WO2013025322A2 - Marker-based prognostic risk score in liver cancer - Google Patents

Abstract

Methods and compositions for the prognosis and classification of cancer, especially liver cancer, are provided. For example, in certain aspects methods for liver cancer prognosis using expression analysis of selected biomarkers are described. In particular, a risk score may be developed to provide a cancer prognosis.

Description

DESCRIPTION

MARKER-BASED PROGNOSTIC RISK SCORE IN LIVER CANCER

BACKGROUND OF THE INVENTION

[0001] This application claims priority to U.S. Application No. 61/523,707 filed on August 15, 2011, the entire disclosure of which is specifically incorporated herein by reference in its entirety without disclaimer. [0002] This invention was made with government support under grant number CA106672 awarded by the National Institute of Health. The government has certain rights in the invention.

1. Field of the Invention

[0003] The present invention relates generally to the fields of oncology, molecular biology, cell biology, and cancer. More particularly, it concerns cancer prognosis or classification using molecular markers.

2. Description of Related Art

[0004] Gene expression profiling studies of various cancers have discovered consistent gene expression patterns associated with pathological or clinical phenotype, elucidating subtypes of cancer previously unidentified with conventional technologies (Lee et al., 2006; van de Vijver et al., 2002; Potti et al, 2006; Lee et al, 2004; Alizadeh et al, 2000). This new technology has been used successfully to predict clinical outcomes and survival rates and to identify potential therapeutic targets and prognostic marker genes. Better understanding of the fundamental biology of these genes may not only improve prognostication but also offer new individualized therapeutic options.

[0005] However, despite many attempts to establish pre-treatment prognostic markers to understand the clinical biology of patients with liver cancers, validated clinical or biomarker parameters are lacking in many aspects. Therefore, there remains a need to discover novel prognostic markers for cancer patients, especially liver cancer patients. SUMMARY OF THE INVENTION

[0006] With the recent advances in gene expression profiling technology, improvement in prediction models for risk assessment in liver cancer, such as hepatocellular carcinoma (HCC), has been reported (Budha et al, 2006; Budhu et al, 2006; Lee et al., 2004a; Lee et al, 2004b; Lee et al, 2004c; Lee et al, 2006; Woo et al, 2008; Roessler et al, 2010; Boyault et al, 2007; Chiang et al, 2008; Simon et al, 2007). However, although these gene expression signatures might better reflect the biological characteristics of HCC tumors, the complexity of prediction models based on such signatures has hampered their clinical usefulness. To overcome this limitation, methods and compositions related to a simple risk scoring system that can provide prognosis of liver cancer patients are provided, for example, to predict recurrence and overall survival (OS) of patients after surgical resection for HCC.

[0007] Certain aspects of the present invention overcome major deficiencies in the art by providing a method of providing a prognosis for a subject determined to have a liver cancer. The method may comprise obtaining expression information of biomarkers in cells of a liver cancer sample of a subject, wherein the biomarkers are one, two, three, four, five, six, seven, eight, nine, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, or all genes selected from the group consisting of ACSL5, ADH1B, ADH6, ALDOA, APOC3, AQP9, ARPC2, BPHL, Clorfl l5, C4BPB, CDOl, CHI3L1, COBLL1, CRAT, CRYL1, CTSC, CXCR4, CYB5A, CYP27A1, CYP2J2, CYP4F12, DDIT4, EPHX2, ETV5, F10, F3, F5, GJB1, GPHN, HN1, HNF4A, IGFBP3, IQGAP1, IQGAP2, ITPR2, KHK, LAMB1, LECT2, MST1, MTSS1, PAH, PFKFB3, PKLR, PKM2, PLG, PLOD2, PPT1, RALA, RGN, RGS1, RGS2, RNASE4, SERPTNA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1, SLC38A1, SPHK1, SULT2A1, TBX3, TM4SF1 and TSPAN3. The expression of the biomarkers may be assessed by testing the sample or obtained from a lab or a technician.

[0008] The method may further comprise providing a prognosis for the subject based on the expression information, wherein, as compared with a reference expression level, increased expression of one or more genes selected from the group consisting of ACSL5, ADH1B, ADH6, APOC3, AQP9, BPHL, Clorfl l5, C4BPB, CDOl, CHI3L1, COBLL1, CRAT, CRYL1, CYB5A, CYP27A1, CYP2J2, CYP4F12, EPHX2, F10, F5, GJB1, GPHN, HNF4A, IQGAP2, ITPR2, KHK, LECT2, MST1, MTSS1, PAH, PKLR, PLG, RGN, RNASE4, SERPINA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1, SULT2A1 and TBX3 indicates a good prognosis, and/or increased expression of one or more genes selected from the group consisting of ALDOA, ARPC2, CTSC, CXCR4, DDIT4, ETV5, F3, HN1, IGFBP3, IQGAP1 , LAMB1, PFKFB3, PKM2, PLOD2, PPT1, RALA, RGS1, RGS2, SLC38A1, SPHK1, TM4SF1 and TSPAN3 indicates a poor prognosis. The reference expression level may be obtained from a control subject who does not have a liver cancer, a non-cancerous liver sample. In other aspects, the reference expression level may be a predetermined value, for example, as determined by statistic analysis of a group of control subjects or control samples. In further aspects, the methods may be combined with any other prognosis or classification methods, such as staging or classification by the absence or presence of invasion, such as vasculature invasion.

[0009] In certain aspects, the method of providing the prognosis may further comprise generating a risk score based on the expression information. In particular aspects, the risk score may be defined as a weighted sum of expression levels of the biomarkers. For example, the risk score may be calculated based on a summation of the expression level of the selected biomarkers multiplied with a corresponding regression coefficient. The regression coefficient may be calculated according to a regression analysis of the correlation between the expression level of the biomarker genes and survival of a control group. [0010] To improve data processing efficiency, the risk score may be generated on a computer. For example, the risk score may be generated by a computer readable medium comprising machine executable instructions suitable for generating a risk score. The method for providing the prognosis may also comprise classifying a group of subjects based on the risk score by comparing the risk score of each subject in the group with a reference value of risk score. In a still further aspect, the reference value may be a representative risk score of a control group, such as a median or an average. The control group for risk score generation may be a group with known or available expression information and survival data, which may be a good prognosis group, a poor prognosis group, a high risk group, or a low risk group. [0011] In order to obtain expression information of the biomarkers, the method may comprise obtaining or receiving a cancer sample of the subject. For example, the sample may be prepared by a clinician, a lab technician, a machine or a robot, or stored after preparation and retrieved or received before the sample testing. For maintenance of the sample quality during storage and handling, the sample may be paraffin-embedded or frozen. Testing the sample to assess expression may be comprised in the claimed methods.

[0012] The skilled artisan will understand that any methods known in the art for assessing gene expression can be used in certain aspects of the present methods and compositions. The testing to assess gene expression may comprise RNA quantification, such as obtaining RNA of the sample, reverse transcription, amplification and/or probe hybridization. The techniques that may be used in the testing for RNA quantification may include, but not limited to, cDNA microarray, quantitative RT-PCR, in situ hybridization, Northern blotting, nuclease protection, or a combination thereof. In particular, cDNA microarray may be used for its high-throughput and high efficiency. Quantitative RT-PCR may also be used alone or in combination with other quantification methods for validation or confirmation. Alternatively, the testing may comprise antibody detection for expression at a protein level, such as immunohistochemistry, an ELISA, a radioimmunoassay (RIA), an immunoradiometric assay, a fluoroimmunoassay, a chemiluminescent assay, a bio luminescent assay, a gel electrophoresis, a Western blot analysis, or a combination thereof.

[0013] In certain aspects of the invention, the poor prognosis may comprise high risk of recurrence, poor survival, or a low response to a conventional therapy such as surgery, chemotherapy and/or radiation therapy relative to one or more control subjects. The good prognosis may comprise low risk of recurrence, good survival, or a high response to a conventional therapy relative to one or more control subjects. The control subjects may be subjects that do not have a cancer, like a liver cancer.

[0014] Based on the prognosis identification, the methods may comprise reporting the prognosis identification to the subject, or further aspects, prescribing or administering a treatment to the subject: for example, such a treatment would be a conventional therapy like surgery, chemotherapy and/or radiation therapy to the subject if a good prognosis is identified, or an alternative treatment other than surgery, chemotherapy and radiation therapy to the subject if a poor prognosis is identified.

[0015] In certain aspects, the subject may be a human or any mammal. The subject may have previously received treatment by surgery or chemotherapy. In a particular aspect, the subject may have been determined to have or not to have vasculature invasion. The subject may have a liver cancer or have a risk of a liver cancer, or may be determined to have a liver cancer or a risk for a liver cancer. Non-limiting examples of liver cancer include hepatocellular carcinoma, chonlagiocarcinoma and hepatoblasoma.

[0016] There may also be provided a kit or an array comprising a plurality of antigen-binding fragments that bind to expression products of biomarkers or a plurality of primers or probes that bind to transcripts of two or more of the biomarkers described above to assess expression levels, wherein the kit or array is housed in a container.

