Tumor mutational burden as a predictive biomarker for molecularly matched therapy in two independent pan-cancer cohorts.

person Damian Tobias Rieke Charité– Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charité Comprehensive Cancer Center; German Cancer Consortium (DKTK), Berlin, Germany info_outline Damian Tobias Rieke, Till de Bortoli, Manuela Benary, Peter Horak, Mario Lamping, Sebastian Stintzing, Inge Tinhofer, Serge Leyvraz, Reinhold Schäfer, Frederick Klauschen, Ulrich Keller, Albrecht Stenzinger, Stefan Froehling, Razelle Kurzrock, Ivan Jelas, Ulrich Keilholz

Authors person Damian Tobias Rieke Charité– Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charité Comprehensive Cancer Center; German Cancer Consortium (DKTK), Berlin, Germany info_outline Damian Tobias Rieke, Till de Bortoli, Manuela Benary, Peter Horak, Mario Lamping, Sebastian Stintzing, Inge Tinhofer, Serge Leyvraz, Reinhold Schäfer, Frederick Klauschen, Ulrich Keller, Albrecht Stenzinger, Stefan Froehling, Razelle Kurzrock, Ivan Jelas, Ulrich Keilholz Organizations Charité– Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charité Comprehensive Cancer Center; German Cancer Consortium (DKTK), Berlin, Germany, Charité - Universitätsmedizin Berlin, Berlin, Germany, National Centre for Tumor Diseases, Heidelberg, Germany, Charite - Universitätsmedizin Berlin, Berlin, Germany, Charité– Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Hematology, Oncology and Cancer Immunology; German Cancer Consortium (DKTK), Berlin, Germany, Heidelberg University Hospital, Heidelberg, Germany, Worldwide Innovative Network for Personalized Cancer Therapy, Paris, France Abstract Disclosures Research Funding Other Deutsches Konsortium für Translationale Krebsforschung (DKTK) Background: Personalized cancer therapy based on molecular tumor aberrations has shown efficacy in a subset of tumors. Novel biomarkers are warranted to help extend this benefit to a larger patient population. Tumor mutational burden (TMB) is an established predictive biomarker for immune checkpoint inhibition. Its role for molecularly matched therapy is unknown. Methods: Comprehensive molecular profiling including whole-exome and RNA-sequencing was performed in 104 patients with advanced cancer within the DKTK MASTER program. 55 patients received a systemic therapy excluding immunotherapy. TMB and survival were analyzed in patients receiving molecularly matched therapy (n=34) or non-molecularly matched therapy (n=21) as well as in an independent published cohort of patients receiving molecularly matched (n=68) or non-molecularly matched (n=40) therapy (excluding immunotherapy). TMB and co-occurring driver mutations were analyzed in the DKTK MASTER cohort. Results: The median TMB of 1.68 mutations per megabase (mut/Mb) in the 34 patients receiving molecularly matched therapy was used to stratify patients into TMB-high and -low groups. Median overall survival (OS) and progression-free survival (PFS) were significantly shorter in the TMB-high than in the TMB-low group (OS: 4 months [95% CI, 3.3 to 7.6] versus 19 months [95%CI, 10.0 to not reached] p < 0.001, Hazard Ratio (HR) 4.4 [95%CI, 1.9 to 10.2], p<0.001; PFS 1.8 months [95%CI, 1.1 to 3.6] versus 8.5 months [95% CI, 2.8 to 17.0] p = 0.004, HR 2.9 [95%CI, 1.4 to 6.2], p=0.005). No significant differences were observed in patients receiving non-molecularly informed systemic therapy (OS HR 0.7 [95%CI, 0.2 to 2.6], p=0.635 and PFS HR 1.2 [95%CI, 0.4 to 3.5] p=0.701). In the validation cohort, shorter progression-free survival was also identified in the TMB-high group (median TMB cut-off of 4 mut/Mb; HR 2.2 [95%CI, 1.3 to 3.7] p=0.005) treated with molecularly matched therapy but not with non-molecularly informed therapy (HR 1.1 [95%CI, 0.5 to 2.2] p=0.848). TMB was associated with co-occurring driver mutations (n=104, r = 0.78 [95%CI, 0.68 to 0.85], p<0.001). Conclusions: High TMB was previously established as a positive predictive biomarker associated with efficacy of immune checkpoint inhibitor treatments. Our study reveals high TMB as a negative predictive biomarker associated with low efficacy of molecularly informed systemic therapy, potentially due to co-occurring driver mutations. Thus, TMB is a candidate predictive pan-cancer biomarker for combined precision oncology and immunotherapy programs.