Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma

Talal El Zarif; Karl Semaan; Marc Eid; Ji-Heui Seo; Simon Garinet; Matthew P Davidsohn; Pranshu Sahgal; Brad Fortunato; John Canniff; Amin H Nassar; Sarah Abou Alaiwi; Ziad Bakouny; Gitanjali Lakshminarayanan; Hunter Savignano; Kevin Lyons; Sayed Matar; Atef Ali; Eddy Saad; Renee Maria Saliby; Paulo Cordeiro; Ziwei Zhang; Nourhan El Ahmar; Yasmin Nabil Laimon; Chris Labaki; Valisha Shah; Dory Freeman; Jillian O’Toole; Gwo-Shu Mary Lee; Justin Hwang; Mark Pomerantz; Sabina Signoretti; Eliezer M Van Allen; Wanling Xie; Jacob E Berchuck; Srinivas R Viswanathan; David A Braun; Toni K Choueiri; Matthew L Freedman; Sylvan C Baca

doi:10.1016/j.celrep.2024.114350

. Author manuscript; available in PMC: 2025 Mar 22.

Published in final edited form as: Cell Rep. 2024 Jun 12;43(6):114350. doi: 10.1016/j.celrep.2024.114350

Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma

Talal El Zarif ^1,^2,^3,^*, Karl Semaan ^2,^3,^4,^*, Marc Eid ^2,³, Ji-Heui Seo ^2,³, Simon Garinet ², Matthew P Davidsohn ², Pranshu Sahgal ², Brad Fortunato ², John Canniff ², Amin H Nassar ⁵, Sarah Abou Alaiwi ⁶, Ziad Bakouny ⁷, Gitanjali Lakshminarayanan ², Hunter Savignano ², Kevin Lyons ², Sayed Matar ⁸, Atef Ali ⁹, Eddy Saad ², Renee Maria Saliby ², Paulo Cordeiro ^2,^3,⁴, Ziwei Zhang ², Nourhan El Ahmar ⁸, Yasmin Nabil Laimon ⁸, Chris Labaki ^2,¹⁰, Valisha Shah ², Dory Freeman ², Jillian O’Toole ², Gwo-Shu Mary Lee ², Justin Hwang ⁹, Mark Pomerantz ², Sabina Signoretti ⁸, Eliezer M Van Allen ^2,⁴, Wanling Xie ¹¹, Jacob E Berchuck ², Srinivas R Viswanathan ^2,^12,^13,¹⁴, David A Braun ^5,¹⁵, Toni K Choueiri ^2,^{^}, Matthew L Freedman ^2,^3,^{^}, Sylvan C Baca ^2,^3,^4,^{^}

^1.Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

^2.Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA.

^3.Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

^4.The Eli and Edythe L. Broad Institute, Cambridge, MA, USA

^5.Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

^6.Section of Cardiology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

^7.Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

^8.Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA.

^9.Department of Medicine, University of Minnesota Masonic Cancer Center, Minneapolis, MN, USA.

^10.Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.

^11.Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA.

^12.Department of Medicine, Harvard Medical School; Boston, MA, USA.

^13.Cancer Program, Broad Institute of MIT and Harvard; Cambridge, MA, USA.

^14.Department of Medicine, Brigham and Women’s Hospital; Boston, MA, USA.

^15.Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA

co-first authors

^{^}

co-senior and corresponding authors

Lead contact: Sylvan C. Baca.

Author contributions:

T.E.Z and K.S. are co-first authors of this work. T.E.Z, K.S., T.K.C., M.L.F., and S.C.B conceived the study; K.S. and T.E.Z. analyzed the data with assistance from B.F., K.L., A.A., P.C., Z.Z., V.S., J.H., and W.X. under the joint supervision of T.K.C., M.L.F., and S.C.B; T.E.Z. and K.S. wrote the manuscript under the joint supervision of T.K.C., M.L.F., and S.C.B; T.E.Z performed ChIP-seq and MeDIP-seq experiments with assistance from M.E., J.H.S, S.A.A, and A.H.N; M.P.D., G.L., H.S., and J.C. performed cfChIP-seq and cfMeDIP-seq experiments under the supervision of M.L.F; S.G. performed CRISPRa experiments under the supervision of S.R.V; P.S. performed the western blot experiments under supervision of M.L.F; Z.B., E.S., R.M.S., C.L., D.F., J.O., G.S.M.L., M.P., E.M.V.A., J.E.B., and D.A.B. assisted with procuring clinical samples. S.G., S.R.V. S.M., N.E.A., and Y.N.L. assisted with annotation of clinical samples under the supervision of S.S. All authors assisted in writing and revising the manuscript. T.K.C., M.L.F., and S.C.B. are co-senior authors of this work.

^✉

Correspondance: Toni K Choueiri: toni_choueiri@dfci.harvard.edu; Matthew L Freedman: matthew_freedman@dfci.harvard.edu; Sylvan C Baca: sylvan_baca@dfci.harvard.edu

PMCID: PMC11929466 NIHMSID: NIHMS2056089 PMID: 38870013

Summary:

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and heightened response to immune checkpoint inhibitors (ICIs). Two major barriers to improving outcomes for sRCC are the limited understanding of its gene regulatory programs and the low diagnostic yield of tumor biopsies due to spatial heterogeneity. Herein, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries from tissue and plasma samples from 50 patients with RCC and healthy volunteers. By profiling histone modifications and DNA methylation, we identified highly recurrent epigenomic reprogramming enriched in sRCC. Furthermore, CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs, and FOSL1 expression was associated with response to ICIs in RCC in two randomized clinical trials. Finally, we established a blood-based diagnostic approach using detectable sRCC epigenomic signatures in patient plasma, providing a framework for discovering epigenomic correlates of tumor histology via liquid biopsy.

Graphical Abstract

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Introduction:

Sarcomatoid differentiation is a histopathologic feature observed in 5–20% of renal cell carcinoma (RCC) tumors across all histologies¹, including clear cell RCC (ccRCC), the most common subtype of kidney cancer^2,3. 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^4–6. Diagnosing sRCC is clinically important because it can respond particularly well to immune checkpoint inhibitors (ICIs)^6–10, potentially due to increased PD-L1 expression on tumor cells and prominent immune cell infiltration^6,10–12.

The molecular underpinnings of sarcomatoid differentiation are poorly understood, partly due to a paucity of cell line models that recapitulate sRCC biology¹⁰. 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/CDKN2B¹⁰ and increased expression of cell cycle genes (i.e., E2F7)⁶. Epithelioid and sRCC components from the same patient generally share mutations, suggesting a common clonal origin^10,13–16, but there are no pathognomonic alterations that distinguish sarcomatoid differentiation. In contrast to transcriptional and genomic alterations in sRCC, the epigenomic programs that drive sarcomatoid differentiation remain uncharacterized¹.

