Sarcomatoid Renal Cell Carcinoma Has a Distinct Molecular Pathogenesis, Driver Mutation Profile, and Transcriptional Landscape Free

Renal cell carcinoma (RCC) is an important contributor to cancer-specific mortality (1). Sarcomatoid RCC (SRCC), which is characterized by a biphasic epithelioid carcinomatous (E-) component and a spindle cell sarcomatous (S-) component, represents an important and understudied subset of RCC. SRCC comprises up to 20% of stage IV RCC cases and shows aggressive behavior irrespective of the parent subtype with which it shares histologic characteristics (2–4). No established therapies are effective against SRCC (5). Current clinical thinking conceptualizes SRCC as a single entity (6); however, a thorough molecular understanding of the disease has been limited by the dearth of genome-wide examinations, both within specific RCC subtypes and across different parent RCC subtypes.

Large-scale multi-institutional, multiplatform genomic studies of RCC have been reported, notably The Cancer Genome Atlas (TCGA) data on clear-cell RCC (ccRCC), papillary RCC (PapRCC), and chromophobe RCC (ChRCC; refs. 7–10). Genome-wide molecular characterization of SRCC has heretofore been based on formalin-fixed, paraffin-embedded (FFPE) tissues and largely confined to the sarcomatoid ccRCC (SccRCC) subtype (11–14).

The aim of this study was to comprehensively examine the mutational, copy number, and transcriptional characteristics of SRCC and to compare these data with those of nonsarcomatoid RCC (RCC). We found that SRCC is not a single entity but rather is highly influenced by the parent RCC subtype with which it shares histologic characteristics. Moreover, we show that SccRCC exhibits a different early molecular pathogenesis, driver mutation, and transcriptional profile than ccRCC. These findings will improve the molecular classification of RCC and provide a means for predicting aggressive sarcomatoid tumors preoperatively as well as suggesting therapeutic targets, thus aiding in the management of this disease.

For this retrospective study, we obtained 40 frozen SRCC samples and nonneoplastic kidney control samples from the tissue bank of The University of Texas MD Anderson Cancer Center (Houston, TX) after informed consent and using an institutional review board–approved protocol (IRB# LAB 08-670). The study was conducted in accordance with the Declaration of Helsinki. Lesional foci representing the E- or S- component of SRCC as well as nonneoplastic kidney controls were marked on hematoxylin and eosin–stained slides, with all cases reviewed by at least two genitourinary pathologists (Supplementary Fig. S1). These foci were macrodissected from the frozen samples prior to the extraction of tissue analytes. We identified a second cohort of SRCC cases (n = 32) by examining surgical pathology reports and digital pathologic images from TCGA. The clinicopathologic features of the SRCCs derived from the MD Anderson and TCGA cohorts are summarized in Supplementary Table S1.

Genomic DNA was extracted using the AllPrep DNA/RNA Mini Kit according to the manufacturer's instructions (QIAGEN). DNA concentration was quantified using a NanoDrop 1000 spectrophotometer (NanoDrop Technologies) and the Quant-iT PicoGreen Kit (Life Technologies) individually. Paired-end whole-exome sequencing (WES) was performed on frozen sarcomatoid renal tumors and corresponding normal controls using the HiSeq2000 platform. DNA mutations and copy numbers were assessed from WES data using in-house pipelines. Data were analyzed within and across sample subtypes, with details provided in Supplementary Methods S1.

Total RNA was isolated using the miRNeasy Mini Kit according to the manufacturer's instructions (QIAGEN). RNA was quantitatively assessed using the NanoDrop 1000 spectrophotometer (NanoDrop Technologies) and qualitatively assessed using the Agilent 2100 bioanalyzer (Agilent Technologies).

RNA-sequencing (RNA-Seq) was performed on frozen and FFPE tissues with paired-end sequencing using the HiSeq2000 platform. Gene expression was assessed using an in-house pipeline that uses tophat (15) and htseq (16), among other tools. The details are provided in Supplementary Methods S2.

DNA methylation profiling was performed from genomic DNA extracted from frozen sarcomatoid renal tumors and corresponding normal kidney controls using the Illumina Infinium MethylationEPIC BeadChip platform. The details are provided in Supplementary Methods S3. Assessment of gene silencing by DNA methylation was carried out as previously described (17).

