SIGNIFICANCE:We describe a paradigm wherein combined high TLS and low tissue-resident exhausted CD8+ T cells are required for optimal response to PD-1 blockade in RCC. This analysis identifies key determinants of response to PD-1 blockade in advanced RCC and suggests avenues for future immune modulation through rational combination therapy strategies.

Translocation renal cell carcinoma (tRCC) is an aggressive subtype of kidney cancer driven by TFE3 gene fusions, which act via poorly characterized downstream mechanisms. Here we report that TFE3 fusions transcriptionally rewire tRCCs toward oxidative phosphorylation (OXPHOS), contrasting with the highly glycolytic nature of most other renal cancers. Reliance on this TFE3 fusion-driven OXPHOS programme renders tRCCs vulnerable to NADH reductive stress, a metabolic stress induced by an imbalance of reducing equivalents. Genome-scale CRISPR screening identifies tRCC-selective vulnerabilities linked to this metabolic state, including EGLN1, which hydroxylates HIF-1α and targets it for proteolysis. Inhibition of EGLN1 compromises tRCC cell growth by stabilizing HIF-1α and promoting metabolic reprogramming away from OXPHOS, thus representing a vulnerability for OXPHOS-dependent tRCC cells. Our study defines tRCC as being dependent on a mitochondria-centred metabolic programme driven by TFE3 fusions and nominates EGLN1 inhibition as a therapeutic strategy in this cancer. Li et al. show that the TFE3 driver fusion transcriptionally upregulates an OXPHOS programme in translocation renal cell carcinoma that renders this cancer vulnerable to reductive stress, induced by imbalance of reducing equivalents.

4536 Background: We have previously shown that levels of antigen-experienced but not terminally exhausted CD8 + TILs (i.e. CD8 + PD1 + TIM3 - LAG3 - TILs) are associated with improved clinical outcomes in patients (pts) with metastatic clear cell RCC (mccRCC) treated with nivolumab monotherapy within three independent clinical trials (CheckMate-010, CheckMate-025, HCRN GU16-260). Here, we aimed to evaluate the performance of this biomarker in patients with mccRCC treated with nivolumab plus ipilimumab (Nivo+Ipi) versus sunitinib (Sun) as part of the CheckMate-214 (CM-214) clinical trial. Methods: Pre-treatment, formalin fixed paraffin embedded (FFPE) tumor samples from the CM-214 clinical trial were stained with a multiplex immunofluorescence (mIF) assay and the density of CD8+PD1+TIM3-LAG3- TILs (mIF biomarker) was quantified using Halo image analysis software. The correlation between the mIF biomarker and clinical outcomes, including progression-free survival (PFS), and objective response rate (ORR), was evaluated using Cox proportional hazards and logistic regression models. The significance level was set at 5% (2-sided). Results: Following quality control, mIF data were obtained for 255 intermediate-and poor-risk patients (Nivo+Ipi= 136, Sun= 119). A comparison between patients with mIF biomarker data available and those without, showed no imbalance with regards to ORR and PFS in the Nivo+Ipi group. However, in the Sun group, pts with mIF biomarker data showed a higher ORR compared to pts without available data (33% vs 24%). In Nivo+Ipi-treated pts, the mIF biomarker measured as continuous variable showed a trend for a positive association with ORR (OR = 1.23, p = 0.1162) but no association with PFS (HR 0.97, p = 0.688). In Sun-treated pts, the mIF biomarker measured as continuous variable showed a trend for a positive association with ORR (OR = 1.32, p = 0.0726) and a significant positive association with PFS (HR= 0.78, p = 0.004. However, given that Sun-treated pts with available mIF data were enriched for responders, results obtained in the Sun group should be interpreted with caution. Conclusions: In contrast to Nivo monotherapy, pre-treatment levels of CD8+ PD1+ TIM-3-LAG-3- TILs were not associated with improved clinical outcomes to Nivo+Ipi. These results suggest that the baseline levels of intratumoral T-cell inflammation do not represent a strong determinant of response to anti-CTLA4-based therapy in mccRCC, which is consistent with the knowledge that anti-CTLA4 therapy (Ipi) can recruit new T cells into the tumor. The association between CD8+ PD1+ TIM-3-LAG-3- TILs and outcomes on sunitinib needs further investigation.

