WO2011130385A1

WO2011130385A1 - Biomarkers for hepatocellular cancer

Info

Publication number: WO2011130385A1
Application number: PCT/US2011/032285
Authority: WO
Inventors: Xin Wei Wang; Anuradha Budhu
Original assignee: The United States Of America, As Represented By The Secretary, Department Of Health And Human Servic
Priority date: 2010-04-13
Filing date: 2011-04-13
Publication date: 2011-10-20

Abstract

Disclosed are various biomarkers of hepatocellular cancer (HCC). The present invention also provides various methods of using the biomarkers, including methods for diagnosis of HCC, methods of determining predisposition to HCC, methods of monitoring progression/regression of HCC, methods of assessing efficacy of compositions for treating HCC, as well as other methods based on biomarkers of HCC.

Description

BIOMARKERS FOR HEPATOCELLULAR CANCER

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority under 35 U.S.C. § 119(e) from U.S. Provisional Application No. 61/323,420, filed April 13, 2010, which is incorporated herein by reference in its entirety.

REFERENCE TO A SEQUENCE LISTING

This application contains a Sequence Listing submitted electronically as a text file. The text file, named "6137NCI-28-PCT_seq_listing_ST25.txt," has a size in bytes of 1KB, and was recorded on April 13, 2010. The information contained in the text file is incorporated herein by reference in its entirety pursuant to 37 CFR § 1.52(e)(5).

FIELD OF THE INVENTION

The present invention generally relates to biomarkers, methods and assay kits for the identification of hepatocellular cancer patients predicted to respond to chemotherapy.

BACKGROUND OF THE INVENTION

Hepatocellular carcinoma (HCC) represents an extremely poor prognostic cancer that remains one of the most common and aggressive malignancies worldwide. With current diagnostic methods, HCC patients are often diagnosed with end-stage cancer and have poor survival. In addition, treatment options remain minimal and relatively unsuccessful and thus patient outcome remains dismal. HCC is also a very heterogeneous disease, which adds to the difficulty of clinical diagnosis and prognosis. HCC heterogeneity is thought to occur via lineage-specific tumor subtypes, some of which retain stem-cell features making them highly aggressive form of this disease. Therefore, there is a need for an accurate method to identify HCC and its proclivity for metastases/relapse, particularly at early stages of this disease.

SUMMARY OF THE INVENTION

The present invention relates to biomarkers of HCC, methods for diagnosis of HCC, methods of determining predisposition to HCC, methods of monitoring progression/regression of HCC, methods of assessing efficacy of compositions for treating HCC, methods of screening compositions for activity in modulating biomarkers of HCC, methods of treating HCC, as well as other methods based on biomarkers of HCC. In one embodiment, the invention provides a method for determining if a subject has hepatocellular cancer (HCC), the method comprising analyzing a biological sample from a subject to determine the level of a marker or plurality of markers for hepatocellular cancer in the sample, wherein the one or more markers are selected from the group consisting of: i) markers selected from Tables 1, 2, 3 and/or 4; ii) fragments of markers selected from Tables 1, 2, 3 and/or 4; iii) successors of markers selected from Tables 1, 2, 3 and/or 4; iv) modified versions of markers selected from Tables 1, 2, 3 and/or 4; combinations of markers of i), ii) iii) and iv); and comparing the level of the marker or plurality of markers in the sample to hepatocellular cancer-positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of markers to diagnose whether the subject has hepatocellular cancer.

In certain embodiments, the marker or plurality of markers are selected from the group consisting of: i) markers selected from Table 2; ii) fragments of markers selected from Table 2; iii) successors of markers selected from Table 2; iv) modified versions of markers selected from Table 2; combinations of markers of i), ii) iii) and iv); and a plurality of markers comprising the marker set of N-acetylasparagine, pipecolate, kynurenine, tryptophan, valerylcarnitine, glucarate (saccharate), scyllo-inositol, caproate (6:0), 1-oleoylglycerol (1-monoolein), adenosine adenosine 5 '-monophosphate (AMP), and glycylleucine.

In some embodiments, the hepatocellular cancer is early stage hepatocellular cancer (TNM stage I).

In some embodiments, the markers are selected from the group consisting of N- acetylasparagine, kynurenine, valerylcarnitine and combinations of the foregoing, and wherein these markers are upregulated compared to hepatocellular cancer-negative reference levels.

In other embodiments, the markers are selected from the group consisting of: 2- oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, and both 2- oleoylglycerophosphocholine and 2-linoleoylglycerophosphocholine.

In some embodiments, the markers are selected from the group consisting of: i) markers selected from Table 4; ii) fragments of markers selected from Table 4; iii) successors of markers selected from Table 4; iv) modified versions of markers selected from Table 4; combinations of markers of i), ii) iii) and iv); and a plurality of markers comprising the marker set of 4-methyl-2-oxopentanoate, glucuronate, Isobar: fructose 1,6- diphosphate, glucose 1 ,6-diphosphate, 2-phosphoglycerate, 6-phosphogluconate, sedoheptulose-7-phosphate, heme, palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], stearidonate (18:4n3), palmitoleate (16: ln7), eicosenoate (20: ln9 or 11), docosadienoate (22:2n6), dihomo-linoleate (20:2n6), oleate (18:ln9), 10- heptadecenoate (17: ln7), linoleate (18:2n6), myristoleate (14: ln5), cis-vaccenate (18: ln7), palmitate (16:0),

1 -linoleoylglycerophosphoethanolamine, 2-linoleoylglycerophosphoethanolamine, 1 -stearoylglycerophosphoethanolamine, 1 -arachidonoylglycerophosphoethanolamine, 2-linoleoylglycerophosphocholine, 2-palmitoylglycerophosphocholine,

2-arachidonoylglycerophosphocholine, 1-palmitoylglycerophosphocholine,

2-oleoylglycerophosphocholine, adenosine 5 '-diphosphate (ADP), adenosine

5 '-monophosphate (AMP), 3-aminoisobutyrate, uridine, ophthalmate, glutathione, reduced (GSH), taurocholenate sulfate, and galacturonate.

In some embodiments, the hepatocellular cancer-positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of markers are associated with hepatic stem cell (HpSC) HCC subtype, mature hepatocyte (MH) HCC subtype, or both.

The invention also provides a method for determining patient outcome in hepatocellular cancer, the method comprising analyzing a biological sample from a subject to determine the level(s) of one or more markers for hepatocellular cancer in the sample, wherein the one or more markers are selected from the group consisting of: i) markers selected from Table 3; ii) fragments of markers selected from Table 3; iii) successors of markers selected from Table 3; iv) modified versions of markers selected from Table 3; combinations of markers of i), ii) iii) and iv); and a plurality of markers comprising the marker set of glucuronate, 6-phosphogluconate, palmitoleate (16: ln7), palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], linoleate (18:2n6), stearidonate (18:4n3), dihomo-linoleate (20:2n6), docosadienoate (22:2n6), eicosenoate (20: ln9 or 11),

1- linoleoylglycerophosphoethanolamine, 2-oleoylglycerophosphocholine,

2- linoleoylglycerophosphocholine, adenosine 5 '-monophosphate (AMP), adenosine

5 '-diphosphate (ADP), 3-aminoisobutyrate, uridine, ophthalmate, ergothioneine, and galacturonate.

In some embodiments, the level of the marker or plurality of markers in the sample as compared to hepatocellular cancer-positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of markers is indicative of heptaocellular cancer outcome. In some embodiments, the markers are selected from the group consisting of:

2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, and both

2-oleoylglycerophosphocholine and 2-linoleoylglycerophosphocholine and downregulation of the markers compared to hepatocellular cancer-negative reference levels is associated with an increase in patient survival.

In some embodiments, the biological sample is a tumor tissue, and/or a body fluid, such as blood, serum, plasma, urine, or saliva.

In some embodiments, the standard level or reference range is determined according to a statistical procedure for risk prediction, such as using a Hazard ratio.

In some embodiments, the subject has a hepatitis B viral infection.

In other embodiments, the level of the marker or plurality of markers is detected with a reagent that specifically detects the marker or plurality of markers, such as an antibody, an antibody derivative, an antibody fragment, and/or an aptamer.

The invention also provides a method for monitoring the progression of heptaocellular cancer in a subject, the method comprising measuring the expression level of a marker or a plurality of markers in a first biological sample obtained from the subject, wherein the marker or plurality of markers comprise a plurality of markers selected from the group consisting of: markers i) selected from Tables 1, 2, 3 and/or 4; ii) fragments of markers selected from Tables 1, 2, 3 and/or 4; iii) successors of markers selected from Tables 1, 2, 3 and/or 4; iv) modified versions of markers selected from Tables 1, 2, 3 and/or 4; and combinations of markers of i), ii) iii) and iv); measuring the expression level of the marker or plurality of markers in a second biological sample obtained from the subject; and comparing the expression level of the marker or plurality of markers measured in the first sample with the level of the marker measured in the second sample.

In some embodiments, the first biological sample from the subject is obtained at a time t₀, and the second biological sample from the subject is obtained at a later time t . The first biological sample and the second biological sample may be obtained from the subject more than once over a range of times.

The invention further provides a method of assessing the efficacy of a treatment for heptaocellular cancer in a subject, the method comprising comparing: the expression level of a marker or plurality of markers measured in a first sample obtained from the subject at a time to, wherein the marker is selected from the group consisting of: i) markers selected from Tables 1, 2, 3 and/or 4; ii) fragments of markers selected from Tables 1, 2, 3 and/or 4; iii) successors of markers selected from Tables 1, 2, 3 and/or 4; iv) modified versions of markers selected from Tables 1, 2, 3 and/or 4; and combinations of markers of i), ii) iii) and iv); and the level of the marker or plurality of markers in a second sample obtained from the subject at time ti; wherein a change in the level of the marker or plurality of markers in the second sample relative to the first sample is an indication that the treatment is efficacious for treating heptaocellular cancer in the subject.

In some embodiments, the time to is before the treatment has been administered to the subject, and the time t is after the treatment has been administered to the subject. In some embodiments, the comparing is repeated over a range of times and in some embodiments, the time to is before the treatment has been administered to the subject, and the time ti is after the treatment has been administered to the subject.

The invention further provides a kit for heptaocellular cancer comprising a means to detect the expression of a marker or plurality of markers selected from the group consisting of: i) markers selected from Tables 1, 2, 3 and/or 4; ii) fragments of markers selected from Tables 1, 2, 3 and/or 4; iii) successors of markers selected from Tables 1, 2, 3 and/or 4; iv) modified versions of markers selected from Tables 1, 2, 3 and/or 4; and combinations of markers of i), ii) iii) and iv).

In some embodiments, the means to detect comprises binding ligands that specifically detect the markers. In some embodiments, the means to detect comprises binding ligands disposed on an assay surface. In some embodiments, the assay surface comprises a chip, array, or fluidity card. The binding ligands may comprise antibodies or binding fragments thereof.

In some embodiments, the assay system comprises a control selected from the group consisting of: information containing a predetermined control level of the marker or plurality of markers that has been correlated with good patient outcome; information containing a predetermined control level of the marker or plurality of markers that has been correlated with poor patient outcome; and both of the foregoing.

In some embodiments, the assay system comprises a control selected from the group consisting of information containing a predetermined control level of the marker or plurality of markers that has been correlated with associated with HpSC HCC subtype; information containing a predetermined control level of the marker or plurality of markers that has been correlated with MH HCC subtype; and both of the foregoing. Other features and advantages of the invention will become apparent to one of skill in the art from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1A-D show identification of tumor-related metabolites. In Fig. 1A, a VENN diagram is shown representing the results of class comparison analyses between tumor or nontumor tissue among hepatic stem cell (HpSC) HCC subtype tissues, mature hepatocyte (MH) HCC subtype tissues or among all HCC cases (ALL) at p<0.05 with 1000 permutations of the class label. In total there are 253 metabolites that were differentially expressed in tumor tissues. Fig. 1 B shows the number of upregulated, downregulated or total metabolites in the tumor vs. nontumor comparison among all superpathways or within each subpathway. Fig. 1C shows the number of upregulated, downregulated or total metabolites in the tumor vs. nontumor comparison among the lipid pathway. In Fig. ID, a receiver operator characteristic (ROC) curve is shown depicting the sensitivity and specificity of the 253 tumor- specific metabolites following class prediction analysis using the Bayesian compound covariate algorithm. AUC: Area under the curve.

Figure 2A-E show identification of early tumor-related metabolites. In Fig. 2A, a VENN diagram is shown representing the results of class comparison analyses between tumor or nontumor tissue among HCC with early (TNM stage I) or late disease (TNM stage II or III) at p<0.05 with 1000 permutations of the class label. In Fig. 2B, a receiver operator characteristic (ROC) curve is shown depicting the sensitivity and specificity of the 17 early tumor-specific metabolites following class prediction analysis using the Bayesian compound covariate algorithm. AUC: Area under the curve. Fig.2C shows a scatter plot depicting the expression levels of an amino acid-related metabolite, N- acetylasparagine, that is upregulated in tumor tissues of patients with early stage HCC. Data is shown as the mean +/- standard deviation. The cutoff for statistical significance is p<0.05. Fig. 2D shows a scatter plot depicting the expression levels of an amino acid- related metabolite, Kynurenine, that is upregulated in tumor tissues of patients with early stage HCC. Data is shown as the mean +/- standard deviation. The cutoff for statistical significance is p<0.05. Fig. 2E shows a scatter plot depicting the expression levels of an amino acid-related metabolite, Valerylcarnatine, that is upregulated in tumor tissues of patients with early stage HCC. Data is shown as the mean +/- standard deviation. The cutoff for statistical significance is p<0.05. Figure 3A-E shows identification of prognostic metabolites. In Fig. 1C, a VENN diagram is shown representing the results of class comparison analyses between hepatic stem cell (HpSC) HCC subtype tumor tissues and mature hepatocyte (MH) HCC subtype tumor tissues with that of tumor or nontumor tissue at p<0.05 with 1000 permutations of the class label. The 28 metabolites from the class comparison analysis above, were tested for their association with outcome based on Cox regression analysis. Fig. 3B shows a Kaplan Meier curve to plot the association of lysolipids 2-oleoylglycerophosphocholine with outcome. High and low risk designation was made using a median cutoff of metabolite expression. The log-rank p-value is shown. Fig. 3C shows a Kaplan Meier curve to plot the association of lysolipids linoleoylglycerophosphocholine with outcome. High and low risk designation was made using a median cutoff of metabolite expression. The log-rank p-value is shown. Fig. 3D shows a Kaplan Meier curve of overall survival of HCC patients sub-grouped based on the combined expression of the lysolipids in Fig. 3B and Fig. 3C. High and low risk designation was made using a median cutoff of metabolite expression. The log-rank p-value is shown.

Figure 4 shows schematically the HCC metbaolomics study design.

Figure 5 is a Venn diagram showing tumor-specific metabolites for the class comparison of Tumor vs. Nontumor, p<0.05; FDR<20%.