Fluorescence in situ hybridization (FISH) using a commercial probe set (VHL/CEN3q; Abnova; catalog number FG0029) was performed on FFPE-derived tissue microarray sections from two patient cohorts. The first cohort comprised patients with SccRCC: n = 88 patients, median age at surgery was 59, with 83% dead of disease and median follow-up of 11 months. The second cohort comprised patients with ccRCC lacking sarcomatoid features: n = 66 patients (Fuhrman grade 3, n = 57; Fuhrman grade 2, n = 9), median age at surgery was 58, with 23% dead of disease and median follow-up of 70 months. The detailed FISH methodology is provided in Supplementary Methods S4.

TCGA exome sequencing and RNA-Seq data derived from SRCC and non-SRCC cases were evaluated using the Broad Institute Firehose pipeline. TCGA data downloaded from the TCGA data coordinating center site were also used to compare SccRCC with non-SccRCC cases in terms of gene-level copy number, arm-level chromosomal copy number, exome-based somatic mutations, and VHL methylation status.

For RNA-Seq data differentially expressed gene analysis, we used the “edgeR” package (18) in R to calculate the fold change and to adjust the P value. We analyzed canonical pathways and functional networks with Ingenuity Pathway Analysis (Ingenuity Systems). The Kaplan–Meier survival analysis was used with the log-rank test to assess the statistical significance of the differences between stratified survival groups using the “survival” package in R. Other standard statistical tests were used to analyze the clinical and genomics data, including the χ² test, Fisher exact test, Mann–Whitney–Wilcoxon test, and Cox proportional hazard analysis. Significance was defined as P < 0.05. Analyses were primarily performed using R. Heat maps were plotted based on the pheatmap function in the “pheatmap” package.

We examined SRCC derived from three different parent subtypes (SccRCC; sarcomatoid ChRCC or SChRCC; and sarcomatoid PapRCC or SPapRCC) and their biphasic E- and S- morphologic components. The overall mutational landscape of SRCC found in our cohort is schematically represented in Fig. 1A. Supplementary Table S2 summarizes the mutations found in our cohort; detailed sequencing data are in the process of being deposited in database of Genotypes and Phenotypes (accession numbers pending). Using both local and TCGA data, we found SRCC of different subtypes to show a mutational burden similar to that of RCC (refs. 7–9; Fig. 2A). Unsupervised clustering of copy number and transcriptional data showed that SRCC segregated according to the parent subtype rather than to the E- or S- morphologic components, as illustrated in Fig. 2B and C. Potentially actionable mutations, involving pan-cancer T200 panel genes (19), were distributed across all SRCC subtypes (Supplementary Table S2). However, mutations in 3p21-25 genes (VHL, PBRM1, SETD2, and BAP1) were only seen in SccRCC (Fig. 2D).

Despite the large number of nonsynonymous mutations that were found in SRCC, relatively few genes were recurrently mutated. Indeed, among the top 30 recurrently mutated genes, only 9 were significant in SRCC according to the MutSig algorithm (q < 0.1; Fig. 1B and Supplementary Table S3A). Some gene mutations were shared across different subtypes of SRCC, most notably TP53, PTEN, NF2, and RELN (Fig. 1C).

We next assessed whether mutations in the aforementioned genes were significantly enriched in sarcomatoid tumors compared with nonsarcomatoid tumors within the same parent RCC subtype using institutional and TCGA-derived data (Supplementary Table S3B). PTEN was more frequently mutated in SccRCC versus ccRCC; TP53 mutations were associated with SccRCC and SPapRCC; and NF2 mutations were associated with SPapRCC only. RELN mutations were significantly higher in SRCC across all subtypes.

Biallelic loss of tumor suppressor genes at 3p21-25 is fundamental to the pathogenesis of ccRCC (20, 21). Loss of VHL is thought to be the initiating event, followed by inactivation of other 3p21-25 genes, such as PBRM1 and BAP1 (22). Numerous additional chromosomal copy-number alterations have been proposed as contributing to the progression of ccRCC (23). In terms of arm-spanning copy-number changes, fewer chromosomal losses at 3p were associated with high-grade SccRCC (Supplementary Table S4). Differences in gene-level copy number were more dramatic, with significantly less segmental 3p21-25 deletion seen in high-grade and SccRCC spanning the VHL, PBRM1, BAP1, and SETD2 genes (Supplementary Table S5).

