TY - JOUR T1 - Multi-model preclinical platform predicts clinical response of melanoma to immunotherapy JF - bioRxiv DO - 10.1101/727826 SP - 727826 AU - Eva PĂ©rez-Guijarro AU - Howard H. Yang AU - Romina E. Araya AU - Rajaa El Meskini AU - Helen T. Michael AU - Suman Kumar Vodnala AU - Kerrie L. Marie AU - Cari Graff-Cherry AU - Sung Chin AU - Anthony J. Iacovelli AU - Alan Kulaga AU - Anyen Fon AU - Aleksandra M. Michalowski AU - Willy Hugo AU - Roger S. Lo AU - Nicholas P. Restifo AU - Terry Van Dyke AU - Shyam K. Sharan AU - Romina S. Goldszmid AU - Zoe Weaver Ohler AU - Maxwell P. Lee AU - Chi-Ping Day AU - Glenn Merlino Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/08/06/727826.abstract N2 - Although immunotherapy has revolutionized cancer treatment, only a subset of patients demonstrates durable clinical benefit. Definitive predictive biomarkers and targets to overcome resistance remain unidentified, underscoring the urgency to develop reliable immunocompetent models for mechanistic assessment. Here we characterize a panel of syngeneic mouse models representing the main molecular and phenotypic subtypes of human melanomas and exhibiting their range of responses to immune checkpoint blockade (ICB). Comparative analysis of genomic, transcriptomic and tumor-infiltrating immune cell profiles demonstrated alignment with clinical observations and validated the correlation of T cell dysfunction and exclusion programs with resistance. Notably, genome-wide expression analysis uncovered a melanocytic plasticity signature predictive of patient outcome in response to ICB, suggesting that the multipotency and differentiation status of melanoma can determine ICB benefit. Our comparative preclinical platform recapitulates melanoma clinical behavior and can be employed to identify new mechanisms and treatment strategies to improve patient care. ER -