Figure 6 shows the expression analysis of 28 tumor and subtype-specific metabolites in tumor and nontumor tissue. A S-Plot is shown representing the results of class comparison analyses comparing the expression of 28 prognostic metabolites between tumor or nontumor tissue among hepatic stem cell (HpSC) HCC subtype tissues (gray dots) or mature hepatocyte (MH) HCC subtype tissues (black dots) at p<0.05 with 1000 permutations of the class label.

Figure 7 shows the hierarchical clustering of integrated metabolite and gene surrogate signatures. A hierarchical cluster is shown representing the correlation between metabolites and their gene surrogates (gray: positive correlation; black: negative correlation).

Figure 8 shows the survival analysis based on gene-surrogate expression. A Kaplan-Meier survival curve demonstrates that the gene surrogates of tumor and survival- related metabolites are significantly associated with patient survival.

Figure 9 shows the top networks resulting from the pathway analysis of gene surrogates. Ingenuity pathway analysis (IP A) of metabolite-related gene surrogates was used to identify the signaling pathways associated with these genesets. The PI3K and MYC pathways were top networks associated with these genes. The shaded gene symbols are those imported into IPA from the original gene surrogate list.

Figure 10 shows the global search for gene surrogates of 6 fatty acid metabolites. The schematic shows the correlation analysis between gene expression and metabolite expression for the fatty acid metabolites represented in cluster 1 in Fig. 7. Filtering criteria was employed to determine the most correlated metabolite-gene pairs, which were then tested for their capacity to predict survival outcome in two independent cohorts.

Figure 11A-B shows survival analysis of fatty acid-related gene surrogates in two cohorts. These figures (11A and 11B) are Kaplan-Meier survival curves demonstrating that gene surrogates for fatty acid metabolites are significantly associated with patient survival in two independent cohorts, LCI and LEC, respectively. In both figures, the darker (upper) curves are the survival curves of the low risk cohort and the lighter (lower) curves are the high risk cohorts.

DETAILED DESCRIPTION OF THE INVENTION

The present inventors have discovered biological markers whose presence and measurement levels are indicative of hepato-cellular carcinoma (HCC). The present inventors have discovered metabolites that are differentially expressed in biological samples obtained from hepatocellular cancer subjects and that have been compared to clinical outcomes. The levels and activities of these markers, along with clinical parameters, can be used as biological markers indicative of hepatocellular cancer, including early stage (TNM stage I). The invention also relates to the identification of a number of metabolites and related molecules that are expressed in patients with hepatocellular cancer and that discriminate between patients having a high and low probability of survival. The biomarkers include low molecular weight molecules.

According to one definition, a biological marker ("biomarker" or "marker") is "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacological responses to therapeutic interventions." NIH Biomarker Definitions Working Group (1998). Biomarkers can also include patterns or ensembles of characteristics indicative of particular biological processes. The biomarker measurement can increase or decrease to indicate a particular biological event or process. In addition, if a biomarker measurement typically changes in the absence of a particular biological process, a constant measurement can indicate occurrence of that process.

The terminology used herein is for describing particular embodiments and is not intended to be limiting. As used herein, the singular forms "a," "and" and "the" include plural referents unless the content and context clearly dictate otherwise. Thus, for example, a reference to "a marker" includes a combination of two or more such markers. Unless defined otherwise, all scientific and technical terms are to be understood as having the same meaning as commonly used in the art to which they pertain. For the purposes of the present invention, the following terms are defined below.

As used herein, the term "marker" includes metabolite or small molecule markers.

Metabolite or small molecule means organic and inorganic molecules which are present in a cell. The term does not include large macromolecules, such as large proteins (e.g., proteins with molecular weights over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), large nucleic acids (e.g., nucleic acids with molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000), or large polysaccharides (e.g., polysaccharides with a molecular weights of over 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). The small molecules of the cell are generally found free in solution in the cytoplasm or in other organelles, such as the mitochondria, where they form a pool of intermediates which can be metabolized further or used to generate large molecules, called macromolecules. The term "small molecules" includes signaling molecules and intermediates in the chemical reactions that transform energy derived from food into usable forms. Examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed during cellular processes, and other small molecules found within the cell.

Marker measurements may be of the absolute values (e.g., the molar concentration of a molecule in a biological sample) or relative values (e.g., the relative concentration of two molecules in a biological sample). The quotient or product of two or more measurements also may be used as a marker. For example, some physicians use the total blood cholesterol as a marker of the risk of developing coronary artery disease, while others use the ratio of total cholesterol to HDL cholesterol.

In the invention, the markers are primarily used for diagnostic and prognostic purposes. However they may also be used for therapeutic, drug screening and patient stratification purposes (e.g., to group patients into a number of "subsets" for evaluation), as well as other purposes described herein, including evaluation of the effectiveness of a hepatocellular cancer therapeutic.

The present invention is based on the findings of a study designed to identify biological markers for HCC. Samples of tumor tissue from patients with HCC who underwent surgical resection were analyzed using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. The HCC patients had hepatitis B viral infection. Metabolomic profiling includes tissue procurement, histopathological examination, metabolite extraction and separation, mass spectrometry-based detection, spectral analysis, data normalization, delineation of class-specific metabolites and altered pathways, validation of class-specific metabolites and their functional characterization. The term metabolomics refers to the study of cellular metabolites. Metabolites are products of altered pathways in cancer and are released into circulatory blood and urine which can serve as excellent non-invasive diagnostic/prognostic markers.

The markers of the present invention were identified by comparing the levels measured in tumor samples obtained from HCC patients with the levels measured in non- tumor samples obtained from the same patients. Measurement values of the biomarkers were found to differ in biological samples from patients with HCC as compared to biological samples from normal controls. In preferred embodiments, such differences were statistically significant. Accordingly, it is believed that these biomarkers are indicators of HCC.

The present invention includes all methods relying on correlations between the biomarkers described herein and the presence of HCC, early stage HCC, and HCC having stem cell features. In a preferred embodiment, the invention provides methods for determining whether a candidate drug is effective at treating HCC by evaluating the effect it has on the biomarker values. In this context, the term "effective" is to be understood broadly to include reducing or alleviating the signs or symptoms of HCC, improving the clinical course of the disease, or reducing any other objective or subjective indicia of the disease. Different drugs, doses and delivery routes can be evaluated by performing the method using different drug administration conditions. The method may also be used to compare the efficacy of two different drugs or other treatments or therapies for HCC.

It is expected that the biomarkers described herein will be measured in combination with other signs, symptoms and clinical tests of HCC, such as CT scans or HCC biomarkers reported in the literature. Likewise, more than one of the biomarkers of the present invention may be measured in combination. Measurement of the biomarkers of the invention along with any other markers known in the art, including those not specifically listed herein, falls within the scope of the present invention.

The practice of the invention employs, unless otherwise indicated, conventional methods of analytical biochemistry, microbiology, molecular biology and recombinant DNA techniques generally known within the skill of the art. Such techniques are explained fully in the literature. (See, e.g., Sambrook et al. Molecular Cloning: A Laboratory Manual. 3rd, ed., Cold Spring Harbor Laboratory, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 2000; DNA Cloning: A Practical Approach, Vol. I & II (Glover, ed.); Oligonucleotide Synthesis (Gait, ed., Current Edition); Nucleic Acid Hybridization (Hames & Higgins, eds., Current Edition); Transcription and Translation (Hames & Higgins, eds., Current Edition); CRC Handbook of Parvoviruses, Vol. I & II (Tijessen, ed.); Fundamental Virology, 2nd Edition, Vol. I & II (Fields and Knipe, eds.)).

As used herein, a component (e.g., a marker) is referred to as "differentially expressed" in one sample as compared to another sample when the method used for detecting the component provides a different level or activity when applied to the two samples. A component is referred to as "increased" or "upregulated" in the first sample if the method for detecting the component indicates that the level or activity of the component is higher in the first sample than in the second sample (or if the component is detectable in the first sample but not in the second sample). Conversely, a component is referred to as "decreased" or "downregulated" in the first sample if the method for detecting the component indicates that the level or activity of the component is lower in the first sample than in the second sample (or if the component is detectable in the second sample but not in the first sample). In particular, marker is referred to as "increased" ("upregulated") or "decreased" ("downregulated") in a sample (or set of samples) obtained from a hepatocellular cancer subject (or a subject who is suspected of having hepatocellular cancer, or is at risk of developing hepatocellular cancer) if the level or activity of the marker is higher or lower, respectively, compared to the level of the marker in a sample (or set of samples) obtained from a non-hepatocellular cancer subject, or a reference value or range.

A number of comparison studies were performed to identify the markers listed using various groups of hepatocellular cancer and non-hepatocellular cancer patients. Tables 1-4 list markers that were found to be differentially expressed with statistical significance. Accordingly, it is believed that these biomarkers are indicators of hepatocellular cancer. Particularly preferred markers of the invention include acetylasparagine, kynurenine, valerylcarnitine, 2-oleoylglycerophosphocholine, 2- linoleoylglycerophosphocholine, and combinations of the foregoing. Accordingly, in one aspect, the invention provides biomarkers of hepatocellular cancer. In one embodiment, the invention provides an isolated component listed in Tables 1-4. As used herein, a compound is referred to as "isolated" when it has been separated from at least one component with which it is naturally associated. For example, a metabolite can be considered isolated if it is separated from contaminants including polypeptides, polynucleotides and other metabolites. Isolated molecules can be either prepared synthetically or purified from their natural environment. Standard quantification methodologies known in the art can be employed to obtain and isolate the molecules of the invention.

In particular, the inventors have discovered unique sets of metabolite biomarkers that are associated with HCC, early stage HCC, HCC outcome and an HCC stem-cell subtype. The HCC metabolite signature can discriminate HCC tumors from nontumorous tissue with 88-97 percent accuracy (sensitivity and specificity: 0.76-1.0; positive and negative predictive values: 0.81-1.0) with permutation p-values <0.0001 among seven separate class prediction algorithms. (Fig. 1A and Table 1 : 253 Diagnostic HCC Metabolites). The majority of 253 metabolites are downregulated in tumors (Fig. IB), however among certain pathways such as the lipid pathway, the majority of metabolites are upregulated in tumors (Fig. 1C). Receiver operator characteristic (ROC) curves for the 253 diagnostic metabolites is shown in Fig. ID. A subset of these metabolites can make this determination even in those patients with early disease defined as TNM stage I with 62-78 percent accuracy (sensitivity and specificity: 0.53-0.9; positive and negative predictive values: 0.65-0.89) with permutation p-values <0.0001 among seven separate class prediction algorithms. (Fig. 2A and Table 2: 17 Early Diagnostic HCC Metabolites). An ROC for the 17 early diagnostic metabolites is shown in Fig. 2B. Among the 17 early diagnostic metabolites, three amino-acid related metabolites are upregulated in tumor tissues (Fig. 2C, D and E). In addition, metabolites were found that can predict HCC patient outcome (28 Prognostic HCC Metabolites) with hazard ratios between 0.49 and 1.54 (Fig. 3A and Table 3). Among the prognostic metabolites, 2 lysolipid metabolites are significantly associated with outcome and can be used in concert to increase prognostic accuracy (Fig. 3B, C and D). Moreover, a set of metabolites can distinguish HCC associated with stem cell features (Table 4) with 70-77 percent accuracy (sensitivity and specificity: 0.6-0.87; positive and negative predictive values: 0.72-0.83) with permutation p-values < 0.01 among seven separate class prediction algorithms.

In one embodiment, the invention provides a marker or plurality of markers of hepatocellular cancer in which the marker or plurality of markers is selected from Table 1 , is a fragment, precursor, successor, or modified version of a marker or plurality of markers of Table 1, or combinations of any of these markers.

In one embodiment, the invention provides a marker or plurality of markers of early stage hepatocellular cancer defined as TNM stage I, in which the marker or plurality of markers is selected from Table 2, is a fragment, precursor, successor, or modified version of a marker or plurality of markers of Table 2, or combinations of any of these markers. In one embodiment, the marker or plurality of markers comprises N- acetylasparagine, kynurenine, valerylcarnitine, and combinations of the foregoing.

In one embodiment, the invention provides a marker or plurality of markers of hepatocellular cancer which are associated with patient survival, in which the marker or plurality of markers is selected from Table 3, is a fragment, precursor, successor, or modified version of a marker or plurality of markers of Table 3, or combinations of any of these markers. In one embodiment, the markers are 2-oleoylglycerophosphocholine, 2- linoleoylglycerophosphocholine, or both 2-oleoylglycerophosphocholine and 2- linoleoylglycerophosphocholine.

In one embodiment, the invention provides a marker or plurality of markers of hepatocellular cancer associated with stem-cell features and thus aggressive forms of HCC, in which the marker or plurality of markers is selected from Table 4, is a fragment, precursor, successor, or modified version of a marker or plurality of markers of Table 4, or combinations of any of these markers.

Some variation is inherent in the measurements of the physical and chemical characteristics of the markers. The magnitude of the variation depends to some extent on the reproducibility of the separation means and the specificity and sensitivity of the detection means used to make the measurement. Preferably, the method and technique used to measure the markers is sensitive and reproducible. Markers corresponding to the markers identified in Tables 1-4 reflect a single marker appearing in a database for which the component was a match. Such a selection is not meant to limit the marker to those corresponding to the markers disclosed in Tables 1- 4. Accordingly, in another embodiment, the invention provides a marker that is a fragment, precursor, successor or modified version of a marker described in Tables 1-4. In another embodiment, the invention includes a molecule that comprises a foregoing fragment, precursor, successor or modified polypeptide.

Another embodiment of the present invention relates to an assay system including a plurality of antibodies, or antigen binding fragments thereof, or aptamers for the detection of the expression of biomarkers differentially expressed in patients with hepatocellular cancer. The plurality of antibodies, or antigen binding fragments thereof, or aptamers consist of antibodies, or antigen binding fragments thereof, or aptamers that selectively bind to proteins differentially expressed in patients with hepatocellular cancer, and that can be detected as protein products using antibodies or aptamers. In addition, the plurality of antibodies, or antigen binding fragments thereof, or aptamers comprise antibodies, or antigen binding fragments thereof, or aptamers that selectively bind to proteins or portions thereof (e.g., peptides) encoded by any of the genes from the tables provided herein.

Certain embodiments of the present invention utilize a plurality of biomarkers that have been identified herein as being differentially expressed in subjects with hepatocellular cancer. As used herein, the terms "patient," "subject" and "a subject who has hepatocellular cancer" and "hepatocellular cancer subject" are intended to refer to subjects who have been diagnosed with hepatocellular cancer. The terms "non-subject" and "a subject who does not have hepatocellular cancer" are intended to refer to a subject who has not been diagnosed with hepatocellular cancer, or who is cancer-free as a result of surgery to remove the diseased tissue. A non-hepatocellular cancer subject may be healthy and have no other disease, or they may have a disease other than hepatocellular cancer.

