Renal cancer gene expression signature

Abstract

Provided herein are markers and methods to improve management of renal cancer. In some embodiments, the disclosure relates to a panel of biomarkers and use thereof to improve management of renal cell carcinoma, including clear cell renal cell carcinoma.

Description

[0001] Attorney Docket No. UM-39857.601 RENAL CANCER GENE EXPRESSION SIGNATURE PRIORITY STATEMENT This application claims priority to U.S. Provisional Application No. 63 / 447,101, filed February 21, 2023, the entire contents of which are incorporated herein by reference for all purposes. FIELD The present disclosure relates to markers and methods to improve management of renal cancer. In some embodiments, the disclosure relates to a panel of biomarkers and use thereof to improve management of renal cell carcinoma, including clear cell renal cell carcinoma. BACKGROUND Renal cell carcinoma (RCC) accounts for approximately 4.2% of all newly diagnosed cancer cases in the United States annually. Although surgery is curative in most patients, approximately 20% experience recurrence or distant metastasis. There is a critical unmet need to develop prognostic biomarkers to improve the management of patients renal cell carcinoma, including biomarkers to identity patients that require additional therapy following surgery to reduce the risk of disease recurrence. SUMMARY In some aspects, provided herein are methods involving determining expression of a panel of genes obtained from a subject. In some embodiments, the subject is at risk of having cancer, and expression of the panel of genes in the subject is used to diagnose cancer in the subject. In some embodiments, the subject has or is at risk of having cancer, and expression of the panel of genes in the subject is used to prognose cancer (e.g. determine the severity of cancer, predict disease outcome) in the subject. In some embodiments, the subject has or is at risk of having cancer, and expression of the panel of genes is used to determine a treatment for the subject. The methods provided herein allow for personalized cancer medicine wherein high-risk patients (e.g. patients predicted as having a poor outcome) can be treated with aggressive cancer Attorney Docket No. UM-39857.601 treatment regimens and low-risk patients are not unnecessarily exposed to such aggressive treatments and are instead flagged for surveillance of the cancer. In some embodiments, the subject has or is at risk of having cancer and has received one or more treatments for cancer, and expression of the panel of genes in the subject is used to monitor treatment efficacy in the subject and / or determine whether additional treatments are needed in the subject. In some embodiments, the cancer is renal cancer. In some embodiments, the cancer is renal cell carcinoma. In some embodiments, the cancer is clear cell renal cell carcinoma (ccRCC). In some embodiments, the methods provided herein comprise determining expression of a panel of genes in a sample obtained from a subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the panel of genes comprises at least 6 genes. In some embodiments, the panel of genes comprises at least 10 genes. In some embodiments, the panel of genes comprises at least 12 genes. In some embodiments, the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the panel comprises less than 50 genes in total. In some embodiments, the panel comprises less than 25 genes in total. In some embodiments, the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the methods further comprise assigning a score to the subject based upon the expression of the panel of genes in the sample. A high score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score. Expression of the panel of genes can be determined by any suitable method, including at the RNA level, DNA level, or protein level. In some embodiments, expression of the panel of genes is determined by RNA sequencing. In some embodiments, the sample is a tumor sample. For example, the sample can be a tumor sample obtained from a subject during a biopsy procedure to diagnose cancer. In some embodiments, the sample can be a tumor sample obtained from a subject during a surgical procedure to treat the cancer (e.g. a partial nephrectomy or total / radical nephrectomy). In some Attorney Docket No. UM-39857.601 embodiments, the subject has renal cancer (e.g. renal cell carcinoma, clear cell renal cell carcinoma), and the sample is a tumor sample obtained from the subject during a partial or total / radical nephrectomy to treat the cancer. In some embodiments, the subject is a human. In some embodiments, the methods provided herein further comprise predicting disease outcome in the subject based upon the expression of the panel of genes in the sample and / or the score assigned to the subject. In some embodiments, the methods further comprise predicting poor disease outcome in the subject when the expression of one or more genes in the panel is increased in the sample compared to a control and / or when score assigned to the subject is above a threshold value. In some embodiments, poor disease outcome comprises reduced progression free survival (PFS) and / or disease specific survival (DSS) in the patient. In some embodiments, the methods further comprise providing an aggressive cancer treatment regimen to the subject when the expression of one or more genes in the panel is increased in the sample compared to a control and / or or when score assigned to the subject is above a threshold value. In some embodiments, the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof. In some embodiments, the methods comprise monitoring (e.g. surveilling) the subject when poor disease outcome is not predicted (e.g. when the score assigned to the subject is not above a threshold value). In some aspects, provided herein are methods of predicting disease outcome in a subject having or at risk of having renal cancer. In some embodiments, methods of predicting disease outcome in a subject comprise determining expression of a panel of genes in a sample obtained from the subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2. In some embodiments, the methods further comprise predicting disease outcome based upon the expression of the panel of genes in the subject. For example, in some embodiments the methods further comprise predicting poor disease outcome in the subject when expression of one or more genes in the panel is increased compared to a control. In some embodiments, methods of predicting disease outcome in the subject comprise assigning score to the subject based upon the expression of the panel of genes in the sample, and predicting disease outcome in the subject based upon the score assigned to the subject. A high Attorney Docket No. UM-39857.601 score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score. In some embodiments, the panel of genes comprises at least 6 genes. In some embodiments, the panel of genes comprises at least 10 genes. In some embodiments, the panel of genes comprises at least 12 genes. In some embodiments, the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the panel comprises less than 50 genes in total. In some embodiments, the panel comprises less than 25 genes in total. In some embodiments, the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. Expression of the panel of genes can be determined by any suitable method, including at the RNA level, DNA level, or protein level. In some embodiments, expression of the panel of genes is determined by RNA sequencing. In some embodiments, methods of predicting disease outcome comprise predicting poor disease outcome in the subject when the score assigned to the subject is above a threshold value. In some embodiments, a poor disease outcome comprises reduced progression free survival (PFS) and / or disease specific survival (DSS) in the patient. In some embodiments, the methods further comprise treating the patient with an aggressive cancer treatment regimen when poor disease outcome is predicted. In some embodiments, the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof. In some embodiments, the methods comprise monitoring (e.g. surveilling) the subject when poor disease outcome is not predicted (e.g. when the score assigned to the subject is not above a threshold value). In some embodiments, the subject has received a first treatment regimen for renal cancer. In some embodiments, first treatment regimen comprises a surgical procedure. In some embodiments, the renal cancer is clear cell renal cell carcinoma. In some aspects, provided herein are methods of treating a subject having or at risk of having renal cancer. In some aspects, methods of treating a subject having or at risk of having renal cancer comprise determining expression of a panel of genes in a sample obtained from the subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, Attorney Docket No. UM-39857.601 BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2, and determining a treatment for the subject based upon the expression of the panel of genes. In some embodiments, the methods comprise assigning score to the subject based upon the expression of the panel of genes in the sample, and providing a treatment to the subject based upon the score assigned to the subject. A high score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score. In some embodiments, the panel of genes comprises at least 6 genes. In some embodiments, the panel of genes comprises at least 10 genes. In some embodiments, the panel of genes comprises at least 12 genes. In some embodiments, the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the panel comprises less than 50 genes in total. In some embodiments, the panel comprises less than 25 genes in total. In some embodiments, the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. In some embodiments, the panel comprises less than 50 genes in total. In some embodiments, the panel comprises 25 genes in total. In some embodiments, the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2. Expression of the panel of genes can be determined by any suitable method, including at the RNA level, DNA level, or protein level. In some embodiments, expression of the panel of genes is determined by RNA sequencing. In some embodiments, the subject has received a first treatment regimen for renal cancer. In some embodiments, the first treatment regimen comprises a surgical procedure. In some embodiments, the renal cancer is clear cell renal cell carcinoma. In some embodiments, the methods comprise providing an aggressive cancer treatment regimen when the score assigned to the subject is above a threshold value. In some embodiments, the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof. In some embodiments, the methods comprise monitoring (e.g. surveilling) the subject when poor disease outcome is not predicted (e.g. when the score assigned to the subject is not above a threshold value). Attorney Docket No. UM-39857.601 In some aspects, provided herein are kits. The kits find use in the methods described herein. In some embodiments, provided herein is a kit comprising reagents for detecting one or more genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2, wherein the kit detects less