Sarcomatoid differentiation is spatially heterogeneous, and sRCC and epithelioid RCC components often co-exist within a tumor. Therefore, the detection of sarcomatoid differentiation using tumor biopsy is challenging. In two case series, 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^17,18. This limitation is compounded by the fact that patients with metastatic RCC are increasingly diagnosed from single tissue biopsies as cytoreductive nephrectomies have fallen out of favor in metastatic RCC^19,20. Therefore, tractable biomarkers that reflect the aggregate burden of cancer are needed for sRCC diagnoses. To this end, recent studies have shown the promise of using blood biopsy to detect circulating epigenomic signatures of histologic variants of cancer^21–24.

In this study, we characterized the epigenomes of sarcomatoid and epithelioid ccRCC tumors from pathologically reviewed clinical tissue specimens. We hypothesized that sarcomatoid differentiation is driven by the activation of cis-regulatory elements (CREs) bound by a set of master transcription factors (TFs). We identified marked and consistent differences in the epigenomic profiles of sRCC compared to epithelioid ccRCC. Based on differential regulatory elements, we nominated candidate TFs that may drive epigenomic changes that characterize sRCC. We found that TFs implicated by epigenomic profiling are upregulated in sRCC and that their expression levels are associated with clinical outcomes across three clinical cohorts, including TCGA^2,3 and two large phase III randomized clinical trials: Javelin Renal 101 (JR101)²⁵ and IMmotion (IM151). We demonstrated that the expression of a candidate TF in an epithelioid ccRCC cell line model activates sarcomatoid-like gene expression patterns. Finally, we applied tissue-derived epigenomic signatures to detect sRCC from circulating chromatin in plasma using epigenomic liquid biopsy (Figure 1). Overall, our findings uncover epigenomic changes in gene regulation that may drive sarcomatoid differentiation in RCC and suggest an approach for blood-based detection of sarcomatoid differentiation to guide therapy selection.

**(A)** Epigenomic profiling of frozen tissue samples from patients with epithelioid or sarcomatoid ccRCC. **(B)** Stratification of tissue samples by subtype-specific epigenomic features and their correlation with clinical outcomes in three cohorts. **(C)** Detection of sarcomatoid-like gene expression patterns following activation of expression of a candidate transcription factor in an RCC cell line model. **(D)** 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, cfDNA: cell-free DNA.

Results:

We generated 107 tissue- and plasma-based epigenomic libraries 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

Using tissue samples, we examined the epigenomic landscape of sRCC (Table S1, Figure 2A). We performed chromatin immunoprecipitation sequencing (ChIP-seq) for post-translational histone modifications (H3K27ac and H3K4me2) and methylated CpG dinucleotide sequencing (MeDIP-seq). H3K27ac is associated with active promoters and enhancers²⁶, while H3K4me2 ChIP-seq captures active and poised promoters in addition to enhancers^27,28. 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 (Figure 2B). H3K27ac and H3K4me2 ChIP-seq signals overlapped different genomic annotations, with H3K4me2 capturing more promoters and H3K27ac capturing more promoter-distal CREs (Figure 2B), consistent with prior reports^29–31. Principal component analyses (PCA) (Figure 2C–E) and unsupervised hierarchical clustering (Figure S1A) of H3K27ac and H3K4me2 peaks and methylated CpG islands cleanly separated epithelioid and sRCC samples. Our results indicate consistent and widespread 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, Figure 2F). Signal profiles across the different epigenetic marks are displayed for representative genes in Figure S1B.

To evaluate the biological significance of sRCC- and epithelioid-enriched H3K27ac CREs (Figure 3A), we evaluated the enrichment of gene ontology (GO) terms of genes near differential CREs using GREAT³². We noted significant enrichment at the top 1,621 sRCC CREs (q < 0.01, LFC > 1) for gene pathways involved in immune cell activation and stimulation of an immune response (Figure 3B). These results are consistent with previous reports demonstrating that sRCC tumors show increased inflammatory gene expression signatures and CD8+ T cell infiltration and have higher expression of PD-1/PD-L1^10,12, which may partly explain their responsiveness to ICIs^6–8,10. Additionally, sRCC CREs demonstrated enrichment of annotations related to cellular morphology and extracellular matrix organization, which are expected in the setting of EMT, a defining feature of sarcomatoid differentiation³³. 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 (Figure 3C), likely reflecting the activity of hypoxia-inducible factors (i.e., HIF2α) that are implicated in angiogenesis and RCC carcinogenesis³⁴. These findings are consistent with observations from the IM151 trial³⁵, where sRCC tumors exhibited more immunogenic and less angiogenic gene expression profiles than their epithelioid counterparts⁶. Furthermore, comparing paired sarcomatoid and epithelioid samples from the same individuals (n=3), the aggregate H3K27ac signal at sarcomatoid-enriched CREs was consistently higher in the matched sarcomatoid samples compared to their epithelioid counterparts. This result indicates that epigenomic characteristics that define RCC were robust to variation across individuals (Figure S1C).

Differential methylation analysis showed that genes near hyper-methylated regions in sarcomatoid RCC were enriched for annotations related to regulation of stem cell differentiation and cell fate commitment, suggesting that these key pathways may contribute to sarcomatoid differentiation (Figure S1D). To evaluate the relationship of gene expression with sRCC-enriched H3K27ac ChIP-seq and MeDIP-seq sites (Tables S2 and S3, respectively), we incorporated RNA-seq data of genes upregulated in sRCC (n=6058) and epithelioid RCC (n=3734) from TCGA (FDR q < 0.05). Genes with higher expression in sRCC were enriched for overlap with CREs that have higher H3K27ac in sRCC (hypergeometric p < 2×10⁻¹⁶; Figure S1E). This result is expected, given that H3K27ac correlates with the activity of CREs. In contrast, sites with higher DNA methylation in sRCC did not overlap genes with higher expression (p = 1), consistent with the suppressive role of DNA methylation at gene promoters. These results suggest that the differential epigenomic features we identified may drive differences in gene expression between sRCC and epithelioid RCC.