The plurality of biomarkers within the above-limitation includes at least two or more biomarkers (e.g., at least 2, 3, 4, 5, 6, and so on, in whole integer increments, up to all of the possible biomarkers) identified by the present invention, and includes any combination of such biomarkers. Such biomarkers are selected from any of the markers listed in the Tables provided herein. In a preferred embodiment, the plurality of biomarkers used in the present invention includes all of the biomarkers in the marker set that has been demonstrated to be predictive of survival (high or low; also referred to as "good outcome" and "poor outcome" herein) in a hepatocellular cancer patient.

The markers of the invention are useful in methods for diagnosing hepatocellular cancer, determining the extent and/or severity of the disease, monitoring progression of the disease and/or response to therapy. Such methods can be performed in human and non-human subjects. The markers are also useful in methods for treating hepatocellular cancer and for evaluating the efficacy of treatment for the disease. Such methods can be performed in human and non-human subjects. The markers may also be used as pharmaceutical compositions or in kits. The markers may also be used to screen candidate compounds that modulate their expression. The markers may also be used to screen candidate drugs for treatment of hepatocellular cancer. Such screening methods can be performed in human and non-human subjects.

Markers may be isolated by any suitable method known in the art. Markers can be purified from natural sources by standard methods known in the art (e.g., chromatography, centrifugation, differential solubility, immunoassay). In one embodiment, markers may be isolated from a biological sample using the methods disclosed herein. In another embodiment, polypeptide markers may be isolated from a sample by contacting the sample with substrate-bound antibodies or aptamers that specifically bind to the markers.

The present invention also encompasses molecules which specifically bind the markers of the present invention. As used herein, the term "specifically binding," refers to the interaction between binding pairs (e.g., an antibody and an antigen or aptamer and its target). In some embodiments, the interaction has an affinity constant of at most 10^"6 moles/liter, at most 10^"7 moles/liter, or at most 10^"8 moles/liter. In other embodiments, the phrase "specifically binds" refers to the specific binding of one protein to another (e.g., an antibody, fragment thereof, or binding partner to an antigen), wherein the level of binding, as measured by any standard assay (e.g., an immunoassay), is statistically significantly higher than the background control for the assay. For example, when performing an immunoassay, controls typically include a reaction well/tube that contain antibody or antigen binding fragment alone (i.e., in the absence of antigen), wherein an amount of reactivity (e.g., non-specific binding to the well) by the antibody or antigen binding fragment thereof in the absence of the antigen is considered to be background. Binding can be measured using a variety of methods standard in the art including enzyme immunoassays (e.g., ELISA), immunoblot assays, etc.).

The binding molecules include antibodies, aptamers and antibody fragments. As used herein, the term "antibody" refers to an immunoglobulin molecule capable of binding an epitope present on an antigen. The term is intended to encompasses not only intact immunoglobulin molecules such as monoclonal and polyclonal antibodies, but also bi- specific antibodies, humanized antibodies, chimeric antibodies, anti-idiopathic (anti-ID) antibodies, single-chain antibodies, Fab fragments, F(ab') fragments, fusion proteins and any modifications of the foregoing that comprise an antigen recognition site of the required specificity. As used herein, an aptamer is a non-naturally occurring nucleic acid having a desirable action on a target. A desirable action includes, but is not limited to, binding of the target, catalytically changing the target, reacting with the target in a way which modifies/alters the target or the functional activity of the target, covalently attaching to the target as in a suicide inhibitor, facilitating the reaction between the target and another molecule, in the preferred embodiment, the action is specific binding affinity for a target molecule, such target molecule being a three dimensional chemical structure other than a polynucleotide that binds to the nucleic acid ligand through a mechanism which predominantly depends on Watson/Crick base pairing or triple helix binding, wherein the nucleic acid ligand is not a nucleic acid having the known physiological function of being bound by the target molecule.

In one aspect, the invention provides antibodies or aptamers that specifically bind to a component listed in Tables 1-4, or to a molecule that comprises a foregoing component (e.g., a protein comprising a polypeptide or dipeptide identified in a table of the invention). In another embodiment, the invention provides antibodies or aptamers that specifically bind to a component that is a fragment, modification, precursor or successor of a marker described in Tables 1-4, or to a molecule that comprises a foregoing component.

Another embodiment of the present invention relates to a plurality of aptamers, antibodies, or antigen binding fragments thereof, for the detection of the expression of biomarkers differentially expressed in patients with hepatocellular cancer. The plurality of aptamers, antibodies, or antigen binding fragments thereof, consists of antibodies, or antigen binding fragments thereof, that selectively bind to proteins differentially expressed in patients with hepatocellular cancer, and that can be detected using antibodies or aptamers. In addition, the plurality of aptamers, antibodies, or antigen binding fragments thereof, comprises antibodies, or antigen binding fragments thereof, that selectively bind to proteins or portions thereof (peptides) encoded by any of the genes from the tables provided herein.

According to the present invention, a plurality of aptamers, antibodies, or antigen binding fragments thereof, refers to at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, and so on, in increments of one, up to any suitable number of antibodies, or antigen binding fragments thereof, including, in one embodiment, antibodies representing all of the biomarkers described herein, or antigen binding fragments thereof.

Certain antibodies that specifically bind polypeptide markers or polynucleotide markers of the invention may already be known and/or available for purchase from commercial sources. In any event, the antibodies of the invention may be prepared by any suitable means known in the art. For example, antibodies may be prepared by immunizing an animal host with a marker or an immunogenic fragment thereof (conjugated to a carrier, if necessary). Adjuvants (e.g., Freund's adjuvant) optionally may be used to increase the immunological response. Sera containing polyclonal antibodies with high affinity for the antigenic determinant can then be isolated from the immunized animal and purified.

Alternatively, antibody-producing tissue from the immunized host can be harvested and a cellular homogenate prepared from the organ can be fused to cultured cancer cells. Hybrid cells which produce monoclonal antibodies specific for a marker can be selected. Alternatively, the antibodies of the invention can be produced by chemical synthesis or by recombinant expression. For example, a polynucleotide that encodes the antibody can be used to construct an expression vector for the production of the antibody. The antibodies of the present invention can also be generated using various phage display methods known in the art.

Antibodies or aptamers that specifically bind markers of the invention can be used, for example, in methods for detecting components listed in Tables 1-4 using methods and techniques well-known in the art. In some embodiments, for example, the antibodies are conjugated to a detection molecule or moiety (e.g., a dye, and enzyme) and can be used in ELISA or sandwich assays to detect markers of the invention.

In another embodiment, antibodies or aptamers against a polypeptide marker or polynucleotide marker of the invention can be used to assay a tissue sample (e.g., a hepatocellular tumor tissue) for the marker. The antibodies or aptamers can specifically bind to the marker, if any, present in the tissue sections and allow the localization of the marker in the tissue. Similarly, antibodies or aptamers labeled with a radioisotope may be used for in vivo imaging or treatment applications.

Another aspect of the invention provides compositions comprising a marker of the invention, a binding molecule that is specific for a marker (e.g., an antibody or an aptamer), an inhibitor of a marker, or other molecule that can increase or decrease the level or activity of a polypeptide marker or polynucleotide marker. Such compositions may be pharmaceutical compositions formulated for use as a therapeutic.

Alternatively, the invention provides a composition that comprises a component that is a fragment, modification, precursor or successor of a marker described in Tables 1- 4, or to a molecule that comprises a foregoing component.

In another embodiment, the invention provides a composition that comprises an antibody or aptamer that specifically binds to a polypeptide or a molecule that comprises a foregoing antibody or aptamer.

The present invention also provides methods of detecting the biomarkers of the present invention. The practice of the present invention employs, unless otherwise indicated, conventional methods of analytical biochemistry, microbiology, molecular biology and recombinant DNA techniques within the skill of the art. Such techniques are explained fully in the literature. (See, e.g., Sambrook, J. et al. Molecular Cloning: A Laboratory Manual. 3rd, ed., Cold Spring Harbor Laboratory, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 2000; DNA Cloning: A Practical Approach, Vol. I & II (D. Glover, ed.); Oligonucleotide Synthesis (N. Gait, ed., Current Edition); Nucleic Acid Hybridization (B. Hames & S. Higgins, eds., Current Edition); Transcription and Translation (B. Hames & S. Higgins, eds., Current Edition); CRC Handbook of Parvoviruses, Vol. I & II (P. Tijessen, ed.); Fundamental Virology, 2nd Edition, Vol. I & II (B. N. Fields and D. M. Knipe, eds.)).

The markers of the invention may be detected by any method known to those of skill in the art, including without limitation LC-MS, GC-MS, immunoassays, hybridization and enzyme assays. The detection may be quantitative or qualitative. A wide variety of conventional techniques are available, including mass spectrometry, chromatographic separations, 2-D gel separations, binding assays (e.g., immunoassays), competitive inhibition assays, and so on. Any effective method in the art for measuring the presence/absence, level or activity of a marker is included in the invention. It is within the ability of one of ordinary skill in the art to determine which method would be most appropriate for measuring a specific marker. Thus, for example, an ELISA assay may be best suited for use in a physician's office while a measurement requiring more sophisticated instrumentation may be best suited for use in a clinical laboratory. Regardless of the method selected, it is important that the measurements be reproducible.

The markers of the invention can be measured by mass spectrometry, which allows direct measurements of analytes with high sensitivity and reproducibility. A number of mass spectrometric methods are available. As will be appreciated by one of skill in the art, many separation technologies may be used in connection with mass spectrometry. For example, a wide selection of separation columns is commercially available. In addition, separations may be performed using custom chromatographic surfaces (e.g., a bead on which a marker specific reagent has been immobilized). Molecules retained on the media subsequently may be eluted for analysis by mass spectrometry.

In other embodiments, the level of the markers may be determined using a standard immunoassay, such as sandwiched ELISA using matched antibody pairs and chemiluminescent detection. Commercially available or custom monoclonal or polyclonal antibodies are typically used. However, the assay can be adapted for use with other reagents that specifically bind to the marker. Standard protocols and data analysis are used to determine the marker concentrations from the assay data.

A number of the assays discussed above employ a reagent that specifically binds to the marker. Any molecule that is capable of specifically binding to a marker is included within the invention. In some embodiments, the binding molecules are antibodies or antibody fragments. In other embodiments, the binding molecules are non-antibody species, such as aptamers. Thus, for example, the binding molecule may be an enzyme for which the marker is a substrate. The binding molecules may recognize any epitope of the targeted markers.

As described above, the binding molecules may be identified and produced by any method accepted in the art. Methods for identifying and producing antibodies and antibody fragments specific for an analyte are well known. Examples of other methods used to identify the binding molecules include binding assays with random peptide libraries (e.g., phage display) and design methods based on an analysis of the structure of the marker. The markers of the invention also may be detected or measured using a number of chemical derivatization or reaction techniques known in the art. Reagents for use in such techniques are known in the art, and are commercially available for certain classes of target molecules.

Finally, the chromatographic separation techniques described above also may be coupled to an analytical technique other than mass spectrometry such as fluorescence detection of tagged molecules, NMR, capillary UV, evaporative light scattering or electrochemical detection.

In one embodiment, the present invention provides a method for determining whether a subject has hepatocellular cancer. In another aspect, the invention provides methods for diagnosing hepatocellular cancer in a subject. These methods comprise obtaining a biological sample from a subject suspected of having hepatocellular cancer, or at risk for developing hepatocellular cancer, detecting the level or activity of one or more biomarkers in the sample, and comparing the result to the level or activity of the marker(s) in a sample obtained from a non-hepatocellular cancer subject, or to a reference range or value. As used herein, the term "biological sample" includes a sample from any body fluid or tissue (e.g., serum, plasma, blood, cerebrospinal fluid, urine, saliva, hepatocellular cancer tissue). Typically, the standard biomarker level or reference range is obtained by measuring the same marker or markers in a set of normal controls. Measurement of the standard biomarker level or reference range need not be made contemporaneously; it may be a historical measurement. Preferably the normal control is matched to the patient with respect to some attribute(s) (e.g., age). Depending upon the difference between the measured and standard level or reference range, the patient can be diagnosed as having hepatocellular cancer or as not having hepatocellular cancer. In some embodiments, hepatocellular cancer is diagnosed in the patient if the expression level of the biomarker or biomarkers in the patient sample is statistically more similar to the expression level of the biomarker or biomarkers that has been associated with hepatocellular cancer than the expression level of the biomarker or biomarkers that has been associated with the normal controls.

What is presently referred to as hepatocellular cancer may turn out to be a number of related, but distinguishable conditions. Classifications may be made, and these types may be further distinguished into subtypes. Indeed, HCC heterogeneity may be attributed to lineage-specific tumor subtypes (Lee et al, Nat Med 12, 410-6, 2006; Yamashita et al, Cancer Res, 68, 1451-61 2008; Zaret and Grompe, Science 2008; Yamashita et al, Gastroenterology 2009), some of which retain stem-cell features making them highly aggressive forms of HCC, and metabolites associated with stem cell features are disclosed herein. Furthermore, HCC can develop with the background of hepatitis B viral infection, hepatitis C viral infection, alcohol related disease and fatty acid disease. Any and all of the various forms of hepatocellular cancer are intended to be within the scope of the present invention. Indeed, by providing a method for subsetting patients based on biomarker measurement level, the compositions and methods of the present invention may be used to uncover and define various forms of the disease.

The methods of the present invention may be used to make the diagnosis of hepatocellular cancer, independently from other information such as the patient's symptoms or the results of other clinical or paraclinical tests. However, the methods of the present invention may be used in conjunction with such other data points.

Because a diagnosis is rarely based exclusively on the results of a single test, the method may be used to determine whether a subject is more likely than not to have hepatocellular cancer, or is more likely to have hepatocellular cancer than to have another disease, based on the difference between the measured and standard level or reference range of the biomarker. Thus, for example, a patient with a putative diagnosis of hepatocellular cancer may be diagnosed as being "more likely" or "less likely" to have hepatocellular cancer in light of the information provided by a method of the present invention. If a plurality of biomarkers are measured, at least one and up to all of the measured biomarkers must differ, in the appropriate direction, for the subject to be diagnosed as having (or being more likely to have) hepatocellular cancer. In some embodiments, such difference is statistically significant.

The biological sample may be of any tissue or fluid, including a serum or tissue sample, but other biological fluids or tissue may be used. Possible biological samples include, but are not limited to, blood, plasma, urine, saliva, and hepatocellular tissue. In some embodiments, the level of a marker may be compared to the level of another marker or some other component in a different tissue, fluid or biological "compartment." Thus, a differential comparison may be made of a marker in tissue and serum. It is also within the scope of the invention to compare the level of a marker with the level of another marker or some other component within the same compartment. As will be apparent to those of ordinary skill in the art, the above description is not limited to making an initial diagnosis of hepatocellular cancer, but also is applicable to confirming a provisional diagnosis of hepatocellular cancer or "ruling out" such a diagnosis. Furthermore, an increased or decreased level or activity of the marker(s) in a sample obtained from a subject suspected of having hepatocellular cancer, or at risk for developing hepatocellular cancer, is indicative that the subject has or is at risk for developing hepatocellular cancer.

The invention also provides a method for determining a subject's risk of developing hepatocellular cancer, the method comprising obtaining a biological sample from a subject, detecting the level or activity of a marker in the sample, and comparing the result to the level or activity of the marker in a sample obtained from a non- hepatocellular cancer subject, or to a reference range or value wherein an increase or decrease of the marker is correlated with the risk of developing hepatocellular cancer.

