Methods, kits and systems for determining sarcomatoid differentiation of renal cell carcinoma and methods for treating based on the same

Abstract

Methods and systems are provided for determining sarcomatoid differentiation of renal cell carcinoma (RCC). In various embodiments, the present disclosure relates to the use of one or more histone modifications, DNA methylation, and / or expression of one or more transcription factors that are characteristic of RCC with sarcomatoid features (sRCC). In some embodiments, differential modifications are detected and quantified at one or more genomic loci. In various embodiments, a determination of sarcomatoid subtype is useful, e.g., for identifying, selecting treatment for, and / or treating a subject having sRCC.

Description

DFS-33825 (DFCI 3382.W01WO) METHODS, KITS AND SYSTEMS FOR DETERMINING SARCOMATOID DIFFERENTIATION OF RENAL CELL CARCINOMA AND METHODS FOR TREATING BASED ON THE SAME CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No.63 / 601,164, filed November 20, 2023, the disclosure of which is incorporated by reference herein in its entirety. SEQUENCE LISTING

[0002] This application contains a Sequence Listing which has been physically transmitted in electronic form in an XML format and is hereby incorporated by reference in its entirety. Said XML format, created on November 15, 2024 , is identified as DFS-33825_SL.xml and is 180,621,312 bytes in size. BACKGROUND

[0003] Renal cell carcinoma (RCC) is among the most common types of kidney cancer in adults. RCC is sometimes referred to as hypernephroma, renal adenocarcinoma, renal cancer, or kidney cancer. Sarcomatoid differentiation is a histopathologic feature observed in 5-20% of renal cell carcinoma (RCC) tumors across all histologies, including clear cell RCC (ccRCC), the most common subtype of kidney cancer. Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival. Challenges contributing to the poor survival of sRCC subjects include: a limited understanding of sRCC gene regulatory programs and difficulty identifying sarcomatoid differentiation on tumor biopsy due to spatial heterogeneity. Thus, there is a continuing need for means to improve outcomes for sRCC. SUMMARY

[0004] The present disclosure includes, among other things, technologies for the determination of sarcomatoid differentiation status of RCC and for the detection, monitoring, and / or treatment of RCC based on sarcomatoid differentiation status. In various embodiments, the present disclosure relates to the measurement of histone modifications and / or DNA methylation in a sample obtained or derived from a subject. The present disclosure provides, among other things, differential sites of histone modifications and / or DNA methylation (e.g., as measured in cell-free DNA (cfDNA)) that are characteristic of sRCC, and which in various embodiments are useful, e.g., for detecting, monitoring, selecting treatment for, and / or treating sRCC.

[0005] In a first aspect, there is provided a method of determining if a subject has a sarcomatoid renal cell carcinoma (sRCC) or determining a sarcomatoid / epithelioid subtype of a renal ^^1 ^ ^DFS-33825 (DFCI 3382.W01WO) cell carcinoma (RCC) in the subject. The method comprises quantifying (in a sample obtained or derived from the subject) a(i) one or more histone modifications and / or a(ii) DNA methylation at one or more genomic loci and / or b) expression of one or more transcription factors. The sample optionally comprises cell-free DNA (cfDNA) from a liquid biopsy sample.

[0006] In a second aspect, there is provided a method of treating a subject having sarcomatoid renal cell carcinoma (sRCC). The method comprises administering an sRCC therapy to the subject determined to have sRCC according to a method comprises quantifying (in a sample obtained or derived from the subject) a(i) one or more histone modifications and / or a(ii) DNA methylation at one or more genomic loci and / or b) expression of one or more transcription factors. The sample optionally comprises cell-free DNA (cfDNA) from a liquid biopsy sample. In some embodiments, the sRCC therapy comprises administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject. In some embodiments, the RCC therapy comprises a combination therapy, e.g., comprises administering a therapeutically effective amount of an immune checkpoint inhibitor (ICI) and a tyrosine kinase inhibitor (TKI). In some embodiments, the TKI is or comprises bevacizumab, a small molecule tyrosine kinase inhibitor, a soluble VEGF decoy receptor, or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2).

[0007] In a third aspect, there is provided a method for monitoring the development of sarcomatoid renal cell carcinoma (sRCC) in a subject. The method comprises a) quantifying, in a sample derived from a subject at a first time point, histone modification and / or DNA methylation of one or more genomic loci. The method further comprises b) quantifying, in a sample derived from a subject at a second, subsequent time point, histone modification and / or DNA methylation of the one or more genomic loci. The method also comprises c) comparing the histone modification and / or DNA methylation from steps a) and b), thereby monitoring the development of sRCC in the subject.

[0008] In a fourth aspect, there is provided a method for monitoring the development of sarcomatoid renal cell carcinoma (sRCC) in a subject. The method comprises a) quantifying, in a sample derived from a subject at a first time point, histone modification and / or DNA methylation of one or more genomic loci. The method further comprises b) quantifying, in a sample derived from a subject at a second, subsequent time point, histone modification and / or DNA methylation of the one or more genomic loci. The method further comprises c) comparing the histone modification and / or DNA methylation from steps a) and b), thereby monitoring the development of sRCC in the subject.

[0009] In a fifth aspect, there is provided a method of assessing the efficacy of an agent for treating sRCC in a subject. The method comprises quantifying in a sample obtained or derived from the subject at a first point in time differential H3K27ac and / or methylation relative to a control of one or more of the genomic loci listed in SEQ ID NOs: 7-58011 (e.g., a sample comprising cell-free DNA (cfDNA) from a liquid biopsy sample); and quantifying in a second biological sample obtained or derived from the subject at a second point in time after treating with the agent, the differentialDFS-33825 (DFCI 3382.W01WO) H3K27ac and / or methylation relative to a control of one or more of the genomic loci listed in SEQ ID NOs: 7-58011 (e.g., a biological sample comprising cell-free DNA (cfDNA) from a liquid biopsy sample). In some embodiments, an increased differential H3K27ac and / or methylation determined in the second sample relative to the first sample indicates that the agent does not treat sRCC in the subject; and a decreased differential H3K27ac and / or methylation determined in the second sample relative to the first sample indicates that the agent treats sRCC in the subject.

[0010] In a sixth aspect, there is provided a method of treating a subject having renal cell carcinoma (RCC), the method comprising treating the subject having RCC, wherein the RCC was determined to have FOSL1 expression above a threshold or reference level, with a therapeutically effective amount of an immune checkpoint inhibitor.

[0011] In a seventh aspect, there is provided a method treating a subject having sarcomatoid (sRCC), the method comprising administering a sarcomatoid RCC (sRCC) therapeutic agent to the subject, wherein the subject has been determined to have a validated epigenetic profile indicative of sRCC based on analysis of a sample obtained or derived from the subject, wherein the sample optionally comprises cell-free DNA (cfDNA). In some embodiments, the presence of the validated epigenetic profile has been determined using a validated classifier, wherein the validated classifier was trained on a set of histone modification profiles and / or DNA methylation profiles from sarcomatoid RCC samples and epithelioid RCC to identify epigenomic profile associated with each RCC type, and a threshold was selected such that the validated classifier predicts sarcomatoid RCC, with an area under the receiver operating characteristic (AUROC) of 0.35 to 1.0 (e.g., 0.4 to 1.0, 0.5 to 1.0, 0.6 to 1.0, or 0.7 to 1.0). In some embodiments, a) the classifier was trained on differential H3K27ac modification profiles and / or differential methylation profiles and / or b) the liquid biopsy sample is blood, plasma, serum, or urine.

[0012] In an eighth aspect, there is provided a kit comprising reagents for determining histone modification and / or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from SEQ ID NOs: 7-58011. In some embodiments, the kit comprises: a) reagents for quantifying H3K27ac for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 7- 25925; b) reagents for quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 25926-58011; c) one or more antibodies for use in ChIP-seq, further optionally wherein the antibody specifically binds H3K27ac modified histones; d) one or more methyl-binding domains for use in MBD-seq; e) reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample; and / or f) instructions for determining if a subject has sRCC.

[0013] In further aspects, there are provided non-transitory computer readable storage media encoded with a computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform a method as described herein. In further aspects, there are provided computer systems comprising a memoryDFS-33825 (DFCI 3382.W01WO) and one or more processors coupled to the memory, wherein the one or more processors are configured to perform operations to perform the method as described herein. In further aspects, there are provided systems for determining if an RCC is sarcomatoid RCC subtype in a subject, the system comprising a sequencer configured to generate a sequencing data set from a sample; and a non- transitory computer readable storage medium and / or a computer system as described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings included herein are for illustration purposes only and not for limitation.

[0015] FIGs.1A-1D show tissue- and plasma-based epigenomic characterization of sRCC. FIG.1A shows epigenomic profiling of frozen tissue samples from patients with epithelioid or sarcomatoid ccRCC. FIG.1B shows stratification of tissue samples by subtype-specific epigenomic features and their correlation with clinical outcomes in three cohorts. FIG.1C shows detection of sarcomatoid-like gene expression patterns following activation of expression of a candidate transcription factor in an RCC cell line model. FIG.1D shows plasma-based epigenomic profiling of patients with ccRCC and healthy volunteers. ccRCC: clear cell renal cell carcinoma, TF: Transcription Factor: ICI: Immune Checkpoint Inhibitors TKI: Tyrosine Kinase Inhibitors, LP-WGS: low pass whole genome sequencing.

[0016] FIGs.2A-2G show H3K27ac, H3K4me2, and methylated CpG dinucleotide regions between sarcomatoid and epithelioid ccRCC reflect distinct epigenomic programs. FIG.2A shows epigenomic datasets generated from tissue samples. FIG.2B shows distribution of H3K27ac, H3K4me2, and MeDIP peaks by genomic regions. FIG.2C shows a scatter plot of the correlation between H3K27ac and H3K4me2 ChIP-seq peaks. FIGs.2D-2F show principal component analysis (PCA) plots of the H3K27ac, H3K4me2, and MeDIP peaks in epithelioid and sarcomatoid ccRCC, respectively. FIG.2G shows Venn-Diagrams of the subtype-enriched H3K27ac, H3K4me2, and MeDIP peaks.

[0017] FIGs.3A-3G show sarcomatoid-enriched CREs are associated with immune system activation and drivers of EMT. FIG.3A shows heatmaps of normalized H3K27ac tag densities at differential H3K27ac cis- regulatory elements (CREs) between epithelioid and sarcomatoid ccRCC samples located ±2^kb from peak center. FIG.3B shows GREAT analysis of sarcomatoid-enriched H3K27ac peaks (n^=^1,621). FIG.3C shows GREAT analysis of epithelioid-enriched H3K27ac peaks (n^=^3,386). FIG.3D shows three top non-redundant significantly enriched nucleotide motifs present in sarcomatoid-specific sites by de novo motif analysis. FIG.3E shows H3K27ac profiles near FOSL1 in five representative samples of each ccRCC subtype (epithelioid and sarcomatoid), normalized to signal at GAPDH. FIG.3F shows three significantly enriched nucleotide motifs present in epithelioid-specific sites by de novo motif analysis. FIG.3G shows H3K27ac profiles near EPAS1DFS-33825 (DFCI 3382.W01WO) normalized to GAPDH in five representative samples of each ccRCC subtype (epithelioid and sarcomatoid).

[0018] FIGs.4A-4D show that FOSL1 is upregulated in sRCC and is associated with clinical outcomes. FIG.4A shows boxplots of FOSL1 expression levels in three clinical cohorts (TCGA, JR101, and IM151). FIG.4B shows progression-free survival in patients in JR101 by FOSL1 levels. FIG.4C shows overall survival in patients with sRCC and epithelioid ccRCC divided by expression of FOSL1 (High vs. Low) in the TCGA cohort. FIG.4D shows progression-free survival in patients with sRCC and epithelioid ccRCC divided by expression of FOSL1 (High vs. Low) in the sunitinib arms of JR101 and IM151. TPM: Transcripts per million, TCGA: The Cancer Genome Atlas, JR101: Javelin Renal 101, IM151: Immotion151, Epi: epithelioid ccRCC, AveAxi: avelumab plus axitinib, AtezoBev: atezolizumab plus bevacizumab, SUN: sunitinib.

[0019] FIGs.5A-5D show that expression of FOSL1 in an epithelioid ccRCC cell line activates sRCC transcriptional programs. FIG.5A shows principal Component Analysis (PCA) plot of three replicates of RNA-sequencing data from two conditions (FOSL1 CRISPRa and negative control Caki-1 cells). FIG.5B shows a volcano plot showing upregulated and downregulated genes in FOSL1 CRISPRa vs. negative control Caki-1 cells. FIG.5C shows normalized enrichment scores of pathways upregulated in FOSL1 CRISPRa Caki-1 cells. FIG.5D shows gene set enrichment analysis (GSEA) of genes upregulated in sarcomatoid ccRCC from JR101 in FOSL1 CRISPRa Caki-1 cells. ccRCC: clear cell renal cell carcinoma, NES: normalized enrichment score, FDR: false-detection rate. JR101: Javelin Renal 101.

[0020] FIGs.6A-6G show that tissue-informed epigenomic signatures enable the detection of sRCC in plasma. FIG.6A shows a schematic demonstrating the measurement of cfChIP and cfMeDIP signals at transcription factor (TF) binding sites differentially methylated regions, respectively. FIG.6B shows epigenomic datasets generated from plasma. FIG.6C shows H3K4me3 cfChIP profiles at PAX8 in plasma of patients with sRCC (n=2), epithelioid ccRCC (n=2) and healthy volunteers (n=4). FIG.6D shows aggregated H3K27ac cfChIP-seq signals in kidney cancer and healthy plasma at REs identified by ATAC-seq in ccRCC. FIG.6E shows aggregated cfMeDIP-seq signals at tissue-informed upregulated sarcomatoid-specific DMRs and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. FIG.6F shows aggregated H3K27ac cfChIP- seq signals at tissue-informed upregulated sarcomatoid-specific H3K27ac sites and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. FIG.6G shows aggregated H3K27ac cfChIP-seq signal at HIF2a binding sites for ccRCC and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. FIGs.6D-6G: thick lines show the mean signal across all samples in the indicated class; shaded area represent the standard errors of the means; boxplots indicate the area under the curve for the aggregate H3K27ac or MeDIP profiles for each sample. Wilcoxon test p-values are indicated for comparison across the groups: 6D: ccRCC (solidDFS-33825 (DFCI 3382.W01WO) black), healthy (angled up dashes), epithelioid ccRCC (horizontal dashes), and sRCC (horizontal dashes). ccRCC: clear cell renal cell carcinoma, Sarc: sarcomatoid ccRCC (sRCC), Epi: epithelioid ccRCC, RE: regulatory elements, DMR: differentially methylated regions.

[0021] FIG.7 shows unsupervised hierarchical clustering of the H3K27ac (panel A), H3K4me2 (panel B), and MeDIP (panel C) peaks in epithelioid and sarcomatoid ccRCC tissue samples.

[0022] FIG.8A shows progression-free survival in patients in IM151 by FOSL1 levels. FIG. 8B shows progression-free survival in patients with sRCC and epithelioid ccRCC divided by expression of FOSL1 (High vs. Low) in the avelumab plus axitinib and atezolizumab plus bevacizumab arms of JR101 and IM151, respectively. TPM: Transcripts per million, TCGA: The Cancer Genome Atlas, JR101: Javelin Renal 101, IM151: IMmotion151, AveAxi: avelumab plus axitinib. AtezoBev: atezolizumab plus bevacizumab; Epi, high: epithelioid ccRCC with high FOSL1, Epi, low: epithelioid ccRCC with low FOSL1, and Sarcom.: sarcomatoid.

[0023] FIG.9 shows gene set enrichment analysis (GSEA) of genes upregulated in sarcomatoid ccRCC from JR101 in FOSL1 OE Caki-1 cells. DETAILED DESCRIPTION

[0024] The present disclosure is based, at least in part, on the identification of highly recurrent epigenomic reprogramming, as assessed by histone modifications and DNA methylation, that distinguish renal cell carcinoma with sarcomatoid differentiation (sRCC) from non-sarcomatoid renal cell carcinoma (RCC). The present disclosure demonstrates, inter alia, that sarcomatoid differentiation of a renal cell carcinoma (RCC) in a subject can be determined by detecting and quantifying the presence of histone modifications and / or DNA methylation at one or more genomic loci in cell-free DNA (cfDNA) from a liquid biopsy sample, e.g., a plasma sample obtained or derived from the subject. This represents the first epigenomic characterization of sRCC from patient tissues and plasma. It is further demonstrated that differential histone modifications (e.g., histone acetylation marks such as H3K27ac) and / or DNA methylation can be combined into multimodal classifiers to determine sarcomatoid differentiation status of RCC. The present disclosure is the first to uniquely identify elements that dictate gene regulatory programs, including transcription factors (TFs) and the enhancers they bind to activate sRCC-associated gene expression which can inform therapy and biomarker development. The present disclosure is also based, at least in part, on the demonstration that differential modification of a transcription factor, FOSL1, and / or differential modification of FOSL1 binding sites are associated with sarcomatoid RCC.It is also demonstrated that increased expression of a transcription factor, FOSL1, is associated with improved outcomes for immune checkpoint inhibitor therapy.DFS-33825 (DFCI 3382.W01WO) Renal cell carcinoma (RCC)

[0025] Renal cell carcinoma (RCC) is the most common type of kidney tumor and one of the top 10 most common cancers. Renal cell carcinoma (RCC) encompasses a heterogeneous group of cancers derived from renal tubular epithelial cells. According to certain reports, the incidence of RCC is increasing.

[0026] Various means of detecting RCC are known in the art. These include physical examination, blood tests (e.g., complete blood count and blood chemistry tests), urinalysis, urine cytology, computed tomography (CT), Magnetic resonance imaging (MRI), ultrasound, x-ray, angiography, kidney biopsy, and others.

[0027] Patients with RCC can present with a range of symptoms and many are asymptomatic until the disease is advanced. In contemporary series, fewer patients have the typical symptoms and there is an increased frequency of incidental diagnosis due to radiologic procedures performed for other indications. Over 50% of RCCs are asymptomatic and detected incidentally. At presentation, approximately 25 percent of individuals either have distant metastases or advanced locoregional disease.

[0028] There are various subtypes of RCC, including clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC). The most common RCC subtype is ccRCC, accounting for ~75% of cases; pRCC and chRCC account for 15%-20% and 5% of cases, respectively. Due to the predominance of clear cell histology, tumors with non-clear cell histology are sometimes grouped as non-clear cell RCC (nccRCC). Distinguishing RCC subtypes guides treatment selection. Sarcomatoid differentiation of RCC (sRCC)

[0029] Sarcomatoid differentiation is a histopathologic feature observed in 5-20% of RCC tumors across all histologies, including clear cell RCC (ccRCC), the most common subtype of kidney cancer. RCC with sarcomatoid features (sRCC) exhibits an aggressive phenotype characterized by epithelial-to-mesenchymal transition (EMT) and responds poorly to vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR TKIs) compared to non-sarcomatoid or “epithelioid” RCC. Diagnosis of sRCC is clinically important because it responds particularly well to immune checkpoint inhibitors (ICIs), potentially due to increased PD-L1 expression on tumor cells and prominent immune cell infiltration.

[0030] The molecular underpinnings of sarcomatoid differentiation are poorly understood, in part due to a paucity of cell line models that faithfully recapitulate sRCC biology. Recent studies have therefore focused on the analysis of clinical sRCC tissues to identify genomic and transcriptomic features that may drive the aggressive behavior of sRCC. Compared to epithelioid ccRCC, sRCC is enriched for several mutations and transcriptional features, such as deletions of CDKN2A / CDKN2B and increased expression of cell cycle genes (i.e., E2F7), but both groups are typically notDFS-33825 (DFCI 3382.W01WO) distinguishable based on next-generation sequencing and typically share mutual mutational profiles, suggesting a common clonal origin. Contrary to transcriptional studies, data on the epigenomic programs that drive EMT in sRCC, are lacking and remain poorly understood.

[0031] Sarcomatoid differentiation is spatially heterogeneous, and components of sRCC and epithelioid RCC often co-exist within a tumor. Because sarcomatoid differentiation is spatially heterogeneous, sampling error poses a major challenge in the diagnosis of sRCC, and low sensitivity for detection of sarcomatoid differentiation from tissue biopsies is a problem in the art. Therefore, the detection of sarcomatoid differentiation using tumor biopsy is challenging. In some instances, biopsies detected sarcomatoid features in only 7.5% and 9.1% of patients who were later found to have clear evidence of sRCC in their nephrectomy specimens. This limitation is compounded by the fact that patients with metastatic RCC are increasingly diagnosed from single tissue biopsies as cytoreductive nephrectomies are being routinely performed in select patients with RCC only. Therefore, tractable biomarkers that reflect the aggregate burden of cancer are needed for sRCC diagnoses. To this end, recent studies have shown promise in the detection of clonally related histologic variants using epigenomic signatures from plasma.

[0032] Accurate diagnosis of sRCC is important since patients usually benefit more from ICI-based regimens than from TKIs only. Accurate diagnosis of sRCC is important since patients usually benefit more from ICI-based regimens than from TKIs only. Since liquid biopsies can sample heterogeneity across tumor sites in advanced cancers, a ctDNA-based correlate of sarcomatoid differentiation could overcome issues of sampling error.

[0033] The present disclosure describes, inter alia, characterization of the epigenomes of sarcomatoid and epithelioid ccRCC tumors from pathologically reviewed clinical tissue specimens. As described in the Examples below, the epigenomic landscape of sRCC was characterized by profiling 107 epigenomic libraries in tissue and plasma samples from 50 patients with RCC and healthy volunteers. Without being bound by any particular scientific theory, it was hypothesized that sarcomatoid differentiation is driven by the activation of cis- regulatory elements (CREs) bound by a set of master TFs. The present disclosure identified marked and consistent differences in the epigenomic profiles of sRCC vs. epithelioid ccRCC. Based on differential regulatory elements, candidate TFs were identified that may drive epigenomic changes that characterize sRCC. Activation of a candidate TF in an epithelioid ccRCC cell line model activates sarcomatoid-like gene expression patterns and these candidate TFs were upregulated in sRCC across three clinical cohorts and their expression levels were associated with clinical outcomes. For example, sRCC epigenomic programs were enriched for FOSL1-bound DNA motifs and characterized by increased immune activation and decreased response to hypoxia. Activation of FOSL1 expression in a non-sRCC cell line induced an sRCC-like gene expression program. Using data from TCGA and two randomized clinical trialsDFS-33825 (DFCI 3382.W01WO) (JR101 and IM151), the present disclosure shows that FOSL1 expression is a biomarker predictive of response to ICIs in RCC.