[0017] In a further aspect, the kit may also comprise instructions that as compared with a reference expression level, increased expression of one or more genes selected from the group consisting of ACSL5, ADH1B, ADH6, APOC3, AQP9, BPHL, Clorfl l5, C4BPB, CDOl , CHI3L1, COBLL1, CRAT, CRYL1 , CYB5A, CYP27A1, CYP2J2, CYP4F12, EPHX2, F10, F5, GJB1, GPHN, HNF4A, IQGAP2, ITPR2, KHK, LECT2, MST1, MTSS1, PAH, PKLR, PLG, RGN, RNASE4, SERPINA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1, SULT2A1 and TBX3 indicates a good prognosis, and/or increased expression of one or more genes selected from the group consisting of ALDOA, ARPC2, CTSC, CXCR4, DDIT4, ETV5, F3, HN1, IGFBP3, IQGAP1, LAMB1, PFKFB3, PKM2, PLOD2, PPT1, RALA, RGS1, RGS2, SLC38A1, SPHK1, TM4SF1 and TSPAN3 indicates a poor prognosis.

[0018] Embodiments discussed in the context of methods and/or compositions of the invention may be employed with respect to any other method or composition described herein. Thus, an embodiment pertaining to one method or composition may be applied to other methods and compositions of the invention as well.

[0019] As used herein the terms "encode" or "encoding" with reference to a nucleic acid are used to make the invention readily understandable by the skilled artisan; however, these terms may be used interchangeably with "comprise" or "comprising" respectively.

[0020] As used herein the specification, "a" or "an" may mean one or more. As used herein in the claim(s), when used in conjunction with the word "comprising", the words "a" or "an" may mean one or more than one.

[0021] The use of the term "or" in the claims is used to mean "and/or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and "and/or." As used herein "another" may mean at least a second or more.

[0022] Throughout this application, the term "about" is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, or the variation that exists among the study subjects.

[0023] Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

[0025] FIGS. 1A-1C. Stratification of HCC patients in NCI cohort with 65-gene expression signature. (1A) Hierarchical clustering of patients with expression data for 65 genes. Patients were subdivided into two clusters, Left (L) and Right (R). (IB) Kaplan-Meier plots for overall survival (OS) of the two subgroups. (1C) Kaplan-Meier plots for recurrence- free survival (RFS) of the two subgroups.

[0026] FIGS. 2A-2C. Risk score and prognosis of HCC patients. (2A) Risk scores in the NCI cohort. Each bar represents the risk score for an individual patient. (2B & 2C) Kaplan- Meier plots of the two subgroups stratified by risk score in the NCI cohort. OS, overall survival; RFS, recurrence free survival.

[0027] FIGS. 3A-3C. Overall survival and recurrence-free survival of HCC patients stratified by risk score in the Korean and LCI HCC cohorts. HCC patients in the Korean cohort (3 A & 3B) and LCI cohort (3C) were stratified by 65-gene risk score. [0028] FIGS. 4A-4D. Kaplan Meier survival plots of overall survival (OS) and recurrence free survival (RFS) of HCC patients in the NCI cohort. Patients were stratified by NCI proliferation signatures (4A, 4B) or SNU recurrence signature (4C, 4D). P- values were obtained using the log-rank test. [0029] FIG. 5. Construction of prediction model according to the 65-gene expression signature. Schematic overview of the strategy used to construct prediction models and evaluate predicted outcomes based on gene expression signatures.

[0030] FIGS. 6A-6C. Kaplan-Meier plots of overall survival (OS) of HCC patients stratified by BCLC stage and risk score. Patients were stratified by BCLC stages (6A) and risk score (6B and 6C). P- values were obtained using the log-rank test.

[0031] FIGS. 7A-7D. Kaplan-Meier plots of overall survival (OS) of HCC patients stratified by AJCC stage and risk score. Patients were stratified by AJCC stages (7A) and risk score (7B, 7C, and 7D). -values were obtained using the log-rank test.

[0032] FIGS. 8A-8D. Kaplan-Meier plots of overall survival (OS) of HCC patients stratified by vasculature invasion and risk score. Patients were stratified by vasculature invasion (8A), risk score (8B and 8C), or both (8D). -values were obtained using the log- rank test. RS, risk score.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0033] The instant invention overcomes several major problems with current cancer prognosis in providing methods and compositions using novel combination of biomarkers identified by expression profiling and survival analysis of liver cancer patients. Particularly, in providing a cancer prognosis such as predicting recurrence of liver cancer after a potential curative therapy, a new risk scoring system and method have been developed, which outperform currently available staging systems and are independent of other clinical variables.

[0034] For example, the prognostic risk score can easily quantify the likelihood of recurrence and overall survival (OS) in hepatocellular carcinoma (HCC) patients who have undergone surgical resection as primary treatment. Several lines of evidence strongly support that the risk score is an independent and significant predictor of prognosis as described in the Examples.

[0035] Classification of human cancers into more homogenous clinical groups such as stages and grades significantly improved the treatment of patients by standardizing patient care. Molecular classification of cancers further improved patient care by enabling the development of treatments tailored to the abnormalities present in each patient's cancer cells. Currently, decision-making for liver cancer (such as HCC) treatment in the clinical setting is mainly based on clinical data, which is best reflected in BCLC staging and its associated treatment algorithm (Bruix and Llovet, 2003). However, this staging method offers little or almost no information about biological characteristics of HCC that would be very critical for tailored treatment in future. Importantly, risk scores provided herein reflects biological characteristics of tumors such as activation of AKT and CTNNB1 as well as prognostic characteristics. Thus, it would provide an opportunity for developing rationalized clinical trials to identify best-fitting treatments for patients since specific inhibitors of AKT are already available and currently under evaluation in clinical or pre-clinical studies for targeted treatment of cancer patients (Pal et al., 2010).

[0036] Thus, the use of a risk score as described herein can provide a prognosis in liver cancer patients in a reliable and reproducible manner across independent patient cohorts. Such methods and risk scores may offer information about the potential benefits of adjuvant therapies after surgical resection, patterns of recurrence, and impact of subsequent therapies.

[0037] Further embodiments and advantages of the invention are described below.

I. Definitions

[0038] "Prognosis" refers to as a prediction of how a patient will progress, and whether there is a chance of recovery. "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. Prognosis also includes prediction of favorable responses to cancer treatments, such as a conventional cancer therapy.

[0039] By "subject" or "patient" is meant any single subject for which therapy is desired, including humans, cattle, dogs, guinea pigs, rabbits, chickens, and so on. Also intended to be included as a subject are any subjects involved in clinical research trials not showing any clinical sign of disease, or subjects involved in epidemiological studies, or subjects used as controls.

[0040] As used herein, "increased expression" refers to an elevated or increased level of expression in a cancer sample relative to a suitable control (e.g., a non-cancerous tissue or cell sample, a reference standard), wherein the elevation or increase in the level of gene expression is statistically-significant (p<0.05). Whether an increase in the expression of a gene in a cancer sample relative to a control is statistically significant can be determined using an appropriate t-test (e.g., one-sample t-test, two-sample t-test, Welch's t-test) or other statistical test known to those of skill in the art. Genes that are overexpressed in a cancer can be, for example, genes that are known, or have been previously determined, to be overexpressed in a cancer.

[0041] As used herein, "decreased expression" refers to a reduced or decreased level of expression in a cancer sample relative to a suitable control (e.g., a non-cancerous tissue or cell sample, a reference standard), wherein the reduction or decrease in the level of gene expression is statistically-significant (p<0.05). In some embodiments, the reduced or decreased level of gene expression can be a complete absence of gene expression, or an expression level of zero. Whether a decrease in the expression of a gene in a cancer sample relative to a control is statistically significant can be determined using an appropriate t-test (e.g., one-sample t-test, two-sample t-test, Welch's t-test) or other statistical test known to those of skill in the art. Genes that are underexpressed in a cancer can be, for example, genes that are known, or have been previously determined, to be underexpressed in a cancer.

[0042] In a further embodiment, the marker level may be compared to the level of the marker from a control, wherein the control may comprise one or more tumor samples (e.g., liver cancer samples) taken from one or more patients determined as having a good prognosis ("good prognosis" control) or a poor prognosis ("poor prognosis" control), or both. [0043] The control may comprise data obtained at the same time (e.g., in the same hybridization experiment) as the patient's individual data, or may be a stored value or set of values, e.g. stored on a computer, or on computer-readable media. If the latter is used, new patient data for the selected marker(s), obtained from initial or follow-up samples, can be compared to the stored data for the same marker(s) without the need for additional control experiments.

