NZ711680B2 - Gene expression profile algorithm for calculating a recurrence score for a patient with kidney cancer - Google Patents

Abstract

The present invention provides algorithm-based molecular assays that involve measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The present invention also provides methods of obtaining a quantitative score for a patient with kidney cancer based on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence. In particular, the gene subsets are: a vascular normalization gene group comprising APOLD1, EDNRB, NOS3 and PPAP2B; an immune response gene group comprising CCL5, CEACAM1 and CX3CL1; a cell growth/division gene group comprising EIF4EBP1, LMNB1 and TUBB2A; and IL-6. on measurement of expression levels of genes from a biological sample obtained from a kidney cancer patient. The genes may be grouped into functional gene subsets for calculating the quantitative score and the gene subsets may be weighted according to their contribution to cancer recurrence. In particular, the gene subsets are: a vascular normalization gene group comprising APOLD1, EDNRB, NOS3 and PPAP2B; an immune response gene group comprising CCL5, CEACAM1 and CX3CL1; a cell growth/division gene group comprising EIF4EBP1, LMNB1 and TUBB2A; and IL-6.

Description

GENE EXPRESSION PROFILE ALGORITHM FOR CALCULATING A RECURRENCE SCORE FOR A PATIENT WITH KIDNEY CANCER CAL FIELD The present disclosure relates to molecular stic assays that provide information concerning gene expression profiles to determine prognostic information for cancer patients. Specifically, the present disclosure provides an algorithm comprising genes, or co-expressed genes, the expression levels of which may be used to determine the likelihood that a kidney cancer patient will experience a positive or a negative clinical e. The t disclosure provides gene expression information useful for calculating a recurrence score for a patient with kidney cancer.

INTRODUCTION The American Cancer Society’s estimates that in 2013 there will be about 65,150 new cases of kidney cancer and about 13,680 deaths from kidney cancer in the United States. (American Cancer Society, Kidney Cancer (Adult) Renal Cell Carcinoma Overview, ble online at /www.cancer.org/acs/groups/cid/documents/webcontent/OO3052-pdf.pdf).

Renal cell carcinoma (RCC), also called renal adenocarcinoma or hypernephroma, is the most common type of kidney cancer, accounting for more than 9 out of 10 cases of kidney cancer, and it accounts for approximately 2-3% of all malignancies. (Id. ; National Comprehensive Cancer Network Guidelines (NCCN) Clinical Practice Guidelines in gy, Kidney Cancer, Version 1.2013.) For n reasons, the rate of RCC has increased by 2% per year for the past 65 years. (NCCN Clinical Practice Guidelines in Oncology, Kidney Cancer.) There are multiple subtypes of RCC, including clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, collecting duct renal cell carcinoma, and unclassified renal cell carcinoma. Clear cell renal cell oma ) is the most common subtype of renal cell carcinoma, with about 7 out of 10 patients with RCC having ccRCC. (American Cancer Society, Kidney Cancer (Adult) Renal Cell Carcinoma Overview) Evaluation and staging of RCC includes visualization via imaging methods, such as computed tomographic (CT) scan, ultrasound, or magnetic resonance imaging (MRI), and physical and laboratory evaluations. Needle-biopsy may be performed to diagnose RCC and guide llance of disease. Physicians classify tumors based on al and pathological features, such as tumor stage, regional lymph node status, tumor size, nuclear grade, and histologic necrosis. Such designations can be subjective, and there is a lack of concordance among pathology laboratories in making such determination (Al-Ayanti M et al. (2003) Arch Pathol Lab Med 127, 593-596), highlighting the need for more objective designations.

Treatment of RCC varies depending on the stage of the cancer, the patient’s overall health, the likely side effects of treatment, the chances of curing the e, the s of improving survival, and/or relieving symptoms associated with the cancer.

Surgery is the main treatment for RCC that can be removed. (American Cancer Society Kidney Cancer (Adult) Renal Cell oma Overview.) Even after surgical excision, 20- % of patients with localized tumors experience relapse, most of which occur within three years. (NCCN Clinical Practice ines in Oncology, Kidney Cancer.) Lung metastasis is the most common site of distant relapse, occurring in 50-60% of patients. (161.) If a t has a small tumor, e. g., < 3 cm, however, the physician may not perform surgery, instead opting to monitor the tumor’s growth. Such active surveillance may allow some patients to avoid surgery and other treatments. In non-surgical candidates, particularly the y and those with competing health risks, ablative techniques, such as cryosurgery or radiofrequency ablation, or active surveillance may be used.

Physicians require prognostic information to help them make informed treatment decisions for patients with RCC and recruit appropriate high risk patients into clinical trials in order to increase the tical power of the trial. Existing methods are based on subjective measures and therefore may provide rate prognostic information.

This ation ses molecular assays that involve measurement of expression level(s) of one or more genes or gene subsets from a biological sample obtained from a kidney cancer patient. For example, the likelihood of a clinical e may be bed in terms of a quantitative score based on observed clinical features of the disease or recurrence-free interval.

In addition, this application discloses s of obtaining a recurrence score (RS) for a patient with kidney cancer based on measurement of expression level(s) of one or more genes or gene subsets from a biological sample obtained from a kidney cancer patient.

The present disclosure provides a method for obtaining a recurrence score for a patient with kidney cancer comprising measuring a level of at least one RNA transcript, or expression product thereof, in a tumor sample ed from the patient. The RNA transcript, or expression product thereof, may be selected from , EDNRB, NOS3, PPA2B, EIF4EBP1, LMNBl, TUBB2A, CCL5, CEACAMl, CX3CL1, and IL-6. The method comprises normalizing the gene expression level against a level of at least one reference RNA transcript, or expression product thereof, in the tumor sample. In some embodiments, normalization may include compression of gene expression measurements for low expressing genes and/or genes with ear onal forms. The method also comprises ing the normalized level to a gene subset. The gene subset may be selected from a vascular normalization group, a cell growth/division group, and an immune response group. In some ments, APOLDl, EDNRB, N083, and PPA2B are assigned to the vascular normalization group. In various embodiments, EIF4EBPl, LMNBl, and TUBB2A are assigned to the cell growth/division group. In other embodiments, CCL5, CEACAMl, and CX3CLl are assigned to the immune se group. The method also comprises weighting the gene subset according to its contribution to the assessment of risk of cancer recurrence. The method further comprises calculating a recurrence score for the t using the weighted gene subsets and the normalized levels. The method may further comprise creating a report comprising the recurrence score.

The present disclosure also provides a method of ting a likelihood of a clinical outcome for a t with kidney cancer. The method comprises determining a level of one or more RNA transcripts, or an expression product thereof, in a tumor sample obtained from the patient. The one or more RNA transcripts is selected from APOLDl, EDNRB, NOS3, PPA2B, EIF4EBP1, LMNBl, TUBB2A, CCL5, CEACAMl, CX3CL1, and IL-6.

The method also comprises assigning the one or more RNA ripts, or an expression product thereof, to one or more gene subsets. The method also comprises assigning the normalized level to a gene subset. The gene subset may be selected from a ar normalization group, a cell growth/division group, and an immune response group. In some embodiments, APOLDl, EDNRB, N083, and PPA2B are assigned to the vascular normalization group. In various embodiments, P1, LMNBl, and TUBB2A are assigned to the cell growth/division group. In other embodiments, CCL5, CEACAMl, and CX3CLl are assigned to the immune response group. The method further comprises calculating a quantitative score for the patient by weighting the level of one or more RNA transcripts, or an expression product f, by their contribution to the assessment of the likelihood of a clinical outcome. The method additionally comprises predicting a likelihood of a clinical outcome for the patient based on the quantitative score. In some embodiments, an increase in the quantitative score correlates with an increased likelihood of a negative al outcome. In some embodiments, the al outcome is cancer recurrence.

In some embodiments of the present disclosure, the kidney cancer is renal cell oma. In other embodiments, the kidney cancer is clear cell renal cell carcinoma.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows predictiveness curves and 95% confidence als for patients with Stage 1 ccRCC (A) and patients with Stage 2 or Stage 3 ccRCC (B) based on the algorithm described in the Examples.

DETAILED DESCRIPTION DEFINITIONS Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and lar Biology 2nd ed., J. Wiley & Sons (New York, NY 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, NY 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.

One skilled in the art will recognize many methods and materials similar or equivalent to those bed , which could be used in the practice of the present ion. Indeed, the present invention is in no way limited to the methods and als described herein. For purposes of the invention, the ing terms are defined below.

The terms “tumor” and “lesion” as used herein, refer to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

The terms “cancer,” “cancerous,” and “carcinoma” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer in the t sure include cancer of the , such as renal cell carcinoma (RCC, renal cell cancer, or renal cell adenocarcinoma), clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, collecting duct renal cell carcinoma, unclassified renal cell carcinoma, transitional cell carcinoma, Wilms tumor, and renal sarcoma.

As used herein, the terms “kidney cancer,77 CCrenal cancer,” or “renal cell carcinoma” refer to cancer that has arisen from the kidney.

The terms “renal cell cancer” or “renal cell oma” (RCC), as used herein, refer to cancer which originates in the lining of the proximal convoluted tubule. More specifically, RCC encompasses several vely common histologic subtypes: clear cell renal cell carcinoma, ary (chromophil), chromophobe, collecting duct oma, and medullary carcinoma. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Incidence of ccRCC is increasing, comprising 80% of localized disease and more than 90% of metastatic disease.

The “pathology” includes all phenomena that compromise the well-being of the t. This includes, without limitation, abnormal or uncontrollable cell , metastasis, interference with the normal oning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological se, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or , such as lymph nodes, etc.

The America Joint Committee on Cancer (AJCC) staging system (7th ed., 2010) (also referred to as the TNM (tumor, node, asis) system) for kidney cancer uses Roman numerals I through IV (1-4) to describe the extent of the disease. (Edge, SB, et al., AJCC Cancer Staging Manual, (7th Ed. 2010.)) In general, the lower the number, the less the cancer has spread. A higher number, such as stage IV, generally reflects a more serious cancer. The TNM staging system is as follows: Primary Tumor (T) Tx Primary tumor cannot be assessed T0 No evidence of primary tumor Tl Tumor 7 cm or less in greatest dimension, limited to the kidney 2014/040003 Tla Tumor 4 cm or less in greatest dimension, limited to the kidney le Tumor more than 4 cm but not more than 7 cm in greatest dimension, limited to the kidney T2 Tumor more than 7 cm in greatest dimension, limited to the kidney T2a Tumor more than 7 cm but less than or equal to 10 cm in the greatest dimension, limited to the kidney T2b Tumor more than 10 cm, limited to the kidney T3 Tumor extends into major veins or perinephric tissues but not into the teral adrenal gland and not beyond Gerota’s fascia T3a Tumor grossly s into the renal vein or its segmental (muscle containing) branches, or tumor invades perirenal and/or renal sinus fat but not beyond Gerota’ s fascia T3b Tumor grossly extends into the vena cava below the diaphragm T3c Tumor grossly extends into the vena cava above the diaphragm or invades the wall of the vena cava T4 Tumor invades beyond Gerota’a fascia (including contiguous ion into the ipsilateral adrenal gland) Regional Lymph Nodes (N) NX Regional lymph nodes cannot be ed N0 N0 regional lymph node metastasis N1 Metastasis in al lymph node(s) Distant Metastasis (M) M0 N0 distant metastasis Ml Distant metastasis Anatomic Stage/Prognostic Groups Stage 1 T1 N0 M0 Stage II T2 N0 M0 Stage III T2 N0 M0 Stage IV T4 Any N M0 Any T Any N M1 The term “early stage renal cancer”, as used herein, refers to Stages 1-3.

