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• W4360748368 abstract "You have accessJournal of UrologyCME1 Apr 2023MP04-10 MACHINE LEARNING PREDICTS METABOLIC BIOMARKERS OF RESPONSE TO ANTI-VEGF DRUG SUNITINIB IN CCRCC Katiana Vazquez-Rivera, X. Amy Xie, Stephen Reese, Ritesh Kotecha, Martin Voss, Robert Motzer, Wesley Tansey, Ari Hakimi, and Ed Reznik Katiana Vazquez-RiveraKatiana Vazquez-Rivera More articles by this author , X. Amy XieX. Amy Xie More articles by this author , Stephen ReeseStephen Reese More articles by this author , Ritesh KotechaRitesh Kotecha More articles by this author , Martin VossMartin Voss More articles by this author , Robert MotzerRobert Motzer More articles by this author , Wesley TanseyWesley Tansey More articles by this author , Ari HakimiAri Hakimi More articles by this author , and Ed ReznikEd Reznik More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003215.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Clear cell renal cell carcinoma (ccRCC) is a metabolic disease with multiple mutated genes involved in the regulation of cellular metabolic processes, however it remains unknown how ccRCC metabolites are associated with response to anti-VEGF or immunotherapy agents. Metabolomic signatures may serve as a biomarker of response or serve as future points of intervention for targeted agents. Thus, we sought to characterize metabolite signatures associated with response to anti-VEGF therapy in patients with ccRCC. METHODS: A machine learning algorithm called T-MIRTH (Transcriptomics-Metabolite Imputation via Rank-Transformation and Harmonization) was developed to identify key metabolites. In short, this algorithm imputes metabolite abundances from RNA sequencing data by modeling metabolite-RNA covariation across datasets with paired metabolomics and transcriptomics data. 262 well-predicted metabolites were used to test the association between imputed levels of individual metabolites by T-MIRTH and progression free survival (PFS) in 7 published sunitinib clinical trials in advanced ccRCC. Using a multivariable Cox proportional hazard model, metabolomic signatures were correlated with progression-free survival and regression results were aggregated using a random effects meta-analysis model. RESULTS: Our T-MIRTH algorithm was validated using 3 ccRCC datasets with paired metabolomics and transcriptomics data and we demonstrated that T-MIRTH can accurately predict metabolite levels using RNA sequencing data. We then demonstrated using the ccRCC TCGA that T-MIRTH predicted metabolic differences between both tumor/normal samples and high/low-stage samples. Our algorithm was able to identify 262 validated metabolites. The results from our meta-analysis across 7 clinical trial demonstrated 7 metabolites significantly associated with improved PFS in the sunitinib arm (FDR < 0.05). In particular, high levels of 1-methylimidazole acetate had improved PFS within the sunitinib arm across all trials. CONCLUSIONS: Using a novel algorithm, we identified 7 metabolites that were associated with improved PFS in patients treated with sunitinib across 7 clinical trials. These metabolites may serve as biomarkers of response and may also be important targets for future therapeutics. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e37 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Katiana Vazquez-Rivera More articles by this author X. Amy Xie More articles by this author Stephen Reese More articles by this author Ritesh Kotecha More articles by this author Martin Voss More articles by this author Robert Motzer More articles by this author Wesley Tansey More articles by this author Ari Hakimi More articles by this author Ed Reznik More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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• W4360748368 title "MP04-10 MACHINE LEARNING PREDICTS METABOLIC BIOMARKERS OF RESPONSE TO ANTI-VEGF DRUG SUNITINIB IN CCRCC" @default.
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