[0044] A good or bad prognosis may, for example, be assessed in terms of patient survival, likelihood of disease recurrence or disease metastasis (patient survival, disease recurrence and metastasis may for example be assessed in relation to a defined timepoint, e.g. at a given number of years after cancer surgery (e.g. surgery to remove one or more tumors) or after initial diagnosis. In one embodiment, a good or bad prognosis may be assessed in terms of overall survival or disease free survival. The good or bad prognosis may be relative to

[0045] For example, "good prognosis" may refers to the likelihood that a patient afflicted with cancer, particularly liver cancer, will remain disease-free (i.e., cancer-free). "Poor prognosis" may be used to mean the likelihood of a relapse or recurrence of the underlying cancer or tumor, metastasis, or death. Cancer patients classified as having a "good prognosis" remain free of the underlying cancer or tumor. In contrast, "bad prognosis" cancer patients experience disease relapse, tumor recurrence, metastasis, or death. In particular embodiments, the time frame for assessing prognosis and outcome is, for example, less than one year, one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, or more years. In certain aspects, the relevant time for assessing prognosis or disease-free survival time may begin the surgical removal of the tumor or suppression, mitigation, or inhibition of tumor growth. Thus, for example, in particular embodiments, a "good prognosis" refers to the likelihood that a liver cancer patient will remain free of the underlying cancer or tumor for a period of at least five, such as for a period of at least ten years. In further aspects of the invention, a "poor prognosis" refers to the likelihood that a liver cancer patient will experience disease relapse, tumor recurrence, metastasis, or death within less than ten years, such as less than five years. Time frames for assessing prognosis and outcome provided herein are illustrative and are not intended to be limiting. [0046] The term "high risk" means the patient is expected to have a distant relapse in a shorter period less than a predetermined value (for example, from a control), for example in less than 5 years, preferably in less than 3 years. The term "low risk" means the patient is expected to have a distant relapse in a shorter period less than a predetermined value, for example, after 5 years, preferably in less than 3 years. Time frames for assessing risks provided herein are illustrative and are not intended to be limiting.

[0047] The term "antigen binding fragment" herein is used in the broadest sense and specifically covers intact monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g. bispecific antibodies) formed from at least two intact antibodies, and antibody fragments.

[0048] The term "primer," as used herein, is meant to encompass any nucleic acid that is capable of priming the synthesis of a nascent nucleic acid in a template-dependent process. Primers may be oligonucleotides from ten to twenty and/or thirty base pairs in length, but longer sequences can be employed. Primers may be provided in double-stranded and/or single-stranded form, although the single-stranded form is preferred.

II. Risk Scores

[0049] The biomarkers as used herein may be related to cancer prognosis, for example, prediction of survival, recurrence, or therapy response. In a particular embodiment, the differential patterns of expression of a plurality of these biomarkers may be used to predict the survival outcome of a subject with cancer. Certain biomarkers tend to be over-expressed in long-term survivors, whereas other biomarkers tend to be over- expressed in short-term survivors. The unique pattern of expression of a plurality of biomarkers in a subject (i.e., the gene signature) may be used to generate a risk score of survival. Subjects with a high risk score may have a short survival time (e.g., less than about 2 years) after surgical resection. Subjects with a low risk score may have a longer survival time (e.g., more than about 3 years) after resection.

[0050] Regardless of the technique used to measure the differential expression of a plurality of biomarkers, the expression of each biomarker may be converted into an expression value. These expression values then will be used to calculate a risk score of survival for a subject with cancer using statistical methods well known in the art. The risk scores may be calculated using a principal components analysis. The risk scores may also be calculated using a partial Cox regression analysis. In a preferred embodiment, the risk scores may be calculated using a univariate Cox regression analysis. [0051] The scores generated may be used to classify patients into high or low risk score, wherein a high risk score is associated with a poor prognosis, such as a short survival time or a poorer survival, and a low risk score is associated with a good prognosis, such as a long survival time or a better survival. The cut-off value may be derived from a control group of cancer patients as a median risk score. In further aspects, there may be two or more major prognostic subgroups. Although two-group classification (e.g., high vs. low risk) of cancer prognosis is largely supported by the results of previous studies (Budhu et ah, 2006; Hoshida et ah, 2008; Lee et ah, 2004b; Lee et ah, 2004c; Lee et ah, 2006; Woo et ah, 2008) it may be contemplated that there are more than two prognostic groups of liver cancer patients, given the genetic heterogeneity of the disease. Because these methods provided herein generate continuous risk scores, it is easy to adjust cutoff criteria to restratify liver cancer patients according to the degree of genetic heterogeneity.

[0052] In addition, the risk score might be developed by incorporating genomic data from surrounding tissues that does not overlap with but complementary to those from tumor tissues. The risk score may also be combined with other clinical characteristics or demographic information.

[0053] In a preferred embodiment of this method, a tissue sample may be collected from a subject with a cancer, for example, a liver cancer. The collection step may comprise surgical resection. The sample of tissue may be stored in RNAIater or flash frozen, such that RNA may be isolated at a later date. RNA may be isolated from the tissue and used to generate labeled probes for a nucleic acid microarray analysis. The RNA may also be used as a template for qRT-PCR in which the expression of a plurality of biomarkers is analyzed. The expression data generated may be used to derive a risk score, e.g., using the Cox regression classification method to obtain regression coefficients as the weight of each corresponding biomarker gene expression. The risk score may be used to predict whether the subject will be a short-term or a long-term cancer survivor.

[0054] Biomarker genes that may be used in cancer prognosis or risk score generation may be one or more selected from Table 1 below. Table 1 - Biomarker genes for cancer prognosis

GENE Description GenBank accession number

(incorporated herein by reference)

PAH phenylalanine hydroxylase BG433489

PFKFB3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 NM 004566

PKLR pyruvate kinase, liver and RBC BF110802

PKM2 pyruvate kinase, muscle NM 002654

PLG plasminogen M74220

PLOD2 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 AI754404

PPT1 palmitoyl-protein thioesterase 1 NM 000310

RALA v-ral simian leukemia viral oncogene homolog A (ras NM_005402

related)

RGN regucalcin (senescence marker protein-30) D31815

RGS1 regulator of G-protein signaling 1 S59049

RGS2 regulator of G-protein signaling 2, 24kDa NM 002923

RNASE4 ribonuclease, RNase A family, 4 NM 002937

SERPIN serpin peptidase inhibitor, clade A (alpha- 1 NM_016186

A10 antiproteinase, antitrypsin), member 10

SERPIN serpin peptidase inhibitor, clade C (antithrombin), D29832

CI member 1

SERPINF serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, NM 000934

2 pigment epithelium derived factor), member 2

SFTPC surfactant protein C J03553

SLC22A7 solute carrier family 22 (organic anion transporter), NM_006672

member 7

SLC2A2 solute carrier family 2 (facilitated glucose transporter), NM 000340

member 2

SLC30A1 solute carrier family 30 (zinc transporter), member 1 AI972416

SLC38A1 solute carrier family 38, member 1 NM 030674

SPHK1 sphingosine kinase 1 NM 021972

SULT2A sulfotransferase family, cytosolic, 2A, NM_003167

1 dehydroepiandrosterone (DHEA)-preferring, member 1

TBX3 T-box 3 NM 016569

TM4SF1 transmembrane 4 L six family member 1 All 89753

TSPAN3 tetraspanin 3 NM_005724

III. Cancer and Cancer Samples

[0055] The expression of a plurality of biomarkers may be measured in a sample of cells from a subject with cancer. The type and classification of the cancer can and will vary. The cancer may be an early stage cancer, i.e., stage I or stage II, or it may be a late stage cancer, i.e., stage III or stage IV. In a particular aspect, the cancer may be a cancer of the liver. [0056] Liver cancer or hepatic cancer is properly considered to be a cancer which starts in the liver, as opposed to a cancer which originates in another organ and migrates to the liver, known as a liver metastasis. The most frequent liver cancer is hepatocellular carcinoma (HCC). Hepatocellular carcinoma (HCC), is the third leading cause of cancer death worldwide. Although surgical resection for HCC provides best chance for cure, the prognosis after surgery differs considerably among patients. Because of this clinical heterogeneity, predicting the recurrence or survival of HCC patients after surgical resection remains challenging.

[0057] In further aspects, this liver cancer may also be a variant type that consists of both HCC and cholangiocarcinoma components. The cells of the bile duct coexist next to the bile ducts that drain the bile produced by the hepatocytes of the liver. Liver cancers which arise from the blood vessel cells in the liver are known as hemangioendotheliomas. Additional examples of liver cancer include but are not limited to: mesenchymal tissue, sarcoma, or hepatoblastoma. Cholangiocarcinoma (bile duct cancers), also included herein, account for 1 or 2 out of every 10 cases of liver cancer. These cancers start in the small tubes (called bile ducts) that carry bile to the intestine. The liver cancer may also include angiosarcoma and hemangiosarcoma, which are rare forms of cancer that start in the blood vessels of the liver. Lymphoma of liver, a rare form of lymphoma that usually have diffuse infiltration to liver, is also included in certain aspects of the invention. [0058] Other types of cancer include, but are not limited to, anal cancer, bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, colon cancer, duodenal cancer, endometrial cancer, eye cancer, gallbladder cancer, head and neck cancer, larynx cancer, non- small cell lung cancer, small cell lung cancer, lymphomas, melanoma, mouth cancer, ovarian cancer, pancreatic cancer, penal cancer, prostate cancer, rectal cancer, renal cancer, skin cancer, testicular cancer, thyroid cancer, and vaginal cancer.