Reference to tumor “grade” for renal cell carcinoma as used herein refers to a grading system based on microscopic ance of tumor cells. According to the TNM staging system of the AJCC, the various grades of renal cell carcinoma are: GX (grade of differentiation cannot be assessed); Gl (well differentiated); G2 (moderately differentiated); and G3-G4 (poorly differentiated/undifferentiated). ased grade” as used herein refers to classification of a tumor at a grade that is more advanced, e. g., Grade 4 (G4) 4 is an increased grade relative to Grades 1, 2, and 3. Tumor grading is an important prognostic factor in renal cell oma. H. Rauschmeier, et al., World J Urol 2:103-108 .

The terms “necrosis” or “histologic necrosis” as used herein refer to the death of living cells or tissues. The presence of necrosis may be a prognostic factor in cancer. For example, necrosis is commonly seen in renal cell carcinoma (RCC) and has been shown to be an adverse prognostic factor in certain RCC es. V. Foria, et al., J Clin Pathol 58(1):39- 43 (2005).

The terms “nodal invasion” or “node-positive (N+)” as used herein refer to the presence of cancer cells in one or more lymph nodes associated with the organ (e.g., drain the organ) containing a primary tumor. Assessing nodal invasion is part of tumor g for most cancers, including renal cell carcinoma.

The term “prognosis” is used herein to refer to the prediction of the likelihood that a cancer patient will have a -attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as kidney cancer.

The term “prognostic gene” is used herein to refer to a gene, the expression of which is correlated, positively or negatively, with a hood of cancer recurrence in a cancer patient d with the standard of care. A gene may be both a prognostic and predictive gene, depending on the association of the gene expression level with the corresponding endpoint. For example, using a Cox proportional hazards model, if a gene is only prognostic, its hazard ratio (HR) does not change when measured in patients treated with the standard of care or in patients treated with a new intervention.

The term “prediction” is used herein to refer to the likelihood that a cancer patient will have a particular response to treatment, whether positive ficial response”) or negative, following surgical removal of the primary tumor. For example, ent could include targeted drugs, immunotherapy, or chemotherapy.

The terms “predictive gene” and nse indicator gene” are used interchangeably herein to refer to a gene, the expression level of which is associated, vely or negatively, with likelihood of beneficial response to ent. A gene may be both a prognostic and predictive gene, and vice versa, depending on the correlation of the gene expression level with the corresponding endpoint (e. g., likelihood of survival without recurrence, likelihood of cial response to treatment). A predictive gene can be identified using a Cox proportional hazards model to study the interaction between gene expression levels and the effect of treatment [comparing patients treated with treatment A to ts who did not receive ent A (but may have ed standard of care, e.g. treatment B)]. The hazard ratio (HR) for a predictive gene will change when measured in untreated/standard of care patients versus patients treated with treatment A.

As used herein, the term “expression level” as applied to a gene refers to the normalized level of a gene product, e. g., the normalized value determined for the RNA expression level of a gene or for the polypeptide expression level of a gene.

The term “gene product” or “expression product” are used herein to refer to the RNA transcription products (transcripts) of the gene, including mRNA, and the polypeptide products of such RNA transcripts. A gene product can be, for example, an unspliced RNA, an mRNA, a splice variant mRNA, a microRNA, a fragmented RNA, a polypeptide, a post-translationally modified polypeptide, a splice variant polypeptide, etc.

The term “RNA transcript” as used herein refers to the RNA transcription products of a gene, for example, mRNA, an unspliced RNA, a splice variant mRNA, a micro RNA, and a fragmented RNA.

Unless indicated otherwise, each gene name used herein corresponds to the Official Symbol assigned to the gene and provided by Entrez Gene (URL: www.ncbi.nlm.nih.gov/sites/entrez) as of the filing date of this application.

The terms “correlated” and “associated” are used interchangeably herein to refer to the association between two measurements (or measured entities). The disclosure provides genes and gene subsets, the expression levels of which are associated with a particular outcome measure, such as for example the association between the expression level of a gene and the hood of clinical outcome. For example, the increased expression level of a gene may be positively correlated (positively associated) with an increased hood of good al outcome for the patient, such as an increased likelihood of long-term survival 2014/040003 without recurrence of the cancer, and the like. Such a positive correlation may be demonstrated statistically in various ways, e. g. by a low hazard ratio for cancer ence or death. In another example, the increased expression level of a gene may be negatively ated (negatively associated) with an increased hood of good al outcome for the patient. In that case, for example, the patient may have a decreased hood of long- term survival without recurrence of the cancer, and the like. Such a negative correlation indicates that the patient likely has a poor prognosis, and this may be demonstrated statistically in s ways, e. g., a high hazard ratio for cancer recurrence or death.

“Correlated” is also used herein to refer to the ation between the expression levels of two different genes, such that expression level of a first gene can be substituted with an expression level of a second gene in a given thm in view of their correlation of expression. Such “correlated expression” of two genes that are substitutable in an algorithm y involves gene expression levels that are positively correlated with one another, e. g., if increased expression of a first gene is positively correlated with an outcome (e. g., increased likelihood of good clinical outcome), then the second gene that is co-expressed and exhibits correlated expression with the first gene is also positively ated with the same outcome.

A “positive clinical outcome” can be assessed using any endpoint indicating a benefit to the patient, including, without limitation, (1) inhibition, to some extent, of tumor growth, including slowing down and complete growth arrest; (2) reduction in the number of tumor cells; (3) reduction in tumor size; (4) inhibition (i.e., reduction, slowing down or complete stopping) of tumor cell infiltration into adjacent peripheral organs and/or tissues; (5) inhibition of metastasis; (6) ement of anti-tumor immune se, possibly resulting in sion or rejection of the tumor; (7) relief, to some extent, of one or more symptoms associated with the tumor; (8) increase in the length of survival following treatment; and/or (9) decreased mortality at a given point of time following treatment.

Positive clinical se may also be expressed in terms of various measures of clinical outcome. Positive clinical outcome can also be considered in the context of an individual’s outcome relative to an outcome of a population of patients having a comparable clinical diagnosis, and can be assessed using various endpoints such as an increase in the duration of Recurrence-Free interval (RFI), an increase in the time of survival as compared to Overall Survival (OS) in a population, an increase in the time of Disease-Free Survival (DFS), an increase in the duration of Distant Recurrence-Free Interval (DRFI), and the like. An increase WO 94078 in the likelihood of positive clinical response corresponds to a decrease in the likelihood of cancer recurrence.

The term “risk classification” means a level of risk (or likelihood) that a subject will experience a ular clinical outcome. A subject may be classified into a risk group or classified at a level of risk based on the methods of the present disclosure, e. g. high, medium, or low risk. A “risk group” is a group of subjects or individuals with a similar level of risk for a particular al outcome.

The term “long-term” survival is used herein to refer to survival for a particular period of time, e. g., for at least 3 years, or for at least 5 years.

The terms “recurrence” and “relapse” are used herein, in the context of potential clinical outcomes of cancer, to refer to a local or distant metastases. Identification of a recurrence could be done by, for example, CT imaging, ultrasound, arteriogram, or X-ray, biopsy, urine or blood test, physical exam, or research center tumor registry.

The term rence-Free al (RFI)” is used herein to refer to the time (in years) from randomization to first kidney cancer recurrence or death due to recurrence of kidney cancer.

The term “Overall Survival (OS)” is used herein to refer to the time (in years) from randomization to death from any cause.

The term se-Free Survival (DFS)” is used herein to refer to the time (in years) from randomization to first kidney cancer recurrence or death from any cause.

The calculation of the es listed above in practice may vary from study to study depending on the definition of events to be either censored or not censored.

The term “Hazard Ratio (HR)” as used herein refers to the effect of an explanatory variable on the hazard or risk of an event (i.e. recurrence or death). In proportional hazards regression models, the HR is the ratio of the ted hazard for two groups (e. g. patients with two different stages of cancer) or for a unit change in a continuous variable (e. g. one standard deviation change in gene expression).

The term “microarray” refers to an ordered arrangement of hybridizable array ts, e. g., oligonucleotide or polynucleotide probes, on a ate.

The term “polynucleotide,” when used in singular or plural generally refers to any polyribonucleotide or polydeoxyribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides are defined herein to include, without limitation, single- and double-stranded RNA, and RNA including single- and double- stranded regions, hybrid molecules comprising DNA and RNA that may be -stranded or, more typically, double-stranded or include - and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons, are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined . In general, the term ucleotide” embraces all chemically, enzymatically and/or lically modified forms of unmodified cleotides, as well as the al forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.

The term “oligonucleotide” refers to a relatively short polynucleotide, including, without limitation, single-stranded ibonucleotides, single- or double- stranded cleotides, RNArDNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by al methods, for e using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.

As used herein, the term “expression level” as applied to a gene refers to the level of the expression product of a gene, e. g. the normalized value determined for the RNA expression product of a gene or for the ptide expression level of a gene.

The term “CT” as used herein refers to threshold cycle, the cycle number in quantitative polymerase chain reaction (qPCR) at which the fluorescence generated within a reaction well exceeds the defined threshold, i.e. the point during the reaction at which a sufficient number of amplicons have accumulated to meet the d threshold.

The term “Cp” as used herein refers to “crossing point.” The Cp value is ated by determining the second derivatives of entire qPCR amplification curves and their maximum value. The Cp value ents the cycle at which the increase of fluorescence is highest and where the thmic phase of a PCR begins.

The terms “threshold” or “thresholding” refer to a procedure used to account for non-linear onships between gene sion measurements and clinical response as well as to further reduce variation in ed gene expression measurements and patient scores induced by low expressing genes. When thresholding is applied, all measurements below or above a threshold are set to that threshold value. Non-linear onship between gene expression and outcome could be examined using smoothers or cubic splines to model gene expression in Cox PH regression on recurrence free interval or logistic regression on recurrence . Variation in reported patient scores could be examined as a function of variability in gene expression at the limit of quantitation and/or detection for a particular gene.

As used herein, the term “amplicon,” refers to pieces of DNA that have been synthesized using amplification ques, such as polymerase chain ons (PCR) and ligase chain reactions.

“Stringency” of hybridization reactions is readily inable by one of ordinary skill in the art, and generally is an empirical calculation ent upon probe length, washing temperature, and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes need lower temperatures.