[0034] The Examples below also describe application of tissue-derived epigenomic signatures to detect sRCC in plasma using epigenomic-based liquid biopsy techniques as outlined in FIGs.1A-1D. These Examples demonstrate that epigenomic signatures of sRCC are detectable in patient plasma, establishing an approach for blood-based diagnosis of this clinically important phenotype. These findings enable clinical and molecular stratification in RCC, inform liquid biopsies for the detection of sRCC, and provide a framework that can be applied to other challenging tumor types. Treatments for RCC

[0035] RCC can be treated by surgical and non-surgical approaches. While surgical approaches are common, they usually cannot cure RCC in advanced cases. Non-surgical therapies for RCC include immunotherapy, targeted therapy, and other approaches. Treatment selection for RCC is guided by the particular RCC subtype. Clear cell RCC

[0036] In some embodiments, a RCC is clear cell RCC (ccRCC). Surgical approaches can be used in the treatment of ccRCC. Once ccRCC is diagnosed, surgery can be used to remove the cancer and part of the kidney surrounding it.

[0037] Various targeted therapies are also available for the treatment of clear cell RCC (ccRCC). The term “targeted therapy” refers to administration of agents that selectively interact with a chosen biomolecule to thereby treat cancer. Immunotherapies using cancer-targeted antibodies are one example of a targeted therapy. Broadly, the field of cancer-targeted antibodies includes, for example, immune checkpoint inhibitors (e.g., anti-PD-1, anti-PD-L1, anti-PD-L2, and / or anti-CTLA- 4 antibody therapeutics), which are well known in the art. For example, “PD-1 pathway inhibitors” can block or otherwise reduce the interaction between PD-1 and one or both of its ligands (PD-L1 and PD-L2) such that the immunoinhibitory signaling otherwise generated by the interaction is blocked or otherwise reduced.

[0038] Targeted therapy for clear cell RCC can include antiangiogenic agents and other agents that slow tumor growth, e.g., by interfering with a step in tumor growth. Certain exemplary targeted therapies target the VEGF pathway, on which certain RCC tumors are particularly dependent. VEGF-targeted therapy can inhibit tumor vascularization. Exemplary agents include pazopanib, sunitinib, cabozantinib, axitinib, sorafenib, bevacizumab, lenvatinib, and tivozanib. Other targeted therapies for clear cell RCC can block mammalian target of rapamycin (mTOR). These include temsirolimus and everolimus.

[0039] Targeted immunotherapy for clear cell RCC can include, e.g., anti-CTLA-4 immunotherapy, anti-PD-1 immunotherapy, and anti-PD-L1 immunotherapy. Examples of anti-DFS-33825 (DFCI 3382.W01WO) CTLA-4 antibody therapeutics include ipilimumab and tremelimumab. Examples of anti-PD-1 antibody therapeutics include nivolumab, pembrolizumab, and avelumab. Nivolumab can be administered in combination with other therapeutics such as ipilimumab. Nivolumab can also be administered in combination with cabozantinib (a targeted therapy). Pembrolizumab can be administered in combination with either axitinib or lenvatinib, which target blood vessels. Avelumab, atezolizumab, and durvalumab are anti-programmed cell death ligand 1 (PD-L1) checkpoint inhibitors. Avelumab can be administered in combination with axitinib.

[0040] Further non-surgical therapies for clear cell RCC can include chemotherapy, radiation therapy, thermal ablation, cryosurgery, hormone therapy, and others.

[0041] In various embodiments, a treatment regimen for clear cell RCC can include a vascular endothelial growth factor (VEGF) inhibitor. In various embodiments, a treatment regimen for clear cell RCC can include a vascular endothelial growth factor (VEGF) inhibitor in combination with an immune checkpoint inhibitor (ICI)(e.g., antibody therapeutic). In certain instances, a treatment regimen for clear cell RCC can include a combination of programmed cell death 1 protein (PD-1) checkpoint inhibitor, cytotoxic T-lymphocyte antigen 4 (CTLA-4) checkpoint inhibitor, and vascular endothelial growth factor (VEGF) inhibitor. As is known in the art, ccRCC responds well to VEGF inhibitor therapy (including, for example, bevacizumab, a small molecule tyrosine kinase inhibitor (e.g., sunitinib, sorafenib, or pazopanib), a soluble VEGF decoy receptor (e.g., aflibercept), or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2) (e.g., ramucirumab)). As is further known in the art, nccRCC responds poorly to VEGF inhibitor therapy. Papillary RCC

[0042] In some embodiments, a RCC is papillary RCC. Papillary RCC can be treated by checkpoint inhibitor immunotherapy and / or vascular endothelial growth factor receptor (VEGFR) inhibitor therapy, in particular with respect to “Type 2” papillary RCC in connection with the advanced hereditary leiomyomatosis and renal cell cancer (HLRCC). Bevacizumab in combination with erlotinib is a preferred therapy for patients with papillary RCC. Other available therapies include nivolumab, pembrolizumab, and combination therapies such as combination of nivolumab and ipilimumab. For papillary RCC, in some instances cabozantinib is preferred to other VEGF pathway inhibitors. Sunitinib is a reasonable alternative for those who decline cabozantinib. Alternative, but less preferred, agents for treatment of papillary RCC include everolimus or temsirolimus.

[0043] Treatment of papillary RCC with checkpoint inhibitor therapies such as pembrolizumab or nivolumab has demonstrated greater response and survival than antiangiogenic agents or chemotherapy. Combination of nivolumab and ipilimumab for papillary RCC is supported by observational studies conducted in heterogeneous populations of non-clear cell RCC. Targeted therapies such as antiangiogenic agents (e.g., VEGFR inhibitors) and mammalian target of rapamycin (mTOR) inhibitors have also demonstrated efficacy in papillary RCC. However, compared to clearDFS-33825 (DFCI 3382.W01WO) cell RCC, targeted therapy may not work as well for treatment of papillary RCC. The choice of regimen depends on disease context. For most patients who choose VEGFR inhibitors or who are ineligible for immunotherapy, cabozantinib can be preferred to other VEGF pathway inhibitors, as this approach improved PFS in a randomized trial. For those who decline cabozantinib, sunitinib is a reasonable alternative, as its efficacy over the mTOR inhibitor everolimus has been established in randomized clinical trials. Alternative, but less preferred, agents for those with poor-risk papillary RCC include mTOR inhibitors such as everolimus or temsirolimus. Observational studies and early phase II trials have suggested modest efficacy for the VEGFR inhibitors such as pazopanib and axitinib for papillary RCC.

[0044] For those with papillary RCC due to hereditary leiomyomatosis, and who are also eligible for antiangiogenic therapy, bevacizumab in combination with erlotinib can be recommended. This preference is based on the results of a nonrandomized trial that demonstrated high response rates in this population. There are limited data regarding other inhibitors of the VEGF pathway in this rare subgroup.

[0045] In patients with treatment-naïve advanced papillary RCC, cabozantinib improved PFS and objective response rates compared with sunitinib in a randomized trial. Data suggest that mTOR inhibitors, such as everolimus and temsirolimus, may be effective in patients with poor-risk non-clear cell RCC. However, these agents are becoming less preferred given the demonstrated efficacy of immunotherapy in patients with intermediate- and poor-risk disease.

[0046] Agents that inhibit the c-MET pathway have activity for therapy against papillary RCC. Exemplary agents can include crizotinib, foretinib, and savolitinib.

[0047] Surgical approaches are also available for treatment of papillary RCC. In addition, non-surgical therapies for papillary RCC can include chemotherapy, radiation therapy, thermal ablation, cryosurgery, hormone therapy, and others. Treatments of papillary RCC can include combinations of any papillary RCC and / or RCC therapies and / or therapeutic agents disclosed herein. In various embodiments, a pRCC therapy can include one or more of an epigenetic modifier, a targeted therapy, a chemotherapy, a radiation therapy, an immunotherapy, and / or a hormonal therapy

[0048] In various embodiments, a treatment regimen for pRCC does not include a vascular endothelial growth factor (VEGF) inhibitor (e.g., does not include one or more, or all, of bevacizumab, a small molecule tyrosine kinase inhibitor (e.g., sunitinib, sorafenib, or pazopanib), a soluble VEGF decoy receptor (e.g., aflibercept), or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2) (e.g., ramucirumab)). As is known in the art, pRCC responds poorly to VEGF inhibitor therapy. Chromophobe RCC

[0049] Primary therapies for chromophobe RCC include targeted agents such as mammalian target of rapamycin (mTOR) inhibitors (e.g., everolimus) or vascular endothelial growth factorDFS-33825 (DFCI 3382.W01WO) receptor (VEGFR) inhibitors (e.g., sunitinib), at least in party because many chromophobe RCC tumors are characterized by mTOR upregulation. Limited data also show some efficacy for bevacizumab plus erlotinib in these tumors. Alternatively, some experts offer a combination of lenvatinib and everolimus, either as off-label initial therapy or subsequent therapy after progression on alternative VEGFR inhibitors. Data on therapies for chromophobe RCC are often based on limited data, given the relative frequency of this RCC subtype.

[0050] Surgical approaches are also available for treatment of chromophobe RCC. In addition, non-surgical therapies for chromophobe RCC can include chemotherapy, radiation therapy, thermal ablation, cryosurgery, hormone therapy, and others. Treatments of chromophobe RCC can include combinations of any chromophobe RCC and / or RCC therapies and / or therapeutic agents disclosed herein. In various embodiments, a chRCC therapy can include one or more of an epigenetic modifier, a targeted therapy, a chemotherapy, a radiation therapy, an immunotherapy, and / or a hormonal therapy.

[0051] In various embodiments, a treatment regimen for chRCC does not include a vascular endothelial growth factor (VEGF) inhibitor (e.g., does not include one or more, or all, of bevacizumab, a small molecule tyrosine kinase inhibitor (e.g., sunitinib, sorafenib, or pazopanib), a soluble VEGF decoy receptor (e.g., aflibercept), or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2) (e.g., ramucirumab)). As is known in the art, chRCC responds poorly to VEGF inhibitor therapy. Treatments for sarcomatoid RCC (sRCC)

[0052] Sarcomatoid RCC responds particularly well to treatment with immune checkpoint inhibitors (ICIs), potentially due to increased PD-L1 expression on tumor cells and prominent immune cell infiltration. (Motzer, R. J. et al. Cancer Cell 38, 803-817.e804, (2020); Choueiri, T. K. et al. ESMO Open 6, 100101, (2021); Tannir, N. M. et al. Clin Cancer Res 27, 78-86, (2021); Rini, B. I. et al. Journal of Clinical Oncology 37, 4500-4500, (2019); Bakouny, Z. et al. Nature Communications 12, 808, (2021); Saliby, R. M. et al. Cancer Immunology Research 11, 1114-1124, (2023); Morisue, R. et al. International Journal of Cancer n / a, doi:10.1002 / ijc.34680; Kawakami, F. et al. Cancer 123, 4823-4831, (2017).)

[0053] In some embodiments, a therapy for sarcomatoid RCC comprises administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject. Immune checkpoint inhibitor therapy for sarcomatoid RCC can include, e.g., anti-PD-L1 immunotherapy, anti- PD-1 immunotherapy, and anti-CTLA-4 immunotherapy.

[0054] Examples of anti-PD-1 antibody therapeutics include nivolumab, pembrolizumab, avelumab and cemiplimab. Nivolumab can be administered in combination with other therapeutics such as ipilimumab. These agents target PD-1, a protein on T cells that normally helps keep these cells from attacking other cells in the body. By blocking PD-1, these therapeutic agents boost theDFS-33825 (DFCI 3382.W01WO) immune response against cancer cells. This can shrink some tumors or slow their growth. Nivolumab can also be administered in combination with cabozantinib (a targeted therapy). Pembrolizumab can be administered in combination with either axitinib or lenvatinib, which target blood vessels.

[0055] Examples of anti-PD-L1 antibody therapeutics include avelumab, atezolizumab, and durvalumab. These agents target PD-L1, a protein related to PD-1 that is found on some tumor cells and immune cells. Blocking this protein can help boost the immune response against cancer cells. This can shrink some tumors or slow their growth. Avelumab can be administered in combination with axitinib.

[0056] Examples of anti-CTLA-4 antibody therapeutics include ipilimumab and tremelimumab. Ipilimumab and tremelimumab are also immunotherapies that boost the immune response, but they block CTLA-4, another protein on T cells that normally helps keep them in check. These therapeutic agents are typically used along with a PD-1 inhibitor (ipilimumab with nivolumab, and tremelimumab with durvalumab); they are not used alone.

[0057] In some embodiments, a therapy for sarcomatoid RCC is a combination therapy comprising an immune checkpoint inhibitor (e.g., as described herein) and a tyrosine kinase inhibitor (TKI). In some embodiments, a TKI is a small molecule tyrosine kinase inhibitor (e.g., sunitinib, sorafenib, or pazopanib). In some embodiments, a TKI is a VEGF-targeted therapy (e.g., pazopanib, sunitinib, cabozantinib, axitinib, sorafenib, bevacizumab, lenvatinib, and tivozanib). In some certain embodiments, a combination therapy for treating sRCC is a combination of (i) avelumab and axitinib or (ii) atezolizumab and bevacizumab. Subjects and Samples

[0058] A sample analyzed using methods and compositions provided herein can be any biological sample and / or any sample including nucleic acid. In some embodiments, a sample includes circulating tumor DNA (ctDNA) derived from a biological sample. In various embodiments, a sample analyzed using methods and compositions provided herein can be a sample from a mammal. In various embodiments, a sample analyzed using methods and compositions provided herein can be a sample from a human subject.

[0059] In various instances, a human subject is a subject diagnosed or seeking diagnosis as having, diagnosed as or seeking diagnosis as at risk of having, and / or diagnosed as or seeking diagnosis as at immediate risk of having, a cancer such as a sarcomatoid RCC. In various instances, a human subject is a subjected identified as a subject in need of sarcomatoid RCC screening. In certain instances, a human subject is a subjected identified as in need of sarcomatoid RCC screening by a medical practitioner.

[0060] The subject may not have undergone previous treatments for RCC, such as the treatments recited in this disclosure including but not limited to surgical resection. In otherDFS-33825 (DFCI 3382.W01WO) embodiments, the subject has undergone previous treatments for RCC, such as the treatments recited in this disclosure including but not limited to surgical resection.

[0061] In various embodiments a subject has one or more biomarkers and / or risk factors for RCC. The major established risk factors for RCC include excess body weight (e.g., clinical overweight or obesity, in particular clinical obesity), hypertension and smoking (e.g., cigarette smoking), which were factors in approximately half of all diagnosed cases in one US study. RCC incidence increases markedly with age and is higher for men than women. Other medical conditions that have been associated with RCC in epidemiological studies include chronic kidney disease, hemodialysis, kidney transplantation, acquired kidney cystic disease, a previous RCC diagnosis and diabetes mellitus. In certain embodiments, a human subject is identified as in need of RCC screening based on age, e.g., due to an age equal to or greater than 50 years, e.g., an age equal to or greater than 50, 55, 60, 65, 70, 75, 80, 85, or 90 years and / or based on body mass index (e.g., clinical overweight or clinical obesity). In various instances, a human subject is a subject not diagnosed as having, not at risk of having, not at immediate risk of having, not diagnosed as having, and / or not seeking diagnosis for a cancer such as a RCC, or any combination thereof.

[0062] In various embodiments, a sample from a subject, e.g., a human can be obtained from a liquid biopsy. In certain embodiments, a sample and / or reference is obtained from serum, plasma, or urine. In certain embodiments, the sample is serum. In certain embodiments, a sample comprises circulating tumor DNA (ctDNA). In certain embodiments, a sample is derived from about 1 mL of blood obtained from the subject. In certain embodiments, a sample is derived from about 0.5-2 mL of blood obtained from the subject, e.g., about 0.5 to 1.75 mL, about 0.5 to 1.5 mL, about 0.75 to 1.25 mL or about 0.9 to 1.1 mL of blood.

[0063] In various embodiments, a sample is a sample of cell-free DNA (cfDNA). cfDNA is typically found in human biofluids (e.g., plasma, serum, or urine) in short, double-stranded fragments. The concentration of cfDNA is typically low, but can significantly increase under particular conditions, including without limitation pregnancy, autoimmune disorders, myocardial infarction, and cancer. Circulating tumor DNA (ctDNA) is the component of cell-free DNA specifically derived from cancer cells. ctDNA can be present in human biofluids bound to leukocytes and erythrocytes or not bound to leukocytes and erythrocytes. Various tests for detection of tumor-derived ctDNA are based on detection of genetic or epigenetic modifications that are characteristic of cancer (e.g., of a relevant cancer). Genetic or epigenetic factors characteristic of cancer can include, without limitation, oncogenic or cancer-associated mutations in tumor-suppressor genes, activated oncogenes, chromosomal disorders, histone modifications (e.g., histone methylation and / or histone acetylation), chromatin accessibility, binding of one or more transcription factors and / or DNA methylation.

[0064] In various embodiments, ctDNA comprises less than 30%, less than 20%, or less than 10% of the cfDNA in the liquid biopsy sample obtained from the subject, e.g., less than 9%, 8%, 7%,DFS-33825 (DFCI 3382.W01WO) 6%, 5%, 4%, 3%, 2% or less than 1% of the cfDNA in the sample. In some embodiments, the percentage of ctDNA in the liquid biopsy sample is assessed using ichorCNA which estimates the percentage of ctDNA in a sample probabilistically (see Adalsteinsson et al., Nat Commun (2017) 8(1):1324).

[0065] cfDNA and ctDNA can provide a real-time or nearly real time metric of status of a source tissue. cfDNA and ctDNA demonstrate a half-life in blood of about 2 hours, such that a sample taken at a given time provides a relatively timely reflection of the status of a source tissue.

[0066] Various methods of isolating nucleic acids from a sample (e.g., of isolating cfDNA from blood or plasma) are known in the art. Nucleic acids can be isolated using, without limitation, standard DNA purification techniques, by direct gene capture (e.g., by clarification of a sample to remove assay-inhibiting agents and capturing a target nucleic acid, if present, from the clarified sample with a capture agent to produce a capture complex and isolating the capture complex to recover the target nucleic acid).

[0067] Reagents and protocols for obtaining and analyzing cfDNA and ctDNA, such as circulating in blood or other tissue, are commercially available as described in the Examples and well- known in the art.

[0068] In various embodiments, samples can be collected from individuals repeatedly over a period of time (e.g., once daily, weekly, monthly, annually, biannually, etc.). In various embodiments, such samples can be used to verify results from earlier detections and / or to identify an alteration in biological pattern because of, for example, disease progression, resistance to therapy, treatment, remission, and the like. For example, subject samples can be taken and monitored every month, every two months, or combinations of one, two, or three-month intervals according to the present disclosure. In various embodiments, samples can be collected for monitoring over time beginning at or at certain clinically determined stages, such as at resistance to a therapy, before radiographic progression, after radiographic progression, and / or at tissue biopsy. In addition, the sRCC features obtained at different points in time can be conveniently compared with each other, as well as with those of normal controls during the monitoring period, thereby providing the subject’s own values, as an internal, or personal, control for long-term monitoring.

[0069] Samples include materials prepared by processes including, without limitation, steps such as concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives, addition of calibrants, addition of protease inhibitors, addition of denaturants, desalting, concentration and / or extraction of sample nucleic acids, and / or amplification of sample nucleic acids (e.g., by PCR or other nucleic acid amplification techniques). Samples also include materials prepared by techniques that isolate, e.g., nucleosomes or transcription factors and / or nucleic acids associated with nucleosomes or transcription factors.DFS-33825 (DFCI 3382.W01WO)

[0070] Removal from a sample of proteins that are not desirable for a relevant purpose or context (e.g., high abundance, uninformative, or undetectable proteins) can be achieved using high affinity reagents, high molecular weight filters, ultracentrifugation and / or electrodialysis. High affinity reagents include antibodies or other reagents (e.g., aptamers) that selectively bind to high abundance proteins. Sample preparation can also include ion exchange chromatography, metal ion affinity chromatography, gel filtration, hydrophobic chromatography, chromatofocusing, adsorption chromatography, isoelectric focusing and related techniques. Molecular weight filters include membranes that separate molecules on the basis of size and molecular weight. Such filters may further employ reverse osmosis, nanofiltration, ultrafiltration and microfiltration. Ultracentrifugation is the centrifugation of a sample at about 15,000-60,000 rpm while monitoring with an optical system the sedimentation (or lack thereof) of particles. Electrodialysis is a procedure which uses an electromembrane or semipermeable membrane in a process in which ions are transported through semi-permeable membranes from one solution to another under the influence of a potential gradient. Since the membranes used in electrodialysis may have the ability to selectively transport ions having positive or negative charge, reject ions of the opposite charge, or to allow species to migrate through a semipermeable membrane based on size and charge, it renders electrodialysis useful for concentration, removal, or separation of electrolytes.

[0071] Separation and purification in the present disclosure may include any procedure known in the art, such as capillary electrophoresis (e.g., in capillary or on-chip) or chromatography (e.g., in capillary, column or on a chip). Electrophoresis is a method that can be used to separate ionic molecules under the influence of an electric field. Electrophoresis can be conducted in a gel, capillary, or in a microchannel on a chip. Examples of gels used for electrophoresis include starch, acrylamide, polyethylene oxides, agarose, or combinations thereof. A gel can be modified by its cross-linking, addition of detergents, or denaturants, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and incorporation of a pH gradient. Examples of capillaries used for electrophoresis include capillaries that interface with an electrospray.

[0072] Capillary electrophoresis (CE) is preferred for separating complex hydrophilic molecules and highly charged solutes. CE technology can also be implemented on microfluidic chips. Depending on the types of capillary and buffers used, CE can be further segmented into separation techniques such as capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), capillary isotachophoresis (cITP) and capillary electrochromatography (CEC). An embodiment to couple CE techniques to electrospray ionization involves the use of volatile solutions, for example, aqueous mixtures containing a volatile acid and / or base and an organic such as an alcohol or acetonitrile.

[0073] Capillary isotachophoresis (cITP) is a technique in which the analytes move through the capillary at a constant speed but are nevertheless separated by their respective mobilities.DFS-33825 (DFCI 3382.W01WO) Capillary zone electrophoresis (CZE), also known as free-solution CE (FSCE), is based on differences in the electrophoretic mobility of the species, determined by the charge on the molecule, and the frictional resistance the molecule encounters during migration, which is often directly proportional to the size of the molecule. Capillary isoelectric focusing (CIEF) allows weakly-ionizable amphoteric molecules, to be separated by electrophoresis in a pH gradient. CEC is a hybrid technique between traditional high performance liquid chromatography (HPLC) and CE.

[0074] Separation and purification techniques used in the present disclosure can include any chromatography procedures known in the art. Chromatography can be based on the differential adsorption and elution of certain analytes or partitioning of analytes between mobile and stationary phases. Different examples of chromatography include, but not limited to, liquid chromatography (LC), gas chromatography (GC), high performance liquid chromatography (HPLC), etc.