than 100 genes in total. In some embodiments, the kit detects less than 50 genes in total. In some embodiments, the kit detects less than 25 genes in total. DESCRIPTION OF THE DRAWINGS FIGs. 1A-1E show whole transcriptomic profiling of the discovery cohort. FIG. 1A is a schematic showing whole transcriptome sequencing was performed on a total of 110 patients who had radical nephrectomy for clear cell renal cell carcinoma (ccRCC) matched based on stage and grade of disease. After excluding patients who failed quality control (QC) parameters, a total of 50 patients with recurrence and 41 without recurrence were retained for downstream analyses. FIG. 1B shows a comparison of the discovery cohort with TCGA ccRCC data (validation cohort A). The variance between the two cohorts was adjusted using R package limma which significantly reduced the variance on PC1 axis from 34.2% (top panel) to 11.6% (bottom panel). FIG. 1C shows unsupervised hierarchical clustering of top 10% variably expressed genes clustered the samples into three major groups. From left to right, gene expression changes in the clusters were correlated with higher risk of recurrence. FIG. 1D, FIG. 1E show volcano (FIG. 1D) and pathway enrichment plots (FIG. 1E) highlighting top differentially expressed genes and pathways enriched among patients with recurrence versus no recurrence. FIGs. 2A-2D show performance of a 15-gene (15G) signature to predict progression free survival (PFS) and disease specific survival (DSS) in the discovery cohort (n = 91). FIG. 2A, FIG. 2B show Kaplan-Meier survival analysis plots using the novel 15G prognostic signature developed as described herein and as shown in FIG. 6 to predict the risk of progression and / or death from ccRCC. The signature was used to stratify patients into low vs. high risk using a Youden cut-point index. Kaplan-Meier survival analysis was performed demonstrating worse PFS and poorer DSS in patients classified as high-risk by the gene signature compared to low- risk. FIG. 2C, FIG. 2D show that in a multivariable Cox proportional hazard analyses adjusting Attorney Docket No. UM-39857.601 for clinicopathologic variables, a high-risk gene signature score was significantly associated with worse PFS and DSS (both p < 0.001). FIGs. 3A-3D show validation of the15G signature in The Cancer Genome Atlas (TCGA) ccRCC cohort (validation cohort A; n = 382). FIG. 3A, FIG. 3B show Kaplan-Meier survival analysis plots of patients with ccRCC in TCGA . Patients were classified as low vs. high-risk using the 15G signature (as described in Figures 2 and 6). Consistent with findings from the discovery cohort, Kaplan-Meier survival curve analysis demonstrated worse PFS and DSS in patients classified as high-risk by the gene signature compared to low-risk. FIG. 3C, FIG. 3D show that in a multivariable Cox proportional hazard analyses adjusting for clinicopathologic variables, a high-risk gene signature score was significantly associated with worse PFS and DSS (both p < 0.001). FIGs. 4A-4D show testing of the 15G signature in a combined independent external ccRCC datasets (validation cohort B; n = 279). Four external ccRCC cohorts with oncologic outcomes data were assembled, namely: Seishi Ogawa Japanese ccRCC cohort (n=87); International Cancer Genome Consortium ccRCC cohort (ICGC; n=81); GSE22541 ccRCC cohort (n=20); and Clinical Proteomics Tumor Analysis Consortium ccRCC cohort (CPTAC; n=91)] to constitute validation cohort B. Notably, in this combined cohort, a z-score summation of the 15 genes was used since these datasets were quantified using different next generation sequencing platforms. Nevertheless, the z-score approach yielded consistent results. FIG. 4A-4B show Kaplan-Meier survival analysis plots. Patients were classified as low vs. high-risk using the 15G signature (as described in Figures 2 and 6). Consistent with findings from the discovery and validation cohort A, Kaplan-Meier survival curve analysis demonstrated worse PFS and DSS in patients classified as high-risk by the gene signature. FIG. 4C, FIG. 4D show multivariable Cox proportional hazard analyses adjusting for clinicopathologic variables demonstrated worse DSS (p=0.002) but not PFS (p>0.05) in patients classified as high-risk compared to low-risk. FIGs. 5A-5F show characterization of the molecular underpinnings of the 15G signature. FIG. 5A and FIG. 5B show heatmaps displaying key gene expression changes between samples predicted to be at high-risk vs low-risk in discovery (FIG. 5A) and validation cohort A (TCGA) (FIG. 5B). FIG. 5C, FIG. 5D show gene set enrichment analyses (GSEA) using cancer hallmark pathways and XCell immune subtypes plots displaying differences in pathways enriched Attorney Docket No. UM-39857.601 between samples classified as high-risk versus low-risk of recurrence by the 15G signature in both the discovery and validation cohort A. Concordance in pathways that were significantly enriched in high and low risk patients across the two cohorts was observed. FIG. 6 is a schematic showing the development of the novel 15G signature described herein to predict progression free survival (PFS) and disease specific survival (DSS) in the discovery cohort (n = 91). A total of 13,971 genes that passed upstream quality control (QC) analyses metrics was tested using a multivariate cox proportional hazards model for biomarker identification. Adjustments were made for clinicopathologic variables such as age, sex, grade, and tumor stage. A total of 1017 and 1611 genes were significantly associated with PFS and DSS, respectively, of which 729 genes were common between both outcome measures. Fold- change (FC) differences and average expression values were calculated to rank the genes. Genes with FC >1.25 and average expression value >1 but with non-zero coefficients were retained for model discovery. A 15-fold lasso and elastic-net regularized linear cox model (R package glmnet) was used to train a multiplex Cox proportional hazards model comprising 15 genes (15G) with non-zero coefficients to predict recurrence. FIGs. 7A-7F show multivariable Cox proportional hazard analyses in the discovery cohort. Multivariable Cox proportional hazards analyses was performed in the discovery cohort to compare the performance of three models: clinicopathologic variables only, clinicopathologic variables plus cell cycle progression (CCP) score, and clinicopathologic variables plus CCP and the 15G signature. Association of the 15G signature with progression free survival (PFS) is shown in FIGS. 7A-7C. FIG. 7A-7B show clinicopathologic variables alone (c-index = 0.6) or with addition of CCP (c-index = 0.59) were not significantly associated with PFS. FIG. 7C shows the 15G signature was independently associated with PFS adjusting for clinicopathologic variables and CCP (c-index = 0.75). Association of the 15G signature with disease specific survival (DSS) is shown in FIG. 7D-7E. FIG. 7D shows in a clinicopathologic variables only model (c-index = 0.7), Fuhrman grade was independently associated with DSS. FIG. 7E shows addition of CCP to clinicopathologic variables did not improve the performance of the model (c- index =0.69). FIG. 7F shows the 15G signature was independently associated with DSS adjusting for clinicopathologic variables and CCP (c-index = 0.81). Attorney Docket No. UM-39857.601 FIGS. 8A-8F show multivariable Cox proportional hazard analyses in validation cohort A (TCGA). The independent performance of the 15G signature in the TCGA ccRCC cohort was validated. Association of the 15G signature with progression free survival (PFS) is shown in FIG. 8A-8C. FIG.8A shows in a clinicopathologic variables only model (c-index = 0.71), male sex and tumor stage were independently associated with PFS. FIG. 8B shows CCP was independently associated with PFS adjusting for clinicopathologic variables (c-index =0.77). FIG. 8C shows the 15G signature was independently associated with PFS adjusting for clinicopathologic variables and CCP (c-index = 0.79), consistent with findings in the discovery cohort (Figure 7). Association of the 15G signature with disease specific survival (DSS) is shown in FIG. 8D-8F. FIG. 8D shows in a clinicopathologic variables only model (c-index = 0.76), age and higher tumor stage (T3-4) were independently associated with DSS. FIG. 8E shows CCP was not independently associated with DSS adjusting for clinicopathologic variables (c-index =0.84). FIG. 8F shows the 15G signature was independently associated with PFS adjusting for clinicopathologic variables and CCP (c-index = 0.85), consistent with findings in the discovery cohort (Figure 7). FIGs. 9A-9F show multivariable Cox proportional hazard analyses in validation cohort B. The independent performance of the 15G signature in four combined external ccRCC cohorts was validated (see Figure 3 for combined cohort details). Association of the 15G signature with progression free survival (PFS) is shown in FIG. 9A-9C. FIG. 9A shows in a clinicopathologic variables only model (c-index = 0.79), Fuhrman grade and higher tumor stage (T3-4) were independently associated with PFS. FIG. 9B shows CCP was not an independently associated with PFS adjusting for clinicopathologic variables (c-index =0.8). FIG. 9C shows the 15G signature was not independently associated with PFS adjusting for clinicopathologic variables and CCP (c-index = 0.8). Association of the 15G signature with disease specific survival (DSS) is shown in FIG. 9D-9F. FIG. 9D shows in a clinicopathologic variables only model (c-index = 0.72), age and higher tumor stage (T3-4) were independently associated with DSS. FIG. 9E shows CCP was not independently associated with DSS adjusting for clinicopathologic variables (c-index =0.72). FIG. 9F shows the 15G signature was independently associated with DSS adjusting for clinicopathologic variables and CCP (c-index = 0.76), consistent with findings in the discovery and validation cohort A (Figures 7-8). Attorney Docket No. UM-39857.601 DEFINITIONS Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments described herein, some preferred methods, compositions, devices, and materials are described herein. However, before the present materials and methods are described, it is to be understood that this invention is not limited to the particular molecules, compositions, methodologies, or protocols herein described, as these may vary in accordance with routine experimentation and optimization. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the embodiments described herein. Unless otherwise defined, all 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. However, in case of conflict, the present specification, including definitions, will control. Accordingly, in the context of the embodiments described herein, the following definitions apply. As used herein and in the appended claims, the singular forms “a”, “an” and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to “a peptide amphiphile” is a reference to one or more peptide amphiphiles and equivalents thereof known to those skilled in the art, and so forth. As used herein, the term “comprise” and linguistic variations thereof denote the presence of recited feature(s), element(s), method step(s), etc. without the exclusion of the presence of additional feature(s), element(s), method step(s), etc. Conversely, the term “consisting of” and linguistic variations thereof, denotes the presence of recited feature(s), element(s), method step(s), etc. and excludes any unrecited feature(s), element(s), method step(s), etc., except for ordinarily-associated impurities. The phrase “consisting essentially of” denotes the recited feature(s), element(s), method step(s), etc. and any additional feature(s), element(s), method step(s), etc. that do not materially affect the basic nature of the composition, system, or method. Many embodiments herein are described using open “comprising” language. Such embodiments encompass multiple closed “consisting of” and / or “consisting essentially of” embodiments, which may alternatively be claimed or described using such language. As used herein, the terms “co-administration” and “co-administering” refer to the administration of at least two agent(s) or therapies to a subject. In some embodiments, the co- Attorney Docket No. UM-39857.601 administration of two or more agents or therapies is concurrent. In other embodiments, a first agent / therapy is administered prior to a second agent / therapy. Those of skill in the art understand that the formulations and / or routes of administration of the various agents or therapies used may vary. The appropriate dosage for co-administration can be readily determined by one skilled in the art. In some embodiments, when agents or therapies are co-administered, the respective agents or therapies are administered at lower dosages than appropriate for their administration alone. Thus, co-administration is especially desirable in embodiments where the co- administration of the agents or therapies lowers the requisite dosage of a potentially harmful (e.g., toxic) agent(s), and / or when co-administration of two or more agents results in sensitization of a subject to beneficial effects of one of the agents via co-administration of the other agent. As used herein, the term “control” is used in the broadest sense and is inclusive of values obtained based upon subjects not having cancer (e.g. control subjects) or obtained based upon subjects having cancer for which a poor prognosis was not predicted. For example, a control value may be a value of gene expression based upon data collected from one or more control subjects not afflicted with or at risk of having cancer. As another example, a control value may be a value obtained from one or more subjects having cancer, but for whom a good prognosis was predicted (e.g. prolonged progression free survival, prolonged disease-specific survival, etc.). The term “gene” refers to a nucleic acid (e.g., DNA) sequence that comprises coding sequences necessary for the production of a polypeptide, precursor, or RNA (e.g., rRNA, tRNA). The polypeptide can be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or functional properties (e.g., enzymatic activity, ligand binding, signal transduction, immunogenicity, etc.) of the full-length or fragment are retained. The term also encompasses the coding region of a structural gene and the sequences located adjacent to the coding region on both the 5′ and 3′ ends for a distance of about 1 kb or more on either end such that the gene corresponds to the length of the full-length mRNA. Sequences located 5′ of the coding region and present on the mRNA are referred to as 5′ non-translated sequences. Sequences located 3′ or downstream of the coding region and present on the mRNA are referred to as 3′ non-translated sequences. The term “gene” encompasses both cDNA and genomic forms of a gene. A genomic form or clone of a gene contains the coding region Attorney Docket No. UM-39857.601 interrupted with non-coding sequences termed “introns” or “intervening regions” or “intervening sequences.” Introns are segments of a gene that are transcribed into nuclear RNA (hnRNA); introns may contain regulatory elements such as enhancers. Introns are removed or “spliced out” from the nuclear or primary transcript; introns therefore are absent in the messenger RNA (mRNA) transcript. The mRNA functions during translation to specify the sequence or order of amino acids in a nascent polypeptide. As used herein, the term “poor prognosis” is used interchangeably with “poor disease outcome” or “poor outcome” to indicate an undesirable cancer outcome, including increased likelihood of cancer recurrence, increased likelihood of metastasis, reduced progression free survival (PFS), reduced disease-specific survival (DFS), or a combination thereof. As used herein, the term “primer” refers to an oligonucleotide, whether occurring naturally as in a purified restriction digest or produced synthetically, that is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product that is complementary to a nucleic acid strand is induced, (e.g., in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH). The primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. If double stranded, the primer is first treated to separate its strands before being used to prepare extension products. In some embodiments, the primer is an oligodeoxyribonucleotide. The primer should be sufficiently long to prime the synthesis of extension products in the presence of the inducing agent. The exact lengths of the primers will depend on many factors, including temperature, source of primer and the use of the method. As used herein, the term “probe” refers to an oligonucleotide (i.e., a sequence of nucleotides), whether occurring naturally as in a purified restriction digest or produced synthetically, recombinantly or by PCR amplification, that is capable of hybridizing to at least a portion of another oligonucleotide of interest. A probe may be single-stranded or double- stranded. Probes are useful in the detection, identification, and isolation of particular gene sequences. It is contemplated that any probe used in the present invention will be labeled with any “reporter molecule,” so that is detectable in any detection system, including, but not limited to enzyme (e.g., ELISA, as well as enzyme-based histochemical assays), fluorescent, radioactive, and luminescent systems. Attorney Docket No. UM-39857.601 The terms "sample" and "biological sample" are used interchangeably to refer to any biological sample obtained from an individual including body fluids, body tissue (e.g., tumor tissue), cells, or other sources. Body fluids are, for example, blood and blood products (e.g. whole blood, capillary blood, venous blood, plasma, serum, etc.), urine, saliva, semen, synovial fluid, and spinal fluid. Samples also include tissue, such as tumor tissue. Methods for obtaining tissue biopsies and body fluids from mammals are well known in the art. In some embodiments, the sample is a “tumor sample”. A "tumor sample" herein is a sample derived from, or comprising tumor cells from a patient's tumor. Examples of tumor samples herein include, but are not limited to, tumor biopsies, circulating tumor cells, circulating plasma proteins, ascitic fluid, primary cell cultures or cell lines derived from tumors or exhibiting tumor-like properties, as well as preserved tumor samples, such as formalin-fixed, paraffin- embedded tumor samples or frozen tumor samples. As used herein, the terms “treat,” “treatment,” and “treating” refer to reducing the amount or severity of a particular condition, disease state (e.g., cancer), or symptoms thereof, in a subject presently experiencing or afflicted with the condition or disease state. The terms do not necessarily indicate complete treatment (e.g., total elimination of the condition, disease, or symptoms thereof). In some embodiments, “treating” cancer refers to reducing the size of a tumor, reducing the number of tumors, and / or completely eliminating the tumor from a subject. "Treatment,” encompasses any administration or application of a therapeutic or technique for a disease (e.g., in a mammal, including a human), and includes inhibiting the disease, arresting its development, relieving the disease, causing regression, or restoring or repairing a lost, missing, or defective function; or stimulating an inefficient process. DETAILED DESCRIPTION The prognosis of clear cell Renal Cell Carcinoma (ccRCC) continues to be poor, with 30% of patients diagnosed with metastatic disease at initial presentation and 30% of patients with clinically localized disease likely to develop metastases following treatment. Currently, clinicopathologic variables incorporated in nomograms are utilized to risk stratify patients with ccRCC. There is an ongoing need to improve upon the performance of nomograms or risk calculators for estimating the biological trajectory of the disease. Identification of biomarkers that can identify patients likely to develop recurrence / metastasis, as well as select patients with Attorney Docket No. UM-39857.601 aggressive ccRCC likely to benefit from systemic therapy in the neoadjuvant and / or adjuvant setting, represents an unmet need. Molecular characterization of ccRCC can improve diagnostic, prognostic, and therapeutic strategies in the management of this disease. The prognostic accuracy of individual somatic mutations, however, is limited in localized kidney cancer given intratumoral heterogeneity of these mutations. Gene expression data when measured across a panel of genes has facilitated prognostication in malignancies including breast and prostate cancer. In kidney cancer, multi- gene expression-based classifiers are limited by poor performance and lack of robust validation data. The present disclosure addresses this need and provides a multi-gene expression signature to improve risk stratification for kidney cancer, in particular for ccRCC. The gene expression signature provided herein provides valuable prognostic information, beyond standard clinicopathologic parameters for risk stratification of patients, and facilitates optimal treatment allocation to patients in need. In some embodiments, provided herein is a 15-gene expression signature (15G score) that provides prognostic information independent of clinico-pathologic variables in ccRCC patients who have undergone nephrectomy. This signature also provides insights into the molecular mechanisms of ccRCC and provides a tool for the identification of novel therapeutic targets. The 15G signature stratifies patients into low-risk versus high-risk groups for identifying patients for surveillance versus treatment. In some aspects, provided herein are methods. In some embodiments, provided herein are methods comprising determining expression of a panel of genes (e.g. a gene expression signature) in a sample obtained from a subject. “Determining expression of a panel of genes” is used interchangeably herein with determining or measuring a “gene expression signature” in a subject. In some embodiments, provided herein are methods comprising determining expression of a panel of genes in a sample obtained from the subject, and assigning a score to the subject based upon the expression of the panel of genes. In some embodiments, the score assigned to the subject is indicative of disease outcome in the subject. For example, in some embodiments a high score is indicative of increased expression of the panel of genes in the subject, and is