Renal Cell Carcinoma Cohort Research Articles

Evaluating trophinin associated protein as a biomarker of prognosis and therapy response in renal cell carcinoma

BackgroundTrophinin Associated Protein (TROAP) has been implicated in some tumors, yet its role in renal cell carcinoma (RCC) remains underexplored. This study aims to elucidate the prognostic and therapeutic implications of TROAP in RCC, encompassing different subtypes.MethodsFirstly, we identified the expression patterns of TROAP across various tumors within the TCGA pan-cancer cohort. Subsequently, the prognostic significance of TROAP was validated in three TCGA RCC cohorts and a local cohort. Finally, we conducted functional enrichment analysis, somatic mutations and copy number variations, assessed therapeutic response cohorts, and performed in vitro experiments to explore the biological characteristics of TROAP.ResultsTROAP serves as an unfavorable factor in both the TCGA RCC datasets and our local cohort. Functional enrichment analysis and in vitro experiments have demonstrated its oncogene effect in promoting tumor progression. Additionally, the relationship between TROAP expression and gene mutations in RCC appears to be limited. Furthermore, elevated TROAP expression is associated with reduced efficacy of RCC therapies, including nivolumab and everolimus.ConclusionsOur findings illustrate TROAP as a pivotal biomarker for prognosis and therapeutic response in RCC. Elevated TROAP expression is indicative of aggressive tumor behavior and resistance to conventional therapies, making it a valuable target for personalized treatment strategies in RCC management.

Adjuvant Everolimus in Non–Clear Cell Renal Cell Carcinoma

Clinical trial data on adjuvant therapy in patients with non-clear cell renal cell carcinoma (RCC) are scant. To evaluate the effect of adjuvant everolimus after nephrectomy on recurrence-free survival (RFS) and overall survival (OS) in patients with localized papillary and chromophobe RCC. This prespecified subgroup analysis of a phase 3 randomized clinical trial, EVEREST, included patients enrolled between April 1, 2011, and September 15, 2016. Eligible patients had fully resected RCC at intermediate-high risk (pT1 grade 3-4, N0 to pT3a grade 1-2, N0) or very-high risk (pT3a grade 3-4 to pT4 any grade or N+) for recurrence who had received radical or partial nephrectomy. Final analyses was completed in March 2022. The intervention group received 54 weeks of everolimus (10 mg orally daily); the control group received a matching placebo. The main outcomes were RFS, OS, and rates of adverse events. For testing the hazard ratio (HR) for treatment effect, a Cox regression model was used for both OS and RFS. Of 1545 adult patients with treatment-naive, nonmetastatic, fully resected RCC in EVEREST, 109 had papillary RCC (median [range] age, 60 [19-81] years; 82 [75%] male; 50 patients [46%] with very high-risk disease) and 99 had chromophobe RCC (median [range] age 51 [18-71] years; 53 [54%] male; 34 patients [34%] with very high-risk disease). Among 57 patients with papillary RCC in the intervention group, 26 (46%) completed 54 weeks of treatment, and among 53 patients with chromophobe RCC in the intervention group, 26 (49%) completed 54 weeks of treatment. With a median (IQR) follow-up of 76 (61-96) months, adjuvant everolimus did not improve RFS compared with placebo in either papillary RCC (5-year RFS: 62% vs 70%; HR, 1.19; 95% CI, 0.61-2.33; P = .61) or chromophobe RCC (5-year RFS: 79% vs 77%; HR, 0.89; 95% CI, 0.37-2.13; P = .79). In the combined non-clear RCC cohort, grade 3 or higher adverse events occurred in 48% of patients who received everolimus and 9% of patients who received placebo. In this clinical trial assessing the use of adjuvant everolimus, postoperative everolimus did not show evidence of improved RFS among patients with papillary or chromophobe RCC, and results from the study do not support adjuvant everolimus for this cohort. However, since the lower bounds of the 95% CIs were 0.61 and 0.89, respectively, potential treatment benefit in these subgroups cannot be ruled out. ClinicalTrials.gov Identifier: NCT01120249.

PD-L1 as a Urine Biomarker in Renal Cell Carcinoma-A Case Series and Proof-of-Concept Study.

Renal cell carcinoma (RCC) is among the most lethal urologic malignancies once metastatic. Current treatment approaches for metastatic RCC (mRCC) involve immune checkpoint inhibitors (ICIs) that target the PD-L1/PD-1 axis. High PD-L1 expression in tumor tissue has been identified as a negative prognostic factor in RCC. However, the role of PD-L1 as a liquid biomarker has not yet been fully explored. Herein, we analyze urine levels of PD-L1 in mRCC patients before and after either ICI therapy or surgical intervention, as well as in a series of patients with treatment-naïve RCC. The mid-stream urine of patients with mRCC (n = 4) or treatment-naïve RCC, i.e., prior to surgery from two centers (cohort I, n = 49: cohort II, n = 29) was analyzed for PD-L1 by ELISA. The results from cohort I were compared to a control group consisting of patients treated for non-malignant urologic diseases (n = 31). In the mRCC group, urine PD-L1 levels were measured before and after tumor nephrectomy (n = 1) or before and after ICI therapy (n = 3). Exosomal PD-L1 in the urine was analyzed in selected patients by immunoblotting. A strong decrease in urine PD-L1 levels was found after tumor nephrectomy or following systemic treatment with ICIs. In patients with treatment-naïve RCC (cohort I), urine PD-L1 levels were significantly elevated in the RCC group in comparison to the control group (median 59 pg/mL vs. 25.7 pg/mL, p = 0.011). PD-L1 urine levels were found to be elevated, in particular, in low-grade RCCs in cohorts I and II. Exosomal PD-L1 was detected in the urine of a subset of patients. In this proof-of-concept study, we show that PD-L1 can be detected in the urine of RCC patients. Urine PD-L1 levels were found to correlate with the treatment response in mRCC patients and were significantly elevated in treatment-naïve RCC patients.