Next, we analyzed differential H3K27ac CREs (q < 0.01, LFC > 1) for the enrichment of nucleotide motifs to identify TFs that bind to and activate these CREs. The top three motifs that were highly enriched at sarcomatoid-specific CREs were FOSL1, ETV4, and E2F7 (Figure 3D). The FOSL1 gene demonstrated a higher H3K27ac signal in sarcomatoid vs. epithelioid ccRCC samples (Figure 3E). The FOSL1 TF is a subunit of the AP-1 complex implicated in invasiveness and EMT in prostate³⁶, breast^37–39, and kidney cancers⁴⁰. Conversely, motif enrichment analysis of CREs downregulated in sRCC identified FOXO1, EPAS1, and HNF1β as the top candidate TFs (Figure 3F). EPAS1, which encodes HIF2α, is densely marked with H3K27ac in epithelioid ccRCC compared to sRCC (Figure 3G) and is a central TF in ccRCC oncogenesis^34,41. Overall, differential CREs pointed to biologically plausible TFs that may activate EMT programs in sRCC and implicated decreased activity of kidney lineage (HNF1β)⁴² and hypoxia-related (HIF2α) TFs in sarcomatoid differentiation.

Candidate sarcomatoid TFs are associated with clinical outcomes independently of histology

Having nominated TFs that may drive EMT-associated gene regulatory programs of sRCC, we tested whether expression levels of these TFs in RCC are associated with clinical outcomes. Using transcriptomic data from TCGA, JR101, and IM151, we identified genes that were significantly upregulated or downregulated in sRCC tumors (DESeq2, FDR q < 0.05) and selected the upregulated TFs. Strikingly, the top three sRCC TFs we nominated (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 S4).

Given the association of sarcomatoid differentiation with response to ICI, we tested whether candidate sRCC TFs were also associated with response to ICIs in patients with RCC. We focused on FOSL1 since it (1) was the top-ranked TF in the motif analysis, (2) showed increased H3K27ac signal at the FOSL1 gene locus, and (3) had the strongest association with shorter PFS in the clinical trial cohorts we examined. FOSL1 expression was higher in sRCC vs. epithelioid ccRCC in the TCGA KIRC cohort (q = 4.91×10⁻¹¹, LFC = 1.73), JR101 (q = 8.51×10⁻¹², LFC = 1.23), and IM151 (q = 2.11×10⁻¹¹, LFC = 0.64) (Figure 4A). We further used the paired sarcomatoid and epithelioid samples from the same individuals in our tissue cohort (n=3) and showed that the aggregate H3K4me2 and H3K27ac signals were consistently higher in the matched sarcomatoid samples compared to their epithelioid counterparts at the FOSL1 locus and FOSL1 protein binding sites, respectively (Figures S2A and S2B). These results demonstrate that FOSL1 is a differentiating biomarker that is upregulated in sRCC independent of inter-individual variability.

In a multivariable analysis accounting for the IMDC risk groups, prognostic factors in RCC⁴, patients with high (> median) FOSL1-expressing tumors had improved PFS when treated with ICI plus TKI combinations vs. sunitinib in both JR101 (adjusted HR: 0.53, 95%CI: 0.41 – 0.70, p <0.001, Figure 4B) and IM151 (adjusted HR: 0.71, 95%CI: 0.56 – 0.90, p = 0.004; Figure S2C). 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, Figure 4B) and IM151 (adjusted HR: 1.02; 95%CI: 0.80 – 1.32, p = 0.86, Figure S2C). We included an interaction term 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) (Figure 4B, Table S4). These results indicate that FOSL1 expression may be a biomarker for prolonged PFS in patients receiving ICIs + VEGFR TKIs in RCC.

Notably, while FOSL1 was upregulated in sRCC, many epithelioid ccRCC tumors also exhibited elevated FOSL1 expression levels (Figure 4A). We hypothesized that these tumors may behave similarly to sRCC and demonstrate poor outcomes with sunitinib and improved outcomes with ICI plus TKI combinations. Accordingly, we stratified patients 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) (Figure 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 JR101 (median PFS = 6.5 vs. 11.3 months; adjusted HR: 1.54; 95% CI: 1.14 – 2.06; p = 0.004) and IM151 (median PFS = 8.3 vs. 11.8 months; adjusted HR: 1.35; 95% CI: 1.03 – 1.76; p = 0.03, Figure 4D), and similar PFS compared to sRCC tumors in the sunitinib arm of both trials (Table S5). There were no significant differences in PFS between FOSL1-high and FOSL1-low epithelioid ccRCC subgroups in the ICI arms, suggesting that all subgroups derived similar benefit (Figure S2D). Taken together, the above findings indicate that elevated FOSL1-high epithelioid RCC is associated with worse outcomes when treated with TKI-only regimens but better outcomes with the addition of ICIs, similar to sRCC. These findings implicate FOSL1 expression as a biomarker that recapitulates clinical outcomes in patients with ccRCC, independent of observable sarcomatoid differentiation on histology.

FOSL1 induces sarcomatoid-associated gene expression changes in epithelioid ccRCC

To test whether FOSL1 drives processes observed in sRCC tumors (i.e., cell cycle progression and EMT)^10,43, we endogenously activated FOSL1 expression in the Caki-1 ccRCC cell line using CRISPRa. We evaluated the transcriptomic and epigenomic differences induced by activating endogenous FOSL1 expression using CRISPRa. In the absence of immune infiltration, RNA-seq data from triplicates of FOSL1 CRISPRa vs. control Caki-1 cells were cleanly separated in the PCA plot (Methods, Figure 5A). We next compared the differentially expressed genes (q < 0.05) between the two conditions. 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 = 1.62×10⁻¹⁰), and strikingly, EPAS1 was downregulated (LFC = −0.40, q = 9.95×10⁻⁶). The downregulation of EPAS1 suggests that FOSL1 may mediate the suppressed hypoxia and angiogenesis signaling observed in sRCC (Figure 5B).

Figure 5. — **(A)** Principal Component Analysis (PCA) plot of RNA-sequencing data from two conditions (*FOSL1* CRISPRa and negative control Caki-1 cells, n=3 independent biological replicates). **(B)** Volcano plot showing upregulated and downregulated genes in *FOSL1* CRISPRa vs. negative control Caki-1 cells (n=3 independent biological replicates). **(C)** Representative immunoblot image of FOSL1 and HIF2α protein levels in Caki-1 cell lines (negative control and *FOSL1* CRISPRa). **(D-E)** Quantification of FOSL1 and HIF2α protein levels represented as mean ± SD band intensity normalized to GAPDH levels (n=3 independent biological replicates). Protein expression level differences between negative control and *FOSL1* CRISPRa Caki-1 cells were analyzed by an unpaired t-test. Statistical significance p values are: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Error bars represent the standard deviation from the mean. **(F)** Normalized enrichment scores of pathways upregulated in *FOSL1* CRISPRa Caki-1 cells. **(G)** 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.