476 Background: ChRCC is a rare form of kidney cancer with a poor prognosis in the metastatic setting, in part due to very limited responses to immune checkpoint inhibitors (ICIs), as compared to clear cell RCC (ccRCC). The mechanisms underlying the poor response of ChRCC to ICIs remain largely uncharacterized. We therefore investigated at the single-cell resolution the cellular and molecular determinants of anti-tumor immunity in ChRCC. Methods: ChRCC samples with matched normal kidney specimens were evaluated using single-cell RNA (scRNA-seq) and single-cell T-cell receptor (scTCR-seq) sequencing. Similar data (scRNA-seq and scTCR-seq) was obtained for ccRCC samples (Braun DA. et al., 2021). T cell clonotypes were inferred and classified into their degree of expansion (poorly, moderately and highly expanded). Diversity metrics (normalized Shannon’s entropy) were calculated. Using a previously described methodology (Young M.D. et al., 2018), the cell of origin (COi) of ChRCC was inferred from scRNA-seq data of normal kidney samples, followed by differential gene expression (DGE) and pathway analysis (DPA) between the putative COi and ChRCC cells to identify potential mediators of diminished immune responses. Immunohistochemistry (IHC) of ChRCC and ccRCC samples was used to assess CD8+ and PD-1+ immune cell populations. Results: Analysis of the scTCR-seq data identified a higher proportion of poorly expanded clonotypes in ChRCC as compared to ccRCC (p=0.05), along with a lower proportion of highly expanded clonotypes (p=0.07). Normalized (Shannon’s) entropy was found to be higher in ChRCC versus ccRCC (p<0.05). Analysis of annotated scRNA-seq data identified a lower proportion of CD8+ and CD4+ T-cells among immune cells in ChRCC vs. ccRCC (44.6 vs. 9.6% and 12.3 vs. 3.2%, respectively). DGE between ChRCC and its putative COi (alpha-intercalated cell) showed a lower expression of HLA class I genes in ChRCC (p<0.05). DPA showed a marked downregulation of antigen presentation and protein processing pathways in ChRCC (p<0.05). IHC analysis showed a markedly low infiltration of CD8+ and PD-1+ immune populations in ChRCC, as compared to ccRCC. Conclusions: ChRCC cells have marked downregulation of HLA class I genes and antigen processing pathways related to their COi. Additionally, ChRCC tumors have poor infiltration of T-cells, which show a low degree of clonal expansion. These mechanisms may help to explain the limited anti-tumor immunity and ultimately, the poor response to ICIs seen among patients with ChRCC.

4543 Background: High expression of the receptor tyrosine kinase AXL has been associated with resistance to targeted agents and anti-PD-1 therapy in multiple tumor types, including metastatic clear cell Renal Cell Carcinoma (mccRCC). Cabozantinib (cabo), a multitargeted tyrosine kinase inhibitor (TKI) active against VEGFR, MET, and AXL, is approved for the treatment of mccRCC, but no predictive biomarkers are available. Here, we analyzed tumor samples from the METEOR trial to assess whether AXL expression predicts improved clinical outcomes with cabo relative to everolimus (evero, mTOR inhibitor) in patients (pts) with mccRCC. Methods: Double immunohistochemistry staining for AXL and CD45/CD163 (immune cell/ macrophage markers) was performed on 278 pretreatment tumor tissues from the METEOR trial (cabo=147; evero=131). Image analysis algorithms were used to assess the % of AXL+ tumor cells (TC), % of AXL+ immune cells (IC), and combined % of AXL+ TC and IC (TC/IC). Optimal cutoffs, based on the minimum p-value approach, were determined at the 17 th , 30 th , and 22 nd percentile for TC, IC, and TC/IC, respectively. The association with progression-free survival (PFS) was assessed using Cox regression adjusted for IMDC risk groups, the presence of bone metastases, and the number of previous VEGFR TKI treatments. The association with objective response rate (ORR) (cabo arm only due to low response rate in evero arm) was reported descriptively. Results: We observed a strong correlation between % AXL+ TC and % AXL+ IC (Spearman correlation coefficient 0.85; p-value <0.001). In the evero arm, high % AXL+ TC and high % AXL+ TC/IC were significantly associated with shorter PFS (median 1.9 vs. 4.1 months and 1.9 vs 4.2 months, respectively); a similar trend was observed for % AXL+ IC. In the cabo arm, % AXL+ TC, % AXL+ IC, and % AXL+ TC/IC were not associated with PFS or ORR (Table). The magnitude of increase in PFS for pts receiving cabo versus evero was larger in pts with high % AXL+ TC compared to pts with low % AXL+ TC (HR: 0.30 vs. 0.51, p-interaction=0.18) and in pts with high % AXL+ TC/IC compared to pts with low % AXL+ TC/IC (HR: 0.30 vs 0.53, p-interaction=0.12). Conclusions: Our finding that the improvement in outcome with cabo relative to evero is more evident in pts with high levels of AXL expression supports that the antitumor activity of cabo in mccRCC is mediated, in part, by AXL inhibition. Our work identifies AXL expression as a potential predictive biomarker for cabo-based therapies in mccRCC. [Table: see text]