[0075] In some embodiments, whole blood is collected from a subject, and a plasma layer is separated by centrifugation. Cell free DNA may be then extracted from the plasma using methods known in the art. In some embodiments, isolated cell free DNA can be used to detect methylation of genomic loci or other genomic and / or epigenomic alterations of biomarkers. Histone modifications

[0076] Histone methylation is understood to increase or decrease expression of associated coding sequences, depending on which histone residue is methylated. Histone methylation is an essential modification that can cause monomethylation (me1), dimethylation (me2), and trimethylation (me3) of several amino acids, thus directly affecting heterochromatin formation, gene imprinting, X chromosome inactivation, and gene transcriptional regulation. Histone methyltransferases promote monomethylation, dimethylation, or trimethylation of histones while histone demethylases promote demethylation of histones. In general, lysine (Lys or K), arginine (Arg or R), and rarely histidine (His or H) are the most common histone methyl acceptors. Histone methylation only occurs at specific lysine and arginine sites of histone H3 and H4. In histone H3, lysine 4, 9, 26, 27, 36, 56, and 79 and arginine 2, 8, and 17 can be methylated. By comparison, histone H4 has fewer methylation sites, in which only lysine 5, 12, and 20 and arginine 3 can be methylated. Histone methylation is often associated with transcriptional activation or inhibition of downstream genes. The methylation of histone H3K4, R8, R17, K26, K36, K79, H4R3, and K12 can activate gene transcription. However, the methylation of histone H3K9, K27, K56, H4K5, and K20 can inhibit gene transcription. Thus, for example, H3K4 methylation generally activates gene expression, while H3K27 methylation generally represses gene expression.

[0077] Histone acetylation occurs predominantly at lysine residues and is generally understood to increase expression of associated coding sequences. Without wishing to be bound by any theory, acetylation of lysine residues is thought to neutralize lysine’s positive charge and thereby cause histones to drift away from DNA, which has a negative charge. The released structureDFS-33825 (DFCI 3382.W01WO) facilitates access to transcriptional machinery such as transcription factors and RNA polymerase II. Histone acetylation and deacetylation are generally catalyzed by histone acetyltransferases (HATs) and HDACs, respectively. Acetyl-CoA can be a source and co-factor of acetylation. In regulatory regions, HATs can acetylate histones and recruit HAT-containing complexes to activate the transcriptional process. For instance, H3K9ac and H3K27ac levels can be associated with promoter and enhancer activities. Furthermore, H3K27ac enhances not only the kinetics of transcriptional activation, but also accelerates the transition of RNA polymerase II from the initiation state to the elongation state.

[0078] Differential modification of a genomic locus (e.g., differential histone methylation and / or differential histone acetylation) can refer to, or be determined by or detected as, a comparative difference or change in modification status of one or more genomic loci between a first sample, condition, disease, or state and a second or reference sample, condition, disease, or state. Those of skill in the art will appreciate that a reference is typically produced by measurement using a methodology identical, similar, or comparable to that by which a compared non-reference measurement was taken.

[0079] A reference can be a value or set of values that are predetermined or derived from a sample or set of samples. A reference can be a sample or set of samples. A reference value can be a predetermined threshold value, a value that varies in accordance with circumstances (e.g., according to patient subpopulation, age, weight, or other variables), or a ratio. Reference ratios can be ratios relating to the modification of multiple loci within individual samples and / or references, or across or between samples and / or references. In various embodiments, a reference can have or represent a normal, non-diseased state. In some embodiments, such as for staging of disease or for evaluating the efficacy of treatment, a reference can have or represent a diseased state, e.g., a renal cell cancer, stage of renal cell cancer, or subtype of renal cell cancer, e.g., sarcomatoid renal cell cancer.

[0080] In some instances, a reference is a non-contemporaneous sample from the same source, e.g., a prior sample from the same source, e.g., from the same subject. In some instances, a reference for the modification status of one or more genomic loci (e.g., one or more differentially modified genomic loci) can be the modification status of the one or more genomic loci (e.g., one or more differentially modified genomic loci) in a sample (e.g., a sample from a subject), or a plurality of samples, known to represent a particular state (e.g., sarcomatoid RCC or epithelioid RCC).

[0081] The present disclosure includes the discovery of genomic loci that are differentially modified and / or differentially methylated in sarcomatoid RCC (e.g., compared to epithelioid RCC). SEQ ID NOs: 7-58011 include genomic loci that are differentially modified and / or differentially methylated in sRCC.

[0082] In some illustrative but non-limiting embodiments of the present disclosure differential modification can refer to a differential (e.g., between a sample and a reference) with anDFS-33825 (DFCI 3382.W01WO) absolute log2(fold-change) that is greater than or equal to 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, or more, or any range in between, inclusive, e.g., as measured according to an assay provided herein.

[0083] Enhancers are genomic loci that can be differentially modified in and / or between conditions, diseases, and other states. Enhancers are cis-acting DNA regulatory regions that are thought to bind trans-acting proteins that contribute to expression patterns of associated genes. Chromatin ImmunoPrecipitation sequencing (ChIP-seq) of histone modifications (e.g., acetylation) have identified millions of enhancers in mammalian genomes. The number of active enhancers in any given cell type is estimated to be in the tens of thousands. Certain transcription factors (TFs), sometimes referred to as “master” transcription factors, associate with active enhancers with important impacts on gene expression and cell function. Certain such transcription factors preferentially associate with enhancers that regulate genes required for establishing cell identity and function, including enhancer domains known as “super-enhancers”. Moreover, master TFs can participate in inter-connected auto-regulatory circuitries or “cliques” that are self-reinforcing, show marked cell selectivity, and function to maintain cell state and / or cell survival.

[0084] RCC epigenetic data known in the art is often derived from cell lines, which diverge from their original tumors and may not represent all histologic subtypes.

[0085] A sarcomatoid RCC subtype can be distinguished, e.g., for purposes of detection and / or treatment, using genomic loci (e.g., enhancers) that are differentially modified (e.g., differentially acetylated and / or differentially methylated) relative to non-sarcomatoid (e.g., epithelioid) RCC. Techniques for Detecting and Quantifying Histone Modifications and Transcription Factor Binding

[0086] Various techniques of molecular biology are well known in the art and / or disclosed in the present application for detecting and quantifying histone modifications and / or transcription factor binding. In some embodiments, the methods, kits and systems of present disclosure involve the detection and quantification of histone modifications and / or transcription factor binding in samples, e.g., in liquid biopsy samples including cfDNA such as plasma samples including cfDNA. Chromatin ImmunoPrecipitation (ChIP) is one technique of molecular biology useful in detecting and quantifying histone modifications and transcription factor binding in samples. CUT&RUN or CUT&Tag are other more recent techniques that can also be used to detect and quantify histone modifications and transcription factor binding sites.

[0087] ChIP can involve various steps including one or more of fixation, sonication, immunoprecipitation, and analysis of the immunoprecipitated DNA. ChIP has become a very widely used tissue-based technique for determining the in vivo location of binding sites of various transcription factors and histones. Because the proteins are captured at the sites of their binding withDFS-33825 (DFCI 3382.W01WO) DNA, ChIP helps to detect DNA-protein interactions that take place in living cells. More importantly, ChIP can be coupled to many commonly used molecular biology techniques such as PCR and real- time PCR, PCR with single-stranded conformational polymorphism, Southern blot analysis, Western blot analysis, cloning, and microarray. The resulting versatility has increased the potential of this technique.

[0088] ChIP of tissue samples usually involves cross-linking of the chromatin-bound proteins by formaldehyde, followed by sonication or nuclease treatment to obtain small DNA fragments. Immunoprecipitation can be then carried out using specific antibodies to the DNA-binding protein of interest. The DNA can be then released from the proteins and analyzed using various methods. ChIP has also been used to study RNA-protein interactions. X-ChIP methods utilize fixed chromatin fragmented by sonication, while the N-ChIP methods utilize native chromatin, which can be unfixed and nuclease digested.

[0089] The first step of the technique can be the cross-linking of DNA and proteins. Formaldehyde is one of the most used cross-linking agents. One advantage of using formaldehyde can be the ease of reversibility of the cross-links and its ability to form bonds that span approximately 2 angstroms. This means that formaldehyde can bind molecules in close association with each other. Generally, formaldehyde can be added to the medium in the cell culture flask or plate. It enters the cells through the cell membrane and cross-links the proteins to the chromatin. Formaldehyde fixation of tumor tissues has also been done. Other cross-linking agents that have been used include chemicals such as methylene blue and acridine orange, cisplatin, dimethylarsinic acid, potassium chromate, and ultraviolet (UV) light and lasers.

[0090] Harvested chromatin can be sonicated in one or more sonication cycles. DNA can be typically broken into to 100–500 bp fragments to pinpoint the location of the DNA sequence of interest. An alternative to sonication can be nuclease digestion of the chromatin, e.g., in N-ChIP methods. Purification of chromatin can be achieved using a cesium chloride (CsCl) gradient centrifugation.

[0091] Chromatin can be immunoprecipitated using one or more antibodies that bind a target epitope. For example, an antibody used in ChIP can selectively bind a particular transcription factor or one or more particular histone modifications, such as one or more particular histone acetylation modifications or histone methylation modifications. In some embodiments, an antibody used to bind a target epitope can be a “pan” antibody (e.g., a pan-acetylation antibody, a pan-methylation antibody, an antibody that binds a group of histone modifications associated with increased transcription activation, and / or an antibody that binds a group of histone modifications associated with increased transcription repression). The antibody against the protein of interest is allowed to bind to the protein- DNA complex, and the complex can be then precipitated. Immunosorbants commonly used to separate the antigen-antibody complex from the lysate include salmon sperm DNA-protein A-DFS-33825 (DFCI 3382.W01WO) Sepharose®, protein G, magnetic beads, and other engineered immunoprecipitation systems known to those of skill in the art.

[0092] Immunoprecipitated DNA can be eluted. Once the DNA of interest is isolated, many detection and quantification methods can be used to study the isolated gene fragments. Commonly utilized methods include PCR, real-time PCR, slot blot hybridization, microarray techniques, and deep or next-generation sequencing. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. ChIP- seq can be used to map DNA-binding proteins, e.g., transcription factor binding sites and histone modifications in a genome-wide manner.

[0093] Cell-free Chromatin ImmunoPrecipitation sequencing (cfChIP-seq) involves applying ChIP-seq to samples that include cell-free DNA, e.g., liquid biopsy samples including cfDNA such as plasma samples including cfDNA (e.g., see Sadeh et al., Nat Biotechnol (2021) 39: 586–598 and Jang et al., Life Sci Alliance (2023) 6(12):e202302003 the entire contents of each of which are incorporated herein by reference). In some embodiments, cfChIP-seq uses antibodies or antibody fragments that bind specific histone modifications (e.g., H3K4me3 and / or H3K27ac) and / or transcription factors that are coupled (covalently or non-covalently) to beads, e.g., magnetic beads such as Dynabeads® magnetic beads and incubated with a volume, e.g., about 1 mL of thawed plasma obtained from a subject. Without limitation, exemplary antibodies that bind H3K4me3 include PA5- 27029 (available from Thermo Fisher Scientific in Waltham, MA) and C15410003 (available from Diagenode in Denville, NJ) and exemplary antibodies that bind H3K27ac include ab21623 or ab4729 (both available from Abcam in Cambridge, UK) and C15210016 (available from Diagenode in Denville, NJ).

[0094] In some embodiments, the antibodies or antibody fragments can be covalently coupled to beads, e.g., epoxy beads. In some embodiments, the antibodies or antibody fragments can be non-covalently coupled to beads, e.g., Protein A or Protein G beads such as Dynabeads® Protein A or Dynabeads® Protein G beads. After washing, a cfDNA library is then typically prepared from the captured cfDNA. Library preparation can be done on-bead or after releasing the captured cfDNA by digestion of bound histones, e.g., using proteinase K. The cfDNA library is then sequenced to generate reads of captured cfDNA sequences, e.g., by next-generation sequencing (NGS) as is known in the art. The reads are then analyzed, e.g., aligned and counted using standard bioinformatic techniques as is known in the art. A cfChIP-seq bioinformatic pipeline can include, e.g., alignment of sequence reads to a reference genome (e.g., human genome build hg19) with BWA or Bowtie2. Aligned reads can be used to call and quantify peaks as compared to a reference.

[0095] CUT&Tag involves antibody-based binding of a target protein, e.g., transcription factor or histone modification of interest, where antibody incubation is directly followed by the shearing of the chromatin and library preparation (see Kaya-Okur et al., Nat Comm (2019) 10:1930).DFS-33825 (DFCI 3382.W01WO)

[0096] CUT&RUN is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing (see Skene and Henikoff, Elife (2017) 6:1-35, Skene et al., Nat Protoc (2018) 13:1006-1019). As only targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has low background levels. Techniques for Detecting and Quantifying DNA Methylation

[0097] Various techniques of molecular biology are well known in the art and / or disclosed in the present application for detecting and quantifying DNA methylation. In some embodiments, the methods, kits and systems of the present disclosure involve the detection and quantification of chromatin accessibility in samples, e.g., in liquid biopsy samples including cfDNA such as plasma samples including cfDNA. Bisulfite sequencing (BS-Seq), Whole Genome Bisulfite Sequencing (WGBS), Methylated DNA ImmunoPrecipitation sequencing (MeDIP-seq), or Methyl-CpG-Binding Domain sequencing (MBD-seq) are exemplary techniques of molecular biology useful in detecting and quantifying chromatin accessibility in samples.

[0098] Bisulfite sequencing (BS-Seq) or Whole-Genome Bisulfite Sequencing (WGBS) is a well-established protocol to detect methylated cytosines in genomic DNA. In this method, genomic DNA is treated with sodium bisulfite and then sequenced, providing single-base resolution of methylated cytosines in the genome. Upon bisulfite treatment, unmethylated cytosines are deaminated to uracils which, upon sequencing, are converted to thymidines. Simultaneously, methylated cytosines resist deamination and are read as cytosines. The location of the methylated cytosines can then be determined by comparing treated and untreated sequences.

[0099] MeDIP-seq was first reported by Weber et al., Nat Genet (2005) 37:853–862. In a typical MeDIP-seq protocol, antibody or antibody-fragment that binds 5-methylcytidine (5mC) is used to enrich methylated DNA fragments, then these fragments are sequenced and analyzed. If using 5mC-specific antibodies or antibody fragments, methylated DNA is isolated from genomic DNA via immunoprecipitation. Anti-5mC antibodies are incubated with fragmented genomic DNA and precipitated, followed by DNA purification and sequencing.

[0100] Methyl-CpG-Binding Domain sequencing (MBD-seq) is similar to MeDIP-seq except that it uses methyl binding domain (MBD) proteins instead of antibodies or antibody fragments to bind methylated DNA. In a typical MBD-seq protocol, genomic DNA is first sonicated and incubated with tagged MBD proteins that can bind methylated cytosines. The protein-DNA complex is then precipitated with antibody-conjugated beads that are specific to the MBD protein tag, followed by DNA purification and sequencing. FOSL1

[0101] The term “FOSL1” is intended to include fragments, variants (e.g., allelic variants), and derivatives thereof. Representative human FOSL1 cDNA and human FOSL1 protein sequencesDFS-33825 (DFCI 3382.W01WO) are well-known in the art and are publicly available from the National Center for Biotechnology Information (NCBI) (see, for example, ncbi.nlm.nih.gov / gene / 8061). For example, human FOSL1 (NP_001287773.1) is encodable by the transcript (NM_001300844.2). Other human FOSL1 isoforms include, for example, NP_001287784.1, NP_001287785.1, NP_001287786.1, and NP_005429.1, which are encodable by the respective transcripts NM_001300855.2, NM_001300856.2, NM_001300857.2, NM_005438.5. Nucleic acid and polypeptide sequences of FOSL1 orthologs in organisms other than humans are well-known and include, for example, chimpanzee FOSL1 (XP_016776758.1 and XP_001170402.2, which may encoded by XM_016921269.2 and XM_001170402.6) and mouse FOSL1 (NP_034365.1 and NM_010235.2).

[0102] Reagents are well-known for detecting FOSL1 expression and can be readily obtained and / or designed by a person having ordinary skill in the art. For example, anti-FOSL1 antibodies suitable for detecting FOSL1 protein are well-known in the art and include, for example, antibodies SAB1406640 (Sigma-Aldrich), OTI12F9 (OriGene, TrueMABTM), ARP31377_P050 (Aviva Systems Biology), and Mouse Anti-FOSL1 Antibody (1E3) (Creative Biolabs). In addition, multiple siRNA, shRNA, CRISPR constructs for modulating FOSL1 expression can be found in the commercial product lists of a variety of companies, such as open reading frame (ORF) clones, CRISPR knockouts, and RNA interference (RNAi) clones, such as siRNA and shRNA clones (e.g., GA105397 and KN402104 CRISPR knockouts (OriGene), FOSL1 siRNA and shRNA (Santa Cruz Biotechnology), TL312944 shRNA (OriGene), MIRT006142 and MIRT006546 (mIRTarBase ) etc.). It is to be noted that the term can further be used to refer to any combination of features described herein regarding FOSL1 molecules. For example, any combination of sequence composition, percentage identify, sequence length, domain structure, functional activity, etc. can be used to describe a FOSL1 molecule encompassed by the present invention. HIF2a

[0103] The term “HIF2a” or “EPAS1” is intended to include fragments, variants (e.g., allelic variants), and derivatives thereof. HIF2a is encoded by Epas1. Representative human HIF2a cDNA and human HIF2a protein sequences are well-known in the art and are publicly available from the National Center for Biotechnology Information (NCBI) (see, for example, ncbi.nlm.nih.gov / gene / 2034). For example, human HIF2a (NP_001421.2) is encodable by the transcript (NM_00130.5). Nucleic acid and polypeptide sequences of HIF2a orthologs in organisms other than humans are well-known and include, for example, mouse HIF2a (NP_034267.3 and NM_010137.3).

[0104] Reagents are well-known for detecting HIF2a expression and can be readily obtained and / or designed by a person having ordinary skill in the art. For example, anti-HIF2a antibodies suitable for detecting HIF2a protein are well-known in the art and include, for example, antibodies AF2886 and AF2997 (R&D Systems), AP23352 and AP51440PU (OriGene, TrueMABTM), GT125DFS-33825 (DFCI 3382.W01WO) (GeneTex), and Orb401083 and Orb1031015 (Biorbyt). In addition, multiple siRNA, shRNA, CRISPR constructs for modulating HIF2a expression can be found in the commercial product lists of a variety of companies, such as open reading frame (ORF) clones, CRISPR knockouts, and RNA interference (RNAi) clones, such as siRNA and shRNA clones (e.g., GA10143 and GA201249 CRISPR knockouts (OriGene), TL315484V and TR500609 shRNA (Santa Cruz Biotehnology), etc.). It is to be noted that the term can further be used to refer to any combination of features described herein regarding HIF2a molecules. For example, any combination of sequence composition, percentage identify, sequence length, domain structure, functional activity, etc. can be used to describe a HIF2a molecule encompassed by the present invention. Classifiers

[0105] In some embodiments, the present disclosure provides methods for obtaining a classifier, e.g., a validated classifier that can be used to determine RCC sarcomatoid differentiation. In some embodiments, a subject is determined to have a validated epigenetic profile indicative of sarcomatoid RCC or epithelioid RCC based on analysis of a biological sample, optionally of cell-free DNA (cfDNA) from a liquid biopsy sample, obtained or derived from the subject, wherein the presence of the validated epigenetic profile has been determined using a validated classifier.

[0106] For illustration purposes and without limitation, in an exemplary embodiment of the present disclosure, the validated classifier may be obtained by determining comparing epigenomic profiles from a cohort of subjects who have previously been determined to have sarcomatoid RCC and a cohort of healthy subjects or subjects who have previously been determined to have epithelioid RCC, wherein the genomic profiles comprise profiles for one or more histone modifications and / or DNA methylation. In some embodiments, a classifer is trained to identify differentially modified loci between the epigenomic profiles of samples from subjects with sarcomatoid RCC and epithelioid RCC.

[0107] In some embodiments, a threshold value is selected such that the validated classifier predicts sarcomatoid RCC, with an area under the receiver operating characteristic (AUROC) that is at least about 0.35, at least about 0.4, at least about 0.5, at least about 0.6, at least about 0.7, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. In some embodiments, the AUROC for determining if a subject has or is at risk of developing sarcomatoid RCC can be about 0.35 to about 1.0, about 0.4 to about 1.0, about 0.5 to about 1.0, about 0.6 to about 1.0, about 0.7 to about 1.0). In some embodiments, the AUROC for determining if a subject has or is at risk of developing sarcomatoid RCC can be about 0.35 to about 1.0, about 0.4 to about 1.0, about 0.5 to about 1.0, about 0.6 to about 1.0, about 0.7 to about 1.0).

[0108] In some embodiments, the AUROC for determining if a subject has or is at risk of developing sarcomatoid ccRCC, can be about 0.4 to about 1.0, about 0.5 to about 1.0, about 0.6 toDFS-33825 (DFCI 3382.W01WO) about 1.0, about 0.7 to about 1.0). In some embodiments, the AUROC for determining if a subject has or is at risk of developing sarcomatoid pRCC can be about 0.35 to about 1.0, about 0.4 to about 1.0, about 0.5 to about 1.0, about 0.6 to about 1.0, about 0.7 to about 1.0). In some embodiments, the AUROC for determining if a subject has or is at risk of developing sarcomatoid chRCC can be about 0.35 to about 1.0, about 0.4 to about 1.0, about 0.5 to about 1.0, about 0.6 to about 1.0, about 0.7 to about 1.0). In certain embodiments of the present disclosure, the area under the receiver operating characteristic (AUROC) for determining if a subject has or is at risk of developing sarcomatoid can be greater than 0.7. In certain embodiments of the present disclosure, a subject has been previously determined to have renal cell carcinoma.