This finding of markedly less 3p21-25 deletions in SccRCC was maintained across a broad range of bioinformatics thresholds used for calling copy loss. We next considered whether a greater burden of contaminating normal diploid cells may have confounded our results. However, the distribution of tumor purities did not differ significantly between high grade (SccRCC + grade 4) versus lower grades (grades 1–3) of ccRCC (P = 0.28). We then used the FISH platform to assay SccRCC and ccRCC for VHL copy number. We found that sarcomatoid features correlated with significantly higher VHL/3q ratios as shown in Supplementary Fig. S2. Using FISH as the basis for copy-number calls, SccRCC showed fewer VHL deletions compared with ccRCC (P = 0.001), and high-grade ccRCC showed fewer VHL deletions compared with lower grade ccRCC tumors (P = 0.000049).

Finally, we considered whether copy-neutral LOH contributed to LOH at 3p in those cases that did not show simple 3p21-25 loss. Our analysis of SNP data from TCGA showed copy-neutral LOH to be an exceedingly rare event in ccRCC (∼1%) and to involve only one case of SccRCC (Supplementary Methods S5 and Supplementary Figs. S3–S7). We assessed independent ccRCC samples assayed by SNP array (GSE12808, GSE14670, GSE11447, and GSE9469) and also found a low incidence of copy-neutral LOH (∼2%) that was not correlated to tumor grade.

SccRCC exhibited less concurrent mutation and copy loss (i.e., two-hit loss) of the VHL and PBRM1 genes than did ccRCC across different histologic grades (Table 1). Gene silencing by methylation is another mechanism of gene inactivation. Epigenetic silencing has been reported to be mutually exclusive with mutation for VHL in ccRCC (7, 24), which is consistent with our data where VHL mutations and methylation did not overlap (Fig. 1D). We only found methylation of the VHL gene and not for the PBRM1, SETD2, or BAP1 genes (Supplementary Fig. S8). Notably, after taking methylation status into account as well as mutation and LOH, SccRCC still showed fewer two-hit losses of VHL compared with ccRCC [P < 0.001; odds ratio (OR), 0.22].

With respect to ccRCC, we found that mutations in TP53, PTEN, RELN, BAP1, and SETD2 were individually associated with or anticorrelated (PBRM1) with sarcomatoid change (Table 2). A mutation in TP53, PTEN, or RELN was correlated with sarcomatoid change in ccRCC (P = 1.64E–06; OR = 6.53), including grade 4 (P = 4.68E–02; OR = 3.2; Supplementary Table S6). Further, mutant allelic frequencies in TP53, PTEN, and RELN were lower than in VHL or BAP1 (P = 0.035), suggesting that they occurred later in the evolution of the tumor. When comparing the mutational landscape of SccRCC versus ccRCC, profiling of only the E- component of SccRCC revealed similar results compared with profiling of both E- and S- components (Table 2 and Supplementary Table S6). A survival analysis showed that a mutation in TP53, PTEN, or RELN was associated with reduced overall survival in ccRCC (Fig. 3A; P = 8.72E–07). In contrast, deletion of 3p21-25 genes or a two-hit loss of VHL was associated with increased overall survival (Fig. 3B and C).

Because SRCC practically always presents as advanced-stage disease, we compared the transcriptional profile of sarcomatoid tumors with that of advanced-stage nonsarcomatoid tumors across all three RCC subtypes. SRCC showed enrichment of several pathways, including TGFβ1 signaling (activated, P = 5.67E–25; Fig. 4A; Supplementary Table S7A).

We next assessed only SccRCC versus advanced-stage ccRCC. Using TCGA data, we found a large number of differentially expressed genes (n = 2,362 genes; FDR < 0.1) that translated into differential regulation of numerous signaling cascades, as illustrated in Fig. 4B and Supplementary Table S7B. Notably, alteration of the VEGF (activated, P = 1.8E–20), TGFβ1 (activated, P = 2.7E–20), and TP53 (inhibited, P = 1.8E–17) pathways was seen in SccRCC. At the level of cellular function, SccRCC showed enrichment of cell growth, movement, and proliferation processes. We performed the same analysis using expression profiling data from local samples (GSE59266) derived from FFPE tissues and found approximately 600 genes that overlapped between the two independent datasets. A pathway analysis based on the overlapping genes revealed enrichment of similar cellular functions and pathways (Supplementary Fig. S9).