The invention also provides methods for determining the stage or severity of hepatocellular cancer, the method comprising obtaining a biological sample from a subject, detecting the level or activity of a marker in the sample, and comparing the result to the level or activity of the marker in a sample obtained from a non- hepatocellular cancer subject, or to a reference range or value wherein an increase or decrease of the marker is correlated with the stage or severity of the disease.

In another aspect, the invention provides methods for monitoring the progression of the disease in a subject who has hepatocellular cancer, the method comprising obtaining a first biological sample from a subject, detecting the level or activity of a marker in the sample, and comparing the result to the level or activity of the marker in a second sample obtained from the subject at a later time, or to a reference range or value wherein an increase or decrease of the marker is correlated with progression of the disease.

Cancer prognosis generally refers to a forecast or prediction of the probable course or outcome of the cancer. As used herein, cancer prognosis includes the forecast or prediction of any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer, and/or likelihood of metastasis in a patient susceptible to or diagnosed with a cancer. Prognostic for cancer means providing a forecast or prediction of the probable course or outcome of the cancer. In some embodiments, prognostic for cancer comprises providing the forecast or prediction of (prognostic for) any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer, and/or likelihood of metastasis in a patient susceptible to or diagnosed with a cancer. Markers of Table 3 and 4 and their variants are particularly useful in prognostic methods.

The marker expression measurement values for the markers listed in Tables 1-4 are differentially expressed in hepatocellular cancer samples. For markers that are increased or upregulated, a significant difference in the elevation of the measured value of one or more of the markers indicates that the patient has (or is more likely to have, or is at risk of having, or is at risk of developing, and so forth) hepatocellular cancer. For markers that are decreased or downregulated, a significant difference in the depression of the measured value of one or more of the markers indicates that the patient has (or is more likely to have, or is at risk of having, or is at risk of developing, and so forth) hepatocellular cancer. If only one biomarker is measured, then that value must change (either increase or decrease) to indicate hepatocellular cancer. If more than one biomarker is measured, then a diagnosis of hepatocellular cancer can be indicated by a change in only one biomarker, all biomarkers, or any number in between. In some embodiments, multiple markers are measured, and a diagnosis of hepatocellular cancer is indicated by changes in multiple markers. For example, a panel of markers may include markers that are increased in level or activity in hepatocellular cancer subject samples as compared to non-hepatocellular cancer subject samples, markers that are decreased in level or activity in hepatocellular cancer subject samples as compared to non-hepatocellular cancer subject samples, or a combination thereof. Measurements can be of (i) a biomarker of the present invention, (ii) a biomarker of the present invention and another factor known to be associated with hepatocellular cancer (e.g., alpha- fetoprotein (AFP), abdominal ultrasound, helical CT scan and/or triple phase CT scan); (iii) a plurality of biomarkers of the present invention, (iv) a plurality of biomarkers comprising at least one biomarker of the present invention and at least one biomarker reported in the literature; or (v) any combination of the foregoing. Furthermore, the amount of change in a biomarker level may be an indication of the relative likelihood of the presence of the disease.

The marker(s) may be detected in any biological sample obtained from the subject, by any suitable method known in the art (e.g., immunoassays, hybridization assay) see supra. In some embodiments, the marker(s) are detected in a tumor sample obtained from the patient by surgical procedure(s).

In an alternative embodiment of the invention, a method is provided for monitoring a hepatocellular cancer patient over time to determine whether the disease is progressing. The specific techniques used in implementing this embodiment are similar to those used in the embodiments described above. The method is performed by obtaining a biological sample, such as serum or tissue, from the subject at a certain time (t;); measuring the level of at least one of the biomarkers in the biological sample; and comparing the measured level with the level measured with respect to a biological sample obtained from the subject at an earlier time (to). Depending upon the difference between the measured levels, it can be seen whether the marker level has increased, decreased, or remained constant over the interval (tj-to). A further deviation of a marker in the direction indicating hepatocellular cancer, or the measurement of additional increased or decreased hepatocellular cancer markers, would suggest a progression of the disease during the interval. Subsequent sample acquisitions and measurements can be performed as many times as desired over a range of times Ϊ₂ to t„.

The ability to monitor a patient by making serial marker level determinations would represent a valuable clinical tool. Rather than the limited "snapshot" provided by a single test, such monitoring would reveal trends in marker levels over time. In addition to indicating a progression of the disease, tracking the marker levels in a patient could be used to predict exacerbations or indicate the clinical course of the disease. For example, as will be apparent to one of skill in the art, the biomarkers of the present invention could be further investigated to distinguish between any or all of the known forms of hepatocellular cancer or any later described types or subtypes of the disease. In addition, the sensitivity and specificity of any method of the present invention could be further investigated with respect to distinguishing hepatocellular cancer from other diseases or to predict relapse or remission.

In an analogous manner, administration of a chemotherapeutic drug or drug combination can be evaluated or re-evaluated in light of the assay results of the present invention. For example, the drug(s) can be administered differently to different subject populations, and measurements corresponding to administration analyzed to determine if the differences in the inventive biomarker signature before and after drug administration are significant. Results from the different drug regiments can also be compared with each other directly. Alternatively, the assay results may indicate the desirability of one drug regimen over another, or indicate that a specific drug regimen should or should not be administered to a hepatocellular cancer patient. In one embodiment, the finding of elevated levels of the markers of the present invention in a hepatocellular cancer patient is indicative of a good prognosis for response to treatment with chemotherapeutic agents. In another embodiment, the absence of elevated levels of the markers of the present invention in a hepatocellular cancer patient is indicative of a poor prognosis for response to treatment.

In another aspect, the invention provides methods for screening candidate compounds for use as therapeutic compounds. In one embodiment, the method comprises screening candidate compounds for those that provide clinical progress following administration to a hepatocellular cancer patient from which a tumor sample has been shown to have elevated levels of the markers of the present invention.

In an analogous manner, the markers of the present invention can be used to assess the efficacy of a therapeutic intervention in a subject. The same approach described above would be used, except a suitable treatment would be started, or an ongoing treatment would be changed, before the second measurement (i.e., after t₀ and before tj). The treatment can be any therapeutic intervention, such as drug administration, dietary restriction or surgery, and can follow any suitable schedule over any time period as appropriate for the intervention. The measurements before and after could then be compared to determine whether or not the treatment had an effect effective. As will be appreciated by one of skill in the art, the determination may be confounded by other superimposed processes (e.g., an exacerbation of the disease during the same period).

In a further embodiment, the markers may be used to screen candidate drugs, for example, in a clinical trial, to determine whether a candidate drug is effective in treating hepatocellular cancer. At time t₀, a biological sample is obtained from each subject in population of subjects diagnosed with hepatocellular cancer. Next, assays are performed on each subject's sample to measure levels of a biological marker. In some embodiments, only a single marker is monitored, while in other embodiments, a combination of markers, up to the total number of factors, is monitored. Next, a predetermined dose of a candidate drug is administered to a portion or sub-population of the same subject population. Drug administration can follow any suitable schedule over any time period. In some cases, varying doses are administered to different subjects within the sub-population, or the drug is administered by different routes. At time ti, after drug administration, a biological sample is acquired from the sub-population and the same assays are performed on the biological samples as were previously performed to obtain measurement values. As before, subsequent sample acquisitions and measurements can be performed as many times as desired over a range of times Ϊ2 to t_n. In such a study, a different sub-population of the subject population serves as a control group, to which a placebo is administered. The same procedure is then followed for the control group: obtaining the biological sample, processing the sample, and measuring the biological markers to obtain a measurement chart.

Specific doses and delivery routes can also be examined. The method is performed by administering the candidate drug at specified dose or delivery routes to subjects with hepatocellular cancer; obtaining biological samples, such as serum or tissue, from the subjects; measuring the level of at least one of the biomarkers in each of the biological samples; and, comparing the measured level for each sample with other samples and/or a standard level. Typically, the standard level is obtained by measuring the same marker or markers in the subject before drug administration. Depending upon the difference between the measured and standard levels, the drug can be considered to have an effect on hepatocellular cancer. If multiple biomarkers are measured, at least one and up to all of the biomarkers must change, in the expected direction, for the drug to be considered effective. Preferably, multiple markers must change for the drug to be considered effective, and preferably, such change is statistically significant.

As will be apparent to those of ordinary skill in the art, the above description is not limited to a candidate drug, but is applicable to determining whether any therapeutic intervention is effective in treating hepatocellular cancer.

In a typical embodiment, a subject population having hepatocellular cancer is selected for the study. The population is typically selected using standard protocols for selecting clinical trial subjects. For example, the subjects are generally healthy, are not taking other medication, and are evenly distributed in age and sex. The subject population can also be divided into multiple groups; for example, different sub-populations may be suffering from different types or different degrees of the disorder to which the candidate drug is addressed. The stratification of the patient population may be made based on the levels of biomarkers of the present invention.

In general, a number of statistical considerations must be made in designing the trial to ensure that statistically significant changes in biomarker measurements can be detected following drug administration. The amount of change in a biomarker depends upon a number of factors, including strength of the drug, dose of the drug, and treatment schedule. It will be apparent to one skilled in statistics how to determine appropriate subject population sizes. Preferably, the study is designed to detect relatively small effect sizes.

The subjects optionally may be "washed out" from any previous drug use for a suitable period of time. Washout removes effects of any previous medications so that an accurate baseline measurement can be taken. At time t₀, a biological sample is obtained from each subject in the population. Next, an assay or variety of assays is performed on each subject's sample to measure levels of particular biomarkers of the invention. The assays can use conventional methods and reagents, as described above. If the sample is blood, then the assays typically are performed on either serum or plasma. For other fluids or tissues, additional sample preparation steps are included as necessary before the assays are performed. The assays measure values of at least one of the biological markers described herein. In some embodiments, only a single marker is monitored, while in other embodiments, a combination of factors, up to the total number of markers, is monitored. The markers may also be monitored in conjunction with other measurements and factors associated with hepatocellular cancer (e.g., MRI imaging). The number of biological markers whose values are measured depends upon, for example, the availability of assay reagents, biological fluid, and other resources.

Next, a predetermined dose of a candidate drug is administered to a portion or sub- population of the same subject population. Drug administration can follow any suitable schedule over any time period, and the sub-population can include some or all of the subjects in the population. In some cases, varying doses are administered to different subjects within the sub-population, or the drug is administered by different routes. Suitable doses and administration routes depend upon specific characteristics of the drug. At time ti, after drug administration, another biological sample (the '¾ sample") is acquired from the sub-population. Typically, the sample is the same type of sample and processed in the same manner as the sample acquired from the subject population before drug administration (the "t₀ sample"). The same assays are performed on the ti sample as on the t_o sample to obtain measurement values. Subsequent sample acquisitions and measurements can be performed as many times as desired over a range of times ¾ to t_n.

Typically, a different sub-population of the subject population is used as a control group, to which a placebo is administered. The same procedure is then followed for the control group: obtaining the biological sample, processing the sample, and measuring the biological markers to obtain measurement values. Additionally, different drugs can be administered to any number of different sub-populations to compare the effects of the multiple drugs. As will be apparent to those of ordinary skill in the art, the above description is a highly simplified description of a method involving a clinical trial. Clinical trials have many more procedural requirements, and it is to be understood that the method is typically implemented following all such requirements.

Paired measurements of the various biomarkers are now available for each subject. The different measurement values are compared and analyzed to determine whether the biological markers changed in the expected direction for the drug group but not for the placebo group, indicating that the candidate drug is effective in treating the disease. In preferred embodiments, such change is statistically significant. The measurement values at time ti for the group that received the candidate drug are compared with standard measurement values, preferably the measured values before the drug was given to the group, i.e., at time t₀. Typically, the comparison takes the form of statistical analysis of the measured values of the entire population before and after administration of the drug or placebo. Any conventional statistical method can be used to determine whether the changes in biological marker values are statistically significant. For example, paired comparisons can be made for each biomarker using either a parametric paired t-test or a non-parametric sign or sign rank test, depending upon the distribution of the data.

In addition, tests may be performed to ensure that statistically significant changes found in the drug group are not also found in the placebo group. Without such tests, it cannot be determined whether the observed changes occur in all patients and are therefore not a result of candidate drug administration.

As indicated in Tables 1-4, some of the marker measurement values are higher in samples from hepatocellular cancer patients. A significant change in the appropriate direction in the measured value of one or more of the markers indicates that the drug is effective. If only one biomarker is measured, then that value must increase or decrease to indicate drug efficacy. If more than one biomarker is measured, then drug efficacy can be indicated by change in only one biomarker, all biomarkers, or any number in between. In some embodiments, multiple markers are measured, and drug efficacy is indicated by changes in multiple markers. Measurements can be of both biomarkers of the present invention and other measurements and factors associated with hepatocellular cancer (e.g., measurement of biomarkers reported in the literature and/or CT imaging). Furthermore, the amount of change in a biomarker level may be an indication of the relatively efficacy of the drug.

In addition to determining whether a particular drug is effective in treating hepatocellular cancer, biomarkers of the invention can also be used to examine dose effects of a candidate drug. There are a number of different ways that varying doses can be examined. For example, different doses of a drug can be administered to different subject populations, and measurements corresponding to each dose analyzed to determine if the differences in the inventive biomarkers before and after drug administration are significant. In this way, a minimal dose required to effect a change can be estimated. In addition, results from different doses can be compared with each other to determine how each biomarker behaves as a function of dose. Based on the results of drug screenings, the markers of the invention may be used as theragnostics; that is, they can be used to individualize medical treatment.

In another aspect, the invention provides a kit for detecting marker(s) of the present invention. The kit may be prepared as an assay system including any one of assay reagents, assay controls, protocols, exemplary assay results, or combinations of these components designed to provide the user with means to evaluate the expression level of the marker(s) of the present invention.

In another aspect, the invention provides a kit for diagnosing hepatocellular cancer in a patient including reagents for detecting at least one polypeptide or polynucleotide marker in a biological sample from a subject.

The kits of the invention may comprise one or more of the following: an antibody, wherein the antibody specifically binds with a marker, a labeled binding partner to the antibody, a solid phase upon which is immobilized the antibody or its binding partner, instructions on how to use the kit, and a label or insert indicating regulatory approval for diagnostic or therapeutic use. The invention further includes microarrays comprising markers of the invention, or molecules, such as antibodies, which specifically bind to the markers of the present invention. In this aspect of the invention, standard techniques of microarray technology are utilized to assess expression of the polypeptides biomarkers and/or identify biological constituents that bind such polypeptides. Protein microarray technology is well known to those of ordinary skill in the art and is based on, but not limited to, obtaining an array of identified peptides or proteins on a fixed substrate, binding target molecules or biological constituents to the peptides, and evaluating such binding. Arrays that bind markers of the invention also can be used for diagnostic applications, such as for identifying subjects that have a condition characterized by expression of polypeptide biomarkers, e.g., hepatocellular cancer.