Hybridization generally depends on the ability of denatured DNA to re-anneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and hybridizable sequence, the higher the relative ature which can be used. As a result, it follows that higher relative temperatures would tend to make the reaction conditions more stringent, while lower temperatures less so. For additional details and explanation of stringency of hybridization reactions, see Ausubel et al., Current Protocols in Molecular Biology, Wiley Interscience Publishers, (1995).

“Stringent conditions” or “high stringency conditions”, as defined herein, typically: (1) employ low ionic strength and high temperature for washing, for example 0.015 M sodium chloride/0.0015 M sodium e/0.1% sodium dodecyl sulfate at 500C; (2) employ during hybridization a denaturing agent, such as formamide, for example, 50% (v/v) formamide with 0.1% bovine serum n/0.1% Ficoll/0.1% polyvinylpyrrolidone/50mM 2014/040003 sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM sodium citrate at 420C; or (3) employ 50% formamide, 5 X SSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5 X Denhardt's solution, ted salmon sperm DNA (50 _g/ml), 0.1% SDS, and 10% dextran sulfate at 420C, with washes at 420C in 0.2 X SSC (sodium chloride/sodium citrate) and 50% formamide, followed by a high-stringency wash consisting of 0.1 X SSC containing EDTA at 550C.

“Moderately stringent conditions” may be identified as bed by Sambrook et al., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and include the use of g solution and hybridization conditions (e. g., temperature, ionic strength and %SDS) less stringent that those described above. An eXample of moderately stringent conditions is overnight incubation at 370C in a on comprising: % formamide, 5 X SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium ate (pH 7.6), 5 X Denhardt's solution, 10% dextran sulfate, and 20 mg/ml red sheared salmon sperm DNA, followed by g the filters in l X SSC at about 37-500C.

The skilled n will recognize how to adjust the temperature, ionic strength, etc. as necessary to accommodate factors such as probe length and the like.

The terms “splicing” and “RNA splicing” are used interchangeably and refer to RNA processing that removes introns and joins eXons to produce mature mRNA with uous coding sequence that moves into the cytoplasm of a eukaryotic cell.

As used herein, the term “eXon” refers to any segment of an interrupted gene that is represented in the mature RNA product. As used herein, the term “intron” refers to any segment of DNA that is transcribed but removed from within the transcript by splicing together the eXons on either side of it. “Intronic RNA” refers to mRNA derived from an intronic region of DNA. Operationally, eXonic sequences occur in the mRNA sequence of a gene as defined by Ref. SEQ ID numbers. Operationally, intron sequences are the intervening ces within the genomic DNA of a gene.

The term “co-eXpressed”, as used herein, refers to a statistical correlation n the eXpression level of one gene and the eXpression level of another gene. Pairwise co-eXpression may be calculated by s methods known in the art, e. g., by calculating Pearson correlation coefficients or Spearman correlation coefficients. Co-eXpressed gene cliques may also be identified using a graph theory. An analysis of co-eXpression may be calculated using normalized eXpression data.

A “computer-based system” refers to a system of hardware, software, and data e medium used to analyze information. The minimum hardware of a patient computer- based system comprises a central processing unit (CPU), and hardware for data input, data output (e. g., display), and data storage. An ordinarily skilled artisan can readily iate that any currently available computer-based systems and/or components thereof are suitable for use in connection with the methods of the present disclosure. The data storage medium may comprise any cture sing a recording of the present information as described above, or a memory access device that can access such a manufacture.

To “record” data, programming or other information on a computer readable medium refers to a process for storing information, using any such s as known in the art. Any convenient data storage structure may be chosen, based on the means used to access the stored ation. A variety of data processor programs and formats can be used for storage, e. g. word processing text file, database format, etc.

A “processor” or “computing means” references any hardware and/or software ation that will perform the functions required of it. For example, a suitable processor may be a mmable digital microprocessor such as available in the form of an electronic controller, mainframe, server or personal computer op or portable). Where the sor is programmable, suitable programming can be communicated from a remote on to the processor, or previously saved in a computer program product (such as a portable or fixed computer readable storage medium, whether magnetic, optical or solid state device based). For example, a magnetic medium or optical disk may carry the programming, and can be read by a suitable reader communicating with each processor at its corresponding station.

The terms “surgery” or “surgical resection” are used herein to refer to surgical removal of some or all of a tumor, and y some of the surrounding tissue. es of al techniques include laparoscopic procedures, biopsy, or tumor ablation, such as cryotherapy, radio ncy ablation, and high intensity ultrasound. In cancer patients, the extent of tissue removed during surgery depends on the state of the tumor as observed by a surgeon. For example, a partial nephrectomy indicates that part of one kidney is removed; a simple nephrectomy entails removal of all of one ; a radical nephrectomy, all of one kidney and neighboring tissue (e. g., adrenal gland, lymph nodes) removed; and bilateral nephrectomy, both kidneys removed.

ALGORITHM-BASED METHODS AND GENE SUBSETS The present disclosure provides an thm-based molecular diagnostic assay for determining an expected clinical outcome, e. g., prognosis. The cancer can be, for example, renal cell carcinoma or clear cell renal cell carcinoma. The present disclosure also provides a method for obtaining a recurrence score for a patient with kidney cancer. For example, the sion levels of the prognostic genes may be used to obtain a recurrence score for a patient with kidney cancer. The algorithm-based assay and associated ation provided by the ce of the methods of the present invention facilitate optimal treatment decision-making in kidney cancer. For example, such a clinical tool would enable physicians to identify patients who have a low likelihood of recurrence and therefore may be able to forgo adjuvant treatment. Similarly, such a tool may also enable physicians to identify patients who have a high likelihood of recurrence and who may be good candidates for adjuvant treatment.

As used herein, a itative score” is an arithmetically or mathematically calculated cal value for aiding in simplifying or disclosing or informing the analysis of more complex quantitative ation, such as the correlation of certain expression levels of the disclosed genes or gene subsets to a likelihood of a clinical outcome of a kidney cancer patient. A quantitative score may be determined by the ation of a specific algorithm.

The algorithm used to calculate the tative score in the methods disclosed herein may group the expression level values of genes. The grouping of genes may be performed at least in part based on knowledge of the relative contribution of the genes according to physiologic functions or component cellular characteristics, such as in the groups sed herein. A quantitative score may be determined for a gene group (“gene group score”). The formation of , in addition, can facilitate the mathematical ing of the contribution of various expression levels of genes or gene subsets to the quantitative score. The weighting of a gene or gene group representing a physiological process or ent cellular characteristic can reflect the contribution of that process or characteristic to the pathology of the cancer and clinical outcome, such as recurrence or upgrading/upstaging of the cancer. The present invention provides an algorithm for calculating the tative scores, for example, as set forth in the Examples. In an embodiment of the invention, an se in the tative score indicates an increased likelihood of a negative clinical outcome.

In an embodiment, a quantitative score is a rence score,” which indicates the likelihood of a cancer recurrence, upgrading or upstaging of a cancer, adverse pathology, non-organ-confined disease, high-grade disease, and/or high-grade or non-organ- confined disease. An increase in the recurrence score may correlate with an increase in the likelihood of cancer recurrence, upgrading or upstaging of a cancer, adverse pathology, non- organ-confined disease, rade disease, and/or high-grade or non-organ-confined disease.

The gene subsets of the present invention include a vascular normalization gene group, an immune response gene group, a cell growth/division gene group, and IL-6.

The gene subset identified herein as the “vascular normalization group” es genes that are involved with vascular and/or angiogenesis functions. The vascular normalization group includes, for example, APOLDl, EDNRB, N083, and PPA2B.

The gene subset identified herein as the “cell growth/division group” includes genes that are involved in key cell growth and cell division pathway(s). The cell growth/division group includes, for example, EIF4EBP1, LMNBl, and TUBB2A.

The gene subset identified herein as the “immune response group” includes genes that are involved in functions of the immune system. The immune se group includes, for example, CCL5, l, and CX3CLl.

Additionally, sion levels of certain individual genes may be used for calculating the ence score. For example, the expression level of IL-6 may be used to calculate the recurrence score. Although IL-6 may be involved in immune responses it may also be involved in other ical processes making it less suitable to be grouped with other immune related genes.

The present invention also provides methods to determine a threshold expression level for a particular gene. A threshold expression level may be calculated for a specific gene. A threshold expression level for a gene may be based on a normalized expression level. In one example, a CT threshold expression level may be calculated by assessing functional forms using logistic regression or Cox proportional hazards regression.

The present invention further provides s to determine genes that co- express with particular genes identified by, e. g., quantitative RT-PCR CR), as validated biomarkers relevant to a particular type of cancer. The co-expressed genes are themselves useful biomarkers. The co-expressed genes may be substituted for the genes with which they co-express. The s can e identifying gene cliques from rray data, izing the microarray data, computing a pairwise Spearman correlation matrix for the array probes, filtering out significant co-expressed probes across ent studies, building a graph, mapping the probe to genes, and generating a gene clique report. The expression levels of one or more genes of a gene clique may be used to calculate the likelihood that a t with kidney cancer will ence a positive clinical outcome, such as a reduced likelihood of a cancer recurrence.

Any one or more combinations of gene groups may be assayed in the method of the present ion. For example, a ar normalization gene group may be assayed, alone or in combination, with a cell growth/division gene group, an immune response gene group, and or Il-6. In addition, any number of genes within each gene group may be assayed.

In a specific embodiment of the invention, a method for predicting a clinical outcome for a patient with kidney cancer comprises measuring an expression level of at least one gene from a vascular normalization gene group, or a co-expressed gene thereof, and at least one gene from a cell growth/division gene group, or a co-expressed gene thereof. In another embodiment, the expression level of at least two genes from a vascular normalization gene group, or a co-expressed gene thereof, and at least two genes from a cell growth/division gene group, or a co-expressed gene thereof, are measured. In yet another embodiment, the expression levels of at least three genes are measured from each of the vascular normalization gene group and the cell growth/division gene group. In a further embodiment, the sion levels of at least four genes from the vascular normalization gene group and at least three genes from the cell growth/differentiation gene group are measured.

In another embodiment of the invention, at least one gene from a vascular normalization gene group, or a co-expressed gene thereof, and at least one gene from an immune response gene group, or a co-expressed gene f are measured. In another embodiment, the expression level of at least two genes from a vascular normalization gene group, or a co-expressed gene thereof, and at least two genes from an immune response gene group, or a ressed gene thereof, are measured. In yet r embodiment, the expression levels of at least three genes are measured from each of the ar normalization gene group and the immune response gene group. In a further embodiment, the expression levels of at least four genes from the vascular ization gene group and at least three genes from the immune se gene group are measured.