[0059] In certain aspects, the sample of cells or tissue sample may be obtained from the subject with cancer by biopsy or surgical resection. The type of biopsy can and will vary, depending upon the location and nature of the cancer. A sample of cells, tissue, or fluid may be removed by needle aspiration biopsy. For this, a fine needle attached to a syringe is inserted through the skin and into the organ or tissue of interest. [0060] The needle may be guided to the region of interest using ultrasound or computed tomography (CT) imaging. Once the needle is inserted into the tissue, a vacuum is created with the syringe such that cells or fluid may be sucked through the needle and collected in the syringe. A sample of cells or tissue may also be removed by incisional or core biopsy. For this, a cone, a cylinder, or a tiny bit of tissue is removed from the region of interest. CT imaging, ultrasound, or an endoscope is generally used to guide this type of biopsy. Lastly, the entire cancerous lesion may be removed by excisional biopsy or surgical resection.

[0061] Once a sample of cells or sample of tissue is removed from the subject with cancer, it may be processed for the isolation of RNA or protein using techniques well known in the art and disclosed in standard molecular biology reference books, such as Ausubel et al. (2003). A sample of tissue may also be stored in RNAIater (Ambion; Austin TX) or flash frozen and stored at -80 °C for later use. The biopsied tissue sample may also be fixed with a fixative, such as formaldehyde, paraformaldehyde, or acetic acid/ethanol. The fixed tissue sample may be embedded in wax (paraffin) or a plastic resin. The embedded tissue sample (or frozen tissue sample) may be cut into thin sections. RNA or protein may also be extracted from a fixed or wax-embedded tissue sample.

[0062] The subject with cancer may be a mammalian subject. Mammals may include primates, livestock animals, and companion animals. Primates may include humans, New World monkeys, Old World monkeys, gibbons, and great apes. Livestock animals may include horses, cows, goats, sheep, deer (including reindeer) and pigs. Companion animals may include dogs, cats, rabbits, and rodents (including mice, rats, and guinea pigs). In an exemplary embodiment, the subject is a human.

IV. Expression Assessment

[0063] In certain aspects, this invention entails measuring expression of one or more prognostic biomarkers in a sample of cells from a subject with cancer. The expression information may be obtained by testing cancer samples by a lab, a technician, a device, or a clinician.

[0064] The pattern or signature of expression in each cancer sample may then be used to generate a risk score for cancer prognosis or classification, such as predicting cancer survival or recurrence. The level of expression of a biomarker may be increased or decreased in a subject relative to other subjects with cancer. The expression of a biomarker may be higher in long- term survivors than in short-term survivors. Alternatively, the expression of a biomarker may be higher in short-term survivors than in long-term survivors.

[0065] Expression of one or more of biomarkers identified by the inventors could be assessed to predict or report prognosis or prescribe treatment options for cancer patients, especially liver cancer patients.

[0066] The expression of one or more biomarkers may be measured by a variety of techniques that are well known in the art. Quantifying the levels of the messenger R A (mRNA) of a biomarker may be used to measure the expression of the biomarker. Alternatively, quantifying the levels of the protein product of a biomarker may be to measure the expression of the biomarker. Additional information regarding the methods discussed below may be found in Ausubel et al. (2003) or Sambrook et al. (1989). One skilled in the art will know which parameters may be manipulated to optimize detection of the mRNA or protein of interest.

[0067] A nucleic acid microarray may be used to quantify the differential expression of a plurality of biomarkers. Microarray analysis may be performed using commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GeneChip® technology (Santa Clara, CA) or the Microarray System from Incyte (Fremont, CA). For example, single-stranded nucleic acids {e.g., cDNAs or oligonucleotides) may be plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific nucleic acid probes from the cells of interest. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescently labeled deoxynucleotides by reverse transcription of RNA extracted from the cells of interest. Alternatively, the RNA may be amplified by in vitro transcription and labeled with a marker, such as biotin. The labeled probes are then hybridized to the immobilized nucleic acids on the microchip under highly stringent conditions. After stringent washing to remove the non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. The raw fluorescence intensity data in the hybridization files are generally preprocessed with the robust multichip average (RMA) algorithm to generate expression values. [0068] Quantitative real-time PCR (qRT-PCR) may also be used to measure the differential expression of a plurality of biomarkers. In qRT-PCR, the RNA template is generally reverse transcribed into cDNA, which is then amplified via a PCR reaction. The amount of PCR product is followed cycle-by-cycle in real time, which allows for determination of the initial concentrations of mRNA. To measure the amount of PCR product, the reaction may be performed in the presence of a fluorescent dye, such as SYBR Green, which binds to double- stranded DNA. The reaction may also be performed with a fluorescent reporter probe that is specific for the DNA being amplified.

[0069] A non-limiting example of a fluorescent reporter probe is a TaqMan® probe (Applied Biosystems, Foster City, CA). The fluorescent reporter probe fluoresces when the quencher is removed during the PCR extension cycle. Multiplex qRT-PCR may be performed by using multiple gene-specific reporter probes, each of which contains a different fluorophore. Fluorescence values are recorded during each cycle and represent the amount of product amplified to that point in the amplification reaction. To minimize errors and reduce any sample-to-sample variation, qRT-PCR may be performed using a reference standard. The ideal reference standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment.

[0070] Suitable reference standards include, but are not limited to, mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin. The level of mRNA in the original sample or the fold change in expression of each biomarker may be determined using calculations well known in the art. [0071] Immunohistochemical staining may also be used to measure the differential expression of a plurality of biomarkers. This method enables the localization of a protein in the cells of a tissue section by interaction of the protein with a specific antibody. For this, the tissue may be fixed in formaldehyde or another suitable fixative, embedded in wax or plastic, and cut into thin sections (from about 0.1 mm to several mm thick) using a microtome. Alternatively, the tissue may be frozen and cut into thin sections using a cryostat. The sections of tissue may be arrayed onto and affixed to a solid surface (i.e., a tissue microarray). The sections of tissue are incubated with a primary antibody against the antigen of interest, followed by washes to remove the unbound antibodies. The primary antibody may be coupled to a detection system, or the primary antibody may be detected with a secondary antibody that is coupled to a detection system. The detection system may be a fluorophore or it may be an enzyme, such as horseradish peroxidase or alkaline phosphatase, which can convert a substrate into a colorimetric, fluorescent, or chemiluminescent product. The stained tissue sections are generally scanned under a microscope. Because a sample of tissue from a subject with cancer may be heterogeneous, i.e., some cells may be normal and other cells may be cancerous, the percentage of positively stained cells in the tissue may be determined. This measurement, along with a quantification of the intensity of staining, may be used to generate an expression value for the biomarker.

[0072] An enzyme-linked immunosorbent assay, or ELISA, may be used to measure the differential expression of a plurality of biomarkers. There are many variations of an ELISA assay. All are based on the immobilization of an antigen or antibody on a solid surface, generally a microtiter plate. The original ELISA method comprises preparing a sample containing the biomarker proteins of interest, coating the wells of a microtiter plate with the sample, incubating each well with a primary antibody that recognizes a specific antigen, washing away the unbound antibody, and then detecting the antibody-antigen complexes. The antibody-antibody complexes may be detected directly. For this, the primary antibodies are conjugated to a detection system, such as an enzyme that produces a detectable product. The antibody-antibody complexes may be detected indirectly. For this, the primary antibody is detected by a secondary antibody that is conjugated to a detection system, as described above. The microtiter plate is then scanned and the raw intensity data may be converted into expression values using means known in the art.

[0073] An antibody microarray may also be used to measure the differential expression of a plurality of biomarkers. For this, a plurality of antibodies is arrayed and covalently attached to the surface of the microarray or biochip. A protein extract containing the biomarker proteins of interest is generally labeled with a fluorescent dye.

[0074] The labeled biomarker proteins are incubated with the antibody microarray. After washes to remove the unbound proteins, the microarray is scanned. The raw fluorescent intensity data may be converted into expression values using means known in the art.

[0075] Luminex multiplexing microspheres may also be used to measure the differential expression of a plurality of biomarkers. These microscopic polystyrene beads are internally color-coded with fluorescent dyes, such that each bead has a unique spectral signature (of which there are up to 100). Beads with the same signature are tagged with a specific oligonucleotide or specific antibody that will bind the target of interest (i.e., biomarker mR A or protein, respectively). The target, in turn, is also tagged with a fluorescent reporter. Hence, there are two sources of color, one from the bead and the other from the reporter molecule on the target. The beads are then incubated with the sample containing the targets, of which up 100 may be detected in one well. The small size/surface area of the beads and the three dimensional exposure of the beads to the targets allows for nearly solution-phase kinetics during the binding reaction. The captured targets are detected by high-tech fluidics based upon flow cytometry in which lasers excite the internal dyes that identify each bead and also any reporter dye captured during the assay. The data from the acquisition files may be converted into expression values using means known in the art.

[0076] In situ hybridization may also be used to measure the differential expression of a plurality of biomarkers. This method permits the localization of mRNAs of interest in the cells of a tissue section. For this method, the tissue may be frozen, or fixed and embedded, and then cut into thin sections, which are arrayed and affixed on a solid surface. The tissue sections are incubated with a labeled antisense probe that will hybridize with an mRNA of interest. The hybridization and washing steps are generally performed under highly stringent conditions. The probe may be labeled with a fluorophore or a small tag (such as biotin or digoxigenin) that may be detected by another protein or antibody, such that the labeled hybrid may be detected and visualized under a microscope. Multiple mRNAs may be detected simultaneously, provided each antisense probe has a distinguishable label. The hybridized tissue array is generally scanned under a microscope. Because a sample of tissue from a subject with cancer may be heterogeneous, i.e., some cells may be normal and other cells may be cancerous, the percentage of positively stained cells in the tissue may be determined. This measurement, along with a quantification of the intensity of staining, may be used to generate an expression value for each biomarker.