The assay system preferably also includes one or more controls. The controls may include: (i) a control sample for detecting sensitivity to a chemotherapeutic agent or agents being evaluated for use in a patient; (ii) a control sample for detecting resistance to the chemotherapeutic(s); (iii) information containing a predetermined control level of markers to be measured with regard to the chemotherapeutic sensitivity or resistance (e.g., a predetermined control level of a marker of the present invention that has been correlated with sensitivity to the chemotherapeutic(s) or resistance to the chemotherapeutic).

In another embodiment, a means for detecting the expression level of the marker(s) of the invention can generally be any type of reagent that can include, but are not limited to, antibodies and antigen binding fragments thereof, peptides, binding partners, aptamers, enzymes, and small molecules. Additional reagents useful for performing an assay using such means for detection can also be included, such as reagents for performing immunohistochemistry or another binding assay.

The means for detecting of the assay system of the present invention can be conjugated to a detectable tag or detectable label. Such a tag can be any suitable tag which allows for detection of the reagents used to detect the marker of interest and includes, but is not limited to, any composition or label detectable by spectroscopic, photochemical, electrical, optical or chemical means. Useful labels in the present invention include: biotin for staining with labeled streptavidin conjugate, magnetic beads (e.g., Dynabeads™), fluorescent dyes (e.g., fluorescein, texas red, rhodamine, green

3 125 35 14 32

fluorescent protein, and the like), radiolabels (e.g., H, I, S, C, or P), enzymes (e.g., horse radish peroxidase, alkaline phosphatase and others commonly used in an ELISA), and colorimetric labels such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex, etc.) beads.

In addition, the means for detecting of the assay system of the present invention can be immobilized on a substrate. Such a substrate can include any suitable substrate for immobilization of a detection reagent such as would be used in any of the previously described methods of detection. Briefly, a substrate suitable for immobilization of a means for detecting includes any solid support, such as any solid organic, biopolymer or inorganic support that can form a bond with the means for detecting without significantly affecting the activity and/or ability of the detection means to detect the desired target molecule. Exemplary organic solid supports include polymers such as polystyrene, nylon, phenol-formaldehyde resins, and acrylic copolymers (e.g., polyacrylamide). The kit can also include suitable reagents for the detection of the reagent and/or for the labeling of positive or negative controls, wash solutions, dilution buffers and the like. The assay system can also include a set of written instructions for using the system and interpreting the results.

The assay system can also include a means for detecting a control marker that is characteristic of the cell type being sampled can generally be any type of reagent that can be used in a method of detecting the presence of a known marker (at the nucleic acid or protein level) in a sample, such as by a method for detecting the presence of a biomarker described previously herein. Specifically, the means is characterized in that it identifies a specific marker of the cell type being analyzed that positively identifies the cell type. For example, in a hepatocellular tumor assay, it is desirable to screen hepatocellular cancer cells for the level of the biomarker expression and/or biological activity. Therefore, the means for detecting a control marker identifies a marker that is characteristic of a hepatocellular cell, so that the cell is distinguished from other cell types, such as a connective tissue or inflammatory cells. Such a means increases the accuracy and specificity of the assay of the present invention. Such a means for detecting a control marker include, but are not limited to: a probe that hybridizes under stringent hybridization conditions to a nucleic acid molecule encoding a protein marker; PCR primers which amplify such a nucleic acid molecule; an aptamer that specifically binds to a conformationally-distinct site on the target molecule; and/or an antibody, antigen binding fragment thereof, or antigen binding peptide that selectively binds to the control marker in the sample. Nucleic acid and amino acid sequences for many cell markers are known in the art and can be used to produce such reagents for detection.

The assay systems and methods of the present invention can be used not only to identify patients that are predicted to survive or be responsive to treatment, but also to identify treatments that can improve the responsiveness of cancer cells which are resistant to treatment, and to develop adjuvant treatments that enhance the response of the treatment and survival.

EXAMPLES

Example 1

This example shows the identification of biological markers for HCC.

Samples of tumor tissue from patients with HCC who underwent surgical resection were analyzed using liquid chromatography-mass spectrometry and gas chromatography- mass spectrometry. The HCC patients had hepatitis B viral infection. The HCC markers were identified by comparing the levels measured in tumor samples obtained from HCC patients with the levels measured in non-tumor samples obtained from the same patients. Measurement values of the biomarkers were found to differ in biological samples from patients with HCC as compared to biological samples from normal controls.

The HCC metabolomics study design is shown schematically in Fig. 4. Metabolomic profiling included tissue procurement, histopathological examination, metabolite extraction and separation, mass spectrometry-based detection, spectral analysis, data normalization, delineation of class-specific metabolites and altered pathways, validation of class-specific metabolites and their functional characterization. The metabolomic profiling techniques are described in more detail in Lawton, et al., "Analysis of the adult human plasma metabolome," Pharmacogenomics . 2008 Apr;9(4):383-97; Sreekumar, et al., "Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression," Nature 457, 910-914 (12 February 2009); U.S. Pat. Nos. 7,005,255 and 7,329,489; 7,635,556; 7,682,783; 7,682,784; and 7,550,258; and U.S Pat. App. Pub. No. 2006/0134677; the entire contents of each of which are hereby incorporated herein by reference.

Bioinformatic analyses were performed as follows. Class Comparison (p<0.05, lOxCV, FDR <20%) was performed on all samples: Tumor ("T") vs. NonTumor ("NT"); HpSC: T vs. NT; and MH: T vs. NT. Class prediction was done on the basis of T vs. NT, 7 algorithms; HpSC vs. MH (Tumor); HpSC vs. MH (Nontumor). Tumor metabolites were compared with subtype metabolites. Outcome Analysis was performed to determine survival-associated metabolites. Pathway Analysis and Data Integration followed.

For tumor-specific metabolites, for the class comparison of Tumor vs. Nontumor, p<0.05; FDR<20%, the following table and Venn diagram in Fig. 5 show the results obtained:

The results of this analysis are shown in Fig. 1. The VENN diagram in Fig. 1A represents the results of class comparison analyses between tumor and nontumor tissue among hepatic stem cell (HpSC) HCC subtype tissues, mature hepatocyte (MH) HCC subtype tissues or among all HCC cases (ALL) at p<0.05 with 1000 permutations of the class label. In total 253 metabolites were identified that were differentially expressed in tumor tissues (see table 1). Fig. 1 B shows the number of upregulated, downregulated or total metabolites in the tumor vs. nontumor comparison among all superpathways, as well as within each subpathway. Fig. 1C shows the number of upregulated, downregulated or total metabolites in the tumor vs. nontumor comparison in the lipid pathway. The majority of the 253 metabolites were downregulated in tumors (Fig. IB). However, in the lipid pathway, the majority of metabolites were upregulated in tumors (Fig. 1C).

The class comparison analysis was followed by a class prediction analysis (Tumor vs.Nontumor). The following table shows the performance of classifiers during cross validation (lOxCV; 1000 permutations). The number of metabolites in the classifier was 253:

Class prediction: 253 metabolites (T vs NT)

A receiver operator characteristic (ROC) curve was plotted to depict the sensitivity and specificity of the 253 tumor- specific metabolites following class prediction analysis using the Bayesian compound covariate algorithm (Fig. ID; the term AUC refers to "Area Under Curve"). The sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) values were as shown in the table below.

In the subtype-specific class prediction analysis (HpSC vs.MH), the following table shows the performance of classifiers during cross validation (lOxCV; 1000 permutations).

Class prediction: 48 metabolites: TUMOR (HpSC ys MH)

Class prediction: 16 metabolites: NQNTUMOR (HpSC vs Hj

Thus, the results obtained showed that in HpSC and MH HCC subtypes,

54% (253/469) of metabolites show altered expression in the tumor tissues when compared to nontumor tissues; the majority of HCC metabolites are downregulated in tumor tissues (72%; 181/253); the metabolites altered in tumors can effectively predict tumor or nontumor tissue during cross validation; and subtype-specific metabolites can differentiate HpSC and MH in tumor tissue, but not in nontumor tissue.

Example 4:

This example illustrates the identification of HCC tumor prognostic gene surrogates.

As explained previously in Example 1, comparison of the metabolic profiles of HpSC samples vs. MH samples led to the identification of 48 tumor-specific metabolites which could significantly differentiate between these two groups. Further, as explained in Example 3, 28 of these were found to be associated with patient outcome (overall survival).

Gene expression studies were conducted in parallel to compare the same samples. Comparison of the gene expression profiles of HpSC samples vs. MH samples led to identification of 169 tumor-specific genes which could significantly differentiate these two groups and were associated with patient outcome. Since the metabolomics studies and the transcriptomics studies were performed on the same samples and were tested statistically for the same phenotype (i.e. association with tumors, subtype and survival), the 169 genes were essentially gene surrogates for the 28 metabolites.

A search was conducted to determine how the 28 metabolites and the 169 genes were related by performing integration analysis based on their expression correlation. 15 of the 28 metabolites and 121 of the 169 genes were found to be highly correlated. The Kaplan-Meier survival curve in Fig. 8 demonstrates that these 121 gene surrogates are significantly associated with patient survival.

Fig. 7 shows a hierarchical cluster representing the correlation between the 15 metabolites and their gene surrogates. As can be seen in the Fig. 7, the 15 metabolites formed two clusters. (Gray represents a positive correlation; and black represents negative correlation.) Cluster 1 contained 6 metabolites related to lipid metabolism.

The transcriptomics data was searched for the gene surrogates of the 6 lipid metabolites and a set of 273 genes was found to be highly correlated with the six metabolites. Fig. 10 shows the schematic of the global search employed for the identification of the gene surrogates of the 6 lipid metabolites. Filtering criteria were employed to determine the most correlated metabolite-gene pairs.

It was hypothesized that since the 6 metabolites were survival related, their 273 gene surrogates would also be associated with patient survival. This hypothesis was tested by investigating the capacity of the 273 genes to predict survival outcome in two independent cohorts, LCI and LEC. The results of this analysis are shown in Fig. 11. The Kaplan-Meier survival curves shown in Fig. 11 demonstrate that the gene surrogates for the lipid metabolites are significantly associated with patient survival in the LCI and LEC cohorts.

Ingenuity pathway analysis (IP A) of the 273 gene surrogates was performed to identify the signaling pathways associated with these genesets. The PI3K and MYC pathways were found to be the top networks associated with these genes. (See Fig. 9) Shaded gene symbols are those imported into IPA from the original gene surrogate list.

Those skilled in the art will appreciate, or be able to ascertain using no more than routine experimentation, further features and advantages of the invention based on the above-described embodiments. Accordingly, the invention is not to be limited by what has been particularly shown and described, except as indicated by the appended claims. All publications and references are herein expressly incorporated by reference in their entirety.

Table 1: 253 Diagnostic HCC Metabolites"

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker^b direction FD ^d

(T vs. NT)^C c p-value p-value

Amino acid Alanine and aspartate N-acetylaspartate (NAA) 1.56 up 0.0091 0.0026 0.0061 metabolism

N-acetylasparagine 1.55 up 0.0181 0.0061 0.0054

N-acetylalanine 1.32 up 0.0039 0.0009 0.0008 alanine 0.85 down 0.0751 0.0347 0.0343

Butanoate metabolism 2 -aminobutyrate 0.50 down 0.0000 0.0000 0.0001

Creatine metabolism creatine 0.66 down 0.0004 0.0001 0.0001

Cysteine, methionine,

N- acety lmethionine 2.31 up 0.0310 0.0114 0.0160 SAM, taurine

taurine 1.59 up 0.027 0.0055 0.0081 metabolism

methionine 0.85 down 0.0253 0.0092 0.0103

2-hydroxybutyrate (AHB) 0.77 down 0.0022 0.0005 0.0002

S-adenosylhomocysteine (SAH) 0.44 down 0.0000 0.0000 < le-07 cysteine 0.31 down 0.0000 0.0000 < le-07

Glutamate metabolism

pyroglutamine 0.51 down 0.0000 0.0000 < le-07 gamma- aminobutyrate (GABA) 0.43 down 0.0059 0.0016 0.0019

Glycine, serine and N-(2-furoyl)glycine 1.75 up 0.0733 0.0337 0.0393 threonine metabolism

N- acety lthreonine 1.37 up 0.037 0.0061 0.0073 threonine 0.86 down 0.0529 0.0224 0.0230

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value betaine 0.68 down 0.0155 0.0051 0.0044 glycine 0.63 down 0.0003 0.0000 < le-07

Lysine metabolism

pipecolate 0.78 down 0.0497 0.0208 0.0204

N2-acetyllysine 0.57 down 0.032 0.0070 0.0147 lysine 0.78 down 0.0008 0.0001 0.0001 methylglutaroylcarnitine 0.42 down 0.0041 0.0010 0.0008

Phenylalanine & phenylalanine 0.83 down 0.0169 0.0056 0.0076 tyrosine metabolism

tyrosine 0.72 down 0.0001 0.0000 < le-07 phenol sulfate 0.67 down 0.0223 0.0080 0.0079

Polyamine metabolism

5-methylthioadenosine (MTA) 4.65 up < le-07 < le-07 < le-07 spermidine 1.30 up 0.0421 0.0169 0.0143 putrescine 0.65 down 0.0236 0.0085 0.0088

Tryptophan

kynurenine 1.54 up 0.0660 0.0293 0.0315 metabolism

tryptophan 0.82 down 0.0191 0.0066 0.0083

C-glycosyltryptophan 0.67 down 0.0009 0.0002 0.0002

Urea cycle; arginine-, arginine 1.31 up 0.0319 0.0120 0.0118 proline-, metabolism

ornithine 0.56 down 0.0004 0.0000 < le-07

Valine, leucine and alpha-hydroxyisovalerate 1.23 up 0.0647 0.0284 0.0290

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker^b direction FD ^d

(T vs. NT)^C c p-value p-value isoleucine metabolism

leucine 0.81 down 0.0045 0.0012 0.0014 valine 0.81 down 0.0006 0.0001 < le-07 propionylcarnitine 0.61 down 0.0475 0.0197 0.0209 tiglyl carnitine 0.50 down 0.0001 0.0000 < le-07 isobutyrylcarnitine 0.28 down 0.0000 0.0000 < le-07

Carbohydrate Aminosugars N- acety lneuraminate 0.69 down 0.0009 0.0002 0.0001 metabolism N- acety lmannos amine 0.55 down 0.0009 0.0002 0.0001

Fructose, mannose, fructose 0.74 down 0.0707 0.0324 0.0315 galactose, starch, and maltose 0.61 down 0.0071 0.0020 0.0016 sucrose metabolism tagatose 0.46 down 0.0383 0.0148 0.0137 mannose 0.46 down 0.0004 0.0000 < le-07 sorbitol 0.33 down 0.0000 0.0000 < le-07

Glycolysis, lactate 1.20 up 0.0396 0.0156 0.0141 gluconeogenesis, glycerate 0.66 down 0.040 0.0104 0.0114 pyruvate metabolism glucuronate 0.40 down 0.0000 0.0000 < le-07 glucose 0.28 down 0.0000 0.0000 < le-07