CRISPRa activates gene expression to physiologic levels under the activity of endogenous gene promoters, leading to lower levels of expression compared to ectopic overexpression experiments⁴⁴. Therefore, we tested differences in expression at the protein level, confirming that FOSL1 and HIF2α were upregulated (2.1-fold) and downregulated (0.69-fold, respectively (Figures 5C–E). Gene set enrichment analysis (GSEA) of FOSL1 CRISPRa vs. negative control Caki-1 cells revealed upregulation of seven gene sets (Figure 5F) with enrichment of programs related to cell cycle (E2F targets, G2M checkpoints) or proliferation (MYC targets), similar to prior observations^6,10. Furthermore, the H3K27ac signal at the promoters of upregulated genes with FOSL1 CRISPRa were increased compared to control Caki-1 cells, whereas they were decreased at the promoters of downregulated genes in CRISPRa FOSL1 (Figure S3A). Taken together, the observed changes in gene expression correlated with biologically relevant changes in epigenomic H3K27ac signals and followed expected trends for both upregulated and downregulated genes in each condition.

We next validated our findings in an external cohort. Using gene expression data from the JR101 trial, we defined 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. 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 (Figure 5G), indicating the robustness of our findings. The epithelioid ccRCC gene set is similarly enriched among the downregulated genes (NES = −1.43, q = 0.01, Figure S3B). In summary, these data demonstrate that overexpression of a single TF-encoding gene (FOSL1) upregulates transcriptional programs observed in sRCC.

Epigenomic signatures of sRCC are detectable from circulating chromatin

Detecting sarcomatoid features in RCC tumors is clinically important because this histologic feature predicts poor response to TKIs and heightened response to ICIs. However, the spatial heterogeneity of sarcomatoid differentiation may lead to false negative results when evaluated with tumor biopsy, likely leading to substantial underdiagnosis¹². To overcome this challenge, we investigated whether we could detect epigenomic features of sRCC in patient plasma (which can sample heterogeneity across tumor sites)^45,46 as a proxy for histopathologic sarcomatoid features. Using a novel liquid biopsy approach for epigenomic profiling from plasma²³, we profiled H3K4me3, H3K27ac and DNA methylation in plasma samples from an independent group of patients, separate from those whose tumor tissue samples were profiled. This cohort included plasma from patients with sarcomatoid ccRCC (n=5), epithelioid ccRCC (n=5), and healthy volunteers without a history of cancer (n=9) (Table S6, Figures 6A and 6B). We inferred the circulating tumor DNA (ctDNA) content in plasma samples by applying ichorCNA to LPWGS data⁴⁷. The average estimated ctDNA content (± standard deviation) was 0.024 (± 0.022) in plasma from patients with RCC (n=10) and 0 (± 0) in healthy volunteers (n=9) (Table S6). 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 (Figure 6C). PAX8 is a histopathologic marker for RCC⁴⁸ that is involved in kidney embryogenesis⁴⁹ and RCC. Furthermore, plasma H3K27ac signal was elevated in RCC patient plasma at RCC-specific regulatory elements previously defined in TCGA tumors based on chromatin accessibility⁵⁰. These findings confirmed that our plasma-based epigenomic assay can detect signals from circulating chromatin originating from RCC.

Figure 6. — **(A)** Schematic demonstrating the measurement of cfChIP and cfMeDIP signals at TF binding sites differentially methylated regions, respectively. **(B)** Epigenomic datasets generated from plasma. **(C)** H3K4me3 cfChIP profiles at PAX8 in plasma of patients with sRCC (n=2), epithelioid ccRCC (n=2) and healthy volunteers (n=4). **(D)** Aggregated H3K27ac cfChIP-seq signals in kidney cancer and healthy plasma at REs identified by ATAC-seq in ccRCC. **(E)** Aggregated cfMeDIP-seq signals at tissue-informed upregulated sarcomatoid-specific DMRs and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. **(F)** Aggregated H3K27ac cfChIP-seq signals at tissue-informed upregulated sarcomatoid-specific H3K27ac sites and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. **(G)** Aggregated H3K27ac cfChIP-seq signal at HIF2α binding sites for ccRCC and comparison between sRCC, epithelioid ccRCC, and healthy plasma, respectively. **(D-G)** Dark lines show the mean signal across all samples in the indicated class. Gray shades 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: ccRCC (red), healthy (gray), epithelioid ccRCC (blue), and sRCC (orange). ccRCC: clear cell renal cell carcinoma, Sarc: sarcomatoid ccRCC (sRCC), Epi: epithelioid ccRCC, RE: regulatory elements, DMR: differentially methylated regions.

Next, we evaluated whether we could capture sarcomatoid-specific epigenomic features in plasma. We focused on the set of sarcomatoid-enriched DMRs in tissue samples to inform cfMeDIP-seq in plasma. We found that there were higher aggregate methylation values at these sites in sRCC plasma samples compared to plasma from patients with epithelioid ccRCC (p = 7.9 × 10⁻³) or healthy volunteers (p = 4.7 × 10⁻⁴, Figure 6E). Similarly, sRCC-enriched CREs that were defined from tissue ChIP-seq data showed increased H3K27ac cfChIP-seq signal in plasma from patients with sRCC vs. those with epithelioid ccRCC (p = 7.9 × 10⁻³) or healthy volunteers (p = 3.4 × 10⁻⁴, Figure 6F). Importantly, we observed decreased H3K27ac in plasma at binding sites for HIF2α in sRCC compared to epithelioid ccRCC (p = 7.9 × 10⁻³; Figure 6G), consistent with decreased activity of this TF in sRCC. This finding aligns with observations that sarcomatoid differentiation is associated with the down-regulation of EPAS1 and HIF2α activity⁶, similar to its downregulation in the FOSL1 CRISPRa cell line model (Figure 5B). Overall, these results serve as proof of concept for the minimally invasive detection of sRCC from circulating chromatin using epigenomic signatures.

Discussion:

In this study, we present the first epigenomic characterization of sRCC from patient tissues and plasma. We demonstrate that highly recurrent gene regulatory programs are activated in sRCC compared to epithelioid ccRCC. These regulatory programs likely drive differential gene expression that has previously been reported between these groups^10,14. Identification of sRCC-enriched CREs enabled us to nominate candidate TFs that drive sRCC gene regulatory programs. Using data from TCGA and two clinical trials, we show that these TFs are upregulated in sRCC and that their expression levels are associated with worse clinical outcomes. We identify FOSL1 expression as a driver of sRCC and a biomarker in RCC. Finally, we identify epigenomic fingerprints of sRCC in plasma based on circulating DNA methylation and histone modifications.