449 Background: Sarcomatoid differentiation (SD) in renal cell carcinoma (RCC) is associated with poor survival and heightened response to immune checkpoint blockade. Detection of SD can be challenging due to spatial heterogeneity and sampling error. Herein, we introduce a novel tissue–informed epigenomic approach to noninvasively identify sarcomatoid differentiation in patients with RCC from cell-free DNA (cfDNA) using <1mL of plasma. Methods: Methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) was performed on pathologically reviewed clear cell RCC frozen tissue samples with and without SD (sarcomatoid-RCC and epithelioid-RCC, resp.) collected at the Dana-Farber Cancer Institute. Differentially methylated regions (DMRs) between sarcomatoid and epithelioid subtypes were identified using DESeq2 (false discovery rate of q < 0.01). Then, MeDIP-seq was performed on cell-free DNA (cfMeDIP-seq) from patients with sarc-RCC and epi-RCC. A Sarcomatoid Methylation Score (SMS) was derived for each sample by aggregating the methylated cfDNA signal at tissue-derived sarcomatoid-enriched DMRs (sarc-DMRs), while normalizing to signal at epithelioid-enriched DMRs (epi-DMRs). Scores were compared between the two groups using a Wilcoxon rank-sum test. A classifier was built to distinguish sarc-RCC from epi-RCC based on SMS and its performance was evaluated using the area under the receiver operating characteristic (AUROC) curve. Results: We identified 32,086 DMRs between 9 sarc-RCC and 10 epi-RCC tissue samples at a false discovery rate of q < 0.01, among which We selected 16,083 DMRs that are enriched in sarcomatoid vs. epithelioid. We generated high-quality cfMeDIP-seq profiles from plasma of 46 patients, 13 with sarc-RCC and 33 with epi-RCC. SMS were significantly higher in sarc-RCC vs. epi-RCC plasma samples (p = 6.7×10 -8 ). These scores achieved an AUROC curve of 0.95 for classifying patients with sarc-RCC from patients with epi-RCC. Conclusions: We present the first proof of concept study for the detection of sarcomatoid differentiation in RCC based on tissue-informed assessment of DNA methylation signals in cfDNA using <1mL of plasma. This approach could overcome the challenges of spatial heterogeneity and sampling error that make identification of sarc-RCC difficult. More generally, it establishes a paradigm for identifying histologic subtypes of cancer based on their epigenomic correlates from cfDNA, with possible therapeutic implications in real-time.

Autologous therapeutic tumor-infiltrating lymphocyte (TIL) therapy is a promising strategy to enhance antitumor immunity. Optimization of ex vivo TIL expansion could expand current immunotherapy options. Previous attempts to generate TIL in renal cell carcinoma (RCC) have been technically challenging. We applied a second-generation manufacturing process, currently used to generate the melanoma TIL product lifileucel, in RCC. Resected primary and metastatic RCC samples were processed using the Gen 2 manufacturing process comprising of pre-Rapid Expansion Protocol (pre-REP) and REP steps. We assessed REP TILs for viability and performed phenotypic and functional characterization. We correlated the tumor immune microenvironment (TIME) with successful TIL expansion. Eight of 11 RCC samples underwent successful REP. Three failed cases demonstrated low CD8/FoxP3 ratio and high expression of PD-1 within FoxP3 cells. Expression of exhaustion markers differed between the TIME and expanded TILs; the latter had a TIM3-high/PD-1-low phenotype but retained functional capacity comparable to lifileucel. The Gen 2 manufacturing process used for lifileucel successfully expanded functional TILs from RCC samples, enabling further study in a clinical trial. TIME features such as low CD8/FoxP3 ratio and high PD-1 expression within FoxP3 cells warrant study as potential biomarkers of successful TIL expansion.

Renal cell carcinoma with sarcomatoid differentiation (sRCC) is associated with poor survival and a 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 the 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.

Abstract Purpose: Programmed cell death protein 1 (PD-1) expression on CD8+TIM-3−LAG-3− tumor-infiltrating cells predicts positive response to PD-1 blockade in metastatic clear-cell renal cell carcinoma (mccRCC). Because inhibition of PD-1 signaling in regulatory T cells (Treg) augments their immunosuppressive function, we hypothesized that PD-1 expression on tumor-infiltrating Tregs would predict resistance to PD-1 inhibitors. Experimental Design: PD-1+ Tregs were phenotyped using multiparametric immunofluorescence in ccRCC tissues from the CheckMate-025 trial (nivolumab: n = 91; everolimus: n = 90). Expression of CD8, PD-1, TIM-3, and LAG-3 was previously determined (Ficial and colleagues, 2021). Clinical endpoints included progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). Results: In the nivolumab (but not everolimus) arm, high percentage of PD-1+ Tregs was associated with shorter PFS (3.19 vs. 5.78 months; P = 0.021), shorter OS (18.1 vs. 27.7 months; P = 0.013) and marginally lower ORR (12.5% vs. 31.3%; P = 0.059). An integrated biomarker (PD-1 Treg/CD8 ratio) was developed by calculating the ratio between percentage of PD-1+Tregs (marker of resistance) and percentage of CD8+PD-1+TIM-3−LAG-3− cells (marker of response). In the nivolumab (but not everolimus) arm, patients with high PD-1 Treg/CD8 ratio experienced shorter PFS (3.48 vs. 9.23 months; P < 0.001), shorter OS (18.14 vs. 38.21 months; P < 0.001), and lower ORR (15.69% vs. 40.00%; P = 0.009). Compared with the individual biomarkers, the PD-1 Treg/CD8 ratio showed improved ability to predict outcomes to nivolumab versus everolimus. Conclusions: PD-1 expression on Tregs is associated with resistance to PD-1 blockade in mccRCC, suggesting that targeting Tregs may synergize with PD-1 inhibition. A model that integrates PD-1 expression on Tregs and CD8+TIM-3−LAG-3− cells has higher predictive value.

448 Background: There is evidence that intratumoral myeloid cell infiltration can modulate response to immune checkpoint inhibitors in some tumor types, but its role in ccRCC remains unclear. Here, we investigated the role of tumor-infiltrating CD163-positive macrophages as a determinant of clinical outcome to anti-PD-1 therapy in patients (pts) with advanced ccRCC treated with first-line nivolumab therapy as part of the HCRN GU16-260 clinical trial. Methods: Primary tumor tissues from pts with ccRCC (n= 67) were analyzed by multiparametric immunofluorescence (IF). Image analysis algorithms were used to assess the density of CD163-positive cells (dCD163) in tumor areas with high CD8+ T cell-infiltrates. In addition, dCD163 was also assessed in randomly selected tumor areas. Log-transformed densities were correlated with objective response rate (ORR) and progression-free survival (PFS) using Cox proportional hazards and binary logistic (modeling odds of attaining objective response) regression analysis, respectively. Alpha level was set a priori at 5% (2-sided). The functional form of CD163 for the PFS endpoint was obtained using a smoothed martingale residual plot. Results: Analysis of dCD163, assessed in high-CD8+ cell tumor areas and measured as a continuous variable, showed a positive association with ORR (OR= 2.21, p= 0.002) and PFS (HR= 0.77, p= 0.028). However, visual inspection of the dCD163 functional form plot for PFS revealed a threshold effect at the raw (log-transformed) value of 262.5 (5.6) cell/mm 2 . Similar to the entire cohort, in patients with dCD163 below this threshold (n= 54), dCD163 was positively associated with PFS (HR= 0.61, p<.001). In contrast, in patients with dCD163 above this threshold (n=13), results suggested the presence of a negative association between dCD163 and PFS (HR= 1.36, p= 0.726). Of note, similar findings were obtained by analyzing dCD163 assessed in random tumor areas. Conclusions: Levels of tumor-infiltrating CD163-positive macrophages are associated with improved clinical outcomes to first-line nivolumab in pts with advanced ccRCC up to a density threshold, above which an association with poor outcomes is noted. Further investigations into the role of tumor-infiltrating myeloid cells in predicting outcomes to anti-PD-1-based therapies are ongoing. Clinical trial information: NCT05403541 .

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 (1) a limited understanding of its gene regulatory programs and (2) difficulty identifying sarcomatoid differentiation on tumor biopsies due to spatial heterogeneity. To address these challenges, we characterized the epigenomic landscape of sRCC by profiling 107 epigenomic libraries in tissue and plasma samples from 50 patients with RCC and healthy volunteers. We identified highly recurrent epigenomic reprogramming, as assessed by histone modifications and DNA methylation, that distinguishes sRCC from non-sarcomatoid RCC. Computational analysis of RCC epigenomic profiles and CRISPRa experiments implicated the transcription factor FOSL1 in activating sRCC-associated gene regulatory programs. Analysis of two randomized clinical trials identified FOSL1 expression as a predictive biomarker of response to ICIs in RCC. Finally, we demonstrate that epigenomic signatures of sRCC are detectable in patient plasma, establishing an approach for blood-based diagnosis of this clinically important phenotype. These findings provide a framework for the discovery and non-invasive detection of epigenomic correlates of tumor histology via liquid biopsy.

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Key Clinical Messages Behçet's disease (BD) or syndrome is a chronic, recurrent, multisystem, inflammatory vasculitis disorder with findings of oral aphthous ulcers, genital ulcers, and uveitis. Gastrointestinal (GI) involvement can be the initial presentation as presented in this case. Abstract Behçet's disease (BD) or syndrome is a chronic, recurrent, multisystem, inflammatory vasculitis disorder of unknown etiology with classical findings of oral aphthous ulcers, genital ulcers, and ocular involvements including chronic anterior, intermediate, posterior, and even panuveitis. Gastrointestinal involvement in BD usually presents with chronic diarrhea, hematochezia as the disease affects ileocecal area which might be similar to presentation of inflammatory bowel diseases. Here, we report a case of undiagnosed BD who presented with chronic diarrhea for 4 months, leading to the diagnosis of BD and responded well to corticosteroid therapy.

4558 Background: ChRCC is an uncommon kidney cancer variant that has a poor prognosis in the metastatic setting, with limited response to current standard-of-care immune checkpoint inhibitors (ICIs) used for other RCC histologies. We evaluated the tumor-immune microenvironment of ChRCC and other related oncocytic neoplasms to better understand the immunophenotype of these tumors. Methods: We performed paired single-cell RNA sequencing (scRNA-seq), single-cell T-cell receptor sequencing (scTCR-seq), and CD45 immunohistochemistry (IHC) of ChRCC, renal oncocytoma (RO) and low-grade oncocytic tumor (LOT) tumor and matched normal samples. Bulk RNA-sequencing (RNA-seq) data of clear cell RCC (ccRCC), papillary RCC (pRCC) and ChRCC were additionally analyzed using The Cancer Genome Atlas (TCGA) kidney cancer cohorts. T cell antigenic specificities from scTCR-seq were inferred using a comprehensive database of annotated T-cell receptor sequences (VDJdb). Single-cell transcriptomic signatures were used to infer the tumor specificity (Oliveira G, Nature, 2021 and Lowery FJ, Science, 2022) and viral specificity (Oliveira G, Nature, 2021) of CD8+ T-cells from ChRCC, as compared to those from ccRCC (Braun DA, Cancer Cell, 2021). Results: ChRCC and other oncocytic tumors had a lower infiltration of CD45+ immune cells as compared to ccRCC (p<0.01). Single-cell analysis was performed on 46,817 cells from 5 tumors (ChRCC: n=3, RO: n=1 and LOT: n=1) and 4 normal samples. Across all tumors, CD8+ T cell clusters displayed a lower expression of immune checkpoints (i.e. PDCD1 [PD-1], CTLA4, LAG3, HAVCR2 [TIM-3], and TIGIT) as compared to ccRCC. This was further validated in a bulk RNA-seq analysis using TCGA data, with a significantly lower expression of all immune checkpoints in ChRCC compared to both ccRCC (p<0.01) and papillary RCC (pRCC; p<0.01). Analysis of the T cell receptor repertoire (scTCR-seq) of ChRCC, RO and LOT samples did not show any pattern of clonal expansion, and a higher proportion of T cells in ChRCC were inferred to have a viral specificity, as compared to ccRCC (0.79 vs. 0.1%, respectively). CD8+ T cells from ChRCC (vs. ccRCC) displayed a significantly lower expression of two signatures of tumor specificity (p<0.01), and a higher expression of the viral-specific signature (p<0.01). Conclusions: In ChRCC, there is low infiltration by CD45+ immune cells. Although infiltrating CD8+ T cells have a predominantly non-exhausted immune phenotype, they likely lack anti-tumor specificity (i.e. are “bystander” T cells). These findings may help to understand the molecular basis for the lack of response to immunotherapy recently identified among patients with advanced ChRCC, and support future therapeutic strategies to increase infiltration of tumor-specific T cells into the tumor microenvironment.

ABSTRACT Inactivation of pVHL tumor suppressor in clear cell Renal Cell Carcinoma (ccRCC) increases the abundance of Histone H3 lysine 27 acetylation (H3K27ac). We hypothesized that H3K27ac, a marker of transcriptional activation, drives the expression of critical oncogenes in ccRCC. Using H3K27ac ChIP-Seq; RNA-Seq; an in vivo positive selection screen; cell-based functional studies; and clinical validations; here, we report the identification of the SLC1A1/EAAT3 aspartate (Asp) and glutamate (Glu) transporter as a ccRCC oncogene. pVHL loss promotes SLC1A1 expression in a HIF-independent manner. Importantly, SLC1A1 inactivation depletes Asp/Glu-derived metabolites, impedes ccRCC growth both in vitro and in vivo , and sensitizes ccRCCs to metabolic therapeutics (e.g., glutaminase blockers). Finally, in human ccRCC biospecimens, higher SLC1A1 expression is associated with metastatic disease and clusters with elevated expression of other solute carriers, but not HIF/Hypoxia pathways. Altogether, our studies identify a HIF-independent metabolic hub in ccRCC and credential SLC1A1 as an actionable ccRCC oncogene. STATEMENT OF SIGNIFICANCE Targeting chronic HIF activation underlies many therapeutic strategies in ccRCC; but, unfortunately, is not curative. SLC1A1, instead, represents a HIF-independent ccRCC dependency, which is targetable alone and together with other antimetabolites, such as glutaminase inhibitors. These observations identify an actionable metabolic program that functions independent of HIF in ccRCC.

Abstract Background Sarcomatoid differentiation of RCC (sRCC) is associated with poor survival. Recent studies showed marked response of sRCC to immune checkpoint blockade (ICB). While distinctive patterns of gene expression in sRCC have been identified, the gene regulatory programs and TFs that drive SD remain unknown. The aim of this study is to nominate TFs responsible for SD and to investigate their association with the clinical outcomes of patients with RCC. Methods Chromatin immunoprecipitation and sequencing (ChIP-seq) for H3K27ac – a histone modification associated with active regulatory elements – was performed on pathologically reviewed sRCC and non-sRCC samples collected at the Dana-Farber Cancer Institute. Regulatory elements that were differentially active between the two groups were identified based on levels of H3K27ac (Benjamini-Hochberg q<0.01, log-fold change (LFC) threshold=1). Enrichment of specific TF binding motifs at activated regulatory elements in sRCC was assessed using HOMER. Differential gene expression analysis of TFs was performed using DESeq2 on RNA-seq data from TCGA. A Mann-Whitney U test was performed on RNA-seq data from the IMmotion151 and Javelin Renal 101 clinical trials to compare mean expression level of TFs in transcript per million (TPM) in these trials. Patients with non-sRCC enrolled in the IMmotion151 trials were divided into quartiles based on gene expression levels of candidate TFs. Progression-free survival (PFS) was compared between non-sRCC patients stratified by expression quartiles as well as patients with sRCC using a multivariable Cox proportional-hazards model accounting for age and IMDC risk score. To validate these findings, a similar analysis was performed in the Javelin Renal 101 trial. Results We obtained high-quality H3K27ac ChIP-seq profiles for 9 sRCC and 17 non-sRCC samples. We identified 278 candidate regulatory elements with increased H3K27ac levels in sRCC vs. non-sRCC. These regulatory elements were enriched for nucleotide motifs bound by the TFs FOSL1 and E2F7. Differential expression analysis between 48 sRCC vs. 493 non-sRCC samples showed that FOSL1 and E2F7 were significantly upregulated in sRCC vs. non-sRCC (LFC=1.7, q=5e-11; LFC=1.8, q=1.3e-20; resp.). Mean TPMs of FOSL1 and E2F7 were significantly increased in sRCC vs. non-sRCC in IMmotion151 cohort and Javelin Renal 101 (all p<0.001). Among patients who received sunitinib, those with the highest quartile of FOSL1 and E2F7 expression showed significantly shorter PFS in IMmotion151 patients compared to patients with the lowest quartile of expression (HR=1.6, 95%CI=1.3-2.2, p=0.008 & HR=2.6, 95%CI=1.8-3.7, p<0.001; resp.). Furthermore, patients with highest quartile of expression showed similar PFS compared to patients with sRCC (p=0.56 and p=0.64; resp.). These results were validated in the sunitinib arm of the Javelin Renal 101 cohort (Figure). Figure: Kaplan-Meier curves of progression-free survival (PFS) in patients with in the sunitinib arm of Javelin Renal 101 by sarcomatoid differentiation and FOSL1 expression levels. Conclusions This is the first study to characterize the epigenomic landscape of sRCC by integrating ChIP-seq and RNA-seq data. Our findings implicated FOSL1 and E2F7 as transcriptional regulators of SD with prognostic relevance. These TFs seems to be associated with aggressive behavior in non-sRCC as well. Further studies are underway to functionally validate these results.

Recent advances in our understanding of the molecular alterations underlying different types of renal cell carcinoma (RCC), as well as the implementation of immune checkpoint inhibitors in the treatment of patients with advanced disease, have significantly expanded the role of pathologists in the management of RCC patients and in the identification of predictive biomarkers that can guide patient treatment. In this chapter, we examine pathologists' evolving role in patient care and the development of precision medicine strategies for RCC.

4549 Background: The HCRN GU16-260 study showed that nivolumab (nivo) monotherapy is active in treatment-naïve patients (pts) with RCC. Although efficacy correlated with tumor cell (TC) PD-L1 expression, more clinically relevant biomarkers are needed. We previously showed that levels of CD8+ tumor-infiltrating lymphocytes (TIL) expressing PD-1 but not TIM-3 and LAG-3 (CD8+ PD1+ TIM3- LAG3-) were associated with response to nivo in previously treated clear cell RCC (ccRCC) pts (Pignon, 2019; Ficial, 2021). Here, we sought to validate these findings in the first-line setting. Methods: Primary tumor tissues from pts with ccRCC (n= 67) and non-ccRCC (n= 13) were analyzed by multiparametric immunofluorescence (IF), and the density of CD8+PD-1+TIM-3-LAG-3- TIL was quantified by image analysis. The correlation of the log-transformed biomarkers with objective response rate (ORR) and progression-free survival (PFS) was assessed in the ccRCC cohort and the combined (ccRCC + non-ccRCC) cohort (n=80) using Cox or logistic regression analysis, or Chi-square/Fisher’s exact test (as appropriate) for statistical inference – alpha level was set at 5% (1-sided). Results: In both the ccRCC and the combined cohort, density of CD8+PD-1+TIM-3-LAG-3- TIL (IF biomarker) measured as continuous variable was associated with ORR (ccRCC: OR = 1.54, p = 0.017; combined: OR = 1.35, p = 0.040) and PFS (ccRCC: HR = 0.79, p = 0.015; combined: HR = 0.85, p = 0.016). At a cutoff optimized for maximum sensitivity for ORR, ccRCC patients with a high (61/67) vs low (6/67) IF biomarker had higher ORR (44.3% vs. 0% p = 0.039) and longer median PFS (10.9 months vs. 4.2 months, p = 0.016). Of note, when combined with the IF biomarker, TC PD-L1 expression (≥1%) further separated the clinical outcomes of ccRCC pts. Conclusions: High levels of CD8+ PD1+ TIM-3-LAG-3- TILs were associated with response to first-line nivo in pts with advanced RCC and the combination with TC PD-L1 status further separated the outcomes of pts with ccRCC, validating our previous findings. Further investigations into the role of myeloid inflammation as determinant of clinical outcome to nivo therapy are ongoing. Clinical trial information: NCT03117309 . [Table: see text]