Nucleotide sugars, ribulose 5-phosphate 0.70 down 0.068 0.0216 0.0230 pentose metabolism 6-phosphogluconate 0.70 down 0.0312 0.01 16 0.0108 ribonate 0.60 down 0.0019 0.0004 0.0005 ribose 5-phosphate 0.46 down 0.024 0.0048 0.0161

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker^b direction FD ^d

(T vs. NT)^C c p-value p-value ribose 0.39 down 0.0007 0.0001 0.0004 ribitol 0.37 down 0.0001 0.0000 0.0001 xylitol 0.34 down 0.0000 0.0000 < le-07

Cofactors and Ascorbate and aldarate glucarate (saccharate) 0.58 down 0.0163 0.0054 0.0066 vitamins metabolism gulono- 1 ,4-lactone 0.37 down 0.0003 0.0000 0.0003

Folate metabolism 5 -methyl tetrahydro folate (5MeTHF) 0.64 down 0.0469 0.0192 0.0221

Nicotinate and nicotinamide 0.47 down < le-07 < le-07 0.0001 nicotinamide nicotinamide adenine dinucleotide

metabolism (NAD+) 0.23 down 0.008 0.0009 0.0033

Pyridoxal metabolism pyridoxal 0.60 down 0.0001 0.0000 < le-07

Riboflavin metabolism riboflavin (Vitamin B2) 0.51 down 0.0000 0.0000 < le-07 flavin adenine dinucleotide (FAD) 0.43 down 0.0000 0.0000 < le-07

Tocopherol

metabolism alpha-tocopherol 0.43 down 0.0058 0.0015 0.0024

Vitamin B6

metabolism pyridoxate 0.46 down 0.0818 0.0384 0.0329

Energy Krebs cycle fumarate 0.50 down < le-07 < le-07 < le-07 malate 0.42 down < le-07 < le-07 < le-07

Oxidative

phosphorylation acetylphosphate 1.21 up 0.0394 0.0154 0.0126

Lipid Bile acid metabolism scholate 0.45 down 0.0912 0.0439 0.0409 glycochenodeoxycholate 0.38 down 0.0126 0.0039 0.0041

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value glycocholate 0.25 down 0.0041 0.0010 0.0008

Carnitine metabolism butyrylcarnitine 11.32 up 0.0001 0.0000 0.0006 hexanoylcarnitine 4.18 up 0.0096 0.0028 0.0027 palmitoylcarnitine 3.67 up 0.0127 0.0040 0.0046 stearoylcarnitine 3.06 up 0.0237 0.0086 0.0069 oleoylcarnitine 2.49 up 0.0660 0.0292 0.0268 deoxycarnitine 2.10 up 0.0001 0.0000 < le-07 valerylcarnitine 1.97 up 0.0548 0.0234 0.0256

2-methylmalonyl carnitine 1.89 up 0.0032 0.0007 0.0008 acetylcarnitine 1.76 up 0.0212 0.0074 0.0086

3 -dehydrocarnitine 1.47 up 0.0008 0.0001 0.0001 glutamyl carnitine 0.49 down 0.0004 0.0001 0.0001

Diacylglycerol 1 ,2-dipalmitoylglycerol 1.93 up 0.0793 0.0370 0.0308

Essential fatty acid docosahexaenoate (DHA; 22:6n3) 0.78 down 0.0510 0.0215 0.0195 eicosapentaenoate (EPA; 20:5n3) 0.57 down 0.0004 0.0001 < le-07 docosapentaenoate (n3 DPA;

22:5n3) 0.52 down 0.0016 0.0003 0.0005 linolenate [alpha or gamma; (18:3n3

or 6)] 0.37 down 0.0004 0.0001 0.0001

Glycerolipid choline phosphate 2.13 up 0.0014 0.0003 0.0005 metabolism phosphoethanolamine 2.12 up 0.0001 0.0000 < le-07 choline 0.75 down 0.0006 0.0001 0.0001

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value ethanolamine 0.73 down 0.0091 0.0026 0.0033 glycerol 0.62 down 0.0097 0.0028 0.0036 glycerophosphorylcholine (GPC) 0.38 down 0.0003 0.0000 < le-07 glycerol 3-phosphate (G3P) 0.25 down < le-07 < le-07 < le-07

Inositol metabolism scyllo-inositol 0.65 down 0.0111 0.0034 0.0029 myo-inositol 0.60 down 0.0215 0.0076 0.0064

Ketone bodies 3-hydroxybutyrate (BHBA) 0.60 down 0.0000 0.0000 < le-07

Long chain fatty acid docosadienoate (22:2n6) 2.09 up 0.0004 0.0001 0.0001 docosatrienoate (22:3n3) 2.08 up 0.0042 0.0010 0.0007 eicosenoate (20: ln9 or 11) 1.59 up 0.0673 0.0300 0.0282 dihomo-linoleate (20:2n6) 1.73 up 0.040 0.0101 0.0124 palmitoleate (16: ln7) 1.65 up 0.1 14 0.0445 0.0464 stearidonate (18:4n3) 0.38 down 0.097 0.0237 0.0233 linoleate (18:2n6) 0.58 down 0.0035 0.0008 0.0004

Lysolipid 1- oleoylglycerophosphoethanolamine 1.70 up 0.0032 0.0007 0.0010

2- oleoylglycerophosphoethanolamine 1.38 up 0.0849 0.0401 0.0351

1-palmitoylglycerophosphoinositol 1.36 up 0.0759 0.0353 0.0340

1 -oleoylglycerophosphocholme 2.26 up 0.130 0.0416 0.0417

2 -oleoylglyceropho sphocholine 2.24 up 0.128 0.0403 0.0399

1 -oleoylglycerophosphomositol 1.42 up 0.128 0.0404 0.0421

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

2-linoleoylglycerophosphocholine 0.25 down 0.040 0.0102 0.0104

1- linoleoylglycerophosphoethanolami

ne 0.77 down 0.0912 0.0440 0.0450

Medium chain fatty caproate (6:0) 1.46 up 0.0127 0.0040 0.0052 acid heptanoate (7:0) 1.24 up 0.0060 0.0016 0.0025 caprylate (8:0) 1.23 up 0.0217 0.0077 0.0077

5-dodecenoate (12: ln7) 1.22 up 0.0912 0.0442 0.0444 pelargonate (9:0) 1.18 up 0.0383 0.0148 0.0113

Mono acylglycerol 1 -oleoylglycerol (1-monoolein) 0.67 down 0.073 0.0242 0.0265

Sphingolipid phytosphingosine 0.50 down 0.0702 0.0320 0.0280

Sterol/Steroid 7 -alpha-hydroxy cholesterol 1.99 up 0.0475 0.0196 0.0185 cholesterol 0.81 down 0.082 0.0176 0.0190 squalene 0.69 down 0.094 0.0336 0.0412

7-beta-hydroxycholesterol 1.86 up 0.0067 0.0019 0.0022 dehydroisoandrosterone sulfate

(DHEA-S) 0.69 down 0.0042 0.001 1 0.0016

Nucleotide Purine metabolism, xanthine 0.62 down 0.0000 0.0000 < le-07

(hypo)xanthine/inosin

e containing xanthosine 0.22 down 0.0000 0.0000 < le-07

Purine metabolism, adenosine 5 '-diphosphate (ADP) 0.73 down 0.091 0.0313 0.0392 adenine containing adenosine 0.67 down 0.0204 0.0071 0.0066

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value adenosine 5'-monophosphate

(AMP) 0.57 down 0.0909 0.0433 0.0430 adenosine 3 '-monophosphate (3'- AMP) 0.47 down 0.0026 0.0005 0.0006 adenosine 2 '-monophosphate (2'- AMP) 0.39 down < le-07 < le-07 < le-07 cytidine 5 '-monophosphate (5'-

Pyrimidine CMP) 1.47 up 0.1 14 0.0431 0.0383 metabolism, cytidine cytidine 5 '-monophosphate (5'- containing CMP) 1.47 up 0.1 14 0.0431 0.0383

Purine metabolism,

guanine containing guanosine 1.91 up 0.0027 0.0006 0.0005

Purine metabolism,

urate metabolism urate 0.47 down 0.0000 0.0000 < le-07

Pyrimidine

metabolism, thymine

containing; Valine,

leucine and isoleucine

metabolism/ 3 - aminoi sobutyrate 2.34 up 0.053 0.0152 0.0185

Pyrimidine 5,6-dihydrouracil 3.00 up 0.0000 0.0000 < le-07 metabolism, uracil adenine 2.86 up 0.0012 0.0002 0.0003 containing uridine 0.78 down 0.0008 0.0001 0.0001 beta-alanine 0.48 down 0.0002 0.0000 < le-07

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Peptide Dipe tide glycylproline 0.71 down 0.0059 0.0016 0.0017 glycylleucine 0.67 down 0.0393 0.0153 0.0140 g-glutamyl gamma-glutamylphenylalanine 0.78 down 0.0688 0.0309 0.0341 gamma-glutamylvaline 0.76 down 0.0494 0.0206 0.0207 gamma-glutamylleucine 0.64 down 0.0025 0.0005 0.0010 gamma-glutamyltyrosine 0.60 down 0.0000 0.0000 < le-07 gamma-glutamylmethionine 0.55 down 0.0113 0.0035 0.0029 gamma-glutamylglutamine 0.34 down 0.0317 0.01 18 0.0120 gamma-glutamylglutamate 0.27 down 0.0007 0.0001 0.0002

Glutathione ophthalmate 5.88 up 0.112 0.0326 0.0294 metabolism 5-oxoproline 0.72 down 0.0443 0.0179 0.0180

VGAHAGEYGAEALER (SEQ ID

Polypeptide NO: 1) 0.47 down 0.0849 0.0402 0.0370 unknown unknown Y-11583 13.98 up 0.058 0.011 1 0.0306

Y-13418 3.39 up 0.0000 0.0000 < le-07

Y-14837 2.99 up 0.0038 0.0009 0.0007

Y-13387 2.75 up 0.0001 0.0000 < le-07

Y- 12051 2.31 up 0.071 0.0142 0.0165

Y-14634 2.28 up 0.0037 0.0009 0.0010

Y-12794 2.19 up 0.121 0.0365 0.0432

Y-12627 2.15 up 0.114 0.0443 0.0484

Y-11081 2.14 up 0.101 0.0264 0.0393

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Y- 12990 2.12 up 0.0023 0.0005 0.0001

Y-04051 2.12 up 0.137 0.0466 0.0210

Y- 11596 2.08 up 0.0208 0.0072 0.0084

Y-13557 2.00 up 0.0029 0.0006 0.0006

Y-05229 1.99 up 0.0004 0.0001 < le-07

Y-05491 1.94 up 0.0000 0.0000 < le-07

Y-14256 1.71 up 0.062 0.0196 0.0236

Y- 13421 1.71 up 0.0592 0.0256 0.0268

Y-11319 1.68 up 0.045 0.0125 0.0115

Y-08994 1.65 up 0.124 0.0493 0.0498

Y- 13621 1.58 up 0.0144 0.0046 0.0018

Y-12100 1.56 up 0.0110 0.0034 0.0033

Y-06272 1.47 up 0.097 0.0350 0.0318

Y-11255 1.44 up 0.0548 0.0234 0.0124

Y-08889 1.26 up 0.0415 0.0165 0.0152

Y-11476 1.24 up 0.0099 0.0030 0.0032

Y- 13549 1.16 up 0.0611 0.0266 0.0261

Y-10611 0.85 down 0.0320 0.0121 0.0129

Y-11315 0.83 down 0.0027 0.0006 0.0008

Y- 13496 0.80 down 0.0041 0.0010 0.0006

Y-12056 0.77 down 0.0052 0.0014 0.0006

Y- 11677 0.77 down 0.1013 0.0496 0.0509

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Y-08166 0.76 down 0.0004 0.0000 0.0002

Y-03094 0.72 down 0.0129 0.0041 0.0027

Y-11560 0.71 down 0.0967 0.0471 0.0503

Y-13505 0.70 down 0.0912 0.0442 0.0427

Y-06350 0.70 down 0.0557 0.0240 0.0236

Y- 11245 0.70 down 0.054 0.0156 0.0177

Y- 12092 0.69 down 0.0374 0.0143 0.0144

Y-06667 0.69 down 0.0019 0.0004 0.0003

Y- 12465 0.67 down 0.0418 0.0167 0.0174

Y-03090 0.67 down 0.028 0.0060 0.0034

Y- 14606 0.65 down 0.0103 0.0031 0.0037

Y-12860 0.64 down 0.0702 0.0318 0.0317

Y- 11593 0.64 down 0.0035 0.0008 0.0006

Y-04523 0.62 down 0.0020 0.0004 < le-07

Y- 14904 0.62 down 0.0064 0.0018 0.0016

Y- 13684 0.61 down 0.0695 0.0314 0.0378

Y-10445_200 0.61 down 0.0179 0.0060 0.0068

Y- 13007 0.61 down 0.0151 0.0049 0.0050

Y-04599 0.59 down 0.0281 0.0103 0.0143

Y- 14088 0.57 down 0.0009 0.0002 0.0005

Y-12450 0.57 down 0.0639 0.0279 0.0258

Y-11585 0.57 down 0.0415 0.0165 0.0172

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Y-12128 0.56 down 0.0366 0.0139 0.0152

Y- 12206 0.55 down 0.0003 0.0000 < le-07

Y- 13497 0.54 down 0.0030 0.0007 0.0009

Y-11914 0.51 down 0.0042 0.0011 0.0010

Y-11578 0.50 down 0.0184 0.0063 0.0051

Y- 12792 0.50 down 0.0004 0.0001 0.0001

Y-11612_200 0.50 down 0.0007 0.0001 0.0002

Y-14596 0.49 down 0.0089 0.0025 0.0036

Y- 11303 0.48 down 0.077 0.0161 0.0250

Y-06650 0.48 down 0.0099 0.0029 0.0033

Y- 14089 0.47 down 0.0004 0.0001 0.0003

Y-14594 0.47 down 0.0169 0.0057 0.0055

Y- 14091 0.47 down 0.0002 0.0000 < le-07

Y-04015 0.47 down 0.0002 0.0000 < le-07

Y- 12800 0.46 down 0.0001 0.0000 < le-07

Y-14938 0.46 down 0.0107 0.0032 0.0025

Y- 13073 0.46 down 0.0049 0.0013 0.0016

Y-13554 0.45 down 0.0020 0.0004 0.0010

Y- 11546 0.45 down 0.0465 0.0189 0.0198

Y-13180 0.44 down 0.104 0.0385 0.0635

Y-11575 0.44 down 0.0153 0.0050 0.0038

Y-12663 0.42 down 0.0030 0.0007 0.0008

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Y-13520 0.41 down 0.0044 0.0011 0.0010

Y- 13685 0.41 down 0.057 0.0170 0.0614

Y- 12802 0.39 down 0.0064 0.0018 0.0018

Y-11786 0.37 down < le-07 < le-07 < le-07

Y-12873 0.37 down 0.0004 0.0001 < le-07

Y- 13423 0.36 down 0.0004 0.0000 0.0001

Y-01497_200 0.36 down 0.0003 0.0000 < le-07

Y- 14625 0.36 down < le-07 < le-07 < le-07

Y-13396 0.36 down 0.0035 0.0008 0.0009

Y- 11687 0.35 down 0.0000 0.0000 < le-07

Y-12095_200 0.35 down < le-07 < le-07 < le-07

Y-10597 0.35 down < le-07 < le-07 < le-07

Y-11261 0.34 down 0.0004 0.0001 0.0001

Y-13519 0.34 down 0.0011 0.0002 0.0003

Y-12801 0.34 down 0.037 0.0062 0.0080

Y-12689 0.33 down 0.0001 0.0000 0.0331

Y-14599 0.26 down 0.0000 0.0000 0.0001

Y-10457_200 0.18 down < le-07 < le-07 < le-07

Y-10613 0.16 down 0.0003 0.0000 0.0002

Xenobiotics Chemical glycerol 2 -phosphate 0.45 down 0.0003 0.0000 < le-07

Sugar, sugar galacturonate 0.51 down 0.0003 0.0000 < le-07 substitute, starch erythritol 0.47 down 0.0001 0.0000 < le-07

Fold Change Parametri Permutation

Super Pathway Sub Pathway Marker* direction FD ^d

(T vs. NT)^C c p-value p-value

Food component/plant ergothioneine 0.58 down 0.011 0.0015 0.0004 ^a Metabolites are listed in alphabetical order by super-pathway and then by sub-pathway.

b Markers are listed in order of fold change differences between tumor and nontumor tissue.

c Fold changes represent metabolites with signifcant (p<0.05) differences between tumor (T) versus nontumor tissue (NT).