[0077] The number of biomarkers whose expression is measured in a sample of cells from a subject with cancer may vary. Since the risk score is based upon the differential expression of the biomarkers, a higher degree of accuracy should be attained when the expression of more biomarkers is measured; however, a large number of biomarkers in the gene signature would hamper the clinical usefulness. In a certain embodiment, the differential expression of a selectede number of biomarkers may be measured. V. Statistical and Informatical Methods

[0078] In certain aspects of the invention, expression information of the biomarkers may be analyzed by statistical and informatical methods to help provide prognosis prediction and treatment prescription. Those methods may comprise processing the test data stored on a data storage device by using a tangible computer readable medium having computer usable program code executable to perform operations for the statistic analysis and prediction/prescription output, or for assisting risk score generation as described above. A. Kaplan-Meier method

[0079] The Kaplan-Meier method (also known as the product limit estimator) estimates the survival function from life-time data. In medical research, it might be used to measure the fraction of patients living for a certain amount of time after treatment.

[0080] A plot of the Kaplan-Meier method of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations ("clicks") is assumed to be constant.

[0081] An important advantage of the Kaplan-Meier curve is that the method can take into account "censored" data— losses from the sample before the final outcome is observed (for instance, if a patient withdraws from a study). On the plot, small vertical tick-marks indicate losses, where patient data has been censored. When no truncation or censoring occurs, the Kaplan-Meier curve is equivalent to the empirical distribution.

[0082] A method might involve grouping patients into categories, for instance, those with Gene A profile and those with Gene B profile. In the graph, patients with Gene B die much more quickly than those with gene A. After two years about 80% of the Gene A patients still survive, but less than half of patients with Gene B.

B. Log-rank test

[0083] In statistics, the log-rank test (sometimes called the Mantel-Cox test) is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right censored (technically, the censoring must be non- informative). It is widely used in clinical trials to establish the efficacy of new drugs compared to a control group (often a placebo) when the measurement is the time to event (such as a heart attack).

[0084] The logrank test statistic compares estimates of the hazard functions of the two groups at each observed event time. It is constructed by computing the observed and expected number of events in one of the groups at each observed event time and then adding these to obtain an overall summary across all time points where there is an event.

[0085] The logrank statistic can be derived as the score test for the Cox proportional hazards model comparing two groups. It is therefore asymptotically equivalent to the likelihood ratio test statistic based from that model.

C. Proportional hazards analysis

[0086] Proportional hazards models are a sub-class of survival models in statistics. For the purposes of simplification, consider survival models to consist of two parts: the underlying hazard function, describing how hazard (risk) changes over time, and the effect parameters, describing how hazard relates to other factors - such as the choice of treatment, in a medical example. The proportional hazards assumption is the assumption that effect parameters multiply hazard: for example, if taking drug X halves a hazard at time 0, it also halves the hazard at time 1, or time 0.5, or time t for any value of t. The effect parameter(s) estimated by any proportional hazards model can be reported as hazard ratios. [0087] It was observed that if the proportional hazards assumption holds (or, is assumed to hold) then it is possible to estimate the effect parameter(s) without any consideration of the hazard function. This approach to survival data is called application of the Cox proportional hazards model, sometimes abbreviated to Cox model or to proportional hazards model.

[0088] Other proportional hazards models exist. Another approach to survival data is to assume that the proportional hazards assumption holds, but in addition to assume that the hazard function follows a known form. For example, assuming the hazard function to be the Weibull hazard function gives the Weibull proportional hazards model (in which the survival times follow a Weibull distribution).

D. Hierarchical clustering

[0089] Clustering is the assignment of objects into groups (called clusters) so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. [0090] Besides the term data clustering (or just clustering), there are a number of terms with similar meanings, including cluster analysis, automatic classification, numerical taxonomy, botryology and typological analysis.

[0091] Hierarchical clustering builds (agglomerative), or breaks up (divisive), a hierarchy of clusters. The traditional representation of this hierarchy is a tree (called a dendrogram), with individual elements at one end and a single cluster containing every element at the other. Agglomerative algorithms begin at the leaves of the tree, whereas divisive algorithms begin at the root. The former method builds the hierarchy from the individual elements by progressively merging clusters. [0092] In transcriptomics, clustering is used to build groups of genes with related expression patterns (also known as coexpressed genes). Often such groups contain functionally related proteins, such as enzymes for a specific pathway, or genes that are co-regulated. High throughput experiments using expressed sequence tags (ESTs) or DNA microarrays can be a powerful tool for genome annotation, a general aspect of genomics. VI. Cancer Treatments

[0093] Biomarkers and a new "risk score" system that can predict the likelihood of recurrence or overall survival in liver cancer patients in this invention can be used to identify patients who will get benefit of conventional single or combined modality therapy before treatment begins. In the same way, the invention can identify those patients who do not get much benefit from such conventional single or combined modality therapy and can offer them alternative treatment(s).

[0094] In certain aspects of the present invention, conventional cancer therapy may be applied to a subject wherein the subject is identified or reported as having a good prognosis based on the assessment of the biomarkers as disclosed. On the other hand, at least an alternative cancer therapy may be prescribed, as used alone or in combination with conventional cancer therapy, if a poor prognosis is determined by the disclosed methods or kits.

[0095] Conventional cancer therapies include one or more selected from the group of chemical or radiation based treatments and surgery. Chemotherapies include, for example, cisplatin (CDDP), carboplatin, procarbazine, mechlorethamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, chlorambucil, busulfan, nitrosurea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP 16), tamoxifen, raloxifene, estrogen receptor binding agents, taxol, gemcitabien, navelbine, farnesyl-protein tansferase inhibitors, transplatinum, 5-fluorouracil, vincristin, vinblastin and methotrexate, or any analog or derivative variant of the foregoing.

[0096] Radiation therapy that cause DNA damage and have been used extensively include what are commonly known as γ-rays, X-rays, and/or the directed delivery of radioisotopes to tumor cells. Other forms of DNA damaging factors are also contemplated such as microwaves and UV-irradiation. It is most likely that all of these factors effect a broad range of damage on DNA, on the precursors of DNA, on the replication and repair of DNA, and on the assembly and maintenance of chromosomes. Dosage ranges for X-rays range from daily doses of 50 to 200 roentgens for prolonged periods of time (3 to 4 wk), to single doses of 2000 to 6000 roentgens. Dosage ranges for radioisotopes vary widely, and depend on the half-life of the isotope, the strength and type of radiation emitted, and the uptake by the neoplastic cells.

[0097] The terms "contacted" and "exposed," when applied to a cell, are used herein to describe the process by which a therapeutic construct and a chemotherapeutic or radiotherapeutic agent are delivered to a target cell or are placed in direct juxtaposition with the target cell. To achieve cell killing or stasis, both agents are delivered to a cell in a combined amount effective to kill the cell or prevent it from dividing.

[0098] Approximately 60% of persons with cancer will undergo surgery of some type, which includes preventative, diagnostic or staging, curative and palliative surgery. Curative surgery is a cancer treatment that may be used in conjunction with other therapies, such as the treatment of the present invention, chemotherapy, radiotherapy, hormonal therapy, gene therapy, immunotherapy and/or alternative therapies.

[0099] Curative surgery includes resection in which all or part of cancerous tissue is physically removed, excised, and/or destroyed. Tumor resection refers to physical removal of at least part of a tumor. In addition to tumor resection, treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and microscopically controlled surgery (Mohs' surgery). It is further contemplated that the present invention may be used in conjunction with removal of superficial cancers, precancers, or incidental amounts of normal tissue. [00100] Laser therapy is the use of high-intensity light to destroy tumor cells. Laser therapy affects the cells only in the treated area. Laser therapy may be used to destroy cancerous tissue and relieve a blockage in the esophagus when the cancer cannot be removed by surgery. The relief of a blockage can help to reduce symptoms, especially swallowing problems. Photodynamic therapy (PDT), a type of laser therapy, involves the use of drugs that are absorbed by cancer cells; when exposed to a special light, the drugs become active and destroy the cancer cells. PDT may be used to relieve symptoms of liver cancer such as difficulty swallowing.

[00101] Upon excision of part of all of cancerous cells, tissue, or tumor, a cavity may be formed in the body. Treatment may be accomplished by perfusion, direct injection or local application of the area with an additional anti-cancer therapy. Such treatment may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments may be of varying dosages as well. [00102] Alternative cancer therapy include any cancer therapy other than surgery, chemotherapy and radiation therapy in the present invention, such as immunotherapy, gene therapy, hormonal therapy or a combination thereof. Subjects identified with poor prognosis using the present methods may not have favorable response to conventional treatment(s) alone and may be prescribed or administered one or more alternative cancer therapy per se or in combination with one or more conventional treatments.