Multiple regions of a given tumor were sequenced in a subset of SRCC cases encompassing different RCC subtypes and different histologic components (E- or S-), as shown in Fig. 1A and Supplementary Table S8. The S- component showed a significantly higher overall mutational load than did the E- component across all RCC subtypes (P = 0.0215) and in the ccRCC subtype (P = 0.0199; Supplementary Fig. S10A and S10B). The mutational load among cancer-related genes (25), however, did not show significant differences between E-and S-. Moreover, despite the biphasic components harboring private mutations that were unique to that region, no single-gene mutation was significantly enriched in E- or S- regions. Interestingly, mutations in cancer-related genes were more commonly shared among the E- and S- components as compared with all gene mutations: for example, 66% shared cancer-related mutations in SRCC versus 19% shared total mutations in RCC, P = 1.15E–09; and 62% shared cancer-related mutations in SccRCC versus 19% shared total mutations in ccRCC, P = 3.57E–05. An Ingenuity Pathway Analysis of the mRNA expression profile of paired E- and S- components of SccRCC revealed activation of TGFβ1 signaling (P = 2.85E–25) as the top upstream regulator (Fig. 4C and Supplementary Table S7C). Network functions that were altered included those involved in cellular assembly and organization as well as cell movement. Together, these pathways provide potential targets for future clinical trials.

SRCC is often considered and managed as a single clinical entity, regardless of the underlying parent subtype with which it is associated. For instance, the National Comprehensive Cancer Network guidelines do not distinguish between sarcomatoid tumors of clear-cell or non–clear-cell origin in terms of recommendations for management (6). Molecular characterization of SRCC has generally been limited to the clear-cell subtype using targeted single-platform studies (11, 13, 14, 26). Our multiplatform, genome-wide analysis shows subtype-specific differences and that SRCC clusters according to parent subtype. In support of our observations, Tickoo and colleagues demonstrated upregulation of hypoxia-inducible factor (HIF) pathway markers in only the clear-cell subtype of SRCC (27). These results suggest that SRCC is not a single disease entity, but rather that its molecular biology reflects its parent subtype, which should be taken into account when managing this disease.

The signal molecular alteration in ccRCC is loss of VHL on 3p (through deletion, mutation, or methylation), leading to activation of HIF pathway molecules such as VEGF and CAIX (22). This has led to the implementation of targeted antiangiogenic agents that abrogate HIF signaling with improvements in progression-free survival (28). Other 3p genes involved in chromatin remodeling (PBRM1, SETD2, and BAP1) were subsequently described as important tumor suppressors that are lost during the pathogenesis of ccRCC (22). The relationship between VHL's mutational status and tumor progression is controversial (29–32), and we did not find a mutation in VHL to correlate with sarcomatoid features or with aggressive disease (Table 2). However, sarcomatoid ccRCC differs from non-SccRCC molecularly in that there is significantly less 3p21-25 deletion spanning the VHL, PBRM1, BAP1, and SETD2 genes and less two-copy loss of VHL and PBRM1. This is consistent with historic cytogenetic studies that have shown the presence of 3p deletions to be associated with lower grade and lower stage disease, absence of sarcomatoid features (33), and improved survival outcomes in ccRCC (33–35). More recent molecular evidence also supports our findings in that LOH at the VHL locus (36) and two-hit loss of the VHL gene in ccRCC (24) were associated with lower stage, lower grade disease and with the absence of sarcomatoid features. Regulation of PBRM1 is postulated to be influenced by tumor differentiation, with fewer mutations in higher grade disease and more aggressive tumors (12, 37, 38). By contrast, BAP1 and SETD2 have been reported to show more mutations with disease progression (7, 12, 39). Taken together, it appears that high-grade ccRCC tumors—exemplified by SccRCC—are prone not to show LOH at 3p21-25 and, notably, to retain a copy of the wild-type VHL and PBRM1 genes. Our analyses challenge the dogma that all ccRCC tumors show a 3p LOH/deletion event (22, 40). Rather, it appears that SccRCC tumor clones show a distinct early molecular pathogenesis, as illustrated in Fig. 5. This suggests that although SccRCC and ccRCC may originate from a common precursor, the molecular events are different, and SccRCC does not evolve linearly from low-grade ccRCC. Consequently, diagnostic molecular assays that fail to demonstrate a 3p or VHL deletion in a suspected ccRCC case do not necessarily rule out a clear-cell subtype of RCC, particularly when associated with higher grade or sarcomatoid features.