In a r embodiment of the invention, an expression level of at least one gene from a vascular normalization gene group, or a co-expressed gene thereof, and IL-6 are measured. In another embodiment, the expression level of at least two genes from a vascular normalization gene group, or a co-expressed gene thereof, and IL-6 are measured. In yet another embodiment, the sion levels of at least three genes from the vascular normalization gene group and IL-6 are measured. In a further embodiment, the expression levels of at least four genes from the vascular normalization gene group and IL-6 are Additionally, an expression level of at least one gene from a vascular normalization gene group, or a co-expressed gene thereof, and at least one gene from an immune response gene group, or a co-expressed gene thereof is measured. In another ment, the expression level of at least two genes from a vascular normalization gene group, or a co-expressed gene thereof, and at least two genes from an immune response gene group, or a co-expressed gene thereof, are measured. In yet another embodiment, the expression levels of at least three genes are measured from each of the ar ization gene group and the immune response gene group. In a further embodiment, the expression levels of at least four genes from the vascular normalization gene group and at least three genes from the immune response gene group are measured.

In a specific embodiment of the invention, a method for predicting a al outcome for a patient with kidney cancer comprises measuring an expression level of at least one gene from a cell /division gene group, or a co-expressed gene f, and at least one gene from an immune response gene group, or a co-expressed gene thereof. In another embodiment, the expression level of at least two genes from a cell growth/division gene group, or a co-expressed gene thereof, and at least two genes from an immune response gene group, or a co-expressed gene thereof, are measured. In yet another embodiment, the expression levels of at least three genes are measured from each of the cell growth/division gene group and the immune response gene group.

In a further ment of the invention, an expression level of at least one gene from a cell growth/division gene group, or a co-expressed gene thereof, and IL-6 are measured. In another embodiment, the expression level of at least two genes from a cell growth/division gene group, or a co-expressed gene thereof, and IL-6 are measured. In yet another embodiment, the sion levels of at least three genes from the cell growth/division gene group and IL-6 are measured.

In a further embodiment of the invention, an expression level of at least one gene from an immune response gene group, or a co-expressed gene thereof, and IL-6 are measured. In another embodiment, the expression level of at least two genes from an immune response gene group, or a co-expressed gene thereof, and IL-6 are measured. In yet r embodiment, the expression levels of at least three genes from the immune response gene group and IL-6 are measured.

In an additional embodiment of the invention, an expression level of at least one gene from a vascular normalization gene group, or a co-expressed gene thereof, at least one gene from a cell growth/division gene group, or a co-expressed gene thereof, and at least one gene from an immune response gene group are measured. In r embodiment, the expression level of at least two genes from a vascular normalization gene group, or a co- expressed gene thereof, at least two genes from a cell growth/division gene group, or a coexpressed gene thereof, and at least two genes from an immune se gene group are measured. In yet another embodiment, the expression levels of at least three genes are measured from each of the vascular normalization gene group, the cell growth/division gene group, and the immune response gene group. In a further embodiment, the expression levels of at least four genes from the vascular normalization gene group, at least three genes from the cell /differentiation gene group, and at least three genes from the immune response gene group are measured.

In another embodiment of the invention, an expression level of at least one gene from a ar normalization gene group, or a co-expressed gene thereof, at least one gene from a cell /division gene group, or a co-expressed gene thereof, at least one gene from an immune response gene group, and IL-6 are measured. In another embodiment, the sion level of at least two genes from a vascular normalization gene group, or a co- sed gene thereof, at least two genes from a cell growth/division gene group, or a co- expressed gene thereof, at least two genes from an immune response gene group, and IL-6 are ed. In yet another embodiment, the expression levels of at least three genes are measured from each of the vascular normalization gene group, the cell growth/division gene group, and the immune response gene group, and IL-6. In a further embodiment, the expression levels of at least four genes from the vascular normalization gene group, at least three genes from the cell growth/differentiation gene group, at least three genes from the immune se gene group, and IL-6 are measured.

Additionally, expression levels of one or more genes that do not fall within the gene subsets described herein may be measured with any of the combinations of the gene subsets described herein. Alternatively, any gene that falls within a gene subset may be ed separately from the gene subset, or in another gene subset.

In a specific embodiment, the method of the invention comprises measuring the expression levels of the specific ations of genes and gene subsets shown in the Examples. In a further embodiment, gene group score(s) and quantitative s) are calculated according to the thm(s) shown in the Examples. In certain embodiments, the method of the invention comprises measuring expression levels of the cancer-related genes APOLDl, CCL5, CEACAMl, CX3CL1, EDNRB, EIF4EBP1, IL6, LMNBl, NOS3, PPAP2B, and , and the reference genes AAMP, ARFl, ATPSE, GPXl, and RPLPl, normalizing the expression levels of one or more of the cancer-related genes against the expression levels of one or more of the reference genes, assigning the normalized expression levels to gene subsets, weighting the gene subset according to its contribution to cancer recurrence, calculating a recurrence score using the weighted gene subset and the normalized levels, and ng a report comprising the recurrence score.

In certain embodiments, the method of the invention comprises measuring expression levels of certain subgroups of cancer-related genes ed from the group consisting of: (1) APOLDl, N083, and EMCN; (2) APOLDl, NOS3, IL6, 1L8, and EMCN; (3) CEACAMl, , IL6, and IL8; (4) EIF4EBP1 and LMNBl; (5) APOLDl, EDNRB, and NOS3; (6) APOLDl, EDNRB, and PPAP2B; (7) APOLDl, N083, and PPAP2B; (8) EDNRB, N083, and PPAP2B; (9) APOLDl and NOS3; (10) N083 and PPAP2B; (11) APOLDl, NOS3, PPAP2B, and CEACAMl; (12) APOLDl, NOS3, PPAP2B, and CX3CL1; (13) APOLDl, NOS3, CEACAMl, and CX3CL1; (14) APOLDl, PPAP2B, CEACAMl, and CX3CL1; (15) NOS3, PPAP2B, CEACAMl, and CX3CL1; (16) APOLDl, NOS3, l, CX3CL1, and P1; (17) NOS3, PPAP2B, CEACAMl, CX3CL1, and EIF4EBP1; (18) APOLDl, NOS3, CEACAMl, , and LMNBl; (19) NOS3, , CEACAMl, CX3CL1, and LMNBl; (20) APOLDl, NOS3, CEACAMl, CX3CL1, and TUBB2A; and (21) NOS3, PPAP2B, CEACAMl, CX3CL1, and TUBB2A and the reference genes AAMP, ARFl, ATPSE, GPXl, and RPLPl, normalizing the sion levels of one or more of the subgroups of -related genes against the expression levels of one or more of the reference genes, and creating a report comprising the risk of recurrence.

In certain embodiments, the risk of recurrence is estimated from a hazard ratio calculated using the normalized expression levels of one or more subgroups of cancer-related genes.

Various technological approaches for determination of expression levels of the disclosed genes are set forth in this specification, including, without limitation, RT-PCR, microarrays, high-throughput sequencing, serial analysis of gene sion (SAGE) and Digital Gene Expression (DGE), which will be discussed in detail below. In particular aspects, the sion level of each gene may be determined in relation to various features of the sion ts of the gene including exons, introns, protein epitopes and protein activity.

The expression product that is assayed can be, for example, RNA or a polypeptide. The expression product may be fragmented. For example, the assay may use primers that are complementary to target sequences of an sion product and could thus measure full transcripts as well as those fragmented expression products containing the target sequence. Further information is provided in Tables A and B.

The RNA expression product may be assayed directly or by detection of a cDNA product resulting from a PCR-based amplification method, e.g., quantitative reverse transcription polymerase chain reaction (qRT-PCR). (See e. g., U.S. Patent No. 7,587,279).

Polypeptide expression product may be assayed using immunohistochemistry (IHC) by proteomics techniques. Further, both RNA and polypeptide expression products may also be assayed using microarrays.

CLINICAL UTILITY Currently, of the expected al outcome for RCC patients is based on subjective determinations of a tumor’s clinical and ogic es. For example, physicians make decisions about the appropriate surgical procedures and adjuvant therapy based on a renal tumor’s stage, grade, and the presence of necrosis. Although there are standardized measures to guide pathologists in making these decisions, the level of concordance n pathology laboratories is low. (See Al-Ayanti M et al. (2003) Arch Pathol Lab Med 127, 593-596) It would be useful to have a reproducible molecular assay for determining and/or ming these tumor characteristics.

In addition, rd al criteria, by themselves, have limited ability to tely estimate a patient’s prognosis. It would be useful to have a reproducible molecular assay to assess a patient’s prognosis based on the biology of his or her tumor. Such information could be used for the purposes of t counseling, selecting patients for clinical trials (e. g., adjuvant trials), and understanding the biology of renal cell carcinoma. In addition, such a test would assist physicians in making surgical and treatment endations based on the biology of each patient’s tumor. For example, a c test could stratify RCC patients based on risk of recurrence and/or likelihood of long-term survival t recurrence (relapse, metastasis, etc.). There are several g and planned clinical trials for RCC ies, including adjuvant radiation and chemotherapies. It would be useful to have a genomic test able to identify high-risk patients more accurately than standard clinical criteria, thereby further enriching an adjuvant RCC population for study.

This would reduce the number of patients needed for an adjuvant trial and the time needed for definitive testing of these new agents in the adjuvant g.

Finally, it would be useful to have a lar assay that could predict a patient’s likelihood to d to specific treatments. Again, this would facilitate individual treatment decisions and recruiting patients for clinical , and increase physician and patient ence in making healthcare decisions after being sed with cancer.

METHODS OF ASSAYING EXPRESSION LEVELS OF A GENE PRODUCT Methods of expression profiling include methods based on sequencing of polynucleotides, methods based on hybridization analysis of polynucleotides, and proteomics- based methods. Representative methods for sequencing-based analysis include Massively Parallel cing (see e. g., Tucker et al., The American J. Human Genetics 85:142-154, 2009) and Serial Analysis of Gene Expression (SAGE). Exemplary methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106247- 283 (1999)); RNase protection assays (Hod, hniques 13:852-854 (1992)); and PCR- based methods, such as reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Antibodies may be employed that can recognize sequence-specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid es or DNA-protein duplexes.

Nucleic Acid Sequencing-Based Methods Nucleic acid sequencing technologies are suitable methods for expression is. The principle underlying these s is that the number of times a cDNA sequence is detected in a sample is directly related to the ve RNA levels corresponding to that sequence. These methods are sometimes referred to by the term Digital Gene Expression (DGE) to reflect the discrete numeric property of the resulting data. Early 2014/040003 methods applying this principle were Serial Analysis of Gene Expression (SAGE) and Massively el Signature Sequencing . See, e. g., S. Brenner, et al., Nature Biotechnology 18(6):630-634 (2000).

More recently, the advent of “next-generation” cing technologies has made DGE simpler, higher throughput, and more affordable. As a result, more laboratories are able to utilize DGE to screen the expression of more nucleic acids in more individual patient samples than previously possible. See, e. g., J. Marioni, Genome Research 18(9):1509- 1517 (2008); R. Morin, Genome Research 18(4):610-621 (2008); A. Mortazavi, Nature Methods 5(7):621-628 (2008); N. Cloonan, Nature Methods 5(7):613-619 . Massively parallel sequencing methods have also enabled whole genome or transcriptome sequencing, allowing the analysis of not only coding but also non-coding sequences. As reviewed in Tucker et al., The American J. Human Genetics 85:142-154 (2009), there are several commercially available massively parallel sequencing platforms, such as the na Genome Analyzer (Illumina, Inc., San Diego, CA), Applied Biosystems SOLiDTM Sequencer (Life Technologies, Carlsbad, CA), Roche GS-FLX 454 Genome Sequencer (Roche Applied Science, Germany), and the Helicos® Genetic Analysis Platform (Helicos Biosciences Corp., Cambridge, MA). Other developing technologies may be used. e Transcription PCR [RT-PCR) The ng material is typically total RNA isolated from a human tumor, usually from a primary tumor. Optionally, normal tissues from the same patient can be used as an internal control. RNA can be extracted from a tissue sample, e. g., from a sample that is fresh, frozen (e. g. fresh frozen), or paraffin-embedded and fixed (e. g. in-fixed).