[0109] A person of ordinary skill will appreciate that other methods can be used to obtain a classifier, e.g., a validated classifier that can be used to determine RCC sarcomatoid differentiation and that the present disclosure is not limited to classifiers obtained in accordance with this method. Exemplary Genomic Loci

[0110] The present disclosure includes the identification of exemplary genomic loci that are differentially modified in sarcomatoid vs non-sarcomatoid (e.g., epithelioid) renal cell cancer. See SEQ ID NOs: 7-58011 which correspond to the chromosomal coordinates of a differentially modified genomic loci; each sequence lists the corresponding the genomic locus as a qualifier value. The genomic loci in SEQ ID NOs: 7-12874 and SEQ ID NOs: 25926-42008 are “Sarcomatoid Locus Up” loci, which are those whose increase correlates with sarcomatoid RCC; while genomic loci in SEQ ID NOs: 12875-25925 and SEQ ID NOs: 42009-58011 are “Sarcomatoid Locus Down”, which are those whose increase correlate with epithelioid RCC). Specifically, SEQ ID NOs: 7-12874 correspond to H3K27ac Sarcomatoid Locus Up genomic loci where H3K27ac modification increase correlates with sarcomatoid RCC; SEQ ID NOs: 12875-25925 correspond to H3K27ac Sarcomatoid Locus Down genomic loci where H3K27ac modification increase correlates with epithelioid RCC; SEQ ID NOs: 25926-42008 correspond to MeDIP Sarcomatoid Locus Up genomic loci where methylation increase correlates with sarcomatoid RCC; SEQ ID NOs: 42009-58011 correspond to MeDIP Sarcomatoid Locus Down genomic loci where methylation increase correlates with epithelioid RCC. The sequences correspond to genomic loci which are based on human genome build hg19.

[0111] The present disclosure is not limited to methods that use the exact same chromosomal coordinates that are recited in SEQ ID NOs: 7-58011. The present disclosure encompasses methods that use any of the genomic loci in SEQ ID NOs: 7-58011and also subregions thereof, i.e., references herein to methods that involve detecting and / or quantifying one or more histone modifications, binding of one or more transcription factors, and / or DNA methylation at one or more genomic loci of SEQ ID NOs: 7-58011encompasses methods that detect these marks anywhere within these genomic loci including within any subregions. For example, where SEQ ID NO: 11 references chr2:242209816-242213036 as a genomic locus for detecting and / or quantifying H3K27acDFS-33825 (DFCI 3382.W01WO) modification, this encompasses methods that detect and / or quantify H3K27ac modification at any position or sub-region of chr2:242209815-242213036, e.g., methods that detect and / or quantify H3K27ac modification within chr2:242209915-242212036, etc. In some embodiments, a subregion may span at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 or at least 3000 contiguous base pairs that are located between the lower and upper coordinates of a genomic locus recited in SEQ ID NOs: 7-58011. In some embodiments, a subregion may span less than 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 or at least 3000 contiguous base pairs that are located between the lower and upper coordinates of a genomic locus recited in SEQ ID NOs: 7- 58011. In some embodiments, a subregion may have the same central coordinate as a genomic locus recited in SEQ ID NOs: 7-58011. In some embodiments, a subregion may have a different central coordinate as a genomic locus recited in SEQ ID NOs: 7-58011. It is also to be understood that the lower / upper coordinates of the genomic loci in SEQ ID NOs: 7-58011 are approximate and that the present disclosure encompasses methods where any one or more of the genomic loci are expanded by increasing the size of the genomic locus by 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40% or up to 50% in one or both directions.

[0112] In some embodiments, a classifier is generated using a set of differentially modified genomic loci that are correlated with sarcomatoid RCC. Sequence reads that fall into each selected genomic locus are analyzed and counted, e.g., as described herein including the Examples. In some embodiments, counts from genomic loci that are correlated with sarcomatoid are aggregated. Other ways of using the genomic loci and related sequencing data to generate and apply a classifier to determine sarcomatoid differentiation status are described herein and known in the art, e.g., without limitation, methods that use a learning statistical classifier system or a combination of learning statistical classifier systems.

[0113] In some embodiments, exemplary genomic loci from SEQ ID NOs: 7-25925 or SEQ ID NOs: 25926-58011are used in a monomodal classifier, e.g., a classifier that uses a single histone modification (e.g., H3K4me3 or H3K27ac) or DNA methylation at one or more genomic loci for purposes of determining sarcomatoid differentiation status. In some embodiments, exemplary genomic loci from SEQ ID NOs: 7-25925 and / or SEQ ID NOs: 25926-58011 are used in combination in a multimodal classifier, e.g., a classifier that uses more than one histone modification (e.g., H3K4me3 and H3K27ac) or one or more histone modifications (e.g., H3K4me3 and / or H3K27ac) and DNA methylation at one or more genomic loci for purposes of determining sarcomatoid differentiation status. Differential H3K27ac modification

[0114] Genomic loci demonstrating differential H3K27ac modification in sarcomatoid renal cell cancer are provided in SEQ ID NOs: 7-25925 which includes the chromosomal coordinates of each genomic locus. The genomic loci of SEQ ID NOs: 7-128754 are “Sarcomatoid Locus Up” loci,DFS-33825 (DFCI 3382.W01WO) whose presence and / or increase correlate with sarcomatoid RCC; while genomic loci of SEQ ID NOs: 12875-25925 are “Sarcomatoid Locus Down”, which are those whose presence and / or increase correlate with epithelioid RCC). The genomic loci are based on human genome build hg19.

[0115] A person of skill in the art will recognize that the methods disclosed herein do not require that every genomic locus listed in SEQ ID NOs: 7-25925 be assessed for H3K27ac modification. Instead, a subset of loci may be assessed for H3K27ac modification. Subsets of the genomic loci of SEQ ID NOs: 7-25925 can be selected (e.g., for use in determining sarcomatoid differentiation status) based on various performance criteria, e.g., to select genomic loci that demonstrate differential modification with a particular level of statistical significance and / or a particular threshold of differential between relevant states (e.g., the presence and / or increase of sarcomatoid locus up loci, a ratio of sarcomatoid locus up loci as compared to sarcomatoid locus down loci, and / or a measured log2(fold-change)). Subsets of the genomic loci may also be selected based on an algorithm, e.g., during the process of obtaining a classifier. Those of skill in the art will appreciate that such subsets of loci of SEQ ID NOs: 7-25925, and loci included in such subsets, are together, individually, and / or in randomly selected subsets, at least as informative (e.g., as statistically significant and / or reliable) for uses disclosed herein, e.g., for determining sarcomatoid differentiation status. See also the Examples of the present disclosure for experiments showing that informative classifiers can be generated using many different combinations of the loci. The present disclosure particularly includes, among other things, subsets of the genomic loci of SEQ ID NOs: 7-25925, which have an absolute log2(fold-change) of 10.0 or higher, 9.5 or higher, 8.5 or higher, 8.0 or higher, 7.5 or higher, 7.0 or higher, 6.5 or higher, 6.0 or higher, 5.5 or higher, 5.0 or higher, 4.5 or higher, 4.0 or higher, 3.5 or higher, 3.0 or higher, 2.5 or higher, 2.0 or higher, 1.9 or higher, 1.8 or higher, 1.7 or higher, 1.6 or higher, 1.5 or higher, 1.4 or higher, 1.3 or higher, 1.2 or higher, 1.1 or higher, 1.0 or higher, 0.9 or higher, 0.8 or higher, 0.7 or higher, 0.6 or higher, 0.5 or higher, or 0.1 or higher. The present disclosure also includes subsets of the genomic loci of SEQ ID NOs: 7-25925, which have an absolute log2(fold-change) of 10.0 or higher, 9.5 or higher, 8.5 or higher, 8.0 or higher, 7.5 or higher, 7.0 or higher, 6.5 or higher, 6.0 or higher, 5.5 to less than 6.0, 5.0 to less than 5.5, 4.5 to less than 5.0, 4.0 to less than 4.5, 3.8 to less than 4.0, 3.6 to less than 3.8, 3.4 to less than 3.6, 3.2 to less than 3.4, 3.0 to less than 3.2, 2.8 to less than 3.0, 2.6 to less than 2.8, 2.4 to less than 2.6, 2.2 to less than 2.4, 2.0 to less than 2.2, 1.8 to less than 2.0, 1.6 to less than 1.8, 1.4 to less than 1.6, 1.2 to less than 1.4, 1.0 to less than 1.2, 0.8 to less than 1.0, 0.6 to less than 0.8, or 0.1 to less than 0.5.

[0116] In various embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci, or any range in between, inclusive, such as 3K-13K sarcomatoid locus up loci, all sarcomatoid locus up loci, etc. loci identifiedDFS-33825 (DFCI 3382.W01WO) in SEQ ID NOs: 7-12874 (or any subset thereof) are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with an epithelioid renal cell cancer). In certain embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least a number of loci identified in a SEQ ID NOs: 7-12874 (or any subset thereof) having a lower bound selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1000 (1K) sarcomatoid locus up loci, and an upper bound selected from 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, or all sarcomatoid locus up loci is found to be H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In certain particular embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (e.g., about 1 to about 13000, about 5 to about 10000, about 10 to about 8000, about 25 to about 200, about 5, about 10, about 20, or about 50 loci) are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% of sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In certain embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least a percent of sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 having a lower bound selected from 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10%, and an upper bound selected from 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% is found to be H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0117] In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) of the top 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci, or any range in between, inclusive, such as 3K-13K sarcomatoid locus up loci, all sarcomatoid locus up loci, etc. sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer) (wherein, e.g., the “top” loci refers to the group of sarcomatoid locus up, or sarcomatoid locus down, loci, in different sections,DFS-33825 (DFCI 3382.W01WO) respectively, starting with the highest absolute log2(fold-change)). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 10 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 is H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 is H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 is H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 10 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0118] In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 10 sarcomatoid locus up loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or 10) identified in SEQ ID NOs: 7-12874 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or 25) and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K,DFS-33825 (DFCI 3382.W01WO) 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 7- 12874 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 50 sarcomatoid locus up loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or at least 25, at least 30, at least 35, at least 40, at least 45, or 50) identified in SEQ ID NOs: 7-12874 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least five of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least five of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 7-12874 (or any subset thereof) in total are H3K27ac modified as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0119] In various embodiments, differentially H3K27ac modified refers to an acetylation status characterized by an increase or decrease in a value measuring acetylation (e.g., of read counts and / or normalized read counts for a given genomic locus), and / or a mean, median and / or mode thereof, and / or a log thereof (e.g., log base 2 (log2)), of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6- fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, 25-fold, 30-fold, 35-fold, 40-fold, 45-fold, 50- fold, or greater, or any range in between, inclusive, such as 1% to 50%, 50% to 2-fold, 25% to 50- fold, 25% to 30-fold, 25% to 20-fold, 25% to 16-fold, 30% to 16-fold, 50% to 16-fold, 70% to 16- fold, 2-fold to 16-fold, 2.2-fold to 16-fold, 2.6-fold to 16-fold, 3-fold to 16-fold, 3.4-fold to 16-fold, 4- fold to 16-fold, 4.5-fold to 16-fold, 5.2-fold to 16-fold, 6-fold to 16-fold, 7-fold to 16-fold, or 8-foldDFS-33825 (DFCI 3382.W01WO) to 16-fold, as compared to a reference, optionally where the statistical significance of the increase or decrease is at least 5e-2, 1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5, 5e-6, or 1e-6. In various embodiments, an increase or decrease in a value measuring acetylation can be, or is expressed as, a log2(fold-change), e.g., a log2(fold-change) of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7- fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, or greater, or any range in between, inclusive, such as an increase or decrease of 0.1-fold to 10-fold, 0.2-fold to 5-fold, 0.2-fold to 4.0-fold, 0.4-4.0-fold, 0.4- fold to 4.0-fold, 0.6-fold to 4.0-fold, 0.8-fold to 4.0-fold, 1.0-fold to 4.0-fold.1.2-fold to 4.0-fold.1.4- fold to 4.0-fold, 1.6-fold to 4.0-fold, 1.8-fold to 4.0-fold, 2.0-fold to 4.0-fold, 2.2-fold to 4.0-fold, 2.4- fold to 4.0-fold, 2.6-fold to 4.0-fold, 2.8-fold to 4.0-fold, or 3.0-fold to 4.0-fold, optionally where the statistical significance of the increase or decrease is at least 5e-2, 1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5, 5e-6, or 1e-6.

[0120] In various embodiments, sarcomatoid locus down loci provided in SEQ ID NOs: 12875-25925 can be used to generate a ratio of sarcomatoid locus up loci to sarcomatoid locus down loci. In various embodiments, the sarcomatoid locus down loci parameters can match the sarcomatoid locus up loci parameters (e.g., any description of sarcomatoid locus up loci described for differential H3K27ac modification described above). A representative, non-limiting example involves using the top 50 sarcomatoid locus down loci as a reference to compare against the top 50 sarcomatoid locus up loci). In various embodiments, the sarcomatoid locus down loci parameters can be defined according to any description of sarcomatoid locus up loci described for differential histone acetylation described above, but do not match the sarcomatoid locus up loci parameters. A representative, non-limiting example involves using all sarcomatoid locus down loci as a reference to compare against the top 50 sarcomatoid locus up loci). In various embodiments, a classifier is based on the ratio of the sarcomatoid locus up loci parameters compared to the sarcomatoid locus down loci parameters. In various embodiments, the ratio used in comparison to a reference (e.g., a sample from a healthy subject or a subject with an epithelioid renal cell cancer). Differential DNA methylation

[0121] Genomic loci demonstrating differential DNA methylation in sarcomatoid renal cell cancer are provided in SEQ ID NOs: 25926-58011 which shows the chromosomal coordinates of each genomic locus. Genomic loci of SEQ ID NOs: 25926-42008 are “Sarcomatoid Locus Up”, which are those whose presence and / or increase correlates with sarcomatoid RCC; while genomic loci of SEQ ID NOs: 42009-58011 are “Sarcomatoid Locus Down”, which are those whose presence and / or increase correlate with epithelioid RCC). The genomic loci are based on human genome build hg19.

[0122] A person of skill in the art will recognize that the methods disclosed herein do not require that every genomic locus listed in SEQ ID NOs: 25926-58011 be assessed for DNADFS-33825 (DFCI 3382.W01WO) methylation. Instead, a subset of loci may be assessed for DNA methylation. Subsets of the genomic loci of SEQ ID NOs: 25926-58011 can be selected (e.g., for use in determining sarcomatoid differentiation status) based on various performance criteria, e.g., to select genomic loci that demonstrate differential modification with a particular level of statistical significance and / or a particular threshold of differential between relevant states (e.g., the presence and / or increase of sarcomatoid locus up loci, a ratio of sarcomatoid locus up loci as compared to sarcomatoid locus down loci, and / or a measured log2(fold-change)). Subsets of the genomic loci may also be selected based on an algorithm, e.g., during the process of obtaining a classifier. Those of skill in the art will appreciate that such subsets of loci of SEQ ID NOs: 25926-58011, and loci included in such subsets, are together, individually, and / or in randomly selected subsets, at least as informative (e.g., as statistically significant and / or reliable) for uses disclosed herein, e.g., for determining sarcomatoid differentiation status. See also the Examples of the present disclosure for experiments showing that informative classifiers can be generated using many different combinations of the loci. The present disclosure particularly includes, among other things, subsets of the genomic loci of SEQ ID NOs: 25926-58011, which have an absolute log2(fold-change) of 10.0 or higher, 9.5 or higher, 8.5 or higher, 8.0 or higher, 7.5 or higher, 7.0 or higher, 6.5 or higher, 6.0 or higher, 5.5 or higher, 5.0 or higher, 4.5 or higher, 4.0 or higher, 3.5 or higher, 3.0 or higher, 2.5 or higher, 2.0 or higher, 1.9 or higher, 1.8 or higher, 1.7 or higher, 1.6 or higher, 1.5 or higher, 1.4 or higher, 1.3 or higher, 1.2 or higher, 1.1 or higher, 1.0 or higher, 0.9 or higher, 0.8 or higher, 0.7 or higher, 0.6 or higher, 0.5 or higher, or 0.1 or higher. The present disclosure also includes subsets of the genomic loci of SEQ ID NOs: 25926-58011, which have an absolute log2(fold-change) of 10.0 or higher, 9.5 or higher, 8.5 or higher, 8.0 or higher, 7.5 or higher, 7.0 or higher, 6.5 or higher, 6.0 or higher, 5.5 to less than 6.0, 5.0 to less than 5.5, 4.5 to less than 5.0, 4.0 to less than 4.5, 3.8 to less than 4.0, 3.6 to less than 3.8, 3.4 to less than 3.6, 3.2 to less than 3.4, 3.0 to less than 3.2, 2.8 to less than 3.0, 2.6 to less than 2.8, 2.4 to less than 2.6, 2.2 to less than 2.4, 2.0 to less than 2.2, 1.8 to less than 2.0, 1.6 to less than 1.8, 1.4 to less than 1.6, 1.2 to less than 1.4, 1.0 to less than 1.2, 0.8 to less than 1.0, 0.6 to less than 0.8, or 0.1 to less than 0.5.

[0123] In various embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 1.5K, 2K, 2.5K, 3K, 3.5K, 4K, 4.5K, 5K, 6.5K, 7K, 8.8K, 9K, 9.5K, 10K, 10.5K, 11K, 11.5K, 12K, 12.5K, 13K, 13.5K, 14K, 14.5K, 15K, 15.5K, 16K, 16.5K, or all, sarcomatoid locus up loci, or any range in between, inclusive, such as 3K-16.5K sarcomatoid locus up loci, all sarcomatoid locus up loci, etc loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In certain embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at leastDFS-33825 (DFCI 3382.W01WO) a number of loci identified in a SEQ ID NOs: 25926-42008 (or any subset thereof) having a lower bound selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1000 (1K) sarcomatoid locus up loci, and an upper bound selected from 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all sarcomatoid locus up loci is found to be differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In certain particular embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (e.g., about 1 to about 16,500, about 5 to about 15,000, about 10 to about 14,000, about 25 to about 200, about 5, about 10, about 20, or about 50 loci) are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% of sarcomatoid locus up loci identified in SEQ ID NOs: 25926-58011 are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In certain embodiments, a sample or subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least a percent of sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 having a lower bound selected from 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%, 2%, 3%, 4%, 5%, or 10%, and an upper bound selected from 1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 75%, or 100% is found to be differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0124] In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one (e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) of the top 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci, or any range in between, inclusive, such as 3K-16.5K sarcomatoid locus up loci, all sarcomatoid locus up loci, etc. sarcomatoid locus up loci identified in SEQ ID NOs: 25926- 42008 are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer) (wherein, e.g., the “top” loci refers to the group of sarcomatoid locus up, or sarcomatoid locus down, in different sections, respectively, starting with the highest absolute log2(fold-change)). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 10 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 is differentially DNADFS-33825 (DFCI 3382.W01WO) methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 is differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least one of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 is differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 10 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In some embodiments, a subject from which the sample is obtained or derived, is determined to have a sarcomatoid differentiation status if at least five of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0125] In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 10 sarcomatoid locus up loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or 10) identified in SEQ ID NOs: 25926-42008 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 25 loci identified in SEQ ID NOs: 25926-42008 (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or 25) and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject withDFS-33825 (DFCI 3382.W01WO) epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least one of the top 50 sarcomatoid locus up loci (e.g., at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10, at least 15, at least 20, or at least 25, at least 30, at least 35, at least 40, at least 45, or 50) identified in SEQ ID NOs: 25926-42008 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least five of the top 25 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000 (1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer). In various embodiments, a sample or subject from which the sample is derived, is determined to have a sarcomatoid differentiation status if at least five of the top 50 sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 and at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 750, 1000(1K), 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, 11K, 12K, 13K, 14K, 15K, 16K, 16.5K, or all, sarcomatoid locus up loci identified in SEQ ID NOs: 25926-42008 (or any subset thereof) in total are differentially DNA methylated as compared to a reference (e.g., a sample from a healthy subject or a subject with epithelioid renal cell cancer).

[0126] In various embodiments, differentially DNA methylated refers to a methylation status characterized by an increase or decrease in a value measuring methylation (e.g., of read counts and / or normalized read counts for a given genomic locus), and / or a mean, median and / or mode thereof, and / or a log thereof (e.g., log base 2 (log2)), of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7- fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, 25-fold, 30-fold, 35-fold, 40-fold, 45-fold, 50-fold, or greater, or any range in between, inclusive, such as 1% to 50%, 50% to 2-fold, 25% to 50-fold, 25% to 30-fold, 25% to 20-fold, 25% to 16-fold, 30% to 16-fold, 50% to 16-fold, 70% to 16-fold, 2-fold to 16-fold, 2.2-fold to 16-fold, 2.6-fold to 16-fold, 3-fold to 16-fold, 3.4-fold to 16-fold, 4-fold to 16- fold, 4.5-fold to 16-fold, 5.2-fold to 16-fold, 6-fold to 16-fold, 7-fold to 16-fold, or 8-fold to 16-fold, as compared to a reference, optionally where the statistical significance of the increase or decrease is at least 5e-2, 1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5, 5e-6, or 1e-6. In various embodiments, an increase or decrease in a value measuring methylation can be, or is expressed as, a log2(fold-change),DFS-33825 (DFCI 3382.W01WO) e.g., a log2(fold-change) of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 75%, 100%, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, 20-fold, or greater, or any range in between, inclusive, such as an increase of 0.1- fold to 10-fold, 0.2-fold to 5-fold, 0.2-fold to 4.0-fold, 0.4-4.0-fold, 0.4-fold to 4.0-fold, 0.6-fold to 4.0-fold, 0.8-fold to 4.0-fold, 1.0-fold to 4.0-fold.1.2-fold to 4.0-fold.1.4-fold to 4.0-fold, 1.6-fold to 4.0-fold, 1.8-fold to 4.0-fold, 2.0-fold to 4.0-fold, 2.2-fold to 4.0-fold, 2.4-fold to 4.0-fold, 2.6-fold to 4.0-fold, 2.8-fold to 4.0-fold, or 3.0-fold to 4.0-fold, optionally where the statistical significance of the increase or decrease is at least 5e-2, 1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5, 5e-6, or 1e-6.

[0127] In various embodiments, sarcomatoid locus down loci provided in SEQ ID NOs: 42009-58011 can be used to generate a ratio of sarcomatoid locus up loci to sarcomatoid locus down loci. In various embodiments, the sarcomatoid locus down loci parameters can match the sarcomatoid locus up loci parameters (e.g., any description of sarcomatoid locus up loci described for differential DNA methylation described above). A representative, non-limiting example involves using the top 50 sarcomatoid locus down loci as a reference to compare against the top 50 sarcomatoid locus up loci). In various embodiments, the sarcomatoid locus down loci parameters can be defined according to any description of sarcomatoid locus up loci described for differential histone acetylation described above, but do not match the sarcomatoid locus up loci parameters. A representative, non-limiting example involves using all sarcomatoid locus down loci as a reference to compare against the top 50 sarcomatoid locus up loci). In various embodiments, a classifier is based on the ratio of the sarcomatoid locus up loci parameters compared to the sarcomatoid locus down loci parameters. In various embodiments, the ratio used in comparison to a reference (e.g., a sample from a healthy subject or a subject with an epithelioid renal cell cancer). Applications

[0128] Methods and compositions of the present disclosure include analysis of differentially modified genomic loci to determine RCC sarcomatoid differentiation status and / or subtype of a subject. Methods and compositions of the present disclosure can be used in any of a variety of applications. For example, methods and compositions of the present disclosure can be used in detecting and / or treating sarcomatoid RCC.

[0129] In various embodiments, sRCC detection using methods and compositions of the present disclosure can be applied to an asymptomatic human subject. As used herein, a subject can be referred to as “asymptomatic” if the subject does not report, and / or demonstrate by non-invasively observable indicia (e.g., without one, several, or all of device-based probing, tissue sample analysis, bodily fluid analysis, surgery, or sRCC screening), sufficient characteristics of RCC to support a medically reasonable suspicion that the subject is likely suffering from RCC, and / or from cancer. Detection of early stage RCC can be achieved using methods and compositions of the presentDFS-33825 (DFCI 3382.W01WO) disclosure, with attendant medical benefits including potential for early treatment and attendant improvement in therapeutic outcomes.

[0130] In various embodiments, sRCC detection using methods and compositions of the present disclosure can be applied to a symptomatic human subject. As used herein, a subject can be referred to as “symptomatic” if the subject report, and / or demonstrates by non-invasively observable indicia (e.g., without one, several, or all of device-based probing, tissue sample analysis, bodily fluid analysis, surgery, or RCC detecting), sufficient characteristics of RCC to support a medically reasonable suspicion that the subject is likely suffering from RCC, and / or from cancer. For example, in various embodiments a sample from a subject, optionally where the subject has a tumor that is not known or suspected of being RCC, can be assayed according to one or more embodiments of the present disclosure to determine if the tumor is sRCC. In various embodiments a sample from a subject, where the subject has a renal cell carcinoma that is known or suspected of being sRCC, can assayed according to one or more embodiments of the present disclosure to determine if the tumor is sRCC.

[0131] Those of skill in the art will appreciate that regular, preventative, and / or prophylactic screening to detect sRCC improves diagnosis of sRCC, including and / or particularly early stage cancer. Thus, the present disclosure provides, among other things, methods and compositions particularly useful for the diagnosis and treatment of early stage RCC. Generally, and particularly in embodiments in which sRCC detection in accordance with the present disclosure is carried out annually, and / or in which a subject is asymptomatic at time of detecting, methods and compositions of the present disclosure are especially likely to detect early stage sRCC. In various embodiments, detecting in accordance with methods and compositions of the present disclosure reduces sRCC mortality, e.g., by early sRCC diagnosis.

[0132] In various embodiments sRCC detection in accordance with the present disclosure is performed once for a given subject or multiple times for a given subject. In various embodiments, sRCC detection in accordance with the present disclosure is performed on a regular basis, e.g., every six months, annually, every two years, every three years, every four years, every five years, or every ten years.

[0133] In various embodiments, sRCC detection using methods and compositions disclosed herein provide a diagnosis of sRCC status and / or subtype. In other instances, sRCC detection using methods and compositions disclosed herein will be indicative of sRCC diagnosis but not definitive for sRCC diagnosis. In various instances in which methods and compositions of the present disclosure are used to detect sRCC, detection using methods and compositions of the present disclosure can be followed by a further diagnosis-confirmatory assay, which further assay can confirm, support, undermine, or reject a diagnosis resulting from prior detecting, e.g., detecting in accordance with theDFS-33825 (DFCI 3382.W01WO) present disclosure. As used herein, a diagnosis-confirmatory assay can be a sRCC assay that provides a diagnosis recognized as definitive by medical practitioners, e.g., histological confirmation.

[0134] In various embodiments, sRCC detection according to one or more methods and / or compositions disclosed herein is followed by treatment of sRCC. In various embodiments, treatment of sRCC includes administration of a therapeutic regimen including one or more sRCC therapies provided herein, including without limitation immune checkpoint inhibitor therapy.

[0135] Those of skill in the art will appreciate that treatments of sRCC provided herein can be utilized, e.g., as determined by a medical practitioner, alone or in any combination, in any order, regimen, and / or therapeutic program

[0136] In various embodiments, methods and compositions can be used to detect the utility of a therapeutic agent (e.g., a candidate or putative therapeutic agent) to treat sRCC. For example, the therapeutic agent can be applied to a subject or sample, and methods and / or compositions of the present disclosure could be used to detect the effect of the therapeutic agent on an sRCC.

[0137] In various embodiments, methods and compositions can be used to detect the clinical efficacy of a course of therapy for sRCC. For example, methods and / or compositions of the present disclosure could be used to determine the presence, absence, or state of sRCC in an RCC patient over the course of treatment. Methods and / or compositions of the present disclosure could be used in conjunction with, or confirmed by, other means of determining sRCC status including, for example measurements of tumor size or character by techniques such as CT, PET, mammogram, ultrasound, palpation, histology, caliper measurement after biopsy or surgical resection, or by various qualitative, quantitative, or semi quantitative scoring systems including without limitation residual cancer burden (Symmans et al. (2007) J. Clin. Oncol.25:4414-4422, the contents of which are incorporated herein by reference in its entirety) or Miller-Payne score (Ogston et al., (2003) Breast (Edinburgh, Scotland) 12:320-327, the contents of which are incorporated herein by reference in its entirety) in a qualitative fashion like “pathological complete response” (pCR), “clinical complete remission” (cCR), “clinical partial remission” (cPR), “clinical stable disease” (cSD), or “clinical progressive disease” (cPD).

[0138] In some embodiments, methods and compositions for sRCC detection provided herein can inform treatment and / or payment (e.g., reimbursement for or reduction of cost of medical care, such as detecting or treatment) decisions and / or actions, e.g., by individuals, healthcare facilities, healthcare practitioners, health insurance providers, governmental bodies, or other parties interested in healthcare cost.

[0139] In some embodiments, methods and compositions for sRCC detection provided herein can inform decision making relating to whether health insurance providers reimburse a healthcare cost payer or recipient (or not), e.g., for (1) detection itself (e.g., reimbursement for detecting otherwise unavailable, available only for periodic / regular detecting, or available only for temporally- and / or incidentally- motivated detecting); and / or for (2) treatment, including initiating,DFS-33825 (DFCI 3382.W01WO) maintaining, and / or altering therapy, e.g., based on detected sRCC status and / or subtype. For example, in some embodiments, methods and compositions for sRCC detection provided herein are used as the basis for, to contribute to, or support a determination as to whether a reimbursement or cost reduction will be provided to a healthcare cost payer or recipient. In some instances, a party seeking reimbursement or cost reduction can provide results of sRCC detection conducted in accordance with the present specification together with a request for such reimbursement or reduction of a healthcare cost. In some instances, a party making a determination as to whether or not to provide a reimbursement or reduction of a healthcare cost will reach a determination based in whole or in part upon receipt and / or review of results of sRCC detection conducted in accordance with the present specification.

[0140] To classify sarcomatoid features of RCC patient plasma, an assay for histone- associated marks (e.g., H3K27ac, or H3K4me2) can be performed on patient plasma. Sequencing reads can be aligned to a reference genome (e.g., human genome build hg19). For a given sample, reads mapping to filtered tissue-specific genomic loci of the present disclosure (e.g., for all or a subset of genomic loci provided in one or more, or all, of SEQ ID NOs: 7-58011, for respective modifications and RCC subtypes) are collected. Read counts at assayed sites are normalized to the total number of reads overlapping all ChIP-seq peaks across all RCC subtypes. Normalized read counts represent scores for each subtype, indicating the relative modification and / or activity of histology-specific genomic loci. These scores can be compared to scores from a reference panel of RCC patient plasma. A single predicted histologic subtype can be assigned based on cutoffs established from training data that maximize classification accuracy in a training cohort.

[0141] For the avoidance of any doubt, those of skill in the art will appreciate from the present disclosure that methods and compositions for RCC detection of the present specification are at least for in vitro use. Accordingly, all aspects and embodiments of the present disclosure can be performed and / or used at least in vitro.

[0142] Those of skill in the art will also appreciate that, in certain embodiments, methods of the present disclosure can be implemented on and / or in conjunction with a computer program and computer system. A computer system can also store and manipulate data generated by methods of the present disclosure that comprise a plurality of genomic locus modification status and / or accessibility status changes / profiles, which data can be used by a computer system in implementing methods disclosed herein. In certain embodiments, a computer system receives modification status and / or accessibility status data; (ii) stores the data; and (iii) compares the data in any number of ways described herein (e.g., analysis relative to appropriate references), e.g., to detect sRCC. In certain embodiments, a computer system (i) compares the genomic locus modification and / or accessibility status to a reference; and (ii) outputs an indication of whether the modification status and / orDFS-33825 (DFCI 3382.W01WO) accessibility status of the genomic locus is significantly different from the reference and / or provides a determination regarding RCC sarcomatoid differentiation.

[0143] Numerous types of computer systems can be used to implement methods of the present disclosure according to knowledge possessed by a skilled artisan in the bioinformatics and / or computer arts. Several software components can be loaded into memory during operation of such a computer system. The software components can comprise both software components that are standard in the art and components that are special to the present disclosure (e.g., dCHIP software described in Lin et al. (2004) Bioinformatics 20, 1233-1240, the contents of which are incorporated herein by reference in its entirety; radial basis machine learning algorithms (RBM) known in the art). Methods of the present disclosure can also be programmed or modeled in mathematical software packages that allow symbolic entry of equations and high-level specification of processing, including specific algorithms to be used, thereby freeing a user of the need to procedurally program individual equations and algorithms. Such packages include, e.g., Matlab from Mathworks (Natick, Mass.), Mathematica from Wolfram Research (Champaign, Ill.), S-Plus from MathSoft (Seattle, Wash.), R from R Foundation for Statistical Computing (Vienna, Austria), Python from Python Software Foundation (Wilmington, DE), or Perl from Perl Foundation (Holland, MI). Other programs contemplated herein are disclosed in the Examples. In certain embodiments, a computer comprises a database for storage of genomic locus modification status and / or accessibility status data. Such stored profiles can be accessed and used to perform comparisons of interest at a later point in time. For example, genomic locus modification status and / or accessibility status data of a sample derived from the non-cancerous tissue of a subject and / or profiles generated from population-based distributions of informative loci of interest in relevant populations of the same species can be stored and later compared to that of a sample derived from the cancerous tissue of the subject or tissue suspected of being cancerous of the subject. In addition to the exemplary program structures and computer systems described herein, other, alternative program structures and computer systems will be readily apparent to the skilled artisan.

[0144] Various algorithms can be applied to the comparison, between samples and references, of the modification status and / or accessibility status of genomic loci differentially methylated in RCC. In various embodiments, an algorithm can be a single learning statistical classifier system. Other suitable statistical algorithms are well known to those of skill in the art. For example, learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (e.g., panel of markers of interest) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a classification tree (e.g., random forest) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem. Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (e.g., decision / classification trees such as random forests, classification and regression treesDFS-33825 (DFCI 3382.W01WO) (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action-value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming. Other learning statistical classifier systems include support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ). In certain embodiments, methods of the present disclosure can include sending classification results to a clinician, e.g., an oncologist.

[0145] In various embodiments, the area under the receiver operating characteristic (AUROC) for determining if a subject has or is at risk of developing sRCC is equal to or greater than 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, or 0.95. Kits

[0146] The present disclosure includes kits for detecting modification and / or accessibility of one or more genomic loci. Kits of the present disclosure can include, e.g., reagents such as buffers and / or antibodies useful in the detection of histone modification of genomic loci. In certain embodiments, a kit of the present disclosure can include at least one antibody that selective binds a histone modification selected from H3K9ac, H3K14ac, H3K18ac, H3K23ac, H3K27ac, H3K4me1, H3K4me2, or H3K4me3, or pan acetylation. A kit of the present disclosure can include instructional materials disclosing or describing the use of the kit in a method of sRCC detection and / or treatment disclosed herein. In various embodiments, a kit of the present disclosure can include one or more therapeutic agents useful in the treatment of sRCC, e.g., as disclosed herein, optionally in combination with instruction materials for treatment of sRCC. Systems

[0147] The present disclosure includes systems for detecting modification and / or accessibility of one or more genomic loci. In some embodiments, the present disclosure provides systems for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and / or DNA methylation at one or more genomic loci. Systems of the present disclosure can include a sequencer configured to generate a sequencing data set from a sample; and a non-transitory computer readable storage medium and / or a computer system.DFS-33825 (DFCI 3382.W01WO)

[0148] In some embodiments, the non-transitory computer readable storage medium is encoded with a computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform a method of the present disclosure.

[0149] In some embodiments, the computer system comprises a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform a method of the present disclosure.

[0150] In some embodiments, the sequencer is configured to generate a Whole Genome Sequencing (WGS) data set from the sample. In some embodiments, the system also includes a sample preparation device configured to prepare the sample for sequencing from a biological sample, optionally a liquid biopsy sample. The sample preparation device may include reagents for quantifying one or more histone modifications, chromatin accessibility, binding of one or more transcription factors, and / or DNA methylation at one or more genomic loci in cell-free DNA (cfDNA) from the biological sample, optionally the liquid biopsy sample.

[0151] Systems of the present disclosure can include, e.g., reagents such as buffers and / or antibodies useful in the detection and quantification of histone modifications. In certain embodiments, a system of the present disclosure can include at least one antibody that selective binds H3K4me3 modifications. In certain embodiments, a system of the present disclosure can include at least one antibody that selective binds H3K27ac modifications. A system of the present disclosure can include instructional materials disclosing or describing the use of the system in a method of determining sarcomatoid RCC status and / or treatment disclosed herein.

[0152] In some embodiments, a system of the present disclosure comprises reagents for quantifying one or more histone modifications and / or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from SEQ ID NOs: 7-58011.

[0153] In some embodiments, the system comprises reagents for quantifying H3K27ac for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 7-25925. In some embodiments, the system comprises one or more antibodies for use in ChIP-seq, optionally wherein the one or more antibodies specifically bind H3K27ac-modified histones.

[0154] In some embodiments, the system comprises reagents for quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 25926-58011. In some embodiments, the system comprises one or more methyl-binding domains for use in MBD-seq.

[0155] In some embodiments, the system comprises reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample. In some embodiments, the sequencer comprises reagents for library preparation for sequencing. In some embodiments, the sequencer comprises reagents for sequencing. In some embodiments, the system comprises instructions for determining if a subject has sarcomatoid renal cell cancer.DFS-33825 (DFCI 3382.W01WO) CERTAIN DEFINITIONS

[0156] Unless otherwise defined, all terms of art, notations, and other scientific terms or terminology used herein are intended to have the meanings commonly understood by those of skill in the art to which this application pertains.

[0157] Further, nomenclature used herein is as commonly used in the art as can be seen by reference to, e.g., informatics.jax.org / mgihome / nomen / gene.shtml and genenames.org / .

[0158] As used herein, the following terms have the meanings as ascribed to them below, unless specified otherwise or obvious from context.

[0159] The term "or" is understood to be inclusive. Unless specifically stated or obvious from context, as used herein, the terms "a", "an", and "the" are understood to be singular or plural.

[0160] About: The term “about” is understood as within a range of normal tolerance in the art, for example within 2 standard deviations of the mean. “About” may be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. All numerical values provided herein are modified by the term about. Ranges provided herein are understood to be shorthand for all values within the range, including fractions / decimals. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50.

[0161] Administration: As used herein, the term “administration” refers to the act of the attending physician or caregiver, prescribing the agent for administration and thereby causing the application of an agent to a subject, through ingestion, infusion, injection, or any other means, whether self-administered or administered by a clinician or other qualified care giver

[0162] Agent: As used herein, the term “agent” may refer to any chemical entity, including without limitation any of one or more of an atom, molecule, compound, amino acid, polypeptide, nucleotide, nucleic acid, protein, protein complex, liquid, solution, saccharide, polysaccharide, lipid, or combination or complex thereof.

[0163] Antibody: As used herein, the term “antibody” refers to a polypeptide that includes one or more canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular antigen (e.g., a heavy chain variable domain, a light chain variable domain, and / or one or more CDRs). Thus, the term antibody includes, without limitation, human antibodies, non-human antibodies, synthetic and / or engineered antibodies, fragments thereof, and agents including the same. Antibodies can be naturally occurring immunoglobulins (e.g., generated by an organism reacting to an antigen). Synthetic, non-naturally occurring, or engineered antibodies can be produced by recombinant engineering, chemical synthesis, or other artificial systems or methodologies known to those of skill in the art.DFS-33825 (DFCI 3382.W01WO)

[0164] As is well known in the art, typical human immunoglobulins are approximately 150 kD tetrameric agents that include two identical heavy (H) chain polypeptides (about 50 kD each) and two identical light (L) chain polypeptides (about 25 kD each) that associate with each other to form a structure commonly referred to as a “Y-shaped” structure. Typically, each heavy chain includes a heavy chain variable domain (VH) and a heavy chain constant domain (CH). The heavy chain constant domain includes three CH domains: CH1, CH2 and CH3. A short region, known as the “switch”, connects the heavy chain variable and constant regions. The “hinge” connects CH2 and CH3 domains to the rest of the immunoglobulin. Each light chain includes a light chain variable domain (VL) and a light chain constant domain (CL), separated from one another by another “switch.” Each variable domain contains three hypervariable loops known as “complement determining regions” (CDR1, CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1, FR2, FR3, and FR4). In each VH and VL, the three CDRs and four FRs are arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, and FR4. The variable regions of a heavy and / or a light chain are typically understood to provide a binding moiety that can interact with an antigen. Constant domains can mediate binding of an antibody to various immune system cells (e.g., effector cells and / or cells that mediate cytotoxicity), receptors, and elements of the complement system. Heavy and light chains are linked to one another by a single disulfide bond, and two other disulfide bonds connect the heavy chain hinge regions to one another, so that the dimers are connected to one another and the tetramer is formed. When natural immunoglobulins fold, the FR regions form the beta sheets that provide the structural framework for the domains, and the CDR loop regions from both the heavy and light chains are brought together in three-dimensional space so that they create a single hypervariable antigen binding site located at the tip of the Y structure.

[0165] In some embodiments, an antibody is a polyclonal, monoclonal, monospecific, or multispecific antibody (e.g., a bispecific antibody). In some embodiments, an antibody includes at least one light chain monomer or dimer, at least one heavy chain monomer or dimer, at least one heavy chain-light chain dimer, or a tetramer that includes two heavy chain monomers and two light chain monomers. Moreover, the term “antibody” can include (unless otherwise stated or clear from context) any art-known constructs or formats utilizing antibody structural and / or functional features including without limitation intrabodies, domain antibodies, antibody mimetics, Zybodies®, Fab fragments, Fab’ fragments, F(ab’)2 fragments, Fd’ fragments, Fd fragments, isolated CDRs or sets thereof, single chain antibodies, single-chain Fvs (scFvs), disulfide-linked Fvs (sdFv), polypeptide-Fc fusions, single domain antibodies (e.g., shark single domain antibodies such as IgNAR or fragments thereof), cameloid antibodies, camelized antibodies, masked antibodies (e.g., Probodies®), affybodies, anti-idiotypic (anti-Id) antibodies (including, e.g., anti-anti-Id antibodies), Small Modular ImmunoPharmaceuticals (SMIPs), single chain or Tandem diabodies (TandAb®), VHHs, Anticalins®, Nanobodies®, minibodies, BiTE®s, ankyrin repeat proteins or DARPINs®, Avimers®, DARTs, TCR-DFS-33825 (DFCI 3382.W01WO) like antibodies, Adnectins®, Affilins®, Trans-bodies®, Affibodies®, TrimerX®, MicroProteins, Fynomers®, Centyrins®, KALBITOR®s, CARs, engineered TCRs, and antigen-binding fragments of any of the above.

[0166] In various embodiments, an antibody includes one or more structural elements recognized by those skilled in the art as a complementarity determining region (CDR) or variable domain. In some embodiments, an antibody can be a covalently modified (“conjugated”) antibody (e.g., an antibody that includes a polypeptide including one or more canonical immunoglobulin sequence elements sufficient to confer specific binding to a particular antigen, where the polypeptide is covalently linked with one or more of a therapeutic agent, a detectable moiety, another polypeptide, a glycan, or a polyethylene glycol molecule). In some embodiments, antibody sequence elements are humanized, primatized, chimeric, etc., as is known in the art.

[0167] An antibody including a heavy chain constant domain can be, without limitation, an antibody of any known class, including but not limited to, IgA, secretory IgA, IgG, IgE and IgM, based on heavy chain constant domain amino acid sequence (e.g., alpha (^), delta (^), epsilon (^), gamma (^) and mu (µ)). IgG subclasses are also well known to those in the art and include but are not limited to human IgG1, IgG2, IgG3 and IgG4. “Isotype” refers to the Ab class or subclass (e.g., IgM or IgG1) that is encoded by the heavy chain constant region genes. As used herein, a “light chain” can be of a distinct type, e.g., kappa (^) or lambda (^), based on the amino acid sequence of the light chain constant domain. In some embodiments, an antibody has constant region sequences that are characteristic of mouse, rabbit, primate, or human immunoglobulins. Naturally-produced immunoglobulins are glycosylated, typically on the CH2 domain. As is known in the art, affinity and / or other binding attributes of Fc regions for Fc receptors can be modulated through glycosylation or other modification. In some embodiments, an antibody may lack a covalent modification (e.g., attachment of a glycan) that it would have if produced naturally. In some embodiments, antibodies produced and / or utilized in accordance with the present invention include glycosylated Fc domains, including Fc domains with modified or engineered such glycosylation.

[0168] Antibody fragment: As used herein, an “antibody fragment” refers to a portion of an antibody or antibody agent as described herein, and typically refers to a portion that includes an antigen-binding portion or variable region thereof. An antibody fragment can be produced by any means. For example, in some embodiments, an antibody fragment can be enzymatically or chemically produced by fragmentation of an intact antibody or antibody agent. Alternatively, in some embodiments, an antibody fragment can be recombinantly produced (i.e., by expression of an engineered nucleic acid sequence. In some embodiments, an antibody fragment can be wholly or partially synthetically produced. In some embodiments, an antibody fragment (particularly an antigen-binding antibody fragment) can have a length of at least about 50, 60, 70, 80, 90, 100, 110,DFS-33825 (DFCI 3382.W01WO) 120, 130, 140, 150, 160, 170, 180, 190 amino acids or more, in some embodiments at least about 200 amino acids.

[0169] Sample: As used herein, the term “sample” typically refers to cells, tissue or fluid obtained or derived from an animal or human. In some embodiments, a sample can be or include blood, blood components, cell-free DNA (cfDNA), circulating-tumor DNA (ctDNA), ascites, biopsy samples, surgical specimens, cell-containing body fluids, sputum, saliva, feces, urine, cerebrospinal fluid, peritoneal fluid, pleural fluid, lymph, gynecological fluids, secretions, excretions, skin swabs, vaginal swabs, oral swabs, nasal swabs, washings or lavages such as a ductal lavages or bronchoalveolar lavages, aspirates, scrapings, or bone marrow. In some embodiments, a sample is or includes cells obtained from a single subject or from a plurality of subjects. A sample can be a “primary sample” obtained directly from a subject, or can be a “processed sample”, i.e., a sample that was derived from a primary sample, e.g., via dilution, purification, mixing with one or more reagents, or any other processing step(s) as described herein.

[0170] Blood component: As used herein, the term “blood component” refers to any component of whole blood, including red blood cells, white blood cells, plasma, platelets, endothelial cells, mesothelial cells, epithelial cells, and cell-free DNA. Blood components also include the components of plasma, including proteins, metabolites, lipids, nucleic acids, and carbohydrates, and any other cells that can be present in blood, e.g. due to pregnancy, organ transplant, infection, injury, or disease.

[0171] Cancer: As used herein, the terms “cancer,” “malignancy,” “tumor,” and “carcinoma,” are used interchangeably to refer to a disease, disorder, or condition in which cells exhibit or exhibited relatively abnormal, uncontrolled, and / or autonomous growth, so that they display or displayed an abnormally elevated proliferation rate and / or aberrant growth phenotype. In some embodiments, a cancer can include one or more tumors. In some embodiments, a cancer can be or include cells that are precancerous (e.g., benign), malignant, pre-metastatic, metastatic, and / or non- metastatic. In some embodiments, a cancer can be or include a solid tumor. In some embodiments, a cancer is or includes a renal cell carcinoma (RCC) (e.g., RCC of sarcomatoid subtype). In some embodiments, a cancer can be associated with sarcomatoid features.

[0172] Combination therapy: As used herein, the term “combination therapy” refers to administration to a subject of to two or more agents or regimens such that the two or more agents or regimens together treat a disease, condition, or disorder of the subject. In some embodiments, the two or more therapeutic agents or regimens can be administered simultaneously, sequentially, or in overlapping dosing regimens. Those of skill in the art will appreciate that combination therapy includes but does not require that the two agents or regimens be administered together in a single composition, nor at the same time.DFS-33825 (DFCI 3382.W01WO)

[0173] Differentially modified: As used herein, the term “differentially modified” describes a genomic locus for which histone modification status and / or DNA methylation status differs between a first condition or sample and a second condition or sample (e.g., a standard or reference). A differentially modified genomic locus can include a greater or smaller number or frequency of histone modification(s) and / or DNA methylations under a selected condition of interest, such as sarcomatoid RCC, as compared a reference state, such as epithelioid RCC.

[0174] Methylation Status: As used herein, “methylation status” of a genomic locus refers to the frequency with which DNA sequences corresponding to the genomic locus are identified in an assay for detection of DNA methylated sequences and / or the density (e.g., the measured density) of DNA methylation corresponding to the genomic locus. Methylation status can be determined by various assays known in the art, including without limitation Bisulfite sequencing (BS-Seq), Whole Genome Bisulfite Sequencing (WGBS), Methylated DNA ImmunoPrecipitation sequencing (MeDIP- seq), or Methyl-CpG-Binding Domain sequencing (MBD-seq). Where two samples are separately analyzed by the same assay or comparable assays for detection of DNA methylated sequences, differences in methylation status of genomic loci can be detected. Methylation status can be compared to a standard or reference. A sample that has a methylation status that differs from a standard or reference can be referred to as differentially modified.

[0175] Modification Status or Histone Modification Status: As used herein, “modification status” or “histone modification status” of a genomic locus refers to the frequency with which DNA sequences corresponding to the genomic locus are identified in an assay for detection of DNA sequences associated with histones bearing one or more histone modifications (e.g., one or more particular histone modifications) and / or the density (e.g., the measured density) of histone modifications (e.g., one or more particular histone modifications) corresponding to the genomic locus. Modification status can be determined by various assays known in the art, including without limitation CHiP-seq as one example. Other well-known assays include CUT&RUN (Cleavage Under Targets and Release Using Nuclease) sequencing and CUT&Tag (Cleavage Under Targets and Tagmentation). Where two samples are separately analyzed by the same assay or comparable assays for detection of DNA sequences associated with histones bearing one or more histone modifications (e.g., one or more particular histone modifications), differences in modification status of genomic loci can be detected. Modification status can be compared to a standard or reference. A sample that has a modification status that differs in modification status or histone modification status from a standard or reference can be referred to as differentially modified.

[0176] Regulatory sequence: As used herein in the context of expression of a nucleic acid coding sequence, a regulatory sequence is a nucleic acid sequence that controls expression of a coding sequence. In some embodiments, a regulatory sequence can control or impact one or more aspects of gene expression (e.g. cell-type-specific expression, inducible expression, etc.).DFS-33825 (DFCI 3382.W01WO)

[0177] Subject: As used herein, the term “subject” refers to an organism, typically a mammal (e.g. a human). In some embodiments, a subject is suffering from a disease, disorder or condition (e.g., RCC, e.g., sRCC). In some embodiments, a subject is susceptible to a disease, disorder, or condition. In some embodiments, a subject displays one or more symptoms or characteristics of a disease, disorder or condition. In some embodiments, a subject is not suffering from a disease, disorder or condition. In some embodiments, a subject does not display any symptom or characteristic of a disease, disorder, or condition. In some embodiments, a subject has one or more features characteristic of susceptibility to or risk of a disease, disorder, or condition. In some embodiments, a subject is a subject that has been tested for a disease, disorder, or condition, and / or to whom therapy has been administered. The terms "subject" and “patient” are used interchangeably herein and indicate a human.

[0178] Therapeutic agent: As used herein, the term “therapeutic agent” refers to any agent that elicits a desired pharmacological effect when administered to a subject. In some embodiments, an agent is considered to be a therapeutic agent if it demonstrates a statistically significant effect across an appropriate population. In some embodiments, the appropriate population can be a population of model organisms or a human population. In some embodiments, an appropriate population can be defined by various criteria, such as a certain age group, gender, genetic background, preexisting clinical conditions, etc. In some embodiments, a therapeutic agent is a substance that can be used for treatment of a disease, disorder, or condition (e.g., RCC, e.g., sRCC). In some embodiments, a therapeutic agent is an agent that has been or is required to be approved by a government agency before it can be marketed for administration to humans. In some embodiments, a therapeutic agent is an agent for which a medical prescription is required for administration to humans.

[0179] Therapeutically effective amount: As used herein, “therapeutically effective amount” or “effective amount” refers to a quantity sufficient to reduce the signs / symptoms of cancer, e.g., sRCC. When an effective amount or therapeutically effective amount is indicated, the precise amount of the indicated agents to be administered may be determined by a physician with consideration of the potency of the agent(s) and the age, weight, the extent of progression of disease, and / or other condition(s) of the patient (subject)..

[0180] Treatment: As used herein, the terms “treat,” “treating,” “treatment,” and the like refer to reducing or ameliorating sRCC and / or symptoms associated therewith. It will be appreciated that, although not precluded, treating sRCC does not require that the symptoms associated therewith be completely eliminated. As used herein "treatment" or "treating," includes any beneficial or desirable effect on the symptoms or pathology of sRCC and may include even minimal reductions in one or more measurable markers of the disease.DFS-33825 (DFCI 3382.W01WO) EXAMPLES

[0181] The present Examples demonstrate that significant epigenomic reprogramming occurs in sarcomatoid differentiation of RCC, identifies candidate TFs driving sRCC, and provides methods and systems for blood-based detection of sarcomatoid differentiation to guide therapy selection. Example 1: sRCC-Specific Differentially Modified Genomic Loci and Master Transcription Factors

[0182] A total of 107 tissue- and plasma-based epigenomic libraries were generated from 50 individuals across different epigenomic assays. Pathologically reviewed tissue samples were derived from 31 individual patients with ccRCC (10 sarcomatoid and 21 epithelioid). Plasma was collected from 19 individuals: 5 with sRCC, 5 with epithelioid ccRCC, and 9 healthy volunteers. Sarcomatoid and epithelioid ccRCC exhibit distinct epigenomic profiles

[0183] Using tissue samples from ccRCC patients (Table S1), the epigenomic landscape of sRCC was examined (FIG.2A). Specifically, chromatin immunoprecipitation sequencing (ChIP-seq) was performed for post-translational histone modifications (H3K27ac and H3K4me2) and methylated CpG dinucleotide sequencing (MeDIP-seq) for DNA methylation. H3K27ac is associated with active promoters and enhancers, while H3K4me2 ChIP-seq maps active and poised promoters and enhancers. Peaks for each epigenetic mark overlapped expected genomic annotations. For example, 61% of H3K27ac peaks overlapped with non-promoter regions of annotated genes, consistent with the capture of promoter-distal active enhancers (FIG.2B). H3K27ac and H3K4me2 ChIP-seq signals were strongly correlated between all the samples (Pearson correlation, r^=^0.83, p < 2.2x10-16) but overlapped different genomic annotations, with H3K4me2 capturing more promoters and H3K27ac capturing more promoter-distal CREs (FIG.2B, FIG.2C). Principal component analyses (PCA) (FIGs.2D-2F) and unsupervised hierarchical clustering (FIG.7) of H3K27ac and H3K4me2 peaks and methylated CpG islands cleanly separated epithelioid and sRCC samples. This highlighted the consistent differences in the epigenomic features between groups, with 25,919 H3K27ac, 44,133 H3K4me2, and 51,464 MeDIP peaks upregulated in sRCC or epithelioid ccRCC (q < 0.05, FIG.2G).

[0184] Unlike other epigenetic marks, H3K27ac enables identification of active programs of gene regulation by capturing active promoters and enhancers. To evaluate the biological significance of sRCC- and epithelioid-enriched H3K27ac CREs (FIG.3A), the enrichment of gene ontology (GO) terms of genes near differential CREs was evaluated using GREAT. (McLean, C. Y. et al. Nature Biotechnology 28, 495-501, (2010)). Significant enrichment at the top 1,621 sRCC CREs (q < 0.01, LFC > 1) was observed for gene pathways involved in immune cell activation and stimulation of an immune response (FIG.3B). These results demonstrate that sRCC tumors show increased inflammatory gene expression signatures and CD8+ T cell infiltration, and have higher expression of PD-1 / L1, which may partly explain their responsiveness to ICIs. Additionally, enrichment wasDFS-33825 (DFCI 3382.W01WO) observed for processes implicated in shifts in cellular morphology and extracellular matrix organization A similar analysis of the top 3,386 epithelioid-specific H3K27ac CREs (q < 0.01, LFC > 1) showed enrichment for genes involved in the response to hypoxia (Fig.3C), likely reflecting the activity of hypoxia-inducible factors (i.e., HIF2a) that are implicated in angiogenesis and RCC carcinogenesis.

[0185] Next, differential H3K27ac CREs (q < 0.01, LFC > 1) were analyzed for enrichment of nucleotide motifs to identify TFs that may drive CRE activation. The top three motifs that were highly enriched at sarcomatoid-specific CREs were FOSL1, ETV4, and E2F7 (FIG.3D). The FOSL1 gene demonstrated a higher H3K27ac signal in sarcomatoid vs. epithelioid ccRCC samples (FIG.3E). Conversely, motif enrichment analysis of putative enhancers downregulated in sRCC identified FOXO1, EPAS1, and HNF1^ as the top candidate TFs (Fig.3F). EPAS1, which encodes HIF2a, is densely marked with H3K27ac in epithelioid vs. sRCC and is a central TF in ccRCC oncogenesis (FIG.3G). Overall, differential CREs pointed to biologically plausible TFs that may activate EMT programs in sRCC and implicated decreased activity of developmental (HNF1^) and hypoxia related (HIF2^) TFs in sarcomatoid differentiation. Candidate sarcomatoid TFs predict clinical outcomes independent of histologic subtyping

[0186] Having identified TFs that may drive EMT-associated gene regulatory programs of sRCC, next it was tested whether their expression levels in RCC are associated with clinical outcomes. Transcriptomic data from the TCGA cohort (Ricketts, C. J. et al. The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep 23, 313- 326.e315, (2018)) and two large phase III randomized clinical trials: Javelin Renal 101 (JR101)(Motzer, R. J. et al. New England Journal of Medicine 380, 1103-1115, (2019)) and IM151 cohorts were analyzed. In JR101, patients were randomized to the avelumab plus axitinib combination or sunitinib arms, while in the IM151 cohort, they were randomized to atezolizumab plus bevacizumab combination or sunitinib arms. Genes significantly upregulated or downregulated in sRCC tumors were identified from the three cohorts (FDR q < 0.05) and selected the upregulated TFs. Strikingly, the top three TFs that were previously identified (FOSL1, E2F7, and ETV4) were upregulated in sRCC and were associated with improved progression-free survival (PFS) in patients with RCC who were treated with ICI plus TKI combinations vs. TKIs alone (Table S2).

[0187] Further analysis was performed on FOSL1 since it was the top-ranked TF in the motif analysis, had an evident increase in the H3K27ac signal at the FOSL1 gene locus, and had the strongest association with shorter PFS in the clinical trials. FOSL1 expression was higher in sRCC vs. epithelioid ccRCC in the TCGA KIRC cohort (q = 4.91x10-11, LFC = 1.73), JR101 (q = 8.51x10-12, LFC = 1.23), and the IM151 cohort (q = 2.11x10-11, LFC = 0.64) (FIG.4A). In a multivariable analysis accounting for the IMDC risk groups, patients with high (> median) FOSL1-expressing tumors had improved PFS when treated with ICI plus TKI combinations vs. sunitinib in both theDFS-33825 (DFCI 3382.W01WO) JR101 cohort (adjusted HR: 0.53, 95%CI: 0.41 – 0.70, p <0.001, FIG.4B) and the IM151 cohort (adjusted HR: 0.71, 95%CI: 0.56 – 0.90, p = 0.004; Fig.8A). Patients with low (< median) FOSL1- expressing tumors had similar PFS regardless of ICI use in both JR101 (adjusted HR: 0.85; 95%CI: 0.63 - 1.13; p = 0.25, FIG.4B) and IM151 (adjusted HR: 1.02; 95%CI: 0.80 - 1.32, p = 0.86, FIG. 8A). An interaction term was included between treatment arms (ICI + TKI vs. TKI) and FOSL1 expression (high vs. low) in the Cox regression analysis for PFS, with treatment arm and baseline FOSL1 expression as two other independent variables. Compared to sunitinib, ICI + TKI combinations improved PFS for patients with high FOSL1-expressing tumors but offered no benefit for patients with low FOSL1 in JR101 (interaction p-value = 0.03) and IM151 (interaction p-value = 0.04) (FIG.4B, Table S2). These results indicate that FOSL1 expression may be a potential predictive biomarker for prolonged PFS in patients receiving ICIs + VEGFR TKIs in RCC.

[0188] Notably, while FOSL1 was upregulated in sRCC, many epithelioid ccRCC tumors also exhibited elevated FOSL1 expression levels (FIG.4A). In this context, it was hypothesized that these tumors may behave similarly to sRCC, with worse outcomes with sunitinib compared to improved outcomes with ICI plus TKI combinations. Accordingly, patients were stratified into three groups: epithelioid ccRCC with low FOSL1 or high FOSL1, and sRCC. Strikingly, the OS (time from diagnosis to death or loss of follow-up) of patients with epithelioid ccRCC and high FOSL1 was worse compared to those with low FOSL1 (median OS = 21.2 vs.37.4 months; HR: 1.9; 95% CI: 1.1 – 3.5; p = 0.03) (Fig.4C) and similar to those with sRCC tumors (p = 0.1) in the metastatic ccRCC TCGA cohort. Furthermore, patients with epithelioid ccRCC and high FOSL1 had shorter PFS compared to those with low FOSL1 in both the JR101 (median PFS = 6.5 vs.11.3 months; adjusted HR: 1.54; 95% CI: 1.14 – 2.06; p = 0.004) and IM151 cohort subgroups (median PFS = 8.3 vs.11.8 months; adjusted HR: 1.35; 95% CI: 1.03 – 1.76; p = 0.03, Fig.4D), and similar PFS compared to sRCC tumors in the sunitinib arm of both trials (Table S3). On the other hand, there were no significant differences in PFS between epithelioid ccRCC subgroups in the ICI arms, suggesting that all subgroups derived similar benefit (Fig.8B). Taken together, the above findings indicate that elevated FOSL1 expression is associated with worse outcomes when treated with TKI-only regimens, similar to sRCC, but outcomes may be improved with the addition of ICIs. These findings implicate FOSL1 as a biomarker and are in agreement with clinical outcomes in patients with ccRCC, independent of histopathologic subtyping. FOSL1 induces a sarcomatoid-like gene expression profile in epithelioid ccRCC

[0189] To test whether FOSL1 drives processes observed in sRCC tumors (i.e., cell cycle progression), FOSL1 expression was endogenously activated in the Caki-1 ccRCC cell line using CRISPRa.

[0190] Differentially expressed genes (q < 0.05) were compared between triplicates of FOSL1-expressing vs. control Caki-1 cells as described in the methods set forth in Example 2 (FIG.DFS-33825 (DFCI 3382.W01WO) 5A). Overall, 357 genes were upregulated and 540 downregulated in FOSL1 CRISPRa vs. control Caki-1 cells. As expected, FOSL1 was upregulated in FOSL1 CRISPRa cells (LFC = 0.59, q = 1.62x10-10), but strikingly, EPAS1 was downregulated (LFC = -0.40, q = 9.95x10-6). The downregulation of EPAS1 suggests that FOSL1 may be a causative factor in the suppressed hypoxia and angiogenesis signaling observed in sRCC (FIG.5B). Gene set enrichment analysis (GSEA) of FOSL1 CRISPRa vs. negative control Caki-1 cells revealed upregulation of seven gene sets (FIG.5C) with enrichment of programs related to cell cycle (E2F targets, G2M checkpoints) or proliferation (MYC targets).

[0191] Using gene expression data from the JR101 trial, a set of the top 500 genes that were upregulated in sRCC and a set of the top 500 genes upregulated in epithelioid RCC tumors were defined. GSEA revealed that the sarcomatoid gene set is enriched among upregulated genes in FOSL1 CRISPRa vs. control Caki-1 cells with a normalized enrichment score (NES) = 2.06, q <0.001 (FIG. 5D). The epithelioid ccRCC gene set is similarly enriched among the downregulated genes (NES = - 1.43, q = 0.01, FIG.9). In summary, these data demonstrate that overexpression of a single TF (FOSL1) upregulates transcriptional programs observed in sRCC. Epigenomic signatures of sRCC are detectable from circulating plasma nucleosomes

[0192] Detecting sarcomatoid features in RCC tumors is clinically relevant because sRCC has a poor response to TKIs and heightened response to ICIs, the mainstays of RCC treatment. sRCC detection from a tumor biopsy is challenging due to spatial heterogeneity and potential sampling errorsIt was investigated whether epigenomic features of sRCC could be detected in patient plasma as a proxy for histopathologic sarcomatoid features. Using a recently described liquid biopsy approach for epigenomic profiling from 1 mL of plasma (Baca, S. C. et al. Nature Medicine, doi:10.1038 / s41591-023-02605-z (2023)), H3K4me3, H3K27ac and DNA Methylation were profiled in plasma samples from an independent group of patients with sarcomatoid ccRCC (n=5), epithelioid ccRCC (n=5), and healthy volunteers without a history of cancer (n=9) (Table S4, FIGs.6A-6B). The circulating tumor DNA (ctDNA) content in plasma samples was inferred by applying ichorCNA to LPWGS data. The average (standard deviation) estimated ctDNA content was 0.024 (± 0.022) in plasma samples from patients with RCC (n=10) and 0 (± 0) in plasma samples of healthy volunteers (n=9) (Table S4). Plasma H3K4me3 signal was elevated at the PAX8 gene locus in patients with RCC compared to healthy volunteers, regardless of the presence or absence of sarcomatoid features (FIG. 6C), indicating that epigenomic signals from RCC cells were sampled. Plasma H3K27ac signal was elevated in RCC patient plasma at RCC-specific regulatory elements. These findings confirmed that this plasma-based epigenomic assay can detect RCC-specific signals.

[0193] Next, it was evaluated whether sarcomatoid-specific epigenomic features in plasma could be captured. The set of sarcomatoid-enriched DMRs in tissue samples were focused on to inform cfMeDIP-seq in plasma. It was found that there were higher aggregate methylation values atDFS-33825 (DFCI 3382.W01WO) these sites in sRCC plasma samples compared to plasma samples of patients with epithelioid ccRCC (p = 7.9 x 10-3) or healthy volunteers (p = 4.7 x 10-4, FIG.6E). Similarly, the H3K27ac cfChIP-seq signal was increased at differential H3K27ac sites (derived from tissue samples) in plasma of patients with sRCC vs. in the plasma of patients with epithelioid ccRCC tumors (p = 7.9 x 10-3) or in the plasma of healthy volunteers (p = 3.4 x 10-4, FIG.6F). Importantly, an increase H3K27ac in plasma at binding sites for HIF2^ was observed in epithelioid tumors compared to sRCC (p = 7.9 x 10-3) and healthy plasma (p = 9.9 x 10-4, FIG.6G). These findings align with the observations from the epigenomic and transcriptomic datasets, showing that sarcomatoid differentiation involves down- regulation of EPAS1 and HIF2^ activity. Overall, these results serve as proof of concept for detecting sRCC in plasma using minimally invasive liquid biopsy techniques. Discussion

[0194] These results demonstrate that highly recurrent cis-regulatory programs are activated in sRCC compared to epithelioid ccRCC. These regulatory programs likely drive differential gene expression known to exist between these groups. Characterization of sRCC-enriched CREs enabled identification of candidate TFs that drive sRCC gene regulatory programs. Analyzing data from TCGA and two clinical trials, it was found that these TFs are upregulated in sRCC and that their expression levels are associated with worse clinical outcomes. FOSL1 expression was identified as a driver of sRCC and a predictive biomarker in RCC. Furthermore, the present example identified epigenomic fingerprints of sRCC in plasma based on circulating DNA methylation and histone modifications.

[0195] This further provides information related to the transcriptional regulation in sRCC by identifying key TFs that orchestrate transcriptional programs associated with sarcomatoid differentiation. Specific nucleotide motifs were enriched at sRCC CREs, implicating TFs that may bind to these CREs and activate regulatory networks associated with sarcomatoid differentiation. For example, the data reported herein shows that activation of FOSL1 expression in an epithelial ccRCC cell line (Caki-1) decreased expression of EPAS1 and upregulated genes involved with cell cycle progression and proliferation. Motif enrichment was also observed for ETV4, a member of the E26 transformation-specific (ETS) family of TFs that activate promoters and enhancers of genes involved in cell proliferation, differentiation, and apoptosis. ETV4 and FOSL1 are known to cooperate in promoting ccRCC metastasis and predict worse survival in ccRCC. Without being bound by theory, is it believed that downregulation of EPAS1, which encodes HIF2a, with forced expression of FOSL1 is indicative of a mechanism by which FOSL1 suppresses hypoxia-related or angiogenic pathways and, thus, decreased sensitivity to TKIs.

[0196] The TFs that identified in sarcomatoid differentiation through epigenomic profiling described herein showed elevated expression in sRCC tumors across multiple clinical trial cohorts. Patients with high FOSL1 gene expression levels had better outcomes with ICI+TKIs vs. TKIs alone,DFS-33825 (DFCI 3382.W01WO) whereas those with low FOSL1 levels had similar outcomes across both treatment arms. This was mainly explained by the decreased benefit of TKIs in FOSL1-high patients, mirroring findings in sRCC tumors. Importantly, FOSL1-high tumors without sarcomatoid differentiation also demonstrated poor prognosis compared to FOSL1-low tumors. This finding supports the conclusion that these tumors had epigenomic features of sRCC even though histologic sarcomatoid differentiation was not observed. Clinical stratification based on expression of sRCC TFs may thus augment classification provided by histopathologic findings alone.

[0197] Utilizing epigenomic profiling of circulating DNA methylation and histone modifications, the present example describes detection of signatures of sarcomatoid differentiation in patient plasma that distinguishes sRCC from epithelioid RCC in plasma samples. Importantly, these signatures were defined entirely using tumor tissue, bolstering biological plausibility and interpretability. In addition, signals of decreased enhancer activity at HIF2a binding sites were observed in sRCC plasma compared to epithelioid RCC plasma, consistent with observations from tumor tissue that HIF2a is downregulated in sRCC. Overall, these results show that epigenomic liquid biopsies in RCC can identify patients with sarcomatoid epigenomic features who would benefit from treatment with immune checkpoint inhibitor (ICI)-based regimens.

[0198] In summary, the present example demonstrated that sRCC and epithelioid ccRCC tumors have distinctive epigenomic features. These results established that specific TFs may drive aggressive clinical phenotypes of sRCC and validated FOSL1, a TF upregulated in sRCC, as a predictive biomarker of improved outcomes with ICIs independent of histopathological subtyping. Furthermore, the present example showed that FOSL1 expression induces biological processes seen in sRCC. In addition, it has been shown herein that decreased H3K27ac histone modification at EPAS1 (encoding HIF2a) motifs and binding sites is associated with the presence of sarcomatoid differentiation. Finally, epigenomic liquid biopsy techniques were applied to enable the detection of sRCC in 1 mL of plasma of patients with RCC. Collectively, these findings may improve clinical stratification and therapy selection for patients with RCC. Tables

[0199] The following tables are included in the present application in connection with this Example.DFS-33825 (DFCI3382.W01WO) PIDeM 2em4 K3 H c A72 K3 H diota mocra S atS 3 T 3 T 3 T 3 T 3 T 3 T 3 T 3 T 3 T 3 T 3 T 3 T 2 T 2 T 4 T 3 T 3 T 4 T 3 T 3 T 2 T 1 T e 9 5 9 5 9 6 9 6 9 4 9 4 9 5 9 5 9 6 9 9 9 9 9 9 9 9 9 9 9 9 9 g -0 -0 -0 -0 -0 -0 -0 -0 -06-7-7-6-6-5-6-5-5-7-3-5-6- A 5 5 6 6 4 4 5 5 6 06 07 07 06 06 05 06 05 05 07 03 05 06 ela exele e e e eemela elaamela ellalaam mela ellaamela ellalaam mela ela ela ela ela ela elaS F M MeF M MeFeF M MeF M MeFeF M M M M M M M ylllllllllllllllllllllllllllllllllll l l l lg oe e e e e e e e e e e e e e e e elelelelelelotCsiraCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCrel aCr Cr Cr Cr Cr Cr Cr Creaeaeaeaeaeaeaeae H C C C C C C C C C C C C ClClClClClClClClClC T r 8 T e 5 b 0 T 19 1 1 0 7 59 1 mu 2 7 7 2 2 6 4 3 2 6 8 8 6 0 0.K / K / 6 51 61 60 70 11 32 52 0 2 11NST 98K / 1 83 02 1 6 3 2 8 7 0 14 4 94 4 80 0T 0T 1T 1T 1T 1 1 31 11 20elC 1 5 6 3 3 5 7 7 0 4 0 T T T T T C 1 C C C C T 9T 0 1 1 9 9 2 1 0 1 C C C C C C Cb T T T T T T C C C C C C C C C C Ca R R R R R 2 S R R 2 S R 2 S R R R R R R R R R R R R TDFS-33825 (DFCI 3382.W01WO)PIDeata M D2em4 K3ata H DcA72 ata o o o o o o o o o D N N N N N N N N N e cruoS r yryryryryryryry y oa a a a a ar rma a au m Tirm Pirm Pirm Pirm Pirm Pirm Pirm Pirm PirP ndioiottaaitmnoecrrefafio o o o o o o o o S D N N N N N N N N N *ed arGIIIIII IIIIIIIIIIIIIX X X X X X X X X M M * M M M e x x 0 X M0 M M M 0 X x x g atNaNaNbNaN NNaNaNaS 1 T 1 T 1 T 1 T 1 T 1 T 1 T 1 T 1 T e 9 8 0 5 9 6 9 6 9 9 9 9 9 g -0 -0- -5-7-4-6-6- A 8 4 06 06 05 07 04 06 06 e xelela ela eamelela elaame e eemelalalaS M M F M F F M M M yl l l l l l l lgloelelelelelelele llelotCsiraCr Cr C C C C C Cel ael arel arel arel arel arel areae H C C C C C C ClClC reb mu 02 42 23 24 34 44 84 16 4 g N 2 T 0 2 C T 0 2 T 0 2 2 2 2 2 92 nilT 0T 0T 0T 0T 0T 0T p C C C C C C C C C C C C C C C C C C CmaR R R R R R R R R Rsta*DFS-33825 (DFCI 3382.W01WO)p n 3 8 4poit.0.1.1n 4 .08 .9.c0 0 0oi0t0 0 0 acraet rentinieuela3v.9 - 00.1.ula9.7 0 0v09.3 01.0 p - p ))bibininititin n u wo)u 3)4)w 1So) ) )S.sL1.0.0 .sL 2 3 8 2 6 0 i1.v. . .v x *- 13- 1 1 1 18 -8ve* -0 -7 -3 A R e H.6v 0.5.5B R.8.7.6A(0(0( ozH 0(0 0 ( 5 8 8 7 7et( (A 2 0 2 1.0.07.0( 0.10.8.01 1 1 0 5 R 1 JMI spu e 9oue rl00 30 2u4 9gk a v 0 0 00l-0 a 0 .0.0.v-0.1 6 00.1.si0 0rp00 0 0 p C D MIrofhdegih H)0) )gi ) ) )tsuj.70 .87 .7H 0 . 6 7 d *0 9.9.0 a- 0 - 0 -*0 - 0 - 1 -SF R 1 7 4 R 6 0 7 P H4.04.4.( 0 0 H5.06.06. r0of3(1( ( ( (e5.06.8 05.1 07.6 07.5 08.n 0 olaIK T.svICI2 1_ISF L 4 F 1 4 KelT S 7F T LS 7 2 VT F VTba O F E E O F 2 E T E R T H *DFS-33825 (DFCI 3382.W01WO)n a. .ino 5 3.3.8 1.56.14 n a. .ino 2 1.3.8 1.38.65 d 1 1 1 d 1 1 1 e MeM M M 8 5 9 0 5 9 3 7 9 10 N 71 31 3 61 5 6 1 5 1 N 71 6 5 1 6 91 4 9 1 6 1 51 R JMIC C C C C C C C C C C C C C RsRsRsR C s RsR C s R Rsp -n -n- -o C n n- -s- -o C p n n n n u o orn nC o nnC u o n o n C C o n o n C C G MP MPRsM MRsorG M MRsM MRsT P PT P PT P P T T T T T w h w h h h o Lgio Lgiwo LgiwogH H H LiH mrmArt iAtve sn x eANneB N pu metv U SozU Sora Amte aetgrerA ksiT TrC D MIro 1fL 13 LdSFeelT S F S O T Otsb F Fuja d T A *DFS-33825 (DFCI 3382.W01WO) stnem garftnem hcirneA N Dtckreaeb p mu n rod nevb A ydobitnadiotsaerm outcareaF S yg olotsiH.sv yh y y y y y y y C C h h h h h h h CtlC C C C C C C C a C C C C C C C C C CtlatlatlatlatlatlatlCeR R R R R R R R R Re e e e e eaeR H H H H H H H H e cr Iu CICICIk k k k k k k CICICICICICIC B o F F F F F F F F F F GnaB b GnaB b GnaBnb GaBnb GaBnb GaBnb Gab S D D D D D D D D D D Moib Moib Moib Moib Moib Moib Moib DIl4 1 4 a 6 3 9 8 4 07 37 1 2 7 47 57 3 2 1 4 5 6 4 1 6 8 4 8 9 8 0 4 6 7 55 u 7 0 1 74 95 9 9 9 9 9 0 0 1 4 5 8 1 di1 2 2 viC C C C C 2 2 2 2 2 3 C C C C C C C C C C 0 3 0 0 3 0 0 3 0 0 3 3 4 0 00 00 00 dR RC C C C C 1 1 1 1 1 1 1 nIR R RR R R R R P P P P P P P H H H H H H H I 4 1 4 0 3 1 4 5 23 24 54 1 4 8 6 D 6 3 9 8 4 7 7 7 7 7 _ 7 1 6 6 88 90 47 5 y 1 02 12 74 95 92 92 92 92 92 0 0 1 4 5 8 51 d C C C C C C C C C C 30 3 3 3 3 3 44uStC C C C C C C C C C P 0P 0P 0P 0P 0P 0P S R R R R R R R Rel_ _ _ _ _ _ _ _ R_ R_ H H H H H H H 72 72 72 72 72 72 72 72 72 7 _ 2 7 _ 2 7 _ 2 7 _ 2 7 _ _ _ 2 72 72 7b 2a K K K K K K K K K K K K K K K K K TDFS-33825 (DFCI 3382.W01WO) stnem garftnem hcirneA N Dtckreaeb mu n rod nevb A ydobitnadiotsaermo utcareaF S yg olotsiH.svyh yCtlh y tlh y tlC C C C C C C C C C h yh yh C CtltltlCaeaeaeC C C C C C C Ca a aH H H R R R R R R R R R Re e eR H H H e c k k r BnaBna I I I I I I I I I Ik k k BnaBnaBnu G b G C C C C C C C C C C G G Gaoob o F F F F F F F F F F b o b b S Mib Mib D D D D D D D D D D Mib Moib Moib DI3 l 0 82 2 2 5 a 76 2 4 8 6 1 4 8 4 0 3 1 4 5 3 4 4 7 30 9 7 9 7 7 7 7 7 1 6 6 u di50 90 1 2 12 4 5 9 9 9 9 9 03 03 13 C C C C 2 2 2 2 2 0 0 0 vi0 0 C C C C C C C C C C C 0 0 0 d 1P 1PR RC C C C C 1P 1P 1 nIH HR R RR R R R R P H H H 3 8 DI0 _ 7 22 4 1 4 0 3 1 4 5 23 24 5 y 6 d 5 8 6 3 9 8 4 7 7 7 7 7 9 7 1 6 46 1 0 1 7 9 9 9 9 9 9 0 0 1 u 0 0 2 2 4 5 2 2 2 2 2 t P P C C C C C C C C C C 3 3 3 S H C C C C C C C C C C 0P 0P 0P _ H 7 _7 R_ R_ R_ R_ R_ R_ R_ R R R H H H 2 2 4 4 4 4 4 4 4 _4 _4 _4 _4 _4 _4 K K K K K K K K K K K K K K KDFS-33825 (DFCI 3382.W01WO) stnem garftnem hcirneA N Dtckreaeb m d nevb A ydobitnadiotsaermo utcareaF S yg olotsiH C C C C C C C C C C.svyh y t h y t h y t h y t h y t h y y y t htC C C C C C C C C C h hCl l l l l l lC C C C C C C C C CtltlCaeaeaeaeaeaeaeH H R R R R R R R R R RaeaeR H H H H H H H e c k k k k k k rua anaBnaBnaBna I I I Ik k BnBnBI I I I I IBnaBnao G b o G b o G b o G b o G b G b CF CF CF CF CF CF CF CF CF CF G b G b S Mib Mib Mib Mib Moib Moib D D D D D D D D D D Moib Moib DI1 4 8 6 l 8 a 8 9 4 0 47 5 3 5 0 8 7 22 46 13 49 87 4 07 37 17 4 2 7 57 4 5 6 46 di3 5 8 1 6 8 7 0 9 u 1 4 5 9 9 9 9 9 0 1 0 3 0 0 3 0 0 4 5 9 1 2 2 0 00 00 00 C C C C C 2 2 2 2 2 3 3 C C C C C 0 0 vi1 1 1 1 1 1 C C C C C C C C C C 01 0 d I P P P P P PR R1 n H H H H H HR R RR R R R R P P H H DI1 _ 8 4 8 9 8 0 4 6 7 5 3 8 5 07 22 4 2 5 6 1 4 0 3 1 4 5 4 4 y 4 5 8 1 6 8 7 30 91 8 4 7 7 7 7 7 6 6 d 3 3 3 4 5 9 1 2 2 74 95 9 9 9 9 9 0 1 ut0 0 0 0 0 0 2 2 2 2 2 C C C C C C 3 3 S P P P P P P C C C C H_ H H H H H C C C C C C C C C C 0P 0P 4 _4 _4 _4 _4 _4 Rd Rd Rd Rd Rd Rd Rd Rd Rd Rd Hd Hd K K K K K K m m m m m m m m m m m mDFS-33825 (DFCI 3382.W01WO) stnem garftnem hcirneA N Dtckreaeb m d nevb A ydobitnadiotsaermo utcareaF S yg olotsiH C C C C C C C C C C.svyh yCtlh y tlh y y y y y y tlhtlhtlhtlhtlC C C C C C C C C C h h C C C C C CtltlCaeaeaeaeaeaeaeC C C Ca aH H H H H H H R R R R R R R R R Re eR H H e c k k k k k k r BnaBnBnBnBnBnI I I I I I I I I Ik k BnBnuo G b Gab Gab Gab Gab Gab CF CF CF CF CF CF CF C C C Gab Gab Sob Mob Mob Mob Mob MoF F F Mi i i i i ib D D D D D D D D D D Moib Moib DI1 l 8 4 a 8 9 8 4 0 4 6 5 7 5 3 8 5 0 8 1 7 2 2 2 6 2 4 8 6 1 7 3 4 8 4 0 3 1 4 5 3 4 0 9 7 9 7 7 7 7 7 1 6 u di30 30 30 40 50 90 1 2 12 4 5 9 9 9 9 9 03 03 C C C C 2 2 2 2 2 0 0 vi0 0 0 0 0 0 C C C C C C C C C C C 0 0 d 1P 1P 1P 1P 1 1R RC C C C C 1 1 nIP PR R RR R R R R P P H H H H H H H H DI18 4 8 6 3 8 _ 9 4 5 0 2 y 8 0 7 5 7 2 4 d 4 5 8 1 6 8 6 1 2 2 3 49 8 4 07 3 1 4 5 3 4 ut3 3 3 4 5 9 71 02 12 74 95 9 7 2 9 7 7 7 1 6 2 92 9 9 0 0 S 0P 0P 0P 0P 0P 0P 2 2 C C C C C C C C C C 3 3 Hd Hd Hd Hd Hd Hd C C C C C C C C C C 0P 0P m m m m m m R L R L R L R L R L R L R L R L R L R L H L H LDFS-33825 (DFCI 3382.W01WO) stnem garftnem hcirneA N Dtckreb mu nrod neAv / A / A / A / A / A / A / b N N N N N N N A yd S S S S S S S o G G G G G G G bitWP W W W W W W naL P L P L P L P L P L P Ldiotsaermo utA / A / A / A / A / A / A / caN N N N N N NreaF S yg olotA / AN / A / A / A / A / A / siN N N N N N H.svyh yh yh yh yh y y yCtlCa tlatltltlhtlhtlhtle eaeaeaeaeaeaeR H H H H H H H H e c k k k k k k k r BnaBnaBnaBnaBnaBnBnu G b G b G b G b G b Gab Gao S Moib Moib Moib Moib Moib Moib b Moib DI5 l 4 18 49 84 65 30 8 a 61 84 05 78 51 7 22 u di3 3 3 3 6 8 00 00 00 0 40 50 90 vi1 1 0 0 0 0 d P 1 1 1 1 1 nIP P P P P P H H H H H H H DI_y 54 18 49 84 65 3 8 d 6 8 0 7 5 0 2 ut13 43 53 83 1 7 4 6 28 S 0 0 0 0 5 9 P P P P 0P 0P 0P H L H L H L H L H L H L H LDFS-33825 (DFCI 3382.W01WO) Example 2: Methods from Example 1 Subjects and samples

[0200] Sarcomatoid and epithelioid ccRCC clinically annotated tumor tissue specimens were derived as previously described. (Nassar, A. H. et al. Nature Communications 14, 346, (2023).) All tissue samples included were derived from resected RCC tumors from patient donors who provided explicit written consent per the declaration of Helsinki under an approved IRB protocol at the Dana-Farber Cancer Institute. Plasma samples were collected from patients with sarcomatoid and epithelioid ccRCC. The patients were diagnosed and treated at the Dana-Farber Cancer Institute (DFCI) between 2005 and 2022. All patients provided written informed consent. The use of samples was approved by the DFCI (01- 045 and 09-171) IRB protocols. Studies were conducted in accordance with recognized ethical guidelines. Epigenomic profiling Chromatin immunoprecipitation (ChIP) in RCC tissue specimens.

[0201] For tissue specimens, a 2-mm2core needle was used to obtain one core of tumor tissue from frozen tissue blocks in the areas marked on the corresponding slide enriched for tumor cells of sarcomatoid and non-sarcomatoid regions. Frozen samples were pulverized using the Covaris cryoPREP® system. They were then fixed using 2 mmol / L disuccinimidyl glutarate (DSG) for 10 minutes, followed by 1% formaldehyde buffer for 10 minutes, and quenched with glycine. Chromatin was sheared to 300 to 500 bp using the Covaris E220 ultrasonicator. The resulting chromatin was incubated overnight with the following antibodies (H3K27ac, Diagenode, Catalog No: C15410196 LOT: A1723- 0041D; H3K4me2, Diagenode, Catalog No: C15410035 LOT: A936-0023) coupled with 40^^l protein A and protein G beads (Invitrogen) at 4 degrees Celsius overnight. Five percent of the sample was not exposed to antibodies and was used as a control input. The beads were then washed three times each with Low-Salt Wash Buffer (0.1% sodium dodecyl sulfate (SDS), 1% Triton X-100, 2^mM ethylenediaminetetraacetic acid (EDTA), 20^mM Tris-HCl pH 7.5, 150^mM NaCl), High-Salt Wash Buffer (0.1% SDS, 1% Triton X-100, 2^mM EDTA, 20^mM Tris-HCl pH 7.5, 500^mM NaCl), and LiCl Wash Buffer (10^mM Tris pH 7.5, 250^mM LiCl, 1% NP-40, 1% Na-Doc, 1^mM EDTA) and rinsed with TE buffer (pH 8.0) once. The samples were then de-cross-linked, treated with RNase and proteinase K, and DNA was extracted (Qiagen). DNA sequencing libraries were prepared from purified input and IP sample DNA using the ThruPLEX® DNA-seq Kit (TakaraBio). Libraries were sequenced on an Illumina HiSeq 4000 to generate 150-bp paired-end reads (Novogene). ChIP-seq data analysis

[0202] ChIP-sequencing reads were aligned to the human genome build hg19 using the Burrows- Wheeler Aligner (BWA) version 0.7.17. (Langmead, B., et al. Genome Biol 10, R25, (2009).) Non-DFS-33825 (DFCI 3382.W01WO) uniquely mapped and redundant reads were discarded. MACS v2.1.1.20140616was used for ChIP-seq peak calling with a q-value (FDR) threshold of 0.01. ChIP-seq data quality was evaluated by a variety of measures, including total peak number, FrIP (fraction of reads in peak) score, number of high-confidence peaks (enriched >10-fold over background), and percent of peak overlap with DNAse hypersensitivity (DHS) peaks derived from the ENCODE project. ChIP-seq peaks were assessed for overlap with gene features and CpG islands using annotatr.. IGV v2.8.2 was used to visualize normalized ChIP-seq read counts at specific genomic loci. ChIP-seq heatmaps were generated with deepTools v3.3.1 and show normalized read counts at the peak center ±2^kb unless otherwise noted. Overlap of ChIP-seq peaks was assessed using BEDTools v2.26.0. Peaks were considered overlapping if they shared one or more base pairs. Identification and annotation of histology-specific peaks

[0203] Sample–sample clustering, principal component analysis, and identification of lineage- enriched peaks were performed using Cobra v2.0, a ChIP-seq analysis pipeline implemented with Snakemake.. ChIP-seq data from sarcomatoid and epithelioid RCC tissue samples were compared to identify H3K27ac and H3K4me2 peaks with significant enrichment in the two above groups. Samples from unique individuals were included. A union set of peaks for each histone modification was created using BEDTools, and narrowPeak calls from MACS were used for H3K27ac and H3K4me2. The number of unique aligned reads overlapping each peak in each sample was calculated from BAM files using BEDtools. Read counts for each peak were normalized to the total number of mapped reads for each sample. Quantile normalization was applied to this matrix of normalized read counts. Using DEseq2 v1.14.1, histology-enriched peaks were identified at the indicated FDR-adjusted p-value (padj) and log2 fold-change cutoffs (H3K27ac, padj^<^0.05, |log2 fold-change| >0; H3K4me2, padj^<^0.05, |log2 fold- change| >0;). Unsupervised hierarchical clustering was performed based on Spearman correlation between samples. Principal component analysis was performed using the prcomp R function. Enriched de novo motifs in differential peaks were detected using HOMER version 4.7. The top non-redundant motifs were ranked by adjusted p-value. The GREAT analysis (V3.0) was used to assess for enrichment of Gene Ontology (GO) and MSigDB perturbation annotations among genes near differential ChIP-seq peaks, assigning each peak to the nearest gene within 500^kb. TCGA and Clinical Trials Data

[0204] RNA-seq and clinical data from Javelin Renal101, IMmotion151 and TCGA (The Cancer Genome Atlas) cohorts were analyzed using R (v 4.2) on Rstudio (v 2022.7.2.576). Differential gene expression analysis between sRCC and epithelioid ccRCC tissue samples was computed using DESeq2 and Benjamini-Hochberg false discovery rate correction with q^<^0.05 considered statistically significant.DFS-33825 (DFCI 3382.W01WO) Patients were then stratified based on transcript per millions (TPMs) counts for transcription factors of interest. Survival analysis was computed using survminer R package with p < 0.05 considered statistically significant. Generation of stable CRISPRa FOSL1 cell lines

[0205] Guides were cloned into a pXPR_502, following a previously published cloning protocol (Konermann, S. et al. Nature 517, 583-588, (2015)): sgCtrl: F: 5’- CACCGCGCCAAACGTGCCCTGACGG-3’ (SEQ ID NO: 1), R: 5’- AAACCCGTCAGGGCACGTTTGGCGC-3’ (SEQ ID NO: 2), sgFOSL1: F: 5’- CACCGGGGCTGAACCACTGCGACCG-3’ (SEQ ID NO: 3), R: 5’- AAACCGGTCGCAGTGGTTCAGCCCC-3’ (SEQ ID NO: 4), 24^h before transfection, HEK293T cells were seeded 10cm dishes at a density of 5^×^106cells. PEI-Transfection was performed following the manufacturer’s protocol. Briefly, one solution of Opti-MEM™ (100^^L) and PEI MAX® (40^L) was combined with a DNA mixture of the packaging plasmid pMD2.G (2^g), psPAX2 (3 ^g), and the transfer vector (5 ^g). This mixture was incubated at room temperature 15 minutes and added dropwise on HEK293T cells with fresh media. After an overnight incubation at 37°C, the media was changed and collected after 48 hours. The virus was then concentrated in Amicon® Ultra-1550 kDa, at 1500g for 30 min and stored at -80°C.