indicative of poor disease outcome in the subject. In some embodiments, the subject is a human. In some embodiments, the subject is suspected of having or at risk of cancer. In some embodiments, the subject has or at risk of having renal cancer. In some embodiments, the Attorney Docket No. UM-39857.601 subject has received a first treatment regimen for renal cancer. In some embodiments, the treatment regimen comprises surgery. In some embodiments, provided herein is a method comprising determining expression of a panel of genes in a sample obtained from a subject (e.g. a gene expression signature), wherein the subject has received a first treatment regimen for renal cancer. Gene expression can be determined either at the RNA level (i.e., mRNA or noncoding RNA (ncRNA)) (e.g., miRNA, tRNA, rRNA, snoRNA, siRNA and piRNA) or at the protein level. In some embodiments, measuring gene expression at the mRNA level includes measuring levels of cDNA corresponding to mRNA. In some embodiments, determining expression of a gene comprises determining an RNA level for the gene. In some embodiments, determining expression a gene comprises determining a level of a protein encoded by the gene. Various suitable methods for determining expression of a gene may be employed. In some embodiments, the panel of genes comprises one or more selected from insulin- like growth factor 2 mRNA-binding protein 3 (IGF2BP3), protein tyrosine kinase 6 (PTK6), BolA family member 3 (BOLA3), ubiquinol-cytochrome C reductase hinge protein (UQCRH), transcription factor CP2 like 1 (TFCP2L1), solute carrier family 7 member 8 (SLC7A8), solute carrier family 16 member 1 (SLC16A1), perilipin 5 (PLIN5), transmembrane protein 136 (TMEM136), solute carrier family 7 member 1 (SLC7A1), transmembrane protein 81 (TMEM81), ribosomal protein L22 like 1 (RPL22L1), elongin B (ELOB), IQ motif containing C (IQCC), and transgelin 2 (TAGLN2). In some embodiments, the panel of genes comprises at least 2 genes, at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, at least 9 genes, at least 10 genes, at least 11 genes, at least 12 genes, at least 13 genes, at least 14 genes, or at least 15 genes. In some embodiments, the panel comprises 15 genes. In some embodiments, the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2. In some embodiments, the panel of genes comprises 15 genes, wherein the 15 genes comprise IGF2BP3, PLIN5, TAGLN2, PTK6, TMEM136, BOLA3, SLC7A1, UQCRH, TMEM81, TFCP2L1, RPL22L1, SLC7A8, ELOB, SLC16A1, IQCC, and TAGLN2. In some embodiments, the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2. Attorney Docket No. UM-39857.601 In some embodiments, the method further comprises determining expression of one or more housekeeping genes. As used herein, the term “housekeeping genes” or “normalizing genes” are used interchangeably to refer to the genes whose expression is used to calibrate or normalize the measured expression of the gene of interest (e.g., a gene in the panel of genes). The expression of housekeeping genes should be independent of cancer outcome / prognosis, and the expression of the housekeeping genes should be highly similar among all of the samples. This normalization helps to ensure accurate comparison of expression of the gene panels between different samples. Any suitable housekeeping gene(s) known in the art can be used. One or more housekeeping genes can be used. In some embodiments, the housekeeping genes are selected from ATP synthase F1 subunit epsilon (ATP5E), ADP-ribosylation factor 1 (ARF1), clathrin heavy chain 1 (CLTC1), and phosphoglycerate kinase 1 (PGK1). In some embodiments, one housekeeping gene is used. In some embodiments, 2 housekeeping genes are used. In some embodiments, 3 housekeeping genes are used. In some embodiments, each of ATP5E, ARF1, CLTC1, and PGK1 are used. In some embodiments, a score is generated based upon the expression of the panel of genes. Determining a score based upon the expression of the panel of genes includes methods involving normalizing the expression of genes, and determining the score in the subject based upon the normalized expression of the genes. In some embodiments, a score is generated based upon the normalized expression of each member in the panel of genes. A score may be generated, at least in part, by combining the expression (e.g. normalized expression) for each member of the panel of genes. In some embodiments, the total expression (e.g. total normalized expression) for the panel may be calculated (e.g. by adding the normalized expression of each gene to receive a total score). In some embodiments, the average expression (e.g. average normalized expression) for the panel may be calculated. For example, the normalized expression of each member of the panel of genes may be combined, and that total may be divided by the number of genes to determine the average normalized expression across all genes in the panel. In some embodiments, each member in the panel receives equal weight. In some embodiments, one or more members of the panel is more significant than others (e.g. receives more weight than others). For example, one member of the panel may be at least 10%, 15%, 20%, 25%, 30%, Attorney Docket No. UM-39857.601 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or more than 90% of the total weight given to all of the genes in the panel. Generally speaking, higher scores are indicative of increased expression (e.g. increased average normalized expression, increased total normalized expression) of the panel of genes in the subject compared to expression of the same panel of genes in a second subject. Accordingly, a higher score is indicative of increased expression of a panel of genes comprising one or more of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2 in the subject. In some embodiments, a “high score” means that the score is equal to or above a cutoff value. In some embodiments, a “low score” means that the score is below a cutoff value. In some embodiments, a score above a cutoff value is indicative of increased risk of an undesirable cancer outcome. Accordingly, a subject having a high score may also be referred to herein or in the accompanying figures as a “high risk” subject, whereas a subject having a low score may also be referred to as a “low risk” subject. The terms “cutoff value”, “threshold value”, and “reference value” are used interchangeably and refer to a value above which a poor outcome is predicted, and below which a poor outcome is not predicted. In some embodiments, the threshold value is determined based upon expression of the panel of genes in a subject not having cancer. In some embodiments, the threshold value is determined based upon the average expression of the panel of genes in multiple subjects not having cancer. In some embodiments, a “high” score indicates that the fold change in the expression of the panel of genes is at least 1.25 compared to control expression, such as expression of the panel in a subject not having cancer). In some embodiments, a “low” score indicates that the fold change in the expression of the panel of genes is not at least 1.25 compared to control expression, such as expression of the panel in a subject not having cancer). In some embodiments, a “high” score indicates that expression of the panel of genes is increased by at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least 100% compared to expression of the panel of genes in a control. In some embodiments, a “low” score indicates that expression of the panel of genes is not increased by at least 20% compared to a control expression. Attorney Docket No. UM-39857.601 In some embodiments, RNA levels for the genes are measured, and subsequently used for assigning a score to the subject. In some embodiments, RNA levels are measured by RNA sequencing. RNA sequencing encompasses a variety of suitable sequencing techniques that can be used to determine the quantity and type of RNA present in a sample. In some embodiments, RNA sequencing involves isolating RNA from a sample, generating a cDNA library from the RNA, and then sequencing the cDNA. In some embodiments, RNA sequencing involves sequencing RNA molecules directly, such as through massively-parallel direct RNA-seq. Suitable RNA sequencing methods include sequencing-by-synthesis methods (SBS), reversible terminator sequencing, sequencing by ligation, sequencing by hybridization and ligation, sequencing by hybridization and synthesis, nanopore sequencing, nanoball sequencing, and the like. Suitable platforms for RNA sequencing include, for example, high-throughput sequencing platforms marketed by Illumina (e.g. miSeq, hiSeq), Applied Biosystems Instruments (SOLiD), Complete Genomics (Nanoball sequencing), Pacific Biosciences (e.g. SMRT sequencing), Helicos Biosciences (Helicos sequencing), and Oxford nanopore technologies (Nanopore Sequencing). Any suitable sequencing technique, platform, or modification thereof can be used to determine expression of the panel of genes described herein. In some embodiments, RNA levels for the genes are measured by a reverse transcription reaction to generate cDNA, followed by a quantitative PCR (qPCR) assay. Typically, a cycle threshold (Ct) value is determined for each test gene and each normalizing (e.g. housekeeping) gene. The Ctvalue indicates the number of cycles at which the fluorescence from a qPCR reaction above background is detectable. The Ctvalue for each gene may be normalized by subtracting the Ct value for a housekeeping gene or the average Ct value for multiple housekeeping genes. The normalized values may be converted to an expression value for the gene, which may be an estimate of the copy number for that gene. In some embodiments, the mean expression value for each gene may be averaged. The average expression value may be transformed, such as by a base 2 algorithm (e.g. log2 transformation), to generate the score for the subject. Expression of the panel of genes may be measured from any suitable sample type obtained from the subject. In some embodiments, the sample is a bodily fluid such as blood (e.g. whole blood, capillary blood, venous blood), plasma, serum, urine, saliva, semen, synovial fluid, or spinal fluid. In some embodiments, the sample is a tissue sample. In some embodiments, the Attorney Docket No. UM-39857.601 sample is a tumor sample. The tumor sample may be any sample derived from or comprising cells from a tumor in the subject. For example, the tumor sample may be a tumor biopsy, circulating tumor cells, circulating plasma proteins, ascitic fluid, primary cell cultures or cell lines derived from tumors or exhibiting tumor-like properties, as well as preserved tumor samples, such as formalin-fixed, paraffin- embedded tumor samples or frozen tumor samples. In some embodiments, the expression levels of genes identified herein are measured in tumor tissue. For example, the tumor tissue may be obtained upon surgical resection of the tumor, or by tumor biopsy. The expression level of the identified genes may also be measured in tumor cells recovered from sites distant from the tumor, including circulating tumor cells or body fluid (e.g., urine, blood, blood fraction, etc.). In some embodiments, the subject is afflicted with or at risk of developing cancer. The terms “cancer” and “carcinoma” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. The pathology of cancer includes, for example, abnormal or uncontrollable cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression, or aggravation of inflammatory or immunological response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, blood vessels, etc. In some embodiments, the cancer is renal cancer. As used herein, the terms “renal cancer” or “renal cell carcinoma” refer to cancer that has arisen from the kidney. Renal cancer encompasses several histologic subtypes, including clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), chromophobic renal cell carcinoma, collecting duct renal cell carcinoma (cdRCC), medullary carcinoma, transitional cell carcinoma (TCC), Wilms tumor (WT), renal sarcoma (RS), or unclassified renal cell carcinoma (RCC). In some embodiments, the renal cancer is clear cell renal cell carcinoma (ccRCC). In some embodiments, the subject has received a first treatment regimen for renal cancer (e.g. ccRCC). For example, in some embodiments the subject has received surgery as a first treatment regimen for renal cancer. In some embodiments, the expression of the panel of genes is predictive of cancer outcome in the subject. For example, the expression of the panel of genes, and / or the score assigned to the subject, may be indicative of cancer prognosis (e.g. ccRCC prognosis). In some embodiments, elevated expression of the panel of genes compared to a control and / or a score Attorney Docket No. UM-39857.601 above a threshold value (e.g. a “high score”) is indicative of poor prognosis for the subject. For example, in some embodiments a fold change in the expression of the panel of genes of at least 1.25 (e.g. a fold change of at least 1.25 for each gene in the panel of genes compared to control expression, such as expression of the panel in a subject not having cancer) is indicative of poor prognosis. In some embodiments, a score above a threshold value is indicative of poor prognosis. The threshold value may be determined or assigned based upon expression of the panel of genes in a control subject, such as a subject not having cancer. A score above the threshold value is considered a “high” score, whereas a score below the threshold value is considered a “low” score. In some embodiments, a “high” score indicates that the fold change in the expression of the panel of genes is at least 1.25 compared to control expression, such as expression of the panel in a subject not having cancer), and is indicative of poor prognosis in the subject. In some embodiments, a “low” score indicates that the fold change in the expression of the panel of genes is not at least 1.25 compared to control expression, such as expression of the panel in a subject not having cancer). In some embodiments, a “high” score indicates that expression of the panel of genes is increased by at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or at least 100% compared to expression of the panel of genes in a control, and indicates a poor prognosis in the subject. In some embodiments, a “low” score indicates that expression of the panel of genes is not increased by at least 20% compared to a control expression. In some embodiments, “poor prognosis” or a “poor disease outcome” is used to indicate an undesirable cancer outcome following nephrectomy in the subject. For example, “poor prognosis” or “poor disease outcome” may be indicative of increased likelihood of cancer recurrence, reduced progression free survival, reduced disease-specific survival, or a combination thereof following nephrectomy in the subject. In some embodiments, expression of the panel of genes is predictive of cancer outcome following a first treatment regimen in the subject. For example, in some embodiments, expression of the panel of genes is predictive of cancer outcome following a surgical procedure in the subject. In some embodiments, the methods described herein further comprise selecting an appropriate treatment regimen for the subject. In some embodiments, the method further comprises selecting an appropriate treatment regimen for the subject following an initial Attorney Docket No. UM-39857.601 treatment (e.g. a first treatment regimen) for the cancer. In some embodiments, the first treatment regimen for the cancer comprises a surgical procedure. Suitable surgical procedures include, for example, laparoscopic procedures, biopsy, or tumor ablation, such as cryotherapy, radio frequency ablation, and high intensity ultrasound. Additional suitable surgical procedures include nephrectomy, including a partial nephrectomy (e.g. a removal of the cancer within the kidney), a simple nephrectomy (e.g. removal of the kidney itself) or a radical nephrectomy (e.g. a procedure to remove the kidney, the adrenal gland, surrounding tissue, and sometimes nearby lymph nodes). In some embodiments, the method comprises selecting an appropriate treatment regimen for the subject following the first treatment regimen that the subject has already received. The treatment regimen that is selected based upon expression of the panel of genes may be referred to herein as a “second treatment regimen”, a “second treatment”, a “follow up treatment”, or a “follow up treatment regimen”. In some embodiments the method comprises selecting an appropriate treatment regimen following a surgical procedure to treat renal cancer in the subject, such as a partial nephrectomy, a simple nephrectomy, or a radical nephrectomy. For example, a treatment regimen may be selected based upon expression of one or more genes in the panel and / or the score assigned to the subject. For example, a subject having elevated expression of the panel of genes or a score above a threshold value may be identified as a subject that would benefit from one or more additional therapies following surgery, such as one or more of radiation therapy, immunotherapy, chemotherapy, targeted therapy, or combinations thereof. Suitable treatment regimens include, for example, radiation therapy, immunotherapy, chemotherapy, targeted therapy, or combinations thereof. In some embodiments, a combination of treatment regimens may be employed. In some embodiments, the treatment regimen may comprise immunotherapy. Suitable immunotherapy includes, for example, cytokine immunotherapy, such as with interleukin-2 (IL-2). Other suitable immunotherapies include, for example, interferon therapy, or immune-checkpoint inhibitor therapies, such as PD-1 inhibitors (nivolumab, pembrolizumab, avelumab, etc.) or CTLA-4 inhibitors (e.g. ipilimumab.). In some embodiments, the treatment regimen comprises targeted therapy, such as treatment with antiangiogenic agents including kinase inhibitors or monoclonal antibodies. Suitable kinase inhibitors include mTOR inhibitors (e.g. everoloimus, temsirolimus), and VEGF inhibitors (e.g. sunitinib, pazopanib, cabozantinib, axitinib, sorafenib, levantinib). Attorney Docket No. UM-39857.601 In some embodiments, the treatment regimen comprises therapy with an agent selected from everolimus, aldesleukin, bevacizumab, avelumab, axitinib, belzutifan, cabozantinib-S- Malate, tivozanib hydrochloride), IL-2 (Aldesleukin), ipilimumab, pembrolizumab, lenvatinib mesylate, sorafenib tosylate, nivolumab, pazopanib hydrochloride, sunitinib malate, temsirolimus, tivozanib hydrochloride, pazopanib hydrochloride, or belzutifan. In some embodiments, an aggressive treatment regimen is selected for a subject having elevated expression of one or more genes in the panel compared to a control or having a score above a threshold value. For example, an aggressive treatment regimen may be selected for a subject having elevated expression of one or more of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2. As used herein, the term “aggressive treatment regimen” refers to a treatment at or above the standard level of treatment for the patient (e.g. at or above the standard level of treatment administered to the patient by a physician with knowledge of the patient’s disease state). For example, in some embodiments a standard treatment regimen following surgery (e.g. following a nephrectomy) may comprise surveillance. For example, a patient not having an elevated score (e.g. not having elevated expression of one or more genes in the panel) may be a candidate for surveillance (e.g. monitoring of the cancer), whereas a patient having a high score (e.g. elevated expression of one or more genes in the panel) would not be a good candidate for surveillance and may instead benefit from an aggressive treatment regimen. In some embodiments, an aggressive treatment regimen comprises a plurality of therapies. For example, an aggressive treatment regimen may comprise providing at least two treatment types to the subject. For example, an aggressive treatment regimen may comprise any combination of radiation therapy, immunotherapy, chemotherapy, and targeted therapy. In some embodiments, radiation therapy may be performed in addition to, for example, immunotherapy, chemotherapy, and / or targeted therapy. In some embodiments, an aggressive treatment regimen may comprise a high dose and / or frequent administration of the treatment to the subject compared to a less aggressive treatment regimen. Selection of the appropriate dosage and administration schedule may be performed by a physician, including a physician with knowledge of the subject’s gene expression. In some embodiments, provided herein is a method of treating a subject. In some embodiments, provided herein is a method of treating a subject, wherein the subject has received Attorney Docket No. UM-39857.601 a first treatment regimen for cancer. In some embodiments, the cancer is renal cancer. In some embodiments, the first treatment regimen for renal cancer comprises a nephrectomy (e.g. a partial nephrectomy, a simple nephrectomy, or a radical nephrectomy). In some embodiments, a method of treating a subject comprises determining expression of a panel of genes as described herein. In some embodiments, the methods of treating a subject further comprise assigning a score to the patient based upon expression of the panel of genes. Suitable methods for assigning a score to the patient are described above. The methods further comprise treating the patient with an appropriate treatment regimen (e.g. an appropriate follow up treatment regimen) based upon expression of the panel of genes and / or based upon the score assigned to the subject. For example, a subject having elevated expression of the panel of genes and / or a score above a threshold value may be identified as a subject that would benefit from one or more additional therapies following surgery, such as one or more of radiation therapy, immunotherapy, chemotherapy, targeted therapy, or combinations thereof, as described above. In some embodiments, the methods further comprise treating the patient with an aggressive treatment regimen, as described above, when expression of one or more genes in the panel is elevated in a sample obtained from the subject and / or when the score assigned to the subject is above a threshold value. In some embodiments, the method comprises monitoring (e.g. surveilling) the subject when the expression of the panel of genes is not elevated in the sample and / or the score assigned to the subject is below a threshold value. In some embodiments, provided herein are kits. In some embodiments, provided herein is a kit for determining expression of one or more genes in a panel as described herein. In some embodiments, provided herein is a kit comprising reagents for detecting one or more or each of the genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2. In some embodiments, the kit comprises reagents for detecting one or more of each of the genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2, along with one or more housekeeping genes. In some embodiments, the housekeeping genes are one or more of ATP5E, ARF1, CLTC1, and PGK1. In some embodiments, the kit comprises reagents for detecting less than 100 genes in total. For example, in some embodiments the kit comprises reagents for detecting one or more or each of the genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, Attorney Docket No. UM-39857.601 TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC, and TAGLN2, wherein the kit detects less than 100 genes in total (e.g. less than 100 genes, less than 75 genes, less than 50 genes, less than 40 genes, less than 30 genes, less than 25 genes, etc.). The kit may comprise reagents suitable for determining expression of the one or more genes by any suitable technique, including mRNA-based and protein-based detection. For example, suitable techniques for determining gene expression include sequencing techniques (including DNA sequencing and RNA sequencing techniques), amplification based techniques such as polymerase chain reaction (PCR) based techniques (e.g. PCR, reverse transcription PCR (RT-PCR), qualitative PCR (qPCR), digital PCR, droplet digital PCR), hybridization techniques (e.g. in situ hybridization, fluorescence in situ hybridization, microarray, Southern blot, Northern blot), serial analysis of gene expression (SAGE), Digital Gene Expression (DGE), and immunoassays (e.g. immunoprecipitation, Western blot, ELISA, immunohistochemistry, immunocytochemistry, flow cytometry, immune-PCR, etc.). In some embodiments, the kit comprises oligonucleotides for detecting one or more genes in a panel along with one or more housekeeping genes. In some embodiments, the kit may comprise oligonucleotides, buffers, salts, preservatives, inhibitors (e.g. RNase inhibitors) dNTPs, enzymes, co-factors, primers, probes, and the like. In some embodiments, the kit comprises antibodies for detection of protein, along with suitable additional reagents including buffers, salts, preservatives, inhibitors (e.g. protease inhibitors), enzymes, stabilizers, and the like. In some embodiments, the kit additionally comprises instructions for use. Instructions included in kits can be affixed to packaging material or can be included as a package insert. While the instructions are typically written or printed materials, they are not limited to such. Any medium capable of storing such instructions and communicating them to an end user is contemplated by this disclosure. Such media include, but are not limited to, electronic storage media (e.g., magnetic discs, tapes, cartridges, chips), optical media (e.g., CD ROM), and the like. As used herein, the term "instructions" can include the address of an internet site that provides the instructions. The various components of the kit optionally are provided in suitable containers as necessary. The kit can further include containers for holding or storing a sample. Where appropriate, the kit optionally also can contain reaction vessels, mixing vessels, and other components that facilitate the preparation of reagents Attorney Docket No. UM-39857.601 or the test sample. The kit can also include one or more instrument for assisting with obtaining a test sample, such as a syringe, pipette, forceps, measured spoon, or the like. EXAMPLES Example 1 There is a critical unmet need to develop prognostic biomarkers to improve the management of patients with clear cell renal cell carcinoma (ccRCC). Differential expression gene (DEG) analysis on whole transcriptome RNA next-generation sequencing data from kidney cancer samples was performed, comparing patients with recurrence versus no recurrence after nephrectomy for kidney cancer. A multiplex gene expression signature that is associated with profession-free survival (PFS) was developed. Using publicly available data set for validation, Kaplan-Meier (KM) survival analysis and multivariable cox-proportional hazard testing were utilized to investigate the prognostic impact of the novel gene expression signature on progression free survival (PFS). The data shown herein demonstrates the independent prognostic significance of this novel gene expression signature to predict recurrence after radical nephrectomy for kidney cancer and has the potential to identify patients for additional therapy following surgery. MATERIALS AND METHODS Discovery cohort: Following institutional review board approval, patients with localized kidney cancer (pT1-3) who underwent radical nephrectomy (RNx) for ccRCC and with sufficient tissue available for analysis were retrospectively identified. This case control cohort was constructed to include patients with and without documented clinical recurrence or metastases during follow up. Patients were matched (1:1) based on stage and grade of disease. Formalin-fixed paraffin- embedded (FFPE) specimens corresponding to the nephrectomies were retrieved, and a genitourinary pathologist reviewed the corresponding hematoxylin and eosin (H&E) slides to assess stage, grade and other histopathologic features as well as identify areas for molecular profiling. RNA was isolated from sections of matched tumor normal FFPE specimens using the Qiagen Allprep FFPE DNA / RNA kit (Qiagen, Valencia, CA) as described in Salami et al., JCI Attorney Docket No. UM-39857.601 insight. 2018;3(21):3. Total RNAs were quantitated using a Nanodrop spectrophotometer. RNA quality was determined using Bio-Analyzer. Capture transcriptome sequencing in the discovery cohort: 100 ng - 5 μg of total RNA was used in a reverse transcription reaction, and the obtained cDNA was taken to a second-strand DNA synthesis as described in Cieslik et al., Genome research. 2015;25(9):1372-1381. Following second-strand synthesis, the Sciclone G3 NGS workstation (Perkin Elmer) with the Kappa HT library preparation kit to produce the libraries, which entailed end repair, A-tailing, adapter ligation, size selection on a 3% agarose gel and uridine digestion. Products were then captured with human all exon v4 capture probes as described above. Both capture genome and transcriptome libraries were sequenced using HiSeq 2500 (Illumina) as described in Cieslik et al., Genome research. 2015;25(9):1372-1381. Following sequencing and base calling, RNA-seq data was aligned using STAR (2.4.0g1) to the GRCh38.p1 reference genome, using the “basic” version of gencode 22 to construct the splice junction database. To identify chromosome-level differences in gene expression, the genome was divided into genic and intergenic loci. The total number of reads mapping to each locus was counted using feature Counts. To correct for different sequencing depth and effective library size normalization factors, we applied the TMM function (default settings) on reads mapped to diploid chromosomes. Expression of protein-coding genes was quantified by counting the reads overlapping exons of annotated protein coding genes in strand-specific mode. Plots of locus- level expressions were produced using GViz. Development and validation of a multigene expression prognostic signature: Using the RNAseq data from the discovery cohort, a gene signature was developed to predict recurrence / progression-free survival (PFS) using a 15-fold lasso and elastic-net regularized linear Cox model. A Youden cut-point index (Ruop et al., Biom J. 2008 Jun;50(3):419-30) that maximizes sensitivity and specificity was used to classify patients into high versus low risk for recurrence. Performance of the gene signature was tested in two validation cohorts, A and B. The validation cohort A (n=382) is comprised of patients diagnosed with localized ccRCC in The Cancer Genome Atlas (TCGA) and with oncologic outcomes data available (Sato et al. Nat Genet. 45(8):860–7). Four other publicly available ccRCC gene expression data set [Seishi Ogawa Japanese ccRCC (n=87), International Cancer Genome Consortium ccRCC (ICGC; Attorney Docket No. UM-39857.601 n=81), GSE22541 ccRCC (n=20) and Clinical Proteomics Tumor Analysis Consortium ccRCC (CPTAC; n=91)] (Clark et al., Cell 2019; 179 (4):964-983) were assembled to constitute validation cohort B. In silico derivation of the Myriad Prolaris™ commercially available 31-gene cell cycle progression (mxCCP) score was performed using RNA-seq data for each patient as described (Salami et al., JCI Insight 2018; 3 (21) e123468) to facilitate comparison with the novel multigene signature described herein in multivariable models. Statistical Analyses. Kaplan-Meier (KM) curves and multivariate Cox proportional hazard testing were used to validate the independent prognostic impact of the gene signature on PFS and DSS by comparing hazard ratios [95% confidence intervals (CI)] and p<0.05 considered statistically significant. Results Whole transcriptomic profiling in the discovery cohort. Of the 110 patients included in the discovery cohort, 91 had gene expression data meeting quality control parameters for inclusion in the final analysis (Figure 1A). As presented in Table 1, there were no significant differences in age, sex, tumor size, stage, and grade (all p>0.05) between patients who developed recurrence (n = 50) and those without recurrence (n=41), with a median follow-up 26 and 36 months, respectively. Following batch adjustment, gene expression data from the discovery cohort displayed similar distribution with validation cohort A (TCGA ccRCC; Figure 1B). Patients who had cancer recurrence in the discovery cohort displayed distinct gene expression patterns and pathway enrichment compared with those without recurrence (Figure 1C-E). Table 1. Clinicopathologic data of the discovery cohort. Variable No Recurrence Recurrence P-value Attorney Docket No. UM-39857.601 T2a 8 8 T2b 3 3 se Development of a 15-gene (15G) signature to predict PFS and DSS in the discovery cohort. To identify individual genes associated with PFS and DSS, Cox proportional hazard’s testing was used to evaluate a total of 13, 971 genes adjusting for age, gender, tumor grade, and pT- stage. 1017 and 1611 genes were identified associated with PFS and DSS, respectively. Of the 729 genes associated with both outcome measures, 19 genes with fold-change values >1.25 and average expression >1 were identified. A 15-gene signature (15G) with non-zero coefficients by GLMNET model was then developed to predict PFS (Figure 6 and Table 2). Patients classified as high-risk for recurrence (15G score >8.24 had a shorter PFS and DSS compared to low-risk (both p <0.0001, Figure 2A-B). The 15G score was the only variable independently associated with worse PFS and DSS (PFS: HR=11.08, CI=4.9-25.1; DSS: HR=9.67, CI=3.4-27.7), adjusting for clinical-pathologic variables and mxCCP score in a multivariable model (Figure 7).

[0002] Attorney Docket No. UM-39857.601 Table 2. Known functions of genes constituting the 15G signature. BOLA3 This gene encodes a protein that plays an essential role in the production of iron- sulfur (Fe-S) clusters for the normal maturation of lipoate-containing 2-oxoacid d . n in Attorney Docket No. UM-39857.601 UQCRH UQCRH (Ubiquinol-Cytochrome C Reductase Hinge Protein) is a Protein Coding gene. Among its related pathways are Pathways of neurodegeneration - multiple Validation of the 15G score to predict PFS in validation cohorts A and B. The clinicopathologic characteristics of validation cohorts A and B are shown in Tables 3-4. As described above, validation cohort A (n=382) is comprised of ccRCC patients in The Cancer Genome Atlas (TCGA) with localized disease at the time of diagnosis and with oncologic outcomes data available (Ricketts et al., Cell Rep. 20183; 23(1): 313-326). Following a median follow up 20 months, 82 (21.5%) developed recurrence in validation cohort A. The second validation cohort, B, is composed of four publicly available ccRCC gene expression data set [Seishi Ogawa Japanese ccRCC (n=87), International Cancer Genome Consortium ccRCC (ICGC; n=81), GSE22541 ccRCC (n=20) and Clinical Proteomics Tumor Analysis Consortium ccRCC (CPTAC; n=91)]. The median follow up in validation cohort B was 41 months and an aggregate recurrence of 50 (17.8%). The 15G score was independently associated with worse PFS and DSS in validation cohort A, adjusting for clinical-pathologic variables and mxCCP score in a multivariable model [(n=382), PFS: HR=2.6, CI=1.6-4.3; DSS: HR=3; CI=1.4-6.1; Figures 3 and 8]. Similarly, in validation cohort B (n=279), the 15G score was only independently associated with worse DSS and not PFS (PFS: HR=1.6, CI=0.73-3.6; DSS: HR=3.1; CI=1.5-6.4] adjusting for clinical-pathologic variables and mxCCP (Figures 4 and 9). Table 3. Clinicopathologic data of the validation cohort A. Variable No Recurrence Recurrence P-value Attorney Docket No. UM-39857.601 T2b 2 0 T3a 50 27 Table 4. Clinicopathologic data of the validation cohort B. Variable No Recurrence Recurrence P-value (n = 231) (n = 50) Molecular underpinnings of the 15G signature. The genes constituting the 15G score are responsible for various cellular functions (Table 2), with no overlap with existing kidney cancer signatures. As shown in Figure 5A-B, a similar gene expression patterns among patients predicted by the 15G score to be at high-risk vs low-risk of recurrence was observed in both Attorney Docket No. UM-39857.601 discovery and validation cohort A. Notably, overexpression of IGF2BP3 was significantly associated with recurrence in both the discovery and validation cohort A. Gene set enrichment analyses (GSEA) using cancer hallmark pathways and XCell immune subtypes showed consistent differences in pathways enriched between samples classified as high-risk versus low- risk of recurrence by the 15G score in both the discovery and validation cohort A (Figure 5C- D). Known cancer hallmark pathways e.g. epithelial-mesenchymal transition, mTOR signaling, glycolysis and oxidative phosphorylation as well as cell types e.g. M1 macrophages were enriched in patients with high-risk 15G score compared to low-risk in both discovery and validation cohort A. Kidney cancer is a highly heterogeneous disease, with a wide range of molecular subtypes and clinical outcomes. Developing a comprehensive gene expression signature for this disease can provide valuable insights into its underlying biological mechanisms and facilitate the identification of potential therapeutic targets. In this study, comprehensive gene expression profiling of primary ccRCC samples from a cohort of 110 patients was performed. A 15G score that was strongly associated with worse PFS and DSS was developed. This association was independent of known clinico-pathologic predictors and derived CCP score, suggesting that this genomic expression-based signature is a valuable biomarker that adds additional prognostic information beyond currently available tumor characteristics. To validate these findings, the 15G score was applied to two independent publicly available datasets (combined n = 661) including the TCGA kidney cancer cohort to stratify patients into low-risk and high-risk categories with the latter exhibiting worse PFS and DSS, consistent with the observations in the discovery cohort. Attempts have been made to developed ccRCC genomic classifiers associated with recurrence and death to inform the management of this lethal disease. However, current classifies are limited by lack of improvement over clinico-pathologic data and lack of validation studies. The gene signature provided herein provides a unique pathway agnostic genomic classifier that was significantly associated with recurrence and death from ccRCC across multiple and diverse datasets. The 15G score encompasses genomic signatures enriched for EMT, mTOR signaling, glycolysis and oxidative phosphorylation. Attorney Docket No. UM-39857.601 The 15G score has many uses, including diagnosing, prognosing, and monitoring ccrCC. First, the 15G score can be used as a diagnostic test to accurately diagnose ccRCC by identifying specific genes that are overexpressed in cancerous tissue. Second, as a prognostic test the 15G signature can be used to classify patients into low versus high risk for recurrence following treatment for ccRCC to inform the frequency and intensity of surveillance or monitoring strategies. Third, as a predictive test, the 15G score could be used for treatment selection, such as to identify the most effective treatment for an individual patient by identifying genes that predict drug response in both the adjuvant and neoadjuvant settings. Additionally, the assay performed on biopsy specimens may inform which patients with small renal masses are candidates for surveillance versus treatment. Fourth, the expression of the signature, if detectable in liquid biopsy specimens, can be used to monitor the effectiveness of treatment by measuring changes in gene expression levels over time. Finally, the signature can be used to identify novel therapeutic targets and facilitate the development of new treatments by identifying genes that are important in tumorigenesis and progression of the disease. It is understood that the foregoing detailed description and accompanying examples are merely illustrative and are not to be taken as limitations upon the scope of the disclosure, which is defined solely by the appended claims and their equivalents. Various changes and modifications to the disclosed embodiments will be apparent to those skilled in the art. Such changes and modifications, including without limitation those relating to the chemical structures, substituents, derivatives, intermediates, syntheses, compositions, formulations, or methods of use of the disclosure, may be made without departing from the spirit and scope thereof. Any patents and publications referenced herein are herein incorporated by reference in their entireties.