Whereas mutations in 3p genes were confined to the ccRCC subtype, a handful of other recurrent mutations were shared by SRCC of different subtypes, namely, PTEN, TP53, NF2, and RELN. Although mutations in these genes occurred in all SRCC subtypes, their specific association with sarcomatoid changes within a given subtype varied according to the gene. Among ccRCC, a mutation in PTEN, TP53, or RELN was predictive of the sarcomatoid histologic type, whereas a two-hit inactivation of VHL or PBRM1 was predictive of the non-SccRCC histologic type (Fig. 5). Knowing whether a patient with advanced disease has sarcomatoid features is relevant because of the frequent explosive growth of sarcomatoid tumors that dissuades clinicians from offering cytoreductive nephrectomy prior to initiating systemic therapy. The ability to predict SccRCC is not feasible at present, owing to the lack of radiologic criteria or tissue biomarkers. Moreover, biopsy prediction of RCC tumor grade is notoriously unreliable, including predicting for sarcomatoid change (41).

Assaying for a handful of mutations is feasible using a clinical platform in a Clinical Laboratory Improvement Amendments (CLIA)–certified environment and may be an informative test, with prognostic and management implications. However, the intratumoral mutational heterogeneity of ccRCC, resulting from a putative pattern of branched evolution (40), limits the utility of any mutational screen based on single biopsies, because the majority of mutations are subclonal and, therefore, would not be detected by sampling a single region. Nevertheless, VHL is considered as a clonal mutation, and PBRM1 is sometimes clonal and often shared among different regions (40, 42); thus, assays for these mutations or deletions are less likely to be affected by intratumoral heterogeneity than assays for the other genes that we have proposed. Sampling strategies have not yet been devised to capture subclonal mutations, although early work is promising (42). In this context, our finding, supported by another independent cohort (14), that the S- component showed a higher mutational burden suggests that the higher grade component of a tumor should be assayed as part of a multiregional sampling approach.

The mutational pattern in SccRCC is likely a key driver of its response to existing therapies and may provide insight into new treatment approaches. TP53 mutations have been associated with decreased therapeutic response to antiangiogenic therapies and to PI3K pathway–targeting agents (43, 44). Our recent publication testing the efficacy of PI3K pathway–targeted agents, including MK-2206 and everolimus, demonstrated that mutations in TP53 were associated with reduced response to either agent (44). Duration of response to antiangiogenic agents also appears to be shorter in SccRCC than in RCC (45). Interestingly, SccRCC is associated with increased tissue expression of PD-L1 (46), and emerging data suggest that SccRCC may be associated with heightened response to checkpoint antibodies (E. Jonasch; unpublished observations). The mechanistic link between the unique genomic features of SccRCC and response to checkpoint antibodies is being actively investigated. For PTEN-deficient tumors, several drugs are currently under clinical development in this setting, including PI3K inhibitors, AKT inhibitors, and PARP inhibitors (47). Circulating biomarkers for SccRCC have not yet been formally developed, but a recent study by Pal and colleagues detected a high rate of TP53 mutations (64%) in the circulating tumor DNA of patients with ccRCC treated with later lines of VEGF inhibitors versus first-line treatment (31%; ref. 48). Whether this finding is related to sarcomatoid dedifferentiation remains to be determined.

Transcriptional differences between SccRCC and ccRCC may also be exploited to develop biomarkers of sarcomatoid change, particularly as current technologies enable high-throughput mRNA assays using FFPE material. The major advantage of a transcriptional analysis would appear to be the relatively stable gene expression signature among different regions (12, 13, 40). Our transcriptional analyses suggested that different pathways are enriched in SRCC, particularly TGFβ signaling. These may provide opportunities for therapeutic targeting in addition to biomarkers of tissue of origin.