General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of lar biology, including Ausubel et al., Current ols of Molecular y, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 . In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer’s instructions. For example, total RNA from cells in culture can be ed using Qiagen RNeasy mini-columns. Other cially available RNA isolation kits e MasterPureTM Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, WI), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel- Test). RNA ed from a tumor sample can be isolated, for example, by cesium chloride density gradient centrifugation. The isolated RNA may then be ed of ribosomal RNA as described in US. Pub. No. 2011/0111409.

The sample containing the RNA is then subjected to reverse transcription to produce cDNA from the RNA template, ed by exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse ription step is typically primed using specific primers, random hexamers, or oligo-dT primers, ing on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA), following the manufacturer’s instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

PCR-based methods use a thermostable pendent DNA polymerase, such as a Taq DNA polymerase. For example, TaqMan® PCR typically utilizes the 5’- nuclease activity of Taq or Tth polymerase to yze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5’ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon l of a PCR reaction product.

A third oligonucleotide, or probe, can be designed to facilitate detection of a nucleotide sequence of the amplicon located between the hybridization sites of the two PCR primers.

The probe can be ably labeled, e. g., with a reporter dye and can further be provided with both a fluorescent dye, and a quencher fluorescent dye, as in a TaqMan® probe configuration. Where a TaqMan® probe is used, during the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the ed reporter dye is free from the ing effect of the second fluorophore. One molecule of er dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative retation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700TM Sequence Detection TM (Perkin-Elmer-Applied Biosystems, Foster City, CA, USA), or LightCycler (Roche Molecular Biochemicals, im, Germany). In a preferred embodiment, the 5' nuclease procedure is run on a ime quantitative PCR device such as the ABI PRISM 7700TM ce Detection SystemTM. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 384-well format on a thermocycler. The RT-PCR may be performed in triplicate wells with an equivalent of 2ng RNA input per 10 uL-reaction volume. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data. ] lease assay data are generally initially expressed as a old cycle (“CT”). Fluorescence values are recorded during every cycle and ent the amount of product amplified to that point in the amplification reaction. The threshold cycle (CT) is generally described as the point when the fluorescent signal is first recorded as statistically significant. The Cp value is calculated by determining the second tives of entire qPCR amplification curves and their maximum value. The Cp value ents the cycle at which the increase of fluorescence is highest and where the logarithmic phase of a PCR begins.

To ze errors and the effect of sample-to-sample variation, RT- PCR is usually performed using an internal rd. The ideal internal standard gene (also referred to as a reference gene) is expressed at a constant level among cancerous and non- cancerous tissue of the same origin (i.e., a level that is not significantly different among normal and ous tissues), and is not significantly affected by the experimental treatment (i.e., does not exhibit a significant difference in expression level in the relevant tissue as a result of exposure to chemotherapy). RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehydephosphate- dehydrogenase (GAPDH) and B-actin. Gene expression measurements can be normalized relative to the mean of one or more (e. g., 2, 3, 4, 5, or more) reference genes. Reference- normalized expression measurements can range from 0 to 15, where a one unit increase generally reflects a 2-fold increase in RNA quantity.

Real time PCR is compatible both with quantitative competitive PCR, where an internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a ization gene contained within the sample, or a housekeeping gene for . For further details see, e. g. Held et al., Genome Research 6:986-994 (1996).

Design of PCR Primers and Probes PCR primers and probes can be designed based upon exon, intron, or intergenic sequences present in the RNA transcript of interest. Primer/probe design can be performed using publicly available software, such as the DNA BLAT software developed by Kent, W.J., Genome Res. 12(4):656-64 (2002), or by the BLAST software including its variations.

Where necessary or desired, repetitive sequences of the target sequence can be masked to mitigate non-specific signals. ary tools to accomplish this include the Repeat Masker program available on-line h the Baylor e of Medicine, which screens DNA ces against a y of repetitive elements and returns a query sequence in which the repetitive elements are . The masked sequences can then be used to design primer and probe sequences using any commercially or ise publicly available primer/probe design packages, such as Primer Express (Applied Biosystems); MGB assay- by-design (Applied tems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Rrawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology.

Humana Press, Totowa, NJ, pp 365-386).

Other factors that can influence PCR primer design include primer length, melting ature (Tm), and G/C content, specificity, mentary primer sequences, and 3'-end sequence. In general, l PCR primers are generally 17-30 bases in length, and contain about 20-80%, such as, for example, about 50-60% G+C bases, and exhibit Tm's between 50 and 80 OC, e.g. about 50 to 70 0C.

For further guidelines for PCR primer and probe design see, e.g.

Dieffenbach, CW. et al, “General ts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press,. New York, 1995, pp. 133-155; Innis and d, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T.N. Primerselect: Primer and probe design. Methods MoI. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by nce.

Tables A and B provide further information concerning the primer, probe, and amplicon sequences associated with the Examples disclosed herein.

RAY® System In MassARRAY-based methods, such as the exemplary method developed by Sequenom, Inc. (San Diego, CA) following the isolation of RNA and reverse transcription, the ed cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal rd. The CDNA/competitor mixture is PCR amplified and is subjected to a CR shrimp ne phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR products from the itor and cDNA are ted to primer extension, which generates distinct mass signals for the competitor— and CDNA-derived PCR products. After purification, these products are dispensed on a chip array, which is pre- loaded with components needed for analysis with - assisted laser desorption ionization time-of—flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated. For further details see, e. g. Ding and , Proc. Natl. Acad. Sci. USA 100:3059-3064 (2003).

Other PCR-based Methods Further PCR-based techniques that can find use in the methods disclosed herein include, for example, BeadArray® technology (Illumina, San Diego, CA; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); BeadsArray for Detection of Gene Expression® (BADGE), using the commercially available LuminexlOO LabMAP® system and multiple color-coded microspheres (Luminex Corp., Austin, TX) in a rapid assay for gene expression (Yang et al., Genome Res. 8-1898 (2001)); and high ge expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res. 31(16) e94 (2003).

Microarrays In this method, cleotide sequences of interest ding cDNAs and oligonucleotides) are arrayed on a substrate. The arrayed sequences are then contacted under conditions suitable for specific hybridization with detectably labeled cDNA generated from RNA of a sample. The source of RNA typically is total RNA isolated from a tumor sample, and optionally from normal tissue of the same patient as an internal control or cell lines. RNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e. g. formalin-fixed) tissue samples.

For example, PCR amplified inserts of cDNA clones of a gene to be assayed are applied to a substrate in a dense array. Usually at least 10,000 nucleotide sequences are applied to the substrate. For example, the microarrayed genes, immobilized on the microchip at 10,000 ts each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated h incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest.

Labeled cDNA probes applied to the chip hybridize with icity to each spot of DNA on the array. After washing under ent ions to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance.

] With dual color fluorescence, tely labeled cDNA probes generated from two sources of RNA are ized pair wise to the array. The relative abundance of the transcripts from the two sources corresponding to each ied gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such s have been shown to have the sensitivity required to detect rare transcripts, which are sed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et at, Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed on commercially available ent, following the manufacturer's protocols, such as by using the trix GenChip® technology, or Incyte's microarray technology.

Isolating RNA from Body Fluids Methods of isolating RNA for expression analysis from blood, plasma and serum (see for example, Tsui NB et al. (2002) Clin. Chem. 48,1647-53 and references cited therein) and from urine (see for example, Boom R et al. (1990) J Clin Microbiol. 28, 495-503 and reference cited therein) have been described.

Methods of Isolating RNA from Paraffin-Embedded Tissue The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification primer extension and ication are provided in various published journal articles. (See, e.g., T.E. Godfrey et al,. J. Molec. Diagnostics 2: 84-91 (2000); K. Specht et al., Am. J. Pathol. 158: 419-29 , M. Cronin, et al., Am J Pathol 164:35-42 (2004)). histochemistry Immunohistochemistry methods are also suitable for detecting the expression levels of genes and applied to the method disclosed herein. Antibodies (e. g., monoclonal antibodies) that ically bind a gene product of a gene of interest can be used in such methods. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline atase. atively, unlabeled primary antibody can be used in conjunction with a labeled secondary antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

Proteomics The term “proteome” is defined as the totality of the proteins present in a sample (e. g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein sion in a sample (also referred to as “expression proteomics”). Proteomics typically es the following steps: (1) separation of individual ns in a sample by 2-D gel ophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e. g. my mass spectrometry or N- terminal sequencing, and (3) analysis of the data using bioinformatics.

General Description of the mRNA Isolation, Purification and Amplification ] The steps of a representative ol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are provided in various published l articles. (See, e.g., T.E. Godfrey, et al,. J. Molec. Diagnostics 2: 84-91 (2000); K. Specht et al., Am. J. Pathol. 158: 419-29 (2001), M. Cronin, et al., Am J Pathol 164:35-42 (2004)). , a representative process starts with cutting a tissue sample section (e. g. about 10 um thick sections of a paraffin-embedded tumor tissue sample). The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair is performed if desired. The sample can then be subjected to analysis, e. g., by reverse transcribed using gene specific promoters followed by RT-PCR.

STATISTICAL ANALYSIS OF GENE SION LEVELS IN FICATION OF MARKER GENES FOR USE IN PROGNOSTIC METHODS ] One skilled in the art will recognize that there are many statistical methods that may be used to determine whether there is a significant relationship between an outcome of interest (e. g., likelihood of survival, likelihood of se to chemotherapy) and expression levels of a marker gene as described here. This relationship can be presented as a continuous recurrence score (RS), or patients may be stratified into risk groups (e. g., low, intermediate, high). For example, a Cox proportional hazards regression model may provide an adequate fit to a particular clinical nt (e. g., RFI, DFS, OS). One assumption of the Cox proportional hazards regression model is the proportional hazards assumption, i.e. the assumption that effect parameters multiply the underlying hazard. Assessments of model adequacy may be performed including, but not limited to, examination of the cumulative sum of gale residuals. One skilled in the art would recognize that there are numerous statistical methods that may be used (e.g., Royston and Parmer (2002), smoothing spline, etc.) to fit a flexible parametric model using the hazard scale and the Weibull distribution with natural spline smoothing of the log cumulative s on, with s for treatment (chemotherapy or observation) and RS allowed to be time-dependent. (See, P.