[0206] Lentiviral spinfection was performed on CAKI-1 cells with the following conditions: 200^L of concentrated virus was added to 2 x 105cells in replicates with 4^g / mL of polybrene in 6 well- dishes with 2 mL of media, centrifugated at 1000g during 1h. The media was changed after an overnight incubation, and antibiotic selection started after 48 hours, for 5 days.

[0207] CAKI-1 were first infected with pXPR_109 (dCas9-VP64) and selected with blasticidin (5^g / mL) to establish stable dCas9-VP64-expressing CAKI-1 cells. Subsequently, these cells were infected with pXPR-502 containing guides and selected with puromycin (2 ^g / mL) and blasticidin to maintain the dCas9-VP64 expression. Functional validation RNA-seq data

[0208] Differential gene expression analysis between FOSL1 CRISPRa and negative control Caki-1 cells was computed using DESeq2 and Benjamini-Hochberg false discovery rate correction with q^<^0.05 considered statistically significant. Gene Set Enrichment Analysis (GSEA) was computed between FOSL1 CRISPRa and negative control Caki-1 cells (GSEA q^<^0.01) as previously described. (Subramanian, A. et al. Proceedings of the National Academy of Sciences 102, 15545-15550, (2005).) cfDNA processing and tumor content calculation

[0209] cfDNA samples were processed by the following method. Peripheral blood was collected in EDTA Vacutainer® tubes (BD) and processed within 3 hours of collection. Plasma was separated byDFS-33825 (DFCI 3382.W01WO) centrifugation at 2,500 × g for 10 minutes, transferred to microcentrifuge tubes, and centrifuged at 2,500 × g at room temperature for 10 minutes to remove cellular debris. The supernatant was aliquoted into 1 to 2 mL aliquots and stored at −80°C until DNA extraction. cfDNA was isolated from 1 mL of plasma using the QIAamp® Circulating Nucleic Acids Kit (QIAGEN), eluted in AE buffer, and stored at −80°C. Low- pass whole-genome sequencing (LPWGS) was performed on all cfDNA samples. The ichorCNA R package was used to infer copy-number profiles and cfDNA tumor content from read abundance across bins spanning the genome using default parameters. MeDIP-seq

[0210] MeDIP-seq was performed on tissue and plasma samples according to methods known in the art. (Nuzzo, P. V. et al. Nat Med 26, 1041-1043, (2020).) cfDNA library preparation was performed on 10 ng of DNA using the KAPA® HyperPrep™ Kit (KAPA Biosystems) according to the manufacturer's protocol. Then end-repair, A-tailing, and ligation of NEBNext® adaptors (NEBNext® Multiplex Oligos for Illumina kit, New England BioLabs) was performed. Libraries were digested using the USER™ enzyme (New England BioLabs). ^ DNA, consisting of unmethylated and in vitro methylated DNA, was added to prepared libraries to achieve a total amount of 100 ng DNA. Methylated and unmethylated Arabidopsis thaliana DNA (Diagenode) was added for quality control. MeDIP was performed using the MagMeDIP Kit (Diagenode) following the manufacturer's protocol. Samples were purified using the iPure Kit v2 (Diagenode). Success of the immunoprecipitation was confirmed using qPCR to detect recovery of the spiked-in Arabidopsis thaliana methylated and unmethylated DNA. KAPA® HiFi™ Hotstart ReadyMix (KAPA Biosystems) and NEBNext® Multiplex Oligos for Illumina (New England Biolabs) were added to a final concentration of 0.3 ^mol / L. Libraries were amplified as follows: activation at 95°C for 3 minutes, amplification cycles of 98°C for 20 seconds, 65°C for 15 seconds, 72°C for 30 seconds, and a final extension of 72°C for 1 minute. Samples were pooled and sequenced (Novogene Corporation) on Illumina HiSeq 4000 to generate 150 bp paired-end reads. MeDIP-seq quality control and processing of sequencing reads

[0211] After sequencing, the quality and quantity of the raw reads were examined using FastQC version 0.11.5 (available at bioinformatics.babraham.ac.uk / projects / fastqc) and MultiQC version 1.7. Raw reads were quality, and adapter trimmed using Trim Galore! version 0.6.0 (available at bioinformatics.babraham.ac.uk / projects / trim_galore / ) using default settings in paired-end mode. The trimmed reads then were aligned to hg19 using Bowtie2 version 2.3.5.1 in paired-end mode and all other settings default. The SAMtools version 1.10 software suite was used to convert SAM alignment files to BAM format, sort and index reads, and remove duplicates. The R package RSamtools version 2.2.1 wasDFS-33825 (DFCI 3382.W01WO) used to calculate the number of unique mapped reads. Saturation analyses to evaluate reproducibility of each library were carried out using the R Bioconductor package MEDIPS version 1.38.0. Tissue-informed approach for detection of sarcomatoid features using MeDIP-seq

[0212] To identify differentially methylated regions (DMR) between sarcomatoid and non- sarcomatoid ccRCC samples, first the genome was binned into 300 base-pair windows and tested each window for differential methylation between sarcomatoid and non-sarcomatoid samples using limma- voom (R package limma version 3.42.0) on TMM-normalized counts (R package edgeR version 3.28.0). Only bins with a total count above a fixed threshold were tested for differential methylation, where the threshold was set at 20% of the total number of samples across both groups. The search was restricted to bins within annotated CpG islands and FANTOM5 enhancers and excluded regions of high signal or poor mappability. (Cavalcante, R. G. & Sartor, M. A. Bioinformatics 33, 2381-2383, (2017); Amemiya, H. M. et al. Sci Rep 9, 9354, (2019).) DMRs with read enrichment in sRCC compared with epithelioid RCC at FDR-adjusted P < 0.01 and log2fold-change > 0 were selected. Windows with peaks in MeDIP-seq data from white blood cells (as determined by MACS2, version 2.1.2) were removed to minimize signal from blood cell–derived cfDNA. (Zhang, Y. et al. Genome Biol 9, R137, (2008).) Using the MeDIPs R package, CpG-normalized relative methylation scores (RMS) were calculated across 300 bp windows for each cfDNA sample. (Lienhard, M. et al. Bioinformatics 30, 284-286 (2014); Pelizzola, M. et al. Genome research 18, 1652-1659 (2008).) Then the RMS in cfDNA at sarcomatoid-enriched DMRs were summed for each sample and normalized this value to the sum of RMS values across all 300 bp windows. This value was termed “Sarcomatoid RCC Methylation Value.” The same process was performed for Non- sarcomatoid RCC DMRs to derive a “Non-sarcomatoid RCC Methylation Value.” Then the log2 ratio of the Sarcomatoid RCC Methylation Value to the Non-sarcomatoid RCC Methylation Value was calculated and these values were normalized to the median score in cfDNA from eight healthy cancer-free controls. This value was termed the “Sarcomatoid RCC Risk Score.” cfChIP-seq assay

[0213] 1 ^g of antibody was coupled with 10 ^L protein A (Invitrogen, cat# 10002D) and 10 ^L protein G (Invitrogen, cat# 10004D) for at least 6 hrs at 4 °C with rotation in 0.5 % BSA (Jackson Immunology, cat# 001-000-161) in PBS (Phosphate-Buffered Saline, Gibco, cat# 14190250), followed by blocking with 1% BSA (bovine serum albumin) in PBS for 1 hr at 4°C with rotation. The following antibodies were used: H3K4me3, Thermo Fisher # PA5-27029; H3K27ac, Abcam # ab4729; panAc, Active Motif #39139. Thawed plasma was centrifuged at 3,000g for 15 min at 4°C. The supernatant was precleared with the magnetic beads with 20 ^L protein A and 20 ^L protein G for 2 hrs at 4°C. Then, the precleared and conditioned plasma was subjected to antibody-coupled magnetic beads overnight with rotation at 4 °C. The reclaimed magnetic beads were washed with 1mL of each washing buffer twice.DFS-33825 (DFCI 3382.W01WO) Three washing buffers were used in following order: low salt washing buffer (0.1% SDS, 1% Triton X- 100, 2 mM EDTA, 150mM NaCl, 20 mM Tris-HCl pH 7.5), high salt buffer (0.1 % SDS, 1 % Triton X- 100, 2 mM EDTA, 500 mM NaCl, 20 mM Tris-HCl pH 7.5), and LiCl washing buffer (250 mM LiCl, 1%NP-40, 1% Na Deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 7.5). Subsequently, the beads were rinsed with TE buffer (Fisher Sci, cat# BP2473500), and resuspended and incubated in 100^L of DNA extraction buffer containing 0.1 M NaHCO3, 1% SDS and 0.6 mg / mL Proteinase K (Qiagen, cat#19131) and 0.4 mg / mL RNaseA (Thermo Fisher, cat#12091021) for 10 min for 37°C, for 1 hr for 50°C, and for 90 min at 65°C. DNA was purified through phenol extraction (Invitrogen, cat# 15593031) and ethanol precipitation was performed with 3M NaOAc (Ambion, cat# AM9740) and glycogen (Ambion, cat# AM9510). cfChIP-seq libraries were prepared with ThruPLEX® DNA-Seq Kit (Takara Bio, cat# R400675) following the manufacturer's instructions. After library amplification, the DNA was purified by AMPure XP (Beckman coulter, cat# A63880). The size distribution of the purified libraries was examined using Agilent 2100 Bioanalyzer with a high sensitivity DNA Chip (Agilent, cat# 5067-4626). The library was submitted for the 150 base-pair paired end sequencing on an Illumina NovaSeq6000 system (Novogene Corporation, CA). Statistical tests

[0214] All statistical tests were two-sided except where otherwise indicated. INCORPORATION BY REFERENCE

[0215] All publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. In case of conflict, the present application, including any definitions herein, will control.

[0216] Also incorporated by reference in their entirety are any polynucleotide and polypeptide sequences which reference an accession number correlating to an entry in a public database, such as those maintained by The Institute for Genomic Research (TIGR) on the world wide web at tigr.org and / or the National Center for Biotechnology Information (NCBI) on the World Wide Web at ncbi.nlm.nih.gov. OTHER EMBODIMENTS

[0217] It will be appreciated that the scope of the present disclosure is to be defined by that which may be understood from the disclosure and claims rather than by the specific embodiments that have been presented by way of example. Elements described with respect to one aspect or embodiment of the present disclosure are also contemplated with respect to other aspects or embodiments of the presentDFS-33825 (DFCI 3382.W01WO) disclosure. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

DFS-33825 (DFCI 3382.W01WO) CLAIMS What is claimed is:

1. A method of determining if a subject has a sarcomatoid renal cell carcinoma (sRCC) or determining a sarcomatoid / epithelioid subtype of a renal cell carcinoma (RCC) in the subject, the method comprising quantifying in a sample obtained or derived from the subject: a) at one or more genomic loci, (i) one or more histone modifications, and / or (ii) DNA methylation, and / or b) expression of one or more transcription factors, wherein the sample optionally comprises cell-free DNA (cfDNA) from a liquid biopsy sample.

2. The method of claim 1, wherein the one or more histone modifications are quantified using a histone modification assay that measures H3K27ac, optionally wherein the histone modification assay is selected from ChIP-seq (Chromatin ImmunoPrecipitation sequencing), CUT&RUN (Cleavage Under Targets and Release Using Nuclease) sequencing, and CUT&Tag (Cleavage Under Targets and Tagmentation) sequencing.

3. The method of claim 1 or 2, wherein DNA methylation is quantified using Bisulfite sequencing (BS-Seq), Whole Genome Bisulfite Sequencing (WGBS), Methylated DNA ImmunoPrecipitation sequencing (MeDIP-seq), or Methyl-CpG-Binding Domain sequencing (MBD-seq).

4. The method of any one of claims 1-3, comprising quantifying H3K27ac histone modifications and DNA methylation.

5. The method of any one of claims 1-4, wherein the sample is blood, plasma, serum, or urine.

6. The method of any one of claims 1-5, wherein a) differential histone modification and / or differential DNA methylation at the one or more genomic loci and / or b) differential expression of one or more transcription factors as compared to a reference indicates that the subject has sarcomatoid renal cell carcinoma (sRCC) or determines the sRCC subtype of RCC, optionally wherein the reference is a predetermined threshold, a measurement from a sample, and / or a normalized value, optionally wherein the reference is a measurement from a sample obtained from a cohort of subjects who have previously been determined to have epithelioid RCC.DFS-33825 (DFCI 3382.W01WO) 7. The method of any one of claims 1-7, comprising a) quantifying expression of the FOSL1 transcription factor and / or HIF2a (EPAS1) transcription factor; b) quantifying H3K27ac histone modifications at a) the FOSL1 genomic locus, b) one or more FOSL1 binding motifs or binding sites, c) the HIF2a (EPAS-1) genomic locus, and / or d) one or more HIF2a (EPAS1) binding motifs or binding sites, optionally wherein one or more FOSL1 binding motifs comprise a sequence of X1X2TGAX3TCAX4X5X6(SEQ ID NO: 5), wherein X1is A, T, G, or C, X2is A, T, G, or C, X3 is G or C, X4 is T, G, or C, X5 is A, T, G, or C, and X6 is A, T, G, or C and / or one or more HIF2a binding motifs comprise a sequence of X1CACGTX2X3X4X5 (SEQ ID NO: 6), wherein X1 is A, T, G, or C, X2is A, T or C, X3is T, G or C, X4is A, T, G or C, X5is A, T, G or C; c) quantifying histone modification and / or DNA methylation of one more genomic loci in SEQ ID NOs: 7-58011, optionally i) quantifying H3K27ac modifications for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 7-25925 and / or ii) quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 25926-58011.

8. The method of any one of claims 1-7, wherein the subject has previously been determined to have renal cell carcinoma (RCC), optionally wherein the RCC is clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), or chromophobe renal cell carcinoma (chRCC).

9. A method of treating a subject having sarcomatoid renal cell carcinoma (sRCC), the method comprising administering an sRCC therapy to the subject determined to have sRCC according to a method of any one of claims 1-8, optionally wherein a) if the subject has been determined to have sRCC, the sRCC therapy comprises administering a therapeutically effective amount of an immune checkpoint inhibitor to the subject; b) the RCC therapy comprises administering a therapeutically effective amount of a combination therapy to the subject, wherein the combination therapy comprises an immune checkpoint inhibitor (ICI) and a tyrosine kinase inhibitor (TKI), further optionally wherein the TKI is or comprises bevacizumab, a small molecule tyrosine kinase inhibitor, a soluble VEGF decoy receptor, or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2).

10. The method of claim 9, wherein the immune checkpoint inhibitor is or comprises an immune checkpoint antibody therapeutic selected from: an anti-PD-1 antibody therapeutic, an anti-PD-L1 antibody therapeutic, an anti-PD-L2 antibody therapeutic, and an anti-CTLA-4 antibody therapeutic,DFS-33825 (DFCI 3382.W01WO) optionally wherein the immune checkpoint inhibitor antibody therapeutic is or comprises nivolumab, pembrolizumab, avelumab, atezolizumab, durvalumab, camrelizumab, ipilimumab, or tremelimumab.

11. A method for monitoring the development of sarcomatoid renal cell carcinoma (sRCC) in a subject, the method comprising: a) quantifying, in a sample derived from a subject at a first time point, histone modification and / or DNA methylation of one or more genomic loci; b) quantifying, in a sample derived from a subject at a second, subsequent time point, histone modification and / or DNA methylation of the one or more genomic loci; and c) comparing the histone modification and / or DNA methylation from steps a) and b), thereby monitoring the development of sRCC in the subject.

12. The method of claim 11, wherein quantifying histone modification and / or DNA methylation at the first or second time point comprises: (i) quantifying histone modification and / or DNA methylation of one more genomic loci in SEQ ID NOs: 7-58011; (ii) quantifying H3K27ac histone modifications at the FOSL1 and / or HIF2a (EPAS1) genomic locus; and / or (iii) quantifying H3K27ac histone modifications at one or more FOSL1 binding motifs or binding sites and / or HIF2a (EPAS1) binding motifs or binding sites; and / or wherein the subject has been administered an RCC therapy, wherein the RCC therapy is administered (a) prior to the first time point (b) subsequent to the first time point and prior to the second time point, or (c) subsequent to the second time point, optionally further comprising if the subject is found to have sRCC at the first time point and / or the second time point, then the subject is administered an immune checkpoint inhibitor therapy.

13. A method of assessing the efficacy of an agent for treating sRCC in a subject, the method comprising: quantifying in a sample obtained or derived from the subject at a first point in time differential H3K27ac and / or methylation relative to a control of one or more of the genomic loci listed in SEQ ID NOs: 7-58011, optionally wherein the sample comprises cell-free DNA (cfDNA) from a liquid biopsy sample; quantifying in a second biological sample obtained or derived from the subject at a second point in time after treating with the agent, the differential H3K27ac and / or methylation relative to a control ofDFS-33825 (DFCI 3382.W01WO) one or more of the genomic loci listed in SEQ ID NOs: 7-58011, optionally wherein the biological sample comprises cell-free DNA (cfDNA) from a liquid biopsy sample; wherein an increased differential H3K27ac and / or methylation determined in the second sample relative to the first sample indicates that the agent does not treat sRCC in the subject; and wherein a decreased differential H3K27ac and / or methylation determined in the second sample relative to the first sample indicates that the agent treats sRCC in the subject.

14. A method of treating a subject having renal cell carcinoma (RCC), the method comprising treating the subject having RCC, wherein the RCC was determined to have FOSL1 expression above a threshold or reference level, with a therapeutically effective amount of an immune checkpoint inhibitor, optionally wherein a) the immune checkpoint inhibitor therapy is or comprises administering a therapeutically effective amount of an immune checkpoint inhibitor antibody therapeutic, optionally wherein the immune checkpoint inhibitor antibody therapeutic is or comprises: (i) an anti-PD-1 antibody therapeutic, anti-PD- L1 antibody therapeutic, anti-PD-L2 antibody therapeutic, or anti-CTLA-4 antibody therapeutic; and / or (ii) nivolumab, pembrolizumab, avelumab, atezolizumab, durvalumab, camrelizumab, ipilimumab, or tremelimumab; and / or b) if the subject has been determined to have an elevated level of FOSL1, the RCC therapy comprises administering a therapeutically effective amount of a combination therapy to the subject, wherein the combination therapy comprises an immune checkpoint inhibitor (ICI) and a tyrosine kinase inhibitor (TKI), optionally wherein the TKI is or comprises bevacizumab, a small molecule tyrosine kinase inhibitor, a soluble VEGF decoy receptor, or a humanized monoclonal antibody of VEGF receptor 2 (VEGFR2).

15. A method of treating a subject having sarcomatoid (sRCC), the method comprising: administering a sarcomatoid RCC (sRCC) therapeutic agent to the subject, wherein the subject has been determined to have a validated epigenetic profile indicative of sRCC based on analysis of a sample obtained or derived from the subject, wherein the sample optionally comprises cell-free DNA (cfDNA), wherein the presence of the validated epigenetic profile has been determined using a validated classifier, wherein the validated classifier was trained on a set of histone modification profiles and / or DNA methylation profiles from sarcomatoid RCC samples and epithelioid RCC to identify epigenomic profile associated with each RCC type, and a threshold was selected such that the validated classifierDFS-33825 (DFCI 3382.W01WO) predicts sarcomatoid RCC, with an area under the receiver operating characteristic (AUROC) of 0.35 to 1.0 (e.g., 0.4 to 1.0, 0.5 to 1.0, 0.6 to 1.0, or 0.7 to 1.0), optionally wherein a) the classifier was trained on differential H3K27ac modification profiles and / or differential methylation profiles and / or b) the liquid biopsy sample is blood, plasma, serum, or urine.

16. A kit comprising reagents for determining histone modification and / or DNA methylation at one or more genomic loci, wherein the one or more genomic loci are selected from SEQ ID NOs: 7-58011, optionally wherein the kit a) comprises reagents for quantifying H3K27ac for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 7-25925; b) comprises reagents for quantifying DNA methylation for at least 5, 10, 20, 30, 40, or 50 genomic loci in SEQ ID NOs: 25926-58011; c) comprises one or more antibodies for use in ChIP-seq, further optionally wherein the antibody specifically binds H3K27ac modified histones; d) comprises one or more methyl-binding domains for use in MBD-seq; e) comprises reagents for isolation of cell-free DNA (cfDNA) from a liquid biopsy sample; and / or f) comprises instructions for determining if a subject has sRCC.

17. A non-transitory computer readable storage medium encoded with a computer program, wherein the program comprises instructions that when executed by one or more processors cause the one or more processors to perform operations to perform the method of any one of claims 1-15.

18. A computer system comprising a memory and one or more processors coupled to the memory, wherein the one or more processors are configured to perform operations to perform the method of any one of claims 1-15.

19. A system for determining if an RCC is sarcomatoid RCC subtype in a subject, the system comprising a sequencer configured to generate a sequencing data set, epigenomic data set, and / or gene expression data set from a sample; and a non-transitory computer readable storage medium of claim 17 and / or a computer system of claim 18.

20. The method of any one of claims 1-15, wherein the subject is a mammal, optionally wherein the mammal is a human.

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