Claims

Attorney Docket No. UM-39857.601 CLAIMS We claim:

1. A method comprising determining expression of a panel of genes in a sample obtained from a subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

2. The method of claim 1, wherein the panel of genes comprises at least 6 genes.

3. The method of claim 1, wherein the panel of genes comprises at least 10 genes.

4. The method of claim 1, wherein the panel of genes comprises at least 12 genes.

5. The method of claim 1, wherein the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

6. The method of any one of the preceding claims, wherein the panel comprises less than 50 genes in total.

7. The method of claim 6, wherein the panel comprises less than 25 genes in total.

8. The method of any one of the preceding claims, wherein the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

9. The method of any one of the preceding claims, further comprising assigning a score to the subject based upon the expression of the panel of genes in the sample.Attorney Docket No. UM-39857.601 10. The method of claim 9, wherein a high score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score.

11. The method of any one of the preceding claims, wherein expression of the panel of genes is determined by RNA sequencing.

12. The method of any one of the preceding claims, wherein the sample is a tumor sample.

13. The method of any one of the preceding claims, wherein the subject is a human.

14. The method of any one of the preceding claims, wherein the subject has or at risk of having renal cancer.

15. The method of claim 14, wherein the subject has received a first treatment regimen for renal cancer.

16. The method of claim 15, wherein the first treatment regimen for renal cancer comprises a surgical procedure.

17. The method of any one of claims 14-16, wherein the renal cancer is clear cell renal cell carcinoma.

18. The method of any one of the preceding claims, further comprising predicting poor disease outcome in the subject when the expression of one or more genes in the panel is increased in the sample compared to a control or when the score assigned to the subject is above a threshold value.

19. The method of claim 18, wherein poor disease outcome comprises reduced progression free survival (PFS) and / or disease specific survival (DSS) in the patient.Attorney Docket No. UM-39857.601 20. The method of any one of the preceding claims, further comprising providing an aggressive cancer treatment regimen to the subject when the expression of one or more genes in the panel of genes is increased in the sample compared to a control, the score assigned to the subject is above a threshold value, and / or a poor disease outcome is predicted.

21. The method of claim 20, wherein the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof.

22. A method of predicting disease outcome in a subject having or at risk of having renal cancer, the method comprising: a) determining expression of a panel of genes in a sample obtained from the subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2; b) assigning a score to the subject based upon the expression of the panel of genes in the sample; and c) predicting disease outcome in the subject based upon the score assigned to the subject.

23. The method of claim 22, wherein a high score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score.

24. The method of claim 22 or claim 23, wherein the panel of genes comprises at least 6 genes.

25. The method of claim 24, wherein the panel of genes comprises at least 10 genes.Attorney Docket No. UM-39857.601 26. The method of claim 24, wherein the panel of genes comprises at least 12 genes.

27. The method of claim 24, wherein the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

28. The method of any one of claims 22-27, wherein the panel comprises less than 50 genes in total.

29. The method of claim 28, wherein the panel comprises less than 25 genes in total.

30. The method of any one of claims 22-29, wherein the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

31. The method of any one of claims 22-30, wherein expression of the panel of genes is determined by RNA sequencing.

32. The method of any one of claims 22-31, comprising predicting poor disease outcome in the subject when the score assigned to the subject is above a threshold value.

33. The method of claim 32, further comprising treating the patient with an aggressive cancer treatment regimen when a poor disease outcome is predicted.

34. The method of claim 33, wherein the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof.

35. The method of any one of claims 32-34, wherein a poor disease outcome comprises reduced progression free survival (PFS) and / or disease specific survival (DSS) in theAttorney Docket No. UM-39857.601 patient.

36. The method of any one of claims 22-35, wherein the subject has received a first treatment regimen for renal cancer.

37. The method of claim 36, wherein the first treatment regimen comprises a surgical procedure.

38. The method of any one of claims 22-37, wherein the renal cancer is clear cell renal cell carcinoma.

39. A method of treating a subject having or at risk of having renal cancer, the method comprising: a) determining expression of a panel of genes in a sample obtained from the subject, wherein the panel of genes comprises at least 4 genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2; b) assigning score to the subject based upon the expression of the panel of genes in the sample; and c) providing a treatment to the subject based upon the score assigned to the subject.

40. The method of claim 39, wherein a high score is indicative of increased expression of the panel of genes compared to expression of the equivalent panel of genes for a low score.

41. The method of claim 39 or claim 40, wherein the panel of genes comprises at least 6 genes.

42. The method of claim 41, wherein the panel of genes comprises at least 10 genes.Attorney Docket No. UM-39857.601 43. The method of claim 42, wherein the panel of genes comprises at least 12 genes.

44. The method of claim 43, wherein the panel comprises each of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

45. The method of any one of claims 39-44, wherein the panel comprises less than 50 genes in total.

46. The method of claim 45, wherein the panel comprises less than 25 genes in total.

47. The method of any one of claims 39-46, wherein the panel consists of IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2.

48. The method of any one of claims 39-47, wherein expression of the panel of genes is determined by RNA sequencing.

49. The method of any one of claims 39-48, wherein the subject has received a first treatment regimen for renal cancer.

50. The method of claim 49, wherein the first treatment regimen comprises a surgical procedure.

51. The method of any one of claims 39-50, wherein the renal cancer is clear cell renal cell carcinoma.

52. The method of any one of claims 39-51, comprising providing an aggressive cancer treatment regimen when the score assigned to the subject is above a threshold value.Attorney Docket No. UM-39857.601 53. The method of claim 52, wherein the aggressive cancer treatment regimen comprises one or more therapies selected from radiation therapy, immunotherapy, chemotherapy, targeted therapy, and combinations thereof.

54. A kit comprising reagents for detecting one or more genes selected from IGF2BP3, PTK6, BOLA3, UQCRH, TFCP2L1, SLC7A8, SLC16A1, PLIN5, TMEM136, SLC7A1, TMEM81, RPL22L1, ELOB, IQCC and TAGLN2, wherein the kit detects less than 100 genes in total.

55. The kit of claim 54, wherein the kit detects less than 50 genes in total.

56. The kit of claim 55, wherein the kit detects less than 25 genes in total.

57. The kit of any one of claims 54-56, for use in a method of detecting the one or more genes in a sample obtained from a subject.

58. The kit of claim 57, wherein the subject has or is at risk of having cancer.

59. The kit of claim 58, wherein the cancer is renal cancer.

60. The kit of claim 59, wherein the renal cancer is clear cell renal cell carcinoma.

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