Our findings provide biological insights into the development of sRCC by nominating key TFs that may orchestrate transcriptional programs associated with sarcomatoid differentiation. FOSL1 emerged as a top candidate master TF from motif enrichment analysis of sarcomatoid CREs. FOSL1 is a subunit of AP-1 involved in tumor cell proliferation, survival, and invasiveness⁵¹ that may collaborate with ETV4, another candidate sRCC TF, to promote EMT and metastasis in RCC⁴⁰. Importantly, activation of FOSL1 expression in an epithelial ccRCC cell line decreased expression of EPAS1 and upregulated genes involved with cell cycle progression and proliferation, consistent with expression differences in sRCC tumors observed in prior studies^6,10. The downregulation of the HIF2α gene EPAS1 with forced expression of FOSL1 suggests a mechanism by which FOSL1 may suppress hypoxia-related or angiogenic pathways. This may explain the decreased sensitivity of sRCC to TKIs that target angiogenic pathways and suggests that HIF2α inhibition may be less effective in sRCC tumors.

The TFs we implicated in sarcomatoid differentiation through epigenomic profiling 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, 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 raises the possibility that these tumors had epigenomic features of sRCC even though histologic sarcomatoid differentiation was not observed. Our results suggest that clinical stratification based on the expression of sRCC TFs may augment classification provided by histopathologic findings alone.

This is especially helpful in the context of sRCC tumors that are heterogenous morphologically and clinically with evidence of variable response rates to immunotherapy. Many factors beyond sarcomatoid differentiation have been demonstrated to correlate with immune response, both within the tumor, the immune environment, and even the microbiome^11,52–54. sRCC tumors are a subset of RCC tumors characterized by particularly aggressive disease, who although tend to respond well to immunotherapy, continue to have inferior outcomes compared to epithelioid tumors indicating its unique features. There is also likely heterogeneity in the degree of sarcomatoid differentiation across tumors which may further explain differential response rates in sarcomatoid RCC. Epigenomic profiling enables the identification of gene regulatory programs underlying cell states and may be a cleaner way of further capturing unique features or cancer subgroups with similar clinical and pathologic features. This is clinically valuable since it may aid in improving our understanding of sRCC biology to improve regimen selection in the clinic (i.e., using dual ICI or. VEGF/ICI combination therapies)⁵⁵.

Because sarcomatoid differentiation is spatially heterogeneous, sampling error poses a significant challenge in the diagnosis of sRCC. Prior studies demonstrated low sensitivity (~8%)¹⁷ for detecting sarcomatoid differentiation from tissue biopsies. Accurate diagnosis of sRCC is essential since patients benefit more from ICI-based regimens than TKIs. Since liquid biopsies can sample heterogeneity across tumor sites in advanced cancers^46,56–58, a ctDNA-based correlate of sarcomatoid differentiation could overcome issues of sampling error. Utilizing epigenomic profiling of circulating DNA methylation and histone modifications, we detected signatures of sarcomatoid differentiation in patient plasma that distinguished sRCC from epithelioid RCC in plasma samples with low ctDNA (<0.06). Importantly, these signatures were defined entirely using tumor tissue, bolstering biological plausibility and interpretability. In addition, we observed signals of decreased enhancer activity at HIF2α binding sites in sRCC plasma compared to epitheliod RCC plasma, consistent with observations from tumor tissue that HIF2α is downregulated in sRCC. These findings suggest that epigenomic liquid biopsies in RCC could identify patients with sarcomatoid epigenomic features who would benefit from ICI.

In conclusion, our work demonstrates that sarcomatoid differentiation of RCC is associated with consistent reprogramming of regulatory element activity that is likely driven by FOSL1 and other EMT-related TFs. We implicated FOSL1 as a biomarker of improved outcomes with ICIs independent of histological evidence of sarcomatoid differentiation. These findings may improve clinical stratification and therapy selection for patients with RCC and inform the discovery of new therapies for sRCC, as targeting cancer-associated transcriptional regulation becomes tractable^59,60. Finally, our approach represents a broadly applicable framework for using epigenomic liquid biopsy to infer histologic features that can currently only be assessed from tissue.

Limitations of the study:

Although our cohort represents the largest published epigenomic profiling study of sRCC, our tissue and plasma sample sizes are limited. Furthermore, samples were obtained from a single large academic center where patients may be enriched for unique tumor characteristics. However, it was reassuring that observations seen in tissue samples (i.e., decreased activation of HIF2α signaling pathways) were conserved in an independent plasma cohort. In addition, our functional experiments evaluated one TF in a single epithelioid ccRCC cell line (Caki-1). Validating other candidate TFs across multiple RCC cell lines is warranted. Besides that, our work focused only on ccRCC tumors. Due to the lack of sample availability, we could not characterize the epigenomic profiles of sRCC in non-ccRCC tumors. Moreover, sufficient tissue was not available to perform RNA sequencing or immunohistochemistry to respectively measure gene and protein expression levels in our samples directly. Finally, our bulk epigenomic assays could not capture intratumoral heterogeneity and assess its spatial distribution, which could be further explored in spatial profiling and single-cell studies

STAR★Methods:

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Sylvan C. Baca (sylvan_baca@dfci.harvard.edu).

Materials availability

This study did not generate unique reagents.

Data and code availability

The epigenomic data are available through Gene Expression Omnibus (GEO) under accession numbers GSE268155, GSE188486, GSE243474.
The following public datasets were used: DNAse hypersensitivity sites (https://zenodo.org/record/3838751/files/DHS_Index_and_Vocabulary_hg19_WM20190703.txt.gz), TCGA ATAC-seq peak calls (https://api.gdc.cancer.gov/data/116ebba2-d284-485b-9121-faf73ce0a4ec; lifted over to hg19 from hg38).
This paper does not report original code.
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

Experimental model and study participant details

Sarcomatoid and epithelioid ccRCC clinically annotated tumor tissue specimens were collected under a DFCI/Harvard Cancer Center IRB-approved protocol (01–045) with the written informed consent of all patients. No monetary compensation was offered for patient participation. The samples were then stored fresh and frozen in the Gelb Center biobank at the Dana-Farber Cancer Institute under a protocol approved by the MGB Institutional Review Board ⁶¹. 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.

Method details

Chromatin immunoprecipitation (ChIP) in RCC tissue specimens.