In summary, we have shown that SRCC is not a single entity but rather multiple diseases that segregate according to the underlying parent subtype. With respect to the most common ccRCC subtype, our findings challenge the dogma that LOH at 3p21-25 is an event common to all ccRCC (20, 22, 40). Rather it appears that clinically aggressive, SccRCC has a distinct tumorigenesis, generally lacking copy losses at 3p21-25. Together with its distinct mutational and transcriptomic landscape, this line of investigation will herald a more precise molecular classification of RCC, with diagnostic, prognostic, and therapeutic implications.

N. Tannir reports receiving speakers bureau honoraria from and is a consultant/advisory board member for Bristol-Myers Squibb, Exelixis, GlaxoSmithKline, Nektar, Novartis, and Pfizer, and reports receiving commercial research grants from Bristol-Myers Squibb, Epizyme, Exelexis, and Novartis. F. Monzon has ownership interest (including patents) in Castle Biosciences. B. Czerniak is a consultant/advisory board member for Abott Molecular and Merck Sharp & Dohme Cor. G. Mills has ownership interest (including patents) in Catena Pharmaceuticals, ImmunoMet, Myriad Genetics, PTV Ventures, and Spindletop Ventures, reports receiving speakers bureau honoraria from Allostery, AstraZeneca, ImmunoMet, ISIS Pharmaceuticals, Lilly, MedImmune, Novartis, and Symphogen, is a consultant/advisory board member for Adventist Health, Allostery, AstraZeneca, Catena Pharmaceuticals, Critical Outcome Technologies, ISIS Pharmaceuticals, ImmunoMet, Lilly, MedImmune, Novartis, Precision Medicine, Provista Diagnostics, Signalchem Lifesciences, Symphogen, Takeda/Millennium Pharmaceuticals, Tarveda, and Tau Therapeutics, and reports receiving commercial research grants from Adelson Medical Research Foundation, AstraZeneca, Breast Cancer Research Foundation, Clinical Outcome Technologies, Illumina, Karus, Komen Research Foundation, Nanostring, and Takeda/Millennium Pharmaceuticals. No potential conflicts of interest were disclosed by the other authors.

Conception and design: Z. Wang, J. Karam, E. Jonasch, C. Wood, B. Czerniak, G. Mills, K. Shaw, K. Sircar

Development of methodology: Z. Wang, J.R. Canales, C. Wood, I. Wistuba, K. Chen, K. Sircar

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Karam, F. Kawakami, P. Trevisan, C.-W. Chow, P. Tamboli, N. Tannir, C. Wood, M. Varella-Garcia, B. Czerniak, G. Mills, K. Chen, K. Sircar

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Z. Wang, T.B. Kim, B. Peng, J. Karam, C. Creighton, A. Joon, N. Tannir, C. Wood, F. Monzon, K. Baggerly, B. Czerniak, G. Mills, K. Chen, K. Sircar

Writing, review, and/or revision of the manuscript: Z. Wang, T.B. Kim, B. Peng, J. Karam, A. Joon, F. Kawakami, E. Jonasch, N. Tannir, C. Wood, F. Monzon, I. Wistuba, G. Mills, K. Shaw, K. Chen, K. Sircar

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): C.-W. Chow, P. Tamboli, K. Shaw, K. Chen, K. Sircar

Study supervision: C. Wood, K. Chen, K. Sircar

Other (FISH analyses): M. Varella-Garcia

The authors would like to acknowledge Kim-Anh Vu, Camille Sanchez, and Ann Sutton for technical and secretarial assistance.

Z. Wang and K. Chen are partially supported by NIH grant R01CA172652. C. Creighton is supported by NIH grant CA125123. P. Trevisan is supported partially from the CAPES Foundation. M. Varella-Garcia was supported by NCI-P30CA046934 to the Cancer Center Molecular Pathology Shared Resource. K. Sircar received funding from the University of Texas MD Anderson Cancer Center (Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation and CCSG grant CA016672, the Khalifa Bin Zayed Al Nahyan Foundation) and the Kidney Cancer Research Program (Monteleone Foundation). C.J. Creighton is supported by NIH grant CA125123.

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