Abstract 3457: Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma

Abstract Background: Comprising 90% of renal cancer cases in adults, renal cell carcinoma (RCC) is marked by significant heterogeneity in its tumor microenvironment. This study aims to test the hypothesis that tumor-associated macrophages (TAMs) play a role in modulating RCC progression and patient response to treatment. We explored the prognostic implications of TAM signatures on RCC survival, leveraging immunomics to decipher TAM-related alterations in the tumor microenvironment. Methods: Utilizing independent single-cell RNA-seq data from human RCC samples, we crafted 8 distinct RCC TAM signatures reflective of TAM presence. A LASSO Cox regression model was developed to prognosticate survival, tested against the TCGA dataset and independently validated across multiple RCC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We identified two TAM signatures that correlate positively with patient survival, indicative of the abundance of specific subsets of TAMs in RCC. These signatures showed a strong association of specific TAM markers and macrophage infiltration. Survival analysis demonstrated that specific TAM signature gene expressions are significant prognostic markers. Forest plots underscored their prognostic relevance. A 32-gene risk score model was established, successfully stratifying patients into distinct risk categories, with low-risk patients exhibiting markedly improved overall survival. Conclusions: The study underscores the positive association between abundance of specific TAM subsets of and RCC patient survival. The precision of our LASSO Cox regression model, informed by novel abundance markers of specific TAM subsets, advances our understanding of TAM roles in TIME and RCC progression. These TAM signatures could potentially provide additional insights and targets to enhance the efficacy of RCC treatments. Keywords: Tumor associated macrophage, renal cell carcinoma, prognosis, tumor microenvironment, immunomics. Citation Format: Youngsoo Han, Aidan Shen, Zhaohui Wang, Aliesha Garrett, Chongming Jiang. Distinct signatures of tumor-associated macrophage subpopulations predict survival in renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3457.

Abstract 1371: SLAMF9 promotes tumor progression and immunotherapy resistance by reshaping the tumor microenvironment

Abstract Tumor prognosis and immunotherapy efficacy coincides with the infiltration ratio and functional status of immune cells in the tumor microenvironment (TME). Signaling lymphocytes activating molecule factors (SLAMFs) has been reported to play a key role in regulating tumor immunity. Herein, using multi cohorts, in vitro and in vivo experiments, we showed that SLAMF9 worsens tumor and suppresses immune therapy responses by remodeling TME. A pan-cancer analysis showed that SLAMF9 had relatively high transcriptional levels within tumor tissues compared with normal tissues across most cancer types. High SLAMF9 expression is associated with poor prognosis and anti-PD-L1 therapy resistance in advanced clear cell renal cell carcinoma cohorts and metastatic urothelial cancer cohorts. SLAMF9 expression was markedly upregulated in anti-CTLA-4-resistant murine B16 melanoma compared with parental tumors. In particular, SLAMF9 expression was generally negatively correlated with CD8+T cell infiltration score in 12159 patients of 40 cancer types even using different immune cell infiltrating algorithms. Although in vitro experiments have shown that SLAMF9 knockdown has almost no effect on the malignant phenotype of tumor cells, subcutaneous melanoma and lung adenocarcinoma with SLAMF9 deficiency grew slowly, which featured with increased infiltration of CD8+T and PD-1+cells. More importantly, SLAMF9-knockdown significantly increases the sensitivity of subcutaneous melanoma to anti-CTLA-4 inhibitor or anti-PD-L1 antibodies and improved the survival benefits of tumor-bearing mice. This work highlights SLAMF9 as a new target in reshaping the tumor immune microenvironment by controlling the functional status of CD+8 T cells and to improve the efficacy of immunotherapy. Citation Format: Tao Fan, Chu Xiao, Ziqin Deng, Wenpeng Cai, He Tian, Liyu Wang, Chunxiang Li, Jie He. SLAMF9 promotes tumor progression and immunotherapy resistance by reshaping the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1371.

Implications of MTHFD2 expression in renal cell carcinoma aggressiveness.

