US20100203544A1

US20100203544A1 - DIAGNOSIS AND TREATMENT OF CANCERS WITH MicroRNA LOCATED IN OR NEAR CANCER ASSOCIATED CHROMOSOMAL FEATURES

Info

Publication number: US20100203544A1
Application number: US12/751,463
Authority: US
Inventors: Carlo M. Croce; Chang-Gong Liu; George A. Calin; Cinzia Sevignani
Original assignee: Thomas Jefferson University
Current assignee: Thomas Jefferson University
Priority date: 2004-02-09
Filing date: 2010-03-31
Publication date: 2010-08-12
Also published as: US9506065B2; US20060105360A1; US9150859B2; WO2005078139A2; WO2005078139A3; US8778676B2; US20100234241A1; US20140256040A1; US7723030B2; CA2554818A1; US20150368647A1; US20130330403A1; EP2295604A3; US20170175123A1; EP2295604B1; EP2295604A2; EP1713938A2

Abstract

MicroRNA genes are highly associated with chromosomal features involved in the etiology of different cancers. The perturbations in the genomic structure or chromosomal architecture of a cell caused by these cancer-associated chromosomal features can affect the expression of the miR gene(s) located in close proximity to that chromosomal feature. Evaluation of miR gene expression can therefore be used to indicate the presence of a cancer-causing chromosomal lesion in a subject. As the change in miR gene expression level caused by a cancer-associated chromosomal feature may also contribute to cancerigenesis, a given cancer can be treated by restoring the level of miR gene expression to normal. microRNA expression profiling can be used to diagnose cancer and predict whether a particular cancer is associated with an adverse prognosis. The identification of specific mutations associated with genomic regions that harbor miR genes in CLL patients provides a means for diagnosing CLL and possibly other cancers.

Description

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 11/194,055, filed Jul. 29, 2005, which is a continuation-in-part of International Application No. PCT/US2005/004865, filed Feb. 9, 2005, which claims the benefit of U.S. Provisional Application No. 60/543,119, filed Feb. 9, 2004, U.S. Provisional Application No. 60/542,929, filed Feb. 9, 2004, U.S. Provisional Application No. 60/542,963, filed Feb. 9, 2004, U.S. Provisional Application No. 60/542,940, filed Feb. 9, 2004, U.S. Provisional Application No. 60/580,959, filed Jun. 18, 2004, and U.S. Provisional Application No. 60/580,797, filed Jun. 18, 2004. The entire teachings of the above applications are incorporated herein by reference.

GOVERNMENT SUPPORT

The invention described herein was supported in part by grant nos. P01CA76259, P01CA81534, and P30CA56036 from the National Cancer Institute. The U.S. government has certain rights in this invention

FIELD OF THE INVENTION

The invention relates to the diagnosis of cancers, or the screening of individuals for the predisposition to cancer, by evaluating the status of at least one miR gene located in close proximity to chromosomal features, such as cancer-associated genomic regions, fragile sites, human papilloma virus integration sites, and homeobox genes and gene clusters. The invention also relates to the treatment of cancers by altering the amount of gene product produced from miR genes located in close proximity to these chromosomal features. The invention further provides methods of diagnosing CLL and other cancers by screening for mutations in miR genes.

BACKGROUND OF THE INVENTION

Taken as a whole, cancers are a significant source of mortality and morbidity in the U.S. and throughout the world. However, cancers are a large and varied class of diseases with diverse etiologies. Researchers therefore have been unable to develop treatments or diagnostic tests which cover more than a few types of cancer.
For example, cancers are associated with many different classes of chromosomal features. One such class of chromosomal features are perturbations in the genomic structure of certain genes, such as the deletion or mutation of tumor suppressor genes. The activation of proto-oncogenes by gene amplification or promoter activation (e.g., by viral integration), epigenetic modifications (e.g., a change in DNA methylation) and chromosomal translocations can also cause cancerigenesis. Such perturbations in the genomic structure which are involved in the etiology of cancers are called “cancer-associated genomic regions” or “CAGRs.”
Chromosomal fragile sites are another class of chromosomal feature implicated in the etiology of cancers. Chromosomal fragile sites are regions of genomic DNA which show an abnormally high occurrence of gaps or breaks when DNA synthesis is perturbed during metaphase. These fragile sites are categorized as “rare” or “common.” As their name suggests, rare fragile sites are uncommon. Such sites are associated with di- or tri-nucleotide repeats, can be induced in metaphase chromosomes by folic acid deficiency, and segregate in a Mendelian manner. An exemplary rare fragile site is the Fragile X site.
Common fragile sites are revealed when cells are grown in the presence of aphidocolin or 5-azacytidine, which inhibit DNA polymerase. At least eighty-nine common fragile sites have been identified, and at least one such site is found on every human chromosome. Thus, while their function is poorly understood, common fragile sites represent a basic component of the human chromosome structure.
Induction of fragile sites in vitro leads to increased sister-chromatid exchange and a high rate of chromosomal deletions, amplifications and translocations, while fragile sites have been colocalized with chromosome breakpoints in vivo. Also, most common fragile sites studied in tumor cells contain large, intra-locus deletions or translocations, and a number of tumors have been identified with deletions in multiple fragile sites. Chromosomal fragile sites are therefore mechanistically involved in producing many of the chromosomal lesions commonly seen in cancer cells.
Cervical cancer, which is the second leading cause of female cancer mortality worldwide, is highly associated with human papillomavirus (HPV) infection. Indeed, sequences from the HPV 16 or HPV 18 viruses are found in cells from nearly every cervical tumor cell examined. In malignant forms of cervical cancer, the HPV genome is found integrated into the genome of the cancer cells. HPV preferentially integrates in or near common chromosomal fragile sites. HPV integration into a host cell genome can cause large amplification, deletions or rearrangements near the integration site. Expression of cellular genes near the HPV integration site can therefore be affected, which may contribute to the oncogenesis of the infected cell. These sites of HPV integration into a host cell genome are therefore considered another class of chromosomal feature that is associated with a cancer.
Homeobox genes are a conserved family of regulatory genes that contain the same 183-nucleotide sequence, called the “homeobox.” The homeobox genes encode nuclear transcription factors called “homeoproteins,” which regulate the expression of numerous downstream genes important in development. The homeobox sequence itself encodes a 61 amino acid “homeodomain” that recognizes and binds to a specific DNA binding motif in the target developmental genes. Homeobox genes are categorized as “class I” or “clustered” homeobox genes, which regulate antero-posterior patterning during embryogenesis, or “class II” homeobox genes, which are dispersed throughout the genome. Altogether, the homeobox genes account for more than 0.1% of the vertebrate genome.
The homeobox genes are believed to “decode” external inductive stimuli that signal a given cell to proceed down a particular developmental lineage. For example, specific homeobox genes might be activated in response to various growth factors or other external stimuli that activate signal transduction pathways in a cell. The homeobox genes then activate and/or repress specific programs of effector or developmental genes (e.g., morphogenetic molecules, cell-cycle regulators, pro- or anti-apoptotic proteins, etc.) to induce the phenotype “ordered” by the external stimuli. The homeobox system is clearly highly coordinated during embryogenesis and morphogenesis, but appears to be dysregulated during oncogenesis. Such dysregulation likely occurs because of disruptions in the genomic structure or chromosomal architecture surrounding the homeobox genes or gene clusters. The homeobox genes or gene clusters are therefore considered yet another chromosomal feature which are associated with cancers.
Micro RNAs (miRs) are naturally-occurring 19 to 25 nucleotide transcripts found in over one hundred distinct organisms, including fruit flies, nematodes and humans. The miRs are typically processed from 60- to 70-nucleotide foldback RNA precursor structures, which are transcribed from the miR gene. The miR precursor processing reaction requires Dicer RNase III and Argonaute family members (Sasaki et al. (2003), Genomics 82, 323-330). The miR precursor or processed miR products are easily detected, and an alteration in the levels of these molecules within a cell can indicate a perturbation in the chromosomal region containing the miR gene.
To date, at least 222 separate miR genes have been identified in the human genome. Two miR genes (miR15a and miR16a) have been localized to a homozygously deleted region on chromosome 13 that is correlated with chronic lymphocytic leukemia (Calin et al. (2002), Proc. Natl. Acad. Sci. USA 99:15524-29), and the miR-143/miR145 gene cluster is downregulated in colon cancer (Michael et al. (2003), Mol. Cancer. Res. 1:882-91). However, the distribution of miR genes throughout the genome, and the relationship of the miR genes to the diverse chromosomal features discussed herein, has not been systematically studied.
A method for reliably and accurately diagnosing, or for screening individuals for a predisposition to, cancers associated with such diverse chromosomal features as CAGRs, fragile sites, HPV integration sites and homeobox genes is needed. A method of treating cancers associated with these diverse chromosomal features is also highly desired.

SUMMARY OF THE INVENTION

It has now been discovered that miR genes are commonly associated with chromosomal features involved in the etiology of different cancers. The perturbations in the genomic structure or chromosomal architecture of a cell caused by a cancer-associated chromosomal feature can affect the expression of the miR gene(s) located in close proximity to that chromosomal feature. Evaluation of miR gene expression can therefore be used to indicate the presence of a cancer-causing chromosomal lesion in a subject. As the change in miR gene expression level caused by a cancer-associated chromosomal feature may also contribute to cancerigenesis, a given cancer can be treated by restoring the level of miR gene expression to normal.
The invention therefore provides a method of diagnosing cancer in a subject. The cancer can be any cancer associated with a cancer-associated chromosomal feature. As used herein, a cancer-associated chromosomal feature includes, but is not limited to, a cancer-associated genomic region, a chromosomal fragile site, a human papillomavirus integration site on a chromosome of the subject, and a homeobox gene or gene cluster on a chromosome of the subject. The cancer can also be any cancer associated with one or more adverse prognostic markers, including cancers associated with positive ZAP-70 expression, an unmutated IgV_Hgene, positive CD38 expression, deletion at chromosome 11q23, and loss or mutation of TP53. In one embodiment, the diagnostic method comprises the following steps. In a sample obtained from a subject suspected of having a cancer associated with a cancer-associated chromosomal feature, the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature is evaluated by measuring the level of at least one miR gene product from the miR gene in the sample, provided the miR genes are not miR-15, miR-16, miR-143 or miR-145. An alteration in the level of miR gene product in the sample relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject. In a related embodiment, the diagnostic method comprises evaluating in a sample obtained from a subject suspected of having a cancer associated with a cancer-associated chromosomal feature, the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature, provided the miR gene is not miR-15 or miR-16, by measuring the level of at least one miR gene product from the miR gene in the sample. An alteration in the level of miR gene product in the sample relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject.
The status of the at least one miR gene in the subject's sample can also be evaluated by analyzing the at least one miR gene for a deletion, mutation and/or amplification. The detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. The status of the at least one miR gene in the subject's sample can also be evaluated by measuring the copy number of the at least one miR gene in the sample, wherein a copy number other than two for miR genes located on any chromosome other than a Y chromosome, and other than one for miR genes located on a Y chromosome, is indicative of the subject either having or being at risk for having a cancer. In one embodiment, the diagnostic method comprises analyzing at least one miR gene in the sample for a deletion, mutation and/or amplification, wherein detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. In a related embodiment, the diagnostic method comprises analyzing at least one miR gene in the sample for a deletion, mutation or amplification, provided the miR gene is not miR-15 or miR-16, wherein detection of a deletion, mutation and/or amplification in the miR gene relative to the miR gene in a control sample is indicative of the presence of the cancer in the subject. In a further embodiment, the diagnostic method comprises analyzing the miR-16 gene in the sample for a specific mutation, depicted in SEQ ID NO. 642, wherein detection of the mutation in the miR-16 gene relative to a miR-16 gene in a control sample is indicative of the presence of the cancer in the subject.
The invention also provides a method of screening subjects for a predisposition to develop a cancer associated with a cancer-associated chromosomal feature, by evaluating the status of at least one miR gene located in close proximity to the cancer-associated chromosomal feature in the same manner described herein for the diagnostic method. The cancer can be any cancer associated with a cancer-associated chromosomal feature.
In one embodiment, the level of the at least one miR gene product from the sample is measured by quantitatively reverse transcribing the miR gene product to form a complementary target oligodeoxynucleotide, and hybridizing the target oligodeoxynucleotide to a microarray comprising a probe oligonucleotide specific for the miR gene product. In another embodiment, the levels of multiple miR gene products in a sample are measured in this fashion, by quantitatively reverse transcribing the miR gene products to form complementary target oligodeoxynucleotides, and hybridizing the target oligodeoxynucleotides to a microarray comprising probe oligonucleotides specific for the miR gene products. In another embodiment, the multiple miR gene products are simultaneously reverse transcribed, and the resulting set of target oligodeoxynucleotides are simultaneously exposed to the microarray.
In a related embodiment, the invention provides a method of diagnosing cancer in a subject, comprising reverse transcribing total RNA from a sample from the subject to provide a set of labeled target oligodeoxynucleotides; hybridizing the target oligodeoxynucleotides to a microarray comprising miRNA-specific probe oligonucleotides to provide a hybridization profile for the sample; and comparing the sample hybridization profile to the hybridization profile generated from a control sample, an alteration in the profile being indicative of the subject either having, or being at risk for developing, a cancer. The microarray of miRNA-specific probe oligonucleotides preferably comprises miRNA-specific probe oligonucleotides for a substantial portion of the human miRNome, the full complement of microRNA genes in a cell. The microarray more preferably comprises at least about 60%, 70%, 80%, 90%, or 95% of the human miRNome. In one embodiment, the cancer is associated with a cancer-associated chromosomal feature, such as a cancer-associated genomic region or a chromosomal fragile site. In another embodiment, the cancer is associated with one or more adverse prognostic markers. In a particular embodiment, the cancer is B-cell chronic lymphocytic leukemia. In a further embodiment, the cancer is a subset of B-cell chronic lymphocytic leukemia that is associated with one or more adverse prognostic markers. As used herein, an adverse prognostic marker is any indicator, such as a specific genetic alteration or a level of expression of a gene, whose presence suggests an unfavorable prognosis concerning disease progression, the severity of the cancer, and/or the likelihood of developing the cancer.
The invention further provides a method of treating a cancer associated with a cancer-associated chromosomal feature in a subject. The cancer can be any cancer associated with a cancer-associated chromosomal feature, for example, cancers associated with a cancer-associated genomic region, a chromosomal fragile site, a human papillomavirus integration site on a chromosome of the subject, or a homeobox gene or gene cluster on a chromosome of the subject. Furthermore, the cancer is a cancer associated with a cancer-associated chromosomal feature in which at least one isolated miR gene product from a miR gene located in close proximity to the cancer-associated chromosomal feature is down-regulated or up-regulated in cancer cells of the subject, as compared to control cells. When the at least one isolated miR gene product is down regulated in the subject's cancer cells, the method comprises administering to the subject, an effective amount of at least one isolated miR gene product from the at least one miR gene, such that proliferation of cancer cells in the subject is inhibited. When the at least one isolated miR gene product is up-regulated in the cancer cells, an effective amount of at least one compound for inhibiting expression of the at least one miR gene is administered to the subject, such that proliferation of cancer cells in the subject is inhibited.
The invention further provides a method of treating cancer associated with a cancer-associated chromosomal feature in a subject, comprising the following steps. The amount of miR gene product expressed from at least one miR gene located in close proximity to the cancer-associated chromosomal region in cancer cells from the subject is determined relative to control cells. If the amount of the miR gene product expressed in the cancer cells is less than the amount of the miR gene product expressed in control cells, the amount of miR gene product expressed in the cancer cells is altered by administering to the subject an effective amount of at least one isolated miR gene product from the miR gene, such that proliferation of cancer cells in the subject is inhibited. If the amount of the miR gene product expressed in the cancer cells is greater than the amount of the miR gene product expressed in control cells, the amount of miR gene product expressed in the cancer cells is altered by administering to the subject an effective amount of at least one compound for inhibiting expression of the at least one miR gene, such that proliferation of cancer cells in the subject is inhibited.
The invention further provides pharmaceutical compositions comprising a pharmaceutically acceptable carrier and at least one miR gene product, or a nucleic acid expressing at least one miR gene product, from an miR gene located in close proximity to a cancer-associated chromosomal feature, provided the miR gene product is not miR-15 or miR-16.
The invention still further provides for the use of at least one miR gene product, or a nucleic acid expressing at least one miR gene product, from an miR gene located in close proximity to a cancer-associated chromosomal feature for the manufacture of a medicament for the treatment of a cancer associated with a cancer-associated chromosomal region.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an image of a Northern blot analysis of the expression of miR-16a (upper panel), miR-26a (middle panel), and miR-99a (lower panel) in normal human lung (lane 1) and human lung cancer cells (lanes 2-8). Below the three blots is an image of an ethidium bromide-stained gel indicating the 55 RNA lane loading control. The genomic location and the type of alteration are indicated.

FIG. 2 is a schematic representation demonstrating the position of various miR genes on human chromosomes in relation to HOX gene clusters.

FIG. 3 shows an miRNome expression analysis of 38 individual CLL samples. The main miR-associated CLL clusters are presented. The control samples are: MNC, mononuclear cells; Ly, Diffuse large B cell lymphoma; CD5, selected CD5+ B lymphocytes.

FIG. 4 is an image of a Northern blot analysis of the expression of miR-16a (upper panel), miR-26a (middle panel), and miR-99a (lower panel) in 12 B-CLL samples. Below the three blots is an image of an ethidium bromide-stained gel indicating the 5S RNA lane loading control. miR-16a expression levels varied in these B-CLL cases, and were either low or absent in several of the samples tested. However, the expression levels of miR-26a and miR-99a, both regions not involved in B-CLL, were relatively constant in the tested samples.

FIG. 5 shows Kaplan-Meier curves depicting the relationship between miRNA expression levels and the time from diagnosis to either the time of initial therapy or the present, if therapy had not commenced. The proportion of untreated patients with CLL is plotted against time since diagnosis. The patients are grouped according to the expression profile generated by 11 microRNA genes.

FIG. 6 shows the expression levels of miR-16-1 and miR-15a miRNAs in samples from two patients with a miR-16-1 mutation (see SEQ ID NO. 642) and in CD5+ cell samples from normal patients, both by Northern blot analysis (upper panels) and by miRNACHIP (expression level indicated by numbers below panels).

FIG. 7A is a schematic depicting the locations of mutations affecting various miRNAs. The mutated (below chromosome) and normal (above chromosome) nucleotide base is presented for each mutation/polymorphism. The figure is not drawn to scale.

FIG. 7B depicts the RT-PCR amplification products of primary transcripts corresponding to various mutant miR gene products for which mutations have been identified in B-CLL cells, as well as the length of the amplified genomic DNA (G). GAPDH levels were used for normalization; RT+=reverse transcription, RT−=control without reverse transcription, G=genomic control.

FIG. 7C presents the chromatograms for the genomic regions of samples having either normal miR-16-1/15a (top) or mutated miR-16-1/15a (CtoT)+7 (bottom). The precise position of the precursor (line with period at end) and the location of the mutation (arrowheads) are indicated.

FIG. 7D shows the expression levels by miRNACHIP (MAr) and Northern blot (NB) analysis for miR-16-1 and miR-15a in samples from two normal CD5 pools (CD5+) and from both of the patients carrying the germline (CtoT)+7 mutation (CLL). The Northern blot band intensities were quantified using ImageQuantTL (Nonlinear Dynamics Ltd.). Data are presented as arbitrary units.

FIG. 7E is a Northern blot showing that the germline mutation in the pri-miR-16-1 is associated with abnormal expression of the active, mature miR-16-1 molecule. Levels of expression were assessed in 293 cells transfected with miR-16-1-WT, miR-16-1-MUT or Empty vector (Empty V), as indicated. Untransfected 293 cells were tested as a control. Normalization for loading was performed with a U6 probe (miR-15a; left panel) and the transfection levels were normalized with anti-GFP signal on cell lysates from the same pellet as that used for Northern blotting (miR-16-1; right panel).

