Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

• Main Authors: Liu, David V. (Author), Liu, Derek (Author), Jerby-Arnon, Livnat (Author), Vokes, Natalie I. (Author), Margolis, Claire A. (Author), Conway, Jake (Author), He, Meng Xiao (Author), Elmarakeby, Haitham (Author), Dietlein, Felix (Author), Miao, Diana (Author), Tracy, Adam (Author), Izar, Benjamin (Author), Regev, Aviv (Author), Van Allen, Eliezer M. (Author)
• Other Authors: Broad Institute of MIT and Harvard (Contributor)
• Format: Article
• Language: English
• Published: Springer Science and Business Media LLC, 2020-06-17T18:36:59Z.
• Subjects: Article
• Online Access: Get fulltext

 Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.