Royston, M. , Statistics in Medicine 21(15:2l75-2l97 .) The relationship between recurrence risk and (l) recurrence risk groups; and (2) clinical/pathologic covariates (e. g., number of nodes examined, ogical T stage, tumor grade, lymphatic or vascular invasion, etc.) may also be tested for significance.

In an exemplary embodiment, power calculations were carried for the Cox proportional hazards model with a single non-binary covariate using the method proposed by F. Hsieh and P. Lavori, Control Clin Trials -560 (2000) as implemented in PASS 2008.

GENERAL DESCRIPTION OF EXEMPLARY EMBODIMENTS This disclosure provides a method for obtaining a recurrence score for a patient with kidney cancer by assaying expression levels of certain prognostic genes from a tumor sample obtained from the patient. Such methods involve use of gene subsets that are created based on similar functions of gene products. For example, stic methods disclosed herein involve ng expression levels of gene subsets that include at least one gene from each of a vascular normalization group, an immune response group, and cell growth/division group, and IL-6, and calculating a recurrence score (RS) for the t by weighting the expression levels of each of the gene subsets by their respective butions to cancer recurrence. The weighting may be different for each gene subset, and may be either positive or negative. For example, the vascular normalization gene group score may be weighted by multiplying a factor of -0.45, the immune se gene group score may be weighted by lying a factor of -0.31, the cell growth/division gene group score may be weighted by a factor of +0.27, and the value for IL-6 may be multiplied by a factor of +0.04.

Normalization of Expression Levels The expression data used in the methods disclosed herein can be ized. Normalization refers to a s to correct for (normalize away), for example, differences in the amount of RNA assayed and ility in the y of the RNA used, to remove unwanted sources of systematic variation in CT measurements, and the like. With respect to RT-PCR experiments involving archived fixed paraffin embedded tissue samples, sources of systematic variation are known to include the degree of RNA degradation relative to the age of the patient sample and the type of fixative used to store the sample. Other sources of systematic variation may be attributable to laboratory processing ions.

Assays can provide for normalization by incorporating the expression of certain normalizing genes, which genes are relatively invariant under the nt conditions. Exemplary normalization genes include housekeeping genes. Normalization can be based on the mean or median signal (CT) of all of the assayed genes or a large subset thereof (global normalization approach). In general, the normalizing genes, also referred to as reference genes should be genes that are known to be invariant in kidney cancer as compared to non-cancerous kidney tissue, and are not significantly affected by s sample and process conditions, thus provide for normalizing away extraneous effects.

Unless noted otherwise, normalized expression levels for each mRNA/tested tumor/patient will be expressed as a percentage of the expression level measured in the reference set. A reference set of a iently high number (e. g., 40) of tumors yields a distribution of normalized levels of each mRNA species. The level measured in a particular tumor sample to be analyzed falls at some percentile within this range, which can be determined by methods well known in the art.

In exemplary embodiments, one or more of the following genes are used as references by which the expression data is normalized: AAMP, ARFl, ATPSE, GPXl, and RPLPl. The calibrated weighted e CT measurements for each of the prognostic genes may be normalized relative to the mean of five or more reference genes.

] Those skilled in the art will recognize that ization may be achieved in numerous ways, and the techniques described above are intended only to be exemplary, not exhaustive.

Standardization of sion Levels The expression data used in the methods disclosed herein can be standardized. Standardization refers to a s to effectively put all the genes on a comparable scale. This is performed because some genes will exhibit more variation (a broader range of expression) than others. Standardization is performed by dividing each sion value by its standard deviation across all samples for that gene. Hazard ratios are then interpreted as the proportional change in the hazard for the clinical endpoint (clinical recurrence, biological recurrence, death due to kidney cancer, or death due to any cause) per 1 standard deviation increase in expression.

Bridging Expression Measurements and Calibration An oligonucleotide set represents a forward primer, reverse primer, and probe that are used to build a primer and probe (P3) pool and gene specific primer (GSP) pool. Systematic differences in RT-PCR cycle threshold (CT) measurements can result between different oligonucleotide sets due to inherent variations oligonucleotide ses.

For example, differences in oligonucleotide sets may exist between development, production (used for validation), and future production tide sets. Thus, use of tical calibration procedures to adjust for systematic ences in oligonucleotide sets resulting in ation in the gene coefficients used in calculating RS may be desirable. For example, for each of the genes assayed for use in an algorithm, one may use a scatterplot of CT measurements for production oligonucleotide sets versus CT measurements from a corresponding sample used in different oligonucleotide set to create linear sion model that treats the effect of lot-to- lot differences as a random effect. Examination of such a plot will reveal that the variance of CT measurements increases exponentially as a function of the mean CT. The random effects linear regression model can be evaluated with log-linear variance, to obtain a linear WO 94078 calibration on. A calculated mean squared error (MSE) for the scores can be compared to the MSE if no calibration scheme is used at all.

As another example, a latent le measurement of CT (e.g. first principle component) may be derived from various oligonucleotide sets. The latent variable is a reasonable measure of the “true” underlying CT measurement. Similar to the method described above, a linear regression model may be fit to the sample pairs treating the effects of differences as a random effect, and the ed average CT value adjusted to a calibrated Centering and Data Compression/Scaling Systematic differences in the distribution of t RS due to analytical or sample differences may exist n early development, clinical validation and commercial samples. A constant centering tuning parameter may be used in the algorithm to account for such difference.

Data compression is a procedure used to reduce the variability in observed ized CT values beyond the limit of quantitation (LOQ) of the assay. Specifically, for each of the kidney cancer assay genes, variance in CT measurements increase exponentially as the normalized CT for a gene extends beyond the LOQ of the assay. To reduce such variation, normalized CT values for each gene may be compressed towards the LOQ of the assay.

Additionally, normalized CT values may be rescaled. For example, normalized CT values of the stic and reference genes may be rescaled to a range of 0 to 15, where a one-unit increase generally reflects a 2-fold increase in RNA quantity.

Threshold Values The present invention describes a method to determine a threshold value for expression of a cancer-related gene, comprising measuring an expression level of a gene, or its expression product, in a tumor n obtained from a cancer patient, normalizing the expression level to obtain a normalized sion level, calculating a threshold value for the normalized expression level, and determining a score based on the likelihood of ence or clinically beneficial se to treatment, wherein if the normalized expression level is less than the old value, the threshold value is used to determine the score, and wherein if the normalized expression level is greater or equal to the threshold value, the normalized expression level is used to determine the score. 2014/040003 For example, a threshold value for each cancer-related gene may be determined through ation of the functional form of relationship between gene sion and outcome. Examples of such analyses are presented for Cox PH regression on ence free interval where gene expression is modeled using natural splines and for logistic regression on ence status where gene expression is modeled using a lowess smoother.

In some embodiments, if the relationship between the term and the risk of recurrence is non-linear or expression of the gene is vely low, a old may be used. In an embodiment, when the expression of IL6 is <4 CT the value is fixed at 4 CT.

KITS OF THE INVENTION The als for use in the methods of the present invention are suited for preparation of kits produced in accordance with well-known procedures. The present disclosure thus provides kits comprising , which may include gene-specific or gene- selective probes and/or primers, for tating the expression of the disclosed genes for predicting prognostic outcome or response to treatment. Such kits may optionally contain reagents for the extraction of RNA from tumor samples, in particular fixed paraffin- embedded tissue samples and/or reagents for RNA amplification. In addition, the kits may ally comprise the reagent(s) with an identifying description or label or instructions relating to their use in the methods of the present invention. The kits may comprise containers (including microliter plates suitable for use in an automated implementation of the method), each with one or more of the various reagents (typically in concentrated form) utilized in the methods, ing, for example, pre-fabricated microarrays, s, the appropriate nucleotide triphosphates (e. g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more probes and primers of the present invention (e. g., appropriate length ) or random primers linked to a promoter reactive with the RNA polymerase). Mathematical algorithms used to estimate or quantify prognostic or predictive information are also properly potential components of kits.

REPORTS The methods of this invention, when practiced for commercial diagnostic purposes, generally produce a report or summary of information obtained from the herein-described methods. For example, a report may include information concerning sion levels of prognostic genes, a Recurrence Score, a prediction of the predicted al outcome for a particular t, or thresholds. The methods and reports of this invention can further include storing the report in a database. The method can create a record in a database for the subject and populate the record with data. The report may be a paper report, an auditory report, or an onic record. The report may be displayed and/or stored on a computing device (e. g., ld device, desktop computer, smart device, website, etc.).

It is contemplated that the report is provided to a physician and/or the patient. The receiving of the report can further include establishing a k connection to a server computer that includes the data and report and requesting the data and report from the server computer.

COMPUTER M The values from the assays described above, such as expression data, recurrence score, treatment score and/or benefit score, can be calculated and stored manually.

Alternatively, the above-described steps can be completely or partially performed by a computer m product. The present invention thus provides a computer program product including a computer readable storage medium having a computer program stored on it. The m can, when read by a computer, execute relevant calculations based on values obtained from analysis of one or more biological sample from an individual (e. g., gene expression levels, normalization, thresholding, and conversion of values from assays to a score and/or graphical depiction of likelihood of recurrence/response to chemotherapy, gene co-expression or clique analysis, and the like). The computer program product has stored therein a computer program for performing the calculation.

The present disclosure provides systems for executing the program described above, which system generally includes: a) a central computing environment; b) an input device, operatively connected to the computing environment, to receive patient data, wherein the patient data can include, for example, expression level or other value obtained from an assay using a ical sample from the patient, or rray data, as described in detail above; c) an output device, connected to the computing environment, to e information to a user (e. g., medical nel); and d) an algorithm executed by the central computing nment (e.g., a processor), where the thm is executed based on the data received by the input device, and wherein the algorithm ates a RS, risk or benefit group classification, gene co-expression analysis, thresholding, or other functions described herein.

The methods provided by the present invention may also be automated in whole or in part.

All aspects of the present invention may also be practiced such that a limited number of additional genes that are co-expressed with the disclosed genes, for e as evidenced by statistically meaningful Pearson and/or Spearman correlation coefficients, are included in a prognostic test in addition to and/or in place of disclosed genes.

Having described the invention, the same will be more readily understood through reference to the following Examples, which are provided by way of illustration, and are not intended to limit the ion in any way.

EXAMPLES EXAMPLE 1: SELECTION OF GENES FOR ALGORITHM DEVELOPMENT A gene identification study to identify genes associated with clinical recurrence is described in US. Provisional ation Nos. 61/294,038, filed January 11, 2010, and 61/346,230, filed May 19, 2010, and in US. Application Publication No. 2011/0171633, filed y 7, 2011, and published July 14, 2011 (all of which are hereby incorporated by reference). Briefly, ts with stage I-III ccRCC who underwent nephrectomy at Cleveland Clinic between 1985 and 2003 with ed paraffin-embedded nephrectomy samples were identified. RNA was extracted from 6 x 10 um dissected tumor sections and RNA expression quantified for 732 genes (including 5 reference genes) using . The primary endpoint was recurrence-free al (RFI), defined as time from nephrectomy to first recurrence or death clue to RCC. 931 ts with complete clinical/pathology data and tissue blocks were evaluable. Patient characteristics were: 63% male, median age 61, stage I (68%), II (1 0%) and III (22%), median follow-up of 5.6 years, -year recurrence rates in stage I, II, and III were 10%, 29%, and 45% respectively.