DETAILED DESCRIPTION OF THE INVENTION

All nucleic acid sequences herein are given in the 5′ to 3′ direction. In addition, genes are represented by italics, and gene products are represented by normal type; e.g., mir-17 is the gene and miR-17 is the gene product.
It has now been discovered that the genes that comprise the miR gene complement of the human genome (or “miRNome”) are non-randomly distributed throughout the genome in relation to each other. For example, of 222 human miR genes, at least ninety are located in thirty-six gene clusters, typically with two or three miR genes per cluster (median=2.5). The largest cluster is composed of six genes located on chromosome 13 at 13q31; the miR genes in this cluster are miR-17/miR-18/miR-19a/miR-20/miR-19b1/miR-92-1.
The human miR genes are also non-randomly distributed across the human chromosomal complement. For example, chromosome 4 has a less-than-expected rate of miRs, and chromosomes 17 and 19 contain significantly more miR genes than expected based on chromosome size. Indeed, six of the thirty-six miR gene clusters (17%), containing 16 of 90 clustered genes (18%), are located on chromosomes 17 and 19, which account for only 5% of the entire human genome.
The sequences of the gene products of 187 miR genes are provided in Table 1. The location and distribution of these 187 miR genes in the human genome is given in Tables 2 and 3; see also Example 1. All Tables are located in the Examples section below. As used herein, an “miR gene product” or “miRNA” means the unprocessed or processed RNA transcript from an miR gene. As the miR gene products are not translated into a protein, the term “miR gene products” does not include proteins.
A used herein, “probe oligonucleotide” refers to an oligonucleotide that is capable of hybridizing to a target oligonucleotide. “Target oligonucleotide” or “target oligodeoxynucleotide” refers to a molecule to be detected (e.g., in a hybridization). By “miR-specific probe oligonucleotide” or “probe oligonucleotide specific for an miR” is meant a probe oligonucleotide that has a sequence selected to hybridize to a specific miR gene product, or to a reverse transcript of the specific miR gene product.
The unprocessed miR gene transcript is also called an “miR precursor,” and typically comprises an RNA transcript of about 70 nucleotides in length. The miR precursor can be processed by digestion with an RNAse (such as, Dicer, Argonaut, or RNAse III, e.g., E. coli RNAse III)) into an active 19-25 nucleotide RNA molecule. This active 19-25 nucleotide RNA molecule is also called the “processed miR gene transcript.”
The active 19-25 nucleotide RNA molecule can be obtained from the miR precursor through natural processing routes (e.g., using intact cells or cell lysates) or by synthetic processing routes (e.g., using isolated processing enzymes, such as isolated Dicer, Argonaut, or RNAase III). It is understood that the active 19-25 nucleotide RNA molecule can also be produced directly by biological or chemical syntheses, without having been processed from the miR precursor. For ease of discussion, such a directly produced active 19-25 nucleotide RNA molecule is also referred to as a “processed miR gene product.”
As used herein, “miR gene expression” refers to the production of miR gene products from an miR gene, including processing of the miR precursor into a processed miR gene product.
The human miR genes are closely associated with different classes of chromosomal features that are themselves associated with cancer. As used herein, a “cancer-associated chromosomal feature” refers to a region of a given chromosome, which, when perturbed, is correlated with the occurrence of at least one human cancer. As used herein, a chromosomal feature is “correlated” with a cancer when the feature and the cancer occur together in individuals of a study population in a manner not expected on the basis of chance alone.
A region of a chromosome is “perturbed” when the chromosomal architecture or genomic DNA sequence in that region is disturbed or differs from the normal architecture or sequence in that region. Exemplary perturbations of chromosomal regions include, e.g., chromosomal breakage and translocation, mutations, deletions or amplifications of genomic DNA, a change in the methylation pattern of genomic DNA, the presence of fragile sites, and the presence of viral integration sites. One skilled in the art would recognize that other chromosomal perturbations associated with a cancer are possible.
It is understood that a cancer-associated chromosomal feature can be a chromosomal region where perturbations are known to occur at a higher rate than at other regions in the genome, but where the perturbation has not yet occurred. For example, a common chromosomal breakpoint or fragile site is considered a cancer-associated chromosomal feature, even if a break has not yet occurred. Likewise, a region in the genomic DNA known as a mutational “hotspot” can be a cancer-associated chromosomal feature, even if no mutations have yet occurred in the region.
One class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “cancer-associated genomic region” or “CAGR” (see Table 4). As used herein, a “CAGR” includes any region of the genomic DNA that comprises a genetic or epigenetic change (or the potential for a genetic or epigenetic change) that differs from normal DNA, and which is correlated with a cancer. Exemplary genetic changes include single- and double-stranded breaks (including common breakpoint regions in or near possible oncogenes or tumor-suppressor genes); chromosomal translocations; mutations, deletions, insertions (including viral, plasmid or transposon integrations) and amplifications (including gene duplications) in the DNA; minimal regions of loss-of-heterozygosity (LOH) suggestive of the presence of tumor-suppressor genes; and minimal regions of amplification suggestive of the presence of oncogenes. Exemplary epigenetic changes include any changes in DNA methylation patterns (e.g., DNA hyper- or hypo-methylation, especially in promoter regions). As used herein, “cancer-associated genomic region” or “CAGR” specifically excludes chromosomal fragile sites or human papillomavirus insertion sites.
Many of the known miR genes in the human genome are in or near CAGRs, including 80 miR genes that are located exactly in minimal regions of LOH or minimal regions of amplification correlated to a variety of cancers. Other miR genes are located in or near breakpoint regions, deleted areas, or regions of amplification. The distribution of miR genes in the human genome relative to CAGRs is given in Tables 6 and 7 and in Example 4A below.
As used herein, an miR gene is “associated” with a given CAGR when the miR gene is located in close proximity to the CAGR; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb) of the CAGR. See Tables 6 and 7 and Example 4A below for a description of cancers which are correlated with CAGRs, and a description of miRs associated with those CAGRs.
For example, cancers associated with CAGRs include leukemia (e.g., AML, CLL, pro-lymphocytic leukemia), lung cancer (e.g., small cell and non-small cell lung carcinoma), esophageal cancer, gastric cancer, colorectal cancer, brain cancer (e.g., astrocytoma, glioma, glioblastoma, medulloblastoma, meningioma, neuroblastoma), bladder cancer, breast cancer, cervical cancer, epithelial cancer, nasopharyngeal cancer (e.g., oral or laryngeal squamous cell carcinoma), lymphoma (e.g., follicular lymphoma), uterine cancer (e.g., malignant fibrous histiocytoma), hepatic cancer (e.g., hepatocellular carcinoma), head-and-neck cancer (e.g., head-and-neck squamous cell carcinoma), renal cancer, male germ cell tumors, malignant mesothelioma, myelodysplastic syndrome, ovarian cancer, pancreatic or biliary cancer, prostate cancer, thyroid cancer (e.g., sporadic follicular thyroid tumors), and urothelial cancer.
Examples of miR genes associated with CAGRs include miR-153-2, let-71, miR-33a, miR-34a-2, miR34a-1, let-7a-1, let-7d; let-7f-1, miR-24-1, miR-27b, miR-23b, miR-181a; miR-199b, miR-218-1, miR-31, let-7a-2, let-7g, miR-21, miR-32a-1, miR-33b, miR-100, miR-101-1, miR-125b-1, miR-135-1, miR-142 as, miR-142s; miR-144, miR-301, miR-297-3, miR-155(BIC), miR-26a, miR-17, miR-18, miR-19a, miR-19b1, miR-20, miR-92-1, miR-128a, miR-7-3, miR-22, miR-123, miR-132, miR-149, miR-161; miR-177, miR-195, miR-212, let-7c, miR-99a, miR-125b-2, miR-210, miR-135-2, miR-124a-1, miR-208, miR-211, miR-180, miR-145, miR-143, miR-127, miR-136, miR-138-1, miR-154, miR-134, miR-299, miR-203, miR-34, miR-92-2, miR-19b-2, miR-108-1, miR-193, miR-106a, miR-29a, miR-29b, miR-129-1, miR-182s, miR-182 as, miR-96, miR-183, miR-32, miR-159-1, miR-192 and combinations thereof.
Specific groupings of miR gene(s) that are associated with a particular cancer are evident from Tables 6 and 7, and are preferred. For example, acute myeloid leukemia (AML) is associated with miR-153-2, and adenocarcinoma of the lung or esophagus is associated with let-71. Where more than one miR gene is listed in Tables 6 and 7, it is understood that the cancer associated with those genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes. Subgenera of CAGRs or associated with miR gene(s) would also be evident to one of ordinary skill in the art from Tables 6 and 7.
Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “chromosomal fragile site” or “FRAs” (see Table 4 and Example 2). As used herein, a “FRA” includes any rare or common fragile site in a chromosome; e.g., one that can be induced by subjecting a cell to stress during DNA replication. For example, a rare FRA can be induced by subjecting the cell to folic acid deficiency during DNA replication. A common FRA can be induced by treating the cell with aphidocolin or 5-azacytidine during DNA replication. The identification or induction of chromosomal fragile sites is within the skill in the art; see, e.g., Arlt et al. (2003), Cytogenet. Genome Res. 100:92-100 and Arlt et al. (2002), Genes, Chromosomes and Cancer 33:82-92, the entire disclosures of which are herein incorporated by reference.
Approximately 20% of the known human miR genes are located in (13 miRs) or within 3 Mb (22 miRs) of cloned FRAs. Indeed, the relative incidence of miR genes inside fragile sites occurs at a rate 9.12 times higher than in non-fragile sites. Moreover, after studying 113 fragile sites in a human karyotype, it was found that 61 miR genes are located in the same chromosomal band as a FRA. The distribution of miR genes in the human genome relative to FRAs is given in Table 5 and in Example 2.
As used herein, an miR gene is “associated” with a given FRA when the miR gene is located in close proximity to the FRA; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb) of the FRA. See Table 5 and Example 2 for a description of cancers which are correlated with FRAs, and a description of miRs associated with those FRAs.
For example, cancers associated with FRAs include bladder cancer, esophageal cancer, lung cancer, stomach cancer, kidney cancer, cervical cancer, ovarian cancer, breast cancer, lymphoma, Ewing sarcoma, hematopoietic tumors, solid tumors and leukemia.
Examples of miR genes associated with FRAs include miR-186, miR-101-1, miR-194, miR-215, miR-106b, miR-25, miR-93, miR-29b, miR-29a, miR-96, miR-182s, miR-182 as, miR-183, miR-129-1, let7a-1, let-7d, let-7f-1, miR-23b, miR-24-1, miR-27b, miR-32, miR-159-1, miR-192, miR-125b-1, let-7a-2, miR-100, miR-196-2, miR-148b, miR-190, miR-21, miR-301, miR-142s, miR-142 as, miR-105-1, miR-175 and combinations thereof.
Specific groupings of miR gene(s) that are associated with a particular cancer and FRA are evident from Table 5, and are preferred. For example, FRA7H is correlated with esophageal cancer, and is associated with miR-29b, miR-29a, miR-96, miR-182s, miR-182 as, miR-183, and miR-129-1. FRA9D is correlated with bladder cancer, and is associated with let7a-1, let-7d, let-7f-1, miR-23b, miR-24-1, and miR-27b. Where more than one miR gene is listed in Table 5 in association with a FRA, it is understood that the cancer associated with those miR genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes. Subgenera of CAGRs and/or associated with miR gene(s) would also be evident to one of ordinary skill in the art from Table 5.
Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “human papillomavirus (HPV) integration site” (see Table 4 and Example 3). As used herein, an “HPV integration site” includes any site in a chromosome of a subject where some or all of an HPV genome can insert into the genomic DNA, or any site where some or all of an HPV genome has inserted into the genomic DNA. HPV integration sites are often associated with common FRAs, but are distinct from FRAs for purposes of the present invention. Any species or strain of HPV can insert some or all of its genome into an HPV integration site. However, the most common strains of HPV which insert some or all of their genomes into an HPV integration site are HPV 16 and HPV 18. The identification of HPV integration sites in the human genome is within the skill in the art; see, e.g., Thorland et al. (2000), Cancer Res. 60:5916-21, the entire disclosure of which is herein incorporated by reference.
Thirteen miR genes (7%) are located within 2.5 Mb of seven of the seventeen (45%) cloned integration sites in the human genome. The relative incidence of miRs at HPV16 integration sites occurred at a rate 3.22 times higher than in the rest of the genome. Indeed, four miR genes (miR-21, miR-301, miR-142s and miR-142 as) were located within one cluster of integration sites at chromosome 17q23, in which there are three HPV16 integration events spread over roughly 4 Mb of genomic sequence.
As used herein, an miR gene is “associated” with a given HPV integration site when the miR gene is located in close proximity to the HPV integration site; i.e., when the miR is located within the same chromosomal band or within 3 megabases (3 Mb), preferably within 2.5 Mb, of the HPV integration site. See Table 5 and Example 3 for a description of miRs associated with HPV integration sites.
Insertion of HPV sequences into the genome of subject is correlated with the occurrence of cervical cancer. Examples of miR genes associated with HPV integration sites on human chromosomes include miR-21, miR-301, miR-142 as, miR-142s, miR-194, miR-215, miR-32 and combinations thereof.
Specific groupings of miR gene(s) that are associated with a particular HPV integration site are evident from Table 5, and are preferred. For example, the HPV integration site located in or near FRA9E is associated with miR-32. The HPV integration site located in or near FRA1H is associated with miR-194 and miR-215. The HPV integration site located in or near FRA17B is associated with miR-21, miR-301, miR-142s, and miR-142 as. Where more than one miR gene is listed in Table 5 in relation to an HPV integration site, it is understood that the cancer associated with those miR genes can be diagnosed by evaluating any one of the listed miR genes, or by evaluating any combination of the listed miR genes.
Another class of cancer-associated chromosomal feature which is closely associated with miR genes in the human genome is a “homeobox gene or gene cluster” (see Table 4 and Example 5). As used herein, a “homeobox gene or gene cluster” is a single gene or a grouping of genes, characterized in that the gene or genes have been classified by sequence or function as a class I or class II homeobox gene or contain the 183-nucleotide “homeobox” sequence. Identification and characterization of homeobox genes or gene clusters are within the skill in the art; see, e.g., Cillo et al. (1999), Exp. Cell Res. 248:1-9 and Pollard et al. (2000), Current Biology 10:1059-62, the entire disclosures of which are herein incorporated by reference.
Of the four known class I homeobox gene clusters in the human genome, three contain miR genes: miR-10a and miR-196-1 are in the HOX B cluster on 17q21; miR-196-2 is in the HOX C cluster at 12q13; and miR-10b is in the HOX D cluster at 2q31. Three other miRs (miR-148, miR-152 and miR-148b) are located within 1 Mb of a HOX gene cluster. miR genes are also found within class II homeobox gene clusters; for example, seven microRNAs (miR-129-1, miR-153-2, let-7a-1, let-7f-1, let-7d, miR-202 and miR-139) are located within 0.5 Mb of class II homeotic genes. See Example 5 and FIG. 2 for a description of miRs associated with homeobox genes or gene clusters in the human genome.
Examples of homeobox genes associated with miR genes in the human genome include genes in the HOXA cluster, genes in the HOXB cluster, genes in the HOXC cluster, genes in the HOXD cluster, NK1, NK3, NK4, Lbx, Tlx, Emx, Vax, Hmx, NK6, Msx, Cdx, Xlox, Gsx, En, HB9, Gbx, Msx-1, Msx-2, GBX2, HLX, HEX, PMX1, DLX, LHX2 and CDX2. Examples of homeobox gene clusters associated with miR genes in the human genome include HOXA, HOXB, HOXC, HOXD, extended Hox, NKL, ParaHox, and EHGbox, PAX, PBX, MEIS, REIG and PREP/KNOX1.
Examples of cancers associated with homeobox genes or gene clusters include renal cancer, Wilm's tumor, colorectal cancer, small cell lung cancer, melanoma, breast cancer, prostate cancer, skin cancer, osteosarcoma, neuroblastoma, leukemia (acute lymphocytic leukemia, acute myeloid leukemia, chronic lymphocytic leukemia), glioblastoma multiform, medulloblastoma, lymphoplasmacytoid lymphoma, thyroid cancer, rhabdomyosarcoma and solid tumors.
Examples of miR genes associated with homeobox genes or gene clusters include miR-148, miR-10a, miR-196-1, miR-152, miR-196-2, miR-148b, miR-10b, miR-129-1, miR-153-2, miR-202, miR-139, let-7a, let-7f, let-7d and combinations thereof.
Specific groupings of miR gene(s) that are associated with particular homeobox genes or gene cluster are evident from Example 5 and FIG. 2, and are preferred. For example, homeobox gene cluster HOXA is associated with miR-148. Homeobox gene cluster HOXB is associated with miR-148, miR-10a, miR-196-1, miR-152 and combinations thereof. Homeobox gene cluster HOXC is associated with miR-196-2, miR-148b or a combination thereof. Homeobox gene cluster HOXD, is associated with miR-10b. Where more than one miR gene is associated with a homeobox gene or gene cluster, it is understood that the cancer associated with those genes can be diagnosed by evaluating any one of the miR genes, or by evaluating any combination of the miR genes. In one embodiment, the miR gene or gene product that is measured or analyzed is not miR-15, miR-16, miR-143 and/or miR-145.
Without wishing to be bound by any theory, it is believed that perturbations in the genomic structure or chromosomal architecture of a cell which comprise the cancer-associated chromosomal feature can affect the expression of the miR gene(s) associated with the feature in that cell. For example, a CAGR can comprise an amplification of the region containing an miR gene(s), causing an up-regulation of miR gene expression. Likewise, the CAGR can comprise a chromosomal breakpoint or a deletion that disrupts gene expression, and results in a down-regulation of miR gene expression. HPV integrations and FRAs can cause deletions, amplifications or rearrangement of the surrounding DNA, which can also affect the structure or expression of any associated miR genes. The factors which cause the collected dysregulation of homeobox genes or gene clusters would cause similar disruptions to any associated miR genes. A change in the status of at least one of the miR genes associated with a cancer-associated chromosomal feature in a tissue or cell sample from a subject, relative to the status of that miR gene in a control sample, therefore is indicative of the presence of a cancer, or a susceptability to cancer, in a subject.
Without wishing to be bound by any theory, it is also believed that a change in status of miR genes associated with a cancer-associated chromosomal feature can be detected prior to, or in the early stages of, the development of transformed or neoplastic phenotypes in cells of a subject. The invention therefore also provides a method of screening subjects for a predisposition to developing a cancer associated with a cancer-associated chromosomal feature, by evaluating the status of at least one miR gene associated with a cancer-associated chromosomal feature in a tissue or cell sample from a subject, relative to the status of that miR gene in a control sample. Subjects with a change in the status of one or more miR genes associated with a cancer-associated chromosomal feature are candidates for further testing to determine or confirm that the subjects have cancer. Such further testing can comprise histological examination of blood or tissue samples, or other techniques within the skill in the art.
As used herein, the “status of an miR gene” refers to the condition of the miR gene in terms of its physical sequence or structure, or its ability to express a gene product. Thus, the status of an miR gene in cells of a subject can be evaluated by any technique suitable for detecting genetic or epigenetic changes in the miR gene, or by any technique suitable for detecting the level of miR gene product produced from the miR gene.
For example, the level of at least one miR gene product produced from an miR gene can be measured in cells of a biological sample obtained from the subject. An alteration in the level (i.e., an up- or down-regulation) of miR gene product in the sample obtained from the subject relative to the level of miR gene product in a control sample is indicative of the presence of the cancer in the subject. As used herein, a “subject” is any mammal suspected of having a cancer associated with a cancer-associated chromosomal feature. In one embodiment, the subject is a human suspected of having a cancer associated with a cancer-associated chromosomal feature. As used herein, expression of an miR gene is “up-regulated” when the amount of miR gene product produced from that gene in a cell or tissue sample from a subject is greater than the amount produced from the same gene in a control cell or tissue sample. Likewise, expression of an miR gene is “down-regulated” when the amount of miR gene product produced from that gene in a cell or tissue sample from a subject is less than the amount produced from the same gene in a control cell or tissue sample.
Methods for determining RNA expression levels in cells from a biological sample are within the level of skill in the art. For example, tissue sample can be removed from a subject suspected of having cancer associated with a cancer-associated chromosomal feature by conventional biopsy techniques. In another example, a blood sample can be removed from the subject, and white blood cells isolated for DNA extraction by standard techniques. The blood or tissue sample is preferably obtained from the subject prior to initiation of radiotherapy, chemotherapy or other therapeutic treatment. A corresponding control tissue or blood sample can be obtained from unaffected tissues of the subject, from a normal human individual or population of normal individuals, or from cultured cells corresponding to the majority of cells in the subject's sample. The control tissue or blood sample is then processed along with the sample from the subject, so that the levels of miR gene product produced from a given miR gene in cells from the subject's sample can be compared to the corresponding miR gene product levels from cells of the control sample.
For example, the relative miR gene expression in the control and normal samples can be conveniently determined with respect to one or more RNA expression standards. The standards can comprise, for example, a zero miR gene expression level, the miR gene expression level in a standard cell line, or the average level of miR gene expression previously obtained for a population of normal human controls.
Suitable techniques for determining the level of RNA transcripts of a particular gene in cells are within the skill in the art. According to one such method, total cellular RNA can be purified from cells by homogenization in the presence of nucleic acid extraction buffer, followed by centrifugation. Nucleic acids are precipitated, and DNA is removed by treatment with DNase and precipitation. The RNA molecules are then separated by gel electrophoresis on agarose gels according to standard techniques, and transferred to nitrocellulose filters by, e.g., the so-called “Northern” blotting technique. The RNA is then immobilized on the filters by heating. Detection and quantification of specific RNA is accomplished using appropriately labeled DNA or RNA probes complementary to the RNA in question. See, for example, Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition, Cold Spring Harbor Laboratory Press, 1989, Chapter 7, the entire disclosure of which is incorporated by reference.
Suitable probes for Northern blot hybridization of a given miR gene product can be produced from the nucleic acid sequences provided in Table 1. Methods for preparation of labeled DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition, Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11, the disclosures of which are herein incorporated by reference.
For example, the nucleic acid probe can be labeled with, e.g., a radionuclide such as ³H, ³²P, ³³P, ¹⁴C, or ³⁵S; a heavy metal; or a ligand capable of functioning as a specific binding pair member for a labeled ligand (e.g., biotin, avidin or an antibody), a fluorescent molecule, a chemiluminescent molecule, an enzyme or the like.
Probes can be labeled to high specific activity by either the nick translation method of Rigby et al. (1977), J. Mol. Biol. 113:237-251 or by the random priming method of Fienberg et al. (1983), Anal. Biochem. 132:6-13, the entire disclosures of which are herein incorporated by reference. The latter is the method of choice for synthesizing ³²P-labeled probes of high specific activity from single-stranded DNA or from RNA templates. For example, by replacing preexisting nucleotides with highly radioactive nucleotides according to the nick translation method, it is possible to prepare ³²P-labeled nucleic acid probes with a specific activity well in excess of 10⁸cpm/microgram. Autoradiographic detection of hybridization can then be performed by exposing hybridized filters to photographic film. Densitometric scanning of the photographic films exposed by the hybridized filters provides an accurate measurement of miR gene transcript levels. Using another approach, miR gene transcript levels can be quantified by computerized imaging systems, such the Molecular Dynamics 400-B 2D Phosphorimager available from Amersham Biosciences, Piscataway, N.J.
Where radionuclide labeling of DNA or RNA probes is not practical, the random-primer method can be used to incorporate an analogue, for example, the dTTP analogue 5-(N—(N-biotinyl-epsilon-aminocaproyl)-3-aminoallyl)deoxyuridine triphosphate, into the probe molecule. The biotinylated probe oligonucleotide can be detected by reaction with biotin-binding proteins, such as avidin, streptavidin, and antibodies (e.g., anti-biotin antibodies) coupled to fluorescent dyes or enzymes that produce color reactions.
In addition to Northern and other RNA blotting hybridization techniques, determining the levels of RNA transcripts can be accomplished using the technique of in situ hybridization. This technique requires fewer cells than the Northern blotting technique, and involves depositing whole cells onto a microscope cover slip and probing the nucleic acid content of the cell with a solution containing radioactive or otherwise labeled nucleic acid (e.g., cDNA or RNA) probes. This technique is particularly well-suited for analyzing tissue biopsy samples from subjects. The practice of the in situ hybridization technique is described in more detail in U.S. Pat. No. 5,427,916, the entire disclosure of which is incorporated herein by reference. Suitable probes for in situ hybridization of a given miR gene product can be produced from the nucleic acid sequences provided in Table 1, as described above.
The relative number of miR gene transcripts in cells can also be determined by reverse transcription of miR gene transcripts, followed by amplification of the reverse-transcribed transcripts by polymerase chain reaction (RT-PCR). The levels of miR gene transcripts can be quantified in comparison with an internal standard, for example, the level of mRNA from a “housekeeping” gene present in the same sample. A suitable “housekeeping” gene for use as an internal standard includes, e.g., myosin or glyceraldehyde-3-phosphate dehydrogenase (G3PDH). The methods for quantitative RT-PCR and variations thereof are within the skill in the art.
In some instances, it may be desirable to simultaneously determine the expression level of a plurality of different of miR genes in a sample. In certain instances, it may be desirable to determine the expression level of the transcripts of all known miR genes correlated with cancer. Assessing cancer-specific expression levels for hundreds of miR genes is time consuming and requires a large amount of total RNA (at least 20 μg for each Northern blot) and autoradiographic techniques that require radioactive isotopes. To overcome these limitations, an oligo library in microchip format may be constructed containing a set of probe oligonucleotides specific for a set of miR genes. In one embodiment, the oligo library contains probes corresponding to all known miRs from the human genome. The microchip oligolibrary may be expanded to include additional miRNAs as they are discovered.
The microchip is prepared from gene-specific oligonucleotide probes generated from known miRNAs. According to one embodiment, the array contains two different oligonucleotide probes for each miRNA, one containing the active sequence and the other being specific for the precursor of the miRNA. The array may also contain controls such as one or more mouse sequences differing from human orthologs by only a few bases, which can serve as controls for hybridization stringency conditions. tRNAs from both species may also be printed on the microchip, providing an internal, relatively stable positive control for specific hybridization. One or more appropriate controls for non-specific hybridization may also be included on the microchip. For this purpose, sequences are selected based upon the absence of any homology with any known miRNAs.
The microchip may be fabricated by techniques known in the art. For example, probe oligonucleotides of an appropriate length, e.g., 40 nucleotides, are 5′-amine modified at position C6 and printed using commercially available microarray systems, e.g., the GeneMachine OmniGrid™ 100 Microarrayer and Amersham CodeLink™ activated slides. Labeled cDNA oligomer corresponding to the target RNAs is prepared by reverse transcribing the target RNA with labeled primer. Following first strand synthesis, the RNA/DNA hybrids are denatured to degrade the RNA templates. The labeled target cDNAs thus prepared are then hybridized to the microarray chip under hybridizing conditions, e.g. 6×SSPE/30% formamide at 25° C. for 18 hours, followed by washing in 0.75×TNT at 37° C. for 40 minutes. At positions on the array where the immobilized probe DNA recognizes a complementary target cDNA in the sample, hybridization occurs. The labeled target cDNA marks the exact position on the array where binding occurs, allowing automatic detection and quantification. The output consists of a list of hybridization events, indicating the relative abundance of specific cDNA sequences, and therefore the relative abundance of the corresponding complementary miRs, in the patient sample. According to one embodiment, the labeled cDNA oligomer is a biotin-labeled cDNA, prepared from a biotin-labeled primer. The microarray is then processed by direct detection of the biotin-containing transcripts using, e.g., Streptavidin-Alexa647 conjugate, and scanned utilizing conventional scanning methods. Images intensities of each spot on the array are proportional to the abundance of the corresponding miR in the patient sample.
The use of the array has several advantages for miRNA expression detection. First, the global expression of several hundred genes can be identified in a same sample at one time point. Second, through careful design of the oligonucleotide probes, expression of both mature and precursor molecules can be identified. Third, in comparison with Northern blot analysis, the chip requires a small amount of RNA, and provides reproducible results using 2.5 μg of total RNA. The relatively limited number of miRNAs (a few hundred per species) allows the construction of a common microarray for several species, with distinct oligonucleotide probes for each. Such a tool would allow for analysis of trans-species expression for each known miR under various conditions.
In addition to use for quantitative expression level assays of specific miRs, a microchip containing miRNA-specific probe oligonucleotides corresponding to a substantial portion of the miRNome, preferably the entire miRNome, may be employed to carry out miR gene expression profiling, for analysis of miR expression patterns. Distinct miR signatures may be associated with established disease markers, or directly with a disease state. As described hereinafter in Example 11, two distinct clusters of human B-cell chronic lymphocytic leukemia (CLL) samples are associated with the presence or the absence of Zap-70 expression, a predictor of early disease progression. As described in Examples 11 and 12, two miRNA signatures were associated with the presence of absence of prognostic markers of disease progression, including Zap-70 expression, mutations in the expressed immunoglobulin variable-region gene IgV_Hand deletions at 13q14. Therefore, miR gene expression profiles can be used for diagnosing the disease state of a cancer, such as whether a cancer is malignant or benign, based on whether or not a given profile is representative of a cancer that is associated with one or more established adverse prognostic markers. Prognostic markers that are suitable for this method include ZAP-70 expression, unmutated IgV_Hgene, CD38 expression, deletion at chromosome11q23, loss or mutation of TP53, and any combination thereof.
According to the expression profiling method in one embodiment, total RNA from a sample from a subject suspected of having a cancer is quantitatively reverse transcribed to provide a set of labeled target oligodeoxynucleotides complementary to the RNA in the sample. The target oligodeoxynucleotides are then hybridized to a microarray comprising miRNA-specific probe oligonucleotides to provide a hybridization profile for the sample. The result is a hybridization profile for the sample representing the expression pattern of miRNA in the sample. The hybridization profile comprises the signal from the binding of the target oligodeoxynucleotides from the sample to the miRNA-specific probe oligonucleotides in the microarray. The profile may be recorded as the presence or absence of binding (signal vs. zero signal). More preferably, the profile recorded includes the intensity of the signal from each hybridization. The profile is compared to the hybridization profile generated from a normal, i.e., noncancerous, control sample. An alteration in the signal is indicative of the presence of the cancer in the subject.
Other techniques for measuring miR gene expression are also within the skill in the art, and include various techniques for measuring rates of RNA transcription and degradation.
The status of an miR gene in a cell of a subject can also be evaluated by analyzing at least one miR gene or gene product in the sample for a deletion, mutation or amplification, wherein detection of a deletion, mutation or amplification in the miR gene or gene product relative to the miR gene or gene product in a control sample is indicative of the presence of the cancer in the subject. As used herein, a mutation is any alteration in the sequence of a gene of interest that results from one or more nucleotide changes. Such changes include, but are not limited to, allelic polymorphisms, and may affect gene expression and/or function of the gene product.
A deletion, mutation or amplification in an miR gene or gene product can be detected by determining the structure or sequence of an miR gene or gene product in cells from a biological sample from a subject suspected of having cancer associated with a cancer-associated chromosomal feature, and comparing this with the structure or sequence of a corresponding gene or gene product in cells from a control sample. Subject and control samples can be obtained as described herein. Especially suitable candidate miR genes for this type of analysis include, but are not limited to, miR-16-1, miR-27b, miR-206, miR-29b-2 and miR-187. As described in Examples 13 and 14 herein, specific mutations in these five miR genes have been identified in samples from CLL patients.
In certain embodiments, the present invention provides methods for diagnosing whether a subject has, or is at risk for developing, a cancer, comprising analyzing a miR gene or gene product in a test sample from the subject, wherein the detection of a mutation in the miR gene or gene product in the test sample, relative to a control sample, is indicative of the subject having, or being at risk for developing, cancer. In one embodiment, the method comprises analyzing the status of a miR-16-1 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-16-1 gene for the presence of a mutation, wherein the mutation is a C to T nucleotide substitution at +7 base pairs 3′ of the miR-16-1 precursor coding region (see, e.g., SEQ ID NOS:641 and 642). Suitable cancers to be diagnosed by this method include CLL, among others. In another embodiment, the method comprises analyzing the status of a miR-27b gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-27b gene for the presence of a mutation, wherein the mutation is a G to A nucleotide substitution at +50 base pairs 3′ of the miR-27b precursor coding region (see, e.g., SEQ ID NOS:645 and 646). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL, throat cancer, and lung cancer. In an additional embodiment, the method comprises analyzing the status of a miR-206 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is a G to T nucleotide substitution at position 49 of the miR-206 precursor coding region (see, e.g., SEQ ID NOS:657 and 658). In a related embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is an A to T substitution at −116 base pairs 5′ of the miR-206 precursor coding region (see, e.g., SEQ ID NOS:657 and 659). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL and other leukemias, esophogeal cancer, prostate cancer and breast cancer. In yet another embodiment the method comprises analyzing the status of a miR-29b-2 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-29b-2 gene for the presence of a mutation, wherein the mutation is a G to A nucleotide substitution at +212 base pairs 3′ of the miR-29b-2 precursor coding region (see, e.g., SEQ ID NOS:651 and 652). In a related embodiment, the method comprises analyzing the status of a miR-206 gene for the presence of a mutation, wherein the mutation is an A nucleotide insertion at +107 base pairs 3′ of the miR-29b-2 precursor coding region (see, e.g., SEQ ID NOS:651 and 653). Suitable cancers to be diagnosed by this method include, but are not limited to, CLL and other leukemias, as well as breast cancer. In a further embodiment, the method comprises analyzing the status of a miR-187 gene or gene product. In a particular embodiment, the method comprises analyzing the status of a miR-187 gene for the presence of a mutation, wherein the mutation is a T to C nucleotide substitution at +73 base pairs 3′ of the miR-187 precursor coding region (see, e.g., SEQ ID NOS:654 and 655). Suitable cancers to be diagnosed by this method include, CLL, among others.
Any technique suitable for detecting alterations in the structure or sequence of genes can be used in the practice of the present method. For example, the presence of miR gene deletions, mutations or amplifications can be detected by Southern blot hybridization of the genomic DNA from a subject, using nucleic acid probes specific for miR gene sequences.
Southern blot hybridization techniques are within the skill in the art. For example, genomic DNA isolated from a subject's sample can be digested with restriction endonucleases. This digestion generates restriction fragments of the genomic DNA that can be separated by electrophoresis, for example, on an agarose gel. The restriction fragments are then blotted onto a hybridization membrane (e.g., nitrocellulose or nylon), and hybridized with labeled probes specific for a given miR gene or genes. A deletion or mutation of these genes is indicated by an alteration of the restriction fragment patterns on the hybridization membrane, as compared to DNA from a control sample that has been treated identically to the DNA from the subject's sample. Probe labeling and hybridization conditions suitable for detecting alterations in gene structure or sequence can be readily determined by one of ordinary skill in the art. The miR gene nucleic acid probes for Southern blot hybridization can be designed based upon the nucleic acid sequences provided in Table 1, as described herein. Nucleic acid probe hybridization can then be detected by exposing hybridized filters to photographic film, or by employing computerized imaging systems, such the Molecular Dynamics 400-B 2D Phosphorimager available from Amersham Biosciences, Piscataway, N.J.
Deletions, mutations and/or amplifications of an miR gene can also be detected by amplifying a fragment of these genes by polymerase chain reaction (PCR), and analyzing the amplified fragment by sequencing or by electrophoresis to determine if the sequence and/or length of the amplified fragment from the subject's DNA sample is different from that of a control DNA sample. Suitable reaction and cycling conditions for PCR amplification of DNA fragments can be readily determined by one of ordinary skill in the art.
Deletions of an miR gene can also be identified by detecting deletions of chromosomal markers that are closely linked to the miR gene. Mutations in an miR gene can also be detected by the technique of single strand conformational polymorphism (SSCP), for example, as described in Orita et al. (1989), Genomics 5:874-879 and Hayashi (1991), PCR Methods and Applic. 1:34-38, the entire disclosures of which are herein incorporated by reference. The SSCP technique consists of amplifying a fragment of the gene of interest by PCR; denaturing the fragment and electrophoresing the two denatured single strands under non-denaturing conditions. The single strands assume a complex sequence-dependent intrastrand secondary structure that affects the strands electrophoretic mobility.
The status of an miR gene in cells of a subject can also be evaluated by measuring the copy number of the at least one miR gene in the sample, wherein a gene copy number other than two for miR genes on somatic chromosomes and sex chromosomes in a female, or other than one for miR genes on sex chromosomes in a male, is indicative of the presence of the cancer in the subject.
Any technique suitable for detecting gene copy number can be used in the practice of the present method, including the Southern blot and PCR amplification techniques described above. An alternative method of determining the miR gene copy number in a sample of tissue relies on the fact that many miR genes or gene clusters are closely linked to chromosomal markers or other genes. The loss of a copy of an miR gene in an individual who is heterozygous at a marker or gene closely linked to the miR gene can be inferred from the loss of heterozygosity in the closely linked marker or gene. Methods for determining loss of heterozygosity of chromosomal markers are within the skill in the art.
As discussed above, the human miR genes are closely associated with different classes of chromosomal features that are themselves associated with cancer. These cancers are likely caused, in part, by the perturbation in the chromosome or genomic DNA caused by the cancer-associated chromosomal feature, which can affect expression of oncogenes or tumor-suppressor genes located near the site of perturbation. Without wishing to be bound by any theory, it is believed that the perturbations caused by the cancer-associated chromosomal features also affect the expression level of miR genes associated with the feature, and that this also may also contribute to cancerigenesis. Therefore, a given cancer can be treated by restoring the level of miR gene expression associated with that cancer to normal. For example, if the level of miR gene expression is down-regulated in cancer cells of a subject, then the cancer can be treated by raising the miR expression level. Likewise, if the level of miR gene expression is up-regulated in cancer cells of a subject, then the cancer can be treated by reducing the miR expression level.
The cancers associated with different cancer-associated chromosomal features, and the miR genes associated with these features, are described above and in Tables 5, 6 and 7 and FIG. 2. In the practice of the present method, expression the appropriate miR gene or genes associated with a particular cancer and/or cancer-associated chromosomal features is altered by the compositions and methods described herein. As before, specific groupings of miR gene(s) that are associated with a particular cancer-associated chromosomal feature and/or cancer are evident from Tables 5, 6 and 7 and in FIG. 2, and are preferred. In one embodiment, the method of treatment comprising administering an miR gene product. In another embodiment, the method of treatment comprises administering an miR gene product, provided the miR gene product is not miR-15, miR-16, miR-143 and/or miR-145.
In one embodiment of the present method, the level of at least one miR gene product in cancer cells of a subject is first determined relative to control cells. Techniques suitable for determining the relative level of miR gene product in cells are described above. If miR gene expression is down-regulated in the cancer cell relative to control cells, then the cancer cells are treated with an effective amount of a compound comprising the isolated miR gene product from the miR gene which is down-regulated. If miR gene expression is up-regulated in cancer cells relative to control cells, then the cancer cells are treated with an effective amount of a compound that inhibits miR gene expression. In one embodiment, the level of miR gene product in a cancer cell is not determined beforehand, for example, in those cancers where miR gene expression is known to be up- or down-regulated.
Thus, in the practice of the present treatment methods, an effective amount of at least one isolated miR gene product can be administered to a subject. As used herein, an “effective amount” of an isolated miR gene product is an amount sufficient to inhibit proliferation of a cancer cell in a subject suffering from a cancer associated with a cancer-associated chromosomal feature. One skilled in the art can readily determine an effective amount of an miR gene product to be administered to a given subject, by taking into account factors such as the size and weight of the subject; the extent of disease penetration; the age, health and sex of the subject; the route of administration; and whether the administration is regional or systemic.
For example, an effective amount of isolated miR gene product can be based on the approximate weight of a tumor mass to be treated. The approximate weight of a tumor mass can be determined by calculating the approximate volume of the mass, wherein one cubic centimeter of volume is roughly equivalent to one gram. An effective amount of the isolated miR gene product based on the weight of a tumor mass can be at least about 10 micrograms/gram of tumor mass, and is preferably between about 10-500 micrograms/gram of tumor mass. More preferably, the effective amount is at least about 60 micrograms/gram of tumor mass. Particularly preferably, the effective amount is at least about 100 micrograms/gram of tumor mass. It is preferred that an effective amount based on the weight of the tumor mass be injected directly into the tumor.
An effective amount of an isolated miR gene product can also be based on the approximate or estimated body weight of a subject to be treated. Preferably, such effective amounts are administered parenterally or enterally, as described herein. For example, an effective amount of the isolated miR gene product is administered to a subject can range from about 5-3000 micrograms/kg of body weight, and is preferably between about 700-1000 micrograms/kg of body weight, and is more preferably greater than about 1000 micrograms/kg of body weight.
One skilled in the art can also readily determine an appropriate dosage regimen for the administration of an isolated miR gene product to a given subject. For example, an miR gene product can be administered to the subject once (e.g., as a single injection or deposition). Alternatively, an miR gene product can be administered once or twice daily to a subject for a period of from about three to about twenty-eight days, more preferably from about seven to about ten days. In a preferred dosage regimen, an miR gene product is administered once a day for seven days. Where a dosage regimen comprises multiple administrations, it is understood that the effective amount of the miR gene product administered to the subject can comprise the total amount of gene product administered over the entire dosage regimen.
As used herein, an “isolated” miR gene product is one which is synthesized, or altered or removed from the natural state through human intervention. For example, an miR gene product naturally present in a living animal is not “isolated.” A synthetic miR gene product, or an miR gene product partially or completely separated from the coexisting materials of its natural state, is “isolated.” An isolated miR gene product can exist in substantially purified form, or can exist in a cell into which the miR gene product has been delivered. Thus, an miR gene product which is deliberately delivered to, or expressed in, a cell is considered an “isolated” miR gene product. An miR gene product produced inside a cell by from an miR precursor molecule is also considered to be “isolated” molecule.
Isolated miR gene products can be obtained using a number of standard techniques. For example, the miR gene products can be chemically synthesized or recombinantly produced using methods known in the art. Preferably, miR gene products are chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA/RNA synthesizer. Commercial suppliers of synthetic RNA molecules or synthesis reagents include, e.g., Proligo (Hamburg, Germany), Dharmacon Research (Lafayette, Colo., USA), Pierce Chemical (part of Perbio Science, Rockford, Ill., USA), Glen Research (Sterling, Va., USA), ChemGenes (Ashland, Mass., USA) and Cruachem (Glasgow, UK).
Alternatively, the miR gene products can be expressed from recombinant circular or linear DNA plasmids using any suitable promoter. Suitable promoters for expressing RNA from a plasmid include, e.g., the U6 or H1 RNA pol III promoter sequences, or the cytomegalovirus promoters. Selection of other suitable promoters is within the skill in the art. The recombinant plasmids of the invention can also comprise inducible or regulatable promoters for expression of the miR gene products in cancer cells.
The miR gene products that are expressed from recombinant plasmids can be isolated from cultured cell expression systems by standard techniques. The miR gene products which are expressed from recombinant plasmids can also be delivered to, and expressed directly in, the cancer cells. The use of recombinant plasmids to deliver the miR gene products to cancer cells is discussed in more detail below.
The miR gene products can be expressed from a separate recombinant plasmid, or can be expressed from the same recombinant plasmid. Preferably, the miR gene products are expressed as the RNA precursor molecules from a single plasmid, and the precursor molecules are processed into the functional miR gene product by a suitable processing system, including processing systems extant within a cancer cell. Other suitable processing systems include, e.g., the in vitro Drosophila cell lysate system as described in U.S. published application 2002/0086356 to Tuschl et al. and the E. coli RNAse III system described in U.S. published patent application 2004/0014113 to Yang et al., the entire disclosures of which are herein incorporated by reference.
Selection of plasmids suitable for expressing the miR gene products, methods for inserting nucleic acid sequences into the plasmid to express the gene products, and methods of delivering the recombinant plasmid to the cells of interest are within the skill in the art. See, for example, Zeng et al. (2002), Molecular Cell 9:1327-1333; Tuschl (2002), Nat. Biotechnol, 20:446-448; Brummelkamp et al. (2002), Science 296:550-553; Miyagishi et al. (2002), Nat. Biotechnol. 20:497-500; Paddison et al. (2002), Genes Dev. 16:948-958; Lee et al. (2002), Nat. Biotechnol. 20:500-505; and Paul et al. (2002), Nat. Biotechnol. 20:505-508, the entire disclosures of which are herein incorporated by reference.
In one embodiment, a plasmid expressing the miR gene products comprises a sequence encoding a miR precursor RNA under the control of the CMV intermediate-early promoter. As used herein, “under the control” of a promoter means that the nucleic acid sequences encoding the miR gene product are located 3′ of the promoter, so that the promoter can initiate transcription of the miR gene product coding sequences.
The miR gene products can also be expressed from recombinant viral vectors. It is contemplated that the miR gene products can be expressed from two separate recombinant viral vectors, or from the same viral vector. The RNA expressed from the recombinant viral vectors can either be isolated from cultured cell expression systems by standard techniques, or can be expressed directly in cancer cells. The use of recombinant viral vectors to deliver the miR gene products to cancer cells is discussed in more detail below.
The recombinant viral vectors of the invention comprise sequences encoding the miR gene products and any suitable promoter for expressing the RNA sequences. Suitable promoters include, for example, the U6 or H1 RNA pol III promoter sequences, or the cytomegalovirus promoters. Selection of other suitable promoters is within the skill in the art. The recombinant viral vectors of the invention can also comprise inducible or regulatable promoters for expression of the miR gene products in a cancer cell.
Any viral vector capable of accepting the coding sequences for the miR gene products can be used; for example, vectors derived from adenovirus (AV); adeno-associated virus (AAV); retroviruses (e.g., lentiviruses (LV), Rhabdoviruses, murine leukemia virus); herpes virus, and the like. The tropism of the viral vectors can be modified by pseudotyping the vectors with envelope proteins or other surface antigens from other viruses, or by substituting different viral capsid proteins, as appropriate.
For example, lentiviral vectors of the invention can be pseudotyped with surface proteins from vesicular stomatitis virus (VSV), rabies, Ebola, Mokola, and the like. AAV vectors of the invention can be made to target different cells by engineering the vectors to express different capsid protein serotypes. For example, an AAV vector expressing a serotype 2 capsid on a serotype 2 genome is called AAV 2/2. This serotype 2 capsid gene in the AAV 2/2 vector can be replaced by a serotype 5 capsid gene to produce an AAV 2/5 vector. Techniques for constructing AAV vectors which express different capsid protein serotypes are within the skill in the art; see, e.g., Rabinowitz J. E. et al. (2002), J Virol 76:791-801, the entire disclosure of which is herein incorporated by reference.
Selection of recombinant viral vectors suitable for use in the invention, methods for inserting nucleic acid sequences for expressing RNA into the vector, methods of delivering the viral vector to the cells of interest, and recovery of the expressed RNA products are within the skill in the art. See, for example, Dornburg (1995), Gene Therap. 2:301-310; Eglitis (1988), Biotechniques 6:608-614; Miller (1990), Hum. Gene Therap. 1:5-14; and Anderson (1998), Nature 392:25-30, the entire disclosures of which are herein incorporated by reference.
Preferred viral vectors are those derived from AV and AAV. A suitable AV vector for expressing the miR gene products, a method for constructing the recombinant AV vector, and a method for delivering the vector into target cells, are described in Xia et al. (2002), Nat. Biotech. 20:1006-1010, the entire disclosure of which is herein incorporated by reference. Suitable AAV vectors for expressing the miR gene products, methods for constructing the recombinant AAV vector, and methods for delivering the vectors into target cells are described in Samulski et al. (1987), J. Virol. 61:3096-3101; Fisher et al. (1996), J. Virol., 70:520-532; Samulski et al. (1989), J. Virol. 63:3822-3826; U.S. Pat. No. 5,252,479; U.S. Pat. No. 5,139,941; International Patent Application No. WO 94/13788; and International Patent Application No. WO 93/24641, the entire disclosures of which are herein incorporated by reference. Preferably, the miR gene products are expressed from a single recombinant AAV vector comprising the CMV intermediate early promoter.
In one embodiment, a recombinant AAV viral vector of the invention comprises a nucleic acid sequence encoding an miR precursor RNA in operable connection with a polyT termination sequence under the control of a human U6 RNA promoter. As used herein, “in operable connection with a polyT termination sequence” means that the nucleic acid sequences encoding the sense or antisense strands are immediately adjacent to the polyT termination signal in the 5′ direction. During transcription of the miR sequences from the vector, the polyT termination signals act to terminate transcription.
In the practice of the present treatment methods, an effective amount of at least one compound which inhibits miR gene expression can also be administered to the subject. As used herein, “inhibiting miR gene expression” means that the production of miR gene product from the miR gene in the cancer cell after treatment is less than the amount produced prior to treatment. One skilled in the art can readily determine whether miR gene expression has been inhibited in a cancer cell, using for example the techniques for determining miR transcript level discussed above for the diagnostic method.
As used herein, an “effective amount” of a compound that inhibits miR gene expression is an amount sufficient to inhibit proliferation of a cancer cell in a subject suffering from a cancer associated with a cancer-associated chromosomal feature. One skilled in the art can readily determine an effective amount of an miR gene expression-inhibiting compound to be administered to a given subject, by taking into account factors such as the size and weight of the subject; the extent of disease penetration; the age, health and sex of the subject; the route of administration; and whether the administration is regional or systemic.
For example, an effective amount of the expression-inhibiting compound can be based on the approximate weight of a tumor mass to be treated. The approximate weight of a tumor mass can be determined by calculating the approximate volume of the mass, wherein one cubic centimeter of volume is roughly equivalent to one gram. An effective amount based on the weight of a tumor mass can be at least about 10 micrograms/gram of tumor mass, and is preferably between about 10-500 micrograms/gram of tumor mass. More preferably, the effective amount is at least about 60 micrograms/gram of tumor mass. Particularly preferably, the effective amount is at least about 100 micrograms/gram of tumor mass. It is preferred that an effective amount based on the weight of the tumor mass be injected directly into the tumor.
An effective amount of a compound that inhibits miR gene expression can also be based on the approximate or estimated body weight of a subject to be treated. Preferably, such effective amounts are administered parenterally or enterally, as described herein. For example, an effective amount of the expression-inhibiting compound administered to a subject can range from about 5-3000 micrograms/kg of body weight, and is preferably between about 700-1000 micrograms/kg of body weight, and is more preferably greater than about 1000 micrograms/kg of body weight.
One skilled in the art can also readily determine an appropriate dosage regimen for administering a compound that inhibits miR gene expression to a given subject. For example, an expression-inhibiting compound can be administered to the subject once (e.g., as a single injection or deposition). Alternatively, an expression-inhibiting compound can be administered once or twice daily to a subject for a period of from about three to about twenty-eight days, more preferably from about seven to about ten days. In a preferred dosage regimen, an expression-inhibiting compound is administered once a day for seven days. Where a dosage regimen comprises multiple administrations, it is understood that the effective amount of the expression-inhibiting compound administered to the subject can comprise the total amount of compound administered over the entire dosage regimen.
Suitable compounds for inhibiting miR gene expression include double-stranded RNA (such as short- or small-interfering RNA or “siRNA”), antisense nucleic acids, and enzymatic RNA molecules such as ribozymes. Each of these compounds can be targeted to a given miR gene product and destroy or induce the destruction of the target miR gene product.
For example, expression of a given miR gene can be inhibited by inducing RNA interference of the miR gene with an isolated double-stranded RNA (“dsRNA”) molecule which has at least 90%, for example 95%, 98%, 99% or 100%, sequence homology with at least a portion of the miR gene product. In a preferred embodiment, the dsRNA molecule is a “short or small interfering RNA” or “siRNA.”
siRNA useful in the present methods comprise short double-stranded RNA from about 17 nucleotides to about 29 nucleotides in length, preferably from about 19 to about 25 nucleotides in length. The siRNA comprise a sense RNA strand and a complementary antisense RNA strand annealed together by standard Watson-Crick base-pairing interactions (hereinafter “base-paired”). The sense strand comprises a nucleic acid sequence which is substantially identical to a nucleic acid sequence contained within the target miR gene product.
As used herein, a nucleic acid sequence in an siRNA which is “substantially identical” to a target sequence contained within the target mRNA is a nucleic acid sequence that is identical to the target sequence, or that differs from the target sequence by one or two nucleotides. The sense and antisense strands of the siRNA can comprise two complementary, single-stranded RNA molecules, or can comprise a single molecule in which two complementary portions are base-paired and are covalently linked by a single-stranded “hairpin” area.
The siRNA can also be altered RNA that differs from naturally-occurring RNA by the addition, deletion, substitution and/or alteration of one or more nucleotides. Such alterations can include addition of non-nucleotide material, such as to the end(s) of the siRNA or to one or more internal nucleotides of the siRNA, or modifications that make the siRNA resistant to nuclease digestion, or the substitution of one or more nucleotides in the siRNA with deoxyribonucleotides.
One or both strands of the siRNA can also comprise a 3′ overhang. As used herein, a “3′ overhang” refers to at least one unpaired nucleotide extending from the 3′-end of a duplexed RNA strand. Thus, in one embodiment, the siRNA comprises at least one 3′ overhang of from 1 to about 6 nucleotides (which includes ribonucleotides or deoxyribonucleotides) in length, preferably from 1 to about 5 nucleotides in length, more preferably from 1 to about 4 nucleotides in length, and particularly preferably from about 2 to about 4 nucleotides in length. In a preferred embodiment, the 3′ overhang is present on both strands of the siRNA, and is 2 nucleotides in length. For example, each strand of the siRNA can comprise 3′ overhangs of dithymidylic acid (“TT”) or diuridylic acid (“uu”).
The siRNA can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing dsRNA or siRNA molecules are described in U.S. published patent application 2002/0173478 to Gewirtz and in U.S. published patent application 2004/0018176 to Reich et al., the entire disclosures of which are herein incorporated by reference.
Expression of a given miR gene can also be inhibited by an antisense nucleic acid. As used herein, an “antisense nucleic acid” refers to a nucleic acid molecule that binds to target RNA by means of RNA-RNA or RNA-DNA or RNA-peptide nucleic acid interactions, which alters the activity of the target RNA. Antisense nucleic acids suitable for use in the present methods are single-stranded nucleic acids (e.g., RNA, DNA, RNA-DNA chimeras, PNA) that generally comprise a nucleic acid sequence complementary to a contiguous nucleic acid sequence in an miR gene product. Preferably, the antisense nucleic acid comprises a nucleic acid sequence that is 50-100% complementary, more preferably 75-100% complementary, and most preferably 95-100% complementary to a contiguous nucleic acid sequence in an miR gene product. Nucleic acid sequences for the miR gene products are provided in Table 1. Without wishing to be bound by any theory, it is believed that the antisense nucleic acids activate RNase H or some other cellular nuclease that digests the miR gene product/antisense nucleic acid duplex.
Antisense nucleic acids can also contain modifications to the nucleic acid backbone or to the sugar and base moieties (or their equivalent) to enhance target specificity, nuclease resistance, delivery or other properties related to efficacy of the molecule. Such modifications include cholesterol moieties, duplex intercalators such as acridine or the inclusion of one or more nuclease-resistant groups.
Antisense nucleic acids can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing are within the skill in the art; see, e.g., Stein and Cheng (1993), Science 261:1004 and U.S. Pat. No. 5,849,902 to Woolf et al., the entire disclosures of which are herein incorporated by reference.
Expression of a given miR gene can also be inhibited by an enzymatic nucleic acid. As used herein, an “enzymatic nucleic acid” refers to a nucleic acid comprising a substrate binding region that has complementarity to a contiguous nucleic acid sequence of an miR gene product, and which is able to specifically cleave the miR gene product. Preferably, the enzymatic nucleic acid substrate binding region is 50-100% complementary, more preferably 75-100% complementary, and most preferably 95-100% complementary to a contiguous nucleic acid sequence in an miR gene product. The enzymatic nucleic acids can also comprise modifications at the base, sugar, and/or phosphate groups. An exemplary enzymatic nucleic acid for use in the present methods is a ribozyme.
The enzymatic nucleic acids can be produced chemically or biologically, or can be expressed from a recombinant plasmid or viral vector, as described above for the isolated miR gene products. Exemplary methods for producing and testing dsRNA or siRNA molecules are described in Werner and Uhlenbeck (1995), Nucl. Acids Res. 23:2092-96; Hammann et al. (1999), Antisense and Nucleic Acid Drug Dev. 9:25-31; and U.S. Pat. No. 4,987,071 to Cech et al, the entire disclosures of which are herein incorporated by reference.
Administration of at least one miR gene product, or at least one compound for inhibiting miR gene expression, will inhibit the proliferation of cancer cells in a subject who has a cancer associated with a cancer-associated chromosomal feature. As used herein, to “inhibit the proliferation of a cancer cell” means to kill the cell, or permanently or temporarily arrest or slow the growth of the cell. Inhibition of cancer cell proliferation can be inferred if the number of such cells in the subject remains constant or decreases after administration of the miR gene products or miR gene expression-inhibiting compounds. An inhibition of cancer cell proliferation can also be inferred if the absolute number of such cells increases, but the rate of tumor growth decreases.
The number of cancer cells in a subject's body can be determined by direct measurement, or by estimation from the size of primary or metastatic tumor masses. For example, the number of cancer cells in a subject can be measured by immunohistological methods, flow cytometry, or other techniques designed to detect characteristic surface markers of cancer cells.
The size of a tumor mass can be ascertained by direct visual observation, or by diagnostic imaging methods, such as X-ray, magnetic resonance imaging, ultrasound, and scintigraphy. Diagnostic imaging methods used to ascertain size of the tumor mass can be employed with or without contrast agents, as is known in the art. The size of a tumor mass can also be ascertained by physical means, such as palpation of the tissue mass or measurement of the tissue mass with a measuring instrument, such as a caliper.
The miR gene products or miR gene expression-inhibiting compounds can be administered to a subject by any means suitable for delivering these compounds to cancer cells of the subject. For example, the miR gene products or miR expression inhibiting compounds can be administered by methods suitable to transfect cells of the subject with these compounds, or with nucleic acids comprising sequences encoding these compounds. Preferably, the cells are transfected with a plasmid or viral vector comprising sequences encoding at least one miR gene product or miR gene expression inhibiting compound.
Transfection methods for eukaryotic cells are well known in the art, and include, e.g., direct injection of the nucleic acid into the nucleus or pronucleus of a cell; electroporation; liposome transfer or transfer mediated by lipophilic materials; receptor mediated nucleic acid delivery, bioballistic or particle acceleration; calcium phosphate precipitation, and transfection mediated by viral vectors.
For example, cells can be transfected with a liposomal transfer compound, e.g., DOTAP(N-[1-(2,3-dioleoyloxy)propyl]-N,N,N-trimethyl-ammonium methylsulfate, Boehringer-Mannheim) or an equivalent, such as LIPOFECTIN. The amount of nucleic acid used is not critical to the practice of the invention; acceptable results may be achieved with 0.1-100 micrograms of nucleic acid/10⁵cells. For example, a ratio of about 0.5 micrograms of plasmid vector in 3 micrograms of DOTAP per 10⁵cells can be used.
An miR gene product or miR gene expression inhibiting compound can also be administered to a subject by any suitable enteral or parenteral administration route. Suitable enteral administration routes for the present methods include, e.g., oral, rectal, or intranasal delivery. Suitable parenteral administration routes include, e.g., intravascular administration (e.g., intravenous bolus injection, intravenous infusion, intra-arterial bolus injection, intra-arterial infusion and catheter instillation into the vasculature); peri- and intra-tissue injection (e.g., peri-tumoral and intra-tumoral injection, intra-retinal injection, or subretinal injection); subcutaneous injection or deposition, including subcutaneous infusion (such as by osmotic pumps); direct application to the tissue of interest, for example by a catheter or other placement device (e.g., a retinal pellet or a suppository or an implant comprising a porous, non-porous, or gelatinous material); and inhalation. Preferred administration routes are injection, infusion and direct injection into the tumor.
In the present methods, an miR gene product or miR gene expression inhibiting compound can be administered to the subject either as naked RNA, in combination with a delivery reagent, or as a nucleic acid (e.g., a recombinant plasmid or viral vector) comprising sequences that express the miR gene product or expression inhibiting compound. Suitable delivery reagents include, e.g, the Minis Transit TKO lipophilic reagent; lipofectin; lipofectamine; cellfectin; polycations (e.g., polylysine), and liposomes.
Recombinant plasmids and viral vectors comprising sequences that express the miR gene products or miR gene expression inhibiting compounds, and techniques for delivering such plasmids and vectors to cancer cells, are discussed above.
In a preferred embodiment, liposomes are used to deliver an miR gene product or miR gene expression-inhibiting compound (or nucleic acids comprising sequences encoding them) to a subject. Liposomes can also increase the blood half-life of the gene products or nucleic acids.
Liposomes suitable for use in the invention can be formed from standard vesicle-forming lipids, which generally include neutral or negatively charged phospholipids and a sterol, such as cholesterol. The selection of lipids is generally guided by consideration of factors such as the desired liposome size and half-life of the liposomes in the blood stream. A variety of methods are known for preparing liposomes, for example, as described in Szoka et al. (1980), Ann. Rev. Biophys. Bioeng. 9:467; and U.S. Pat. Nos. 4,235,871, 4,501,728, 4,837,028, and 5,019,369, the entire disclosures of which are herein incorporated by reference.
The liposomes for use in the present methods can comprise a ligand molecule that targets the liposome to cancer cells. Ligands which bind to receptors prevalent in cancer cells, such as monoclonal antibodies that bind to tumor cell antigens, are preferred.
The liposomes for use in the present methods can also be modified so as to avoid clearance by the mononuclear macrophage system (“MMS”) and reticuloendothelial system (“RES”). Such modified liposomes have opsonization-inhibition moieties on the surface or incorporated into the liposome structure. In a particularly preferred embodiment, a liposome of the invention can comprise both opsonization-inhibition moieties and a ligand.
Opsonization-inhibiting moieties for use in preparing the liposomes of the invention are typically large hydrophilic polymers that are bound to the liposome membrane. As used herein, an opsonization inhibiting moiety is “bound” to a liposome membrane when it is chemically or physically attached to the membrane, e.g., by the intercalation of a lipid-soluble anchor into the membrane itself, or by binding directly to active groups of membrane lipids. These opsonization-inhibiting hydrophilic polymers form a protective surface layer that significantly decreases the uptake of the liposomes by the MMS and RES; e.g., as described in U.S. Pat. No. 4,920,016, the entire disclosure of which is herein incorporated by reference.
Opsonization inhibiting moieties suitable for modifying liposomes are preferably water-soluble polymers with a number-average molecular weight from about 500 to about 40,000 daltons, and more preferably from about 2,000 to about 20,000 daltons. Such polymers include polyethylene glycol (PEG) or polypropylene glycol (PPG) derivatives; e.g., methoxy PEG or PPG, and PEG or PPG stearate; synthetic polymers such as polyacrylamide or poly N-vinyl pyrrolidone; linear, branched, or dendrimeric polyamidoamines; polyacrylic acids; polyalcohols, e.g., polyvinylalcohol and polyxylitol to which carboxylic or amino groups are chemically linked, as well as gangliosides, such as ganglioside GM1. Copolymers of PEG, methoxy PEG, or methoxy PPG, or derivatives thereof, are also suitable. In addition, the opsonization inhibiting polymer can be a block copolymer of PEG and either a polyamino acid, polysaccharide, polyamidoamine, polyethyleneamine, or polynucleotide. The opsonization inhibiting polymers can also be natural polysaccharides containing amino acids or carboxylic acids, e.g., galacturonic acid, glucuronic acid, mannuronic acid, hyaluronic acid, pectic acid, neuraminic acid, alginic acid, carrageenan; aminated polysaccharides or oligosaccharides (linear or branched); or carboxylated polysaccharides or oligosaccharides, e.g., reacted with derivatives of carbonic acids with resultant linking of carboxylic groups. Preferably, the opsonization-inhibiting moiety is a PEG, PPG, or derivatives thereof. Liposomes modified with PEG or PEG-derivatives are sometimes called “PEGylated liposomes.”
The opsonization inhibiting moiety can be bound to the liposome membrane by any one of numerous well-known techniques. For example, an N-hydroxysuccinimide ester of PEG can be bound to a phosphatidyl-ethanolamine lipid-soluble anchor, and then bound to a membrane. Similarly, a dextran polymer can be derivatized with a stearylamine lipid-soluble anchor via reductive amination using Na(CN)BH₃and a solvent mixture, such as tetrahydrofuran and water in a 30:12 ratio at 60° C.
Liposomes modified with opsonization-inhibition moieties remain in the circulation much longer than unmodified liposomes. For this reason, such liposomes are sometimes called “stealth” liposomes. Stealth liposomes are known to accumulate in tissues fed by porous or “leaky” microvasculature. Thus, tissue characterized by such microvasculature defects, for example solid tumors, will efficiently accumulate these liposomes; see Gabizon, et al. (1988), Proc. Natl. Acad. Sci., USA, 18:6949-53. In addition, the reduced uptake by the RES lowers the toxicity of stealth liposomes by preventing significant accumulation of the liposomes in the liver and spleen. Thus, liposomes that are modified with opsonization-inhibition moieties are particularly suited to deliver the miR gene products or miR gene expression inhibition compounds (or nucleic acids comprising sequences encoding them) to tumor cells.
The miR gene products or miR gene expression inhibition compounds are preferably formulated as pharmaceutical compositions, sometimes called “medicaments,” prior to administering to a subject, according to techniques known in the art. Pharmaceutical compositions of the present invention are characterized as being at least sterile and pyrogen-free. As used herein, “pharmaceutical formulations” include formulations for human and veterinary use. Methods for preparing pharmaceutical compositions of the invention are within the skill in the art, for example as described in Remington's Pharmaceutical Science, 17th ed., Mack Publishing Company, Easton, Pa. (1985), the entire disclosure of which is herein incorporated by reference.
The present pharmaceutical formulations comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) (e.g., 0.1 to 90% by weight), or a physiologically acceptable salt thereof, mixed with a pharmaceutically-acceptable carrier. The pharmaceutical formulations of the invention can also comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) which are encapsulated by liposomes and a pharmaceutically-acceptable carrier. In one embodiment, the pharmaceutical compositions comprise an miR gene or gene product that is not miR-15, miR-16, miR-143 and/or miR-145.
Preferred pharmaceutically-acceptable carriers are water, buffered water, normal saline, 0.4% saline, 0.3% glycine, hyaluronic acid and the like.
In a preferred embodiment, the pharmaceutical compositions of the invention comprise at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) which is resistant to degradation by nucleases. One skilled in the art can readily synthesize nucleic acids which are nuclease resistant, for example by incorporating one or more ribonucleotides that are modified at the 2′-position into the miR gene products. Suitable 2′-modified ribonucleotides include those modified at the 2′-position with fluoro, amino, alkyl, alkoxy, and O-allyl.
Pharmaceutical compositions of the invention can also comprise conventional pharmaceutical excipients and/or additives. Suitable pharmaceutical excipients include stabilizers, antioxidants, osmolality adjusting agents, buffers, and pH adjusting agents. Suitable additives include, e.g., physiologically bio compatible buffers (e.g., tromethamine hydrochloride), additions of chelants (such as, for example, DTPA or DTPA-bisamide) or calcium chelate complexes (such as, for example, calcium DTPA, CaNaDTPA-bisamide), or, optionally, additions of calcium or sodium salts (for example, calcium chloride, calcium ascorbate, calcium gluconate or calcium lactate). Pharmaceutical compositions of the invention can be packaged for use in liquid form, or can be lyophilized.
For solid pharmaceutical compositions of the invention, conventional nontoxic solid pharmaceutically-acceptable carriers can be used; for example, pharmaceutical grades of mannitol, lactose, starch, magnesium stearate, sodium saccharin, talcum, cellulose, glucose, sucrose, magnesium carbonate, and the like.
For example, a solid pharmaceutical composition for oral administration can comprise any of the carriers and excipients listed above and 10-95%, preferably 25%-75%, of the at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them). A pharmaceutical composition for aerosol (inhalational) administration can comprise 0.01-20% by weight, preferably 1%-10% by weight, of the at least one miR gene product or miR gene expression inhibition compound (or at least one nucleic acid comprising sequences encoding them) encapsulated in a liposome as described above, and a propellant. A carrier can also be included as desired; e.g., lecithin for intranasal delivery.
The invention will now be illustrated by the following non-limiting examples.

EXAMPLES

The following techniques were used in the Examples.
General Methods:
The miR Gene Database
A set of 187 human miR genes was compiled (see Table 1). The set comprises 153 miRs identified in the miR Registry (maintained by the Wellcome Trust Sanger Institute, Cambridge, UK), and 36 other miRs manually curated from published papers (Lim et al., 2003, Science 299:1540; Lagos-Quintana et al., 2001, Science 294:853-858; Lau et al., 2001, Science 294:858-862; Lee et al., 2001, Science 294:862-864; Mourelatos et al., 2002, Genes Dev. 16:720-728; Lagos-Quintana et al., 2002, Curr. Biol. 12:735-739; Dostie et al., 2003, RNA 9:180-186; Houbaviy et al., 2003, Dev. Cell. 5:351-8) or found in the GenBank database accessed through the National Center for Biotechnology Information (NCBI) website, maintained by the National Institutes of Health and the National Library of Medicine Nineteen new human miRs (approximately 10% of the miR set) were found based on their homology with cloned miRs from other species (mainly mouse). For all miRs, the sequence of the precursor was identified using the M Zucker RNA folding program and selecting the precursor sequence that gave the best score for the hairpin structure. The program is available and is maintained by Michael Zucker of Rensselaer Polytechnic Institute.