For tissue specimens, a 2-mm² core 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 μl 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% SDS, 1% Triton X-100, 2 mM 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

ChIP-sequencing reads were aligned to the human genome build hg19 using the Burrows-Wheeler Aligner (BWA) version 0.7.17⁶⁴. Non-uniquely mapped and redundant reads were discarded. MACS v2.1.1.20140616⁶⁵ was 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 annotatr⁶⁶. IGV v2.8.2⁶⁷ was used to visualize normalized ChIP-seq read counts at specific genomic loci. ChIP-seq heatmaps were generated with deepTools v3.3.1⁶⁸ 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

Sample–sample clustering, principal component analysis, and identification of lineage-enriched peaks were performed using Cobra v2.0⁶⁹ (https://bitbucket.org/cfce/cobra/src/master/), a ChIP-seq analysis pipeline implemented with Snakemake⁷⁰. 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 v1.14.1⁷¹, histology-enriched peaks were identified at the indicated FDR-adjusted p-value (p_adj) and log2 fold-change cutoffs (H3K27ac, p_adj < 0.05, |log2 fold-change| >0; H3K4me2, p_adj < 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 analysis³² (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

RNA-seq and clinical data was extracted from the cancer genome atlas (TCGA) and two clinical trials JavelinRenal101 (JR101) that randomized patients to avelumab plus axitinib combination or sunitinib arms, and IMmotion151 (IM151), that randomized patients to atezolizumab plus bevacizumab combination or sunitinib arms. Analyses were performed 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 significant⁷¹. 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

Guides were cloned into a pXPR_502, following a previously published cloning protocol⁶²: sgCtrl: F: 5’-CACCGCGCCAAACGTGCCCTGACGG-3’, R: 5’-AAACCCGTCAGGGCACGTTTGGCGC-3’, sgFOSL1: F: 5’-CACCGGGGCTGAACCACTGCGACCG-3’, R: 5’-AAACCGGTCGCAGTGGTTCAGCCCC-3’ 24 h before transfection, HEK293T cells were seeded 10cm dishes at a density of 5 × 10⁶ cells. PEI-Transfection was performed following the manufacturer’s protocol. Briefly, one solution of Opti-MEM (100 μL) and PEI MAX (40μL) was combined with a DNA mixture of the packaging plasmid pMD2.G (2μg), psPAX2 (3 μg), and the transfer vector (5 μg). 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-15 50 kDa, at 1500g for 30 min and stored at −80°C.

Lentiviral infection was performed on Caki-1 cells with the following conditions: 200μL of concentrated virus was added to 2 × 10⁵ cells in replicates with 4μg/mL of polybren 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.

Caki-1 were first infected with pXPR_109 (dCas9-VP64) and selected with blasticidin (5μg/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 μg/mL) and blasticidin to maintain the dCas9-VP64 expression.

RNA methods

RNA was extracted using the Qiagen RNeasy Mini Kit (Cat No./ID: 74104) as recommended, from frozen tumor samples adjacent to those samples used for ChIP. RNA-seq libraries were constructed from 1 μg total RNA using the Illumina TruSeq Stranded mRNA LT Sample Prep Kit according to the manufacturer’s protocol. Barcoded libraries were pooled and sequenced on the Illumina HiSeq 2500 generating 50-bp paired-end reads.

Functional validation RNA-seq data

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 significant⁷¹. Gene Set Enrichment Analysis (GSEA) was computed between FOSL1 CRISPRa and negative control Caki-1 cells (GSEA q < 0.01)⁷².

Immunoblotting

Cells were washed with cold phosphate buffer saline (Gibco #14190) and lysed in RIPA buffer (Sigma-Aldrich, #R0278–50ML) supplemented with protease inhibitor (Roche, #11873580001) and phosphatase inhibitor (CST #5870S). Protein levels in the protein extracts were quantified using BCA Protein Assay Kit (Pierce, #23227) and the protein extracts were separated using SDS-PAGE under denaturing conditions (Bio-Rad 4x Laemmli sample buffer #1610747 supplemented with β-Mercaptoethanol; Bio-Rad 10x Tris/Glycine/SDS running buffer #1610732; 4–20% Novex^™ Wedgewell^™ Tris-Glycine Gels #4561096) and were transferred to PVDF membranes (iBlot^™ 2 PVDF Regular Stacks, #IB24001) using iBlot^™ 2 Gel Transfer system #IB21001. Membranes were blocked with blocking buffer (InterceptTM Blocking buffer, #927–60001) and incubated with the primary antibodies (FRA1/FOSL1 Rabbit #5281, 1:1000; GAPDH Mouse #97166, 1:2000; HIF2α/EPAS1 Rabbit #NB100–122, 1:1000) diluted in blocking buffer and incubated overnight at +4°C. After primary antibody incubation, membranes were washed 3 times with TBST (CST #9997) and incubated with fluorophore-conjugated secondary antibodies diluted in blocking buffer at room temperature for 1 hour (StarBright^™ Blue 700 Goat Anti-Rabbit IgG #12004161, 1:20,000; StarBright^™ Blue 520 Goat Anti-Mouse IgG #12005867, 1:20,000). Membranes were then scanned using Bio-Rad ChemiDoc^™ MP Imaging System. The fluorescent signal was quantified as mean integrated densities of protein bands with ImageJ (v. 1.54f) and normalized to the corresponding GAPDH signal using GraphPad Prism v10.2.”

cfDNA processing and tumor content calculation

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 by 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 QIAGEN 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 parameters⁴⁷.

MeDIP-seq

MeDIP-seq was performed on tissue and plasma samples using previously published methods⁷⁸. cfDNA library preparation was performed on 10 ng of DNA using the KAPA HyperPrep Kit (KAPA Biosystems) according to the manufacturer’s protocol. We then performed end-repair, A-tailing, and ligation of NEBNext adaptors (NEBNext Multiplex Oligos for Illumina kit, New England BioLabs). Libraries were digested using the USER enzyme (New England BioLabs). λ 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 μmol/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

After sequencing, the quality and quantity of the raw reads were examined using FastQC version 0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and MultiQC version 1.7⁷³. Raw reads were quality, and adapter trimmed using Trim Galore! version 0.6.0 (http://www.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 default⁷⁴. The SAMtools version 1.10 software suite was used to convert SAM alignment files to BAM format, sort and index reads, and remove duplicates⁷⁵. The R package RSamtools version 2.2.1 was 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 1.38.0⁷⁶.