Renal cell carcinoma (RCC) is the most common type of cancer in kidney and is often diagnosed in advanced stages. Until now, there is no reliable biomarker to assess tumor prognosis during histopathological diagnosis. The Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) overexpression has been suggested as prognostic indicator for RCC, however, its protein profile needs to be clarified. This study investigated the MTHFD2 expression in different RCC cohorts, associating it with tumor characteristics and prognostic factors. Gene expression comparisons between non-neoplastic (NN) and tumor samples, as well as patients' survival analysis, were assessed using KM-Plotter tool. MTHFD2 protein pattern was evaluated in 117 RCC by immunohistochemistry and associations with prognosis, clinical and pathological data were investigated. The tumors exhibited higher MTHFD2 transcript levels than NN, being even higher in the metastatic group. Opposite gene expression patterns were found among clear cell renal cell carcinoma (ccRCC) and pappilary renal cell carcinoma (pRCC) subtypes, showing higher and lower expressions compared to NN samples respectively. Overexpression was associated with shorter overall survival for ccRCC and pRCC subtypes, and shorter recurrence-free survival for pRCC. The immunolabeling profile varied according to tumor subtypes, with lower intensity and expression scores in ccRCC compared to pRCC and to chromophobe renal cell carcinoma (chRCC). MTHFD2 protein expression was associated with larger tumors and higher Fuhrman grades. Although prognostic value of protein immunostaining was not confirmed, patients with higher MTHFD2 tended to have lower survival rates in the pRCC group. The results highlight MTHFD2 different patterns according to RCC histological subtypes, revealing marked variations at both the genetic and protein levels. The mRNA indicated tumor prognosis, and greater expression in the tumor samples. Although MTHFD2 immunolabeling suggests tumor aggressiveness, it needs to be validated in other cohorts as potential prognostic factor.

Real world evidence comparison of first-line (1L) immune-oncology(IO)/tyrosine kinase inhibitor (TKI) vs. IO/IO combination therapy in renal cell carcinoma (RCC).

402 Background: IO/IO and IO/TKI are the preferred first-line combinations for treatment of RCC. While these combinations have been superior to sunitinib, no head-to-head clinical trials have been completed, which leaves uncertainty about the preferred combination. Here, we have leveraged ASCO Cancerlinq data to compare overall survival for RCC patients (pts) receiving first line IO/IO vs. IO/TKI combinations. We hypothesized that IO/IO and IO/TKI would have similar survival. Methods: We performed a retrospective cohort study comparing IO/IO vs IO/TKI therapy for pts with metastatic RCC. We used the Atropos health platform, which is a commercial platform and queried ASCO CancerLinq data for this study. We identified pts with kidney cancer who received IO/IO or IO/TKI combinations between 2019 and 2023. For our primary analysis, we included only pts with ICD10 code consistent with clear cell [cc]RCC, but also did a secondary analysis including all pts with kidney cancer. The IO/TKI pts received either avelumab/axitinib, pembrolizumab/axitinib, nivolumab/cabozantinib, pembrolizumab/lenvatinib, or atezolizumab/cabozantinib within two weeks of each other. The IO/IO pts were identified as pts receiving ipilimumab and nivolumab within 2 weeks of each other without interceding TKI. We conducted a survival analysis of time to death using Cox proportional hazards regression to compare the two groups. The analysis was performed under three different confounder adjustment scenarios: without adjustment, with basic matching on sex and age, and with high dimensional propensity score (hdPS) using inverse probability of treatment weighting. Results: A total of 584 pts (286 IO/IO and 296 IO/TKI) with kidney cancer and 146 with ccRCC (75 IO/IO and 71 IO/TKI) were included. Mean age was 63-65 years and 30-35% female sex across groups. Table shows the restricted mean survival time (RMST) and HR for the Cox models. In the ccRCC cohort, 20 pts with IO/IO and 7 with IO/TKI died with RMST of 947 and 1071 days, respectively (hdPS HR 0.35, p=0.03). In the RCC cohort, 76 pts with IO/IO vs 49 with IO/TKI died with RMST 1126 vs 1017 (hdPS HR 0.61, p=0.04). Conclusions: In this exploratory, hypothesis-generating analysis of CancerLinq data, pts treated with IO/TKI had longer OS vs those treated with IO/IO. Limitations include the retrospective nature of the study, low power, lack of randomization, and potential unmeasured confounding factors (e.g., confounding by indication). Further investigation and prospective validation are needed to confirm these findings. [Table: see text]

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

BackgroundInflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. MethodsWe retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. FindingsThe model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. ConclusionIn this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Abstract B017: Single-cell proteogenomic profiling reveals immune cell networks in renal cell carcinoma