TABLE 1

Human miR Gene Product Sequences

		SEQ ID
Name	Precursor Sequence (5′ to 3′)*	NO.

has-let-7a-1-prec	CACTGTGGGATGAGGTAGTAGGTTGTATAGTTTTAGG	1
	GTCACACCCACCACTGGGAGATAACTATACAATCTAC
	TGTCTTTCCTAACGTG

hsa-let-7a-2-prec	AGGTTGAGGTAGTAGGTTGTATAGTTTAGAATTACAT	2
	CAAGGGAGATAACTGTACAGCCTCCTAGCTTTCCT

hsa-let-7a-3-prec	GGGTGAGGTAGTAGGTTGTATAGTTTGGGGCTCTGCC	3
	CTGCTATGGGATAACTATACAATCTACTGTCTTTCCT

hsa-let-7a-4-prec	GTGACTGCATGCTCCCAGGTTGAGGTAGTAGGTTGTA	4
	TAGTTTAGAATTACACAAGGGAGATAACTGTACAGCC
	TCCTAGCTTTCCTTGGGTCTTGCACTAAACAAC

hsa-let-7b-prec	GGCGGGGTGAGGTAGTAGGTTGTGTGGTTTCAGGGCA	5
	GTGATGTTGCCCCTCGGAAGATAACTATACAACCTAC
	TGCCTTCCCTG

hsa-let-7c-prec	GCATCCGGGTTGAGGTAGTAGGTTGTATGGTTTAGAG	6
	TTACACCCTGGGAGTTAACTGTACAACCTTCTAGCTTT
	CCTTGGAGC

hsa-let-7d-prec	CCTAGGAAGAGGTAGTAGGTTGCATAGTTTTAGGGCA	7
	GGGATTTTGCCCACAAGGAGGTAACTATACGACCTGC
	TGCCTTTCTTAGG

hsa-let-7d-v1-prec	CTAGGAAGAGGTAGTAGTTTGCATAGTTTTAGGGCAA	8
	AGATTTTGCCCACAAGTAGTTAGCTATACGACCTGCA
	GCCTTTTGTAG

hsa-let-7d-v2-prec	CTGGCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTGT	9
	GACATTGCCCGCTGTGGAGATAACTGCGCAAGCTACT
	GCCTTGCTAG

hsa-let-7e-prec	CCCGGGCTGAGGTAGGAGGTTGTATAGTTGAGGAGGA	10
	CACCCAAGGAGATCACTATACGGCCTCCTAGCTTTCCC
	CAGG

hsa-let-7f-1-prec	TCAGAGTGAGGTAGTAGATTGTATAGTTGTGGGGTAG	11
	TGATTTTACCCTGTTCAGGAGATAACTATACAATCTAT
	TGCCTTCCCTGA

hsa-let-7f-2-prec	CTGTGGGATGAGGTAGTAGATTGTATAGTTGTGGGGT	12
	AGTGATTTTACCCTGTTCAGGAGATAACTATACAATCT
	ATTGCCTTCCCTGA

hsa-let-7f-2-prec	CTGTGGGATGAGGTAGTAGATTGTATAGTTTTAGGGT	13
	CATACCCCATCTTGGAGATAACTATACAGTCTACTGTC
	TTTCCCACGG

hsa-let-7g-prec	TTGCCTGATTCCAGGCTGAGGTAGTAGTTTGTACAGTT	14
	TGAGGGTCTATGATACCACCCGGTACAGGAGATAACT
	GTACAGGCCACTGCCTTGCCAGGAACAGCGCGC

hsa-let-7i-prec	CTGGCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTGT	15
	GACATTGCCCGCTGTGGAGATAACTGCGCAAGCTACT
	GCCTTGCTAG

hsa-mir-001b-1-	ACCTACTCAGAGTACATACTTCTTTATGTACCCATATG	16
prec	AACATACAATGCTATGGAATGTAAAGAAGTATGTATT
	TTTGGTAGGC

hsa-mir-001b-1-	CAGCTAACAACTTAGTAATACCTACTCAGAGTACATA	17
prec	CTTCTTTATGTACCCATATGAACATACAATGCTATGGA
	ATGTAAAGAAGTATGTATTTTTGGTAGGCAATA

hsa-mir-001b-2-	GCCTGCTTGGGAAACATACTTCTTTATATGCCCATATG	18
prec	GACCTGCTAAGCTATGGAATGTAAAGAAGTATGTATC
	TCAGGCCGGG

hsa-mir-001b-	TGGGAAACATACTTCTTTATATGCCCATATGGACCTGC	19
prec	TAAGCTATGGAATGTAAAGAAGTATGTATCTCA

hsa-mir-001d-	ACCTACTCAGAGTACATACTTCTTTATGTACCCATATG	20
prec	AACATACAATGCTATGGAATGTAAAGAAGTATGTATT
	TTTGGTAGGC

hsa-mir-007-1	TGGATGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATT	21
	TTGTTGTTTTTAGATAACTAAATCGACAACAAATCACA
	GTCTGCCATATGGCACAGGCCATGCCTCTACA

hsa-mir-007-1-	TTGGATGTTGGCCTAGTTCTGTGTGGAAGACTAGTGAT	22
prec	TTTGTTGTTTTTAGATAACTAAATCGACAACAAATCAC
	AGTCTGCCATATGGCACAGGCCATGCCTCTACAG

hsa-mir-007-2	CTGGATACAGAGTGGACCGGCTGGCCCCATCTGGAAG	23
	ACTAGTGATTTTGTTGTTGTCTTACTGCGCTCAACAAC
	AAATCCCAGTCTACCTAATGGTGCCAGCCATCGCA

hsa-mir-007-2-	CTGGATACAGAGTGGACCGGCTGGCCCCATCTGGAAG	24
prec	ACTAGTGATTTTGTTGTTGTCTTACTGCGCTCAACAAC
	AAATCCCAGTCTACCTAATGGTGCCAGCCATCGCA

hsa-mir-007-3	AGATTAGAGTGGCTGTGGTCTAGTGCTGTGTGGAAGA	25
	CTAGTGATTTTGTTGTTCTGATGTACTACGACAACAAG
	TCACAGCCGGCCTCATAGCGCAGACTCCCTTCGAC

hsa-mir-007-3-	AGATTAGAGTGGCTGTGGTCTAGTGCTGTGTGGAAGA	26
prec	CTAGTGATTTTGTTGTTCTGATGTACTACGACAACAAG
	TCACAGCCGGCCTCATAGCGCAGACTCCCTTCGAC

hsa-mir-009-1	CGGGGTTGGTTGTTATCTTTGGTTATCTAGCTGTATGA	27
	GTGGTGTGGAGTCTTCATAAAGCTAGATAACCGAAAG
	TAAAAATAACCCCA

hsa-mir-009-2	GGAAGCGAGTTGTTATCTTTGGTTATCTAGCTGTATGA	28
	GTGTATTGGTCTTCATAAAGCTAGATAACCGAAAGTA
	AAAACTCCTTCA

hsa-mir-009-3	GGAGGCCCGTTTCTCTCTTTGGTTATCTAGCTGTATGA	29
	GTGCCACAGAGCCGTCATAAAGCTAGATAACCGAAAG
	TAGAAATGATTCTCA

hsa-mir-010a-prec	GATCTGTCTGTCTTCTGTATATACCCTGTAGATCCGAA	30
	TTTGTGTAAGGAATTTTGTGGTCACAAATTCGTATCTA
	GGGGAATATGTAGTTGACATAAACACTCCGCTCT

hsa-mir-010b-	CCAGAGGTTGTAACGTTGTCTATATATACCCTGTAGAA	31
prec	CCGAATTTGTGTGGTATCCGTATAGTCACAGATTCGAT
	TCTAGGGGAATATATGGTCGATGCAAAAACTTCA

hsa-mir-015a-2-	GCGCGAATGTGTGTTTAAAAAAAATAAAACCTTGGAG	32
prec	TAAAGTAGCAGCACATAATGGTTTGTGGATTTTGAAA
	AGGTGCAGGCCATATTGTGCTGCCTCAAAAATAC

hsa-mir-015a-prec	CCTTGGAGTAAAGTAGCAGCACATAATGGTTTGTGGA	33
	TTTTGAAAAGGTGCAGGCCATATTGTGCTGCCTCAAA
	AATACAAGG

hsa-mir-015b-	CTGTAGCAGCACATCATGGTTTACATGCTACAGTCAA	34
prec	GATGCGAATCATTATTTGCTGCTCTAG

hsa-mir-015b-	TTGAGGCCTTAAAGTACTGTAGCAGCACATCATGGTTT	35
prec	ACATGCTACAGTCAAGATGCGAATCATTATTTGCTGCT
	CTAGAAATTTAAGGAAATTCAT

hsa-mir-016a-	GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTT	36
chr13	AAGATTCTAAAATTATCTCCAGTATTAACTGTGCTGCT
	GAAGTAAGGTTGAC

hsa-mir-016b-	GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGA	37
chr3	AATATATATTAAACACCAATATTACTGTGCTGCTTTAG
	TGTGAC

hsa-mir-016-prec-	GCAGTGCCTTAGCAGCACGTAAATATTGGCGTTAAGA	38
13	TTCTAAAATTATCTCCAGTATTAACTGTGCTGCTGAAG
	TAAGGT

hsa-mir-017-prec	GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGT	39
	GATATGTGCATCTACTGCAGTGAAGGCACTTGTAGCA
	TTATGGTGAC

hsa-mir-018-prec	TGTTCTAAGGTGCATCTAGTGCAGATAGTGAAGTAGA	40
	TTAGCATCTACTGCCCTAAGTGCTCCTTCTGGCA

hsa-mir-018-prec-	TTTTTGTTCTAAGGTGCATCTAGTGCAGATAGTGAAGT	41
13	AGATTAGCATCTACTGCCCTAAGTGCTCCTTCTGGCAT
	AAGAA

hsa-mir-019a-prec	GCAGTCCTCTGTTAGTTTTGCATAGTTGCACTACAAGA	42
	AGAATGTAGTTGTGCAAATCTATGCAAAACTGATGGT
	GGCCTGC

hsa-mir-019a-	CAGTCCTCTGTTAGTTTTGCATAGTTGCACTACAAGAA	43
prec-13	GAATGTAGTTGTGCAAATCTATGCAAAACTGATGGTG
	GCCTG

hsa-mir-019b-1-	CACTGTTCTATGGTTAGTTTTGCAGGTTTGCATCCAGC	44
prec	TGTGTGATATTCTGCTGTGCAAATCCATGCAAAACTGA
	CTGTGGTAGTG

hsa-mir-019b-2-	ACATTGCTACTTACAATTAGTTTTGCAGGTTTGCATTT	45
prec	CAGCGTATATATGTATATGTGGCTGTGCAAATCCATGC
	AAAACTGATTGTGATAATGT

hsa-mir-019b-	TTCTATGGTTAGTTTTGCAGGTTTGCATCCAGCTGTGT	46
prec-13	GATATTCTGCTGTGCAAATCCATGCAAAACTGACTGT
	GGTAG

hsa-mir-019b-	TTACAATTAGTTTTGCAGGTTTGCATTTCAGCGTATAT	47
prec-X	ATGTATATGTGGCTGTGCAAATCCATGCAAAACTGAT
	TGTGAT

hsa-mir-020-prec	GTAGCACTAAAGTGCTTATAGTGCAGGTAGTGTTTAG	48
	TTATCTACTGCATTATGAGCACTTAAAGTACTGC

hsa-mir-021-prec	TGTCGGGTAGCTTATCAGACTGATGTTGACTGTTGAAT	49
	CTCATGGCAACACCAGTCGATGGGCTGTCTGACA

hsa-mir-021-prec-	ACCTTGTCGGGTAGCTTATCAGACTGATGTTGACTGTT	50
17	GAATCTCATGGCAACACCAGTCGATGGGCTGTCTGAC
	ATTTTG

hsa-mir-022-prec	GGCTGAGCCGCAGTAGTTCTTCAGTGGCAAGCTTTAT	51
	GTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGT
	TGCCCTCTGCC

hsa-mir-023a-prec	GGCCGGCTGGGGTTCCTGGGGATGGGATTTGCTTCCT	52
	GTCACAAATCACATTGCCAGGGATTTCCAACCGACC

hsa-mir-023b-	CTCAGGTGCTCTGGCTGCTTGGGTTCCTGGCATGCTGA	53
prec	TTTGTGACTTAAGATTAAAATCACATTGCCAGGGATTA
	CCACGCAACCACGACCTTGGC

hsa-mir-023-prec-	CCACGGCCGGCTGGGGTTCCTGGGGATGGGATTTGCT	54
19	TCCTGTCACAAATCACATTGCCAGGGATTTCCAACCG
	ACCCTGA

hsa-mir-024-1-	CTCCGGTGCCTACTGAGCTGATATCAGTTCTCATTTTA	55
prec	CACACTGGCTCAGTTCAGCAGGAACAGGAG

hsa-mir-024-2-	CTCTGCCTCCCGTGCCTACTGAGCTGAAACACAGTTGG	56
prec	TTTGTGTACACTGGCTCAGTTCAGCAGGAACAGGG

hsa-mir-024-prec-	CCCTGGGCTCTGCCTCCCGTGCCTACTGAGCTGAAACA	57
19	CAGTTGGTTTGTGTACACTGGCTCAGTTCAGCAGGAA
	CAGGGG

hsa-mir-024-prec-9	CCCTCCGGTGCCTACTGAGCTGATATCAGTTCTCATTT	58
	TACACACTGGCTCAGTTCAGCAGGAACAGCATC

hsa-mir-025-prec	GGCCAGTGTTGAGAGGCGGAGACTTGGGCAATTGCTG	59
	GACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACA
	GTGCCGGCC

hsa-mir-026a-prec	AGGCCGTGGCCTCGTTCAAGTAATCCAGGATAGGCTG	60
	TGCAGGTCCCAATGGCCTATCTTGGTTACTTGCACGGG
	GACGCGGGCCT

hsa-mir-026b-	CCGGGACCCAGTTCAAGTAATTCAGGATAGGTTGTGT	61
prec	GCTGTCCAGCCTGTTCTCCATTACTTGGCTCGGGGACC
	GG

hsa-mir-027a-prec	CTGAGGAGCAGGGCTTAGCTGCTTGTGAGCAGGGTCC	62
	ACACCAAGTCGTGTTCACAGTGGCTAAGTTCCGCCCC
	CCAG

hsa-mir-027b-	AGGTGCAGAGCTTAGCTGATTGGTGAACAGTGATTGG	63
prec	TTTCCGCTTTGTTCACAGTGGCTAAGTTCTGCACCT

hsa-mir-027b-	ACCTCTCTAACAAGGTGCAGAGCTTAGCTGATTGGTG	64
prec	AACAGTGATTGGTTTCCGCTTTGTTCACAGTGGCTAAG
	TTCTGCACCTGAAGAGAAGGTG

hsa-mir-027-prec-	CCTGAGGAGCAGGGCTTAGCTGCTTGTGAGCAGGGTC	65
19	CACACCAAGTCGTGTTCACAGTGGCTAAGTTCCGCCC
	CCCAGG

hsa-mir-028-prec	GGTCCTTGCCCTCAAGGAGCTCACAGTCTATTGAGTTA	66
	CCTTTCTGACTTTCCCACTAGATTGTGAGCTCCTGGAG
	GGCAGGCACT

hsa-mir-029a-2	CCTTCTGTGACCCCTTAGAGGATGACTGATTTCTTTTG	67
	GTGTTCAGAGTCAATATAATTTTCTAGCACCATCTGAA
	ATCGGTTATAATGATTGGGGAAGAGCACCATG

hsa-mir-029a-prec	ATGACTGATTTCTTTTGGTGTTCAGAGTCAATATAATT	68
	TTCTAGCACCATCTGAAATCGGTTAT

hsa-mir-029c-prec	ACCACTGGCCCATCTCTTACACAGGCTGACCGATTTCT	69
	CCTGGTGTTCAGAGTCTGTTTTTGTCTAGCACCATTTG
	AAATCGGTTATGATGTAGGGGGAAAAGCAGCAGC

hsa-mir-030a-prec	GCGACTGTAAACATCCTCGACTGGAAGCTGTGAAGCC	70
	ACAGATGGGCTTTCAGTCGGATGTTTGCAGCTGC

hsa-mir-030b-	ATGTAAACATCCTACACTCAGCTGTAATACATGGATT	71
prec	GGCTGGGAGGTGGATGTTTACGT

hsa-mir-030b-	ACCAAGTTTCAGTTCATGTAAACATCCTACACTCAGCT	72
prec	GTAATACATGGATTGGCTGGGAGGTGGATGTTTACTT
	CAGCTGACTTGGA

hsa-mir-030c-prec	AGATACTGTAAACATCCTACACTCTCAGCTGTGGAAA	73
	GTAAGAAAGCTGGGAGAAGGCTGTTTACTCTTTCT

hsa-mir-030d-	GTTGTTGTAAACATCCCCGACTGGAAGCTGTAAGACA	74
prec	CAGCTAAGCTTTCAGTCAGATGTTTGCTGCTAC

hsa-mir-031-prec	GGAGAGGAGGCAAGATGCTGGCATAGCTGTTGAACTG	75
	GGAACCTGCTATGCCAACATATTGCCATCTTTCC

hsa-mir-032-prec	GGAGATATTGCACATTACTAAGTTGCATGTTGTCACG	76
	GCCTCAATGCAATTTAGTGTGTGTGATATTTTC

hsa-mir-033b-	GGGGGCCGAGAGAGGCGGGCGGCCCCGCGGTGCATT	77
prec	GCTGTTGCATTGCACGTGTGTGAGGCGGGTGCAGTGC
	CTCGGCAGTGCAGCCCGGAGCCGGCCCCTGGCACCAC

hsa-mir-033-prec	CTGTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGT	78
	ACCCATGCAATGTTTCCACAGTGCATCACAG

hsa-mir-034-prec	GGCCAGCTGTGAGTGTTTCTTTGGCAGTGTCTTAGCTG	79
	GTTGTTGTGAGCAATAGTAAGGAAGCAATCAGCAAGT
	ATACTGCCCTAGAAGTGCTGCACGTTGTGGGGCCC

hsa-mir-091-prec-	TCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTG	80
13	ATATGTGCATCTACTGCAGTGAAGGCACTTGTAGCATT
	ATGGTGA

hsa-mir-092-prec-	CTTTCTACACAGGTTGGGATCGGTTGCAATGCTGTGTT	81
13 = 092-1	TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTT
	GG

hsa-mir-092-prec-	TCATCCCTGGGTGGGGATTTGTTGCATTACTTGTGTTC	82
X = 092-2	TATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAGA

hsa-mir-093-prec-	CTGGGGGCTCCAAAGTGCTGTTCGTGCAGGTAGTGTG	83
7.1 = 093-1	ATTACCCAACCTACTGCTGAGCTAGCACTTCCCGAGCC
	CCCGG

hsa-mir-093-prec-	CTGGGGGCTCCAAAGTGCTGTTCGTGCAGGTAGTGTG	84
7.2 = 093-2	ATTACCCAACCTACTGCTGAGCTAGCACTTCCCGAGCC
	CCCGG

hsa-mir-095-prec-4	AACACAGTGGGCACTCAATAAATGTCTGTTGAATTGA	85
	AATGCGTTACATTCAACGGGTATTTATTGAGCACCCAC
	TCTGTG

hsa-mir-096-prec-7	TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCT	86
	CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGG
	AAA

hsa-mir-098-prec-X	GTGAGGTAGTAAGTTGTATTGTTGTGGGGTAGGGATA	87
	TTAGGCCCCAATTAGAAGATAACTATACAACTTACTA
	CTTTCC

hsa-mir-099b-	GGCACCCACCCGTAGAACCGACCTTGCGGGGCCTTCG	88
prec-19	CCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC

hsa-mir-099-prec-	CCCATTGGCATAAACCCGTAGATCCGATCTTGTGGTG	89
21	AAGTGGACCGCACAAGCTCGCTTCTATGGGTCTGTGT
	CAGTGTG

hsa-mir-100-1/2-	AAGAGAGAAGATATTGAGGCCTGTTGCCACAAACCCG	90
prec	TAGATCCGAACTTGTGGTATTAGTCCGCACAAGCTTGT
	ATCTATAGGTATGTGTCTGTTAGGCAATCTCAC

hsa-mir-100-prec-	CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTAT	91
11	TAGTCCGCACAAGCTTGTATCTATAGGTATGTGTCTGT
	TAGG

hsa-mir-101-1/2-	AGGCTGCCCTGGCTCAGTTATCACAGTGCTGATGCTGT	92
prec	CTATTCTAAAGGTACAGTACTGTGATAACTGAAGGAT
	GGCAGCCATCTTACCTTCCATCAGAGGAGCCTCAC

hsa-mir-101-prec	TCAGTTATCACAGTGCTGATGCTGTCCATTCTAAAGGT	93
	ACAGTACTGTGATAACTGA

hsa-mir-101-prec-1	TGCCCTGGCTCAGTTATCACAGTGCTGATGCTGTCTAT	94
	TCTAAAGGTACAGTACTGTGATAACTGAAGGATGGCA

hsa-mir-101-prec-9	TGTCCTTTTTCGGTTATCATGGTACCGATGCTGTATAT	95
	CTGAAAGGTACAGTACTGTGATAACTGAAGAATGGTG

hsa-mir-102-prec-1	CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTTT	96
	CCATCTTTGTATCTAGCACCATTTGAAATCAGTGTTTT
	AGGAG

hsa-mir-102-prec-	CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTAA	97
7.1	ATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTT
	GGGGG

hsa-mir-102-prec-	CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTAA	98
7.2	ATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTT
	GGGGG

hsa-mir-103-2-	TTGTGCTTTCAGCTTCTTTACAGTGCTGCCTTGTAGCA	99
prec	TTCAGGTCAAGCAACATTGTACAGGGCTATGAAAGAA
	CCA

hsa-mir-103-prec-	TTGTGCTTTCAGCTTCTTTACAGTGCTGCCTTGTAGCA	100
20	TTCAGGTCAAGCAACATTGTACAGGGCTATGAAAGAA
	CCA

hsa-mir-103-prec-	TACTGCCCTCGGCTTCTTTACAGTGCTGCCTTGTTGCA	101
5 = 103-1	TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCA
	TTG

hsa-mir-104-prec-	AAATGTCAGACAGCCCATCGACTGGTGTTGCCATGAG	102
17	ATTCAACAGTCAACATCAGTCTGATAAGCTACCCGAC
	AAGG

hsa-mir-105-prec-	TGTGCATCGTGGTCAAATGCTCAGACTCCTGTGGTGGC	103
X.1 = 105-1	TGCTCATGCACCACGGATGTTTGAGCATGTGCTACGGT
	GTCTA

hsa-mir-105-prec-	TGTGCATCGTGGTCAAATGCTCAGACTCCTGTGGTGGC	104
X.2 = 105-2	TGCTCATGCACCACGGATGTTTGAGCATGTGCTACGGT
	GTCTA

hsa-mir-106-prec-X	CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCT	105
	TTTTGAGATCTACTGCAATGTAAGCACTTCTTACATTA
	CCATGG

hsa-mir-107-prec-	CTCTCTGCTTTCAGCTTCTTTACAGTGTTGCCTTGTGGC	106
10	ATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC
	ACAGA

hsa-mir-122a-prec	CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGT	107
	GTCTAAACTATCAAACGCCATTATCACACTAAATAGC
	TACTGCTAGGC

hsa-mir-122a-prec	AGCTGTGGAGTGTGACAATGGTGTTTGTGTCCAAACT	108
	ATCAAACGCCATTATCACACTAAATAGCT

hsa-mir-123-prec	ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAAC	109
	TCGTACCGTGAGTAATAATGCGC

hsa-mir-124a-1-	tccttcctCAGGAGAAAGGCCTCTCTCTCCGTGTTCACAGC	110
prec	GGACCTTGATTTAAATGTCCATACAATTAAGGCACGC
	GGTGAATGCCAAGAATGGGGCT

hsa-mir-124a-1-	AGGCCTCTCTCTCCGTGTTCACAGCGGACCTTGATTTA	111
prec	AATGTCCATACAATTAAGGCACGCGGTGAATGCCAAG
	AATGGGGCTG

hsa-mir-124a-2-	ATCAAGATTAGAGGCTCTGCTCTCCGTGTTCACAGCG	112
prec	GACCTTGATTTAATGTCATACAATTAAGGCACGCGGT
	GAATGCCAAGAGCGGAGCCTACGGCTGCACTTGAAG

hsa-mir-124a-3-	CCCGCCCCAGCCCTGAGGGCCCCTCTGCGTGTTCACA	113
prec	GCGGACCTTGATTTAATGTCTATACAATTAAGGCACG
	CGGTGAATGCCAAGAGAGGCGCCTCCGCCGCTCCTT

hsa-mir-124a-3-	TGAGGGCCCCTCTGCGTGTTCACAGCGGACCTTGATTT	114
prec	AATGTCTATACAATTAAGGCACGCGGTGAATGCCAAG
	AGAGGCGCCTCC

hsa-mir-124a-prec	CTCTGCGTGTTCACAGCGGACCTTGATTTAATGTCTAT	115
	ACAATTAAGGCACGCGGTGAATGCCAAGAG

hsa-mir-124b-	CTCTCCGTGTTCACAGCGGACCTTGATTTAATGTCATA	116
prec	CAATTAAGGCACGCGGTGAATGCCAAGAG

hsa-mir-125a-prec	TGCCAGTCTCTAGGTCCCTGAGACCCTTTAACCTGTGA	117
	GGACATCCAGGGTCACAGGTGAGGTTCTTGGGAGCCT
	GGCGTCTGGCC

hsa-mir-125a-prec	GGTCCCTGAGACCCTTTAACCTGTGAGGACATCCAGG	118
	GTCACAGGTGAGGTTCTTGGGAGCCTGG

hsa-mir-125b-1	ACATTGTTGCGCTCCTCTCAGTCCCTGAGACCCTAACT	119
	TGTGATGTTTACCGTTTAAATCCACGGGTTAGGCTCTT
	GGGAGCTGCGAGTCGTGCTTTTGCATCCTGGA

hsa-mir-125b-1	TGCGCTCCTCTCAGTCCCTGAGACCCTAACTTGTGATG	120
	TTTACCGTTTAAATCCACGGGTTAGGCTCTTGGGAGCT
	GCGAGTCGTGCT

hsa-mir-125b-2-	ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGA	121
prec	GGTATTTTAGTAACATCACAAGTCAGGCTCTTGGGAC
	CTAGGCGGAGGGGA

hsa-mir-125b-2-	CCTAGTCCCTGAGACCCTAACTTGTGAGGTATTTTAGT	122
prec	AACATCACAAGTCAGGCTCTTGGGACCTAGGC

hsa-mir-126-prec	CGCTGGCGACGGGACATTATTACTTTTGGTACGCGCTG	123
	TGACACTTCAAACTCGTACCGTGAGTAATAATGCGCC
	GTCCACGGCA

hsa-mir-126-prec	ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAAC	124
	TCGTACCGTGAGTAATAATGCGC

hsa-mir-127-prec	TGTGATCACTGTCTCCAGCCTGCTGAAGCTCAGAGGG	125
	CTCTGATTCAGAAAGATCATCGGATCCGTCTGAGCTTG
	GCTGGTCGGAAGTCTCATCATC

hsa-mir-127-prec	CCAGCCTGCTGAAGCTCAGAGGGCTCTGATTCAGAAA	126
	GATCATCGGATCCGTCTGAGCTTGGCTGGTCGG

hsa-mir-128a-prec	TGAGCTGTTGGATTCGGGGCCGTAGCACTGTCTGAGA	127
	GGTTTACATTTCTCACAGTGAACCGGTCTCTTTTTCAG
	CTGCTTC

hsa-mir-128b-	GCCCGGCAGCCACTGTGCAGTGGGAAGGGGGGCCGAT	128
prec	ACACTGTACGAGAGTGAGTAGCAGGTCTCACAGTGAA
	CCGGTCTCTTTCCCTACTGTGTCACACTCCTAATGG

hsa-mir-128-prec	GTTGGATTCGGGGCCGTAGCACTGTCTGAGAGGTTTA	129
	CATTTCTCACAGTGAACCGGTCTCTTTTTCAGC

hsa-mir-129-prec	TGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCAA	130
	CAGTAGTCAGGAAGCCCTTACCCCAAAAAGTATCTA

hsa-mir-130a-prec	TGCTGCTGGCCAGAGCTCTTTTCACATTGTGCTACTGT	131
	CTGCACCTGTCACTAGCAGTGCAATGTTAAAAGGGCA
	TTGGCCGTGTAGTG

hsa-mir-131-1-	gccaggaggcggGGTTGGTTGTTATCTTTGGTTATCTAGCTG	132
prec	TATGAGTGGTGTGGAGTCTTCATAAAGCTAGATAACC
	GAAAGTAAAAATAACCCCATACACTGCGCAG

hsa-mir-131-3-	CACGGCGCGGCAGCGGCACTGGCTAAGGGAGGCCCGT	133
prec	TTCTCTCTTTGGTTATCTAGCTGTATGAGTGCCACAGA
	GCCGTCATAAAGCTAgataaccgaaagtagaaatg

hsa-mir-131-prec	GTTGTTATCTTTGGTTATCTAGCTGTATGAGTGTATTG	134
	GTCTTCATAAAGCTAGATAACCGAAAGTAAAAAC

hsa-mir-132-prec	CCGCCCCCGCGTCTCCAGGGCAACCGTGGCTTTCGATT	135
	GTTACTGTGGGAACTGGAGGTAACAGTCTACAGCCAT
	GGTCGCCCCGCAGCACGCCCACGCGC

hsa-mir-132-prec	GGGCAACCGTGGCTTTCGATTGTTACTGTGGGAACTG	136
	GAGGTAACAGTCTACAGCCATGGTCGCCC

hsa-mir-133a-1	ACAATGCTTTGCTAGAGCTGGTAAAATGGAACCAAAT	137
	CGCCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGT
	AGCTATGCATTGA

hsa-mir-133a-2	GGGAGCCAAATGCTTTGCTAGAGCTGGTAAAATGGAA	138
	CCAAATCGACTGTCCAATGGATTTGGTCCCCTTCAACC
	AGCTGTAGCTGTGCATTGATGGCGCCG

hsa-mir-133-prec	GCTAGAGCTGGTAAAATGGAACCAAATCGCCTCTTCA	139
	ATGGATTTGGTCCCCTTCAACCAGCTGTAGC

hsa-mir-134-prec	CAGGGTGTGTGACTGGTTGACCAGAGGGGCATGCACT	140
	GTGTTCACCCTGTGGGCCACCTAGTCACCAACCCTC

hsa-mir-134-prec	AGGGTGTGTGACTGGTTGACCAGAGGGGCATGCACTG	141
	TGTTCACCCTGTGGGCCACCTAGTCACCAACCCT

hsa-mir-135-1-	AGGCCTCGCTGTTCTCTATGGCTTTTTATTCCTATGTG	142
prec	ATTCTACTGCTCACTCATATAGGGATTGGAGCCGTGGC
	GCACGGCGGGGACA

hsa-mir-135-2-	AGATAAATTCACTCTAGTGCTTTATGGCTTTTTATTCC	143
prec	TATGTGATAGTAATAAAGTCTCATGTAGGGATGGAAG
	CCATGAAATACATTGTGAAAAATCA

hsa-mir-135-prec	CTATGGCTTTTTATTCCTATGTGATTCTACTGCTCACTC	144
	ATATAGGGATTGGAGCCGTGG

hsa-mir-136-prec	TGAGCCCTCGGAGGACTCCATTTGTTTTGATGATGGAT	145
	TCTTATGCTCCATCATCGTCTCAAATGAGTCTTCAGAG
	GGTTCT

hsa-mir-136-prec	GAGGACTCCATTTGTTTTGATGATGGATTCTTATGCTC	146
	CATCATCGTCTCAAATGAGTCTTC

hsa-mir-137-prec	CTTCGGTGACGGGTATTCTTGGGTGGATAATACGGATT	147
	ACGTTGTTATTGCTTAAGAATACGCGTAGTCGAGG

hsa-mir-138-1-	CCCTGGCATGGTGTGGTGGGGCAGCTGGTGTTGTGAA	148
prec	TCAGGCCGTTGCCAATCAGAGAACGGCTACTTCACAA
	CACCAGGGCCACACCACACTACAGG

hsa-mir-138-2-	CGTTGCTGCAGCTGGTGTTGTGAATCAGGCCGACGAG	149
prec	CAGCGCATCCTCTTACCCGGCTATTTCACGACACCAGG
	GTTGCATCA

hsa-mir-138-prec	CAGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCAT	150
	CCTCTTACCCGGCTATTTCACGACACCAGGGTTG

hsa-mir-139-prec	GTGTATTCTACAGTGCACGTGTCTCCAGTGTGGCTCGG	151
	AGGCTGGAGACGCGGCCCTGTTGGAGTAAC

hsa-mir-140	TGTGTCTCTCTCTGTGTCCTGCCAGTGGTTTTACCCTAT	152
	GGTAGGTTACGTCATGCTGTTCTACCACAGGGTAGAA
	CCACGGACAGGATACCGGGGCACC

hsa-mir-140as-	TCCTGCCAGTGGTTTTACCCTATGGTAGGTTACGTCAT	153
prec	GCTGTTCTACCACAGGGTAGAACCACGGACAGGA

hsa-mir-140s-prec	CCTGCCAGTGGTTTTACCCTATGGTAGGTTACGTCATG	154
	CTGTTCTACCACAGGGTAGAACCACGGACAGG

hsa-mir-141-prec	CGGCCGGCCCTGGGTCCATCTTCCAGTACAGTGTTGG	155
	ATGGTCTAATTGTGAAGCTCCTAACACTGTCTGGTAAA
	GATGGCTCCCGGGTGGGTTC

hsa-mir-141-prec	GGGTCCATCTTCCAGTACAGTGTTGGATGGTCTAATTG	156
	TGAAGCTCCTAACACTGTCTGGTAAAGATGGCCC

hsa-mir-142as-	ACCCATAAAGTAGAAAGCACTACTAACAGCACTGGAG	157
prec	GGTGTAGTGTTTCCTACTTTATGGATG

hsa-mir-142-prec	GACAGTGCAGTCACCCATAAAGTAGAAAGCACTACTA	158
	ACAGCACTGGAGGGTGTAGTGTTTCCTACTTTATGGAT
	GAGTGTACTGTG

hsa-mir-142s-pres	ACCCATAAAGTAGAAAGCACTACTAACAGCACTGGAG	159
	GGTGTAGTGTTTCCTACTTTATGGATG

hsa-mir-143-prec	GCGCAGCGCCCTGTCTCCCAGCCTGAGGTGCAGTGCT	160
	GCATCTCTGGTCAGTTGGGAGTCTGAGATGAAGCACT
	GTAGCTCAGGAAGAGAGAAGTTGTTCTGCAGC

hsa-mir-143-prec	CCTGAGGTGCAGTGCTGCATCTCTGGTCAGTTGGGAG	161
	TCTGAGATGAAGCACTGTAGCTCAGG

hsa-mir-144-prec	TGGGGCCCTGGCTGGGATATCATCATATACTGTAAGTT	162
	TGCGATGAGACACTACAGTATAGATGATGTACTAGTC
	CGGGCACCCCC

hsa-mir-144-prec	GGCTGGGATATCATCATATACTGTAAGTTTGCGATGA	163
	GACACTACAGTATAGATGATGTACTAGTC

hsa-mir-145-prec	CACCTTGTCCTCACGGTCCAGTTTTCCCAGGAATCCCT	164
	TAGATGCTAAGATGGGGATTCCTGGAAATACTGTTCTT
	GAGGTCATGGTT

hsa-mir-145-prec	CTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTA	165
	AGATGGGGATTCCTGGAAATACTGTTCTTGAG

hsa-mir-146-prec	CCGATGTGTATCCTCAGCTTTGAGAACTGAATTCCATG	166
	GGTTGTGTCAGTGTCAGACCTCTGAAATTCAGTTCTTC
	AGCTGGGATATCTCTGTCATCGT

hsa-mir-146-prec	AGCTTTGAGAACTGAATTCCATGGGTTGTGTCAGTGTC	167
	AGACCTGTGAAATTCAGTTCTTCAGCT

hsa-mir-147-prec	AATCTAAAGACAACATTTCTGCACACACACCAGACTA	168
	TGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT

hsa-mir-148-prec	GAGGCAAAGTTCTGAGACACTCCGACTCTGAGTATGA	169
	TAGAAGTCAGTGCACTACAGAACTTTGTCTC

hsa-mir-149-prec	GCCGGCGCCCGAGCTCTGGCTCCGTGTCTTCACTCCCG	170
	TGCTTGTCCGAGGAGGGAGGGAGGGACGGGGGCTGT
	GCTGGGGCAGCTGGA

hsa-mir-149-prec	GCTCTGGCTCCGTGTCTTCACTCCCGTGCTTGTCCGAG	171
	GAGGGAGGGAGGGAC

hsa-mir-150-prec	CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGC	172
	TGGGCTCAGACCCTGGTACAGGCCTGGGGGACAGGGA
	CCTGGGGAC

hsa-mir-150-prec	CCCTGTCTCCCAACCCTTGTACCAGTGCTGGGCTCAGA	173
	CCCTGGTACAGGCCTGGGGGACAGGG

hsa-mir-151-prec	CCTGCCCTCGAGGAGCTCACAGTCTAGTATGTCTCATC	174
	CCCTACTAGACTGAAGCTCCTTGAGGACAGG

hsa-mir-152-prec	TGTCCCCCCCGGCCCAGGTTCTGTGATACACTCCGACT	175
	CGGGCTCTGGAGCAGTCAGTGCATGACAGAACTTGGG
	CCCGGAAGGACC

hsa-mir-152-prec	GGCCCAGGTTCTGTGATACACTCCGACTCGGGCTCTG	176
	GAGCAGTCAGTGCATGACAGAACTTGGGCCCCGG

hsa-mir-153-1-	CTCACAGCTGCCAGTGTCATTTTTGTGATCTGCAGCTA	177
prec	GTATTCTCACTCCAGTTGCATAGTCACAAAAGTGATCA
	TTGGCAGGTGTGGC

hsa-mir-153-1-	tctctctctccctcACAGCTGCCAGTGTCATTGTCACAAAAGTG	178
prec	ATCATTGGCAGGTGTGGCTGCTGCATG

hsa-mir-153-2-	AGCGGTGGCCAGTGTCATTTTTGTGATGTTGCAGCTAG	179
prec	TAATATGAGCCCAGTTGCATAGTCACAAAAGTGATCA
	TTGGAAACTGTG

hsa-mir-153-2-	CAGTGTCATTTTTGTGATGTTGCAGCTAGTAATATGAG	180
prec	CCCAGTTGCATAGTCACAAAAGTGATCATTG

hsa-mir-154-prec	GTGGTACTTGAAGATAGGTTATCCGTGTTGCCTTCGCT	181
	TTATTTGTGACGAATCATACACGGTTGACCTATTTTTC
	AGTACCAA

hsa-mir-154-prec	GAAGATAGGTTATCCGTGTTGCCTTCGCTTTATTTGTG	182
	ACGAATCATACACGGTTGACCTATTTTT

hsa-mir-155-prec	CTGTTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAA	183
	CTGACTCCTACATATTAGCATTAACAG

hsa-mir-16-2-prec	CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGG	184
	CGTTAAGATTCTAAAATTATCTCCAGTATTAACTGTGC
	TGCTGAAGTAAGGTTGACCATACTCTACAGTTG

hsa-mir-181a-prec	AGAAGGGCTATCAGGCCAGCCTTCAGAGGACTCCAAG	185
	GAACATTCAACGCTGTCGGTGAGTTTGGGATTTGAAA
	AAACCACTGACCGTTGACTGTACCTTGGGGTCCTTA

hsa-mir-181b-	TGAGTTTTGAGGTTGCTTCAGTGAACATTCAACGCTGT	186
prec	CGGTGAGTTTGGAATTAAAATCAAAACCATCGACCGT
	TGATTGTACCCTATGGCTAACCATCATCTACTCCA

hsa-mir-181c-prec	CGGAAAATTTGCCAAGGGTTTGGGGGAACATTCAACC	187
	TGTCGGTGAGTTTGGGCAGCTCAGGCAAACCATCGAC
	CGTTGAGTGGACCCTGAGGCCTGGAATTGCCATCCT

hsa-mir-182-as-	GAGCTGCTTGCCTCCCCCCGTTTTTGGCAATGGTAGAA	188
prec	CTCACACTGGTGAGGTAACAGGATCCGGTGGTTCTAG
	ACTTGCCAACTATGGGGCGAGGACTCAGCCGGCAC

hsa-mir-182-prec	TTTTTGGCAATGGTAGAACTCACACTGGTGAGGTAAC	189
	AGGATCCGGTGGTTCTAGACTTGCCAACTATGG

hsa-mir-183-prec	CCGCAGAGTGTGACTCCTGTTCTGTGTATGGCACTGGT	190
	AGAATTCACTGTGAACAGTCTCAGTCAGTGAATTACC
	GAAGGGCCATAAACAGAGCAGAGACAGATCCACGA

hsa-mir-184-prec	CCAGTCACGTCCCCTTATCACTTTTCCAGCCCAGCTTT	191
	GTGACTGTAAGTGTTGGACGGAGAACTGATAAGGGTA
	GGTGATTGA

hsa-mir-184-prec	CCTTATCACTTTTCCAGCCCAGCTTTGTGACTGTAAGT	192
	GTTGGACGGAGAACTGATAAGGGTAGG

hsa-mir-185-prec	AGGGGGCGAGGGATTGGAGAGAAAGGCAGTTCCTGA	193
	TGGTCCCCTCCCCAGGGGCTGGCTTTCCTCTGGTCCTT
	CCCTCCCA

hsa-mir-185-prec	AGGGATTGGAGAGAAAGGCAGTTCCTGATGGTCCCCT	194
	CCCCAGGGGCTGGCTTTCCTCTGGTCCTT

hsa-mir-186-prec	TGCTTGTAACTTTCCAAAGAATTCTCCTTTTGGGCTTT	195
	CTGGTTTTATTTTAAGCCCAAAGGTGAATTTTTTGGGA
	AGTTTGAGCT

hsa-mir-186-prec	ACTTTCCAAAGAATTCTCCTTTTGGGCTTTCTGGTTTTA	196
	TTTTAAGCCCAAAGGTGAATTTTTTGGGAAGT

hsa-mir-187-prec	GGTCGGGCTCACCATGACACAGTGTGAGACTCGGGCT	197
	ACAACACAGGACCCGGGGCGCTGCTCTGACCCCTCGT
	GTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCA

hsa-mir-188-prec	TGCTCCCTCTCTCACATCCCTTGCATGGTGGAGGGTGA	198
	GCTTTCTGAAAACCCCTCCCACATGCAGGGTTTGCAG
	GATGGCGAGCC

hsa-mir-188-prec	TCTCACATCCCTTGCATGGTGGAGGGTGAGCTTTCTGA	199
	AAACCCCTCCCACATGCAGGGTTTGCAGGA

hsa-mir-189-prec	CTGTCGATTGGACCCGCCCTCCGGTGCCTACTGAGCTG	200
	ATATCAGTTCTCATTTTACACACTGGCTCAGTTCAGCA
	GGAACAGGAGTCGAGCCCTTGAGCAA

hsa-mir-189-prec	CTCCGGTGCCTACTGAGCTGATATCAGTTCTCATTTTA	201
	CACACTGGCTCAGTTCAGCAGGAACAGGAG

hsa-mir-190-prec	TGCAGGCCTCTGTGTGATATGTTTGATATATTAGGTTG	202
	TTATTTAATCCAACTATATATCAAACATATTCCTACAG
	TGTCTTGCC

hsa-mir-190-prec	CTGTGTGATATGTTTGATATATTAGGTTGTTATTTAAT	203
	CCAACTATATATCAAACATATTCCTACAG

hsa-mir-191-prec	CGGCTGGACAGCGGGCAACGGAATCCCAAAAGCAGC	204
	TGTTGTCTCCAGAGCATTCCAGCTGCGCTTGGATTTCG
	TCCCCTGCTCTCCTGCCT

hsa-mir-191-prec	AGCGGGCAACGGAATCCCAAAAGCAGCTGTTGTCTCC	205
	AGAGCATTCCAGCTGCGCTTGGATTTCGTCCCCTGCT

hsa-mir-192-2/3	CCGAGACCGAGTGCACAGGGCTCTGACCTATGAATTG	206
	ACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAATTC
	CATAGGTCACAGGTATGTTCGCCTCAATGCCAG

hsa-mir-192-prec	GCCGAGACCGAGTGCACAGGGCTCTGACCTATGAATT	207
	GACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAATT
	CCATAGGTCACAGGTATGTTCGCCTCAATGCCAGC

hsa-mir-193-prec	CGAGGATGGGAGCTGAGGGCTGGGTCTTTGCGGGCGA	208
	GATGAGGGTGTCGGATCAACTGGCCTACAAAGTCCCA
	GTTCTCGGCCCCCG

hsa-mir-193-prec	GCTGGGTCTTTGCGGGCGAGATGAGGGTGTCGGATCA	209
	ACTGGCCTACAAAGTCCCAGT

hsa-mir-194-prec	ATGGTGTTATCAAGTGTAACAGCAACTCCATGTGGAC	210
	TGTGTACCAATTTCCAGTGGAGATGCTGTTACTTTTGA
	TGGTTACCAA

hsa-mir-194-prec	GTGTAACAGCAACTCCATGTGGACTGTGTACCAATTTC	211
	CAGTGGAGATGCTGTTACTTTTGAT

hsa-mir-195-prec	AGCTTCCCTGGCTCTAGCAGCACAGAAATATTGGCAC	212
	AGGGAAGCGAGTCTGCCAATATTGGCTGTGCTGCTCC
	AGGCAGGGTGGTG

hsa-mir-195-prec	TAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCT	213
	GCCAATATTGGCTGTGCTGCT

hsa-mir-196-1-	CTAGAGCTTGAATTGGAACTGCTGAGTGAATTAGGTA	214
prec	GTTTCATGTTGTTGGGCCTGGGTTTCTGAACACAACAA
	CATTAAACCACCCGATTCACGGCAGTTACTGCTCC

hsa-mir-196-1-	GTGAATTAGGTAGTTTCATGTTGTTGGGCCTGGGTTTC	215
prec	TGAACACAACAACATTAAACCACCCGATTCAC

hsa-mir-196-2-	TGCTCGCTCAGCTGATCTGTGGCTTAGGTAGTTTCATG	216
prec	TTGTTGGGATTGAGTTTTGAACTCGGCAACAAGAAAC
	TGCCTGAGTTACATCAGTCGGTTTTCGTCGAGGGC

hsa-mir-196-prec	GTGAATTAGGTAGTTTCATGTTGTTGGGCCTGGGTTTC	217
	TGAACACAACAACATTAAACCACCCGATTCAC

hsa-mir-197-prec	GGCTGTGCCGGGTAGAGAGGGCAGTGGGAGGTAAGA	218
	GCTCTTCACCCTTCACCACCTTCTCCACCCAGCATGGCC

hsa-mir-198-prec	TCATTGGTCCAGAGGGGAGATAGGTTCCTGTGATTTTT	219
	CCTTCTTCTCTATAGAATAAATGA

hsa-mir-199a-1-	GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTC	220
prec	TCAATGTGTACAGTAGTCTGCACATTGGTTAGGC

hsa-mir-199a-2-	AGGAAGCTTCTGGAGATCCTGCTCCGTCGCCCCAGTG	221
prec	TTCAGACTACCTGTTCAGGACAATGCCGTTGTACAGTA
	GTCTGCACATTGGTTAGACTGGGCAAGGGAGAGCA

hsa-mir-199b-	CCAGAGGACACCTCCACTCCGTCTACCCAGTGTTTAG	222
prec	ACTATCTGTTCAGGACTCCCAAATTGTACAGTAGTCTG
	CACATTGGTTAGGCTGGGCTGGGTTAGACCCTCGG

hsa-mir-199s-prec	GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTC	223
	TCAATGTGTACAGTAGTCTGCACATTGGTTAGGC

hsa-mir-200a-prec	GCCGTGGCCATCTTACTGGGCAGCATTGGATGGAGTC	224
	AGGTCTCTAATACTGCCTGGTAATGATGACGGC

hsa-mir-200b-	CCAGCTCGGGCAGCCGTGGCCATCTTACTGGGCAGCA	225
prec	TTGGATGGAGTCAGGTCTCTAATACTGCCTGGTAATG
	ATGACGGCGGAGCCCTGCACG

hsa-mir-202-prec	GTTCCTTTTTCCTATGCATATACTTCTTTGAGGATCTGG	226
	CCTAAAGAGGTATAGGGCATGGGAAGATGGAGC

hsa-mir-203-prec	GTGTTGGGGACTCGCGCGCTGGGTCCAGTGGTTCTTA	227
	ACAGTTCAACAGTTCTGTAGCGCAATTGTGAAATGTTT
	AGGACCACTAGACCCGGCGGGCGCGGCGACAGCGA

hsa-mir-204-prec	GGCTACAGTCTTTCTTCATGTGACTCGTGGACTTCCCT	228
	TTGTCATCCTATGCCTGAGAATATATGAAGGAGGCTG
	GGAAGGCAAAGGGACGTTCAATTGTCATCACTGGC

hsa-mir-205-prec	AAAGATCCTCAGACAATCCATGTGCTTCTCTTGTCCTT	229
	CATTCCACCGGAGTCTGTCTCATACCCAACCAGATTTC
	AGTGGAGTGAAGTTCAGGAGGCATGGAGCTGACA

hsa-mir-206-prec	TGCTTCCCGAGGCCACATGCTTCTTTATATCCCCATAT	230
	GGATTACTTTGCTATGGAATGTAAGGAAGTGTGTGGT
	TTCGGCAAGTG

hsa-mir-206-prec	AGGCCACATGCTTCTTTATATCCCCATATGGATTACTT	231
	TGCTATGGAATGTAAGGAAGTGTGTGGTTTT

hsa-mir-208-prec	TGACGGGCGAGCTTTTGGCCCGGGTTATACCTGATGCT	232
	CACGTATAAGACGAGCAAAAAGCTTGTTGGTCA

hsa-mir-210-prec	ACCCGGCAGTGCCTCCAGGCGCAGGGCAGCCCCTGCC	233
	CACCGCACACTGCGCTGCCCCAGACCCACTGTGCGTG
	TGACAGCGGCTGATCTGTGCCTGGGCAGCGCGACCC

hsa-mir-211-prec	TCACCTGGCCATGTGACTTGTGGGCTTCCCTTTGTCAT	234
	CCTTCGCCTAGGGCTCTGAGCAGGGCAGGGACAGCAA
	AGGGGTGCTCAGTTGTCACTTCCCACAGCACGGAG

hsa-mir-212-prec	CGGGGCACCCCGCCCGGACAGCGCGCCGGCACCTTGG	235
	CTCTAGACTGCTTACTGCCCGGGCCGCCCTCAGTAACA
	GTCTCCAGTCACGGCCACCGACGCCTGGCCCCGCC

hsa-mir-213-prec	CCTGTGCAGAGATTATTTTTTAAAAGGTCACAATCAAC	236
	ATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG
	CTCACTGAACAATGAATGCAACTGTGGCCCCGCTT

hsa-mir-213-prec-	GAGTTTTGAGGTTGCTTCAGTGAACATTCAACGCTGTC	237
LIM	GGTGAGTTTGGAATTAAAATCAAAACCATCGACCGTT
	GATTGTACCCTATGGCTAACCATCATCTACTCC

hsa-mir-214-prec	GGCCTGGCTGGACAGAGTTGTCATGTGTCTGCCTGTCT	238
	ACACTTGCTGTGCAGAACATCCGCTCACCTGTACAGC
	AGGCACAGACAGGCAGTCACATGACAACCCAGCCT

hsa-mir-215-prec	ATCATTCAGAAATGGTATACAGGAAAATGACCTATGA	239
	ATTGACAGACAATATAGCTGAGTTTGTCTGTCATTTCT
	TTAGGCCAATATTCTGTATGACTGTGCTACTTCAA

hsa-mir-216-prec	GATGGCTGTGAGTTGGCTTAATCTCAGCTGGCAACTGT	240
	GAGATGTTCATACAATCCCTCACAGTGGTCTCTGGGAT
	TATGCTAAACAGAGCAATTTCCTAGCCCTCACGA

hsa-mir-217-prec	AGTATAATTATTACATAGTTTTTGATGTCGCAGATACT	241
	GCATCAGGAACTGATTGGATAAGAATCAGTCACCATC
	AGTTCCTAATGCATTGCCTTCAGCATCTAAACAAG

hsa-mir-218-1-	GTGATAATGTAGCGAGATTTTCTGTTGTGCTTGATCTA	242
prec	ACCATGTGGTTGCGAGGTATGAGTAAAACATGGTTCC
	GTCAAGCACCATGGAACGTCACGCAGCTTTCTACA

hsa-mir-218-2-	GACCAGTCGCTGCGGGGCTTTCCTTTGTGCTTGATCTA	243
prec	ACCATGTGGTGGAACGATGGAAACGGAACATGGTTCT
	GTCAAGCACCGCGGAAAGCACCGTGCTCTCCTGCA

hsa-mir-219-prec	CCGCCCCGGGCCGCGGCTCCTGATTGTCCAAACGCAA	244
	TTCTCGAGTCTATGGCTCCGGCCGAGAGTTGAGTCTGG
	ACGTCCCGAGCCGCCGCCCCCAAACCTCGAGCGGG

hsa-mir-220-prec	GACAGTGTGGCATTGTAGGGCTCCACACCGTATCTGA	245
	CACTTTGGGCGAGGGCACCATGCTGAAGGTGTTCATG
	ATGCGGTCTGGGAACTCCTCACGGATCTTACTGATG

hsa-mir-221-prec	TGAACATCCAGGTCTGGGGCATGAACCTGGCATACAA	246
	TGTAGATTTCTGTGTTCGTTAGGCAACAGCTACATTGT
	CTGCTGGGTTTCAGGCTACCTGGAAACATGTTCTC

hsa-mir-222-prec	GCTGCTGGAAGGTGTAGGTACCCTCAATGGCTCAGTA	247
	GCCAGTGTAGATCCTGTCTTTCGTAATCAGCAGCTACA
	TCTGGCTACTGGGTCTCTGATGGCATCTTCTAGCT

hsa-mir-223-prec	CCTGGCCTCCTGCAGTGCCACGCTCCGTGTATTTGACA	248
	AGCTGAGTTGGACACTCCATGTGGTAGAGTGTCAGTT
	TGTCAAATACCCCAAGTGCGGCACATGCTTACCAG

hsa-mir-224-prec	GGGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATGA	249
	TTGTGCATTGTTTCAAAATGGTGCCCTAGTGACTACAA
	AGCCC

hsA-mir-29b-	CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTTT	250
1 = 102-prec1	CCATCTTTGTATCTAGCACCATTTGAAATCAGTGTTTT
	AGGAG

hsA-mir-29b-	CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTAA	251
2 = 102prec7.1 = 7.2	ATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTT
	GGGGG

hsA-mir-29b-	CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTAA	252
3 = 102prec7.1 = 7.2	ATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTT
	GGGGG

hsa-mir-30* = mir-	GTGAGCGACTGTAAACATCCTCGACTGGAAGCTGTGA	253
097-prec-6	AGCCACAGATGGGCTTTCAGTCGGATGTTTGCAGCTG
	CCTACT

mir-033b	ACCAAGTTTCAGTTCATGTAAACATCCTACACTCAGCT	254
	GTAATACATGGATTGGCTGGGAGGTGGATGTTTACTT
	CAGCTGACTTGGA

mir-101-	TGCCCTGGCTCAGTTATCACAGTGCTGATGCTGTCTAT	255
precursor-9 = mir-	TCTAAAGGTACAGTACTGTGATAACTGAAGGATGGCA
101-3

mir-108-1-small	ACACTGCAAGAACAATAAGGATTTTTAGGGGCATTAT	256
	GACTGAGTCAGAAAACACAGCTGCCCCTGAAAGTCCC
	TCATTTTTCTTGCTGT

mir-108-2-small	ACTGCAAGAGCAATAAGGATTTTTAGGGGCATTATGA	257
	TAGTGGAATGGAAACACATCTGCCCCCAAAAGTCCCT
	CATTTT

mir-123-prec =	CGCTGGCGACGGGACATTATTACTTTTGGTACGCGCTG	258
mir-126-prec	TGACACTTCAAAC
	TCGTACCGTGAGTAATAATGCGCCGTCCACGGCA

mir-123-prec =	ACATTATTACTTTTGGTACGCGCTGTGACACTTCAAAC	259
mir-126-prec	TCGTACCGTGAGTAATAATGCGC

mir-129-1-prec	TGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCAA	260
	CAGTAGTCAGGAAGCCCTTACCCCAAAAAGTATCTA

mir-129-small-	TGCCCTTCGCGAATCTTTTTGCGGTCTGGGCTTGCTGT	261
2 = 129b?	ACATAACTCAATAGCCGGAAGCCCTTACCCCAAAAAG
	CATTTGCGGAGGGCG

mir-133b-small	GCCCCCTGCTCTGGCTGGTCAAACGGAACCAAGTCCG	262
	TCTTCCTGAGAGGTTTGGTCCCCTTCAACCAGCTACAG
	CAGGG

mir-135-small-	AGATAAATTCACTCTAGTGCTTTATGGCTTTTTATTCC	263
	TATGTGATAGTAATAAAGTCTCATGTAGGGATGGAAG
	CCATGAAATACATTGTGAAAAATCA

mir-148b-small	AAGCACGATTAGCATTTGAGGTGAAGTTCTGTTATAC	264
	ACTCAGGCTGTGGCTCTCTGAAAGTCAGTGCAT

mir-151-prec	CCTGTCCTCAAGGAGCTTCAGTCTAGTAGGGGATGAG	265
	ACATACTAGACTGTGAGCTCCTCGAGGGCAGG

mir-155-	CTGTTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAA	266
prec(BIC)	CTGACTCCTACATATTAGCATTAACAG

mir-156 = mir-	CCTAACACTGTCTGGTAAAGATGGCTCCCGGGTGGGT	267
157 = overlap mir-	TCTCTCGGCAGTAACCTTCAGGGAGCCCTGAAGACCA
141	TGGAGGAC

mir-158-small =	GCCGAGACCGAGTGCACAGGGCTCTGACCTATGAATT	268
mir-192	GACAGCCAGTGCTCTCGTCTCCCCTCTGGCTGCCAATT
	CCATAGGTCACAGGTATGTTCGCCTCAATGCCAGC

mir-159-1-small	TCCCGCCCCCTGTAACAGCAACTCCATGTGGAAGTGC	269
	CCACTGGTTCCAGTGGGGCTGCTGTTATCTGGGGCGA
	GGGCCA

mir-161-small	AAAGCTGGGTTGAGAGGGCGAAAAAGGATGAGGTGA	270
	CTGGTCTGGGCTACGCTATGCTGCGGCGCTCGGG

mir-163-1b-small	CATTGGCCTCCTAAGCCAGGGATTGTGGGTTCGAGTC	271
	CCACCCGGGGTAAAGAAAGGCCGAATT

mir-163-3-small	CCTAAGCCAGGGATTGTGGGTTCGAGTCCCACCTGGG	272
	GTAGAGGTGAAAGTTCCTTTTACGGAATTTTTT

mir-175-	GGGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATGA	273
small = mir-224	TTGTGCATTGTTTCAAAATGGTGCCCTAGTGACTACAA
	AGCCC

mir-177-small	ACGCAAGTGTCCTAAGGTGAGCTCAGGGAGCACAGAA	274
	ACCTCCAGTGGAACAGAAGGGCAAAAGCTCATT

mir-180-small	CATGTGTCACTTTCAGGTGGAGTTTCAAGAGTCCCTTC	275
	CTGGTTCACCGTCTCCTTTGCTCTTCCACAAC

mir-187-prec	GGTCGGGCTCACCATGACACAGTGTGAGACTCGGGCT	276
	ACAACACAGGACCCGGGGCGCTGCTCTGACCCCTCGT
	GTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCA

mir-188-prec	TGCTCCCTCTCTCACATCCCTTGCATGGTGGAGGGTGA	277
	GCTTTCTGAAAACCCCTCCCACATGCAGGGTTTGCAG
	GATGGCGAGCC

mir-190-prec	TGCAGGCCTCTGTGTGATATGTTTGATATATTAGGTTG	278
	TTATTTAATCCAACTATATATCAAACATATTCCTACAG
	TGTCTTGCC

mir-197-2	GTGCATGTGTATGTATGTGTGCATGTGCATGTGTATGT	279
	GTATGAGTGCATGCGTGTGTGC

mir-197-prec	GGCTGTGCCGGGTAGAGAGGGCAGTGGGAGGTAAGA	280
	GCTCTTCACCCTTCACCACCTTCTCCACCCAGCATGGCC

mir-202-prec	GTTCCTTTTTCCTATGCATATACTTCTTTGAGGATCTGG	281
	CCTAAAGAGGTATAGGGCATGGGAAGATGGAGC

mir-294-1 (chr16)	CAATCTTCCTTTATCATGGTATTGATTTTTCAGTGCTTC	282
	CCTTTTGTGTGAGAGAAGATA

mir-hes1	ATGGAGCTGCTCACCCTGTGGGCCTCAAATGTGGAGG	283
	AACTATTCTGATGTCCAAGTGGAAAGTGCTGCGACAT
	TTGAGCGTCACCGGTGACGCCCATATCA

mir-hes2	GCATCCCCTCAGCCTGTGGCACTCAAACTGTGGGGGC	284
	ACTTTCTGCTCTCTGGTGAAAGTGCCGCCATCTTTTGA
	GTGTTACCGCTTGAGAAGACTCAACC

mir-hes3	CGAGGAGCTCATACTGGGATACTCAAAATGGGGGCGC	285
	TTTCCTTTTTGTCTGTTACTGGGAAGTGCTTCGATTTTG
	GGGTGTCCCTGTTTGAGTAGGGCATC

hsa-mir-29b-1	CTTCAGGAAGCTGGTTTCATATGGTGGTTTAGATTTAA	664
	ATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTT
	GGGGG

*An underlined sequence within a precursor sequence represents a processed miR transcript. All sequences are human.

Genome Analysis

The BUILD 33 and BUILD 34 Version 1 of the Homo sapiens genome, available at the NCBI website (see above), was used for genome analysis. For each human miR present in the miR database, a BLAST search was performed using the default parameters against the human genome to find the precise location, followed by mapping using the maps available at the Human Genome Resources at the NCBI website. See also Altschul et al. (1990), J. Mol. Biol. 215:403-10 and Altschul et al. (1997), Nucleic Acids Res. 25:3389-3402, the entire disclosures of which are herein incorporated by reference, for a discussion of the BLAST search algorithm. Also, as a confirmation of the data, the human clone corresponding to each miR was identified and mapped to the human genome (see Table 2). Perl scripts for the automatic submission of BLAST jobs and for the retrieval of the search results were based on the LPW, HTML, and HTPP Perl modules and BioPerl modules.

Fragile Site Database

This database was constructed using the Virtual Gene Nomenclature Workshop, maintained by the HUGO Gene Nomenclature Committee at University College, London. For each FRA locus, the literature was screened for publications reporting the cloning of the locus. In ten cases, genomic positions for both centromeric and telomeric ends were found. The total genomic length of these FRA loci is 26.9 Mb. In twenty-nine cases, only one anchoring marker was identified. It was determined, based on the published data, that 3 Mb can be used as the median length for each FRA locus. Therefore, 3 Mb was used as a guideline or window length for considering whether miR were in close proximity to the FRA sites.
The human clones for seventeen HPV 16 integration sites (1S) were also precisely mapped on the human genome. By analogy with the length of a FRA, in the case of HPV16 integration sites, “close” vicinity was defined to be a distance of less than 2 Mb.

PubMed Database

The PubMed database was screened on-line for publications describing cancer-related abnormalities such as minimal regions of loss-of-heterozygosity (minimal LOH) and minimal regions of amplification (minimal amplicons) using the words “LOH and genome-wide,” “amplification and genome-wide” and “amplicon and cancer.” The PubMed database is maintained by the NCBI and was accessed via its website. The data obtained from thirty-two papers were used to screen for putative CAGRs, based on markers with high frequency of LOH/amplification. As a second step, a literature search was performed to determine the presence or absence of the above three types of alterations and to determine the precise location of miRs with respect to CAGRs (see above). Search phrases included the combinations “minimal regions of LOH AND cancer”, and “minimal region amplification AND cancer.” A total of 296 publications were found and manually curated to find regions defined by both telomeric and centromeric markers. One hundred fifty-four minimally deleted regions (median length-4.14 Mb) and 37 minimally amplified regions (median length-2.45 Mb) were identified with precise genomic mapping for both telomeric and centromeric ends involving all human chromosomes except Y. To identify common breakpoint regions, PubMed was searched with the combination “translocation AND cloning AND breakpoint AND cancer.” The search yielded 308 papers, which were then manually curated. Among these papers, 45 translocations with at least one breakpoint precisely mapped were reported.

Statistical Analyses

The incidence of miR genes and their association with specific chromosomes and chromosome regions, such as FRAs and amplified or deleted regions in cancer, was analyzed with random effect Poisson regression models. Under these models, “events” are defined as the number of miR genes, and non-overlapping lengths of the region of interest defined exposure “time” (i.e., fragile site versus non-fragile site, etc.). The “length” of a region was exactly ±1 Mb, if known, or estimated as ±1 Mb if unknown. The random effect used was chromosomal location, in that data within a chromosome were assumed to be correlated. The fixed effect in each model consisted of an indicator variable(s) for the type of region. This model provided the incidence rate ratio (IRR), 2-sided 95% confidence interval of the IRR, and 2-sided p-values for testing the hypothesis that the IRR is 1.0. An IRR significantly greater than 1 indicates an increase in the number of miR genes within a region.
Each model was repeated considering the distribution of miR genes only in the transcriptionally active portion of the genome (about 43% of the genome using the published data), rather than the entire chromosome length, and similar results were obtained. Considering the distribution of miRs only in the transcriptionally active portion of the genome is more conservative, and takes into account the phenomenon of clustering that was observed for the miR genes' genomic location. All computations were completed using STATA v7.0.

Patient Samples and Cell Lines

Patient samples were obtained from twelve chronic lymphocytic leukemia (CLL) patients, and mononuclear cells were isolated through Ficoll-Hypaque gradient centrifugation (Amersham Pharmacia Biotech, Piscataway, N.J.), as previously described (Calin et al., Proc. Natl. Acad. Sci. USA 2002, 99:15524-15529). Samples were then processed for RNA and DNA extraction according to standard protocols as described in Sambrook J et al. (1989), Molecular cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.), the entire disclosure of which is herein incorporated by reference.
Seven human lung cancer cell lines were obtained from the American Type Culture Collection (ATCC; Manassas, Va.) and maintained according to ATCC instructions. These cell lines were: Calu-3, H1299, H522, H460, H23, H1650 and H1573.

Northern Blotting

Total RNA isolation from patient samples and cell lines described above was performed using the Tri-Reagent protocol (Molecular Research Center, Inc). RNA samples (30 μg each) were run on 15% acryl amide denaturing (urea) Criterion recast gels (Bio-Red Laboratories, Hercules, Calif.) and then transferred onto Hyoid-N+ membrane (Amersham Pharmacia Biotech), as previously described (Calin et al., Proc. Natl. Acad. Sci. USA 2002, 99:15524-15529). Hybridization with gamma-³²P ATP labeled probes was performed at 42° C. in 7% SDS, 0.2 M Na₂PO₄, pH 7.0 overnight. Membranes were washed at 42° C., twice in 2×SSPE, 0.1% SDS and twice with 0.5×SSPE, 0.1% SDS. Blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 minutes, and were reprobed several times. As a gel loading control, 5S rRNA was also loaded and was stained with ethidium bromide. Lung tissue RNA was utilized as the normal control; normal lung total RNA was purchased from Clontech (Palo Alto, Calif.).

Example 1

miR Genes are Non-Randomly Distributed in The Human Genome

One hundred eighty-six human genes representing known or predicted miR genes were mapped, based on mouse homology or computational methods, as described above in the General Methods. The results are presented in Table 2. The names were as in the miRNA Registry; for new miR genes, sequential names were assigned. miR 213 from Sanger database is different from miR 213 described in Lim et al. (2003, Science 299:1540). MiR genes in clusters are separated by a forward slash “/”. The approximate location in Mb of each clone is presented in the last column.

TABLE 2

miR Database: Chromosome Location and Clustering

	Chromosome		Loc (Mb)
Name	location	Genes in Cluster	(built 33)

let-7a-1	09q22.2	let-7a-1/let-7f-1/let-7d	90.2-.3
let-7a-2	11q24.1	miR-125b-1/let-7a-2/miR-100	121.9-122.15
let-7a-3	22q13.3	let-7a-3/let-7b	44.7-.8
let-7b	22q13.3	let-7a-3/let-7b	44.7-.8
let-7c	21q11.2	miR-99a/let-7c/miR-125b-2	16.7-.9
let-7d	09q22.2	let-7a-1/let-7f-1/let7d	90.2-.3
let-7e	19q13.4	miR-99b/let-7e/miR-125a	56.75-57
let-7f-1	09q22.2	let-7a-1/let-7f-1/let7d	90.2-.3
let-7f-2	Xp11.2	miR-98/let-7f-2	52.2-.3
let-7g	03p21.3	let-7g/miR-135-1	52.1-.3
let-7i	12q14.1		62.7-.9
miR-	20q13.3	miR-133a-2/miR-1b-2	61.75-.8
001b-2
miR-001d	18q11.1	miR-133a-1/miR-1d	19.25-.4
miR-007-1	09q21.33		80-80.1
miR-007-2	15q25		86.7-.8
miR-007-3	19p13.3		4.7-.75
miR-009-	01q22		153.1-.2
1 (=miR-
131-1)
miR-009-	05q14		87.85-88
2 (=miR-
131-2)
miR-009-	15q25.3		87.5
3 (=miR-
131-3)
miR-010a	17q21.3	miR-196-1/miR-10a	46.95-47.05
miR-010b	02q31		176.85-177
miR-015a	13q14	miR-16a/miR-15a	49.5-.8
miR-015b	03q26.1	miR-15b/miR-16b	161.35-.5
miR-016a	13q14	miR-16a/miR-15a	49.5-.8
miR-016b	03q26.1	miR-15b/miR-16b	161.35-.5
miR-017	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
(=miR-		19b-1/miR-92-1
91)
miR-018	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
		19b-1/miR-92-1
miR-019a	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
		19b-1/miR-92-1
miR-	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
019b-1		19b-1/miR-92-1
miR-	Xq26.2	miR-92-2/miR-19b-2/miR-106a	131.2-.3
019b-2
miR-020	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
		19b-1/miR-92-1
miR-021	17q23.2		58.25-.35
(=miR104-
as)
miR-022	17p13.3		1.4-.6
miR-023a	19p13.2	miR-24-2/miR-27a/miR-23a/miR-	13.75-.95
		181c
miR-023b	09q22.1	miR-24-1/miR27b/miR-23b	90.8-91
miR-024-	09q22.1	miR-24-1/miR27b/miR-23b	90.8-91
1 (=miR-
189)
miR-024-2	19p13.2	miR-24-2/miR-27a/miR-23a/miR-	13.75-.95
		181c
miR-025	07q22	miR-106b/miR-25/miR-93-1	99.25-.4
miR-026a	03p21		37.8-.9
miR-026b	02q35		219.1-.3
miR-027a	19p13.2	miR-24-2/miR-27a/miR-23a/miR-	13.75-.95
		181c
miR-027b	09q22.1	miR-24-1/miR27b/miR-23b	90.8-91
miR-028	03q28		189.65-.85
miR-029a	07q32	miR-29a/miR29b	129.9-130.1
miR-029b	07q32	miR-29a/miR29b	129.9-130.1
(=miR-
102-7.1)
miR-029c	01q32.2-32.3	miR-29c/miR-102	204.6-.7
miR-030a-	06q12-13		72.05-.2
as
miR-030a-	06q12-13		72.05-.2
s (=miR-
097)
miR-030b	08q24.2	miR-30d/miR-30b	135.5
miR-030c	06q13		71.95-72.1
miR-030d	08q24.2	miR-30d/miR-30b	135.5
miR-031	09p21		21.3-.5
miR-032	09q31.2		105.1-.3
miR-033a	22q13.2		40.5-.8
miR-033b	17p11.2		17.6-.7
miR-034	01p36.22		8.8
(=miR-
170)
miR-034a-1	11q23	miR-34a-2/miR 34a-1	111.3-.5
miR-034a-2	11q23	miR-34a-2/miR 34a-1	111.3-.5
miR-092-1	13q31	miR-17/miR-18/miR-19a/miR-20/miR-	90.82
		19b-1/miR-92-1
miR-092-2	Xq26.2	miR-92-2/miR-19b-2/miR-106a	131.2-.3
miR-093-1	07q22	miR-106b/miR-25/miR-93-1	99.25-.4
miR-095	04p16		8-.2
miR-096	07q32	miR-182s/miR-182as/miR-96/miR-	128.9-129
		183
miR-098	Xp11.2	miR-98/let-7f-2	52.2-.3
miR-099a	21q11.2	miR-99a/let-7c/miR-125b-2	16.7-.9
miR-099b	19q13.4	miR-99b/let-7e/miR-125a	56.75-57
miR-100	11q24.1	miR-125b-1/let-7a-2/miR-100	121.9-122.15
miR-101-1	01p31.3		64.85-95
miR-101-2	09p24		4.8-5
miR-102	01q32.2-32.3	miR-29c/miR-102	204.5-.7
miR-103-1	05q35.1		167.8-.95
miR-103-2	20p13		3.82-.90
miR-105-1	Xq28		149.3-.4
miR-106b	07q22	miR-106b/miR-25/miR-93-1	99.25-.4
(=miR-
94)
miR-106a	Xq26.2	miR-92-2/miR-19b-2/miR-106a	131.2-.3
miR-107	10q23.31		91.45-.6
miR-108-1	17q11.1	miR-108-1/miR-193	29.6-.8
miR-108-2	16q13.1		14.3-.5
miR-122a	18q21		55.85-56
miR-123	09q34		132.9-133.05
(=miR-
126)
miR-124a-1	08p23		9.5-.65
miR-124a-2	08q12.2		64.9-65.1
miR-124a-3	20q13.33		62.4-.55
miR-125a	19q13.4	miR-99b/let-7e/miR-125a	56.75-57
miR-125b-1	11q24.1	miR-125b1/let-7a-2/miR-100	121.9-122.1
miR-125b-2	21q11.2	miR-99a/let-7c/miR-125b-2	16.7-.9
miR-127	14q32	miR-127/miR-136	99.2-.4
miR-128a	02q21		136.3-.5
miR-128b	03p22		35.45-.6
miR-129-1	07q32		127.25-.4
miR-129-2	11p11.2		43.65-.75
miR-130a	11q12		57.6-.7
miR-130b	22q11.1		20.2-.4
miR-132	17p13.3	miR-212/miR-132	1.85-2
miR-133a-1	18q11.1	miR-133a-1/miR-1d	19.25-.4
miR-133a-2	20q13.3	miR-133a-2/miR-1b-2	61.75-.8
miR-133b	06p12	miR-206/miR-133b	51.9-52
miR-134	14q32	miR-154/miR-134/miR-299	99.4-.6
miR-135-1	03p21.3	let-7g/miR-135-1	52.1-.3
miR-135-2	12q23		97.85-98
miR-136	14q32	miR-127/miR-136	99.2-.4
miR-137	01p21-22		97.75
miR-138-1	03p21		43.85-.95
miR-138-2	16q12-13		56.55-.7
miR-139	11q13		72.55-.7
miR-140as	16q22.1		69.6-.8
miR-140s	16q22.1		69.6-.8
miR-141	12p13	overlap miR-156 —cluster	6.9-7.05
(=overlap
miR-156)
miR-142-	17q23		56.75-.9
as
miR-142-s	17q23		56.75-.9
miR-143	05q32-33	miR-145/miR-143	148.65-.8
miR-144	17q11.2		27.05
miR-145	05q32-33	miR-145/miR-143	148.65-.8
miR-146	05q34		159.8-.9
miR-147	09q33		116.35-.55
miR-148	07p15		25.6-.8
miR-148b	12q13		54.35-.45
miR-149	02q37.3		241.3-.4
miR-150	19q13		54.6-.8
miR-151	08q24.3		141.4-.5
miR-152	17q21		46.4-.5
miR-153-1	02q36		220.1-.2
miR-153-2	07q36		156.5-.7
miR-154	14q32	miR-154/miR-134/miR-299	99.4-.6
miR-155	21q21		25.85
(BIC)
miR-156	12p13	overlap miR-141 —cluster	6.9-7.05
(=miR-
157)
miR-159-1	11q13	miR-159-1/miR-192	64.9-65
miR-161	08p21		21.8-.9
miR-175	Xq28		148.8-.9
(=miR-
224)
miR-177	08p21		21.25-.35
miR-180	22q11.21-12.2		26.45
miR-181a	09q33.1-34.13		120.85-.95
(=miR-
178-2)
miR-181b	01q31.2-q32.1	miR-213 S/miR-181b	195.2-.35
(=miR-
178 =
miR-213-
LIM)
miR-181c	19p13.3	miR-24-2/miR-27a/miR-23a/miR-	13.75-.95
		181c
miR-182-	07q32	miR-182s/miR-182as/miR-96/miR-	128.9-129
as		183
miR-182-s	07q32	miR-182s/miR-182as/miR-96/miR-	128.9-129
		183
miR-183	07q32	miR-182s/miR-182as/miR-96/miR-	128.9-129
(=miR-		183
174)
miR-184	15q24		76.9-77.1
miR-185	22q11.2		18.35-.45
miR-186	01p31		70.9-71
miR-187	18q12.1		33.25-.4
miR-188	Xp11.23-p11.2		48.35-.5
miR-190	15q21		60.6-.8
miR-191	03p21		48.85-.95
miR-192	11q13	miR-159-1/miR-192	64.9-65
(=miR-
158)
miR-193	17q11.2	miR-108-1/miR-193	29.6-.8
miR-194	01q41	miR-215/miR-194	216.7-.8
(=miR-
159-2)
miR-195	17p13		6.75-.85
miR-196-1	17q21	miR-196-1/miR-10a	46.9-47.1
miR-196-2	12q13		54-54.15
miR-197	01p13		109.2-.3
miR-198	03q13.3		121.3-.4
miR-199a-	19p13.2		10.75-.8
1 (=miR-
199s)
miR-199a-2	01q23.3	miR-214/miR-199a-2	168.7-.8
miR-199as	19p13.2		10.75-.8
(=
antisense
miR-199a-
1)
miR-199b	09q34		124.3-.5
(=miR-
164)
miR-200	01p36.3		0.9-1
miR-202	10q26.3		135
miR-203	14q32.33		102.4-.6
miR-204	09q21.1		66.9-67
miR-205	01q32.2		206.2-.3
miR-206	06p12	miR-206/miR-133b	51.9-52
miR-208	14q11.2		21.8-22
miR-210	11p15		0.55-0.75
miR-211	15q11.2-q12		28.9-29.1
miR-212	17p13.3	miR-212/miR-132	1.9-2.1
miR-213-	01q31.3-q32.1	miR-213 S/miR-181b	195.2-.35
SANGER
miR-214	01q23.3	miR-214/miR-199a-2	168.7-.8
miR-215	01q41	miR-215/miR-194	216.7-.8
miR-216	02p16	miR-217/miR-216	56.2-.4
miR-217	02p16	miR-217/miR-216	56.2-.4
miR-218-1	04p15.32		20.15-.35
miR-218-2	05q35.1		168-.15
miR-219	06p21.2-21.31		33.1-.25
miR-220	Xq25		120.6-.8
miR-221	Xp11.3	miR-222/miR-221	44.35-.45
miR-222	Xp11.3	miR-222/miR-221	44.35-.45
miR-223	Xq12-13.3		63.4-.5
miR-294-1	16q22		65.1-.3
miR-297-3	20q13.2		52.25-.35
miR-299	14q32	miR-154/miR-134/miR-299	99.4-.6
miR-301	17q23		57.5-.7
miR-302	04q25		113.9-114
miR-hes1	19q13.4	miR-hes1/miR-hes2/miR-hes3	58.9-59.05
miR-hes2	19q13.4	miR-hes1/miR-hes2/miR-hes3	58.9-59.05
miR-hes3	19q13.4	miR-hes1/miR-hes2/miR-hes3	58.9-59.05

The distribution of the 186 human miR genes was found to be non-random. Ninety miR genes were located in 36 clusters, usually with two or three genes per cluster (median=2.5). The largest cluster found comprises six genes (miR-17/miR-18/miR-19a/miR-20/miR-19b1/miR-92-1) and is located at 13q31 (Table 2). A significant association of the incidence of miR genes with specific chromosomes was found. Chromosome 4 was found to have a lower than expected rate of miR genes (IRR=0.27; p=0.035). Chromosomes 17 and 19 were found to have significantly more miR genes than expected, based on chromosome size (IRR=2.97, p=0.002 and IRR=3.39, p=0.001, respectively). Six of the 36 miR gene clusters (17%), which contain 16 of 90 clustered genes (18%), are located on these two small chromosomes, which account for only 5% of the entire genome.
Similar results were obtained using a model considering the distribution of miR genes only in the transcriptionally active portion of the genome (see Table 3).
Chromosome 1 is used as the baseline in the model with a rate of miR gene incidence of ˜0.057, which is approximately equal to the overall rate of miR gene incidence across the genome.

TABLE 3

Location of miRs by chromosome and results
of mixed effects Poisson regression model.

Chromosome	Length	# of miRs	IRR	p

1	279	16	—	—
2	251	7	0.49	0.112
3	221	10	0.79	0.557
4	197	3	0.27	0.035
5	198	6	0.53	0.183
6	176	6	0.59	0.277
7	163	13	1.39	0.377
8	148	6	0.71	0.469
9	140	15	1.87	0.082
10	143	2	0.24	0.060
11	148	11	1.29	0.508
12	142	6	0.74	0.523
13	118	8	0	1.000
14	107	7	1.14	0.771
15	100	5	0.87	0.789
16	104	5	0.84	0.731
17	88	15	2.97	0.002
18	86	4	0.81	0.708
19	72	14	3.39	0.001
20	66	5	1.32	0.587
21	45	4	1.55	0.433
22	48	6	2.09	0.123
X	163	12	1.28	0.513
Y	51	0	0	1.000

Example 2

miR Genes Are Located in or Near Fragile Sites

Thirty-five of 186 miRs (19%) were found in (13 miR genes), or within 3 Mb (22 miR genes) of cloned fragile sites (FRA). A set of 39 fragile sites with available cloning information was used in the analysis. Data were available for the exact dimension (mean 2.69 Mb) and position of ten of these cloned fragile sites (see General Methods above). The relative incidence of miR genes inside fragile sites occurred at a rate 9.12 times higher than in non-fragile sites (p<0.001, using mixed effect Poisson regression models; see Tables 3 and 4). The same very high statistical significance was also found when only the 13 miRs located exactly inside a FRA or exactly in the vicinity of the “anchoring” marker mapped for a FRA were considered (IRR=3.21, p<0.001). Among the four most active common fragile sites (FRA3B, FRA16D, FRA6E, and FRA7H), the data demonstrate seven miRs in (miR-29a and miR-29b) or close (miR-96, miR-182s, miR-182 as, miR-183, and miR-129-1) to FRA7H, the only fragile site where no candidate tumor suppressor (TS) gene has been found. The other three of the four most active sites contain known or candidate TS genes; i.e., FHIT, WWOX and PARK2, respectively (Ohta et al., 1996, Cell 84:587-597; Paige et al., 2001, Proc. Natl. Acad. Sci. USA 98:11417-11422; Cesari et al., 2003, Proc. Natl. Acad. Sci. USA 100:5956-5961).
Analysis of 113 fragile sites scattered in the human karyotype showed that 61 miR genes are located in the same cytogenetic positions with FRAs. Thirty-five miR genes were located inside twelve cloned FRAs. These data indicate that more miRs are located in or near FRAs, and that the results described herein represent an underestimation of miR gene/FRA association, likely because the mapping of these unstable regions is not complete.

TABLE 4

Mixed Effect Poisson Regression Results for the Association Between
microRNAs and Several Types of Regions of Interest

	Incidence
	Rate Ratio	95% CI
Region of interest	(IRR)	IRR	p

Cloned Fragile sites vs. non-	9.12	6.22,	<0.001
fragile sites		13.38
HPV16 insertion vs. all other	3.22	1.55, 6.68	<0.002
Deleted region vs. all other	4.08	2.99, 5.56	<0.001
Amplified region vs. all other	3.97	2.31, 6.83	<0.001
HOX Clusters vs. all other	15.77	7.39,	<0.001
		33.62
Homeobox genes vs. all other	2.95	1.63, 5.34	<0.001

Note:
“all other” means all the genome except the regions of interest.

Example 3

miR Genes Are Located in or Near Human Papilloma Virus (HPV) Integration Sites

Because common fragile sites are preferential targets for HPV 16 integration in cervical tumors, and infection with HPV16 or HPV18 is the major risk factor for developing cervical cancer, the association between miR gene locations and HPV16 integration sites in cervical tumors was analyzed. The data indicate that thirteen miR genes (7%) are located within 2.5 Mb of seven of seventeen (45%) cloned integration sites. The relative incidence of miRs at HPV16 integration sites occurred at a rate 3.22 times higher than in the rest of the genome (p<0.002) (Tables 4 and 5). In one cluster of integration sites at chromosome 17q23, where three HPV16 integration sites are spread over roughly 4 Mb of genomic sequence, four miR genes (miR-21, miR-301, miR-142s and miR-142 as) were found.

TABLE 5

Analyzed FRA Sites, Cancer Correlation and HPV Integration Sites

						Distance
				Location		miR-	HPV16
Symbol	Chromosome	Cancer correlation	Type	(Mb)	Closest miR(s)	FRA(Mb)	integration*

FRA1A	1p36
FRA1C	1p31		aphidicolin type,	67.87	miR-186; miR-	3; 3
			common		101-1
FRA1F	1q21	bladder
FRA1H	1q42.1	cervical	5-azacytidine,	216.5	miR-194; miR-	exact	YES
			common		215
FRA2G	2q31	RCC
FRA2I	2q33	chronic myelogenous
		leukemia
FRA3B	3p14.2	esophageal carcinoma,
		lung, stomach, kidney,
		cervical cancer
FRA4B	4q12
FRA4C	4q31.1
FRA5C	5q31.1
FRA5E	5p14
FRA6E	6q26	ovarian
FRA6F	6q21	leukemias and solid
		tumors
FRA7E	7q21.2 or
	21.11
FRA7F	7q22		aphidicolin type,	100.2-107	miR-106b; miR-	less than 1
			common		25; miR-93
FRA7G	7q31.2	ovarian
FRA7H	7q32.3	esophageal	aphidicolin type,	129.8-130.4	miR-29b; miR-	exact; 1
			common		29a;	and 2.5
					miR-96; miR-
					182s; miR-182as;
					miR-183; miR-
					129-1
FRA7I	7q35	breast
FRA8B	8q22.1
FRA8E	8q24.1
FRA9D	9q22.1	bladder	aphidicolin type,	89.5-92	let7a-1; let-7d;	exact
			common		let-7f-1 miR-23b;
					miR-24-1; miR-
					27b
FRA9E	9q32-33.1	ovarian, bladder,	aphidicolin type,	101.3-111.9	miR-32	exact	YES
		cervical	common
FRA10B	10q25.2
FRA10C	10q21
FRA10D	10q22.1
FRA11A	11q13.3	hematopoietic and solid	folic acid type,	66.18-66.9	miR-159-1; miR-	1.2
		tumors	rare		192
FRA11B	11q23.3		folic acid type,	119.1-.2	miR-125b-1; let-	2
			rare		7a-2; miR-100
FRA12A	12q13.1		folic acid type,	53.55	miR-196-2; miR-	1
			rare		148b
FRA13C	13q21.2
FRA15A	15q22		aphidicolin type,	60.93	miR-190	exact
			common
FRA16D	16q23.2	gastric
		adenocarcinoma,
		adenocarcinomas of
		stomach, colon, lung
		and ovary
FRA16E	16p12.1
FRA17B	17q23.1		aphidicolin type,	58.25-58.35	miR-21	exact	YES
			common		miR-301	0.5
					miR-142s; miR-	1.5
					142as
FRA18A	18q12.2	esophageal carcinoma
FRA22A	22q13
FRAXA	Xq27.31
FRAXB	Xp22.3
FRAXE	Xq28
FRAXF	Xq28			146.58	miR-105-1; miR-	2.2
					175

Note:
*other microRNAs located close to HPV16 integration sites were found in relation to FRA5C, FRA11C, FRA12B and FRA12E. Positions are indicated according to Build 33 of the Human Genome.

Example 4A

miR Genes Are Located in or Near Cancer Associated Genomic Regions

Because the miR-FRA-HPV16 association has significance for cancer pathogenesis, miR genes might be involved in malignancies through other mechanisms, such as deletion, amplification, or epigenetic modifications. Thus, a search was performed for reported genomic alterations in human cancers, located in regions containing miR genes. PubMed was searched for reports of CAGR such as minimal regions of loss-of-heterozygosity (LOH) suggestive of the presence of tumor-suppressor genes (TSs), minimal regions of amplification suggestive of the presence of oncogenes (OGs), and common breakpoint regions in or near possible OGs or TSs (see General Methods above). Overall, 98 of 187 (52.5%) miR genes were found to be located in CAGRs (see Tables 6 and 7). Eighty of the miR genes (43%) were found to be located exactly within minimal regions of LOH or minimal regions of amplification described in a variety of tumors, such as lung, breast, ovarian, colon, gastric and hepatocellular carcinoma, as well as leukemias and lymphomas (see Tables 6 and 7).
The analysis showed that on chromosome 9, eight of fifteen mapped miR genes (including six located in clusters), were located inside two regions of deletion on 9q (Simoneau et al., 1999, Oncogene 7:157-163): the clusters let-7a-1/let-7f-1/let-7d and miR-23b/miR-27b/miR-24-1 inside region B at 9q22.3 and miR-181a and miR-199b inside region D at 9q33-34.1 (Table 6). Furthermore, five other miR genes were located less than 2 Mb from the markers with the highest rate of LOH: miR-31 near IFNα, miR-204 near D9S15, miR-181 and miR-147 near GSN, and miR-123 near D9S67.
In breast carcinomas, two different regions of loss at 11 q23, independent from the ATM locus, have been studied extensively: the first spans about 2 Mb between loci D11S1347 and D11S927; the second is located between loci D11S1345 and D11S1316 and is estimated at about 1 Mb (di Iasio et al., 1999, Oncogene 25:1635-1638). Despite extensive effort, the only candidate TS gene found was the PPP2R1B gene, involved in less than 10% of reported cases (Calin et al., 2002, Proc. Natl. Acad. Sci. USA 99:15524-15529; Wang et al., 1998, Science 282:284-7). Both of these minimal LOH regions contained numerous microRNAs: the cluster miR-34-al/miR-34-a2 in the first and the cluster miR-125b1/let-7a-2/miR-100 in the second.
High frequency LOH at 17p13.3 and relatively low TP53 mutation frequency in cases of hepatocellular carcinomas (HCC), lung cancers and astrocytomas indicate the presence of other TSs involved in the development of these tumors. One minimal LOH region correlated with HCC, and located telomeric to TP53 between markers D17S1866 and D17S1574 on chromosome 17, contained three miR genes: miR-22, miR-132, and miR-212. miR-195 is located between ENO3 and TP53 on chromosome 17.
Homozygous deletions (HD) in cancer can indicate the presence of TSs (Huebner et al., 1998, Annu Rev. Genet. 32:7-31), and several miR genes are located in homozygously deleted regions without known TSs. In addition to miR-15a and miR-16a located at 13q14 HD region in B-CLL, the cluster miR-99a/let-7c/miR-125b-2 mapped in a 21p11.1 region of HD in lung cancers and miR-32 at 9q31.2 in a region of HD in various types of cancer. Among the seven regions of LOH and HD on the short arm of chromosome 3, three of the regions harbor miRs: miR-26a in region AP20, miR-138-1 in region 5 at 3p21.3 and the cluster let-7g/miR-135-1 in region 3 at 3p21.1-p21.2. The locations of the miR genes/gene clusters are not likely to be random, because it was found that overall, the relative incidence of miRs in both deleted and amplified regions is highly significant (IRR=4.08, p<0.001 and IRR=3.97, p=0.001, respectively) (Table 4). Thus, these miRs expand the spectrum of candidate TSs.

TABLE 6

Examples of microRNAs Located in Minimal Deleted Regions, Minimal
Amplified Regions, and Breakpoint Regions Involved in Human Cancers*

	Location
	(defining	Size			Known
Chromosome	markers)	Mb	MiR Gene	Histotype	OG/TS

3p21.1-21.2-D	ARP-	7	let-7g/miR-135-1	lung, breast	—
	DRR1			cancer
3p21.3(AP20)-D	GOLGA4-	0.75	miR-26a	epithelial cancer	—
	VILL
3p23-21.31	D3S1768-	12.32	miR-26a; miR-138-1	nasopharyngeal	—
(MDR2)-D	D3S1767			cancer
5q32-D	ADRB2-	2.92	miR-145/miR-143	myelodysplastic	—
	ATX1			syndrome
9q22.3-D	D9S280-	1.46	miR-24-1/mir-27b/miR-	urothelial cancer	PTC,
	D9S1809		23b;		FANCC
			let-7a-1/let-7f-1/let-7d
9q33-D	D9S1826-	0.4	miR-123	NSCLC	—
	D9S158
11q23-q24-D	D11S927-	1.994	miR-34a-1/miR-34a-2	breast, lung	PPP2R1B
	D11S1347			cancer
11q23-q24-D	D11S1345-	1.725	miR-125b-1/let-7a-	breast, lung,	—
	D11S1328		2/miR-100	ovary, cervix
				cancer
13q14.3-D	D13S272-	0.54	miR-15a/miR-16a	B-CLL	—
	D13S25
13q32-33-A	stSG15303-	7.15	miR-17/miR-18/miR-	follicular	—
	stSG31624		19a/miR-20/miR-19b-	lymphoma
			1/miR-92-1
17p13.3-D	D17S1866-	1.899	miR-22; miR-132; miR-	HCC	—
	D17S1574		212
17p13.3-D	ENO3-	2.275	miR-195	lung cancer	TP53
	TP53
17q22-t(8; 17)	miR-142s/		miR-142s; miR-142as	prolymphocytic	c-MYC
	c-MYC			leukemia
17q23-A	CLTC-	0.97	miR-21	neuroblastoma	—
	PPM1D
20q13-A	FLJ33887-	0.55	miR-297-3	colon cancer	—
	ZNF217
21q11.1-D	D21S1911-	2.84	miR-99a/let-7c/miR-	lung cancer	—
	ANA		125b

Note:
OG - oncogene; TS - tumor suppressor gene; D - deleted region; A - amplified region; NSCLC—Non-Small Cell Lung Cancer; HCC—Hepatocellular carcinoma; B-CLL—B-Chronic Lymphocytic Leukemia; PTC - patched homolog (Drosophila*); FANCC - Fanconi anemia, complementation group C; PPP2R1B - protein phosphatase 2, regulatory subunit A (PR 65), β isoform. miR genes in a cluster are separated by a slash.