Clinical/pathology covariates significantly associated with RFI included microscopic is, Fuhrman grade, stage, tumor size and lymph node ement (all p<0.001).

Based on the results of the identification study, 448 genes were icantly (p<0.05, unadjusted; Cox models) associated with RFI. For the majority of these genes (366 (82%)), increased expression was associated with better outcome. Many of the genes were significantly (p<0.05) associated with necrosis (503 genes), Fuhrman grade (494), stage (482), tumor size (492), and nodal status (183). 300 genes were significantly (p<0.05, sted) associated with at least 4 of the 5 pathologic and clinical covariates described above.

A smaller set of 72 genes was selected for developing multi-gene models as follows: 29 genes associated with RFI after adjustment for disease stage, Fuhrman grade, tumor size, necrosis and nodal status controlling false discovery rate (FDR) at 10%; the top 14 genes associated with RFI in univariate analyses; 12 genes that were members of the vascular endothelial growth factor/mammalian target of rapamycin (VEGF/mTOR) arization pathway; and 17 genes from additional biological pathways that were identified by principal component analysis (PCA). These data were used to select the final 11 cancer-related genes and 5 reference genes and to develop a multi-gene thm to predict recurrence of ccRCC for ts with stage I/II/III renal cancer.

EXAMPLE 2: ALGORITHM DEVELOPMENT The genes identified in the studies described in Example 1 were considered for inclusion in the ence Score. A smaller set of 72 genes was selected as 0 29 genes associated with RFI after covariate adjustment and FDR control at 10% using Storey’s procedure y JD (2002) A direct approach to false discovery rates.

Journal of the Royal Statistical Society: Series B 64:479—498; Storey JD, Taylor JE, Siegmund DO (2004) Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society, Series B -205.). 0 14 genes most significant before covariate ment 0 12 genes members of VEGF / mTOR pathways 0 17 genes were selected by principal component is to identify genes from additional ys To ine the association between each of the 72 genes and RFI, univariate and multivariable analyses were used. Tables lA (univariate analysis) and 1B (multivariable analysis) report the Hazard Ratio, 95% confidence interval, Chi-squared, p- value, and q-value for each of the 72 genes.

WO 94078 Table 1A: Univariate analysis for 72 genes: association with RFI Association with RFI Rank Official N HR 95 % CI Chi-Sq p-value q-value 22 A2M 29 ADD1 58 ANGPTL3 26 APOLD1 4 AQPl 34 BUB 1 * 24 C1 30rf15 40 CA12* 42 CASP10 73 CCL5 931 0.99 (0.87,1.13) 0.02 0.894 0.455 48 CCNB1* 66 CCR7 69 CD8A CEACAM1 27 CX3CL1 68 CXCL10 931 0.89 (078,101) 3.29 0.070 0.049 67 CXCL9 47 CYR61 23 EDNRB 53 EGR1 1 EMCN 56 ENO2* 17 EPAS1 31 FLT1 14 FLT4 62 HIF1AN 45 HLA—DPB1 ICAM2 19 ID1 50 IL6* 36 IL8* 65 ITGB1 72 ITGB5 931 0.97 (0.85,1.11) 0.22 0.640 0.341 JAG1 12 KDR 54 KIT 21 KL 55 KRAS 63 LAMB 1 * 9 LDB2 52 LMNB 1 * 49 LOX* 43 MAP2K3 41 MMP14* 6O MTOR 18 NOS3 16 NUDT6 61 PDGFA 33 PDGFB 37 PDGFC 28 PDGFD 57 PDGFRB 3 PPAPZB 32 PRKCH 6 PTPRB 44 PTTG1 * 64 RAF1 RGSS 7 SDPR 39 SGK1 1 1 SHANK3 SNRK 46 SPP1 * 2 TEK 13 TGFBR2 TIMP3 TPX2* 8 TSPAN7 7O TUBB2A* 51 UGCG 38 VCAM1 59 VEGFA Key: Genes marked w1th an sk (*) are assomated such that 1ncreased expresswn IS associated with worse e Genes in bold are the top 10 genes with respect to magnitude of the Hazard Ratio (HR) Table 1B: Multivariable analysis for 72 genes: association with RFI Association with RFI ed for 5 Clin/path Covariates Rank Official by HR Symbol N HR 95% CI Chi-Sq p-value q-value 22 A2M 928 0.93 (080,108) 0.99 0.3191 0.5394 29 ADDl 58 ANGPTL3 26 APOLDl 4 AQPl 34 BUB1* 24 C13orf15 40 CA12* 928 1.08 (095,123) 1.46 0.2267 0.4919 42 CASPlO 73 CCL5 48 CCNB1* 66 CCR7 69 CD8A CEACAMl 27 CX3CL1 68 CXCL10 67 CXCL9 47 CYR61 927 0.93 (0811.08) 0.84 0.3603 0.5581 ........ 23 EDNRB 53 EGRl 927 0.91 (079,104) 1.93 0.1652 0.4234 1 EMCN 56 EN02* 17 EPASl 31 FLTl 928 0.91 (080,105) 1.57 0.2106 0.4919 14 FLT4 926 0.86 (073,102) 3.11 0.0776 0.3289 62 HIFlAN 928 1.02 (090,116) 0.10 0.7571 0.7052 45 HLA—DPBl ICAM2 19 m1 50 IL6* 928 1.04 (092,118) 0.46 0.4994 0.6384 36 1L8 * 928 1.11 (098,126) 2.89 0.0890 0.3350 65 ITGB 1 72 ITGB5 JAG1 (075,103) 12 KDR (074,100) 54 KIT (083,113) 21 KL 55 KRAS 63 LAMB 1 * LDB2 52 LMNB 1 * 49 LOX* (086,112) 43 MAP2K3 (0.79 1.06) 41 MMP14* 60 MTOR (081,107) 18 NOS3 16 NUDT6 61 PDGFA (085,112) 33 PDGFB 08) 37 PDGFC (082,106) 28 PDGFD (079,105) 57 PDGFRB (090,118) PPAPZB 32 PRKCH PTPRB 44 PTTG1 * (088,116) 64 RAF1 (0.92 1.22) RGSS SDPR 39 SGK1 11 SHANK3 SNRK 46 SPP1 * 13 TGFBR2 TIMP3 TPX2* TSPAN7 70 TUBB2A* 51 UGCG 928 0.92 04) 1.88 0.1708 0.4271 928 1.01 (0.88,1.17) 0.03 0.8727 0.7408 Genes marked with an asterisk (*) are ated such that increased expression is associated with worse outcome Genes in bold are the top 10 genes w.r.t. magnitude of the Hazard Ratio (HR) Analysis in this table is adjusted for stage, necrosis status, tumor size, Furhman grade, nodal status.

The 72-gene set was reduced further to 14 genes by exploring the bution of genes to the multi-gene models, consistency of performance across endpoints, and analytical performance. Selection of the final set of 11 genes was based on multivariable analyses which considered all possible combinations of the 14 genes and ranked models by standardized hazard ratio for the multi-gene score r, Journal of Applied Statistics 2012 February; 36(2),399-417) corrected for regression to the mean (RM). This method corrects for selecting among combinations of genes and considers combinations selected from all 732 genes igated in the gene fication study. The identified maximum RM-corrected hazard ratio is unbiased (Crager, Stat Med. 2010 Jan 15;29(1):33-45.)) and provides a realistic estimate of the mance of the given multi-gene model on an independent Additional considerations for gene selection included assay performance of individual genes (heterogeneity) when assessed in fixed paraffin-embedded tumor tissue, level and variability of gene expression, and functional form of the relationship with clinical outcome.

The gene expression panel included cancer-related genes and reference genes, as shown in Table 2.

Table 2: Gene sion Panel Accession Reference Accession Number APOLDl NM_030817 AAMP NM_001087 CCLS NM_002985 ARFl NM_00 1658 CEACAMl RM_001712 ATPSE NM_006886 CX3CL1 '/M_002996 xM_004095 A\M_000600 5573 XVI_000603 PPAPZB )M_003713 TUBBZA F M_001069 Overview of the Al orithm for Obtainin a Recurrence Score After using quantitative RT-PCR to determine the mRNA expression levels of the chosen genes, the genes were grouped into subsets. Genes known to be ated with ar and/or angiogenesis functions were grouped in a lar normalization” gene group. Genes known to be associated with immune function were grouped in an “immune response” gene group. Genes associated with key cell growth and cell division pathway(s) were grouped in a “cell growth/ on” gene group.

The gene expression for some genes may be thresholded if the relationship between the term and the risk of recurrence is non-linear or expression of the gene is relatively low. For example, when the expression of 1L6 is found at <4 CT the value is fixed at 4 CT.

In the next step, the measured tumor level of each mRNA in a subset was multiplied by a coefficient reflecting its relative set contribution to the risk of cancer recurrence. This product was added to the other products between mRNA levels in the subset and their coefficients, to yield a term, e.g. a vascular normalization term, a cell growth/division term, and an immune response term. For example, the immune response term is (0.5 CCL5 + CEACAMl + CX3CL1) / 3 (see the Example below).

] The contribution of each term to the overall recurrence score was then weighted by use of a coefficient. For example, the immune response term was multiplied by - 0.31.

The sum of the terms obtained provided the recurrence score (RS).

A relationship between ence score (RS) and risk of recurrence has been found by measuring expression of the test and reference genes in biopsied tumor specimens from a population of ts with clear cell renal cell carcinoma and applying the algorithm.

The RS scale generated by the algorithm of the t invention can be adjusted in various ways. Thus, while the RS scale specifically described above effectively runs from -3.2 to -0.2, the range could be selected such that the scale run from 0 to , 0 to 50, or 0 to 100, for example. For example, in a particular g approach, scaled recurrence score (RS) is calculated on a scale of 0 to 100. For convenience, 10 CT units are added to each measured CT value, and unscaled RS is calculated as described before. Scaled recurrence score values are ated using the equations shown below.

The Recurrence Score (RS) on a scale from 0 to 100 was derived from the reference-normalized expression measurements as follows: 2014/040003 RSu= - 0.45 X Vascular Normalization Gene Group Score - 0.31 X Immune Response Gene Group Score + 0.27 X Cell Growth/ Division Gene Group Score + 0.04 X IL6 where Vascular Normalization Gene Group Score = (0.5 APOLD1+ 0.5 EDNRB + NOS3 + PPAZB) / 4 Cell Growth/ on Gene Group Score = (EIF4EBP1 + 1.3 LMNBl + TUBB2A) / 3 Immune se Gene Group Score = (0.5 CCL5 + CEACAMl + CX3CL1) / 3 The RSu (Recurrence Score unsealed) is then rescaled to be between 0 and 100: RS = (RSu + 3.7) X 26.4, If (RSu + 3.7) X 26.4<0, then RS=0.