Abstract Introduction: Single-cell proteogenomic profiling has enabled the interrogation of complex milieus of cell types and their corresponding states that interact to regulate the antitumor immune response. While several landmark studies employing single-cell RNA sequencing (scRNAseq) have provided a detailed taxonomy of lymphoid and myeloid phenotypic states within the renal cell carcinoma (RCC) microenvironment, these studies have only been performed on small patient cohorts. As such, the ability to infer coordinated interactions between cellular phenotypes remains limited. Here, we leverage single-cell proteogenomic approaches to map immune cell phenotype co-occurrences across a heterogeneous patient cohort. Experimental procedures: We performed 5’ scRNAseq on 75 dissociated tumor samples from 59 patients undergoing partial or radical nephrectomy using the 10X genomics platform. Paired single-cell TCR/BCR sequencing (scTCR/BCRseq) and cytometry by time-of-flight (CyTOF) were additionally performed on 64 and 48 samples, respectively. Results: scRNAseq captured a total of ~350,000 high quality cells of RCC, stromal and immune (myeloid and lymphoid) origin, across a range of histologies (clear cell 69%, papillary 11%, chromophobe 13%, other 8%) and stages (I 53% II 7%, III 31%, IV 9%), with proportions of major cell types being consistent between scRNAseq and CyTOF. Interestingly, paired scTCRseq revealed a subset of patients (~40%) that displayed intratumoral clonally hyperexpanded TCRs (&amp;gt; 100 copies/clone), with CD8+ cells expressing an exhausted phenotype being most dominant. These samples were also co-enriched for CXCL13+ CD8+ T cells as well as proinflammatory CXCL9/10+ macrophages, a relationship we confirmed upon calculating pairwise correlations between all immune cell phenotype proportions across all samples. Notably, RCC cells from these samples expressed an antigen presentation meta-program identified by non-negative matrix factorization and pathway analysis. A “pro-inflammatory” cell network gene signature was derived and employed across the clear cell RCC (KIRC) cohort of TCGA and IMMotion 151 data sets, to reveal pro-inflammatory enriched RCCs displayed worse survival yet improved response to immunotherapy-based regimens, respectively. Conclusions: Our study provides the largest single-cell proteogenomic characterization of RCC to date, detailing co-occurring immune cell phenotypes across heterogenous patient tumor microenvironments. We leveraged this data to characterize a pro-inflammatory immune cell ecotype of RCC with potential prognostic and predictive implications that warrant further validation. Overall, this data should serve as a fundamental resource for future work developing novel therapeutic strategies and biomarkers against RCC. Citation Format: Keith A Lawson, Shirley Hui, Daniel Stueckmann, Xiaoyu Zhang, Jalna Meens, Lisa Martin, Maria Komisarenko, Julia Szusz, Stephane Chevrier, Sujana Sivapatham, Philip Jonsson, Fred Davis, Cristina Penaranda, Ryan Newton, Nicolas Stransky, Piotr Bielecki, Zhihui Liu, Jennifer Pfeil, Sarah Crome, Dominik Deniffel, Masoom Haider, Jason Lee, Neil Fleshner, Nathan Perlis, Robert Hamilton, Girish Kulkarni, Susan Prendeville, Hartland Jackson, Laurie Ailles, Gromoslaw Smolen, Bernd Bodenmiller, Gary Bader, Antonio Finelli. Single-cell proteogenomic profiling reveals immune cell networks in renal cell carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B017.

Phase I LITESPARK-001 study of belzutifan for advanced solid tumors: Extended 41-month follow-up in the clear cell renal cell carcinoma cohort

BackgroundAccumulation of the HIF-2α transcription factor is an oncogenic event implicated in the tumorigenesis of clear cell renal cell carcinoma (ccRCC). In the phase I LITESPARK-001 study, the first-in-class HIF-2α inhibitor belzutifan demonstrated antitumor activity and an acceptable safety profile for pretreated patients with advanced ccRCC. Updated data with additional follow-up of >40 months are presented. MethodsLITESPARK-001 is an ongoing, open-label, study with a 3+3 dose-escalation design followed by an expansion phase. Patients with ccRCC enrolled at 7 sites received belzutifan 120 mg orally once daily until disease progression, unacceptable toxicity, or patient withdrawal. The data cutoff date was July 15, 2021. The primary end point was identifying the maximum tolerated dose and/or the recommended phase II dose. Secondary end points included objective response rate (ORR) and duration of response (DOR) per RECIST v1.1 by investigator assessment, and safety. ResultsMedian follow-up was 41.2 months (range, 38.2–47.7). Patients received a median of 3 (range 1–9) prior systemic therapies. Of 55 patients, 14 (25%) achieved an objective response. Median DOR was not reached (range 3.1+ to 38.0+ months). Adverse events (AEs) attributed to study treatment by investigator assessment were reported in 53 patients (96%). 22 patients (40%) had grade 3 treatment-related AEs; the most common were anemia (n = 13; 24%) and hypoxia (n = 7; 13%). No grade 4 or 5 treatment-related AEs occurred. ConclusionAfter a median follow-up of 41.2 months, belzutifan monotherapy demonstrated durable antitumor activity in patients with advanced ccRCC and acceptable safety.ClinicalTrials.gov, number NCT02974738 FundingThis work was supported by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

A Phase 2 Trial of Talazoparib and Avelumab in Genomically Defined Metastatic Kidney Cancer

BackgroundAlthough different kidney cancers represent a heterogeneous group of malignancies, multiple subtypes including Von Hippel-Lindau (VHL)-altered clear cell renal cell carcinoma (ccRCC), fumarate hydratase (FH)- and succinate dehydrogenase (SDH)-deficient renal cell carcinoma (RCC), and renal medullary carcinoma (RMC) are affected by genomic instability. Synthetic lethality with poly ADP-ribose polymerase inhibitors (PARPis) has been suggested in preclinical models of these subtypes, and paired PARPis with immune checkpoint blockade (ICB) may achieve additive and/or synergistic effects in patients with previously treated advanced kidney cancers. ObjectiveTo evaluate combined PARPi + ICB in treatment-refractory metastatic kidney cancer. Design, setting, and participantsWe conducted a single-center, investigator-initiated phase 2 trial in two genomically selected advanced kidney cancer cohorts: (1) VHL-altered RCC with at least one prior ICB agent and one vascular endothelial growth factor (VEGF) inhibitor, and (2) FH- or SDH-deficient RCC with at least one prior ICB agent or VEGF inhibitor and RMC with at least one prior line of chemotherapy. InterventionPatients received talazoparib 1 mg daily plus avelumab 800 mg intravenously every 14 d in 28-d cycles. Outcome measurements and statistical analysisThe primary endpoint was objective response rate (ORR) by Immune Response Evaluation Criteria in Solid Tumors at 4 mo, and the secondary endpoints included progression-free survival (PFS), overall survival, and safety. Results and limitationsCohort 1 consisted of ten patients with VHL-altered ccRCC. All patients had previously received ICB. The ORR was 0/9 patients; one patient was not evaluable due to missed doses. In this cohort, seven patients achieved stable disease (SD) as the best response. The median PFS was 3.5 mo (95% confidence interval [CI] 1.0, 3.9 mo). Cohort 2 consisted of eight patients; four had FH-deficient RCC, one had SDH-deficient RCC, and three had RMC. In this cohort, six patients had previously received ICB. The ORR was 0/8 patients; two patients achieved SD as the best response and the median PFS was 1.2 mo (95% CI 0.4, 2.9 mo). The most common treatment-related adverse events of all grades were fatigue (61%), anemia (28%), nausea (22%), and headache (22%). There were seven grade 3–4 and no grade 5 events. ConclusionsThe first clinical study of combination PARPi and ICB therapy in advanced kidney cancer did not show clinical benefit in multiple genomically defined metastatic RCC cohorts or RMC. Patient summaryWe conducted a study to look at the effect of two medications, talazoparib and avelumab, in patients with metastatic kidney cancer who had disease progression on standard treatment. Talazoparib blocks the normal activity of molecules called poly ADP-ribose polymerase, which then prevents tumor cells from repairing themselves and growing, while avelumab helps the immune system recognize and kill cancer cells. We found that the combination of these agents was safe but not effective in specific types of kidney cancer.

The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors.

We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.

DNA Methylation Modification Patterns Identify Distinct Prognosis and Responses to Immunotherapy and Targeted Therapy in Renal Cell Carcinoma.

Considering the remarkable heterogeneity of biological features of renal cell carcinoma (RCC), the current clinical classification that only relies on classic clinicopathological features is in urgent need of improvement. Herein, we aimed to conduct DNA methylation modification patterns in RCC. We retrospectively curated multiple RCC cohorts, comprising TCGA-KIRC, TCGA-KICH, TCGA-KIRP, and E-MTAB-1980. DNA methylation modification patterns were proposed with an unsupervised clustering algorithm based on 20 DNA methylation regulators. Immunological features were characterized using tumor-infiltrating immune cells and immunomodulators. Sensitivity to immuno- or targeted therapy was estimated with submap and Genomics of Drug Sensitivity in Cancer (GDSC). DNA methylation score (DMS) was developed with principal component analysis. Three DNA methylation modification patterns were conducted across RCC patients, namely C1, C2 and C3. Among them, C3 displayed the most remarkable survival advantage. The three patterns presented in agreement with immune phenotypes: immune-desert, immune-excluded, and immune-inflamed, respectively. These patterns displayed distinct responses to anti-PD-1 and targeted drugs. DMS enabled the quantification of DNA methylation status individually as an alternative tool for prognostic estimation. The DNA methylation molecular patterns we proposed are an innovative complement to the traditional classification of RCC, which might contribute to precision medicine.

Final database lock results of the phase 2 cohort of lenvatinib + pembrolizumab for progressive disease after a PD-1/PD-L1-containing therapy in metastatic clear cell renal cell carcinoma