TABLE 7

MicroRNAs Located in Minimal Deleted Regions, Minimal Amplified Regions and
Breakpoint Regions Involved in Human Cancers

	Type of region		Position		Position
Chromosome	(name)	Marker 1	(Mb)	Marker 2	(Mb)

01p31	D	D1S2638	62.92	ARHI	67.885
01p36.3	D	D1S468	3.36	D1s2697	15.23
02q21	D	D2S1334	136.66
02q37	D	D2S125	241.5
03p21.1-21.2	D	ARP	51.5	DRR1	58.5
03p21.3	D (AP20)	GOLGA4	37.25	VILL	38
03p23-21.31	D (MDR2)	D3S1768	34.59	D3S1767	46.91
03q27	t(3; 11)(q27; q23.1)	LAZ3/BCL6	188.75	BOB1/	110.78
				OBF1
04p15.3	D	D4S1608	18.83	D4S404	23.98
05q31-33	D	D5S1480	144.17	D5S820	156.1
05q32	D	ADRB2	148.23	ATX1	151.15
07q32	D	D7S3061	122.84	D7S1804	131.25
07q32-q33	D	D7S2531	130.35	D7S1804	131.69
08p21	D (MRL1)	D8S560	21.61	D8S1820	28.02
08p21	D	D8S282	21.42
08p22	D	D8S254	16.62	SFTP2	22.05
08p23.1	A	D8S1819	6.737	D8S550	10.919
09p21	D (LOH)	IFNA	21.2	D9S171/	22.07
				S1814
09p21	D	IFNA	21.5
09p21	D	IFNA	21.5
09p21	D	CDKN2A, CDKN2B	21.9
09q22	D	D9S280	92.47	D9S1809	93.93
09q22.3	D (reg B)	D9S12	91.21	D9S180 R	96.03
09q32	D	D9S1677	107.35
09q33	D	D9S1826	133.88	D9S158	134.53
09q33-34.1	D (reg D)	GSN	119.45	D9S260	127.09
09q34	D	D9S158	134.54
11p15	D	D11S2071	0.23
11p15.5	D (LOH11B)	HRAS	0.52	D11S1363	1.05
11q13	D	D11S4946	64.35	D11S4939	64.54
11q22	D	D11S940/	100.65	CD3D/	118.7
		S1782		D11S4104
11q22.1-23.2	D (MDR3)	D11S2017	107.05	D11S965	111.3
11q22.3-q25	D	D11S1340	116.12	D11S912	128.16
11q22-q23	D	D11S2106/	108.76	D11S1356	117.454
		S2220
11q23	D	D11S1647	110.34	NCAM2/	112.5
				NCAM1
11q23	D	D11S1345	121.83	D11S1328	123.56
11q23	D	D11S1345	121.83	D11S1328	123.56
11q23.1-23.2	D (LOH)	D11S4167	121.68	D11S4144	122.96
11q23-q24	D	D11S927	109.676	D11S1347	111.67
	(LOH11CR1)
11q23-q24	D	D11S1345	121.835	D11S1328	123.56
	(LOH11CR2)
12p13	t(7; 12)(q36; p13)	TEL(ETV6)	11.83	near HLXB9	156.21
12q13-q14	A	DGKA	54.6	BLOV1	67.4
12q13-q15	A	GLI	56.15	MDM2	67.5
12q22	D	D12S1716	95.45	P382A8AG/	97.47
				D12S296
12q22	D	D12S377/	94.1	D12S296	97.47
		D12S101
13q14.3	D	D13S319/	48.5	D13S25	49.04
		D13S272
13q14	D	D13S260	30.23	AFMa301wb5	48.62
13q14	D	Rb1	46.77	BCMS	48.46
				(DLEU-1)
13q14.3	D (RMD)	D13S272	48.5	AF077401	48.765
13q14.3	D (Reg II)	D13S153	46.68	D13S1289	62.43
13q14.3	D	D13S273	48.11	D13S176	58.31
13q14.3	D	D13S1168	48.28	D13S25	49.04
13q32-33	A	stSG15303	89.7	stSG31624	96.85
14q11.1-q12	D	D14S283	20.67	D14S64	22.55
14q32	D	D14S51	95.56	telomere	105.2
15q11.1-15	D	D15S128	22.67	D15S1012	36.72
17p11.2	A	PNMT	17.351
17p11.2	D	D17S1857	16.61	D17S805/	20.79
				S959
17p11.2	D	D117S1857	16.61	D17S805/	20.79
				S959
17p13	D	D17S578	7.025
17p13.3	D	D17S1866	0.121	D17S1574	2.02
17p13.3	D	ENO3	5.5	TP53	7.775
17p13.3	D	D17S1574	2.02	D17S379	2.46
17q11.1	D	NF1	29.7
17q11.1	D	NF1	29.7
17q11.2	A	MLN 62	27.22
		(TRAF4)
17q11.2	D (NF1 locus)	CYTOR4	29.25	WI-12393	30.52
17q22	t(8; 17)	“BCL3”	56.95	c-MYC	128.7
17q23	A	RAD51C	57.116
17q23	A	RAD51C	57.116
17q23	A	CLTC	58.21	PPM1D	59.18
17q25	A (SRO2)	D17S1306	53.76	D17S1604	58.45
19p13.3	D	D19S886	0.95	D19S216	4.9
19p13.3	D (LOH)	D19S216	4.9	D19S549	5.44
19p13.3	D (HZYG)	D19S894	4.34	D19S395	7.32
19p13.3	D (LOH)	D19S886	0.95	D19S216	4.9
20q13	A	FLJ33887	52.2	ZNF217	52.75
20q13.1	A	ZNF217	52.285
20q13.2	A	D20S854	52.68	D20S120	53.69
20q13.2	A	ZNF217	52.85
21q11.1	D	D21S120/	15.06	ANA	17.9
		S1911
21q21	A	BIC	25.8	BIC	25.9
22q12.2-q13.33	D	D22S280	31.53	D22S274	43.54
22q12.3-q13.33	D	D22S280	31.53	D22S282	42.1
22q12.1	t(4; 22)			MN1	26.5
Xq25-26.1	D	DXS1206	125.08	HPRT	132.31

	Size/			miR
	Distance			Location
Chromosome	(Mb)	Histotype	Closest miR	(Mb)

01p31	4.96	ovarian and	miR-101-1	64.9
		breast cancer
01p36.3	0	Non Small Cell	miR-34	8.8
		Lung Ca.
02q21	0.1	gastric ca.	miR-128a	136.55
02q37	0.2	hepatocellular	miR-149	241.65
		carcinoma
		(HCC)
03p21.1-21.2	7	lung, breast ca.	let-7g/miR-135-1	52.3
03p21.3	0.75	epithelial	miR-26a	38
		malignancies
03p23-21.31	12.32	nasopharyngeal	miR-26a; miR-	38; 44
		carcinoma	138-1
03q27		B cell leukemia	miR-34a-2/miR	110.9
		line (Karpas 231)	34a-1
04p15.3	5.15	primary bladder	miR-218-1	20.25
		ca.
05q31-33	11.93	prostate ca.	miR-145/miR-	148.7
		aggressiveness	143
05q32	2.92	myelodysplastic	miR-145/miR-	148.7
		syndrome	143
07q32	8.41	prostate ca.	miR-129-1; miR-	127.3;
		(aggressiveness)	182s/miR-	129; 130
			182as/miR-
			96/miR-183;
			miR29a/miR-29b
07q32-q33	1.34	prostate ca.	miR-29a/miR-	130
		(aggressiveness)	29b
08p21	6.41	HCC	miR-161; miR-	22; 21.5
			177
08p21	0.1	HCC	miR-177	21.5
08p22	5.43	oral and	miR-161; miR-	22; 21.5
		laryngeal	177
		squamous
		carcinoma.
08p23.1	4.18	malignant	miR-124a-1	9.75
		fibrous
		histiocytomas
		(MFHs)
09p21	0.87	primary bladder	miR-31	21.4
		tumor
09p21
0	lung	miR-31	21.4
		adenocarcinoma
09p21
0	gastric ca.	miR-31	21.4
09p21	0.5	breast ca.	miR-31	21.4
09q22	1.46	urothelial ca.	miR-24-1/miR-	92.9; 92.3
			27b/miR-23b;
			let-7a-1/let-
			7f1/let-7d
09q22.3	4.82	bladder ca.	let-7a-1/let-	92.3; 92.9
			7f1/let-7d; miR-
			24-1/miR-
			27b/miR-23b
09q32	0.2	Small Cell Lung	miR-32	107.15
		Ca., Non-Small
		Cell Lung Ca.
09q33	0.4	NSCLC	miR-123	134.95
09q33-34.1	7.64	bladder ca.	miR-181a; miR-	122.85;
			199b	126.3
09q34	0.4	HCC	miR-123	134.95
11p15	0.4	ovarian ca.	miR-210	0.6
11p15.5	0.53	lung ca.	miR-210	0.6
11q13	0.19	sporadic	miR-159-1/miR-	64.45
		follicular thyroid	192
		tumor
11q22	18.05	lung	miR-34a-1/miR-	111
		adenocarcinoma	34a-2
11q22.1-23.2	4.25	nasopharyngeal	miR-34a-1/miR-	111
		carcinoma	34a-2
11q22.3-q25	12.04	ovarian ca.	miR125b-1/let-	121.5
			7a-2/miR-100
11q22-q23	8.7	chronic	miR-34a-1/miR-	111
		lymphocytic	34a-2
		leukemia
11q23	2.16	lung ca.	miR-34a-1/miR-	111
			34a-2
11q23	1.73	lung	miR125b-1/let-	121.5
		adenocarcinoma	7a-2/miR-100
11q23	1.73	lung	miR125b-1/let-	121.5
		adenocarcinoma	7a-2/miR-100
11q23.1-23.2	1.28	cervical ca.	miR125b-1/let-	121.5
			7a-2/miR-100
11q23-q24	1.994	breast, lung ca.	miR-34a-1/miR-	111
			34a-2
11q23-q24	1.725	breast, lung,	miR125b-1/let-	121.5
		ovary, cervix ca.	7a-2/miR-100
12p13		acute myeloid	miR-153-2	156.6
		leukemia (AML)
12q13-q14	12.8	adenocarcinomas	let-7i	61.35
		of lung and
		esophagus
12q13-q15	11.35	bladder ca.	let-7i	61.3-.45
12q22	2.02	male germ cell	miR-135-2	96.5
		tumors
12q22	3.37	male germ cell	miR-135-2	96.5
		tumors.
13q14.3	0.54	B-Chronic	miR-15a/miR-	48.5
		Lymphocytic	16a
		Leuk (B-CLL)
13q14	18.39	adult	miR-15a/miR-	48.5
		Lymphoblastic	16a
		leukemia
13q14	1.69	lipoma	miR-15a/miR16a	48.5
13q14.3	0.265	CLL	miR-15a/miR-	48.5
			16a
13q14.3	15.75	head-and-neck	miR-15a/miR-	48.5
		squamous-cell	16a
		carcinoma
13q14.3	10.2	oral ca.	miR-15a/miR-	48.5
			16a
13q14.3	0.76	B-CLL	miR-15a/miR-	48.5
			16a
13q32-33	7.15	follicular	miR-17/miR-	89.7
		Lymphoma	18/miR-19a/miR-
			20/miR-19b-
			1/miR-92-1
14q11.1-q12	1.88	malignant	miR-208	21.8
		mesothelioma
14q32	9.64	nasopharyngeal	miR-127/miR-	99.3; 99.5;
		carcinoma	136; miR-	102.5
			154/miR-
			134/miR-299;
			miR-203
15q11.1-15	14.05	malignant	miR-211	29
		mesothelioma.
17p11.2	0.5	breast ca	miR-33b	17.8
17p11.2	4.18	kidney ca (Birt-	miR-33b	17.9
		Hogg-Dube sy)
17p11.2	4.18	medulloblastoma	miR-33b	17.9
		(Smith-Magenis
		syndrome)
17p13	0	HCC	miR-195	7
17p13.3	1.9	HCC	miR-22; miR-	1.75; 2.2
			132; miR-212
17p13.3	2.275	lung ca.	miR-195	7
17p13.3	0.44	lung ca.	miR-132; miR-	2.2
			212
17q11.1	0.3	ovarian ca.	miR-108-1	30
17q11.1	0.3	ovarian ca.	miR-193	30
17q11.2	0.1	primary breast	miR-144	27.35
		ca.
17q11.2	1.27	NF1	miR-108-1/miR-	30; 30
		microdeletion	193
17q22		prolymphocytic	miR-142s/miR-	56.95
		leukemia	142as
17q23	0.25	breast ca.	miR-142s/miR-	56.95
			142as
17q23	0.5	breast ca.	miR-301	57.7
17q23	0.97	neuroblastoma	miR-21	58.45
17q25	4.69	breast ca.	miR-142s; miR-	56.95;
			142as; miR-301;	57.7;
			miR-21	58.45
19p13.3	3.95	lung	miR-7-3	4.75
		adenocarcinoma
19p13.3	0.54	gynecological	miR-7-3	4.75
		tumor in Peutz-
		Jegher's sy
19p13.3	2.98	gynecological	miR-7-3	4.75
		tumor in Peutz-
		Jegher's sy
19p13.3	3.95	pancreatic and	miR-7-3	4.75
		biliary ca
20q13	0.55	colon ca	miR-297-3	52.35
20q13.1	0	ovarian	miR-297-3	52.35
20q13.2	1.01	gastric	miR-297-3	52.35
		adenocarcinoma
20q13.2	0.5	head/neck	miR-297-3	52.35
		squamous
		carcinoma
21q11.1	2.84	lung ca. (cell line	miR-99a/let-	16.8
		MA17)	7c/miR-125b-2
21q21	0.1	colon ca.	miR-155(BIC)	25.85
22q12.2-q13.33	12.01	colorectal ca.	miR-33a	40.6
22q12.3-q13.33	10.57	astrocytomas	miR-33a	40.6
22q12.1		meningioma	miR-180	26.45
Xq25-26.1	7.23	advanced	miR-92-2/miR-	132
		ovarian ca.	19b-2/miR-106a

Note:
D—deletion;
A—amplification;
ca.—cancer;
sy—syndrome.
The distance (in Mb) from the markers used in genome-wide analysis is shown.
miRs in clusters are separated by a slash.
Positions are according to BUILD 34, version 1, of the Human Genome

Example 4B

Effect of Genomic Location on miR Gene Expression

In order to investigate whether the genomic location in deleted regions influences miR gene expression, a set of lung cancer cell lines was analyzed. miR-26a and miR-99a, located at 3p21 and 21q1.2, respectively, are not expressed or are expressed at low levels in lung cancer cell lines. The locations of miR-26a and miR-99a correlate with regions of LOH/HD in lung tumors. However, the expression of miR-16a (located at 13q14) was unchanged in the majority of lung tumor cell lines as compared to normal lung (see FIG. 1).
Several miR genes are located near breakpoint regions, including miR-142s at 50 nt from the t(8; 17) translocation involving chromosome 17 and MYC, and miR-180 at 1 kb from the MN1 gene involved in a t(4; 22) translocation in meningioma (Table 6). The t(8; 17) translocation brings the MYC gene near the miR gene promoter, with consequent MYC over-expression, while the t(4; 22) translocation inactivates the MN1 gene, and possibly inactivates the miR gene located in the same position. Other miR genes are located relatively close to chromosomal breakpoints, such as the cluster miR 34a-1/34a-2 and miR-153-2 (see Table 7). Further supporting a role for miR-122a in cancer, it was found herein that human miR-122a is located in the minimal amplicon around MALT1 in aggressive marginal zone lymphoma (MZL), and was found to be about 160 kb from the breakpoint region of translocation t(11; 18) in mucosa-associated lymphoid tissue (MALT) lymphoma (Sanchez-Izquierdo et al., 2003, Blood 101:4539-4546). Apart from miR-122a, several other miR genes were located in regions particularly prone to cancer-specific abnormalities, such as miR-142s and miR-142 as, located at 17q23 close to a t(8; 17) breakpoint in B cell acute leukemia, and also located within the minimal amplicon in breast cancer and near the FRA17B site, which is also a target for HPV16 integration in cervical tumors (see Tables 5 and 7).

Example 5

MicroRNAs are Located in or Near HOX Gene Clusters

Homeobox-containing genes are a family of transcription factor genes that play crucial roles during normal development and in oncogenesis. HOXB4, HOXB5, HOXC9, HOXC10, HOXD4 and HOXD8, all with miR gene neighbors, are deregulated in a variety of solid and hematopoietic cancers (C_1-10et al., 1999, Exp. Cell Res. 248:1-9; Owebs et al., 2002, Stem Cells 20:364-379). A strong correlation was found between the location of specific miR genes and homeobox (HOX) genes. The miR-10a and miR-196-1 genes are located within the HOX B cluster on 17q21, while miR-196-2 is within the HOX C cluster at 12q13, and miR-10b maps to the HOX D cluster at 2q31 (see FIG. 2). Moreover, three other miRs (miR-148, miR-152 and miR-148b) are close to HOX clusters (less than 1 Mb; see FIG. 2). The 1 Mb distance was selected because some form of long-range coordinated regulation of gene expression was shown to expand up to one megabase to HOX clusters (Kamath et al., 2003, Nature 421:231-7). Such proximity of miR genes to HOX gene clusters is unlikely to have occurred by chance (IRR=15.77; p<0.001) (Table 4). Because collinear expression of, and cooperation between, HOX genes is well demonstrated, these data indicated that miRs are altered along with the HOX genes in human cancers.
Next, it was determined whether miR genes were located within class II HOX gene clusters as well. Fourteen additional human HOX gene clusters (Pollard et al., 2000, Current Biology 10:1059-1062) were analyzed, and seven miR genes (miR-129-1, miR-153-2, let-7a-1, let-7f-1, let-7d, miR-202 and miR-139) were located within 0.5 Mb of class II homeotic genes, a result which was highly unlikely to occur by chance (IRR=2.95, p<0.001) (Table 4).

Example 6

Expression of miR Gene Products in Human Cells

The cDNA sequence encoding the entire miR precursor transcript of an miR gene is separately cloned into the context of an irrelevant mRNA expressed under the control of the cytomegalovirus immediate early (CMV-IE) promoter, according to the procedure of Zeng et al., 2002, Mol. Cell. 9:1327-1333, the entire disclosure of which is herein incorporated by reference.
Briefly, Xho I linkers are placed on the end of double-stranded cDNA sequences encoding an miR precursor, and this construct is separately cloned into the Xho I site present in the pBC12/CMV plasmid. The pBC12/CMV plasmid is described in Cullen, 1986, Cell 46:973-982, the entire disclosure of which is herein incorporated by reference.
pCMV plasmid containing the miR precursor coding sequence is transfected into cultured human 293T cells by standard techniques using the FuGene 6 reagent (Roche). Total RNA is extracted as described above, and the presence of the processed miR transcript is detected by Northern blot analysis with an miR probe specific for the miR transcript.
pCMV-miR is also transfected into cultured human normal cells or cells with proliferative disorders, such as cancer cells. For example, the proliferative disease or cancer cell types include ovarian cancer, breast cancer, small cell lung cancer, sporadic follicular thyroid tumor, chronic lymphocytic leukemia, cervical cancer, acute myeloid leukemia, adenocarcinomas, male germ cell tumor, non-small cell lung cancer, gastric cancer, hepatocellular carcinoma, lung cancer, nasopharyngeal cancer, B-chronic lymphocytic leukemia, lipoma, mesothelioma, kidney cancer, NF1 microdeletion, neuroblastoma, medulloblastoma, pancreatic cancer, biliary cancer, colon cancer, gastric adenocarcinoma, head/neck squamous carcinoma, astrocytoma, meningioma, B cell leukemia, primary bladder cancer, prostate cancer, myelodysplastic syndrome, oral cavity carcinoma, laryngeal squamous carcinoma, and urothelial cancer. Total RNA is extracted as described above, and the presence of processed miR transcripts in the cancer cells is detected by Northern blot analysis with miR specific probes. The transfected cells are also evaluated for changes in morphology, the ability to overcome contact inhibition, and other markers indicative of a transformed phenotype.

Example 7

Preparation of Liposomes Encapsulating miR Gene Products

Liposome Preparation 1—Liposomes composed of lactosyl cerebroside, phosphatidylglycerol, phosphatidylcholine, and cholesterol in molar ratios of 1:1:4:5 are prepared by the reverse phase evaporation method described in U.S. Pat. No. 4,235,871, the entire disclosure of which is herein incorporated by reference. The liposomes are prepared in an aqueous solution of 100 μg/ml processed miR transcripts or 500 μg/ml pCMV-microRNA. The liposomes thus prepared encapsulate either the processed microRNA, or the pCMV-microRNA plasmids.
The liposomes are then passed through a 0.4 polycarbonate membrane and suspended in saline, and are separated from non-encapsulated material by column chromatography in 135 mM sodium chloride, 10 mM sodium phosphate (pH 7.4). The liposomes are used without further modification, or are modified as described herein.
A quantity of the liposomes prepared above are charged to an appropriate reaction vessel to which is added, with stirring, a solution of 20 mM sodium metaperiodate, 135 mM sodium chloride and 10 mM sodium phosphate (pH 7.4). The resulting mixture is allowed to stand in darkness for 90 minutes at a temperature of about 20° C. Excess periodate is removed by dialysis of the reaction mixture against 250 ml of buffered saline (135 mM sodium chloride, 10 mM sodium phosphate, pH 7.4) for 2 hours. The product is a liposome having a surface modified by oxidation of carbohydrate hydroxyl groups to aldehyde groups. Targeting groups or opsonization inhibiting moieties are conjugated to the liposome surface via these aldehyde groups.
Liposome Preparation 2—A second liposome preparation composed of maleimidobenzoyl-phosphatidylethanolamine (MBPE), phosphatidylcholine and cholesterol is obtained as follows. MBPE is an activated phospholipid for coupling sulfhydryl-containing compounds, including proteins, to the liposomes.
Dimyristoylphosphatidylethanolamine (DMPE) (100 mmoles) is dissolved in 5 ml of anhydrous methanol containing 2 equivalents of triethylamine and 50 mg of m-maleimidobenzoyl N-hydroxysuccinimide ester, as described in Kitagawa et al. (1976), J. Biochem. 79:233-236, the entire disclosure of which is herein incorporated by reference. The resulting reaction is allowed to proceed under a nitrogen gas atmosphere overnight at room temperature, and is subjected to thin layer chromatography on Silica gel H in chloroform/methanol/water (65/25/4), which reveals quantitative conversion of the DMPE to a faster migrating product. Methanol is removed under reduced pressure and the products re-dissolved in chloroform. The chloroform phase is extracted twice with 1% sodium chloride and the maleimidobenzoyl-phosphatidylethanolamine (MBPE) purified by silicic acid chromatography with chloroform/methanol (4/1) as the solvent. Following purification, thin-layer chromatography indicates a single phosphate containing spot that is ninhydrin negative.
Liposomes are prepared with MBPE, phosphatidylcholine and cholesterol in molar ratios of 1:9:8 by the reverse phase evaporation method of U.S. Pat. No. 4,235,871, supra, in an aqueous solution of 100 μg/ml processed microRNA or a solution of 500 μg/ml pCMV-miR (see above). Liposomes are separated from non-encapsulated material by column chromatography in 100 mM sodium chloride-2 mM sodium phosphate (pH 6.0).

Example 8

Attachment of Anti-Tumor Antibodies to Liposomes

An appropriate vessel is charged with 1.1 ml (containing about 10 mmoles) of Liposome Preparation 1 (see above) carrying reactive aldehyde groups, or Liposome Preparation 2 (see above). 0.2 ml of a 200 mM sodium cyanoborohydride solution and 1.0 ml of a 3 mg/ml solution of a monoclonal antibody directed against a tumor cell antigen is added to the preparation, with stirring. The resulting reaction mixture is allowed to stand overnight while maintained at a temperature of 4° C. The reaction mixture is separated on a Biogel A5M agarose column (Biorad, Richmond, Calif.; 1.5×37 cm).

Example 9

Inhibition of Human Tumor Growth In Vivo with miR Gene Products

A cancer cell line, such as one of the lung cancer cell lines described above or a tumor-derived cell, is inoculated into nude mice, and the mice are divided into treatment and control groups. When tumors in the mice reach 100 to 250 cubic millimeters, processed miR transcripts encapsulated in liposomes are injected directly into the tumors of the test group. The tumors of the control group are injected with liposomes encapsulating carrier solution only. Tumor volume is measured throughout the study.

Example 10

Oligonucleotide Microchip for Genome-Wide miRNA Profiling

Introduction
A micro-chip microarray was prepared as follows, containing 368 gene-specific oligonucleotide probes generated from 248 miRNAs (161 human, 84 mouse, and 3 arabidopsis) and 15 tRNAs (8 human and 7 mouse). These sequences correspond to human and mouse miRNAs found in the miRNA Registry (June 2003) (Griffiths-Jones, S. (2004) Nucleic Acids Res. 32, D109-D111) or collected from published literature (Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. (2001) Science 294, 853-858; Lim, L. P., Glasner, M. E., Yekta, S., Burge, C. B. & Bartel, D. P. (2003) Science 299, 1540; Mourelatos, Z., Dostie, J., Paushkin, S., Sharma, A., Charroux, B., Abel, L., Rappsilber, J., Mann, M. & Dreyfuss, G. (2002) Genes Dev 16, 720-728). For 76 miRNAs, two different oligonucleotide probes were designed, one containing the active sequence and the other specific for the precursor. Using these distinct sequences, we were able to separately analyze the expression of miRNA and pre-miRNA transcripts for the same gene.
Various specificity controls were used to validate data. For intra-assay validation, individual oligonucleotide-probes were printed in triplicate. Fourteen oligonucleotides had a total of six replicates because of identical mouse and human sequences and therefore were spotted on both human and mouse sections of the array. Several mouse and human orthologs differ only in few bases, serving as controls for the hybridization stringency conditions. tRNAs from both species were also printed on the microchip, providing an internal, relatively stable positive, control for specific hybridization, while Arabidopsis sequences were selected, based on the absence of any homology with known miRNAs from other species, and used as controls for non-specific hybridization.
Materials and Methods
The following materials and methods were employed in designing and testing the microchip.
miRNA Oligonucleotide Probe Design. A total of 281 miRNA precursor sequences (190 Homo sapiens, 88 Mus musculus, and 3 Arabidopsis thaliana) with annotated active sites were selected for oligonucleotide design. These correspond to human and mouse miRNAs found in the miRNA Registry or collected from published literature. All of the sequences were confirmed by BLAST alignment with the corresponding genome and the hairpin structures were analyzed. When two precursors with different length or slightly different base composition for the same miRNAs were found, both sequences were included in the database and the one that satisfied the highest number of design criteria was used. The sequences were clustered by organism using the LEADS platform (Sorek, R., Ast, G. & Graur, D. (2002) Genome Research 12, 1060-1067), resulting in 248 clusters (84 mouse, 161 human, and 3 arabidopsis). For each cluster, all 40-mer oligonucleotides were evaluated for their cross-homology to all genes of the relevant organism, number of bases in alignment to a repetitive element, amount of low-complexity sequence, maximum homopolymeric stretch, global and local G+C content, and potential hairpins (self 5-mers). The best oligonucleotide was selected that contained each active site of each miRNA. This produced a total of 259 oligonucleotides; there were 11 clusters with multiple annotated active sites. Next, we attempted to design an oligonucleotide that did not contain the active site for each cluster, when it was possible to choose such an oligonucleotide that did not overlap the selected oligonucleotide(s) by more than 10 nt. To design each of these additional oligonucleotides, we required <75% global cross-homology and <20 bases in any 100% alignment to the relevant organism, <16 bases in alignments to repetitive elements, <16 bases of low-complexity, homopolymeric stretches of no more than 6 bases, G+C content between 30-70% and no more than 11 windows of size 10 with G+C content outside 30-70%, and no self 5-mers. A total of 76 additional oligonucleotides were designed. In addition, we designed oligonucleotides for 7 mouse tRNAs and 8 human tRNAs, using similar design criteria. We selected a single oligonucleotide for each, with the exception of the human and mouse initiators, Met-tRNA-i, for which we selected two oligonucleotides each (Table 8).

TABLE 8

Oligonucleotides used for the miRNA microarray chip and correspondence with specific human and mouse microRNAs.

Oligonucleotide_name	Corresponding miRNA	Oligonucleotide sequence	Covers active site?	Notes	SEQ ID NO.

ath-miR156a-#1	ath-miR156a	TGACAGAAGAGAGTGAGCACACAAAGGCAATTTGCATATC	yes		286

ath-miR156a-#2	ath-miR156a	CATTGCACTTGCTTCTCTTGCGTGCTCACTGCTCTTTCTG	no		287

ath-miR157a-#1	ath-miR157a	GTGTTGACAGAAGATAGAGAGCACAGATGATGAGATACAA	yes		288

ath-miR157a-#2	ath-miR157a	CATCTTACTCCTTTGTGCTCTCTAGCCTTCTGTCATCACC	no		289

ath-miR180a-#1	ath-miR180	GATGGACGGTGGTGATTCACTCTCCACAAAGTTCTCTATG	no		290

ath-miR180a-#2	ath-miR180	TGAGAATCTTGATGATGCTGCATCGGCAATCAACGACTAT	yes		291

hsa-let-7a-1-prec	let-7a-1	TGAGGTAGTAGGTTGTATAGTTTTAGGGTCACACCCACCA	yes		292

hsa-let-7a-2-prec-#1	let-7a-2	TACAGCCTCCTAGCTTTCCTTGGGTCTTGCACTAAACAAC	no		293

hsa-let-7a-2-prec-#2	let-7a-2	ACTGCATGCTCCCAGGTTGAGGTAGTAGGTTGTATAGTTT	yes		294

hsa-let-7a-3-prec	let-7a-3	GGGTGAGGTAGTAGGTTGTATAGTTTGGGGCTCTGCCCTG	yes		295

hsa-let-7b-prec	let-7b	TGAGGTAGTAGGTTGTGTGGTTTCAGGGCAGTGATGTTGC	yes		296

hsa-let-7c-prec	let-7c	GCATCCGGGTTGAGGTAGTAGGTTGTATGGTTTAGAGTTA	yes		297

hsa-let-7d-prec	let-7d	CCTAGGAAGAGGTAGTAGGTTGCATAGTTTTAGGGCAGGG	yes		298

hsa-let-7d-v1-prec	let-7d (=7d-v1)	CTAGGAAGAGGTAGTAGTTTGCATAGTTTTAGGGCAAAGA	yes		299

hsa-let-7d-v2-prec-#1	let-7i (=let-7d-v2)	TTGGTCGGGTTGTGACATTGCCCGCTGTGGAGATAACTGC	no		300

hsa-let-7d-v2-prec-#2	let-7i (=let-7d-v2)	GCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTGTGACAT	yes	idem mmu-let-	301
				7i-prec

hsa-let-7e-prec	let-7e	GGCTGAGGTAGGAGGTTGTATAGTTGAGGAGGACACCCAA	yes		302

hsa-let-7f-1-prec-#1	let-7f-1	GGTAGTGATTTTACCCTGTTCAGGAGATAACTATACAATC	no		303

hsa-let-7f-1-prec-#2	let-7f-1	GGGATGAGGTAGTAGATTGTATAGTTGTGGGGTAGTGATT	yes		304

hsa-let-7f-2-prec2	let-7f-2	TGAGGTAGTAGATTGTATAGTTTTAGGGTCATACCCCATC	yes		305

hsa-let-7g-prec-#1	let-7g	CTGATTCCAGGCTGAGGTAGTAGTTTGTACAGTTTGAGGG	yes		306

hsa-let-7g-prec-#2	let-7g	TTGAGGGTCTATGATACCACCCGGTACAGGAGATAACTGT	no		307

hsa-miR-001b-1-prec1	miR-001	AATGCTATGGAATGTAAAGAAGTATGTATTTTTGGTAGGC	yes		308

hsa-miR-001b-2-prec	miR-001	TAAGCTATGGAATGTAAAGAAGTATGTATCTCAGGCCGGG	yes		309

hsa-miR-007-1-prec	miR-007-1	TGTTGGCCTAGTTCTGTGTGGAAGACTAGTGATTTTGTTG	yes		310

hsa-miR-007-2-prec-#1	miR-007-2	TACTGCGCTCAACAACAAATCCCAGTCTACCTAATGGTGC	no		311

hsa-miR-007-2-prec-#2	miR-007-2	GGACCGGCTGGCCCCATCTGGAAGACTAGTGATTTTGTTG	yes		312

hsa-miR-007-3-prec-#1	miR-007-3	AGATTAGAGTGGCTGTGGTCTAGTGCTGTGTGGAAGACTA	no		313

hsa-miR-007-3-prec-#2	miR-007-3	TGGAAGACTAGTGATTTTGTTGTTCTGATGTACTACGACA	yes		314

hsa-miR-009-1-#1	miR-009-1 (miR-131-1)	TCTTTGGTTATCTAGCTGTATGAGTGGTGTGGAGTCTTCA	yes		315

hsa-miR-009-1-#2	miR-009-1 (miR-131-1)	TAAAGCTAGATAACCGAAAGTAAAAATAACCCCATACACT	yes		316

hsa-miR-009-2-#1	miR-009-2 (miR-131-2)	GAAGCGAGTTGTTATCTTTGGTTATCTAGCTGTATGAGTG	yes		317

hsa-miR-009-2-#2	miR-009-2 (miR-131-2)	GAGTGTATTGGTCTTCATAAAGCTAGATAACCGAAAGTAA	yes	idem mmu-miR-	318
				009-prec-#2

hsa-miR-009-3-#1	miR-009-3 (miR-131-3)	GGGAGGCCCGTTTCTCTCTTTGGTTATCTAGCTGTATGAG	yes		319

hsa-miR-009-3-#2	miR-009-3 (miR-131-3)	GTGCCACAGAGCCGTCATAAAGCTAGATAACCGAAAGTAG	yes		320

hsa-miR-010a-prec-#1	miR-010a	GTCTGTCTTCTGTATATACCCTGTAGATCCGAATTTGTGT	yes		321

hsa-miR-010a-prec-#2	miR-010a	GTGGTCACAAATTCGTATCTAGGGGAATATGTAGTTGACA	no		322

hsa-miR-010b-prec-#1	miR-010b	TACCCTGTAGAACCGAATTTGTGTGGTATCCGTATAGTCA	yes		323

hsa-miR-010b-prec-#2	miR-010b	GTCACAGATTCGATTCTAGGGGAATATATGGTCGATGCAA	no		324

hsa-miR-015a-2-prec-#1	miR-15-a	CCTTGGAGTAAAGTAGCAGCACATAATGGTTTGTGGATTT	yes		325

hsa-miR-015a-2-prec-#2	miR-15-a	TTTGTGGATTTTGAAAAGGTGCAGGCCATATTGTGCTGCC	no		326

hsa-miR-015b-prec-#1	miR-015-b	GGCCTTAAAGTACTGTAGCAGCACATCATGGTTTACATGC	yes		327

hsa-miR-015b-prec-#2	miR-015-b	TGCTACAGTCAAGATGCGAATCATTATTTGCTGCTCTAGA	no		328

hsa-miR-016a-chr13	miR-016-1	CAATGTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGT	yes		329

hsa-miR-016b-chr3	miR-016-2	GTTCCACTCTAGCAGCACGTAAATATTGGCGTAGTGAAAT	yes		330

hsa-miR-017-prec-#1	miR-017 (miR-091)	GCATCTACTGCAGTGAAGGCACTTGTAGCATTATGGTGAC	yes		331

hsa-miR-017-prec-#2	miR-017 (miR-091)	GTCAGAATAATGTCAAAGTGCTTACAGTGCAGGTAGTGAT	yes		332

hsa-miR-018-prec	miR-018	TAAGGTGCATCTAGTGCAGATAGTGAAGTAGATTAGCATC	yes		333

hsa-miR-019a-prec	miR-019a	TGTAGTTGTGCAAATCTATGCAAAACTGATGGTGGCCTGC	yes		334

hsa-miR-019b-1-prec	miR-019b-1	TTCTGCTGTGCAAATCCATGCAAAACTGACTGTGGTAGTG	yes		335

hsa-miR-019b-2-prec	miR-019b-2	GTGGCTGTGCAAATCCATGCAAAACTGATTGTGATAATGT	yes		336

hsa-miR-020-prec	miR-020	TAAAGTGCTTATAGTGCAGGTAGTGTTTAGTTATCTACTG	yes		337

hsa-miR-021-prec-17-#1	miR-021	GTCGGGTAGCTTATCAGACTGATGTTGACTGTTGAATCTC	yes		338

hsa-miR-021-prec-17-#2	miR-021	TTCAACAGTCAACATCAGTCTGATAAGCTACCCGACAAGG	yes		339

hsa-miR-022-prec	miR-022	TGTCCTGACCCAGCTAAAGCTGCCAGTTGAAGAACTGTTG	yes		340

hsa-miR-023a-prec	miR-023a	TCCTGTCACAAATCACATTGCCAGGGATTTCCAACCGACC	yes		341

hsa-miR-023b-prec	miR-023b	AATCACATTGCCAGGGATTACCACGCAACCACGACCTTGG	yes		342

hsa-miR-024-1-prec-#1	miR-024-1	TTTTACACACTGGCTCAGTTCAGCAGGAACAGGAGTCGAG	yes		343

hsa-miR-024-1-prec-#2	miR-024-1	TCCGGTGCCTACTGAGCTGATATCAGTTCTCATTTTACAC	yes		344

hsa-miR-024-2-prec	miR-024-2	AGTTGGTTTGTGTACACTGGCTCAGTTCAGCAGGAACAGG	yes		345

hsa-miR-025-prec	miR-025	ACGCTGCCCTGGGCATTGCACTTGTCTCGGTCTGACAGTG	yes		346

hsa-miR-026a-prec-#1	miR-026a	TTCAAGTAATCCAGGATAGGCTGTGCAGGTCCCAATGGCC	yes		347

hsa-miR-026a-prec-#2	miR-026a	TCCCAATGGCCTATCTTGGTTACTTGCACGGGGACGCGGG	no		348

hsa-miR-026b-prec	miR-026b	TTCAAGTAATTCAGGATAGGTTGTGTGCTGTCCAGCCTGT	yes		349

hsa-miR-027a-prec	miR-027a	GTCCACACCAAGTCGTGTTCACAGTGGCTAAGTTCCGCCC	yes		350

hsa-miR-027b-prec	miR-027b	CCGCTTTGTTCACAGTGGCTAAGTTCTGCACCTGAAGAGA	yes		351

hsa-miR-028-prec	miR-028	AAGGAGCTCACAGTCTATTGAGTTACCTTTCTGACTTTCC	yes		352

hsa-miR-029a-2-#1	miR-029a	CTAGCACCATCTGAAATCGGTTATAATGATTGGGGAAGAG	yes		353

hsa-miR-029a-2-#2	miR-029a	CCCCTTAGAGGATGACTGATTTCTTTTGGTGTTCAGAGTC	no		354

hsa-miR-029b-2 =	miR-029b (=miR-102-	AGTGATTGTCTAGCACCATTTGAAATCAGTGTTCTTGGGG	yes		355
102prec7.1=7.2	7.1 = 7.2)

hsa-miR-029c-prec	miR-029c	TTTTGTCTAGCACCATTTGAAATCGGTTATGATGTAGGGG	yes		356

hsa-miR-030a-prec-#1	miR-030a-as	GCGACTGTAAACATCCTCGACTGGAAGCTGTGAAGCCACA	yes		357

hsa-miR-030a-prec-#2	miR-030a-s	CACAGATGGGCTTTCAGTCGGATGTTTGCAGCTGCCTACT	yes		358

hsa-miR-030b-prec-#1	miR-030b	TGTAAACATCCTACACTCAGCTGTAATACATGGATTGGCT	yes		359

hsa-miR-030b-prec-#2	miR-030b	ATGGATTGGCTGGGAGGTGGATGTTTACTTCAGCTGACTT	no		360

hsa-miR-030c-prec	miR-030c	TACTGTAAACATCCTACACTCTCAGCTGTGGAAAGTAAGA	yes		361

hsa-miR-030d-prec-#1	miR-030d	TAAGACACAGCTAAGCTTTCAGTCAGATGTTTGCTGCTAC	no		362

hsa-miR-030d-prec-#2	miR-030d	TTGTAAACATCCCCGACTGGAAGCTGTAAGACACAGCTAA	yes		363

hsa-miR-031-prec	miR-031	GGCAAGATGCTGGCATAGCTGTTGAACTGGGAACCTGCTA	yes		364

hsa-miR-032-prec-#1	miR-032	TGTCACGGCCTCAATGCAATTTAGTGTGTGTGATATTTTC	no		365

hsa-miR-032-prec-#2	miR-032	GGAGATATTGCACATTACTAAGTTGCATGTTGTCACGGCC	yes		366

hsa-miR-033b-prec	miR-033b	GTGCATTGCTGTTGCATTGCACGTGTGTGAGGCGGGTGCA	yes		367

hsa-miR-033-prec	miR-33	GTGGTGCATTGTAGTTGCATTGCATGTTCTGGTGGTACCC	yes		368

hsa-miR-034-prec-#1	miR-034 (=miR-170)	GAGTGTTTCTTTGGCAGTGTCTTAGCTGGTTGTTGTGAGC	yes		369

hsa-miR-034-prec-#2	miR-034 (=miR-170)	AGTAAGGAAGCAATCAGCAAGTATACTGCCCTAGAAGTGC	no		370

hsa-miR-092-prec-	miR-092-1	ACAGGTTGGGATCGGTTGCAATGCTGTGTTTCTGTATGGT	no		371
13=092-1-#1

hsa-miR-092-prec-	miR-092-1	TCTGTATGGTATTGCACTTGTCCCGGCCTGTTGAGTTTGG	yes		372
13=092-1-#2

hsa-miR-092-prec-	miR-092-2	GTTCTATATAAAGTATTGCACTTGTCCCGGCCTGTGGAAG	yes		373
X=092-2

hsa-miR-093-prec-	miR-093-1	CCAAAGTGCTGTTCGTGCAGGTAGTGTGATTACCCAACCT	yes		374
7.1=093-1

hsa-miR-095-prec-4	miR-095	CGTTACATTCAACGGGTATTTATTGAGCACCCACTCTGTG	yes		375

hsa-miR-096-prec-7-#1	miR-096	CTCCGCTCTGAGCAATCATGTGCAGTGCCAATATGGGAAA	no		376

hsa-miR-096-prec-7-#2	miR-096	TGGCCGATTTTGGCACTAGCACATTTTTGCTTGTGTCTCT	yes		377

hsa-miR-098-prec-X	miR-098	TGAGGTAGTAAGTTGTATTGTTGTGGGGTAGGGATATTAG	yes		378

hsa-miR-099b-prec-19-	miR-099b	GCCTTCGCCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC	no	idem mmu-miR-	379
#1				099b-prec-#1

hsa-miR-099b-prec-19-	miR-099b	CACCCGTAGAACCGACCTTGCGGGGCCTTCGCCGCACACA	yes	idem mmu-miR-	380
#2				099b-prec-#2

hsa-miR-099-prec-21	miR-099a (=miR-099-	ATAAACCCGTAGATCCGATCTTGTGGTGAAGTGGACCGCA	yes		381
	prec21)

hsa-miR-100-1/2-prec	miR-100	TGAGGCCTGTTGCCACAAACCCGTAGATCCGAACTTGTGG	yes		382

hsa-miR-101-1/2-prec-#1	miR-101-1	CCCTGGCTCAGTTATCACAGTGCTGATGCTGTCTATTCTA	no		383

hsa-miR-101-1/2-prec-#2	miR-101-1	TACAGTACTGTGATAACTGAAGGATGGCAGCCATCTTACC	yes		384

hsa-miR-101-prec-9	miR-101-2	GCTGTATATCTGAAAGGTACAGTACTGTGATAACTGAAGA	yes		385

hsa-miR-102-prec-1	miR-102	TCTTTGTATCTAGCACCATTTGAAATCAGTGTTTTAGGAG	yes		386

hsa-miR-103-2-prec	miR-103-2	GTAGCATTCAGGTCAAGCAACATTGTACAGGGCTATGAAA	yes		387

hsa-miR-103-prec-	miR-103-1 (=miR-103-	TATGGATCAAGCAGCATTGTACAGGGCTATGAAGGCATTG	yes		388
5=103-1	5)

hsa-miR-105-prec-	miR-105-1 (=miR-105-	ATCGTGGTCAAATGCTCAGACTCCTGTGGTGGCTGCTCAT	yes		389
X.1=105-1	prec-X)

hsa-miR-106-prec-X	miR-106a	CCTTGGCCATGTAAAAGTGCTTACAGTGCAGGTAGCTTTT	yes		390

hsa-miR-107-prec-10	miR-107	GGCATGGAGTTCAAGCAGCATTGTACAGGGCTATCAAAGC	yes		391

hsa-miR-122a-prec	miR-122a	CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGTGTC	yes		392

hsa-miR-123-prec-#1	miR-123 = miR-126	GACGGGACATTATTACTTTTGGTACGCGCTGTGACACTTC	yes		393

hsa-miR-123-prec-#2	miR-123 = miR-126	TGTGACACTTCAAACTCGTACCGTGAGTAATAATGCGCCG	yes		394

hsa-miR-124a-1-prec1	miR-124a-1	ATACAATTAAGGCACGCGGTGAATGCCAAGAATGGGGCTG	yes		395

hsa-miR-124a-2-prec	miR-124a-2	TTAAGGCACGCGGTGAATGCCAAGAGCGGAGCCTACGGCT	yes		396

hsa-miR-124a-3-prec	miR-124a-3	TTAAGGCACGCGGTGAATGCCAAGAGAGGCGCCTCCGCCG	yes		397

hsa-miR-125a-prec-#1	miR-125a	TCTAGGTCCCTGAGACCCTTTAACCTGTGAGGACATCCAG	yes		398

hsa-miR-125a-prec-#2	miR-125a	CAGGGTCACAGGTGAGGTTCTTGGGAGCCTGGCGTCTGGC	no		399

hsa-miR-125b-1	miR-125b-1	TCCCTGAGACCCTAACTTGTGATGTTTACCGTTTAAATCC	yes		400

hsa-miR-125b-2-prec-#1	miR-125b-2	TAGTAACATCACAAGTCAGGCTCTTGGGACCTAGGCGGAG	no		401

hsa-miR-125b-2-prec-#2	miR-125b-2	ACCAGACTTTTCCTAGTCCCTGAGACCCTAACTTGTGAGG	yes		402

hsa-miR-127-prec	miR-127	TCGGATCCGTCTGAGCTTGGCTGGTCGGAAGTCTCATCAT	yes		403

hsa-miR-128a-prec-#1	miR-128a	TTGGATTCGGGGCCGTAGCACTGTCTGAGAGGTTTACATT	no	idem mmu-miR-	404
				128-prec-#2

hsa-miR-128a-prec-#2	miR-128a	ACATTTCTCACAGTGAACCGGTCTCTTTTTCAGCTGCTTC	yes		405

hsa-miR-128b-prec-#1	miR-128b	TCACAGTGAACCGGTCTCTTTCCCTACTGTGTCACACTCC	yes		406

hsa-miR-128b-prec-#2	miR-128b	GGGGGCCGATACACTGTACGAGAGTGAGTAGCAGGTCTCA	no		407

hsa-miR-129-prec-#1	miR-129-1/2	TGGATCTTTTTGCGGTCTGGGCTTGCTGTTCCTCTCAACA	yes		408