[00103] Immunotherapeutics, generally, rely on the use of immune effector cells and molecules to target and destroy cancer cells. The immune effector may be, for example, an antibody specific for some marker on the surface of a tumor cell. The antibody alone may serve as an effector of therapy or it may recruit other cells to actually effect cell killing. The antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent. Alternatively, the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a tumor cell target. Various effector cells include cytotoxic T cells and NK cells. [00104] Gene therapy is the insertion of polynucleotides, including DNA or RNA, into an individual's cells and tissues to treat a disease. Antisense therapy is also a form of gene therapy in the present invention. A therapeutic polynucleotide may be administered before, after, or at the same time of a first cancer therapy. Delivery of a vector encoding a variety of proteins is encompassed within the invention. For example, cellular expression of the exogenous tumor suppressor oncogenes would exert their function to inhibit excessive cellular proliferation, such as p53, pl6 and C-CAM.

[00105] Additional agents to be used to improve the therapeutic efficacy of treatment include immunomodulatory agents, agents that affect the upregulation of cell surface receptors and GAP junctions, cytostatic and differentiation agents, inhibitors of cell adhesion, or agents that increase the sensitivity of the hyperproliferative cells to apoptotic inducers. Immunomodulatory agents include tumor necrosis factor; interferon alpha, beta, and gamma; IL-2 and other cytokines; F42K and other cytokine analogs; or MIP-1, MIP-lbeta, MCP-1, RANTES, and other chemokines. It is further contemplated that the upregulation of cell surface receptors or their ligands such as Fas / Fas ligand, DR4 or DR5 / TRAIL would potentiate the apoptotic inducing abilities of the present invention by establishment of an autocrine or paracrine effect on hyperproliferative cells. Increases intercellular signaling by elevating the number of GAP junctions would increase the anti-hyperproliferative effects on the neighboring hyperproliferative cell population. In other embodiments, cytostatic or differentiation agents can be used in combination with the present invention to improve the anti-hyperproliferative efficacy of the treatments. Inhibitors of cell adhesion are contemplated to improve the efficacy of the present invention. Examples of cell adhesion inhibitors are focal adhesion kinase (FAKs) inhibitors and Lovastatin. It is further contemplated that other agents that increase the sensitivity of a hyperproliferative cell to apoptosis, such as the antibody c225, could be used in combination with the present invention to improve the treatment efficacy. [00106] Hormonal therapy may also be used in the present invention or in combination with any other cancer therapy previously described. The use of hormones may be employed in the treatment of certain cancers such as breast, prostate, ovarian, or cervical cancer to lower the level or block the effects of certain hormones such as testosterone or estrogen. This treatment is often used in combination with at least one other cancer therapy as a treatment option or to reduce the risk of metastases. VII. Kits and Nucleic Acid Arrays

[00107] The present invention also encompasses kits for performing the diagnostic and prognostic methods of the invention. Such kits can be prepared from readily available materials and reagents. For example, such kits can comprise any one or more of the following materials: enzymes, reaction tubes, buffers, detergent, primers and probes. In a preferred embodiment, these kits allow a practitioner to obtain samples of neoplastic cells in blood, tears, semen, saliva, urine, tissue, serum, stool, sputum, cerebrospinal fluid and supernatant from cell lysate. In another preferred embodiment these kits include the needed apparatus for performing RNA extraction, RT-PCR, and gel electrophoresis. Instructions for performing the assays can also be included in the kits.

[00108] In a particular aspect, these kits may comprise a plurality of agents for assessing the differential expression of a plurality of biomarkers, wherein the kit is housed in a container. The kits may further comprise instructions for using the kit for assessing expression, means for converting the expression data into expression values and/or means for analyzing the expression values to generate scores that predict survival or prognosis. The agents in the kit for measuring biomarker expression may comprise a plurality of PCR probes and/or primers for qRT-PCR and/or a plurality of antibody or fragments thereof for assessing expression of the biomarkers. In another embodiment, the agents in the kit for measuring biomarker expression may comprise an array of polynucleotides complementary to the mRNAs of the biomarkers of the invention. Possible means for converting the expression data into expression values and for analyzing the expression values to generate scores that predict survival or prognosis may be also included.

VIII. Examples

[00109] The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention. Example 1 - Gene expression signature in HCC

[00110] A list of 65 genes whose expression pattern is significantly associated with the prognosis of HCC has been identified. It is contemplated that expression patterns of a subset or all of these genes would be sufficient to predict the prognosis of HCC patients. [00111] It was first tested whether the expression patterns of the 65 overlapping genes could reliably identify patients with poor prognosis. When the hierarchical clustering method was applied (FIG. 1A), the majority of patients with a poor prognosis (cluster Left [L]) were well separated from those with a better prognosis (cluster Right [R]) by gene expression patterns (P = 5.45xl0^"4 for OS and P = 0.05 for RFS by log-rank test, FIGS. 1B-1C). This statistical significance of the 65 -gene expression signature in discriminating between HCC patients with different prognoses is similar to the two previously known gene expression signatures (FIGS. 4A-4D) (Lee et al, 2004b; Lee et al, 2004c; Lee et al, 2006; Woo et al, 2008). This result strongly suggests that the expression patterns of the 65 genes are sufficient to predict the prognosis of HCC patients, although this data set represents only 5.8% of genes in the NCI proliferation signature and 10.3% of genes in the SNU recurrence signature.

[00112] The robustness of 65-gene expression signature was tested by leave-one-out- cross-validation (LOOCV) approach with use of multiple prediction algorithms (FIG. 5). All of six models generated very high sensitivity and specificity during LOOCV test (Table 4), indicating the robustness of the signature in various prediction models. Table 4. Sensitivity and specificity of 65-gene expression signatures in NCI cohort during LOOCV.

CCP

Class Sensitivity Specificity PPV NPV

Cluster L 0.847 1.0 1.0 0.899

Cluster R 1.0 0.847 0.899 1.0

LDA

Class Sensitivity Specificity PPV NPV

Cluster L 0.847 1.0 1.0 0.899

Cluster R 1.0 0.847 0.899 1.0

INN

Class Sensitivity Specificity PPV NPV

Cluster L 0.864 0.988 0.981 0.908 Cluster R 0.988 0.864 0.908 0.981

3NN

Class Sensitivity Specificity PPV NPV

Cluster L 0.847 0.988 0.98 0.898

Cluster R 0.988 0.847 0.898 0.98

NC

Class Sensitivity Specificity PPV NPV

Cluster L 0.949 0.988 0.982 0.963

Cluster R 0.988 0.949 0.963 0.982

SVM

Class Sensitivity Specificity PPV NPV

Cluster L 0.763 0.988 0.978 0.849

Cluster R 0.988 0.763 0.849 0.978

[00113] Sensitivity is the probability for a class A sample to be correctly predicted as class A. Specificity is the probability for a non class A sample to be correctly predicted as non-A. Positive predictive value (PPV) is the probability that a sample predicted as class A actually belongs to class A. Negative predictive value (NPV) is the probability that a sample predicted as non class A actually does not belong to class A.

[00114] CCP, Compound Covariate Predictor

[00115] LD A, Linear Discriminant Analysis

[00116] INN, One Nearest Neighbor

[00117] 3NN, Three Nearest Neighbor

[00118] NC, Nearest Centroid

[00119] SVM, Support Vector Machines

[00120] LOOCV, leave-one-out-cross-validation.

Example 2 - Development and validation of the 65-gene risk score

[00121] Although the hierarchical clustering method and various prediction models used in the current study are useful for determining the association between gene expression signatures and prognosis, the use of this method has proven to be difficult in clinical practice (Abdullah-Sayani et al., 2006). Thus, the risk score using the Cox regression coefficient of each gene in the prognostic signature was determined. The risk score for each patient was calculated by using the regression coefficient of each gene in the 65 -gene signature (Table 2).

Table 2. Regression coefficients of 65 genes from univariate Cox regression analysis.

GENE Coefficient SE Z-score p-value HR HR 95% CI

PFKFB3 0.521 0.168 3.11 0.0019 1.68 1.21-2.34

PKLR -0.386 0.12 -3.22 0.0013 0.68 0.537-0.86

PKM2 0.358 0.126 2.85 0.0044 1.43 1.12-1.83

PLG -0.256 0.0753 -3.4 0.00067 0.774 0.668-0.897

PLOD2 0.0948 0.134 0.709 0.48 1.1 0.846-1.43

PPT1 0.134 0.18 0.746 0.46 1.14 0.804-1.63

RALA 0.878 0.23 3.81 0.00014 2.41 1.53-3.78

RGN -0.392 0.0965 -4.07 4.80E-05 0.675 0.559-0.816

RGS1 0.255 0.111 2.3 0.021 1.29 1.04-1.6

RGS2 0.268 0.0937 2.86 0.0043 1.31 1.09-1.57

RNASE4 -0.258 0.147 -1.76 0.079 0.772 0.579-1.03

SERPINA10 -0.391 0.143 -2.74 0.0061 0.676 0.511-0.894

SERPINC1 -0.228 0.0601 -3.79 0.00015 0.796 0.708-0.896

SERPINF2 -0.352 0.086 -4.1 4.20E-05 0.703 0.594-0.832

SFTPC -0.269 0.183 -1.48 0.14 0.764 0.534-1.09

SLC22A7 -0.476 0.123 -3.87 0.00011 0.621 0.488-0.79

SLC2A2 -0.38 0.0736 -5.17 2.30E-07 0.684 0.592-0.79

SLC30A1 -0.337 0.144 -2.34 0.019 0.714 0.539-0.946

SLC38A1 0.184 0.141 1.3 0.19 1.2 0.911-1.59

SPHK1 0.356 0.153 2.33 0.02 1.43 1.06-1.93

SULT2A1 -0.351 0.087 -4.04 5.40E-05 0.704 0.593-0.835

TBX3 -0.294 0.15 -1.95 0.051 0.745 0.555-1.0

TM4SF1 0.321 0.104 3.07 0.0021 1.38 1.12-1.69

TSPAN3 0.416 0.184 2.26 0.024 1.52 1.06-2.17

[00122] HCC patients in the NCI cohort were then dichotomized into a high-risk and low-risk group using the 50^th percentile cutoff (8.36) of the risk score as the threshold value (FIG. 2A). The OS and RFS rates were significantly lower in the patient group with the high risk score (P = 1.0 x 10^"4 for OS and P = 0.009 for RFS by the log-rank test; FIGS. 2B-2C).