Fold changes are listed in from largest to smallest within their sub-pathways.

d FDR: False discovery rate

Table 2: 17 Early Diagnostic HCC Metabolites"

Super Fold Change Direction Parametric Permutation

Sub Pathway Marker ^b FD ^d

Pathway (T vs. NT)^C p-value p-value

Amino acid Alanine and aspartate

metabolism N- acetyl asp aragine 2.26 up 0.077 0.0211 0.0625

Lysine metabolism pipecolate 0.71 down 0.077 0.0195 0.0234

Tryptophan metabolism kynurenine 2.27 up 0.060 0.0136 0.0195 tryptophan 0.76 down 0.107 0.0348 0.0352

Valine, leucine and isoleucine

metabolism valerylcarnitine 3.29 up 0.060 0.0123 0.0625

Cofactors and Ascorbate and aldarate

vitamins metabolism glucarate (saccharate) 0.36 down 0.01 1 0.0003 0.0039

Lipid Inositol metabolism scyllo-inositol 0.51 down 0.01 1 0.0004 0.0039

Medium chain fatty acid caproate (6:0) 1.68 up 0.095 0.0280 0.0332

1-oleoylglycerol (1-

Mono acylglycerol monoolein) 0.47 down 0.113 0.0422 0.5000

Nucleotide Purine metabolism, adenine adenosine 0.51 down 0.119 0.0455 0.0469 containing adenosine 5 '-monophosphate

(AMP) 0.31 down 0.103 0.0325 0.0332

Peptide Dipeptide glycylleucine 0.46 down 0.057 0.0102 0.0156

Unknown unknown Y- 11596 2.30 up 0.112 0.0408 0.0430

Y-08994 1.87 up 0.067 0.0161 0.0176

Y-06272 1.36 up 0.116 0.0439 0.0156

Super Fold Change Direction Parametric Permutation

Sub Pathway Marker ^b FD ^d

Pathway (T vs. NT)^C p-value p-value

Y- 14606 0.57 down 0.056 0.0093 0.0137

Y-04599 0.45 down 0.094 0.0276 0.0469 ^a Metabolites are listed in alphabetical order by super-pathway and then by sub-pathway.

c Fold changes represent metabolites with signifcant (p<0.05) differences between tumor (T) versus nontumor tissue (NT) in patients with early HCC (TNM Stage I).

Fold changes are listed in from largest to smallest within their sub-pathways.

Table 3: 28 Prognostic HCC Metabolites"

Fold

Change

Super Hazard OutParametric Permutation DirChange Di¬

Sub-Pathway Marker ^b HpSC

Pathway Ratio' come p-value^d p-value^e ection MH (T rection

(T vs.

vs. NT)^g

NT)^f

Carbohydrate Glycolysis,

gluconeogenesis,

pyruvate

metabolism glucuronate 0.76 poor 0.4587 0.4629 0.32 down 0.50 down

Nucleotide

sugars, pentose

metabolism 6-phosphogluconate 0.96 poor 0.8853 0.8875 0.54 down 0.90 down

Lipid Carnitine palmitoleate (16: ln7) 1.15 good 0.5176 0.5218 1.65 up 0.83 down metabolism palmitoylcarnitine 0.94 poor 0.6830 0.6815 2.00 up 6.37 up oleoylcarnitine 0.80 poor 0.2531 0.2657 1.02 up 4.51 up

Essential fatty linolenate [alpha or

acid gamma; (18:3n3 or 6)] 1.17 good 0.4189 0.4292 0.53 down 0.25 down

Long chain fatty linoleate (18:2n6) 1.19 good 0.5783 0.5905 0.53 down 0.47 down acid stearidonate (18:4n3) 1.14 good 0.4354 0.4416 1.01 up 0.38 down dihomo-linoleate (20:2n6) 1.12 good 0.6287 0.6386 1.73 up 1.12 up docosadienoate (22:2n6) 1.09 good 0.7106 0.7100 2.92 up 1.49 up eicosenoate (20: ln9 or 1 1) 1.03 good 0.8784 0.8785 2.03 up 1.24 up

Lysolipid 1- 1.10 good 0.6873 0.6937 0.98 down 0.61 down

Fold

Change

Super Hazard OutParametric Permutation DirChange Di¬

Sub-Pathway Marker ^b HpSC

Pathway Ratio' come p-value^d p-value^e ection MH (T rection

(T vs.

vs. NT)^g

NT)^f

linoleoylglycerophospho- ethanolamine

2-oleoylglycerophospho- choline 0.72 poor 0.0812 0.0842 0.57 down 2.24 up

2- linoleoylglycerophospho- choline 0.49 poor 0.0089 0.0081 0.25 down 0.95 down

Nucleotide Purine adenosine 5'- metabolism, monophosphate (AMP) 0.94 poor 0.7344 0.7397 0.31 down 0.97 down adenine adenosine 5 '-diphosphate

containing (ADP) 0.78 poor 0.5340 0.5373 0.73 down 1.32 up

Pyrimidine

metabolism,

thymine

containing;

Valine, leucine

and isoleucine

metabolism 3-aminoisobutyrate 1.34 good 0.1746 0.1859 2.34 up 0.99 down

Pyrimidine

metabolism, uridine 1.54 good 0.4537 0.4461 0.90 down 0.68 down

Fold

Change

Super Hazard OutParametric Permutation DirChange Di¬

Sub-Pathway Marker ^b HpSC

Pathway Ratio' come p-value^d p-value^e ection MH (T rection

(T vs.

vs. NT)^g

NT)^f

uracil containing

Glutathione

Peptide metabolism ophthalmate 1.10 good 0.4148 0.4186 0.23 down 5.88 up

Unknown unknown Y-12450 1.40 good 0.0741 0.0734 0.88 down 0.37 down

Y-11319 1.21 good 0.5029 0.5103 1.68 up 1.01 up

Y-11914 1.17 good 0.5722 0.5799 0.71 down 0.37 down

Y- 12465 1.12 good 0.6203 0.6193 0.50 down 0.92 down

Y-11583 0.96 poor 0.6901 0.6944 0.21 down 13.98 up

Y-11261 0.93 poor 0.6039 0.6079 0.19 down 0.60 down

Y- 12051 0.78 poor 0.2040 0.2222 1.07 up 2.31 up

Sugar, sugar

Xenobiotics substitute, starch galacturonate 0.84 poor 0.5950 0.6053 0.39 down 0.65 Down

Food

component/plant ergothioneine 0.66 poor 0.0071 0.0085 0.58 down 1.18 up ^a Metabolites are listed in alphabetical order by super-pathway and then by sub-pathway.

c Hazard ratios are listed in from largest to smallest within their sub-pathways.

d'^e Parametric and permutation p-values deemed significant (p<0.20) are underlined

Fold changes represent metabolites with expression differences between tumor (T) versus nontumor tissue (NT) in HpSC samples. Significant values (p<0.05) are underlined ^g Fold changes represent metabolites with expression differences between tumor (T) versus nontumor tissue (NT) in MH sampli Significant values (p<0.05) are underlined

Table 4: 48 Stem Cell-Related HCC Metabolites"

Fold-change

Parametric

Super Pathway Sub-Pathway Marker ^b HpSC vs. MH Direction FDR^d

p-value (Tumor)^c

Valine, leucine and

Amino acid isoleucine metabolism 4 -methyl -2 -oxopentanoate 0.48 down 0.204 0.0065

Carbohydrate Glycolysis,

gluconeogenesis,

pyruvate metabolism glucuronate 0.58 down 0.249 0.0146

Isobar: fructose 1,6-diphosphate, glucose 1,6- diphosphate 0.41 down 0.204 0.0086

2-phosphoglycerate 0.37 down 0.138 0.0021

Nucleotide sugars,

pentose metabolism 6-phosphogluconate 0.48 down 0.232 0.0127 sedoheptulose-7-phosphate 0.40 down 0.232 0.0124

Cofactors and Hemoglobin and

vitamins porphyrin heme 0.13 down 0.138 0.0026

Lipid Carnitine metabolism palmitoylcarnitine 0.38 down 0.422 0.0487 oleoylcarnitine 0.30 down 0.207 0.0097

Essential fatty acid linolenate [alpha or gamma; (18:3n3 or 6)] 3.05 up 0.126 0.0011

Long chain fatty acid stearidonate (18:4n3) 3.49 up 0.126 0.0012 palmitoleate (16: ln7) 2.51 up 0.204 0.0079 eicosenoate (20: ln9 or 11) 2.18 up 0.401 0.0346

Fold-change

Parametric

Super Pathway Sub-Pathway Marker ^b HpSC vs. MH Direction FD ^d

p-value (Tumor)^c

docosadienoate (22:2n6) 2.13 up 0.264 0.0173 dihomo-linoleate (20:2n6) 2.11 up 0.260 0.0164 oleate (18: ln9) 2.10 up 0.422 0.0498

10-heptadecenoate (17: ln7) 1.99 up 0.286 0.0194 linoleate (18:2n6) 1.91 up 0.184 0.0039 myristoleate (14: ln5) 1.83 up 0.322 0.0233 cis-vaccenate (18: ln7) 1.71 up 0.422 0.0481 palmitate (16:0) 1.53 up 0.412 0.0382

Lysolipid 1 -linoleoylglycerophosphoethanolamine 2.51 up 0.126 0.0006

2-linoleoylglycerophosphoethanolamine 1.94 up 0.204 0.0062

1-stearoylglycerophosphoethanolamine 1.51 up 0.371 0.0303

1-arachidonoylglycerophosphoethanolamine 1.50 up 0.368 0.0275

2-linoleoylglycerophosphocholine 0.38 down 0.422 0.0484

2 -palmitoy Iglyceropho sphocholine 0.36 down 0.126 0.0012

2-arachidonoylglycerophosphocholine 0.35 down 0.204 0.0077

1-palmitoylglycerophosphocholine 0.33 down 0.422 0.0453

2-oleoylglycerophosphocholine 0.30 down 0.193 0.0048

Bile acid metabolism taurocholenate sulfate 2.50 up 0.412 0.0372

Nucleotide Purine metabolism,

adenine containing adenosine 5'-diphosphate (ADP) 0.64 down 0.260 0.0163

Fold-change

Parametric

Super Pathway Sub-Pathway Marker ^b HpSC vs. MH Direction FD ^d

p-value (Tumor)^c

adenosine 5 '-monophosphate (AMP) 0.39 down 0.387 0.0325

Pyrimidine metabolism,

thymine containing;

Valine, leucine and

isoleucine metabolism/ 3 -aminoisobutyrate 2.04 up 0.422 0.0481

Pyrimidine metabolism,

uracil containing uridine 1.31 up 0.371 0.0295

Peptide Glutathione metabolism ophthalmate 0.11 down 0.138 0.0026 glutathione, reduced (GSH) 0.09 down 0.302 0.0212

Unknown unknown Y-12450 2.80 up 0.204 0.0091

Y-11319 2.06 up 0.138 0.0018

Y-13537 1.98 up 0.204 0.0085

Y-11914 1.79 up 0.412 0.0385

Y- 12465 0.46 down 0.232 0.0123

Y- 12051 0.44 down 0.371 0.0304

Y- 11809 0.38 down 0.232 0.0130

Y-11261 0.26 down 0.204 0.0077

Y-11583 0.07 down 0.193 0.0050

Sugar, sugar substitute,

Xenobiotics starch galacturonate 0.61 down 0.422 0.0405

Food component/plant ergothioneine 0.46 down 0.422 0.0415

Table 4: 48 Stem Cell-Related HCC Metabolites" (continued)

Fold Change Fold Change

Permutation

Marker ^b HpSC (T vs. Direction MH (T vs. Direction p-value

NT)^e NT)f

4-methyl-2-oxopentanoate 0.0072 0.98 down 1.11 up glucuronate 0.0159 0.32 down 0.50 down

Isobar: fructose 1,6-diphosphate, glucose

1,6-diphosphate 0.0070 1.44 up 2.04 up

2-phosphoglycerate 0.0022 0.72 down 1.76 up

6-phosphogluconate 0.0137 0.54 down 0.90 down sedoheptulose-7-phosphate 0.0139 0.43 down 1.42 up heme 0.0043 0.55 down 1.04 up palmitoylcarnitine 0.0499 2.00 up 6.37 up oleoylcarnitine 0.011 1 1.02 up 4.51 up linolenate [alpha or gamma; (18:3n3 or 6)] 0.0016 0.53 down 0.47 down stearidonate (18:4n3) 0.0016 1.01 up 0.38 down palmitoleate (16: ln7) 0.0068 1.65 up 0.83 down eicosenoate (20: ln9 or 1 1) 0.0316 2.03 up 1.24 up docosadienoate (22:2n6) 0.0160 2.92 up 1.49 up dihomo-linoleate (20:2n6) 0.0158 1.73 up 1.12 up oleate (18: ln9) 0.0504 0.98 down 0.64 down

10-heptadecenoate (17: ln7) 0.0206 1.55 up 0.97 down linoleate (18:2n6) 0.0056 0.53 down 0.47 down myristoleate (14: ln5) 0.0243 1.39 up 0.91 down cis-vaccenate (18: ln7) 0.0457 1.72 up 1.22 up palmitate (16:0) 0.0379 0.90 down 0.72 down

1-linoleoylglycerophosphoethanolamine 0.0009 0.98 down 0.61 down

2-linoleoylglycerophosphoethanolamine 0.0071 0.97 down 0.68 down

1-stearoylglycerophosphoethanolamine 0.0290 1.12 up 1.01 up

1-arachidonoylglycerophosphoethanolamine 0.0265 1.02 up 0.73 down

2-linoleoylglycerophosphocholine 0.0499 0.25 down 0.95 down

2-palmitoylglycerophosphocholine 0.0014 0.78 down 2.17 up

2-arachidonoylglycerophosphocholine 0.0095 0.38 down 1.57 up

1-palmitoylglycerophosphocholine 0.0474 0.49 down 1.22 up

2-oleoylglycerophosphocholine 0.0047 0.57 down 2.24 up taurocholenate sulfate 0.0380 1.15 up 0.68 down adenosine 5 '-diphosphate (ADP) 0.0174 0.73 down 1.32 up adenosine 5 '-monophosphate (AMP) 0.0291 0.31 down 0.97 down

3 -aminoisobutyrate 0.0495 2.34 up 0.99 down uridine 0.0291 0.90 down 0.68 down ophthalmate 0.0037 0.23 down 5.88 up

glutathione, reduced (GSH) 0.0232 0.22 down 7.16 up

Y-12450 0.0102 0.88 down 0.37 down

Y-1 1319 0.0016 1.68 up 1.01 up

Y-13537 0.0080 1.04 up 0.71 down

Y-1 1914 0.0359 0.71 down 0.37 down

Y- 12465 0.0134 0.50 down 0.92 down

Y- 12051 0.0290 1.07 up 2.31 up

Y- 1 1809 0.0141 0.85 down 2.15 up

Y-1 1261 0.0071 0.19 down 0.60 down

Y-1 1583 0.0070 0.21 down 13.98 up galacturonate 0.0376 0.39 down 0.65 down ergothioneine 0.0408 0.58 down 1.18 up ^a Metabolites are listed in alphabetical order by super-pathway and then by sub-pathway.

c Fold changes represent metabolites with significant (p<0.05) expression differences between hepatic stem cell HCC (HpSC) versus mature hepatocyte HCC (MH) subtypes in tumor tissues.