Tissue-informed approach for detection of sarcomatoid features using MeDIP-seq

To identify differentially methylated regions (DMR) between sarcomatoid and non-sarcomatoid ccRCC samples, we first binned the genome 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)^77,79. 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. We restricted our search to bins within annotated CpG islands and FANTOM5 enhancers and excluded regions of high signal or poor mappability ^66,80. We selected DMRs with read enrichment in sRCC compared with epithelioid RCC at FDR-adjusted P < 0.01 and log₂ fold-change > 0. We removed windows with peaks in MeDIP-seq data from white blood cells (as determined by MACS2, version 2.1.2) to minimize signal from blood cell–derived cfDNA⁶⁵. Using the MeDIPs R package, we calculated CpG-normalized relative methylation scores (RMS) across 300 bp windows for each cfDNA sample^76,81. We then summed RMS in cfDNA at sarcomatoid-enriched DMRs 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.” We then calculated the log₂ ratio of the Sarcomatoid RCC Methylation Value to the Non-sarcomatoid RCC Methylation Value and normalized these values to the median score in cfDNA from eight healthy cancer-free controls. This value was termed the “Sarcomatoid RCC Risk Score.”

cfChIP-seq assay

1 μg of antibody was coupled with 10 μL protein A (Invitrogen, cat# 10002D) and 10 μL 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 (Gibco, cat# 14190250), followed by blocking with 1% BSA in PBS for 1 hr at 4°C with rotation. The following antibodies were used: H3K4me3, Thermo Fisher # PA5–27029; H3K27ac, Abcam # ab4729. Thawed plasma was centrifuged at 3,000g for 15 min at 4°C. The supernatant was precleared with the magnetic beads with 20 μL protein A and 20 μL 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. 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 100μL 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).

Quantification and statistical analysis

Multivariable Cox regression was used to compare outcomes of high vs. low-expression level groups in clinical cohorts, accounting for IMDC risk group. Wilcoxon rank-sum test was used to compare epigenomic signal levels between RCC subtypes and healthy plasma samples, respectively, at sites of interest. Western blot experiments were analyzed by using unpaired Student’s t tests. Statistical significance p values are: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. All statistical tests were two-sided except where otherwise indicated. Statistical analyses were performed using R version 4.2.1.

Supplementary Material

NIHMS2056089-supplement-Supplementary_Material.pdf^{(3.5MB, pdf)}

Supplementary Table 2

Table S2. Differential H3K27ac sites enriched in sarcomatoid RCC

NIHMS2056089-supplement-Supplementary_Table_2.xlsx^{(460.3KB, xlsx)}

Supplementary Table 3

Table S3. Differential methylated regions (DMRs) enriched in sarcomatoid RCC

NIHMS2056089-supplement-Supplementary_Table_3.xlsx^{(536.1KB, xlsx)}

Key Resources Table:

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Rabbit polyclonal anti-H3K27ac	Diagenode	Cat#C15410196
Rabbit polyclonal anti-H3K4me2	Diagenode	Cat#C15410035
Rabbit polyclonal anti-H3K4me3	ThermoFisher	Cat#PA5–27029
Rabbit polyclonal anti-H3K27ac	Abcam	Cat#ab4729
Biological Samples
OCT-embedded frozen human tissue kidney cancer specimens	Dana-Farber Cancer Institute	N/A
Plasma samples from patients with RCC and healthy controls	Dana-Farber Cancer Institute	N/A
Chemicals, Peptides, and Recombinant Proteins
PEI MAX	Polyscience	24765–100
Amicon^® Ultra-15 50 kDa	Millipore Sigma	UFC9050
Polybrene	Santa Cruz Biotechnologies	SC-134220
Critical Commercial Assays
ThruPLEX DNA-Seq Kit	TakaraBio	Cat#R400675
Qiagen RNeasy Mini Kit	Qiagen	Cat#74104
QIAamp Circulating Nucleic Acids Kit	Qiagen	Cat#55114
KAPA HyperPrep Kit	Roche	KK8504
MagMeDIP Kit	Diagenode	Cat#C02010021
iPure Kit v2	Diagenode	Cat#C03010015
KAPA HiFi Hotstart ReadyMix	KAPA Biosystems	KK2602
Deposited Data
ChIP seq and MeDIP seq raw and processed data	This paper, Nassar et al⁶¹, Baca et al²³	Available through Gene Expression Omnibus (GEO) under accession numbers GSE268155, GSE188486, GSE243474.
Experimental Models: Cell Lines
293T	ATCC	CRL-3216
Caki-1	ATCC	HTB-46
Oligonucleotides
sgCtrl: F: 5’-CACCGCGCCAAACGTGCCCTGACGG-3’, R: 5’-AAACCCGTCAGGGCACGTTTGGCGC-3’	Integrated DNA Technologies (IDT)	N/A
sgFOSL1: F: 5’-CACCGGGGCTGAACCACTGCGACCG-3’, R: 5’-AAACCGGTCGCAGTGGTTCAGCCCC-3’	Integrated DNA Technologies (IDT)	N/A
Recombinant DNA
pMD2.G	Didier Trono	Addgene: 12259
psPAX2	Didier Trono	Addgene: 12260
Lenti dCAS-VP64_Blast	Konermann et al⁶²	Addgene: 61425
pXPR-502	Sanson et al⁶³	Addgene: 96923
Software and Algorithms
Burrows-Wheeler Aligner	Langmead et al⁶⁴	https://bio-bwa.sourceforge.net/
MACS	Zhang et al⁶⁵	http://liulab.dfci.harvard.edu/MACS/
Annotatr	Cavalcante et al⁶⁶	https://bioconductor.org/packages/release/bioc/html/annotatr.html
IGV	Robinson et al⁶⁷	https://igv.org/
deepTools	Ramirez et al⁶⁸	https://deeptools.readthedocs.io/en/develop/
CoBRA	Qiu et al⁶⁹	https://bitbucket.org/cfce/cobra/src/master/
Snakemake	Köster et al⁷⁰	https://snakemake.readthedocs.io/en/stable/
DESeq2	Love et al⁷¹	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
GREAT	McLean et al³²	https://great.stanford.edu/great/public/html/
GSEA	Subramanian et al⁷²	https://www.gsea-msigdb.org/gsea/index.jsp
ichorCNA R package	Adalsteinsson et al⁴⁷	https://rdrr.io/github/broadinstitute/ichorCNA/
FastQC	N/A	http://www.bioinformatics.babraham.ac.uk/projects/fastqc
MultiQC	Ewels et al⁷³	https://multiqc.info/
Trim Galore!	N/A	http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
Bowtie2	Langmead et al⁷⁴	http://bowtie-bio.sourceforge.net/bowtie2/
SAMtools	Li et al⁷⁵	https://www.htslib.org/
MEDIPS	Lienhard et al⁷⁶	https://bioconductor.org/packages/release/bioc/html/MEDIPS.html
limma-voom	Law et al⁷⁷	https://rdrr.io/bioc/limma/man/voom.html

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Acknowledgments:

We would like to thank the patients treated at Dana-Farber Cancer Institute who generously donated tissue and blood samples that made this research possible. We are also grateful for the generous support of Debbie and Bob First. S.C.B is supported by the Department of Defense Award W81XWH-21-1-0299, the Kure It Cancer Research Foundation, and the Damon Runyon Cancer Research Foundation. M.L.F. is supported by the Claudia Adams Barr Program for Innovative Cancer Research, the H.L. Snyder Medical Research Foundation, the Cutler Family Fund for Prevention and Early Detection, the Donahue Family Fund, the Department of Defense Awards (W81XWH-21-1-0234, W81XWH-21-1-0339, W81XWH-19-1-0554), NIH Awards (R01CA251555 and R01CA227237), and the Movember PCF Challenge Award. T.K.C. is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE (2P50CA101942-16) and Program (5P30CA006516-56), the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, Pan Mass Challenge, Hinda and Arthur Marcus Fund and Loker Pinard Funds for Kidney Cancer Research at DFCI. M.M. Pomerantz is also supported by John and Ann Hall; Rebecca and Nathan Milikowsky Family Foundation.