[00123] The risk score was validated using expression data of the 65 genes from independent HCC cohort. Gene expression data for 100 tumors from Korean patients with HCC were collected and used as an independent test set. The coefficient and threshold value (8.36) derived from the NCI cohort were directly applied. When patients in the Korean cohort were stratified according to their risk score, the patient group with a low risk score had a significantly better prognosis (P = 5.6 x 10^"5 for OS and P = 4.9 x 10^"4 for RFS, log-rank test) (FIGS. 3A-3B) than patients with a high risk score. The risk score was further validated in another independent cohort (LCI cohort, P = 5.0 x 10^"4 for OS, log-rank test) (FIG. 3C). It is important to point out that the median follow-up time (19 month) for patients in the LCI cohort was much shorter than that of the other two cohorts (33.6 and 48.5 month), indicating that the risk score can predict early recurrence of HCC. Taken together, these results demonstrate that it is possible to determine a risk score on the basis of the expression of a small number of genes.

Example 3 - 65-gene risk score is an independent risk factor for both OS and RFS [00124] Clinical data from two test cohorts were combined and the prognostic association between the newly developed 65-gene risk score and other known clinical risk factors using univariate Cox regression analyses was assessed. In addition to alpha- fetoprotein level, tumor size, grade, vasculature invasion, and BCLC stages, which are already well-known risk factors, the risk score was a significant indicator for OS (Table 3). All relevant clinical variables in a multivariate Cox regression analysis were included. Importantly, the risk score remained the most significant prognostic risk factor (HR 2.49, 95% CI 1.57 - 3.95, P = 1.0 x 10^"4 for OS) (Table 3).

Table 3. Univariate and Multivariate Cox Proportional Hazard Regression Analyses of Clinical Variables Associated with Overall Survival of HCC Patients in Validation Cohort.

[00125] In addition, the drop in concordance index approach was used to estimate how much the new risk score can improve the predictive accuracy of OS after treatment. Briefly, based on four clinical variables that were identified as independent predictors of OS in multivariate analysis (Table 3), four prediction models, each lacking one variable, were generated and compared with the full model containing all variables. In each comparison, the degree of decrease in predictive accuracy was estimated by measuring the drop in concordance index after omitting one variable. The biggest drop in concordance index was observed when the risk score was omitted in the prediction model (Table 5). Taken together, these findings suggest that the risk score retains its prognostic relevance even after the classical clinicopathological prognostic features have been taken into account.

Table 5 Drop in concordance index in multivariable analysis

Drop in concordance index

p-value

(95% CI)

Risk Score

0.036 (0.006 - 0.078) 0.003

(>8.36)

AFP

0.02 (-0.001 - 0.053) 0.038

(>300 ng/ml)

Vasculature Invasion

0.009 (-0.01 - 0.033) 0.21

(yes or no)

Tumor Size

0.029 (0.005 - 0.062) 0.004

(> 3 cm) [00126] The independence of the risk score over current staging systems was further tested. When the risk score was applied to patients with early stage (BCLC stage A) and intermediate and advanced stage (BCLC stage B and C) HCC, it successfully identified high- risk patients in different BCLC stages (FIGS. 6A-6C). The risk score was also independent of American Joint Committee on Cancer (AJCC) stages (FIGS. 7A-7D). [00127] Because vasculature invasion is the clinical variable best known to be significantly associated with recurrence and OS of HCC after surgical resection (Okada et al., 1994; Adachi et al, 1995; Kumada et al, 1997; Vauthey et al, 2002; Poon and Fan, 2003), it was next tested how independent the new risk score is of vasculature invasion. As expected, the prognosis of patients without vasculature invasion was significantly better than that of patients with invasion (FIG. 8A). When the risk score-based stratification was applied separately to invasion-positive and -negative patients, it successfully identified high-risk patients in both subgroups (FIGS. 8B-8C). Importantly, when all stratifications were combined together, the risk score even identified patients without vasculature invasion whose risk was worse than or similar to that of patients with invasion (FIG. 8D).

[00128] Potential association of the risk score with underlying liver diseases by including Child-Pugh class and cirrhosis information into analysis was also examined. As expectedly, Edmondson grade reflecting pathological characteristics of tumors showed incremental association with the risk score. The number of patients with a high risk score is slightly increased in higher grade. However, indices for underlying liver diseases lack any association with the risk score (Table 6), indicating that the risk score does not reflect biological characteristics associated with underlying liver diseases.

Table 6. Association of 65-gene risk score with underlying liver disease in validation cohort.

Characteristics High Low Total P-value*

83 209 292

Edmondson Grade 0.005

1 12 64 76

2 50 116 166

3 16 26 42

4 5 3 8

Child-Pugh Class 0.97

A 73 185 258

B 9 21 30

C 1 3 4

Cirrhosis 0.25

Yes 72 168 240

No 11 39 50

NA. 0 2 2

*%²-test

NA, Not Available

Example 4 - Molecular characteristics of HCC associated with 65-gene risk score

[00129] To gain biological insight from the risk score, gene expression data from the

MSH cohort was used, for whom many biological characteristics are available (Chiang et al., 2008) Ninety-one patients from the MSH cohort were stratified according to the risk score by applying the coefficient and threshold values (8.36) derived from the NCI cohort. All three signaling events (phosphorylation) examined in the previous study with the MSH cohort were significantly associated with the risk score (Table 7).

Table 7. Characteristics of HCC patients in MSH cohort stratified by 65-gene risk score.

Characteristics High Low Total P-value*

27 64 91 pAKT staining 0.003

positive 14 13 27

negative 12 47 59

N.D. 1 4 5 pIGFRl staining 2.2 x 10^"4 positive 11 5 16

negative 13 49 62

N.D. 3 10 13 pRPS6 staining 3.6 x 10^"5 positive 21 19 40

negative 5 40 45

N.D. 1 5 6

CTNNB1 mutation 0.05

yes 4 23 27

no 21 39 60

N.D. 2 2 4

P53 mutation 0.93

yes 3 8 11

no 21 53 74

N.D. 3 3 6

*%²-test

[00130] It was found that a high risk score was significantly associated with enriched phosphorylation of AKT (P = 0.003, %²-test), IGFR1 (P = 2.2 x 10^"4, %²-test), and RPS6 (P =

-5 2

3.6 x 10^" , χ -test). Mutation of TP53 is not associated with the risk score (P = 0.93), whereas a high frequency of mutations of CTNNB1 (beta-catenin) was significantly associated with a low risk score (23/ 27 mutations, P = 0.05, χ -test). To validate the association between risk score and CTNNB1 mutations in HCC, patients in the INSERM cohort (n = 57) were stratified by risk score using same 8.36 cutoff threshold (Boyault et al, 2007). Of 17 HCC tumors with CTNNB1 mutations, 16 were in the low-risk group, and this association was statistically significant (Table 8; P = 0.015, %²-test). Table 8. Association of 65-gene risk score with mutation of CTNNB1 in INSERM cohort.

CTNNB1 mutation High Low Total P-value*

16 41 57 0.015 yes 1 16 17

no 15 25 40

Example 5 - Patients and Methods Patients and Gene Expression Data

[00131] Gene expression and clinical data from the National Cancer Institute (NCI),

Mount Sinai Hospital (MSH), and Liver Cancer Institute (LCI) HCC cohorts, as reported in previous studies, were acquired from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession numbers GSE 1898, GSE4024, GSE9843, and GSE 14520) (Lee et al, 2004b; Lee et al, 2004c; Lee et al, 2006; Roessler et al, 2010; Chiang et al, 2008). Gene expression data from HCC patients at the French National Institute for Health and Medical Research (INSERM) were obtained from ArrayExpress, another public microarray database (accession number E-TABM-36) (Boyault et al, 2007). [00132] In addition to these gene expression data from previous studies, gene expression data from 100 patients with HCC (the Korean cohort) were included as an independent validation cohort for the risk score. Tumor specimens and clinical data were obtained from HCC patients undergoing hepatectomy as primary treatment for HCC at Seoul National University, Seoul, and Chonbuk National University, Jeonju, Korea. One hundred surgically removed frozen HCC specimens were used for microarray experiments. Samples were frozen in liquid nitrogen and stored at -80°C until RNA extraction. The study protocols were approved by the Institutional Review Boards at both institutions, and all participants provided written, informed consent. Gene expression data from the Korean cohort were generated using the Illumina microarray platform (Illumina, San Diego, CA) as described in the method (GSE 16757). Patients in the Korean cohort were followed up prospectively at least once every 3 months after surgery.