hsa-miR-129-prec-#2	miR-129-1/2	CCTCTCAACAGTAGTCAGGAAGCCCTTACCCCAAAAAGTA	no		409

hsa-miR-130a-prec-#1	miR-130a	CCAGAGCTCTTTTCACATTGTGCTACTGTCTGCACCTGTC	no		410

hsa-miR-130a-prec-#2	miR-130a	TGTCTGCACCTGTCACTAGCAGTGCAATGTTAAAAGGGCA	yes		411

hsa-miR-132-prec-#1	miR-132	TGTGGGAACTGGAGGTAACAGTCTACAGCCATGGTCGCCC	yes		412

hsa-miR-132-prec-#2	miR-132	TCCAGGGCAACCGTGGCTTTCGATTGTTACTGTGGGAACT	no		413

hsa-miR-133a-1	miR-133a-1 (=miR-	CCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGTAGCT	yes		414
	133c)

hsa-miR-133a-2	miR-133a-2 (=miR-	TTGGTCCCCTTCAACCAGCTGTAGCTGTGCATTGATGGCG	yes		415
	133d)

hsa-miR-134-prec-#1	miR-134	ATGCACTGTGTTCACCCTGTGGGCCACCTAGTCACCAACC	no		416

hsa-miR-134-prec-#2	miR-134	GTGTGTGACTGGTTGACCAGAGGGGCATGCACTGTGTTCA	yes		417

hsa-miR-135-1-prec	miR-135-1 (=miR-135)	GCCTCGCTGTTCTCTATGGCTTTTTATTCCTATGTGATTC	yes		418

hsa-miR-135-2-prec	miR-135-2	CACTCTAGTGCTTTATGGCTTTTTATTCCTATGTGATAGT	yes		419

hsa-miR-136-prec-#1	miR-136	ATGCTCCATCATCGTCTCAAATGAGTCTTCAGAGGGTTCT	no		420

hsa-miR-136-prec-#2	miR-136	TGAGCCCTCGGAGGACTCCATTTGTTTTGATGATGGATTC	yes		421

hsa-miR-137-prec	miR-137	GGATTACGTTGTTATTGCTTAAGAATACGCGTAGTCGAGG	yes	idem mmu-miR-	422
				137-prec

hsa-miR-138-1-prec	miR-138-1	AGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACG	yes		423

hsa-miR-138-2-prec	miR-138-2	AGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCATCCTC	yes	idem mmu-miR-	424
				138-prec

hsa-miR-139-prec	miR-139	GTGTATTCTACAGTGCACGTGTCTCCAGTGTGGCTCGGAG	yes		425

hsa-miR-140-#1	miR-140-as	GCCAGTGGTTTTACCCTATGGTAGGTTACGTCATGCTGTT	no		426

hsa-miR-140-#2	miR-140-as	TTCTACCACAGGGTAGAACCACGGACAGGATACCGGGGCA	yes		427

hsa-miR-141-prec-#1	miR-141	TTGTGAAGCTCCTAACACTGTCTGGTAAAGATGGCTCCCG	yes		428

hsa-miR-141-prec-#2	miR-141	ATCTTCCAGTACAGTGTTGGATGGTCTAATTGTGAAGCTC	no		429

hsa-miR-142-prec	miR-142-as	CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG	yes	idem mmu-miR-	430
				142-prec

hsa-miR-143-prec	miR-143	CTGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAG	yes		431

hsa-miR-144-prec-#1	miR-144	CGATGAGACACTACAGTATAGATGATGTACTAGTCCGGGC	yes		432

hsa-miR-144-prec-#2	miR-144	CCCTGGCTGGGATATCATCATATACTGTAAGTTTGCGATG	no		433

hsa-miR-145-prec	miR-145	CCTCACGGTCCAGTTTTCCCAGGAATCCCTTAGATGCTAA	yes		434

hsa-miR-146-prec	miR-146	TGAGAACTGAATTCCATGGGTTGTGTCAGTGTCAGACCTC	yes		435

hsa-miR-147-prec	miR-147	GACTATGGAAGCCAGTGTGTGGAAATGCTTCTGCTAGATT	yes		436

hsa-miR-148-prec	miR-148	TGAGTATGATAGAAGTCAGTGCACTACAGAACTTTGTCTC	yes		437

hsa-miR-149-prec	miR-149	CGAGCTCTGGCTCCGTGTCTTCACTCCCGTGCTTGTCCGA	yes		438

hsa-miR-150-prec	miR-150	CTCCCCATGGCCCTGTCTCCCAACCCTTGTACCAGTGCTG	yes		439

hsa-miR-151-prec	miR-151	GTATGTCTCATCCCCTACTAGACTGAAGCTCCTTGAGGAC	yes		440

hsa-miR-152-prec-#1	miR-152	ACTCGGGCTCTGGAGCAGTCAGTGCATGACAGAACTTGGG	yes	idem mmu-miR-	441
				152-prec

hsa-miR-152-prec-#2	miR-152	CCCCGGCCCAGGTTCTGTGATACACTCCGACTCGGGCTCT	no		442

hsa-miR-153-1-prec1	miR-153-1	CAGTTGCATAGTCACAAAAGTGATCATTGGCAGGTGTGGC	yes		443

hsa-miR-153-1-prec2	miR-153-1	CACAGCTGCCAGTGTCATTGTCACAAAAGTGATCATTGGC	yes		444

hsa-miR-153-2-prec	miR-153-2	GCCCAGTTGCATAGTCACAAAAGTGATCATTGGAAACTGT	yes		445

hsa-miR-154-prec1-#1	miR-154	GTGGTACTTGAAGATAGGTTATCCGTGTTGCCTTCGCTTT	yes		446

hsa-miR-154-prec1-#2	miR-154	GCCTTCGCTTTATTTGTGACGAATCATACACGGTTGACCT	no		447

hsa-miR-155-prec	miR-155(BIC)	TTAATGCTAATCGTGATAGGGGTTTTTGCCTCCAACTGAC	yes		448

hsa-miR-181a-prec-#1	miR-181a (=miR-178-2)	TCAGAGGACTCCAAGGAACATTCAACGCTGTCGGTGAGTT	yes		449

hsa-miR-181a-prec-#2	miR-181a (=miR-178-2)	GAAAAAACCACTGACCGTTGACTGTACCTTGGGGTCCTTA	no		450

hsa-miR-181b-prec-#1	miR-181b (=miR-178)	TGAGGTTGCTTCAGTGAACATTCAACGCTGTCGGTGAGTT	yes		451

hsa-miR-181b-prec-#2	miR-181b (=miR-178)	ACCATCGACCGTTGATTGTACCCTATGGCTAACCATCATC	yes		452

hsa-miR-181c-prec-#1	miR-181c	TGCCAAGGGTTTGGGGGAACATTCAACCTGTCGGTGAGTT	yes		453

hsa-miR-181c-prec-#2	miR-181c	ATCGACCGTTGAGTGGACCCTGAGGCCTGGAATTGCCATC	no		454

hsa-miR-182-prec-#1	miR-182-s	AGGTAACAGGATCCGGTGGTTCTAGACTTGCCAACTATGG	no		455

hsa-miR-182-prec-#2	miR-182-s	TTGGCAATGGTAGAACTCACACTGGTGAGGTAACAGGATC	yes		456

hsa-miR-183-prec-#1	miR-183 (=miR-174)	GACTCCTGTTCTGTGTATGGCACTGGTAGAATTCACTGTG	yes		457

hsa-miR-183-prec-#2	miR-183 (=miR-174)	GTCTCAGTCAGTGAATTACCGAAGGGCCATAAACAGAGCA	no		458

hsa-miR-184-prec-#1	miR-184	GACTGTAAGTGTTGGACGGAGAACTGATAAGGGTAGGTGA	yes		459

hsa-miR-184-prec-#2	miR-184	CGTCCCCTTATCACTTTTCCAGCCCAGCTTTGTGACTGTA	no		460

hsa-miR-185-prec-#1	miR-185	GCGAGGGATTGGAGAGAAAGGCAGTTCCTGATGGTCCCCT	yes		461

hsa-miR-185-prec-#2	miR-185	CCTCCCCAGGGGCTGGCTTTCCTCTGGTCCTTCCCTCCCA	no		462

hsa-miR-186-prec	miR-186	CTTGTAACTTTCCAAAGAATTCTCCTTTTGGGCTTTCTGG	yes		463

hsa-miR-187-prec-#1	miR-187	CTCGTGTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCA	yes		464

hsa-miR-187-prec-#2	miR-187	TCACCATGACACAGTGTGAGACTCGGGCTACAACACAGGA	no		465

hsa-miR-188-prec	miR-188	TCACATCCCTTGCATGGTGGAGGGTGAGCTTTCTGAAAAC	yes		466

hsa-miR-190-prec	miR-190	GCAGGCCTCTGTGTGATATGTTTGATATATTAGGTTGTTA	yes		467

hsa-miR-191-prec	miR-191	CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC	yes	idem mmu-miR-	468
				191-prec

hsa-miR-192-2/3-#1	miR-192	TCTGACCTATGAATTGACAGCCAGTGCTCTCGTCTCCCCT	yes		469

hsa-miR-192-2/3-#2	miR-192	CCAATTCCATAGGTCACAGGTATGTTCGCCTCAATGCCAG	no		470

hsa-miR-193-prec-#1	miR-193	AGATGAGGGTGTCGGATCAACTGGCCTACAAAGTCCCAGT	yes		471

hsa-miR-193-prec-#2	miR-193	AGGATGGGAGCTGAGGGCTGGGTCTTTGCGGGCGAGATGA	no		472

hsa-miR-194-prec-#1	miR-194	TGTAACAGCAACTCCATGTGGACTGTGTACCAATTTCCAG	yes		473

hsa-miR-194-prec-#2	miR-194	CCAATTTCCAGTGGAGATGCTGTTACTTTTGATGGTTACC	no		474

hsa-miR-195-prec	miR-195	TCTAGCAGCACAGAAATATTGGCACAGGGAAGCGAGTCTG	yes		475

hsa-miR-196-1-prec-#1	miR-196-1	CTGCTGAGTGAATTAGGTAGTTTCATGTTGTTGGGCCTGG	yes		476

hsa-miR-196-1-prec-#2	miR-196-1	ACACAACAACATTAAACCACCCGATTCACGGCAGTTACTG	no		477

hsa-miR-196-2-prec-#1	miR-196-2	AGAAACTGCCTGAGTTACATCAGTCGGTTTTCGTCGAGGG	no		478

hsa-miR-196-2-prec-#2	miR-196-2	GCTGATCTGTGGCTTAGGTAGTTTCATGTTGTTGGGATTG	yes		479

hsa-miR-197-prec	miR-197	TAAGAGCTCTTCACCCTTCACCACCTTCTCCACCCAGCAT	yes		480

hsa-miR-198-prec	miR-198	TCATTGGTCCAGAGGGGAGATAGGTTCCTGTGATTTTTCC	yes		481

hsa-miR-199a-1-prec	miR-199a-1(=199s)	GCCAACCCAGTGTTCAGACTACCTGTTCAGGAGGCTCTCA	yes		482

hsa-miR-199a-2-prec	miR-199a-2	TCGCCCCAGTGTTCAGACTACCTGTTCAGGACAATGCCGT	yes		483

hsa-miR-199b-prec-#1	miR-199b	GTCTGCACATTGGTTAGGCTGGGCTGGGTTAGACCCTCGG	no		484

hsa-miR-199b-prec-#2	miR-199b	ACCTCCACTCCGTCTACCCAGTGTTTAGACTATCTGTTCA	yes		485

hsa-miR-200a-prec	miR-200a	GTCTCTAATACTGCCTGGTAATGATGACGGCGGAGCCCTG	yes		486

hsa-miR-202-prec	miR-202	GATCTGGCCTAAAGAGGTATAGGGCATGGGAAGATGGAGC	yes		487

hsa-miR-203-prec-#1	miR-203	GTTCTGTAGCGCAATTGTGAAATGTTTAGGACCACTAGAC	yes		488

hsa-miR-203-prec-#2	miR-203	TGGGTCCAGTGGTTCTTAACAGTTCAACAGTTCTGTAGCG	no		489

hsa-miR-204-prec-#1	miR-204	CGTGGACTTCCCTTTGTCATCCTATGCCTGAGAATATATG	yes		490

hsa-miR-204-prec-#2	miR-204	AGGCTGGGAAGGCAAAGGGACGTTCAATTGTCATCACTGG	no		491

hsa-miR-205-prec	miR-205	TCCTTCATTCCACCGGAGTCTGTCTCATACCCAACCAGAT	yes		492

hsa-miR-206-prec-#1	miR-206	TTGCTATGGAATGTAAGGAAGTGTGTGGTTTCGGCAAGTG	yes		493

hsa-miR-206-prec-#2	miR-206	TGCTTCCCGAGGCCACATGCTTCTTTATATCCCCATATGG	no		494

hsa-miR-208-prec	miR-208	ACCTGATGCTCACGTATAAGACGAGCAAAAAGCTTGTTGG	yes		495

hsa-miR-210-prec	miR-210	AGACCCACTGTGCGTGTGACAGCGGCTGATCTGTGCCTGG	yes		496

hsa-miR-211-prec-#1	miR-211	TTCCCTTTGTCATCCTTCGCCTAGGGCTCTGAGCAGGGCA	yes		497

hsa-miR-211-prec-#2	miR-211	GCAGGGACAGCAAAGGGGTGCTCAGTTGTCACTTCCCACA	no		498

hsa-miR-212-prec-#1	miR-212	CCTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC	yes		499

hsa-miR-212-prec-#2	miR-212	CGGACAGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTG	no		500

hsa-miR-213-prec-#1	miR-213	AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG	yes	idem mmu-miR-	501
				213-prec

hsa-miR-213-prec-#2	miR-213	TGTGGACAAGCTCACTGAACAATGAATGCAACTGTGGCCC	no		502

hsa-miR-214-prec	miR-214	TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG	yes	idem mmu-miR-	503
				214-prec

hsa-miR-215-prec-#1	miR-215	CAGGAAAATGACCTATGAATTGACAGACAATATAGCTGAG	yes		504

hsa-miR-215-prec-#2	miR-215	CATTTCTTTAGGCCAATATTCTGTATGACTGTGCTACTTC	no		505

hsa-miR-216-prec-#1	miR-216	CTGGGATTATGCTAAACAGAGCAATTTCCTAGCCCTCACG	no		506

hsa-miR-216-prec-#2	miR-216	GATGGCTGTGAGTTGGCTTAATCTCAGCTGGCAACTGTGA	yes		507

hsa-miR-217-prec-#1	miR-217	GAATCAGTCACCATCAGTTCCTAATGCATTGCCTTCAGCA	no		508

hsa-miR-217-prec-#2	miR-217	TGTCGCAGATACTGCATCAGGAACTGATTGGATAAGAATC	yes		509

hsa-miR-218-1-prec	miR-218-1	GTTGTGCTTGATCTAACCATGTGGTTGCGAGGTATGAGTA	yes		510

hsa-miR-218-2-prec-#1	miR-218-2	TGGTGGAACGATGGAAACGGAACATGGTTCTGTCAAGCAC	no		511

hsa-miR-218-2-prec-#2	miR-218-2	TCGCTGCGGGGCTTTCCTTTGTGCTTGATCTAACCATGTG	yes		512

hsa-miR-219-prec	miR-219	ATTGTCCAAACGCAATTCTCGAGTCTATGGCTCCGGCCGA	yes		513

hsa-miR-220-prec	miR-220	TGTGGCATTGTAGGGCTCCACACCGTATCTGACACTTTGG	yes		514

hsa-miR-221-prec	miR-221	CAACAGCTACATTGTCTGCTGGGTTTCAGGCTACCTGGAA	yes	idem mmu-miR-	515
				221-prec-#1

hsa-miR-222-prec-#1	miR-222	CTTTCGTAATCAGCAGCTACATCTGGCTACTGGGTCTCTG	yes		516

hsa-miR-222-prec-#2	miR-222	GCTGCTGGAAGGTGTAGGTACCCTCAATGGCTCAGTAGCC	no		517

hsa-miR-223-prec	miR-223	GAGTGTCAGTTTGTCAAATACCCCAAGTGCGGCACATGCT	yes		518

hsa-miR-224-prec	miR-224	GGCTTTCAAGTCACTAGTGGTTCCGTTTAGTAGATGATTG	yes		519

HSHELA01	—	GGCCGCAGCAACCTCGGTTCGTATCCGAGTCACGGCACCA	—		520

HSTRNL	—	TCCGGATGGAGCGTGGGTTCGAATCCCACTTCTGACACCA	—		521

HUMTRAB	—	ATGGTAGAGCGCTCGCTTTGCTTGCGAGAGGTAGCGGGAT	—		522

HUMTRF	—	GATCTAAAGGTCCCTGGTTCGATCCCGGGTTTCGGCACCA	—		523

HUMTRMI-#1	—	AGCAGAGTGGCGCAGCGGAAGCGTGCTGGGCCCATAACCC	—	idem	524
				MUSTRMI-#1

HUMTRMI-#2	—	AACCCAGAGGTCGATGGATCGAAACCATCCTCTGCTACCA	—		525

HUMTRN	—	CAATCGGTTAGCGCGTTCGGCTGTTAACCGAAAGGTTGGT	—		526

HUMTRS	—	TCTAGCGACAGAGTGGTTCAATTCCACCTTTCGGGCGCCA	—		527

HUMTRV1A	—	ACGCGAAAGGTCCCCGGTTCGAAACCGGGCGGAAACACCA	—		528

mmu-let-7g-prec	mmu-let-7g	CTGAGGTAGTAGTTTGTACAGTTTGAGGGTCTATGATACC	yes		529

mmu-let-7i-prec	mmu-let-7i	GCTGAGGTAGTAGTTTGTGCTGTTGGTCGGGTTGTGACAT	yes	idem hsa-let-7d-	530
				v2-prec-#2

mmu-miR-001b-prec	mmu-miR-001b	ATTCAGTGCTATGGAATGTAAAGAAGTATGTATTTTGGGT	yes		531

mmu-miR-001d-prec	mmu-miR-001d	CTGCTAAGCTATGGAATGTAAAGAAGTATGTATTTCAGGC	yes		532

mmu-miR-009-prec-#1	mmu-miR-009	ATCTTTGGTTATCTAGCTGTATGAGTGTATTGGTCTTCAT	yes		533

mmu-miR-009-prec-#2	mmu-miR-009-	GAGTGTATTGGTCTTCATAAAGCTAGATAACCGAAAGTAA	yes	idem hsa-miR-	534
				009-2-#2

mmu-miR-010b-prec	mmu-miR-010b	TACCCTGTAGAACCGAATTTGTGTGGTACCCACATAGTCA	yes		535

mmu-miR-023b-prec	mmu-miR-023b	TTGAGATTAAAATCACATTGCCAGGGATTACCACGCAACC	yes		536

mmu-miR-027b-prec	mmu-miR-027b	TTGGTTTCCGCTTTGTTCACAGTGGCTAAGTTCTGCACCT	yes		537

mmu-miR-029b-prec	mmu-miR-029b	TAAATAGTGATTGTCTAGCACCATTTGAAATCAGTGTTCT	yes		538

mmu-miR-030b-prec	mmu-miR-030b	TGTAAACATCCTACACTCAGCTGTCATACATGCGTTGGCT	yes		539

mmu-miR-030e-prec	mmu-miR-030e	TGTAAACATCCTTGACTGGAAGCTGTAAGGTGTTGAGAGG	yes		540

mmu-miR-099a-prec	mmu-miR-099a	CATAAACCCGTAGATCCGATCTTGTGGTGAAGTGGACCGC	yes		541

mmu-miR-099b-prec-#1	mmu-miR-099b	GCCTTCGCCGCACACAAGCTCGTGTCTGTGGGTCCGTGTC	no	idem hsa-miR-	542
				099b-prec-19-#1

mmu-miR-099b-prec-#2	mmu-miR-099b	CACCCGTAGAACCGACCTTGCGGGGCCTTCGCCGCACACA	yes	idem hsa-miR-	543
				099b-prec-19-#2

mmu-miR-100-prec	mmu-miR-100	TGCCACAAACCCGTAGATCCGAACTTGTGCTGATTCTGCA	yes		544

mmu-miR-101-prec	mmu-miR-101	GCTGTCCATTCTAAAGGTACAGTACTGTGATAACTGAAGG	yes		545

mmu-miR-122a-prec-#1	mmu-miR-122a	GTGTCCAAACCATCAAACGCCATTATCACACTAAATAGCT	no		546

mmu-miR-122a-prec-#2	mmu-miR-122a	GCTGTGGAGTGTGACAATGGTGTTTGTGTCCAAACCATCA	yes		547

mmu-miR-123-prec-#1	mmu-miR-123	CATTATTACTTTTGGTACGCGCTGTGACACTTCAAACTCG	yes		548

mmu-miR-123-prec-#2	mmu-miR-123	GACACTTCAAACTCGTACCGTGAGTAATAATGCGCGGTCA	yes		549

mmu-miR-124a-prec	mmu-miR-124a	TAATGTCTATACAATTAAGGCACGCGGTGAATGCCAAGAG	yes		550

mmu-miR-125a-prec	mmu-miR-125a	TCCCTGAGACCCTTTAACCTGTGAGGACGTCCAGGGTCAC	yes		551

mmu-miR-125b-prec-#1	mmu-miR-125b	GCCTAGTCCCTGAGACCCTAACTTGTGAGGTATTTTAGTA	yes		552

mmu-miR-125b-prec-#2	mmu-miR-125b	ATTTTAGTAACATCACAAGTCAGGTTCTTGGGACCTAGGC	no		553

mmu-miR-127-prec	mmu-miR-127	TTCAGAAAGATCATCGGATCCGTCTGAGCTTGGCTGGTCG	yes		554

mmu-miR-128-prec-#1	mmu-miR-128	AGGTTTACATTTCTCACAGTGAACCGGTCTCTTTTTCAGC	yes		555

mmu-miR-128-prec-#2	mmu-miR-128	TTGGATTCGGGGCCGTAGCACTGTCTGAGAGGTTTACATT	no	idem hsa-miR-	556
				128a-prec-#1

mmu-miR-129b-prec	mmu-miR-129b	CTTTTTGCGGTCTGGGCTTGCTGTACATAACTCAATAGCC	yes		557

mmu-miR-129-prec	mmu-miR-129	CTTTTTGCGGTCTGGGCTTGCTGTTTTCTCGACAGTAGTC	yes		558

mmu-miR-130-prec	mmu-miR-130	GTCTAACGTGTACCGAGCAGTGCAATGTTAAAAGGGCATC	yes		559

mmu-miR-131-3-prec	mmu-miR-131-3	AGTGGTGTGGAGTCTTCATAAAGCTAGATAACCGAAAGTA	yes		560

mmu-miR-132-prec	mmu-miR-132	TGTGGGAACCGGAGGTAACAGTCTACAGCCATGGTCGCCC	yes		561

mmu-miR-133-prec	mmu-miR-133	ATCGCCTCTTCAATGGATTTGGTCCCCTTCAACCAGCTGT	yes		562

mmu-miR-134-prec-#1	mmu-miR-134	GCACTCTGTTCACCCTGTGGGCCACCTAGTCACCAACCCT	no		563

mmu-miR-134-prec-#2	mmu-miR-134	TGTGTGACTGGTTGACCAGAGGGGCGTGCACTCTGTTCAC	yes		564

mmu-miR-135-prec	mmu-miR-135	CTATGGCTTTTTATTCCTATGTGATTCTATTGCTCGCTCA	yes		565

mmu-miR-136-prec	mmu-miR-136	GAGGACTCCATTTGTTTTGATGATGGATTCTTAAGCTCCA	yes		566

mmu-miR-137-prec	mmu-miR-137	GGATTACGTTGTTATTGCTTAAGAATACGCGTAGTCGAGG	yes	idem hsa-miR-	567
				137-prec

mmu-miR-138-prec	mmu-miR-138	AGCTGGTGTTGTGAATCAGGCCGACGAGCAGCGCATCCTC	yes	idem hsa-miR-	568
				138-2-prec

mmu-miR-140s-prec	mmu-miR-140s	TTACGTCATGCTGTTCTACCACAGGGTAGAACCACGGACA	yes		569

mmu-miR-141-prec	mmu-miR-141	GAAGTATGAAGCTCCTAACACTGTCTGGTAAAGATGGCCC	yes		570

mmu-miR-142-prec	mmu-miR-142	CCCATAAAGTAGAAAGCACTACTAACAGCACTGGAGGGTG	yes	idem hsa-miR-	571
				142-prec

mmu-miR-143-prec	mmu-miR-143	TGGTCAGTTGGGAGTCTGAGATGAAGCACTGTAGCTCAGG	yes		572

mmu-miR-144-prec	mmu-miR-144	GTTTGTGATGAGACACTACAGTATAGATGATGTACTAGTC	yes		573

mmu-miR-145-prec	mmu-miR-145	ACGGTCCAGTTTTCCCAGGAATCCCTTGGATGCTAAGATG	yes		574

mmu-miR-146-prec	mmu-miR-146	TGAGAACTGAATTCCATGGGTTATATCAATGTCAGACCTG	yes		575

mmu-miR-149-prec	mmu-miR-149	GCTCTGGCTCCGTGTCTTCACTCCCGTGTTTGTCCGAGGA	yes		576

mmu-miR-150-prec	mmu-miR-150	TGTCTCCCAACCCTTGTACCAGTGCTGTGCCTCAGACCCT	yes		577

mmu-miR-151-prec	mmu-miR-151	TATGTCTCCTCCCTACTAGACTGAGGCTCCTTGAGGGACA	yes		578

mmu-miR-152-prec	mmu-miR-152	ACTCGGGCTCTGGAGCAGTCAGTGCATGACAGAACTTGGG	yes	idem hsa-miR-	579
				152-prec-#1

mmu-miR-153-prec	mmu-miR-153	TAATATGAGCCCAGTTGCATAGTGACAAAAGTGATCATTG	yes		580

mmu-miR-154-prec	mmu-miR-154	AGATAGGTTATCCGTGTTGCCTTCGCTTTATTCGTGACGA	yes		581

mmu-miR-155-prec	mmu-miR-155	TTAATGCTAATTGTGATAGGGGTTTTGGCCTCTGACTGAC	yes		582

mmu-miR-181-prec	mmu-miR-181	CCATGGAACATTCAACGCTGTCGGTGAGTTTGGGATTCAA	yes		583

mmu-miR-182-prec	mmu-miR-182	TTTGGCAATGGTAGAACTCACACCGGTAAGGTAATGGGAC	yes		584

mmu-miR-183-prec-#1	mmu-miR-183	AACAGTCTCAGTCAGTGAATTACCGAAGGGCCATAAACAG	no		585

mmu-miR-183-prec-#2	mmu-miR-183	TATGGCACTGGTAGAATTCACTGTGAACAGTCTCAGTCAG	yes		586

mmu-miR-184-prec	mmu-miR-184	TGTGACTCTAAGTGTTGGACGGAGAACTGATAAGGGTAGG	yes		587

mmu-miR-185-prec	mmu-miR-185	GGGATTGGAGAGAAAGGCAGTTCCTGATGGTCCCCTCCCA	yes		588

mmu-miR-186-prec	mmu-miR-186	CAAAGAATTCTCCTTTTGGGCTTTCTCATTTTATTTTAAG	yes		589

mmu-miR-187-prec	mmu-miR-187	GGGCGCTGCTCTGACCCCTCGTGTCTTGTGTTGCAGCCGG	yes		590

mmu-miR-188-prec	mmu-miR-188	TCACATCCCTTGCATGGTGGAGGGTGAGCTCTCTGAAAAC	yes		591

mmu-miR-189-prec	mmu-miR-189	CGGTGCCTACTGAGCTGATATCAGTTCTCATTTCACACAC	yes		592

mmu-miR-190-prec	mmu-miR-190	CTGTGTGATATGTTTGATATATTAGGTTGTTATTTAATCC	yes		593

mmu-miR-191-prec	mmu-miR-191	CAACGGAATCCCAAAAGCAGCTGTTGTCTCCAGAGCATTC	yes	idem hsa-miR-	594
				191-prec

mmu-miR-192-2/3-prec	mmu-miR-192-2/3	CTGACCTATGAATTGACAGCCAGTGCTCTCGTCTCCCCTC	yes		595

mmu-miR-193-prec	mmu-miR-193	TGAGAGTGTCAGTTCAACTGGCCTACAAAGTCCCAGTCCT	yes		596

mmu-miR-194-prec	mmu-miR-194	ATCGGGTGTAACAGCAACTCCATGTGGACTGTGCTCGGAT	yes		597

mmu-miR-195-prec	mmu-miR-195	TAGCAGCACAGAAATATTGGCATGGGGAAGTGAGTCTGCC	yes		598

mmu-miR-196-prec	mmu-miR-196	GTAGGTAGTTTCATGTTGTTGGGCCTGGCTTTCTGAACAC	yes		599

mmu-miR-199as-prec	mmu-miR-199as	GAGGCTGGGACATGTACAGTAGTCTGCACATTGGTTAGGC	yes		600

mmu-miR-200a-prec-#1	mmu-miR-200a	TAGTGTCTGATCTCTAATACTGCCTGGTAATGATGACGGC	yes		601

mmu-miR-200a-prec-#2	mmu-miR-200a	CCGTGGCCATCTTACTGGGCAGCATTGGATAGTGTCTGAT	no		602

mmu-miR-201-prec	mmu-miR-201	TACCTTACTCAGTAAGGCATTGTTCTTCTATATTAATAAA	yes		603

mmu-miR-202-prec	mmu-miR-202	GATCTGGTCTAAAGAGGTATAGCGCATGGGAAGATGGAGC	yes		604

mmu-miR-203-prec-#1	mmu-miR-203	GGTCCAGTGGTTCTTGACAGTTCAACAGTTCTGTAGCACA	no		605

mmu-miR-203-prec-#2	mmu-miR-203	GTAGCACAATTGTGAAATGTTTAGGACCACTAGACCCGGC	yes		606

mmu-miR-204-prec	mmu-miR-204	TTCCCTTTGTCATCCTATGCCTGAGAATATATGAAGGAGG	yes		607

mmu-miR-205-prec	mmu-miR-205	GTCCTTCATTCCACCGGAGTCTGTCTTATGCCAACCAGAT	yes		608

mmu-miR-206-prec	mmu-miR-206	TAGATATCTCAGCACTATGGAATGTAAGGAAGTGTGTGGT	yes		609

mmu-miR-207-prec	mmu-miR-207	GCTGCGGCTTGCGCTTCTCCTGGCTCTCCTCCCTCTCCTT	yes		610

mmu-miR-212-prec-#1	mmu-miR-212	CTTCAGTAACAGTCTCCAGTCACGGCCACCGACGCCTGGC	yes		611

mmu-miR-212-prec-#2	mmu-miR-212	AGCGCGCCGGCACCTTGGCTCTAGACTGCTTACTGCCCGG	no		612

mmu-miR-213-prec	mmu-miR-213	AACATTCATTGCTGTCGGTGGGTTGAACTGTGTGGACAAG	yes	idem hsa-miR-	613
				213-prec-#1

mmu-miR-214-prec	mmu-miR-214	TGTACAGCAGGCACAGACAGGCAGTCACATGACAACCCAG	yes	idem hsa-miR-	614
				214-prec

mmu-miR-215-prec	mmu-miR-215	CAGGAGAATGACCTATGATTTGACAGACCGTGCAGCTGTG	yes		615

mmu-miR-216-prec-#1	mmu-miR-216	GAGATGTCCCTATCATTCCTCACAGTGGTCTCTGGGATTA	no		616

mmu-miR-216-prec-#2	mmu-miR-216	ATGGCTATGAGTTGGTTTAATCTCAGCTGGCAACTGTGAG	yes		617

mmu-miR-217-prec-#1	mmu-miR-217	GCAGATACTGCATCAGGAACTGACTGGATAAGACTTAATC	yes		618

mmu-miR-217-prec-#2	mmu-miR-217	CCCCATCAGTTCCTAATGCATTGCCTTCAGCATCTAAACA	no		619

mmu-miR-218-2-prec-#1	mmu-miR-218-2	GGGCTTTCCTTTGTGCTTGATCTAACCATGTGGTGGAACG	yes		620

mmu-miR-218-2-prec-#2	mmu-miR-218-2	GTGGTGGAACGATGGAAACGGAACATGGTTCTGTCAAGCA	no		621

mmu-miR-219-prec-#1	mmu-miR-219	TCCTGATTGTCCAAACGCAATTCTCGAGTCTCTGGCTCCG	yes		622

mmu-miR-219-prec-#2	mmu-miR-219	CTCTGGCTCCGGCCGAGAGTTGCGTCTGGACGTCCCGAGC	no		623

mmu-miR-221-prec-#1	mmu-miR-221	CAACAGCTACATTGTCTGCTGGGTTTCAGGCTACCTGGAA	yes	idem hsa-miR-	624
				221-prec

mmu-miR-221-prec-#2	mmu-miR-221	GGCATACAATGTAGATTTCTGTGTTTGTTAGGCAACAGCT	no		625

mmu-miR-222-prec	mmu-miR-222	TTGGTAATCAGCAGCTACATCTGGCTACTGGGTCTCTGGT	yes		626

mmu-miR-223-prec	mmu-miR-223	AGAGTGTCAGTTTGTCAAATACCCCAAGTGTGGCTCATGC	yes		627

mmu-miR-224-	mmu-miR-224-(miR-	TAAGTCACTAGTGGTTCCGTTTAGTAGATGGTCTGTGCAT	yes		628
precformer175-#1	175)

mmu-miR-224-	mmu-miR-224-(miR-	TGCATTGTTTCAAAATGGTGCCCTAGTGACTACAAAGCCC	no		629
precformer175-#2	175)

MUSTRF	—	TAGACTGAAGATCTAAAGGTCCCTGGTTCGATCCCGGGTT	—		630

MUSTRM4	—	AATCTGAAGGTCGTGAGTTCGATCCTCACACGGGGCACCA	—		631

MUSTRMI-#1	—	AGCAGAGTGGCGCAGCGGAAGCGTGCTGGGCCCATAACCC	—	idem	632
				HUMTRMI-#1

MUSTRMI-#2	—	CCCATAACCCAGAGGTCGATGGATCGAAACCATCCTCTGC	—		633

MUSTRNAH	—	TGCGTTGTGGCCGCAGCAACCTCGGTTCGAATCCGAGTCA	—		634

MUSTRP2	—	GCTCGTTGGTCTAGGGGTATGATTCTCGCTTTGGGTGCGA	—		635

MUSTRS	—	AGCTGTTTAGCGACAGAGTGGTTCAATTCCACCTTTCGGG	—		636

MUSTRV1MN	—	TTCCGTAGTGTAGTGGTTATCACGCTCGCCTGACACGCGA	—		637

Oligonucleotide primer	—	5′ biotin-AAA-AAA-AAA-AAA-(biotin)AAA-AAA-AAA-AAA-	—		638
#1		NNN-NNN-NN 3′

Oligonucleotide primer	—	5′ biotin-(biotin)-AAA-NNN-NNN-NN 3′	—		639
#2

Oligonucleotide primer	—	5′ GCC-AGT-GAA-TTG-TAA-TAC-GAC-TCA-CTA-TAG-GGA-	—		640
#3		GGC-GGN-NNN-NNN-N 3′

miRNA Microarray Fabrication. 40-mer 5′ amine modified C6 oligonucleotides were resuspended in 50 mM phosphate buffer pH 8.0 at 20 mM concentration. The individual oligonucleotide-probe was printed in triplicate on Amersham CodeLink™ activated slides under 45% humidity by GeneMachine OmniGrid™ 100 Microarrayer in 2×2 pin configuration and 20×20 spot configuration of each subarray. The spot diameter was 100 μm and distance from center to center was 200 μm. The printed miRNA microarrays were further chemically covalently-coupled under 70% humidity overnight. The miRNA microarrays were ready for sample hybridization after additional blocking and washing steps.
Target Preparation. Five μg of total RNA were separately added to a reaction mix in a final volume of 12 μl, containing 1 μg of [3′(N)8-(A)12-biotin-(A)12-biotin 5′] oligonucleotide primer. The mixture was incubated for 10 min at 70° C. and chilled on ice. With the mixture remaining on ice, 4 μl of 5× first-strand buffer, 2 μl 0.1 M DTT, 1 μl of 10 mM dNTP mix and 1 μl Superscript™ II RNaseH⁻ reverse transcriptase (200 U/μl) was added to a final volume of 20 μl, and the mixture incubated for 90 min in a 37° C. water bath. After incubation for first strand cDNA synthesis, 3.5 μl of 0.5 M NaOH/50 mM EDTA was added into 20 μl of first strand reaction mix and incubated at 65° C. for 15 min to denature the RNA/DNA hybrids and degrade RNA templates. Then 5 μl of 1 M Tris-HCl, pH 7.6 (Sigma) was added to neutralize the reaction mix and labeled targets were stored in 28.5 μl at −80° C. until chip hybridization.
Array Hybridization. Labeled targets from 5 μg of total RNA were used for hybridization on each KCC/TJU miRNA microarray containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. All probes on these microarrays were 40-mer oligonucleotides spotted by contacting technologies and covalently attached to a polymeric matrix. The microarrays were hybridized in 6×SSPE/30% formamide at 25° C. for 18 hours, washed in 0.75×TNT at 37° C. for 40 min, and processed using direct detection of the biotin-containing transcripts by Streptavidin-Alexa647 conjugate. Processed slides were scanned using a Perkin Elmer ScanArray® XL5K Scanner with the laser set to 635 nm, at Power 80 and PMT 70 setting, and a scan resolution of 10 microns.
Data Analysis. Images were quantified by QuantArray® Software (PerkinElmer). Signal intensities for each spot were calculated by subtracting local background (based on the median intensity of the area surrounding each spot) from total intensities. Raw data were normalized and analyzed using the GeneSpring® software version 6.1.1 (Silicon Genetics, Redwood City, Calif.). GeneSpring generates an average value of the three spot replicates of each miRNA. Following data transformation (to convert any negative value to 0.01), normalization was performed by using a per-chip 50th percentile method that normalizes each chip on its median allowing comparison among chips. Hierarchical clustering for both genes and conditions were then generated by using standard correlation as a measure of similarity. To highlight genes that characterize each tissue, a per-gene on median normalization was performed, which normalizes the expression of every miRNA on its median among samples.
Samples. HeLa cells were purchased from ATCC and grown as recommended. Mouse macrophage cell line RAW264.7 (established from BALB/c mice) was also used (Dumitru, C. D., Ceci, J. D., Tsatsanis, C., Kontoyiannis, D., Stamatakis, K., Lin, J. H., Patriotis, C., Jenkins, N. A., Copeland, N. G., Kollias, G. & Tsichlis, P. N. (2000) Cell 103, 1071-83). RNA from 20 normal human tissues, including 18 of adult origin (7 hematopoietic: bone marrow, lymphocytes B, T, and CD5+ cells from 2 individuals, peripheral blood leukocytes derived from three healthy donors, spleen, and thymus; and 11 solid tissues, including brain, breast, ovary, testis, prostate, lung, heart, kidney, liver, skeletal muscle, and placenta) and 2 of fetal origin (fetal liver and fetal brain) were assessed for miRNA expression. Each RNA was labeled and hybridized in duplicate and the average expression was calculated. For all the normal tissues, except lymphocytes B, T and CD5+ cells, total RNA was purchased from Ambion (Austin, Tex.).
Cell Preparation. Mononuclear cells (MNC) from peripheral blood of normal donors were separated by Ficoll-Hypaque density gradients. T cells were purified from these MNC by rosetting with neuraminidase treated SRBC and depletion of contaminant monocytes (Cd11b+), natural killer cells (CD16+) and B lymphocytes (CD19+) were purified using magnetic beads (Dynabeads, Unipath, Milano, Italy) and specific monoclonal antibodies (Becton Dickinson, San Jose, Calif.). Total B cells and CD5+ B cells were prepared from tonsils as described (Dono, M., Zupo, S., Leanza, N., Melioli, G., Fogli, M., Melagrana, A., Chiorazzi, N. & Ferrarini, M. (2000) J. Immunol. 164, 5596-604). Briefly, tonsils were obtained from patients in the pediatric age group undergoing routine tonsillectomies, after informed consent. Purified B cells were prepared by rosetting T cells from MNC cells with neuraminidase treated SRBC. In order to obtain CD5+ B cells, purified B cells were incubated with anti CD5 monoclonal antibody followed by goat anti mouse Ig conjugated with magnetic microbeads. CD5+ B cells were positively selected by collecting the cells retained on the magnetic column MS by Mini MACS system (Miltenyi Biotec, Auburn, Calif.). The degree of purification of the cell preparations was higher than 95%, as assessed by flow cytometry.
RNA Extraction and Northern Blots. Total RNA isolation and blots were performed as described (Calin, et al., (2002) Proc Natl Acad Sc USA. 99, 15524-15529). After RNA isolation, the washing step with ethanol was not performed, or if performed, the tube walls were rinsed with 75% ethanol without perturbing the RNA pellet (Lagos-Quintana, et al., (2001) Science 294, 853-858). For reuse, blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 min, and were reprobed. 5S rRNA stained with ethidium bromide served as a loading control.
Quantitative RT-PCR for miRNA Precursors. Quantitative RT-PCR was performed as described (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53). Briefly, RNA was reverse transcribed to cDNA with gene-specific primers and Thermoscript, and the relative amount of each miRNA to both U6 RNA and tRNA for initiator methionine was described using the equation 2^−dCT, where dC_T=(C_TmiRNA−C_{TU6 or HUMTMI RNA}). The miRNAs analyzed included miR-15a, miR-16-1, miR-18, miR-20, miR-21, miR-28-2, miR-30d, miR-93-1, miR-105, miR-124a-2, miR-147, miR-216, miR-219, and miR-224. The primers used were as published (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53).
Microarray Data Submission. All data were submitted using MIAMExpress to Array Express database and each of the 44 samples described here received an ID number ranging from SAMPLE169150SUB621 to SAMPLE 169193SIUB621.
Results
Hybridization Sensitivity. The hybridization sensitivity of the miRNA microarray was tested using various quantities of total RNA from HeLa cells, starting from 2.5 μg up to 20 μg. The coefficients of correlation between the 5 μg experiment versus the 2.5, 10 and 20 μg experiments, were 0.98, 0.99 and 0.97 respectively. These results clearly show high inter-assay reproducibility, even in the presence of large differences in RNA quantities. In addition, standard deviation calculated for miRNA triplicates was below 10% for the vast majority (>95%) of oligonucleotides. All other experiments described here were performed with 5 μg of total RNA.
Microarray specificity. To test the specificity of the microchip, miRNA expression in human blood leukocytes from three healthy donors and 2 samples of mouse macrophages was analyzed. Samples derived from the same type of tissue presented homogenous patterns of miRNA expression. Furthermore, the pattern of hybridization is different for the two species. To confirm microarray results, the same RNA samples from mouse macrophages and HeLa cells were also analyzed by quantitative RT-PCR for a randomly selected set of 14 miRNAs (Schmittgen, T. D., Jiang, J., Liu, Q. & Yang, L. (2004) Nucleic Acid Research 32, 43-53). When we were able to amplify a miRNA precursor for which a correspondent oligonucleotide was present on the chip (hsa-miR-15a, hsa-mir-30d, mmu-miR-219 and mmu-miR-224) the concordance between the two techniques was 100%. Furthermore, it has been reported that expression levels of the active miRNA and the precursor pre-miRNA are different in the same sample (Calin, et al. (2002) Proc Natl Acad Sc USA. 99, 15524-15529; Mourelatos, et al. (2002) Genes Dev 16, 720-728; Lagos-Quintana, et al. (2002) Curr Biol 12, 735-739); in fact, for another 10 miRNAs for which only the oligonucleotide corresponding to the active version was present on the chip, no concordance with quantitative real-time PCR results was observed for the precursor.
The stringency of hybridization was, in several instances, sufficient to distinguish nucleotide mismatches for members of closely related miRNA families and very similar sequences gave distinct expression profiles (for example let-7a-1 and let-7f-2 which are 89% similar in an 88 nucleotide sequence). Therefore, each quantified result represents the specific expression of a single miRNA member and not the combined expression of the entire family. In other cases, when a portion of oligonucleotide was 100% identical for two probes (for example, the 23mer of active molecule present in the 40-mer oligonucleotides for both mir-16 sequences from chromosome 13 and chromosome 3), very similar profiles were observed. Therefore, both sequence similarity and secondary structure influence the cross-hybridization between different molecules on this type of microarray.
miRNA Expression in Normal Human Tissues. To further validate reliability of the microarray, we analyzed a panel of 20 RNAs from human normal tissues, including 18 of adult origin (7 hematopoietic and 11 solid tissues) and 2 of fetal origin (fetal liver and brain). For 15 of them, at least two different RNA samples or two replicates from the same preparation were used (for a detailed list of samples see the above Methods). The results demonstrated that different tissues have distinctive patterns of miRNome expression (defined as the full complement of miRNAs in a cell) with each tissue presenting a specific signature. Using unsupervised hierarchical clustering, the same types of tissue from different individuals clustered together. The hematopoietic tissues presented two distinct clusters, the first one containing CD5+ cells, T lymphocytes, and leukocytes and the second cluster containing bone marrow, fetal liver and B lymphocytes. Of note, RNA of fetal or adult type from the same tissue origin (brain) present different miRNA expression pattern. The results demonstrated that some miRNAs are highly expressed in only one or few tissues, such as miR-1b-2 or miR-99b in brain, and the closely related members miR-133a and miR-133b in skeletal muscle, heart and prostate. The types of normalization of the GeneSpring software (on 50% with or without a per-gene on median normalization) did not influence these results.
To verify these data, Northern blot analysis was performed on total RNA used in the microarray experiments, using four miRNA probes: miR-16-1, miR-26a, miR-99a and miR-223. In each case, the concordance between the two techniques was high: in all instances the highest and the lowest expression levels were concordant. For example high levels of miR-223 expression were found by both techniques in spleen, for miR-16-1 in CD5+ cells, while very low levels were found in brain for both miRNAs. Moreover, in several instances (for example miR-15a), we were able to identify the same pattern of expression for the precursor and the active miR with both microchip and Northern blots.
We also compared the published expression data for cloned human and mouse miRNAs by Northern blot analyses against the microarray results. We found that the concordance with the chip data is high for both pattern and intensity of expression. For example, miR-133 was reported to be strongly expressed only in the skeletal muscle and heart (Sempere, et al. (2003) Genome Biol. 5, R13), precisely as was found with the microarray, while miR-125 and mir-128 were reported to be highly expressed in brain (Sempere, et al. (2003) Genome Biol. 5, R13), a finding confirmed on the microchip.

Example 11

miRNA Profiling of B-Cell Chronic Lymphocytic Leukemia Samples

Introduction
The miRNome expression in 38 individual human B-cell chronic lymphocytic leukemia (CLL) cell samples was determined utilizing the microchip of Example 10. One normal lymph node sample and 5 samples from healthy donors, including two tonsillar CD5+B lymphocyte samples and three blood mononuclear cell (MNC) samples, were included for comparison. As hereinafter demonstrated, two distinct clusters of CLL samples associated with the presence or the absence of Zap-70 expression, a predictor of early disease progression. Two miRNA signatures were associated with presence or absence of mutations in the expressed immunoglobulin variable-region genes or with deletions at 13q14 respectively.
Materials and Methods
The following methods were employed in the miRNome expression study.
Tissue Samples and CLL Samples. 47 samples were used for this study, including 41 samples from 38 patients with CLL, and 6 normal samples, including one lymph node, tonsillar CD5+ B cells from two normal donors and blood mononuclear cells from three normal donors. For three cases, two independent samples were collected and processed. CLL samples were obtained after informed consent from patients diagnosed with CLL at the CLL Research Consortium institutions. Briefly, blood was obtained from CLL patients, mononuclear cells were isolated through Ficoll/Hypaque gradient centrifugation (Amersham Pharmacia Biotech) and processed for RNA extraction according to described protocols (M. Lagos-Quintana, R. Rauhut, W. Lendeckel, T. Tuschl, Science 294, 853-858 (2001)). For the majority of samples clinical and biological information, such as age at diagnosis, sex, Rai stage, presence/absence of treatment, ZAP-70 expression, IgV_Hgene mutation status were available, as provided in Table 9:

TABLE 9

Clinical and biological data for the patients in the two CLL clusters*

Semnification	Dx Age	Sex	% Zap	VH gene	Mut

CLL cluster 1	50.68	F	30.4	VH4-04	Neg
CLL cluster 1	57.4	F	50.6	VH3-33	Pos
CLL cluster 1	67.49	M	0.5	VH3-23	Pos
CLL cluster 1	59.74	M	31.5	VH3-09	Pos
CLL cluster 1	77.49	F	0.3	VH5-51	Pos
CLL cluster 1	58.19	F	3.6	VH3-30/3-30.5	Pos
CLL cluster 1	43	M	41.9	VH4-30.1/4-31	Neg
CLL cluster 1	61.82	M	83.2	VH1-03	Neg
CLL cluster 1	48.44	F	69.3	VH1-69	Neg
CLL cluster 2	72.59	M	2.2	VH3-72	Pos
CLL cluster 2	45.19	M	7.3	VH1-69	Pos
CLL cluster 2	56.39	F	0.6	VH3-15	Pos
CLL cluster 2	61.85	F	0.1	VH3-30	Neg
CLL cluster 2	60.89	F	0.1	VH2-05	Pos
CLL cluster 2	62.66	M	1	VH3-07	Pos
CLL cluster 2	49.85	M	3.6	VH3-74	Pos
CLL cluster 2	70.62	M	0.2	VH3-13	Pos
CLL cluster 2	68.02	F	0.9	VH3-30.3	Pos
CLL cluster 2	46.84	M	62.2	VH3-30/3-30.5	Neg
CLL cluster 2	51.31	F	91.9	VH4-59	Neg
CLL cluster 2	52.6	F	10.6	VH3-07	Pos
CLL cluster 2	56.04	F	0.4	VH3-72	Pos
CLL cluster 2	61.67	M	77.9	VH3-74	Neg
CLL cluster 2	62.14	F	46	VH1-02	Pos
CLL cluster 2	39.29	F	10.1	VH3-07	Neg

*data for ZAP-70 expression were available for 25 patients (25/38, 66%).

Cell Preparation. Mononuclear cells (MNC) from peripheral blood of normal donors were separated by Ficoll-Hypaque density gradients. T cells were purified from these MNC by rosetting with neuraminidase-treated sheep red blood cells (SRBC) and depletion of contaminant monocytes (Cd11b+), natural killer cells (CD16+) and B lymphocytes (CD19+) were purified using magnetic beads (Dynabeads, Unipath, Milano, Italy) and specific monoclonal antibodies (Becton Dickinson, San Jose, Calif.). Total B cells and CD5+ B cells were prepared from tonsillar lymphocytes as described (M. Dono et al., J. Immunol. 164, 5596-604. (2000)). Briefly, tonsils were obtained from patients in the pediatric age group undergoing routine tonsillectomies, after informed consent. Purified B cells were prepared by rosetting T cells from MNC cells with neuraminidase treated SRBC. In order to obtain CD5+ B cells, purified B cells were incubated with anti CD5 monoclonal antibody followed by goat anti mouse Ig conjugated with magnetic microbeads. CD5+ B cells were positively selected by collecting the cells retained on the magnetic column MS by Mini MACS system (Miltenyi Biotec, Auburn, Calif.). The degree of purification of the cell preparations was higher than 95%, as assessed by flow cytometry.
RNA Extraction and Northern Blots. Total RNA isolation and blots were performed as described (G. A. Calin et al., Proc Natl Acad Sc USA. 99, 15524-15529 (2002)). After RNA isolation, the washing step with ethanol was not performed, or if performed, the tube walls were rinsed with 75% ethanol without perturbing the RNA pellet (M. Lagos-Quintana, R. Rauhut, W. Lendeckel, T. Tuschl, Science 294, 853-858 (2001)). For reuse, blots were stripped by boiling in 0.1% aqueous SDS/0.1×SSC for 10 minutes, and were reprobed. 5S rRNA stained with ethidium bromide served as a sample loading control.
Microarray Experiments. RNA blot analysis was performed as described in Example 10, utilizing the microchip of Example 10. Briefly, labeled targets from 5 μg of total RNA was used for hybridization on each miRNA microarray chip containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. The microarrays were hybridized in 6×SSPE/30% formamide at 25° C. for 18 hrs, washed in 0.75×TNT at 37° C. for 40 min, and processed using a method of direct detection of the biotin-containing transcripts by Streptavidin-Alexa647 conjugate. Processed slides were scanned using a Perkin Elmer ScanArray® XL5K Scanner, with the laser set to 635 nm, at Power 80 and PMT 70 setting, and a scan resolution of 10 microns.
Data Analysis. Expression profiles were analyzed in duplicate independent experiments starting from the same cell sample. Raw data were normalized and analyzed in GeneSpring® software version 6.1.1 (Silicon Genetics, Redwood City, Calif.). GeneSpring generated an average value of the three spot replicates of each miRNA. Following data transformation (to convert any negative value to 0.01), normalization was performed by using a per-chip on median normalization method and a normalization to specific samples, expressly to the two CD5+ B cell samples, used as common reference for miRNA expression. Hierarchical clustering for both genes and conditions were generated by using standard correlation as a measure of similarity. To identify genes with statistically significant differences between sample groups (i.e. CLL cells and CD5+ B cells, CLL and MNC, CLL samples with or without IgV_Hmutations or CLL cases with or without 13q14.3 deletion), a Welch's approximate t-test for two groups (variances not assumed equal) with a p-value cutoff of 0.05 and Benjamini and Hochberg False Discovery Rate as multiple testing correction were performed.
Real Time PCR. Quantitative real-time PCR was performed as described by T. D. Schmittgen, J. Jiang, Q. Liu, L. Yang, Nucleic Acid Research 32, 43-53 (2004). Briefly, RNA was reverse transcribed to cDNA with gene-specific primers and Thermoscript and the relative amount of each miRNA to tRNA for initiator methionine was described, using the equation 2^−dCT, where dC_T=(C_TmiRNA−C_{TU6 or HUMTMI RNA}). The set of analyzed miRNAs included miR-15a, miR-16-1, miR-18, miR-20, and miR-21. The primers used were as published (Id.).
Western Blotting. Protein lysates were prepared from the leukemia cells of 7 CLL patients and from isolated tonsillar CD5⁺ B cells. Western blot analysis was performed with a polyclonal Pten antibody (Cell Signaling Technology, Beverly, Mass.) and was normalized using an anti-actin antibody (Sigma, St. Louis, Mo.).
Microarray Data Submission. All data were submitted using MIAMExpress to the Array Express database and each of the 39 CLL samples described here received an ID number ranging from SAMPLE 169194SUB621 to SAMPLE 169234SIUB621.
Results
Comparison of miRNA expression in CLL cells vs. normal CD5+ B cells and normal blood mononuclear cells. Normal CD5+ B cells utilized in this study are considered as normal cell counterparts to CLL B cells. As described in Table 10, two groups of differentially expressed miRNAs, the first composed of 55 genes and the second of 29 genes, had statistically significant differences in expression levels between the various groups (p<0.05 using Welch t-test as described in Materials and Methods, above). Only 6 miRNA are shared between the two lists, confirming the results of Example 10 showing distinct miRNome signatures in CD5⁺ B cells and leukocytes. When both pre-miRNA and mature miRNA were observed to be dysregulated (such as for miR-123, miR-132 or miR-136), the same type of variation in CLL samples with respect to CD5 or MNC was noted in every case. Also, for some miRNA genomic clusters all members were aberrantly regulated (such as the up-regulated 7q32 group encompassing miR-96-miR182-miR183), while for others only some members were abnormally expressed (such as the 13q31 genomic cluster where two out of six members, miR-19 and miR-92-1, were strongly up-regulated and two, miR-17 and miR-20, were moderately down-regulated). Without wishing to be bound by any theory, the results illustrate the complexity of the patterns of miRNA expression in CLL and indicate the existence of mechanisms regulating individual miRNA genes that map in the same chromosome region. In confirmation of the accuracy of the data, miR-223, reported to be expressed at high levels in granulocytes (M. Lagos-Quintana et al., Curr Biol 12, 735-739 (2002)), was expressed at significantly lower levels in the CLL samples than in the MNC, but at about the same level as that noted for CD5⁺ B cells (which generally constitute less than a few percent of blood MNC).

TABLE 10

Differentially expressed miRNAs in CLLs versus CD5+ cells or CLLs versus MNC (bold)*

Oligonucleotide probe	microRNA	Chr location	FRA associated	P-value	Type

hsa-let-7a-2-precNo1	let-7a-2	11q24.1		0.014	Down
hsa-let-7d-v2-precNo2	let-7d-v2-prec	12q14.1		4.29E−04	Down
hsa-let-7f-1-precNo1	let-7f-1	09q22.2	FRA9D	3.09E−29	Down
hsa-mir-009-2No1	miR-9-2	5q14		0.013	up
hsa-mir-010a-precNo2	miR-10a-prec	17q21.3		0.007	up
hsa-mir-010b-precNo1	mir-10b	02q31		1.10E−15	up
hsa-mir-015b-precNo2	mir-15b-prec	03q26.1		5.79E−14	up
hsa-mir-017-precNo2	mir-17-prec	13q31		0.042	Down
hsa-mir-017-precNo2	mir-17-prec	13q31		0.049	Down
hsa-mir-019a-prec	mir-19a	13q31		5.16E−17	up
hsa-mir-020-prec	mir-20a	13q31		0.038	Down
hsa-mir-021-prec-17No2	mir-21-prec	17q23.2	FRA17B	0.044	up
hsa-mir-022-prec	mir-22	17p13.3		7.16E−04	up
hsa-mir-023a-prec	mir-23a	19p13.2		0.011	Down
hsa-mir-024-1-precNo1	mir-24-1	09q22.1	FRA9D	0.002	Down
hsa-mir-024-1-precNo2	mir-24-1-prec	09q22.1	FRA9D	7.35E−20	up
hsa-mir-024-2-prec	mir-24-2	19p13.2		5.69E−17	Down
hsa-mir-025-prec	mir-25	07q22	FRA7F	9.52E−04	Down
hsa-mir-027b-prec	mir-27b	09q22.1	FRA9D	0.046	Down
hsa-mir-029a-2No1	mir-29a-2	07q32	FRA7H	0.013	up
hsa-mir-029a-2No2	mir-29a-2-prec	07q32	FRA7H	0.001	up
hsa-mir-029c-prec	mir-29c	01q32.2-32.3		0.002	up
hsa-mir-030a-precNo1	mir-30a	06q12-13		0.004	Down
hsa-mir-030a-precNo2	mir-30a-prec	06q12-13		0.034	Down
hsa-mir-030d-precNo2	mir-30d-prec	08q24.2		0.008	Down
hsa-mir-033-prec	mir-33	22q13.2		1.56E−18	up
hsa-mir-034precNo1	mir-34	01p36.22		6.00E−06	up
hsa-mir-092-prec-13=092-1No1	mir-92-1	13q31		1.70E−12	up
hsa-mir-092-prec-13=092-1No2	mir-92-prec	13q31		0.021	Down
hsa-mir-092-prec-X=092-2	mir-92-2	Xq26.2		3.38E−04	Down
hsa-mir-092-prec-X=092-2	mir-92-2	Xq26.2		0.042	Down
hsa-mir-096-prec-7No1	mir-96	07q32	FRA7H	1.79E−04	up
hsa-mir-099-prec-21	mir-99	21q11.2		0.001	Down
hsa-mir-101-1/2-precNo1	mir-101	01p31.3	FRA1C	1.26E−08	up
hsa-mir-101-1/2-precNo2	mir-101-prec	01p31.3		0.017	up
hsa-mir-103-prec-5=103-1	mir-103-1	05q35.1		0.002	Down
hsa-mir-103-prec-5=103-1	mir-103-1	05q35.1		0.007	Down
hsa-mir-105-prec-X.1=105-1	mir-105-1	Xq28	FRAXF	1.55E−05	up
hsa-mir-107-prec-10	mir-107	10q23.31		0.002	Down
hsa-mir-123-precNo1	mir-123	09q34		2.80E−16	up
hsa-mir-123-precNo1	mir-123	09q34		0.021	Down
hsa-mir-123-precNo2	mir-123-prec	09q34		0.021	Down
hsa-mir-124a-2-prec	mir-124a-2	08q12.2		4.33E−06	up
hsa-mir-128b-precNo1	mir-128b	03p22		5.05E−07	Down
hsa-mir-128b-precNo2	mir-128-prec	03p22		0.007	up
hsa-mir-130a-precNo2	mir-130a-prec	11q12		0.010	Down
hsa-mir-130a-precNo2	mir-130a-prec	11q12		0.050	up
hsa-mir-132-precNo1	mir-132	11q12		1.68E−07	up
hsa-mir-132-precNo2	mir-132-prec	17p13.3		8.62E−04	up
hsa-mir-134-precNo1	mir-134	14q32		6.01E−08	up
hsa-mir-136-precNo1	mir-136	14q32		0.003	up
hsa-mir-136-precNo2	mir-136-prec	14q32		7.44E−04	up
hsa-mir-137-prec	mir-137	01p21-22		0.013	up
hsa-mir-138-1-prec	mir-138-1	03p21		2.53E−04	up
hsa-mir-140No1	mir-140	16q22.1		2.41E−16	up
hsa-mir-141-precNo1	mir-141	12p13		7.91E−08	up
hsa-mir-141-precNo2	mir-141-prec	12p13		1.39E−08	up
hsa-mir-142-prec	mir-142	17q23	FRA17B	0.004	Down
hsa-mir-145-prec	mir-145	05q32-33		0.021	Down
hsa-mir-146-prec	mir-146	05q34		1.03E−08	Down
hsa-mir-148-prec	mir-148	07p15		3.48E−05	up
hsa-mir-152-precNo1	mir-152	17q21		0.003	up
hsa-mir-152-precNo2	mir-152-prec	17q21		3.35E−05	up
hsa-mir-153-1-prec1	mir-153	02q36		0.005	up
hsa-mir-153-1-prec2	mir-153-prec	02q36		1.48E−08	up
hsa-mir-154-prec1No1	mir-154	14q32		1.14E−10	up
hsa-mir-155-prec	mir-155	21q21		0.029	up
hsa-mir-181b-precNo2	mir-181b-prec	01q31.2-q32.1		3.26E−06	up
hsa-mir-181c-precNo2	mir-181c-prec	19p13.3		0.003	up
hsa-mir-182-precNo2	mir-182-prec	07q32	FRA7H	0.001	up
hsa-mir-183-precNo2	mir-183-prec	07q32	FRA7H	1.26E−23	up
hsa-mir-184-precNo1	mir-184	15q24		0.007	up
hsa-mir-188-prec	mir-188	Xp11.23-p11.2		6.08E−11	up
hsa-mir-190-prec	mir-190	15q21	FRA15A	1.48E−20	up
hsa-mir-191-prec	mir-191	03p21		9.14E−05	Down
hsa-mir-192-2/3No1	mir-192	11q13		2.00E−07	Down
hsa-mir-193-precNo2	mir-193-prec	17q11.2		9.14E−05	up
hsa-mir-194-precNo1	mir-194	01q41	FRA1H	0.002	up
hsa-mir-196-2-precNo1	mir-196-2	12q13	FRA12A	4.94E−08	up
hsa-mir-196-2-precNo2	mir-196-2-prec	12q13	FRA12A	0.040	up
hsa-mir-197-prec	mir-197	01p13		0.003	Down
hsa-mir-200a-prec	mir-200a	01p36.3		9.14E−05	up
hsa-mir-204-precNo2	mir-204-prec	09q21.1		8.55E−04	up
hsa-mir-206-precNo1	mir-206	06p12		0.003	Down
hsa-mir-210-prec	mir-210	11p15		0.009	Down
hsa-mir-212-precNo1	mir-212	17p13.3		0.045	Down
hsa-mir-213-precNo1	mir-213	01q31.3-q32.1		1.47E−33	Down
hsa-mir-217-precNo2	mir-217	02p16		3.85E−09	up
hsa-mir-220-prec	mir-220	Xq25		2.14E−09	Down
hsa-mir-220-prec	mir-220	Xq25		3.16E−05	Down
hsa-mir-221-prec	mir-221	Xp11.3		1.39E−05	Down
hsa-mir-223-prec	mir-223	Xq12-13.3		9.04E−04	Down

* The correlation with fragile sites (FRA) location is as published in Calin et al., Proc Natl Acad Sci USA. 101, 2999-3004 (2004).

As indicated in the CLL vs. CD5+ B cell list of Table 10, several miRNAs located exactly inside fragile sites (miR-183 at FRA7H, miR-190 at FRA15A and miR-24-1 at FRA9D) and miR-213. The mature miR-213 molecule is expressed at lower levels in all the CLL samples, and the precursor miR-213 is reduced in expression in 62.5% of the samples. miR-16-1, at 13q14.3, which we previously reported to be down-regulated in the majority of CLL cases by microarray analysis (G. A. Calin et al., Proc Natl Acad Sc USA. 99, 15524-15529 (2002), was expressed at low levels in 45% of CLL samples. An identical mature miR-16 exists on chromosome 3; because the 40-mer oligonucleotide for both miR-16 sequences from chromosome 13 (miR-16-1) and chromosome 3 (miR-16-2) exhibit the same 23-mer mature sequence, very similar profiles were observed. However, since we observed very low levels of miR-16-2 expression in CLL samples by Northern blot, the expression observed is mainly contributed by miR-16-1. The other miRNA of 13q14.3, miR-15a, was expressed at low levels in ˜25% of CLL cases. Overall, these data demonstrate that CLL is a malignancy with extensive alterations of miRNA expression.
Validation of the microarray data was supplied for four miRNAs by Northern blot analyses: miR-16-1, located within the region of deletion at 13q14.3, miR-26a, on chromosome 3 in a region not involved in the pathogeneses of CLL, and miR-206 and miR-223 that are down-regulated (see above) in the majority of samples. For all four miRNAs, the Northern blot analyses confirmed the data obtained using the microarray. We also performed real-time PCR to measure expression levels of precursor molecules for five genes (miR-15a, miR-16-1, miR-18, miR-21, and miR-30d) and we found results concordant with the chip data.
Unsupervised hierarchical clustering generated two clearly distinguishable miRNA signatures within the set of CLL samples, one closer to the miRNA expression profile observed in human leukocytes and the other clearly different (FIG. 3). A list of the microRNAs differentially expressed between the two main CLL clusters is given in Table 11. The name of each miRNA is as in the miRNA Registry. The disregulation of either active molecule or precursor is specified in the name. The location in minimally deleted or minimally amplified or breakpoint regions or in fragile sites is presented. The top 25 differentially expressed miRNA in these two signatures (at p<0.001) include genes known or suggested to be involved in cancer. The precursor of miR-155 is over-expressed in the majority of childhood Burkitt's lymphoma (M. Metzler, M. Wilda, K. Busch, S. Viehmann, A. Borkhardt, Genes Chromosomes Cancer. 39, 167-9. (2004)), miR-21 is located at the fragile site FRA17B (G. A. Calin et al., Proc Natl Acad Sci US A. 101, 2999-3004. (2004)), miR-26a is at 3p21.3, a region frequently deleted region in epithelial cancers, while miR-92-1 and miR-17 are at 13q32, a region amplified in follicular lymphoma (Id.).

TABLE 11

microRNAs differentially expressed between the two main CLL clusters*.

Oligonucleotide	miRNA	Chr location	P-value	Cancer-associated genomic regions

hsa-miR-017-precNo2	miR-17-prec	13q31	0.00000000	Amp - Folicular Ly/Del - HCC
hsa-miR-020-prec	miR-20	13q31	0.00000000	Amp - Folicular Ly/Del - HCC
hsa-miR-103-2-prec	miR-103-2	20p13	0.00000001
hsa-miR-030d-precNo2	miR-30d-prec	08q24.2	0.00000002
hsa-miR-106-prec-X	miR-106	Xq26.2	0.00000006	Del - advanced ovarian ca.
hsa-miR-026b-prec	miR-26b	02q35	0.00000006
hsa-miR-103-prec-5 = 103-1	miR-103-1	05q35.1	0.00000006
hsa-miR-025-prec	miR-25	07q22	0.00000007	FRA7F
hsa-miR-030a-precNo1	miR-30a	06q12-13	0.00000008
hsa-miR-021-prec-17No1	miR-21	17q23.2	0.00000008	Amp - Neuroblastoma; FRA17B
hsa-miR-107-prec-10	miR-107	10q23.31	0.00000008
hsa-miR-092-prec-13 = 092-1No2	miR-92-1-prec	13q31	0.00000024	Amp - Follicular Ly.
hsa-miR-027a-prec	miR-27a	19p13.2	0.00000024
hsa-miR-023a-prec	miR-23a	19p13.2	0.00000032
hsa-miR-092-prec-X = 092-2	miR-92-2	Xq26.2	0.00000040	Del - Advanced Ovarian ca.
hsa-miR-030b-precNo1	miR-30b	08q24.2	0.000004
hsa-miR-026a-precNo1	miR-26a	03p21	0.000009	Del - Epithelial malignancies
hsa-miR-093-prec-7.1 = 093-1	miR-93-1	07q22	0.000009	Amp - Folicular Ly/Del - HCC; FRA7F
hsa-miR-194-precNo1	miR-194	01q41	0.000015	FRA1H
hsa-miR-155-prec	miR-155	21q21	0.000028	Amp - Colon ca; Childhood Burkit Ly
hsa-miR-153-2-prec	miR-153-2	07q36	0.000028	t(7; 12)(q36; p13) - Acute Myeloid Leukemia
hsa-miR-193-precNo2	miR-193-prec	17q11.2	0.000044	Del - Ovarian ca.
hsa-miR-130a-precNo1	miR-130a	11q12	0.0001
hsa-miR-023b-prec	miR-23b	09q22.1	0.0001	Del - Urothelial Ca.; FRA9D
hsa-miR-030c-prec	miR-30c	06q13	0.0001
hsa-miR-139-prec	miR-139	11q13	0.0001
hsa-miR-144-precNo2	miR-144-prec	17q11.2	0.0001	Amp - Primary Breast ca.
hsa-miR-29b-2 = 102prec7.1 = 7.2	miR-29b-2	07q32	0.0002	Del - Prostate ca agressiveness; FRA7H
hsa-miR-125a-precNo2	miR-125a-prec	19q13.4	0.0002
hsa-miR-224-prec	miR-224	Xq28	0.0002
hsa-miR-211-precNo1	miR-211	Xp11.3	0.0002	Del - Malignant Mesothelioma.
hsa-miR-221-prec	miR-221	Xp11.3	0.0002
hsa-miR-191-prec	miR-191	03p21	0.0002
hsa-miR-018-prec	miR-18	13q31	0.0003	Amp - Follicular Lymphoma
hsa-miR-203-precNo2	miR-203-prec	14q32.33	0.0004	Del - Nasopharyngeal ca.
hsa-miR-217-precNo2	miR-217-prec	02p16	0.0004
hsa-miR-204-precNo2	miR-204-prec	09q21.1	0.0004
hsa-miR-199a-1-prec	miR-199a-1	19p13.2	0.0005
hsa-miR-128b-precNo1	miR-128b	03p22	0.0005
hsa-miR-102-prec-1	miR-102	01q32.2-32.3	0.0005	Del - Prostate ca agressiveness
hsa-miR-140No2	miR-140-prec	16q22.1	0.0006
hsa-miR-199a-2-prec	miR-199a-2	01q23.3	0.0007
hsa-miR-010b-precNo2	miR-10b-prec	02q31	0.0008
hsa-miR-029a-2No1	miR-29a-2	07q32	0.0008	Del - Prostate ca agressiveness; FRA7H
hsa-miR-125a-precNo1	miR-125a	19q13.4	0.0010
hsa-miR-204-precNo1	miR-204	09q21.1	0.0011
hsa-miR-181a-precNo1	miR-181a	09q33.1-34.13	0.0014	Del - Bladder ca
hsa-miR-188-prec	miR-188	Xp11.23-p11.2	0.0014
hsa-miR-200a-prec	miR-200a	01p36.3	0.0014
hsa-miR-024-2-prec	miR-24-2	19p13.2	0.0014
hsa-miR-134-precNo2	miR-134-prec	14q32	0.0016	Del - Nasopharyngeal ca.
hsa-miR-010a-precNo2	miR-10a-prec	17q21.3	0.0018
hsa-miR-029c-prec	miR-29c	01q32.2-32.3	0.0021
hsa-miR-010a-precNo1	miR-10a	17q21.3	0.0022
hsa-let-7d-v2-precNo1	let-7d-v2	12q14.1	0.0022	Del - Urothelial carc; FRA9D
hsa-miR-205-prec	miR-205	01q32.2	0.0023
hsa-miR-129-precNo1	miR-129	07q32	0.0023	Del - Prostate ca agressiveness
hsa-miR-032-precNo2	miR-32-prec	09q31.2	0.0026	Del - Lung ca.; FRA9E
hsa-miR-187-precNo2	miR-187-prec	18q12.1	0.0035
hsa-miR-125b-2-precNo1	miR-125b-2	21q11.2	0.0036	Del - Lung ca.(MA17)
hsa-miR-181c-precNo1	miR-181c	19p13.3	0.0036
hsa-miR-132-precNo2	miR-132-prec	17p13.3	0.0036	Del - HCC
hsa-miR-215-precNo1	miR-215	01q41	0.0036	FRA1H
hsa-miR-136-precNo1	miR-136	14q32	0.0036	Del - Nasopharyngeal ca.
hsa-miR-030a-precNo2	miR-30a-prec	06q12-13	0.0040
hsa-miR-100-1/2-prec	miR-100	11q24.1	0.0040	Del - 0varian Ca.; FRA11B
hsa-miR-218-2-precNo1	miR-218-2	05q35.1	0.0040
hsa-miR-193-precNo1	miR-193	17q11.2	0.0052	Del - Ovarian ca.
hsa-miR-027b-prec	miR-27b	09q22.1	0.0058	Del - Bladder ca; FRA9D
hsa-miR-220-prec	miR-220	Xq25	0.0065
hsa-miR-024-1-precNo1	miR-24-1	09q22.1	0.0065	Del - Urothelial ca.
hsa-miR-019a-prec	miR-19a	13q31	0.0071	Amp - Follicular Ly
hsa-miR-196-2-precNo1	miR-196-2	12q13	0.0082	FRA12A
hsa-miR-022-prec	miR-22	17p13.3	0.0086	Del - HCC
hsa-miR-183-precNo2	miR-183-prec	07q32	0.0086	Del - Prostate ca agressiveness; FRA7H
hsa-miR-128a-precNo2	miR-128a-prec	02q21	0.0105	Del - Gastric Ca
hsa-miR-203-precNo1	miR-203	14q32.33	0.0109	Del - Nasopharyngeal ca.
hsa-miR-033b-prec	miR-33b	17p11.2	0.0109	Amp - Breast ca.
hsa-miR-030d-precNo1	miR-30d	08q24.2	0.0111
hsa-miR-133a-1	miR-133a-1	18q11.1	0.0119
hsa-miR-007-3-precNo2	miR-7-3-prec	22q13.3	0.0128
hsa-miR-021-prec-17No2	miR-21-prec	17q23.2	0.0131	Amp - Neuroblastoma
hsa-miR-208-prec	miR-208	14q11.2	0.0134	Del - Malignant Mesothelioma
hsa-miR-154-prec1No2	miR-154-prec	14q32	0.0146	Del - Nasopharyngeal ca.
hsa-miR-141-precNo2	miR-141-prec	12p13	0.0154
hsa-miR-024-1-precNo2	miR-024-1-prec	09q22.1	0.0169	Del - Urothelial carc; FRA9D
hsa-miR-128a-precNo1	miR-128a	02q21	0.0170	Del - Gastric Ca
hsa-miR-184-precNo2	miR-184-prec	15q24	0.0219
hsa-miR-019b-2-prec	miR-19b-2	13q31	0.0302
hsa-miR-132-precNo1	miR-132	17p13.3	0.0303	Del - Hepatocellular ca. (HCC)
hsa-miR-127-prec	miR-127	14q32	0.0326	Del - Nasopharyngeal ca.
hsa-miR-202-prec	miR-202	10q26.3	0.0333
hsa-let-7g-precNo2	let-7g-prec	03p21.3	0.0350	Del - Lung Ca., Breast Ca.
hsa-miR-222-precNo1	miR-222	Xp11.3	0.0351
hsa-miR-009-1No2	miR-009-1-prec	05q14	0.0382
hsa-miR-136-precNo2	miR-136-prec	14q32	0.0391	Del - Nasopharyngeal ca.
hsa-miR-010b-precNo1	miR-10b	02q31	0.0403
hsa-miR-223-prec	miR-223	Xq12-13.3	0.0407

*The location in minimally deleted or minimally amplified or breakpoint regions or in fragile sites is presented.
HCC—Hepatocellular ca.;
AML—acute myeloid leukemia.

The two clusters may be distinguished by at least one clinico-biological factor. A high difference in the levels of ZAP-70 characterized the two groups: 66% (6/9) patients from the first cluster vs. 25% (4/16) patients from the second one have low levels of ZAP-70 (<20%) (P=0.04 at chi test) (Table 9). The mean value of ZAP-70 was 19% (±31% S.D.) vs. 35% (±30% S.D.), respectively or otherwise the two clusters can discriminate between patients who express and who do not express this protein (at levels<20% ZAP-70 is considered as non-expressed) (Table 9). ZAP-70 is a tyrosine kinase, which is a strong predictor of early disease progression, and low levels of expression are proved to be a finding associated with good prognosis (J. A. Orchard et al., Lancet 363, 105-11 (2004)).
The microarray data revealed specific molecular signatures predictive for subsets of CLL that differ in clinical behavior. CLL cases harbor deletions at chromosome 13q14.3 in approximately 50% of cases (F. Bullrich, C. M. Croce, Chronic Lymphoid leukemia. B. D. Chenson, Ed. (Dekker, New York, 2001)). As a single cytogenetic defect, these CLL patients have a relatively good prognosis, compared with patients with leukemia cells harboring complex cytogenetic changes (H. Dohner et al., N Engl J Med. 343, 1910-6. (2000)). It was also shown that deletion at 13q14.3 was associated with the presence of mutated immunoglobulin V_H(IgV_H) genes (D. G. Oscier et al., Blood. 100, 1177-84 (2002)), another good prognostic factor. By comparing expression data of CLL samples with or without deletions at 13q14, we found that miR-16-1 was expressed at low levels in leukemias harboring deletions at 13q14 (p=0.03, ANOVA test). We also found that miR-24-2, miR-195, miR-203, miR-220 and miR-221 are expressed at significantly reduced levels, while miR-7-1, miR-19a, miR-136, miR-154, miR-217 and the precursor of miR-218-2 are expressed at significantly higher levels in the samples with 13q14.3 deletions, respectively (Table 12). All these genes are located in different regions of the genome and differ in their nucleotide sequences, excluding the possibility of cross-hybridization. Without wishing to be bound by any theory, these results suggest the existence of functional miRNA networks in which hierarchical regulation may be present, with some miRNA (such as miR-16-1) controlling or influencing the expression of other miRNA

TABLE 12

microRNAs signatures associated with prognosis in B-CLL¹.

	Chr.
miRNA	location	P-value	Association	Observation

miR-7-1	9q21.33	0.030	13q14 normal
miR-16-1	13q14.3	0.030	IGVH mutations negative
		0.023	13q14 deleted
miR-19a	13q31	0.024	13q14 normal
miR-24-2	19p13.2	0.033	13q14 deleted
miR-29c	1q32.2-32.3	0.018	IGVH mutations positive	cluster miR-29c-miR 102
miR-102	1q32.2-32.3	0.023	IGVH mutations positive	cluster miR-29c-miR 102
miR-132	17p13.3	0.033	IGVH mutations negative
miR-136	14q32	0.045	13q14 normal
miR-154	14q32	0.020	13q14 normal
miR-186	1p31	0.038	IGVH mutations negative
mir-195	17q13	0.036	13q14 deleted
miR-203	14q32.33	0.026	13q14 deleted
miR-217-prec	2p16	0.005	13q14 normal
miR-218-2	5q35.1	0.019	13q14 normal
miR-220	Xq25	0.026	13q14 deleted
miR-221	Xp11.3	0.021	13q14 deleted

¹The name of each miRNA is as in miRNA Registry and the disregulation of either active molecule or precursor is specified in the name.

The expression of mutated IgV_His a favorable prognostic marker (D. G. Oscier et al., Blood. 100, 1177-84 (2002)). We found a distinct miRNA signature composed of 5 differentially expressed genes (miR-186, miR-132, miR-16-1, miR-102 and miR-29c) that distinguished CLL samples that expressed mutated IgV_Hgene from those that expressed unmutated IgV_Hgenes, indicating that miRNA expression profiles have prognostic significance in CLL. As a confirmation of our results is the observation that the common element between the del 13q14.3-related and the IgV_H-related signatures is miR-16-1. This gene is located in the common deleted region 13q14.3 and the presence of this particular deletion is associated with good prognosis. Therefore, miRNAs expand the spectrum of adverse prognostic markers in CLL, such as expression of ZAP-70, unmutated IgV_H, CD38, deletion at chromosome 11q23, or loss or mutation of TP53.

Example 12

Identification of miRNA Signature Profiles Associated with Prognostic Factors and Disease Survival in B-Cell Chronic Leukemia Samples

Introduction
Knowing that the expression profile of miRNome, the full complement of microRNAs in a cell, is different between malignant CLL cells and normal corresponding cells, we asked whether microarray analysis using the miRNACHIP could reveal specific molecular signatures predictive for subsets of CLL that differ in clinical behavior. The miRNome expression in 94 CLL samples was determined utilizing the microchip of Example 10. miRNA expression profiles were analyzed to determine if distinct molecular signatures are associated with the presence or absence of two prognostic markers, ZAP-70 expression and mutation of the IgV_Hgene. The microarray data revealed that two specific molecular signatures were associated with the presence or absence of each of these markers. An analysis of expression profiles from Zap-70 positive/IgV_Hunmutated (Umut) vs. Zap-70 negative/IgV_Hmutated (Mut) CLL samples revealed a unique signature of 17 genes that can distinguish these two subsets. Our results indicate that miRNA expression profiles have prognostic significance in CLL.
Materials and Methods
Patient Samples and Clinical Database. 94 CLL samples were used for this study, which were obtained after informed consent from patients diagnosed with CLL at the CLL Research Consortium institutions (L. Z. Rassenti et al. N. Engl. J. Med. 351(9):893-901 (2004)). Briefly, blood was obtained from CLL patients and mononuclear cells were isolated through Ficoll/Hypaque gradient centrifugation (Amersham Pharmacia Biotech) and processed for RNA extraction according to described protocols (G. A. Calin et al., Proc. Natl. Acad. Sc. U.S.A. 99, 15524-15529 (2002)). For each sample, clinical and biological information, such as sex, age at diagnosis, Rai stage, presence/absence of treatment, time between diagnosis and therapy, ZAP-70 expression, and IgV_Hgene mutation status, were available and are described in Table 13.

TABLE 13

Characteristics of patients analyzed with the miRNACHIP.

	Characteristic	Value

	Male sex - no. of patients (%)	58 (61.7)
	Age at diagnosis - years
	median	57.3
	range	38.2
	Therapy begun
	No
	No. of patients	53
	Time since diagnosis - months	87.07
	Yes
	No. of patients	41
	Time between diagnosis & therapy - months	40.27
	ZAP-70 level
	≦20%	48
	>20%	46
	IgV_H
	Unmutated (≧98% homology)	57
	Mutated (<98% homology)	37

RNA Extraction and Northern Blots. Total RNA isolation and RNA blotting were performed as described (G. A. Calin et al., Proc Proc. Natl. Acad. Sc. U.S.A. 99, 15524-15529 (2002)).
Microarray Experiments. Microarray experiments were performed as described in Example 11. Of note, for 76 microRNAs on the miRNACHIP, two specific oligonucleotides were synthesized—one identifying the active 22 nucleotide part of the molecule and the other identifying the 60-110 nucleotide precursor. All probes on these microarrays are 40-mer oligonucleotides spotted by contacting technologies and covalently attached to a polymeric matrix.
Data Analysis. After construction of the expression table with Genespring, data normalization was performed by using Bioconductor package. Analyses were carried out using the PAM package (Prediction Analysis of Microarrays) and SAM (Significance Analysis of Microarrays) software. The data were confirmed by Northern blotting for 4 microRNAs in 20 CLL samples, each. All data were submitted using MIAMExpress to the Array Express database.
Analysis of ZAP-70 and Sequence analysis of expressed IgV_H. Analyses were performed as described previously (L. Z. Rassenti et al. N. Engl. J. Med. 351(9):893-901 (2004)). Briefly, ZAP-70 expression was assessed by immunoblot analysis and flow cytometry, while the analysis of expressed IgV_Hwas performed by direct sequencing.
Results
Comparison of miRNA expression in ZAP-70 positive vs. ZAP-70 negative CLL cells. Using 20% as a cutoff for defining ZAP-70 positivity, we constructed two classes that were constituted of 48 ZAP-70-negative and 46 ZAP-70-positive CLL samples, respectively. The analyses carried out using the PAM package identified an expression signature composed of 14 microRNAs (14/190 miRNAs on chip, 7.35%) with a PAM score >±0.02 (Table 14). Using the expression of these microRNAs, it is possible to predict with a low misclassification error (about 0.2 at cross-validation) the type of ZAP-70 expression in a patient's malignant B cells.
Comparison of miRNA expression in IgV_Hpositive vs. IgV_Hnegative CLL cells. The expression of a mutated IgV_Hgene is a favorable prognostic marker (D. G. Oscier et al., Blood. 100, 1177-84 (2002)). ZAP-70 expression is well correlated with the status of the IgV_Hgene. Therefore, we asked whether a specific microRNA signature can predict the mutated (Mut) vs. unmutated (Umut) status of this gene. Using the 98% cutoff for homology with germ-line IgV_H, we identified two groups of patients composed of 37 Umut 98% homology) and 57 Mut (<98% homology). Based on this analysis, 12 microRNAs can be used to correctly predict the Umut vs. Mut status of the gene with a low error (0.02) (Table 14). All of these genes are included in the previous signature.
Comparison of miRNA expression in Zap-70 positive/IgV_HUmut vs. Zap-70 negative/IgV_HMut CLL cells. We divided the 94 CLL cases into 4 groups (Zap-70 positive/IgV_HUmut, Zap-70 positive/IgV_HMut, Zap-70 negative/IgV_HUmut and Zap-70 negative/IgV_HMut), and have found, using the PAM package, that the same unique signature composed of 17 genes can discriminate between the two main groups of patients, Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut. In this case, we observed the lowest classification error (0.015 at cross validation). Only one patient was Zap-70 negative and IgV_HUmut, and therefore was not used in the classification. When the remaining three classes were analyzed, the 10 patients belonging to the Zap-70 positive/IgV_HMut class were always misclassified, which indicates that there are no microRNAs on the miRNACHIP that can compose a different signature. The same unique signature was identified using another algorithm of microarray analysis, SAM, thereby confirming the reproducibility of our results. These results indicate that miRNA expression profiles have prognostic significance in CLL and can be used for diagnosing the disease state of a particular cancer by determining whether or not a given profile is characteristic of a cancer associated with one or more adverse prognostic markers.

TABLE 14

A miRNA signature associated with prediction factors and disease
survival in CLL patients.

				Short
				vs.
		IgV_HMut	Zap70+/IgV_H	Long
	ZAP-70+	vs.	Umut vs.	time to
Signature	vs.	IgV_H	Zap70−/IgV_H	initial
component	Zap-70−	Umut	Mut	therapy	Observation

mir-015a	−0.0728	NA	−0.0372 vs.	NA	cluster	15a/16-1
	vs. 0.076		0.0485		del CLL, prostate
					Ca. 13q13.4 (G. A. Calin
					et al., Proc.
					Natl. Acad. Sic. USA.
					99, 15524-15529
					(2002))
mir-016-1	−0.1396	−0.0852	−0.1444 vs.	NA	del CLL, prostate
	vs.	vs. 0.1312	0.1886		ca. 13q13.4 (G. A. Calin
	0.1457				et al., Proc.
					Natl. Acad. Sci. USA.
					99, 15524-15529
					(2002))
mir-016-2	−0.1615	−0.0969	−0.1619 vs.	NA	identical 16-1/16-2
	vs.	vs. 0.1493	0.2113
	0.1685
mir-023a	−0.0235	0.0647 vs.	−0.0748 vs.	0.0587	cluster 23a/24-2
	vs.	0.0997	0.0977	vs. −0.019
	0.0245
mir-023b	−0.0658	−0.0663	−0.0909 vs.	0.0643	cluster 24-1/23b
	vs.	vs. 0.1021	0.1187	vs. −0.0208	FRA 9D; del
	0.0686				Urothelial ca. 9q22.
					(G. A Calin et al.
					Proc. Natl. Acad. Sci.
					U.S.A.
					101(32): 11755-60
					(2004))
mir-024-1	NA	−0.042 vs.	−0.0427 vs.	NA	FRA 9D; del
		0.0648	0.0558		Urothelial ca. 9q22
					(ref (G. A. Calin et al.
					Proc. Natl. Acad. Sci.
					U.S.A.
					101(32): 11755-60
					(2004))
mir-024-2	NA	NA	−0.0272 vs.	−0.0355	0.0696
				vs. −0.0225
mir-029a	0.0806	0.0887 vs.	0.1139 vs. −0.1487	NA	cluster 29a/29b-1
	vs. −00842	−0.1367			FRA7H; del
					Prostate ca. 7q32
					(G. A. Calin et al.
					Proc. Natl. Acad. Sci.
					U.S.A.
					101(32): 11755-60
					(2004))
mir-29b-2	0.1284	0.1869 vs.	0.2065 vs. −0.2696	NA	1q32.2-32.3
	vs. −0.134	−0.2879
mir-029c	0.1579	0.1846	0.2174 vs. −0.2839	−0.0221
	vs. −0.1648	vs. −0.2844		vs.
				0.0072
mir-146	−0.1518	−0.1167	−0.1803 vs.	0.07
	vs.	vs. 0.1798	0.2354	vs. −0.0227
	0.1584
mir-155	−.0.1015	−0.0743	−0.1155 vs.	0.1409	amp child Burkitt's
	vs.	vs. 0.1145	0.1508	vs. −0.0456	lymphoma, colon
	0.1059				ca. (M. Metzler et
					al. Genes
					Chromosomes
					Cancer.
					Feb; 39(2): 167-9
					(2004)) and (M. Z. Michael
					et al. Mol
					Cancer Res.
					1(12): 882-91 (2003)).
mir-181a	−0.0473	NA	−0.0279 vs.	0.1862	Up-regulated in
	vs.		0.0364	vs. −0.0603	differentiated B ly
	0.0494				(C. Z. Chen et al.
					Science.
					303(5654): 83-6
					(2004)).
mir-195	−0.0679	NA	−0.053 vs.	NA
	vs.		0.0692
	0.0708
mir-221	−0.0812	−0.0839	−0.1157 vs.	0.0343	cluster 221/222
	vs.	vs. 0.1292	0.1511	vs. −0.0111
	0.0848
mir-222	NA	NA	−0.022 vs.	0.0458
			0.0288	vs. −0.0148
mir-223	0.0522	0.1036 vs.	0.1056 vs. −0.1379	NA	Normally
	vs. −0.0544	−0.1596			expression
					restricted to
					myeloid lineage
					(C. Z. Chen et al.
					Science.
					303(5654): 83-6
					(2004)).

Note:
ZAP-70 negative = ZAP-70 expression ≦20%;
ZAP-70 positive = ZAP-70 expression >20%;
IgV_Hunmutated = homology ≧98%;
IgV_Hmutated = homology <98%.
The numbers indicate the PAM scores in the two classes (n score and y score).
mir-29b-2 was previously named mir-102.

Association between miRNA expression and time to initial therapy. Treatment of patients according to the National Cancer Institute Working Group criteria (B. D. Cheson et al. Blood. 87(12):4990-7 (1996)) was performed when symptomatic or progressive disease developed. Of the 94 patients studied, 41 had initiated therapy (Table 13). We examined the relationship between the expression of 190 microRNA genes and either the time from diagnosis to initial therapy (for patients that have begun treatment) or from the time of diagnosis to the present (for those patients who haven't begun treatment), collectively representing the total group of 94 patients in the study. We found that the expression profile generated by a spectrum of 9 microRNAs, all components of the unique signature, can differentiate between two subsets of patients in the group of 94 tested—one subset with a short interval from diagnosis to initial therapy and the second subset with a significantly longer interval (see Table 14 and FIG. 5). The significance of Kaplan-Meier curves improves if we restrict the analyses to the two main groups of 83 patients (the Zap-70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut groups) or if we use only the 17 microRNAs from the signature (P decreases from <0.01 to P<0.005 and P<0.001, respectively). All of the microRNAs which can predict the time to initial therapy, with the exception of mir-29c, are overexpressed in the group characterized by a short interval from diagnosis to initial therapy.

Example 13

Identification of Sequence Alterations in miR Genes Associated with CLL

Introduction
Using tumor DNA from CLL samples, we screened more than 700 kb of tumor DNAs (mean 39 patients/miRNA for mean 500 bp/miRNA) for sequence alterations in each of 35 different miR genes. Very rare polymorphisms or tumor specific mutations were identified in 4 of the 39 CLL cases, affecting one of three different miR genes: miR-16-1, miR-27b and miR-206. In two other miR genes, miR-34b and miR-100, polymorphisms were identified in both CLL and normal samples with similar frequencies.
Materials and Methods
Detection of microRNA mutations. Thirty-five miR genes were analyzed for the presence of a mutation, including 16 members of the miR expression signature identified in Example 12 (mir-15a, mir-16-1, mir-23a, mir-23b, mir-24-1, mir-24-2, mir-27a, mir-27b, mir-29b-2, mir-29c, mir-146, mir-155, mir-181a, mir-221, mir-222, mir-223) and 19 other miR genes selected randomly (let-7a2, let-7b, mir-21, mir-30a, mir-30b, mir-30c, mir-30d, mir-30e, mir-32, mir-100, mir-108, mir-125b1, mir-142-5p, mir-142-3p, mir-193, mir-181a, mir-206, mir-213 and mir-224).
The algorithm for screening for miR gene mutations in CLL samples was performed as follows: the genomic region corresponding to each precursor miRNA from either 39 CLL samples or 3 normal mononuclear cell samples from healthy individuals was amplified, including at least 50 base pairs in the 5′ and 3′ extremities. For the miRNAs located in clusters covering less than one kilobase, the entire corresponding genomic region was amplified and sequenced using the Applied Biosystems Model 377 DNA sequencing system (PE, Applied Biosystems, Foster City, Calif.). When a deviation from the normal sequence was found, a panel of blood DNAs from 95 normal individuals was screened to confirm that the deviation represented a polymorphism. If the sequencing data were normal, an additional panel of 37 CLL cases was screened to determine the frequency of mutations in a total of 76 cancer patients. If additional mutations were found, another set of 65 normal DNAs was screened, to assess the frequency of the specific alteration in a total of 160 normal samples.
In vivo studies of mir-16-1 mutant effects. We constructed two mir-16-1/mir-15a expression vectors—one containing an 832 base pair genomic sequence that included both mir-16-1 and mir-15a, and another nearly identical construct containing the C to T mir-16-1 substitution, as shown in SEQ ID NO. 642—by ligating the relevant open reading frame in a sense orientation into the mammalian expression vector, pSR-GFP-Neo (OligoEngine, Seattle, Wash.). These vectors are referred to as mir-16-1-WT and mir-16-1-MUT, respectively. All sequenced constructs were transfected into 293 cells using Lipofectamine 2000 according to the manufacturer's protocol (Invitrogen, Carlsbad, Calif.). The expression of both mir-16-1-WT and mir-16-1-MUT constructs was assessed by Northern blotting as previously described (G. A. Calin et al., Proc. Natl. Acad. Sc. U.S.A. 99, 15524-15529 (2002)).
Results
Very rare polymorphisms or tumor specific mutations were identified in 4 of the 39 CLL cases, affecting one of three different miR genes: miR-16-1, miR-27b and miR-206 (Tables 15 and 16). In two other miR genes, miR-34b and miR-100, polymorphisms were identified in both CLL and normal samples with similar frequencies (see Tables 15, 16 and Results section below).

TABLE 15

Genetic variations in the genomic sequences of miR genes in CLL
patients.

			Other		miRNA
miRNA	Mutation	CLL (%)	allele	Normals	CHIP	Observation

mir-16-1	C to T	2/76 (2.6)	Deleted	0/160 (0)	Reduced	Heterozygous in
	(see SEQ ID		(FISH,		expression	normal cells
	NO. 642)		LOH)			from both
						patients;
						Previous breast
						cancer; Mother
						died with CLL;
						sister died with
						breast cancer.
mir-27b	G to A	1/39 (2.6)	Normal	0/98 (0)	Normal
	(see SEQ ID				expression
	NO. 646)
mir-206	G to A	1/39 (2.6)	Normal	NA	NA
	(see SEQ ID
	NO. 647)
mir-100	G to A	17/39 (43.5)	Normal	2/3	NA
	(see SEQ ID
	NO. 644)

TABLE 16

Sequences showing genetic variations in the miR genes of CLL
patients.

Name	Precursor Sequence (5′ to 3′)	SEQ ID NO.

hsa-mir-16-1-	GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTT	641
normal	AAGATTCTAAAATTATCTCCAGTATTAACTGTGCTGC
	TGAAGTAAGGTTGACCATACTCTAC

hsa-mir-16-1-	GTCAGCAGTGCCTTAGCAGCACGTAAATATTGGCGTT	642
MUT	AAGATTCTAAAATTATCTCCAGTATTAACTGTGCTGC
	TGAAGTAAGGTTGACCATACT T TAC

hsa-mir-100	CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTA	643
	TTAGTCCGCACAAGCTTGTATCTATAGGTATGTGTCT
	GTTAGGCAATCTCACGGACC

hsa-mir-100-	CCTGTTGCCACAAACCCGTAGATCCGAACTTGTGGTA	644
MUT	TTAGTCCGCACAAGCTTGTATCTATAGGTATGTGTCT
	GTTAGGCAATCTCAC A GACC

hsa-mir-27b-	ACCTCTCTAACAAGGTGCAGAGCTTAGCTGATTGGTG	645
normal	AACAGTGATTGGTTTCCGCTTTGTTCACAGTGGCTAA
	GTTCTGCACCTGAAGAGAAGGTGAGATGGGGACAGT
	TAAGTTGGAGCCGCTGGGGCAGAGGCCGTTGCTGAC
	GGGC

hsa-mir-27b-	ACCTCTCTAACAAGGTGCAGAGCTTAGCTGATTGGTG	646
MUT	AACAGTGATTGGTTTCCGCTTTGTTCACAGTGGCTAA
	GTTCTGCACCTGAAGAGAAGGTGAGATGGGGACAGT
	TAAGTTGGAGCCGCTGGGGCAGAGGCCGTTGCTGAC
	A GGC

has-mir-206	TGCTTCCCGAGGCCACATGCTTCTTTATATCCCCATAT	230
	GGATTACTTTGCTATGGAATGTAAGGAAGTGTGTGGT
	TTCGGCAAGTG

has-mir-206-	TGCTTCCCGAGGCCACATGCTTCTTTATATCCCCATAT	647
MUT	GGATTACTTT A CTATGGAATGTAAGGAAGTGTGTGGT
	TTCGGCAAGTG

hsa-mir-34b-	GTGCTCGGTTTGTAGGCAGTGTCATTAGCTGATTGTA	648
normal	CTGTGGTGGTTACAATCACTAACTCCACTGCCATCAA
	AACAAGGCACAGCATCACCGCCG

hsa-mir-34b-	GTGCTCGGTTTGTAGGCAGTGTCATTAGCTGATTGTA	650
MUT	CTGTGGTGGTTACAATCACTAACTCCACTGCCATCAA
	AACAAGGCACAGCATCACC A CCG

Note:
Each mutation/polymorphism is underlined and indicated in bold in the sequences marked “MUT”.

The miR-16-1 gene is located at 13q13.4. In 2 CLL patients out of 76 screened (2.6%), we found a homozygous C to T polymorphism (compare SEQ ID NO: 641 to SEQ ID NO: 642; Table 16), which is located in a 3′ region of the miR-16-1 precursor (FIG. 7C) with strong conservation in all of the primates analyzed (E. Berezikov et al., Cell 120(1):21-4 (2005)), suggesting that this polymorphism has functional implications. By RT-PCR and Northern blotting we have shown that the precursor miRNA includes the 3′ region harboring the base substitution. Both patients have a significant reduction in mir-16-1 expression in comparison with normal CD5+ cells by miRNACHIP and Northern blotting (FIG. 6, FIG. 7D). Further suggesting a pathogenic role, by FISH and LOH, we found a monoallelic deletion at 13q14.3 in the majority of examined cells. This substitution was not found in any of 160 normal control samples (p<0.05 using chi square analysis). In both patients, the normal cells from mucal mucosa were heterozygous for this abnormality. Therefore, this change is a very rare polymorphism or a germ-line mutation. In support of the latter is the fact that one of the patients has two relatives (mother and sister) who have been diagnosed with CLL and breast cancer, respectively. Therefore, this family fulfills the minimal criteria for “familial” CLL, i.e., two or more cases of B-CLL in first-degree living relatives (N. Ishibe et al., Leuk Lymphoma 42(1-2):99-108 (2001)).
To identify a possible pathogenic effect for this substitution, we inserted both the wild-type sequence of the mir-15a/mir-16-1 cluster, as well as the mutated sequence, into separate expression vectors. We transfected 293 cells, which have a low endogenous expression of this cluster. As a control, 293 cells transfected with an empty vector were tested. The expression levels of both mir-15a and mir-16-1 were significantly reduced in transfectants expressing the mutant construct in comparison to transfectants expressing the wild-type construct (FIG. 7E). The level of expression in transfectants expressing the mutant construct was comparable with the level of endogenous expression in 293 cells (FIG. 7E). Therefore, we conclude that the C to T change in miR-16-1 affects the processing of the pre-miRNA in mature miRNA.
The miR-27b gene is located on chromosome 9. A heterozygous mutation caused by a G to A change in the 3′ region of the miR-27b precursor (compare SEQ ID NO: 645 to SEQ ID NO: 646; Table 16), but within the transcript of the 23b-27b-24-1 cluster, was identified in one out of 39 CLL samples. miRCHIP analysis indicated that miR-27b expression was reduced in this sample. This change has not been found in any of the 98 normal individuals screened to date.
The miR-34b gene is located at 11q23. Four CLL patients out of 39 carried two associated polymorphisms, a G to A polymorphism, as shown in SEQ. ID NO. 650, and a T to G polymorphism located in the 3′ region of the miR-34b precursor. Both polymorphisms were within the transcript of the mir-34b-mir-34c cluster. One patient was found to be homozygous (presenting by FISH heterozygous abnormal chromosome 11q23), while the other three were heterozygous for the polymorphisms. The same frequency of mutation was found in 35 normal individuals tested.

Example 14

Identification of Abnormalities in the Genomic Sequences of miR Genes Associated with CLL

Introduction
Abnormally expressed cancer genes are frequently targets for genetic abnormalities, e.g., mutations that can either activate or inactivate their function. Therefore, we screened 42 microRNAs for germline or somatic mutations.
Materials and Methods
Detection of MicroRNA Gene Mutations.
The genomic region corresponding to each precursor miRNA, including at least 50 additional base pairs (bp) in the 5′ and 3′ extremities (i.e., flanking sequences), was amplified from 40 CLL samples and normal mononuclear cell samples from 3 healthy individuals. For the miRNAs located in clusters that were less than one kilobase (kb) in length, the entire corresponding genomic region was amplified and sequenced using the Applied Biosystems Model 377 DNA sequencing system (PE, Applied Biosystems, Foster City, Calif.). When a deviation from the normal sequence was found, a panel of blood DNAs from 160 normal individuals, as well as an additional panel of 35 CLL cases (total of 75 leukemia patients), were screened to confirm polymorphisms. All subjects were Caucasian, as indicated by medical records of CLL patients and information obtained during an interview for control patients. For 46 CLL patients, personal and/or familial cancer history was known. Forty-two miR genes were screened for germline or somatic mutations, including 15 members of the specific signature identified in Example 12, or members of the same cluster: miR-15a, miR-16-1, miR-23a, miR-23b, miR-24-1, miR-24-2, miR-27a, miR-27b, miR-29b-2, miR-29c, miR-146, miR-155, miR-221, miR-222, miR-223, as well as 27 other microRNAs that were selected randomly: let-7a2, let-7b, miR-17-3p, miR-17-5p, miR-18, miR-19a, miR-19b-1, miR-20, miR-21, miR-30b, miR-30c-1, miR-30d, miR-30e, miR-32, miR-100, miR-105-1, miR-108, miR-122, miR-125b-1, miR-142-5p, miR-142-3p, miR-193, miR-181a, miR-187, miR-206, miR-224, miR-346.
Results
Germline or somatic mutations were identified in miRNA genomic regions in 11 out of 75 (15%) CLL samples. Five different miRNAs were affected by mutations (5/42 miR genes analyzed, 12%): miR-16-1, miR-27b, miR-206, miR-29b-2 and miR-187. None of these mutations were found in a set of 160 individuals without cancer (p<0.0001) (see Table 17). The positions of the various mutations are shown relative to the position of the miR gene in FIG. 7A. All the abnormalities are localized in regions that are transcribed, as shown by RT-PCR (FIG. 7B). Eight of the 11 (73%) patients with abnormal miRNA sequences have a known personal or familial history of CLL or other hematopoietic or solid tumors (Table 17). Sequences containing the identified miR gene mutations, as well as their corresponding wild-type sequences, are shown in Table 16 for miR-16-1 and miR-27b and in Table 18 for miR-29b-2, miR-187 and miR-206. Two mutations were identified in miR-29b-2 and miR-206 (labeled MUTT and MUT2, respectively, in Table 18). In addition, a polymorphism was detected in both CLL and normal samples with similar frequencies for three other miR genes: miR-29c, miR-122a and miR-187 (labeld MUT2) (see Tables 17 and 18).

TABLE 17

Genetic variations in the genomic sequences of miR genes in CLL
patients.

				miRNACHIP
miRNA	Location**	CLL	Normals	expression	Observations

miR-	Germline;	2/75	0/160	Reduced to	Normal allele deleted in
16-1	pri-			15% and 40%	CLL cells in both
	miRNA:			of normal,	patients (FISH, LOH).
	CtoT			respectively	For one patient: History
	substitution				of previous breast
	at +7 bp in				cancer; mother with CLL
	the 3′				(deceased); sister with
	flanking				breast cancer (deceased).
miR-	Germline;	1/75	0/160	Normal	Mother with throat and
27b	pri-				lung cancer at age 58.
	miRNA: G				Father with lung cancer
	toA				at age 57.
	substitution
	at +50bp in
	3′ flanking
	sequence
miR-	pri-	1/75	0/160	Reduced to 75%	Sister with breast cancer
29b-2	miRNA: G				at age 88 (still living).
	to A				Brother with “some type
	substitution				of blood cancer” at age
	at +212 in				70.
	3′ flanking
	sequence
miR-	pri-	3/75	0/160	Reduced to 80%	Both patients have a
29b-2	miRNA: A				family history of
	insertion at				unspecified cancer.
	+107 in 3′
	flanking
	sequence
miR-	pri-	1/75	0/160	NA	Unknown
187	miRNA: T
	to C
	substitution
	at +73 in 3′
	flanking
	sequence
miR-	pre-	2/75	0/160	Reduced to 25%	Prostate cancer; mother
206	miRNA: G				with esophogeal cancer.
	to T				Brother with prostate
	substitution				cancer; sister with breast
	at position				cancer
	49 of
	precursor
miR-	Somatic;	1/75	0/160	Reduced to 25%	Aunt with leukemia
206	pri-			(data only for one	(deceased)
	miRNA:			pt)
	A to T
	substitution
	at
	−116 in 5′
	flanking
	sequence
miR-	pri-	2/75	1/160	NA	Paternal grandmother
29c	miRNA: G				with CLL; sister with
	to A				breast cancer.
	substitution
	at −31 in 5′
	flanking
	sequence
miR-	pre-	1/75	2/160	Reduced to 33%	Paternal uncle with
122a	miRNA: C				colon cancer.
	to T
	substitution
	at position
	53 of
	precursor
miR-	pre-	1/75	1/160	NA	Grandfather with
187	miRNA: G				polycythemia vera.
	to A				Father has a history of
	substitution				cancer but not
	at position				lymphoma.
	34 of
	precursor

For each CLL patient/normal control, more than 12 kb of genomic DNA was sequenced. In total, ~627 kb of tumor DNA and about 700 kb of normal DNA was screened by direct sequencing. The positions of the mutations are reported with respect to the precursor miRNA molecule.
**When normal corresponding DNA from bucal mucosa was available, the alteration was identified as germline when present or somatic when absent, respectively.
FISH—fluorescence in situ hybridization;
LOH—loss of heterozygosity;
NA—not available.

TABLE 18

Sequences showing genetic variations in the miR genes of CLL patients.

	Precursor Sequence (5′ to 3′) +/− 5′ or 3′ flanking genomic	SEQ ID
Name	sequence	NO.

hsa-mir-29b-2-	CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTTT	651
normal	CCATCTTTGTATCTAGCACCATTTGAAATCAGTGTTTT
	AGGAGTAAGAATTGCAGCACAGCCAAGGGTGGACTG
	CAGAGGAACTGCTGCTCATGGAACTGGCTCCTCTCCT
	CTTGCCACTTGAGTCTGTTCGAGAAGTCCAGGGAAGA
	ACTTGAAGAGCAAAATACACTCTTGAGTTTGTTGGGT
	TTTGGGAGAGGTGACAGTAGAGAAGGGGGTTGTGTT
	TAAAATAAACACAGTGGCTTGAGCAGGGGCAGAGG

hsa-mir-29b-2-	CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTTT	652
MUT1 (G to A	CCATCTTTGTATCTAGCACCATTTGAAATCAGTGTTTT
substitution at	AGGAGTAAGAATTGCAGCACAGCCAAGGGTGGACTG
+212 in 3′	CAGAGGAACTGCTGCTCATGGAACTGGCTCCTCTCCT
flanking sequence)	CTTGCCACTTGAGTCTGTTCGAGAAGTCCAGGGAAGA
	ACTTGAAGAGCAAAATACACTCTTGAGTTTGTTGGGT
	TTTGGGAGAGGTGACAGTAGAGAAGGGGGTTGTGTT
	TAAAATAAACACAGTGGCTTGAGCAGGGGCAGA A G

hsa-mir-29b-2-	CTTCTGGAAGCTGGTTTCACATGGTGGCTTAGATTTTT	653
MUT2 (A	CCATCTTTGTATCTAGCACCATTTGAAATCAGTGTTTT
insertion at +107	AGGAGTAAGAATTGCAGCACAGCCAAGGGTGGACTG
in 3′ flanking	CAGAGGAACTGCTGCTCATGGAACTGGCTCCTCTCCT
sequence)	CTTGCCACTTGAGTCTGTTCGAGAAGTCCAGGGAAGA
	A A CTTGAAGAGCAAAATACACTCTTGAGTTTGTTGGG
	TTTTGGGAGAGGTGACAGTAGAGAAGGGGGTTGTGT
	TTAAAATAAACACAGTGGCTTGAGCAGGGGCAGAGG

hsa-mir-187-	GGTCGGGCTCACCATGACACAGTGTGAGACCTCGGG	654
normal	CTACAACACAGGACCCGGGCGCTGCTCTGACCCCTCG
	TGTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCAG
	CAGAGCCTGCTCCGCTTGTCCTGAGGGACTCGACACA
	GGGGACTGCACAGAGACCATGGGAAAGTCCAGGCTC

hsa-mir-187-	GGTCGGGCTCACCATGACACAGTGTGAGACCTCGGG	655
MUT1 (T to C	CTACAACACAGGACCCGGGCGCTGCTCTGACCCCTCG
substitution at +73	TGTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCAG
in 3′ flanking	CAGAGCCTGCTCCGCTTGTCCTGAGGGACTCGACACA
sequence)	GGGGACTGCACAGAGACCATGGGAAAGTCCAGGC C C

hsa-mir-187-	GGTCGGGCTCACCATGACACAGTGTGAGACTCG A GC	656
MUT2 (G to A	TACAACACAGGACCCGGGGCGCTGCTCTGACCCCTCG
substitution at	TGTCTTGTGTTGCAGCCGGAGGGACGCAGGTCCGCAG
position 34 of	CAGAGCCTGCTCCGCTTGTCCTGAGGGACTCGACACA
precursor)	GGGGACTGCACAGAGACCATGGGAAAGTCCAGGCTC

has-mir-206	GATTTAGGATGAGTTGAGATCCCAGTGATCTTCTCGC	657
	TAAGAGTTTCCTGCCTGGGCAAGGAGGAAAGATGCT
	ACAAGTGGCCCACTTCTGAGATGCGGGCTGCTTCTGG
	ATGACACTGCTTCCCGAGGCCACATGCTTCTTTATAT
	CCCCATATGGATTACTTTGCTATGGAATGTAAGGAAG
	TGTGTGGTTTCGGCAAGTG

has-mir-206-	GATTTAGGATGAGTTGAGATCCCAGTGATCTTCTCGC	658
MUT1 (G to T	TAAGAGTTTCCTGCCTGGGCAAGGAGGAAAGATGCT
substitution at	ACAAGTGGCCCACTTCTGAGATGCGGGCTGCTTCTGG
position 49 of	ATGACACTGCTTCCCGAGGCCACATGCTTCTTTATAT
precursor)	CCCCATATGGATTACTTT T CTATGGAATGTAAGGAAG
	TGTGTGGTTTCGGCAAGTG

has-mir-206-	G T TTTAGGATGAGTTGAGATCCCAGTGATCTTCTCGC	659
MUT2 (A to T	TAAGAGTTTCCTGCCTGGGCAAGGAGGAAAGATGCT
substitution at	ACAAGTGGCCCACTTCTGAGATGCGGGCTGCTTCTGG
−116 in 5′ flanking	ATGACACTGCTTCCCGAGGCCACATGCTTCTTTATAT
sequence)	CCCCATATGGATTACTTTTCTATGGAATGTAAGGAAG
	TGTGTGGTTTCGGCAAGTG

hsa-mir-29c-	CGAGGTGCAGACCCTGGGAGCACCACTGGCCCATCT	660
normal	CTTACACAGGCTGACCGATTTCTCCTGGTGTTCAGAG
	TCTGTTTTTGTCTAGCACCATTTGAAATCGGTTATGAT
	GTAGGGGGA

hsa-mir-29c-	C A AGGTGCAGACCCTGGGAGCACCACTGGCCCATCT	661
MUT (G to A	CTTACACAGGCTGACCGATTTCTCCTGGTGTTCAGAG
substitution at −31	TCTGTTTTTGTCTAGCACCATTTGAAATCGGTTATGAT
in 5′ flanking	GTAGGGGGA
sequence)

hsa-mir-122a-	CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGT	662
normal	GTCTAAACTATCAAACGCCATTATCACACTAAATAGC
	TACTGCTAGGC

hsa-mir-122a-	CCTTAGCAGAGCTGTGGAGTGTGACAATGGTGTTTGT	663
MUT (C to T	GTCTAAACTATCAAA T GCCATTATCACACTAAATAGC
substitution at	TACTGCTAGGC
position 53 of
precursor)

Note:
The position of each mutation/polymorphism is underlined and indicated in bold in the sequences marked “MUT”.

Example 15

A Unique MicroRNA Signature Associated with Prognostic Factors and Disease Progression in Chronic Lymphocytic Leukemia

Introduction: In spite of extensive effort, little is known regarding the pathogenic events leading to the initiation and progression of B cell CLL, the most frequent adult leukemia in the Western world. On the contrary, several factors predicting the clinical course have been defined. CLL cells with few or no mutations in the immunoglobulin heavy-chain variable-region gene (IgV_H) or with high expression of the 70-kD zeta-associated protein positive (ZAP-70+) have an aggressive course, whereas patients with mutated clones or few ZAP-70+ B cells have an indolent course (Chiorazzi, N., et al., N. Engl. J. Med. 352:804-815 (2005)). It was also found that genomic aberrations in CLL are important independent predictors of disease progression and survival (Dohner, H., et al., N. Engl. J. Med. 343(26):1910-1916 (2000)). However, the molecular basis of these associations is largely unknown. Here, we performed genome wide expression profiling with the miRNACHIP in a large series of CLL samples with extensive clinical data to examine whether expression of these noncoding genes is associated with factors predicting the clinical course.

Materials and Methods

Patient samples and clinical database. Samples used for this study are described in detail in Example 12.
RNA extraction, Northern blots and miRNACHIP experiments. Procedures were performed as described (Calin, G. A., et al., Proc. Natl. Acad. Sci. USA 101(32):1175-1160 (2004); Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004)). Briefly, labeled targets from 5 μg of total RNA was used for hybridization on each miRNACHIP microarray chip containing 368 probes in triplicate, corresponding to 245 human and mouse miRNA genes. Of note, for 76 microRNAs on the miRNACHIP two specific oligos were synthesized one identifying the active 22 nt part of the molecule and the other for the 60-110 nt precursor (Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004)).
Data analysis. Raw data were normalized and analyzed in GeneSpring® software version 7.2 (Silicon Genetics, Redwood City, Calif.). Expression data were median centered using both GeneSpring normalization option or Global Median normalization of the Bioconductor package, without any substantial difference. Statistical comparisons were done both using the GeneSpring ANOVA tool and the SAM software (Significance Analysis of Microarray). mRNA predictors were calculated by using PAM software (Prediction Analysis of Microarrays); the Support Vector Machine tool of GeneSpring was used for the Cross-validation and Test-set prediction. The Kaplan-Meier plot (“survival analysis” of the PAM software) was used to identify an association between miRNA expression and the time elapsing from CLL diagnosis and the beginning of therapy. miRNAs able to best separate the two groups were identified at the same time. All data were submitted using MIAMExpress to the Array Express database (accession numbers to be received upon revision). We validated the microarray data for 4 miRNAs (miR-16-1, miR-26a, miR-206 and miR-223) in 11 CLL samples and normal CD5 cells by solution hybridization detection as presented elsewhere (Calin, G. A., et al., Proc. Natl. Acad. Sci. USA 101(32):11755-11760 (2004)). Furthermore, miR-15a and miR-16-1 expression in the patients with germline mutation was confirmed by Northern blot.
Analysis of ZAP-70 and Sequence analysis of expressed IgV_H. These experiments were performed as described in Example 12.
Results
Comparison of miRNA expression in Zap-70 positive/IgV_HUmut vs. Zap-70 negative/IgV_HMut CLL cells. In Example 12, a unique signature that can discriminate between the two main groups of CLL patients (i.e., Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut), composed of 17 genes, was identified using the PAM package. Using additional algorithms for statistical and prediction analysis (i.e., SAM and GeneSpring) to validate the PAM signature, we found that a signature composed of 13 mature microRNAs could discriminate (at P<0.01) between Zap70 positive/IgV_HUmut and Zap-70 negative/IgV_HMut patients (Tables 19 and 20). Furthermore, the prediction made using Support Vector Machine correctly classified all patients (Table 20). The majority of miRNAs (9 out of 13) were significantly overexpressed in the group with poor prognosis. The 10 patients belonging to the Zap-70 positive and VhMut group were equally assigned to groups good or poor prognosis, suggesting either that there are no microRNAs on the miRNACHIP whose expression can distinguish these two groups, that these two groups are not different with regard to microRNA expression profiles or that the groups are too small to be correctly classified.
We used the Support Vector Machine algorithm also to predict an additional independent set of 50 CLL samples with known ZAP-70 status (Table 21). When the 13 miRNAs of the identified signature were used, the prediction was made correctly in all cases, confirming, thereby confirming our results. Also confirming the microarray specificity, as reported in Liu, C.-G., et al., Proc. Natl. Acad. Sci. USA 101(26): 9740-9744 (2004), the signature did not include very similar members of the same families, such as miR-23a (1 base difference from miR-23b) and miR-15b (four bases difference from miR-15a), while the identical mature miRNAs miR-16-1 and miR-16-2 were both identified, indicating that the chip is able to discriminate between highly similar iso forms.

TABLE 19

miRNA signature associated with prognostic factors (ZAP70 and IgVH mutations) and disease progression in CLL patients*.

				Group 4
Nr. Crt.	Component	Map	P value	expression**	Putative targets***	Observation****

1	miR-15a	13q14.3	0.018	high	NA	cluster	15a/16-1
						del CLL & Prostate ca.
2	miR-195	17p13	0.017	high	NA	del HCC
3	miR-221	Xp11.3	0.010	high	HECTD2, CDKN1B, NOVA1,	cluster 221/222
					ZFPM2, PHF2
4	miR-23b	9q22.1	0.009	high	FNBP1L, WTAP,	cluster 24-1/23b
					PDE4B, SATB1, SEMA6D	FRA 9D; del Urothelial ca.
5	miR-155	21q21	0.009	high	ZNF537, PICALM, RREB1,	amp child Burkitt's lymphoma
					BDNF, QKI
6	miR-223	Xq12-13.3	0.007	low	PTBP2, SYNCRIP, WTAP,	normally expression restricted to myeloid
					FBXW7, QKI	lineage
7	miR-29a-2	7q32	0.004	low	NA	cluster 29a-2/29b-1
						FRA7H; del Prostate ca.
8	miR-24-1	9q22.1	0.003	high	TOP1, FLJ45187, RSBN1L,	cluster 24-1/23b
					RAP2C, PRPF4B	FRA 9D; del Urothelial ca.
9	miR-29b-2	1q32.2-32.3	0.0007	low	NA
	(miR-102)
10	miR-146	5q34	0.0007	high	NOVA1, NFE2L1, C1orf16,
					ABL2, ZFYVE1
11	miR-16-1	13q14.3	0.0004	high	BCL2, CNOT6L, USP15,	cluster 15a/16-1
					PAFAH1B1, ESRRG	del CLL, prostate ca.
12	miR-16-2	3q26.1	0.0003	high	see miR-16-1	identical miR-16-1
13	miR-29c	1q32.2-32.3	0.0002	low	NA

Note:
*All the members of the signature are mature miRNAs;
**Group 4 includes patients with IgVh mutated and Zap-70 negative, both predictors of poor prognosis.
***top five predictions using TargetScan (Lewis, B. P., et al., Cell 120: 15-20 (2005)) were included.
NA —not available;
for specific gene names - see the NCBI site.
****FRA = fragile site; del = deletion; HCC = hepatocellular carcinoma; ca. = carcinoma.

TABLE 20

List of miRNAs associated with prognostic factors and disease progression in
CLL patients selected by Prediction Analysis of Microarrays (PAM) and ANOVA
analysis (GeneSpring)*.

Nr.	PAM	n−	y+		GeneSpring	Anova
crt.	signature	score	score	map	signature	p-value	map

1	mir-222	−0.022	0.0288	Xp11.2	mir-34-prec	0.048	1p36.22
2	mir-24-2	−0.0272	0.0355	19p13.12	mir-192-2/3-prec	0.0457	11q13
3	mir-181a	−0.0279	0.0364	1q32.1	mir-15a-prec	0.0353	13q14.3
4	mir-15a	−0.0372	0.0485	13q14.3	mir-17	0.0257	13q31
5	mir-24-1	−0.0427	0.0558	9q22.1	mir-15a	0.018	13q14.3
6	mir-195	−0.053	0.0692	17p13	mir-195	0.0175	17p13
7	mir-23a	−0.0748	0.0977	19p13.12	mir-213-prec	0.0153	1q31.3-q32.1
8	mir-23b	−0.0909	0.1187	9q22.1	mir-221	0.0105	Xp11.3
9	mir-223	0.1056	−0.1379	Xq12-13.3	mir-023b	0.00964	9q22.1
10	mir-29a-2	0.1139	−0.1487	7q32	mir-155	0.00959	21q21
11	mir-155	−0.1155	0.1508	21q21	mir-223	0.00774	Xq12-13.3
12	mir-221	−0.1157	0.1511	Xp11.3	mir-132	0.00461	17p13.3
13	mir-16-1	−0.1444	0.1886	13q14.3	mir-029a-2	0.00446	7q32
14	mir-16-2	−0.1619	0.2113	3q26.1	mir-024-1	0.00311	9q22.1
15	mir-146	−0.1803	0.2354	5q34	mir-29b-2 (102)	0.000778	1q32.2-32.3
16	mir-29b-2	0.2065	−0.2696	1q32.2-32.3	mir-146	0.000753	5q34
	(102)
17	mir-029c	0.2174	−0.2839	1q32.2-32.3	mir-016-1	0.00042	13q14.3
18					mir-016-2	0.000327	3q26.1
19					mir-029c	0.000216	1q32.2-32.3

*the list of genes is in ascending order of significance, as represented by score or p value, respectively.

TABLE 21

Predictions of ZAP-70 status and Immunoglobulin heavy
chain variable gene status according to miRNA expression in CLL patients*.

	CLL	True Value	Prediction	n margin	y margin

PANEL 1 - 83 correct predictions,	CLL01	Zap70 < 20 VhM	Zap70 < 20 VhM	1.278	−1.327
0 incorrect predictions	CLL02	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL03	Zap70 < 20 VhM	Zap70 < 20 VhM	1.247	−1.348
	CLL04	Zap70 < 20 VhM	Zap70 < 20 VhM	1.16	−1.388
	CLL05	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL06	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL07	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1.122
	CLL08	Zap70 < 20 VhM	Zap70 < 20 VhM	1.391	−1.595
	CLL09	Zap70 < 20 VhM	Zap70 < 20 VhM	0.953	−1.048
	CLL10	Zap70 < 20 VhM	Zap70 < 20 VhM	1.059	−1.333
	CLL11	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL12	Zap70 < 20 VhM	Zap70 < 20 VhM	0.997	−1.261
	CLL13	Zap70 < 20 VhM	Zap70 < 20 VhM	1.488	−1.841
	CLL14	Zap70 < 20 VhM	Zap70 < 20 VhM	2.171	−2.582
	CLL15	Zap70 < 20 VhM	Zap70 < 20 VhM	1.252	−1.352
	CLL16	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1.188
	CLL17	Zap70 < 20 VhM	Zap70 < 20 VhM	1.19	−1.284
	CLL18	Zap70 < 20 VhM	Zap70 < 20 VhM	1.747	−2.062
	CLL19	Zap70 < 20 VhM	Zap70 < 20 VhM	1.503	−1.833
	CLL20	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL21	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL22	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL23	Zap70 < 20 VhM	Zap70 < 20 VhM	2.047	−2.27
	CLL24	Zap70 < 20 VhM	Zap70 < 20 VhM	1.464	−1.527
	CLL25	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL26	Zap70 < 20 VhM	Zap70 < 20 VhM	1.034	−1.034
	CLL27	Zap70 < 20 VhM	Zap70 < 20 VhM	1.479	−1.617
	CLL28	Zap70 < 20 VhM	Zap70 < 20 VhM	2.355	−2.57
	CLL29	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL30	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL31	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL32	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL33	Zap70 < 20 VhM	Zap70 < 20 VhM	2.229	−2.496
	CLL34	Zap70 < 20 VhM	Zap70 < 20 VhM	2.683	−2.931
	CLL35	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL36	Zap70 < 20 VhM	Zap70 < 20 VhM	2.578	−2.768
	CLL37	Zap70 < 20 VhM	Zap70 < 20 VhM	2.079	−2.34
	CLL38	Zap70 < 20 VhM	Zap70 < 20 VhM	1.745	−1.814
	CLL39	Zap70 < 20 VhM	Zap70 < 20 VhM	1.559	−1.699
	CLL40	Zap70 < 20 VhM	Zap70 < 20 VhM	2.608	−3.005
	CLL41	Zap70 < 20 VhM	Zap70 < 20 VhM	2.357	−2.676
	CLL42	Zap70 < 20 VhM	Zap70 < 20 VhM	1.102	−1.303
	CLL43	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL44	Zap70 < 20 VhM	Zap70 < 20 VhM	2.464	−2.629
	CLL45	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL46	Zap70 < 20 VhM	Zap70 < 20 VhM	1	−1
	CLL47	Zap70 < 20 VhM	Zap70 < 20 VhM	2.074	−2.271
	CLL48	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL49	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.179	1.487
	CLL50	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	0.88
	CLL51	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL52	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.836	2.405
	CLL53	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL54	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.334	1.649
	CLL55	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1.229
	CLL56	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL57	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL58	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.171	1.566
	CLL59	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL60	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL61	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.505	1.976
	CLL62	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.095	1.46
	CLL63	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.297	2.717
	CLL64	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.187	1.381
	CLL65	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL66	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.344	1.479
	CLL67	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.876	2.049
	CLL68	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL69	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.89	1.987
	CLL70	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.658	2.938
	CLL71	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.556	1.967
	CLL72	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.574	2.81
	CLL73	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL74	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL75	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL76	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.671	3.041
	CLL77	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1.376
	CLL78	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL79	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.678	1.914
	CLL80	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.416	2.953
	CLL81	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1	1
	CLL82	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−1.782	1.846
	CLL83	Zap70 > 20 VhUM	Zap70 > 20 VhUM	−2.307	2.716
PANEL 2 - 50 correct predictions, 0 incorrect predictions	CLL95	Zap70 < 20	Zap70 < 20	8.494	−8.494
	CLL96	Zap70 < 20	Zap70 < 20	1	−1
	CLL97	Zap70 < 20	Zap70 < 20	0.763	−0.763
	CLL98	Zap70 < 20	Zap70 < 20	11.19	−11.19
	CLL99	Zap70 < 20	Zap70 < 20	7.561	−7.561
	CLL100	Zap70 < 20	Zap70 < 20	14.51	−14.51
	CLL101	Zap70 < 20	Zap70 < 20	5.585	−5.585
	CLL102	Zap70 < 20	Zap70 < 20	1	−1
	CLL103	Zap70 < 20	Zap70 < 20	10.09	−10.09
	CLL104	Zap70 < 20	Zap70 < 20	5.521	−5.521
	CLL105	Zap70 < 20	Zap70 < 20	7.33	−7.33
	CLL106	Zap70 < 20	Zap70 < 20	3.264	−3.264
	CLL107	Zap70 < 20	Zap70 < 20	7.774	−7.774
	CLL108	Zap70 < 20	Zap70 < 20	5.3	−5.3
	CLL109	Zap70 < 20	Zap70 < 20	4.34	−4.34
	CLL110	Zap70 < 20	Zap70 < 20	1.822	−1.822
	CLL111	Zap70 < 20	Zap70 < 20	3.879	−3.879
	CLL112	Zap70 < 20	Zap70 < 20	8.514	−8.514
	CLL113	Zap70 < 20	Zap70 < 20	5.866	−5.866
	CLL114	Zap70 < 20	Zap70 < 20	10.69	−10.69
	CLL115	Zap70 < 20	Zap70 < 20	4.141	−4.141
	CLL116	Zap70 < 20	Zap70 < 20	1	−1
	CLL117	Zap70 < 20	Zap70 < 20	1	−1
	CLL118	Zap70 < 20	Zap70 < 20	1	−1
	CLL119	Zap70 < 20	Zap70 < 20	10.11	−10.11
	CLL120	Zap70 > 20	Zap70 > 20	−3.109	3.109
	CLL121	Zap70 > 20	Zap70 > 20	−4.722	4.722
	CLL122	Zap70 > 20	Zap70 > 20	−5.166	5.166
	CLL123	Zap70 > 20	Zap70 > 20	−7.828	7.828
	CLL124	Zap70 > 20	Zap70 > 20	−7.468	7.468
	CLL125	Zap70 > 20	Zap70 > 20	−11.44	11.44
	CLL126	Zap70 > 20	Zap70 > 20	−1	1
	CLL127	Zap70 > 20	Zap70 > 20	−6.617	6.617
	CLL128	Zap70 > 20	Zap70 > 20	−7.011	7.011
	CLL129	Zap70 > 20	Zap70 > 20	−7.479	7.479
	CLL130	Zap70 > 20	Zap70 > 20	−9.568	9.568
	CLL131	Zap70 > 20	Zap70 > 20	−5.286	5.286
	CLL132	Zap70 > 20	Zap70 > 20	−5.045	5.045
	CLL133	Zap70 > 20	Zap70 > 20	−1	1
	CLL134	Zap70 > 20	Zap70 > 20	−1	1
	CLL135	Zap70 > 20	Zap70 > 20	−1.324	1.324
	CLL136	Zap70 > 20	Zap70 > 20	−1	1
	CLL137	Zap70 > 20	Zap70 > 20	−1	1
	CLL138	Zap70 > 20	Zap70 > 20	−9.649	9.649
	CLL139	Zap70 > 20	Zap70 > 20	−9.264	9.264
	CLL140	Zap70 > 20	Zap70 > 20	−7.13	7.13
	CLL141	Zap70 > 20	Zap70 > 20	−11.77	11.77
	CLL142	Zap70 > 20	Zap70 > 20	−2.986	2.986
	CLL143	Zap70 > 20	Zap70 > 20	−1	1
	CLL144	Zap70 > 20	Zap70 > 20	−1	1

*Prediction for 83 CLLs, from groups 1 and 4 (see text).
Classification was generated by the ‘Support Vector Machines’ algorithm (Kernel Function used: Polynomial Dot Product (Order 2). Diagonal Scaling Factor: 0).
The miRNA signature associated with prognostic factors was generated using panel 1 samples and then tested to cross validate the panel 1 and to predict the status of samples from panel 2.

Association Between miRNA Expression and Time to Initial Therapy.

This analysis was performed as described in Example 12. All of the microRNAs which can predict the time to initial therapy, with the exception of mir-29c, are overexpressed in the group characterized by a short interval from diagnosis to initial therapy (Table 22). The PAM score for each of the components of microRNA signature associated with the time from diagnosis to initial therapy is presented in Table 23.

TABLE 22

Relative expression levels of microRNAs predictive of the
time interval from diagnosis to initial therapy.

	Short	Long
	interval	interval

	microarray expression

hsa-mir-181a	High	Low
hsa-mir-155	High	Low
hsa-mir-146	High	Low
hsa-mir-024-2	High	Low
hsa-mir-023b	High	Low
hsa-mir-023a	High	Low
hsa-mir-222	High	Low
hsa-mir-221	High	Low
hsa-mir-029c	Low	High

TABLE 23

PAM score for each of the components of microRNA signature
associated with the time from diagnosis to initial therapy.

		1 score	2 score

hsa-mir-181a	0.1862	−0.0603
hsa-mir-155	0.1409	−0.0456
hsa-mir-146	0.07	−0.0227
hsa-mir-024-2	0.0696	−0.0225
hsa-mir-023b	0.0643	−0.0208
hsa-mir-023a	0.0587	−0.019
hsa-mir-222	0.0458	−0.0148
hsa-mir-221	0.0343	−0.0111
hsa-mir-029c	−0.0221	0.0072

Note:
Score 1 characterize the short time;
score 2 the long time from diagnosis to initial therapy in a panel of 94 CLL patients.

The relevant teachings of all publications cited herein that have not explicitly been incorporated by reference, are incorporated herein by reference in their entirety. One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the ends and advantages mentioned, as well as those inherent therein. The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof and, accordingly, reference should be made to the appended claims, rather than to the foregoing specification, as indicating the scope of the invention.

Claims

1. A method of determining increased risk of a human subject developing B-cell chronic lymphocytic leukemia (CLL) or increased likelihood of the presence of CLL in a human subject comprising the steps of:

(i) measuring in a blood sample from the subject the level of one or more microRNAs selected from the group consisting of miR-029a microRNA, miR-029b-2 microRNA and miR-029c microRNA; and

(ii) comparing the level of the one or more microRNAs in the sample to a control level of the one or more microRNAs,

wherein an increase in the level of the one or more microRNAs in the sample relative to the control level of the one or more microRNAs is indicative of the subject either having increased risk of developing CLL or increased likelihood of the presence of CLL.

2. The method of claim 1, wherein the level of the one or more microRNAs in the sample is measured using an assay selected from the group consisting of northern blot analysis, in situ hybridization and quantitative reverse transcriptase polymerase chain reaction.

3. The method of claim 1, wherein the subject has an unmutated IgV_Hgene, positive ZAP-70 expression, or a combination thereof.

4. The method of claim 1, wherein the miR-029a microRNA comprises SEQ ID NO:67 or SEQ ID NO:68.

5. The method of claim 1, wherein the miR-029a microRNA comprises nucleotides 41-62 of SEQ ID NO:68.

6. The method of claim 1, wherein the miR-029b-2 microRNA comprises SEQ ID NO:251.

7. The method of claim 1, wherein the miR-029b-2 microRNA comprises nucleotides 51-70 of SEQ ID NO:251.

8. The method of claim 1, wherein the miR-029c microRNA comprises SEQ ID NO:69.

9. The method of claim 1, wherein the miR-029c microRNA comprises nucleotides 64-85 of SEQ ID NO:69.

10. A method of determining increased risk of a human subject developing B-cell chronic lymphocytic leukemia (CLL) or increased likelihood of the presence of CLL in a human subject comprising the steps of:

(1) reverse transcribing one or more microRNAs selected from the group consisting of miR-029a microRNA, miR-029b-2 microRNA and miR-029c microRNA from a blood sample from the subject to provide at least one target oligodeoxynucleotide for each of the one or more microRNAs;

(2) hybridizing the at least one target oligodeoxynucleotide for each of the one or more microRNAs to a microarray comprising miRNA-specific probe oligonucleotides that include at least one microRNA-specific probe oligonucleotide for each of the one or more microRNAs to provide a hybridization profile for the sample, wherein the hybridization profile includes a hybridization signal for each of the one or more microRNAs; and

(3) comparing the sample hybridization profile to a control hybridization profile, wherein the control hybridization profile includes a control hybridization signal for each of the one or more microRNAs,

wherein a hybridization signal of the one or more microRNAs in the sample hybridization profile that is greater than the control hybridization signal for the microRNA in the control hybridization profile indicates the subject has increased risk of developing CLL or increased likelihood of the presence of CLL.

11. The method of claim 10, wherein the microarray comprises miRNA-specific probes oligonucleotides for a substantial portion of the human miRNome.

12. The method of claim 10, wherein the subject has an unmutated IgV_Hgene, positive ZAP-70 expression, or a combination thereof.

13. A method of determining increased risk of a human subject developing B-cell chronic lymphocytic leukemia (CLL) associated with one or more adverse prognostic markers or increased likelihood of the presence of CLL associated with one or more adverse prognostic markers in a human subject comprising the steps of:

wherein a hybridization signal of the one or more microRNAs in the sample hybridization profile that is greater than the control hybridization signal for the microRNA in the control hybridization profile indicates the subject has increased risk of developing CLL associated with one or more adverse prognostic markers or increased likelihood of the presence of CLL associated with one or more adverse prognostic markers.

14. The method of claim 13, wherein the one or more adverse prognostic markers are selected from the group consisting of: positive ZAP-70 expression; and an unmutated IgV_Hgene; or a combination thereof.

15. The method of claim 13, wherein the subject has an unmutated IgV_Hgene, positive ZAP-70 expression, or a combination thereof.