S.R.V. acknowledges Support from the Doris Duke Charitable Foundation (Clinical Scientist Development Award, 2020101), R01CA279044, and the V Foundation (V Scholar Award, V2022-018). S.R.V.: Consulting (current or past 3 years): Jnana Therapeutics. Research support from Bayer. Spouse is an employee of and holds equity in Kojin Therapeutics. D.A.B. reports honoraria from LM Education/Exchange Services, advisory board fees from Exelixis and AVEO, consulting fees from Merck, Pfizer, and Elephas, equity in Elephas, Fortress Biotech (subsidiary), and CurIOS Therapeutics, personal fees from Schlesinger Associates, Cancer Expert Now, Adnovate Strategies, MDedge, CancerNetwork, Catenion, OncLive, Cello Health BioConsulting, PWW Consulting, Haymarket Medical Network, Aptitude Health, ASCO Post/Harborside, Targeted Oncology, Accolade 2nd.MD, DLA Piper, AbbVie, Compugen, Link Cell Therapies, and Scholar Rock, and research support from Exelixis and AstraZeneca, outside of the submitted work. S.C.B., T.K.C. and M.L.F. are co-founders and shareholders of Precede Biosciences. SCB is supported by grant W81XWH-21-1-0299 from the Department of Defense, a Clinical Investigator Award from the Damon Runyon Cancer Research Foundation, the Kure It Cancer Research Foundation, and the Fund for Innovation in Cancer Informatics.

Footnotes

Declaration of interests:

The remaining authors report no competing interests.

^ Figures in this article were created with BioRender.com.

Supplemental Information:

Document S1. Figures S1–S3 and Table S1, S3–S6.

Table S2-S3. Excel file containing additional data too large to fit in a PDF

References:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material

NIHMS2056089-supplement-Supplementary_Material.pdf^{(3.5MB, pdf)}

Supplementary Table 2

Table S2. Differential H3K27ac sites enriched in sarcomatoid RCC

NIHMS2056089-supplement-Supplementary_Table_2.xlsx^{(460.3KB, xlsx)}

Supplementary Table 3

Table S3. Differential methylated regions (DMRs) enriched in sarcomatoid RCC

NIHMS2056089-supplement-Supplementary_Table_3.xlsx^{(536.1KB, xlsx)}

Data Availability Statement

The epigenomic data are available through Gene Expression Omnibus (GEO) under accession numbers GSE268155, GSE188486, GSE243474.
The following public datasets were used: DNAse hypersensitivity sites (https://zenodo.org/record/3838751/files/DHS_Index_and_Vocabulary_hg19_WM20190703.txt.gz), TCGA ATAC-seq peak calls (https://api.gdc.cancer.gov/data/116ebba2-d284-485b-9121-faf73ce0a4ec; lifted over to hg19 from hg38).
This paper does not report original code.
Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.

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PERMALINK

Epigenomic signatures of sarcomatoid differentiation to guide the treatment of renal cell carcinoma

Talal El Zarif

Karl Semaan

Marc Eid

Ji-Heui Seo

Simon Garinet

Matthew P Davidsohn

Pranshu Sahgal

Brad Fortunato

John Canniff

Amin H Nassar

Sarah Abou Alaiwi

Ziad Bakouny

Gitanjali Lakshminarayanan

Hunter Savignano

Kevin Lyons

Sayed Matar

Atef Ali

Eddy Saad

Renee Maria Saliby

Paulo Cordeiro

Ziwei Zhang

Nourhan El Ahmar

Yasmin Nabil Laimon

Chris Labaki

Valisha Shah

Dory Freeman

Jillian O’Toole

Gwo-Shu Mary Lee

Justin Hwang

Mark Pomerantz

Sabina Signoretti

Eliezer M Van Allen

Wanling Xie

Jacob E Berchuck

Srinivas R Viswanathan

David A Braun

Toni K Choueiri

Matthew L Freedman

Sylvan C Baca

Summary:

Graphical Abstract

Introduction:

Figure 1. Tissue- and plasma-based epigenomic characterization of sRCC.

Results:

Sarcomatoid and epithelioid ccRCC exhibit distinct epigenomic profiles

Figure 2. H3K27ac, H3K4me2, and methylated CpG dinucleotide regions between sarcomatoid and epithelioid ccRCC reflect distinct epigenomic programs.

Figure 3. Sarcomatoid-enriched CREs are associated with immune system activation and drivers of EMT.

Candidate sarcomatoid TFs are associated with clinical outcomes independently of histology

Figure 4. FOSL1 is upregulated in sRCC and is associated with clinical outcomes.

FOSL1 induces sarcomatoid-associated gene expression changes in epithelioid ccRCC

Figure 5. Expression of FOSL1 in an epithelioid ccRCC cell line activates sRCC transcriptional programs.

Epigenomic signatures of sRCC are detectable from circulating chromatin

Figure 6. Tissue-informed epigenomic signatures enable the detection of sRCC in plasma.

Discussion:

Limitations of the study:

STAR★Methods:

Resource availability

Lead contact

Materials availability

Data and code availability

Experimental model and study participant details

Method details

Chromatin immunoprecipitation (ChIP) in RCC tissue specimens.

ChIP-seq data analysis

Identification and annotation of histology-specific peaks

TCGA and Clinical Trials Data

Generation of stable CRISPRa FOSL1 cell lines

RNA methods

Functional validation RNA-seq data

Immunoblotting

cfDNA processing and tumor content calculation

MeDIP-seq

MeDIP-seq quality control and processing of sequencing reads

Tissue-informed approach for detection of sarcomatoid features using MeDIP-seq

cfChIP-seq assay

Quantification and statistical analysis

Supplementary Material

Acknowledgments: