148 PBRM1 mutation improves immunotherapy response in clear-cell renal cell carcinoma patients contingent on metabolic subtypes
Year: 2024
Type: conference-abstract
Abstract: Background PBRM1 is the second most mutated gene in clear-cell renal carcinoma (ccRCC). However, conflicting results exist on the role of PBRM1 mutation in response to immune checkpoint inhibitors (ICIs) in ccRCC clinical trials.1 Using large-scale data-driven analysis of serum metabolomics data, we previously identified three metabolic subtypes of ccRCC patients with ICI differential response.2 Here, we evaluate the integration of metabolic subtyping with PBRM1 mutation status as a means of improving patient stratification in response to cancer immunotherapy. Methods We performed an integrative analysis of serum metabolomics data and genomic sequencing of ccRCC patients in the CheckMate025 Phase III clinical trial of ICI.3 4 We further stratified these subtypes according to PBRM1 loss of function (LOF) and wildtype (WT) and assessed their overall survival (OS) and progression-free survival (PFS) using Kaplan-Meier and Cox proportional hazard survival analysis. We used chi-square statistics to evaluate the distribution of clinical variables across metabolic and genetic subtypes and linear models adjusted for clinical variables to identify significantly different features. We finally used PIUMet5 network analysis to identify biological mechanisms linked to those features. Results We found significant differences in response to ICI treatment between metabolic subtypes of PBRM1 LOF. Over all patients, PBRM1 LOF patients experienced significantly longer OS (HR 0.63, p=0.023) and PFS (HR 0.68, p=0.030) in response to ICI (figure 1a). However, these differences were limited to patients in metabolic subtype 3 (OS: HR 0.33, p=0.003; PFS: HR 0.35, p=0.001) (figure 1b), while PBRM1 LOF in subtypes 1 & 2 was not associated with significant differences in survival (figure 1c). Chi-square analysis further showed that PBRM1 status was not significantly associated with any clinical variables or secondary mutations in each metabolic subtype. PBRM1 LOF patients in subtype 3 consistently had significantly higher OS than the WT patients over multiple subgroups (figure 2). Among PBRM1 LOF patients, subtype 3 had a significantly higher OS (p =0.023, Cox HR 0.41), with 222 metabolite features significantly (FDR p-value <0.05) differing between the metabolic subtypes of PBRM1 LOF patients (figure 3b and 3c). Network-based analysis of these features found the Phosphatidylinositol/PI3K signaling pathway to be highly enriched (corrected p=7.2e-6). Conclusions Our findings explain some of the conflicting reports regarding PBRM1 mutation in responses to ICI. They further suggest that metabolomics provides orthogonal information on genetics to predict the outcome of cancer immunotherapy and could, with further validation, provide guidelines for treatment selection. References Walton J, Lawson K, Prinos P, Finelli A, Arrowsmith C, Ailles L. PBRM1, SETD2 and BAP1 — the trinity of 3p in clear cell renal cell carcinoma. Nat Rev Urol 2022;20(2):96-115. Pirhaji L. Examining response to immunotherapy in clear-cell renal cell carcinoma using baseline serum metabolic signatures. J Clin Oncol 2024;42:2604-2604. Braun DA, Ishii Y, Walsh AM, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med 2020;26(6):909-918. Motzer RJ, Escudier B, McDermott DF, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 2015;373(19):1803-1813. Pirhaji L, Milani P, Leidl M, et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat Methods 2016;13(9):770-776.
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4404065663