Abstract Background Lenvatinib + pembrolizumab is approved for the first-line treatment of advanced renal cell carcinoma (RCC). In the CLEAR trial, the combination showed statistically significant/clinically meaningful improvements in overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) vs sunitinib (Motzer R et al. NEJM. 2021). As immune checkpoint inhibitors (ICIs) have become standards of care as first-line treatment of RCC, an unmet need exists for patients who have disease progression after treatment with ICIs. A phase 1b/2 trial of lenvatinib + pembrolizumab was performed in multiple tumor types enrolling patients who had or had not received prior therapy; the primary analysis of the RCC cohort has been reported previously (Lee C-H et al. Lancet Oncol. 2021). Here, we report the final results of this RCC cohort with an additional 24 months of follow-up. Methods Eligible patients ≥18 years old with measurable disease were treated with lenvatinib 20 mg daily + pembrolizumab 200 mg once every 3 weeks. The primary endpoint (ORR at week 24 [ORRwk24] per immune-related [ir] RECIST by the investigators) has been previously reported (Lee C-H et al. Lancet Oncol. 2021). Secondary endpoints included ORR, duration of response (DOR), PFS (each by the investigators), OS and safety. In the analyses reported herein, efficacy data were grouped by prior therapy patients had received, including ICI-pretreated, treatment-naïve, and previously treated but ICI-naïve. Tumors were assessed by the investigators per irRECIST and RECIST v1.1 (modified to allow assessment of ≤10 target lesions [≤5 per organ]). Results 145 Patients were enrolled, of whom 105 were ICI-pretreated, 23 were treatment-naïve, and 17 were previously treated ICI-naïve. By the final database lock date (August 24, 2022), the median duration of follow-up for OS was 37.7 months (95% CI 35.2–42.7). Efficacy analyses included 104 ICI-pretreated patients and 22 treatment-naïve patients; 1 patient in either group was excluded as they were negative for clear cell disease. Per irRECIST by investigator assessment, ORR was 62.5% for ICI-pretreated patients (median DOR: 14.1 months), 77.3% for treatment-naïve patients (median DOR: 24.2 months), and 52.9% for previously treated ICI-naïve patients (median DOR: 9.0 months) (Table). Notably, 18-month PFS rates were 38.0% for ICI-pretreated patients, 70.5% for treatment-naïve patients, and 36.1% for previously treated ICI-naïve patients (Table). The median OS was 32.1 months for ICI-pretreated patients, 55.8 months for treatment-naïve patients, and 30.3 months for previously treated ICI-naïve patients; 24-month OS rates were &amp;gt;50% in all groups. Additional efficacy data are included in the Table. For those who had their dose of lenvatinib reduced (per recommended algorithms for standard adverse event [AE] management) the median time to first dose reduction was 2.10 months (range 0.1–16.6) for ICI-pretreated patients (n=75), 6.47 months (range 0.3–33.1) for treatment-naïve patients (n=18), and 2.89 months (range 1.0–12.5) for previously treated ICI-naïve patients (n=10). Overall, treatment-related (TR) AEs were reported in 99.3% of all 145 patients; grade ≥3 TRAEs were reported in 66.2%. The most common TRAE was diarrhea (n=88). Treatment-emergent, clinically significant (CS) AEs and AEs of special interest for pembrolizumab (AEOSIs) were reported in 95.2% and 55.9% of patients, respectively. Hypertension was the most common grade ≥3 CSAE (n=37), severe skin reactions were the most common grade ≥3 AEOSI (n=6). The median time to first onset of CSAEs was 0.64 months; the median time to first onset of AEOSIs was 2.07 months. Conclusions Lenvatinib + pembrolizumab demonstrated promising and durable antitumor activity with a manageable safety profile in patients with metastatic RCC, including in those who were ICI-pretreated. To our knowledge, this represents the largest cohort of ICI-pretreated patients with RCC prospectively treated in a clinical trial. CDMRP DOD Funding: no

Potential prognostic and therapeutic value of ANXA8 in renal cell carcinoma: based on the comprehensive analysis of annexins family

BackgroundAnnexins are a family of proteins involved in a wide variety of cellular processes such as inflammation, proliferation, differentiation, apoptosis, migration and membrane repair. However, the role of most Annexins in renal cell carcinoma (RCC) remained unclear.MethodsThe differentially expressed Annexins in RCC compared with normal controls were screened applying the TCGA database. The correlation of differentially expressed Annexins with clinical stages, grades and overall survival was analyzed to explore the clinical significance of Annexins in RCC. Then ANXA8 was selected and further stained in the discover and validation RCC cohort. The correlation of ANXA8 expression with clinical parameter was verified at the protein level. To explore the potential function of ANXA8, ANXA8 was knockdown in the RCC cell line and further analyzed using transcriptome and bioinformatic analysis.ResultsmRNA expression of ANXA1, ANXA2R, ANXA4, ANXA8, ANXA8L1 and ANXA13 were significantly upregulated in RCC compared with normal kidney tissues. In contrast, ANXA3 and ANXA9 mRNA expression was significantly downregulated. Higher expression of ANXA2R, ANXA8 and ANXA8L1 were correlated with worse overall survival, while lower expression of ANXA3, ANXA9 and ANXA13 were associated with worse clinical outcomes in RCC patients. We further demonstrated that ANXA8 expression was significantly increased in RCC compared with normal renal tissues at the protein level. And higher protein expression of ANXA8 was associated with higher clinical grades. Through the bioinformatics analysis and cell cycle analysis, we found knockdown of ANXA8 mainly influenced the cell cycle and DNA replication. The top ten hub genes consist of CDC6, CDK2, CHEK1, CCNB1, ORC1, CHEK2, MCM7, CDK1, PCNA and MCM3.ConclusionsMultiple members of Annexins were abnormally expressed and associated with the prognosis of RCC. The expression of ANXA8 was significantly increased in RCC and associated with poor prognosis. ANXA8 might influence the cell cycle and could be a potential biomarker and therapeutic target for RCC.

The PRO-RCC study: a long-term PROspective Renal Cell Carcinoma cohort in the Netherlands, providing an infrastructure for ‘Trial within Cohorts’ study designs

BackgroundOngoing research in the field of both localized, locally advanced and metastatic renal cell carcinoma has resulted in the availability of multiple treatment options. Hence, many questions are still unanswered and await further research. A nationwide collaborative registry allows to collect corresponding data. For this purpose, the Dutch PROspective Renal Cell Carcinoma cohort (PRO-RCC) has been founded, for the prospective collection of long-term clinical data, patient reported outcome measures (PROMs) and patient reported experience measures (PREMs).MethodsPRO-RCC is designed as a multicenter cohort for all Dutch patients with renal cell carcinoma (RCC). Recruitment will start in the Netherlands in 2023. Importantly, participants may also consent to participation in a ‘Trial within cohorts’ studies (TwiCs). The TwiCs design provides a method to perform (randomized) interventional studies within the registry. The clinical data collection is embedded in the Netherlands Cancer Registry (NCR). Next to the standardly available data on RCC, additional clinical data will be collected. PROMS entail Health-Related Quality of Life (HRQoL), symptom monitoring with optional ecological momentary assessment (EMA) of pain and fatigue, and optional return to work- and/or nutrition questionnaires. PREMS entail satisfaction with care. Both PROMS and PREMS are collected through the PROFILES registry and are accessible for the patient and the treating physician.Trial registrationEthical board approval has been obtained (2021_218) and the study has been registered at ClinicalTrials.gov (NCT05326620).DiscussionPRO-RCC is a nationwide long-term cohort for the collection of real-world clinical data, PROMS and PREMS. By facilitating an infrastructure for the collection of prospective data on RCC, PRO-RCC will contribute to observational research in a real-world study population and prove effectiveness in daily clinical practice. The infrastructure of this cohort also enables that interventional studies can be conducted with the TwiCs design, without the disadvantages of classic RCTs such as slow patient accrual and risk of dropping out after randomization.

Biomarker-driven prospective clinical trial in renal cell carcinoma: Developing machine learning models to allocate patients to treatment arms using RNA sequencing.

4525 Background: RNA sequencing has shown promise in defining the biology of individual renal cell carcinoma (RCC) tumors. However, these biomarkers have not yet been translated to the clinic for prospectively assigning optimal treatments to patients. Challenges for RNA-seq biomarker development include translating classifiers across different assays/platforms, normalization of data collected from single patients in the clinic and establishing robust thresholds for assigning prediction groups. We have designed the prospective phase II OPTIC RCC clinical trial (clinicaltrials.gov NCT05361720) to test the utility of an RNA-seq based biomarker in predicting treatment based on biologic drivers relevant to angiogenesis (anti-angiogenesis; TKI) and immune microenvironment (immunotherapy; IO). Here, we will describe the development and optimization of a machine learning model for assigning individual patients to biologically driven clusters in real time to facilitate RNA-seq based biomarker trials. Methods: We have utilized RNA-seq data from the IMmotion 151 trial (ref) to develop a machine learning model. Clusters were grouped into three based on their association to tumor biology and treatment: (1) Cluster 1+2 (angiogenic signature → TKI+IO therapy), (2) Cluster 4+5 (immune/proliferative signature → dual IO therapy), (3) Cluster 3+6 (neither signature → exclude). Random forest classifier was used to train a multi-class model to predict the three groups. The model is evaluated using bootstrapped cross-validation. Results: Our machine learning classifier was built using 188 genes and has a cross-validation accuracy of 85% and sensitivity of &gt;90% in predicting patients into one of the three biological clusters from our training data. Predictions of the classifier are significantly associated with progression-free survival across different treatments within each of the predicted groups. We also observed significant odds ratios when comparing responders (CR/PR/SD) to non-responders (PD) across the treatment groups. The model was then validated in two independent test sets treated with Angio and IO inhibitors: (1) 61 patient renal cell carcinoma cohort (2) 12 patient clear cell renal cell carcinoma cohort. We observed a significant enrichment of responders to Angio+IO treatment for the predicted Cluster_Angio patients compared to IO treatment (p=0.05), highlighting the importance of matching patients to their optimal treatment. Conclusions: We have developed an accurate machine learning model to assign individual patients to RNA-seq clusters in real time. This classifier will facilitate the prospective OPTIC RCC trial. If successful, our biomarker strategy will serve as a proof of concept for selecting optimal treatments for RCC patients.

Association of a deep learning system with progression-free survival (PFS) in patients with renal cell carcinoma (RCC) treated with immune checkpoint blockades (ICBs).

e16513 Background: Anti-PD-1 based therapies have been standard-of-care options in both the front-line and treatment refractory settings in RCC, but only 20%~60% of patients respond to ICBs. Current biomarkers often fail to predict benefit of ICBs, so new reliable biomarkers are urgently warranted. Methods: Genomic and transcriptomic data were collected from 3 different ICB-treated RCC cohorts, including Miao et al. (n=53, WES), CheckMate (CM, n=261, WES; n=181, RNA-Seq) and JAVELIN (n=354, RNA-Seq) cohorts. Hallmark pathways downloaded from MSigDB database were used to screened for defective pathways that correlated with PFS using WES data of Miao et al. and CM cohorts. A defective pathway was defined as detecting at least one variant. Enrichment scores were calculated using RNA-seq data of CM cohort based on 28 immune cells gene sets provided by Charoentong et al. Immune cell signatures (ICSs) related to PFS were screened out using cox regression analysis. The selected pathways and ICSs were combined to develop a logistic regression model (10-fold cross-validation) to predict prognosis. Gene expression profiles of CM cohort were randomly split into two groups: 70% as the training set, and the remaining 30% as the test set. The predictive performance was validated in JAVELIN cohort independently. Gene sets expression levels were estimated using ssGSEA and NES methods. ROC curves were used to evaluate the predictive accuracy for 6-month PFS. Survival plots were created using the Kaplan-Meier estimator, and data were analyzed by log-rank test. Results: 7 signatures were selected as input features including 4 pathways (DNA_repair, IFN_alpha, EMT and angiogenesis) identified in Miao et al. and CM cohorts, together with 3 ICSs (Activated.CD4.T.cell, Activated.CD8.T.cell, and Immature.dendritic.cell) identified in CM cohort. Our model showed promising results for predicting 6-month PFS in CM dataset with AUC of 0.984 in the training set and 0.893 in the test set. The prediction of our model outperformed that based on TMB (AUC 0.655 vs. 0.587), GEP (AUC 0.746 vs. 0.823) and PD-L1 (AUC 0.823 vs. 0.768) both in the training and test sets, while combining our model with biomarkers above improved the prediction drastically with AUC of 0.996 in the training set and 0.935 in the test set. Meanwhile, our model maintained preferable performance for differentiating longer PFS from shorter PFS with p vale of 0.021 in JAVELIN cohort. Conclusions: We proposed a risk model to predict prognosis, in which feature selection based on hallmark pathways and immune cell subtypes. Our model showed excellent performance in predicting PFS in RCC patients undergoing immunotherapy, outperforming other ICB-related biomarkers. While combining our model with TMB, GEP, and PD-L1 was correlated more strongly with survival.

Uncovering LAG-3 related tumor immunology in renal cell carcinoma and pan-cancer evaluation.

e16517 Background: LAG-3 (lymphocyte activation gene 3) is of interest as a target molecule for next-generation immune checkpoint inhibitors in several solid tumors, including renal cell carcinoma (RCC). The aim of this study is uncovering the tumor immune microenvironment (TIME) of the LAG-3-dominant RCC. Methods: Firstly, we performed an automated single-cell count for immunolabelled LAG-3, TIM-3 and TIGIT and TIME evaluation panel molecules (CD3, CD8, CD39, PD1, PDL1, Ctla4, CD68, CD163, CD47, Ki67, CD73, IDO1, GLUT1, CD34 and D2-40) to 105 primary ccRCC tumor samples and created the new classification model. The TIME and clinical value of LAG-3-dominant RCC were evaluated. Secondly, The TIME features of LAG-3 dominant RCC in metastatic sites was evaluated in 47 metastatic samples. Lastly, mRNA seq data from the public cohort and clinical trial data (RCC, n = 1144, other solid cancers, n = 3691) was analyzed with the digital cytometry and evaluated whether the TIME features of LAG-3 dominant RCC are RCC-specific or applicable to other solid tumors. Results: In our cohort and our validated cohort (n = 96), the LAG-3 group had significantly shorter OS (p = 0.037 and p = 0.003). The LAG-3 high expression RCC also showed shorter OS in the TCGA RCC cohort (p = 0.016). As the result of TIME evaluation, LAG-3 dominant RCCs had statistically high level of CD8, high CD39+CD8+, and high M2 macrophages. In metastasis samples, LAG-3 dominant RCCs preserved high level of CD8, high CD39+CD8+. In public mRNA seq analysis, LAG-3 dominant RCCs showed high level of CD8, high CD8+Tregs and high M2 macrophages. High Tregs in LAG-3 dominant tumor were seen in melanoma, HCC and prostate cancer. High M2 macrophage TIME in LAG-3 dominant tumor was unique to RCC, not seen in other solid cancer. Conclusions: LAG-3 dominant RCC had poor prognosis and could exhibit a unique, immunosuppressive TIME.

Immunotherapy and Immune Infiltration in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Analysis.

On a global scale, renal cell carcinoma (RCC) is the second most common form of cancer and the 10th leading cause of cancer-related deaths. There are about 70% of cases of RCC that are clear cell renal cell carcinomas (ccRCCs). This study explores possible targets for immune therapy in patients with RCC. In the recent years, immunotherapy has been applied to RCC patients. In order to identify genes that are closely associated with immune cells, a weighted gene coexpression network analysis (WGCNA) was conducted. A close association was found between genes involved in MEred and M0 macrophages, M1 macrophages, and M2 macrophages. A prognostic prediction model is subsequently developed by incorporating the OS and the expression level of key genes from the RCC cohort into a univariate COX regression analysis, a multivariate COX regression analysis, and a combined COX regression analysis. We finally discovered that 6 genes are closely associated with the prognosis of RCC patients, including SLC16A12, SLC2A9, IGF2BP2, EMX2, ANK3, and METTL7A. The survival analysis proved the prognostic prediction value of the model. The 1-year, 3-year, and 5-year AUC of ROC curves are 0.759, 0.723, and 0.733, respectively. For clinical ROC curves, the AUC score for risk score, stage, grade, and T stage is 0.759, 0.824, 0722, and 0.736, respectively. The nomogram was constructed for better prognosis prediction of RCC patients. In addition, GSVA and GO enrichment analysis was performed to explore the potential pathways that are closely associated with genes involved in the prognostic prediction model. Accordingly, our study demonstrates that immune cells play a crucial role in RCC infiltration. The development of a prognostic prediction model is a potential new prognostic biomarker and potential immunotherapy target for tumors.