[00133] All patients had undergone surgical resection as their primary treatment. Survival data are not publically available for the MSH and INSERM cohorts; thus, these patients were not used for survival analyses.

Statistical analysis

[00134] BRB-ArrayTools were primarily used for statistical analysis of gene expression data (Simon et al, 2007), and all other statistical analyses were performed in the R language environment. Patient prognoses were estimated using Kaplan-Meier plots and the log-rank test. Multivariate Cox proportional hazards regression analysis was used to evaluate independent prognostic factors associated with OS, and as covariates a 65 -gene risk score, tumor stages, and pathologic characteristics were used (Cox, 1972). A P- value <0.05 indicated statistical significance, and all statistical tests were two-tailed. Cluster analysis was performed with Cluster and TreeView software (Eisen et al, 1998).

Development and validation of 65-gene risk scoring system.

[00135] To generate a risk score, the Cox regression coefficient of each gene among a

65-gene set from the NCI cohort was used (Paik et al, 2004). The risk score for each patient was derived by multiplying the expression level of a gene by its corresponding coefficient (Risk score = sum of Cox coefficient of Gene Gi X expression value of Gene Gi). The patients were thus dichotomized into groups at high or low risk of recurrence using the 50^th percentile (median) cutoff of the risk score as the threshold value. The median risk score in the NCI cohort was 8.36. The coefficient and the threshold value (8.36) derived from the NCI cohort were directly applied to gene expression data from the Korean, LCI, MSH, and INSERM cohorts to divide the rest of the patients into high-risk and low-risk groups.

* * *

[00136] All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

REFERENCES

The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.

Abdullah-Sayani et al, Nat. Clin. Pract. Oncol, 3:501-516, 2006.

Adachi et al, Gastroenterology, 108:768-775, 1995.

Alizadeh et al, Nature, 403(6769):503-511, 2000.

Ausubel et al, In: Current Protocols in Molecular Biology, John Wiley &amp; Sons, NY, 2003.

Boyault et al, Hepatology, 45:42-52, 2007.

Bruix and Llovet, Hepatology, 37:507-509, 2003.

Budhu et al, Cancer Cell, 10:99-1 1 1 , 2006.

Chiang et al, Cancer Res., 68:6779-6788, 2008.

Cox, J. Royal Statis. Soc, 34: 187-220, 1972.

Eisen et al, Proc. Natl Acad. Sci. USA, 95: 14863-14868, 1998.

Hoshida et al, N. Engl. J. Med., 359: 1995-2004, 2008.

Kumada et al, Hepatology, 25:87-92, 1997.

Lee and Thorgeirsson, Gastroenterology, 127:S51-S55, 2004a.

Lee et al, Hepatology, 40:667-676, 2004b.

Lee et al, Nat. Genet., 36: 1306-131 1, 2004c.

Lee et al, Nat. Med., 12:410-416, 2006.

Okada et al, Gastroenterology, 106:1618-1624, 1994.

Pal et al. Expert Opin. Investig. Drugs, 19: 1355-1366, 2010.

Poon and Fan, Surg. Oncol. Clin. N. Am., 12:35-50, viii, 2003.

Potti et al, 2006;

oessler et al, Cancer Res., 70: 10202-10212, 2010.

Sambrook et al, In: Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, Cold Spring Harbor, NY, 1989

Simon et al, Cancer Inform., 3: 11-17, 2007.

van de Vijver et al, N. Engl. J. Med., 347(25): 1999-2009, 2002.

Vauthey et al, J. Clin. Oncol, 20: 1527-1536, 2002.

Woo et al, Clin. Cancer Res., 14:2056-2064, 2008.

Claims

An in vitro method of providing a prognosis for a subject determined to have a liver cancer, comprising: a) obtaining a sample of the liver cancer; b) obtaining expression information of biomarkers in cells of the liver cancer sample, the biomarkers being at least ten genes selected from the group consisting of ACSL5, ADH1B, ADH6, ALDOA, APOC3, AQP9, ARPC2, BPHL, Clorfl l5, C4BPB, CDOl, CHI3L1, COBLL1, CRAT, CRYL1, CTSC, CXCR4, CYB5A, CYP27A1, CYP2J2, CYP4F12, DDIT4, EPHX2, ETV5, F10, F3, F5, GJB1, GPHN, HN1, HNF4A, IGFBP3, IQGAP1, IQGAP2, ITPR2, KHK, LAMB1, LECT2, MST1, MTSS1, PAH, PFKFB3, PKLR, PKM2, PLG, PLOD2, PPT1, RALA, RGN, RGS1, RGS2, RNASE4, SERPINA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1, SLC38A1, SPHK1, SULT2A1, TBX3, TM4SF1 and TSPAN3, wherein expression of said biomarkers is assessed by testing said sample; and b) providing a prognosis for the subject based on the expression information, wherein, as compared with a reference expression level, increased expression of one or more genes selected from the group consisting of ACSL5, ADH1B, ADH6, APOC3, AQP9, BPHL, Clorfl l5, C4BPB, CDOl, CHI3L1, COBLL1, CRAT, CRYL1, CYB5A, CYP27A1, CYP2J2, CYP4F12, EPHX2, F10, F5, GJB1, GPHN, HNF4A, IQGAP2, ITPR2, KHK, LECT2, MST1, MTSS1, PAH, PKLR, PLG, RGN, RNASE4, SERPINA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1, SULT2A1 and TBX3 indicates a good prognosis, and increased expression of one or more genes selected from the group consisting of ALDOA, ARPC2, CTSC, CXCR4, DDIT4, ETV5, F3, HN1, IGFBP3, IQGAP1, LAMB1, PFKFB3, PKM2, PLOD2, PPT1 , RALA, RGS1, RGS2, SLC38A1, SPHK1, TM4SF1 and TSPAN3 indicates a poor prognosis.

The method of claim 1 , wherein providing the prognosis comprises generating a risk score based on the expression information, wherein the risk score is defined as a weighted sum of expression levels of the biomarkers.

The method of claim 2, wherein the risk score is generated on a computer.

4. The method of claim 2, wherein the risk score is generated by a computer readable medium comprising machine executable instructions suitable for generating a risk score.

5. The method of claim 2, wherein providing the prognosis comprises classifying the risk score by comparing the risk score with a reference value.

6. The method of claim 1, wherein said obtaining expression information comprises obtaining or receiving said sample of said subject.

7. The method of claim 6, wherein said sample is paraffin-embedded.

8. The method of claim 6, wherein said sample is frozen.

9. The method of claim 1, wherein said obtaining expression information comprises R A quantification.

10. The method of claim 9, wherein the RNA quantification comprises cDNA microarray, quantitative RT-PCR, in situ hybridization, Northern blotting or nuclease protection.

11. The method of claim 1, wherein said obtaining expression information comprises protein quantification.

12. The method of claim 11, wherein said protein quantification comprises immunohistochemistry, an ELISA, a radioimmunoassay (RIA), an immunoradiometric assay, a fluoroimmunoassay, a chemiluminescent assay, a bioluminescent assay, a gel electrophoresis, or a Western blot analysis.

13. The method of claim 1, wherein said poor prognosis comprises high risk of recurrence, poor survival, or a low response to surgery, chemotherapy and/or radiation therapy.

14. The method of claim 1, further comprising reporting said prognosis.

15. The method of claim 1, further comprising prescribing or administering a treatment to said subject based on said prognosis.

16. The method of claim 1, wherein the subject has previously received treatment by surgery or chemotherapy.

17. The method of claim 1, wherein the subject has been determined not to have vasculature invasion.

18. The method of claim 1, wherein the liver cancer is hepatocellular carcinoma.

19. A kit or an array comprising a plurality of antigen-binding fragments that bind to expression products of biomarkers or a plurality of primers or probes that bind to transcripts of the biomarkers to assess expression levels, the biomarkers comprising at least ten genes selected from the group consisting of ACSL5, ADH1B, ADH6, ALDOA, APOC3, AQP9, ARPC2, BPHL, Clorfl l5, C4BPB, CDOl, CHI3L1,

COBLL1, CRAT, CRYL1 , CTSC, CXCR4, CYB5A, CYP27A1, CYP2J2, CYP4F12, DDIT4, EPHX2, ETV5, F10, F3, F5, GJB1, GPHN, HN1, HNF4A, IGFBP3, IQGAP1, IQGAP2, ITPR2, KHK, LAMB1, LECT2, MST1, MTSS1, PAH, PFKFB3, PKLR, PKM2, PLG, PLOD2, PPT1, RALA, RGN, RGS1, RGS2, RNASE4, SERPINA10, SERPINC1, SERPINF2, SFTPC, SLC22A7, SLC2A2, SLC30A1,

SLC38A1, SPHKl, SULT2A1, TBX3, TM4SF1 and TSPAN3, wherein said kit or array is housed in a container.