If (RSu + 3.7) X 26.4>100, then RS=100.

EXAMPLE 3: PERFORMANCE OF THE ALGORITHM The performance of the final genes included in the algorithm with and without adjustment for correction for regression to the mean with respect to the endpoint of recurrence is summarized in Table 3.

When using es that control the false discovery rate such as Storey’s procedure, increasing the tion of genes with little or no association decreases the identification power even for genes strongly associated with outcome. Therefore, analyzing all of the genes together as one very large set can be eXpected to produce an analysis with lower power to fy truly associated genes. To mitigate this issue, a “separate class” analysis (Efron B. Simultaneous inference: When should hypothesis testing problems be combined. Ann. App]. Statist. 2008;2:197—223.) was done. In the te class analysis, false discovery rates are calculated within each gene class, using ation from all the genes to improve the accuracy of the calculation. Two gene classes were selected prospectively on the basis of prior information and/or belief about their association with cancer recurrence, and the remaining genes places in the third class.

Table 3: Performance of the Genes in the Algorithm Higher eXpreSSion (1' RM- Official p- MLB N Class more (+)/ ASHR SHR (95% CI) Value Corrected Symbol value ASHR less (-) (FDR) ASHR risk 1 2 PPAP2B (—) 2.00 0.50 (045,055) <0.001 <0.001 1.73 1.97 2 1 NOS3 (—) 1.83 0.55 (048,062) <0.001 <0.001 1.59 1.80 3 2 EDNRB (—) 1.78 0.56 (050,063) <0.001 <0.001 1.58 1.76 4 2 APOLDl (—) 1.74 0.57 (051,064) <0.001 <0.001 1.55 1.72 3 CX3CL1 (—) 1.72 0.58 (052,065) <0.001 <0.001 1.45 1.68 6 3 CEACAMl (—) 1.70 0.59 (051,067) <0.001 <0.001 1.42 1.64 7 3 lL6* (+) 1.38 1.38 53) <0.001 <0.001 1.24 1.35 8 3 LMNBl (+) 1.40 1.40 (123,160) <0.001 <0.001 1.22 1.34 9 3 EIF4EBP1 (+) 1.19 1.19 (104,137) 0.010 0.004 1.09 1.16 3 TUBB2A (+) 1.09 1.09 (096,124) 0.200 0.054 1.03 1.07 11 1 CCL5 (—) 1.01 0.99 (0.87,1.13) 0.894 0.125 1.01 1.03 Abbreviations: ASHR = absolute standardized hazard ratio, RM = regression to the mean corrected, FDR = false discovery rate. * 1L6 expression thresholded at 4 CT.

] In the Cox model stratified by stage, the final Recurrence Score yielded absolute standardized HR =2.16 (95% CI 1.89, 2.48) and sion to the mean corrected absolute standardized HR =1.91 (95% CI 1.38, 2.30) for the association with ence.

Performance of the ence Score can also be demonstrated by the predictiveness curves (Hung Y, Pepe MS, Feng Z. (2007). Evaluating the predictiveness of a continuous marker. Biometrics 63:1181-1188.) shown in Figures 1A and 1B. These curves are plots of the estimated risk of recurrence cal axis) against the population quantile (rank) of the risk. The curve as a whole shows the population distribution of risk. More effective prognostic scores separate lower risk patients from higher risk patients, which are reflected by the curve separating from the average risk line. Risk cut-points can then be applied to describe how many patients fall into various risk groups. For example, the cut- points can be used to describe how many patients with stage 1 RCC have a risk > 16%.

EXAMPLE 4: HETEROGENEITY STUDY An internal study examining the variability due to tissue heterogeneity was run on a sample of renal cancer fixed paraffin-embedded tissue (FPET) blocks. Eight (8) patients with two (2) blocks for each patient and three (3) sections within each block were assessed using the methods and algorithm ed in the above Examples. Heterogeneity was ed by assessing between block variability and within block variability. The between block variability measures the biological variability n FPET blocks within the same patient. This es an te of the population level variability. The within block variability captures both the tissue heterogeneity within a block as well as the technical assay- related variability. The normalized individual gene scores as well as the Recurrence Score were calculated and within block, between block and between patient variability estimates were generated. The results of the analysis are listed in tables 4 and 5 below. The high ratio of the between patient variability to the between and within block variability is generally favorable. This tes that the tissue heterogeneity and technical assay related variability is low compared with the clinically ative patient to patient variability in the individual gene measurements and the Recurrence Score.

Table 4: Recurrence Score Variance Component Estimates Variance Lower Upper Component SD 95% 95% .60 10.15 33.32 Table 5: Individual Normalized Gene Variance Component Estimates Gene Variance Comp SD Lower 95% Upper 95% AAMP.1 n Patient 0.39 0.26 0.84 Between Block Within Block APOLD1.1 Between Patient APOLD1.1 n Block APOLD1.1 Within Block ARF1.1 Between Patient ARF1.1 Between Block ARF1.1 Within Block ATP5E.1 Between t ATP5E.1 Between Block ATP5E.1 Within Block CCL5.2 Between Patient CCL5.2 Between Block CCL5.2 Within Block CEACAM1.1 Between Patient CEACAM1.1 Between Block CEACAM1.1 Within Block CX3CL1.1 Between t CX3CL1.1 Between Block CX3CL1.1 Within Block EDNRB.1 Between Patient EDNRB.1 Between Block 0.36 0.23 0.75 Gene Variance Comp SD Lower 95% Upper 95% EDNRB.1 Within Block EIF4EBP1.1 Between Patient EIF4EBP1.1 Between Block EIF4EBP1.1 Within Block Between Patient 0.43 0.28 0.88 Between Block 0.04 0.02 0.12 Within Block 0.04 0.04 0.06 Between Patient 1.24 0.81 2.60 n Block 0.28 0.18 0.62 IL6.3 Within Block 0.19 0.15 0.25 LMNB1.1 Within Block 0.14 0.11 0.18 NOS3.1 Between Patient 0.75 0.48 1.69 NOS3.1 Between Block 0.32 0.21 0.70 NOS3.1 Within Block 0.21 0.17 0.28 PPAP2B.1 Between Patient 0.89 0.58 1.90 PPAP2B.1 Between Block .1 Within Block RPLP1.1 Between Patient RPLP1.1 n Block RPLP1.1 Within Block 0.05 0.04 0.07 TUBB.1 Between Patient 0.52 0.34 1.07 TUBB.1 Between Block 0.00 .

Gene Variance Comp SD Lower 95% Upper 95% TUBB.1 Within Block 0.15 0.12 0.19 EXAMPLE 5: ADDITIONAL MULTI-GENE COMBINATIONS A number of alternative gene models were also evaluated, using either the dataset from the gene identification study or the dataset from the validation study.

Additional representative gene combinations tested on the dataset from the gene identification study are shown in Table 6. Additional representative gene combinations tested on the dataset from the tion study are shown in Table 7. Models 1-4 shown in Table 6 were not tested on the dataset from the validation study, and so are omitted from Table 7.

Those Tables both list calculated coefficients reflecting each gene’s relative weight in an algorithm to predict the risk of cancer recurrence. The measured tumor level of each mRNA encoding the specific genes used in the various models tested (e.g., model 11 included APOLDl, NOS3, PPAPZB, and CEACAMl) was multiplied by the listed coefficient to produce an alternative score. The performance of each alternative score, as ed by te standard hazard ratios and the corresponding 95% ence intervals, is also shown in the Tables. Where two genes are listed in the header row (e. g., APOLDl-EDNRB, IL6-IL8), that column lists the coefficient of the average measured tumor level of the mRNA encoding those two genes. wwomwd. mmwofio. minwmd mwbmmd mmmvvd :ommd owwmmd movfld Ommbod Ommmmd Hmwomd flambmd. omflwmd. mmofl md. moENd. memmd. meflmd. wavmd. mmoflmd. wozumd. :25 . mowwmd. hmHmNd. Nwmvmd. wmmmmd. ommwmd. 2mm”? NSbmd. wammd. iii Hm 9 :wwmd. wade. Nmmed. wvowmd. on??? mumbvd. vwmmvd. meovd. wwmwmd. mVOde. 250 NE 2: thde. SNSd. voommd. whowmd. vwvwmd. Vmwovd. wmmwmd. . H md. mumbmd. wwmwmd. wwbwmd. owmwmd. Hoflmvd. Nmmomd.

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Claims (10)

What is claimed is:

1. A method for classifying a patient with kidney cancer as having a higher risk of recurrence or a lower risk of recurrence comprising: measuring a level of RNA transcripts from each of the genes of each of the following gene s in a tumor sample obtained from the patient: a vascular normalization gene group, an immune se gene group, a cell growth/division gene group, and IL-6; wherein the vascular normalization gene group genes are: , EDNRB, NOS3, and PPAP2B, and the immune response gene group genes are: CCL5, CEACAM1, and , and the cell growth/division gene group genes are: EIF4EBP1, LMNB1, and ; calculating a recurrence score using the following algorithm: RS = -0.45 x vascular normalization gene group score -0.31 x Immune response gene group score + 0.27 x cell growth/division gene group score +0.04 x IL6 where: ar normalization gene group score = (0.5APOLD1 + 0.5EDNRB + NOS3 + PPAP2B)/4 cell growth/division gene group score = (EIF4EBP1 + 1.3LMNB1 + TUBB2A)/3; and immune response gene group score = (0.5CCL5 + CEACAM1 + CX3CL1)/3; and classifying the patient as lower risk or higher risk based on the recurrence score by comparison with average population risk for kidney cancer patients.

2. The method of claim 1, further comprising scaling the RS score using the following algorithm: RS (scaled) = (RS (unscaled) + 3.7) x 26.4 where: If RS = <0, then RS = 0; and If RS = >100, then RS = 100.

3. The method of claim 1, wherein the level of the RNA transcripts are determined by quantitative RT-PCR.

4. The method of claim 1 wherein measuring a level of the RNA transcripts comprises: extracting RNA from a tumor sample ed from the patient; reverse transcribing an RNA transcript of the prognostic genes from the gene subsets to produce a cDNA of the prognostic genes; amplifying the cDNA of the stic genes; producing an amplicon of the RNA transcript of the prognostic genes; assaying a level of the amplicon of the prognostic genes; normalizing the level against the level of an amplicon of at least one reference RNA ript in the tumor sample to provide a normalized amplicon level of the prognostic genes; assigning the normalized amplicon level to the one or more gene subsets; and wherein the method further ses generating a report comprising the RS.

5. The method of any one of claims 1 to 4, wherein the kidney cancer is renal cell carcinoma (RCC).

6. The method of any one of claims 1 to 4, wherein the kidney cancer is clear cell renal cell carcinoma (ccRCC).

7. The method of any one of claims 1 to 6, wherein the tumor sample is to be obtained from a biopsy.

8. The method of any one of claims 1 to 7, wherein the tumor sample is fresh or

9. The method of any one of claims 1 to 7, wherein the tumor sample is paraffinembedded and fixed.

10. The method of any one of claims 1-9, further comprising creating a report listing the RS score for the patient.