Fold changes are listed in from largest to smallest within their sub-pathways.

d FDR: False discovery rate.

e Fold changes represent metabolites with expression differences between tumor (T) versus nontumor tissue (NT) in HpSC samples.

Significant values (p<0.05) are underlined

f Fold changes represent metabolites with expression differences between tumor (T) versus nontumor tissue (NT) in MH samples.

Significant values (p<0.05) are underlined.

Table 5: For tumor-specific metabolites, for the class comparison of Tumor vs. Nontumor, p<0.05; FDR<20%, the following table shows the results obtained:

Up Down Total

All Samples 57 157 214

HpSC 42 133 175

MH 41 110 151

Claims

What is claimed is:

1. A method for determining if a subject has hepatocellular cancer (HCC), the method comprising:

analyzing a biological sample from a subject to determine the level of a marker or plurality of markers for hepatocellular cancer in the sample, wherein the one or more markers are selected from the group consisting of markers in any one of Tables 1, 2, 3 and 4; and

comparing the level of the marker or plurality of markers in the sample to hepatocellular cancer-positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of markers to diagnose whether the subject has hepatocellular cancer.

2. The method of Claim 1, wherein the markers in Tables 1, 2, 3 and 4 include

markers selected from the group consisting of:

i) fragments of markers in any one of Tables 1, 2, 3 or 4; ii) successors of markers in any one of Tables 1, 2, 3 or 4; iii) modified versions of markers in any one of Tables 1, 2, 3 or 4; and iv) combinations of markers of i), ii) and iii).

3. The method of Claim 1, wherein the marker or plurality of markers are selected from the group consisting of markers of Table 2.

4. The method Claim of 3, wherein the marker or plurality of markers are selected from the group consisting of:

i) fragments of markers selected from Table 2;

ii) successors of markers selected from Table 2;

iii) modified versions of markers selected from Table 2; iv) combinations of markers of i), ii), and iii); and

v) a plurality of markers comprising the marker set of N- acetylasparagine, pipecolate, kynurenine, tryptophan, valerylcarnitine, glucarate (saccharate), scyllo-inositol, caproate (6:0), 1-oleoylglycerol (1-monoolein), adenosine adenosine 5 '-monophosphate (AMP), and glycylleucine.

5. The method Claim 3, wherein the hepatocellular cancer is early stage

hepatocellular cancer (TNM stage I).

6. The method Claim 1, wherein the markers are selected from the group consisting of N-acetylasparagine, kynurenine, valerylcarnitine and combinations of the foregoing, and wherein these markers are upregulated compared to hepatocellular cancer-negative reference levels.

7. The method of claim 1, wherein the markers are selected from the group consisting of 2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, and both 2- oleoylglycerophosphocholine and 2-linoleoylglycerophosphocholine.

8. The method of Claim 1, wherein the markers are selected from the group

consisting of markers of Table 4.

9. The method of Claim 8, wherein the markers are selected from the group

consisting of:

i) fragments of markers selected from Table 4;

ii) successors of markers selected from Table 4;

iii) modified versions of markers selected from Table 4; iv) combinations of markers of i), ii) and iii);and

v) a plurality of markers comprising the marker set of 4-methyl-2- oxopentanoate, glucuronate, Isobar: fructose 1,6-diphosphate, glucose 1,6- diphosphate, 2-phosphoglycerate, 6-phosphogluconate, sedoheptulose-7- phosphate, heme, palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], stearidonate (18:4n3), palmitoleate (16: ln7), eicosenoate (20: ln9 or 11), docosadienoate (22:2n6), dihomo-linoleate (20:2n6), oleate (18:ln9), 10- heptadecenoate (17: ln7), linoleate (18:2n6), myristoleate (14: ln5), cis-vaccenate (18: ln7), palmitate (16:0), 1-linoleoylglycerophosphoethanolamine, 2- linoleoylglycerophosphoethanolamine, 1 -stearoylglycerophosphoethanolamine, 1 - arachidonoylglycerophosphoethanolamine, 2-linoleoylglycerophosphocholine, 2- palmitoylglycerophosphocholine, 2-arachidonoylglycerophosphocholine, 1 - palmitoylglycerophosphocholine, 2-oleoylglycerophosphocholine, adenosine 5'- diphosphate (ADP), adenosine 5 '-monophosphate (AMP), 3-aminoisobutyrate, uridine, ophthalmate, glutathione, reduced (GSH), taurocholenate sulfate, and galacturonate; and

wherein the hepatocellular cancer-positive and/or hepatocellular cancer- negative reference levels of the marker or plurality of markers are associated with hepatic stem cell (HpSC) HCC subtype, mature hepatocyte (MH) HCC subtype, or both.

10. A method for determining patient outcome in hepatocellular cancer, the method comprising:

analyzing a biological sample from a subject to determine the level(s) of one or more markers for hepatocellular cancer in the sample, wherein the one or more markers are selected from the group consisting of markers of Table 3.

11. The method of claim 10, wherein the markers of Table 3 include markers selected from the group consisting of:

i) fragments of markers selected from Table 3;

ii) successors of markers selected from Table 3;

iii) modified versions of markers selected from Table 3; iv) combinations of markers of i), ii) and iii);and

v) a plurality of markers comprising the marker set of glucuronate, 6- phosphogluconate, palmitoleate (16: ln7), palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], linoleate (18:2n6), stearidonate (18:4n3), dihomo-linoleate (20:2n6), docosadienoate (22:2n6), eicosenoate (20: ln9 or 11), 1-linoleoylglycerophosphoethanolamine, 2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, adenosine 5 '-monophosphate (AMP), adenosine 5 '-diphosphate (ADP), 3-aminoisobutyrate, uridine, ophthalmate, ergothioneine, and galacturonate,

wherein the level of the marker or plurality of markers in the sample as compared to hepatocellular cancer -positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of markers is indicative of heptaocellular cancer outcome.

12. The method of claim 10, wherein the markers are selected from the group

consisting of 2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, and both 2-oleoylglycerophosphocholine and 2-linoleoylglycerophosphocholine and wherein downregulation of the markers compared to hepatocellular cancer- negative reference levels is associated with an increase in patient survival.

13. The method of claim 1, wherein the biological sample is a tumor tissue.

14. The method of claim 1, wherein the biological sample is a body fluid.

15. The method of claim 14, wherein the body fluid is selected from the group consisting of blood, serum, plasma, urine, and saliva.

16. The method of claim 1, wherein the standard level or reference level is determined according to a statistical procedure for risk prediction.

17. The method of claim 16, wherein the statistical procedure for risk prediction

comprises using a Hazard ratio.

18. The method of claim 1, wherein the subject has a hepatitis B viral infection.

19. The method of claim 1, wherein the level of the marker or plurality of markers is detected with a reagent that specifically detects the marker or plurality of markers.

20. The method of claim 19, wherein the reagent is selected from the group consisting of an antibody, an antibody derivative, an antibody fragment, and an aptamer.

21. A method for monitoring the progression of heptaocellular cancer in a subject, the method comprising:

a) measuring the expression level of a marker or a plurality of markers in a first biological sample obtained from the subject, wherein the marker or plurality of markers comprise a plurality of markers selected from the group consisting of markers in any one of Tables 1, 2, 3 or 4;

b) measuring the expression level of the marker or plurality of markers in a second biological sample obtained from the subject; and

c) comparing the expression level of the marker or plurality of markers measured in the first sample with the level of the marker measured in the second sample.

22. The method of claim 21, wherein the markers of any one of Tables 1, 2, 3 and 4 are selected from the group consisting of:

i) fragments of markers in any one of Tables 1, 2, 3 or 4; ϋ) successors of markers in any one of Tables 1, 2, 3 or 4; iii) modified versions of markers in any one of Tables 1, 2, 3 or 4; and iv) combinations of markers of i), ii) and iii)

The method of claim 21, wherein the first biological sample from the subject is obtained at a time to, and the second biological sample from the subject is obtained at a later time ti.

24. The method of claim 21, wherein the first biological sample and the second biological sample are obtained from the subject more than once over a range of times.

25. A method of assessing the efficacy of a treatment for heptaocellular cancer in a subject, the method comprising comparing:

a) the expression level of a marker or plurality of markers measured in a first sample obtained from the subject at a time to, wherein the marker is selected from the group consisting of markers of Tables 1, 2, 3 and 4; and

b) the expression level of the marker or plurality of markers in a second sample obtained from the subject at a time ti;

wherein a change in the level of the marker or plurality of markers in the second sample relative to the first sample is an indication that the treatment is efficacious for treating heptaocellular cancer in the subject.

The method of claim 25, wherein the markers of Tables 1, 2, 3 and 4 are selected from the group consisting of:

i) fragments of markers in any one of Tables 1, 2, 3 or 4; ii) successors of markers in any one of Tables 1, 2, 3 or 4;

iii) modified versions of markers in any one of Tables 1, 2, 3 or 4; and iv) combinations of markers of i), ii) and iii).

The method of claim 25, wherein the time to is before the treatment has been administered to the subject, and the time ti is after the treatment has been administered to the subject.

The method of claim 25, wherein the comparing is repeated over a range of times. The method of claim 28, wherein the time to is before the treatment has been administered to the subject, and the time ti is after the treatment has been administered to the subject.

The method of any one of claims 1, 10, and 21 further comprising:

detecting in the biological sample a level of gene expression of marker genes selected from the group consisting of:

i) genes having at least 95% sequence identity with a gene that is a surrogate for a marker of Table 3;

ii) genes having at least 95% sequence identity with a gene that is a surrogate for a marker selected from the group consisting of glucuronate, 6- phosphogluconate, palmitoleate (16: ln7), palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], linoleate (18:2n6), stearidonate (18:4n3), dihomo-linoleate (20:2n6), docosadienoate (22:2n6), eicosenoate (20: ln9 or 11), 1-linoleoylglycerophosphoethanolamine, 2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, adenosine 5 '-monophosphate (AMP), adenosine 5 '-diphosphate (ADP), 3-aminoisobutyrate, uridine, ophthalmate, ergothioneine, and galacturonate, or homologs or variants thereof;

iii) homologs of marker genes of i) or ii), wherein the genes detected share 100% sequence identity with the corresponding marker genes in i) or ii), respectively;

iv) polypeptides encoded by the marker genes of i) and ii);

v) fragments of polypeptides of iv); and

viii) a polynucleotide which is fully complementary to the marker genes of i) or ϋ);

comparing the level of the marker or plurality of marker genes in the sample to hepatocellular cancer-positive and/or hepatocellular cancer-negative reference levels of the marker or plurality of marker genes.

31. A kit for heptaocellular cancer comprising a means to detect the expression of a marker or plurality of markers selected from the group consisting of markers of Tables 1, 2, 3 and 4.

32. The kit of claim 30, wherein the markers in any one of Tables 1, 2, 3 or 4 include markers selected from the group consisting of:

i) fragments of markers in any one of Tables 1, 2, 3 or 4; ϋ) successors of markers in any one of Tables 1, 2, 3 or 4; iii) modified versions of markers in any one of Tables 1, 2, 3 or 4; and iv) combinations of markers of i), ii) and iii).

33. The kit of claim 30, wherein the means to detect comprises binding ligands that specifically detect the markers.

34. The kit of claim 30, wherein the means to detect comprises binding ligands disposed on an assay surface.

35. The kit of claim 33, wherein the assay surface comprises a chip, array, or fluidity

36. The kit of claim 31 , wherein the binding ligands comprise antibodies or binding fragments thereof.

37. The kit of claim 31 , further comprising: a control selected from the group consisting of:

a) information containing a predetermined control level of the marker or plurality of markers that has been correlated with good patient outcome; b) information containing a predetermined control level of the marker or plurality of markers that has been correlated with poor patient outcome; and

c) both a) and b).

38. The kit of claim 31 , further comprising: a control selected from the group consisting of:

a) information containing a predetermined control level of the marker or plurality of markers that has been correlated with associated with HpSC HCC subtype;

b) information containing a predetermined control level of the marker or plurality of markers that has been correlated with MH HCC subtype; and c) both a) and b).

39. The kit of claim 31 , further comprising a means to detect the expression of a

marker gene or plurality of marker genes selected from the group consisting of: i) genes having at least 95% sequence identity with a gene that is a surrogate for a marker of Table 3; and,

ii) genes having at least 95% sequence identity with a gene that is a surrogate for a marker selected from the group consisting of glucuronate, 6- phosphogluconate, palmitoleate (16: ln7), palmitoylcarnitine, oleoylcarnitine, linolenate [alpha or gamma; (18:3n3 or 6)], linoleate (18:2n6), stearidonate (18:4n3), dihomo-linoleate (20:2n6), docosadienoate (22:2n6), eicosenoate (20: ln9 or 11), 1-linoleoylglycerophosphoethanolamine, 2-oleoylglycerophosphocholine, 2-linoleoylglycerophosphocholine, adenosine 5 '-monophosphate (AMP), adenosine 5 '-diphosphate (ADP), 3-aminoisobutyrate, uridine, ophthalmate, ergothioneine, and galacturonate, or homologs or variants thereof.