and gmerlino@helix.nih.gov # Lead Contact 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.Immune checkpoint blockade (ICB) has become the first-line treatment for metastatic melanoma, the deadliest skin cancer. Antibodies inhibiting CTLA-4 and PD-1/PD-L1 signaling pathways have been approved, as monotherapies or in combination, for more than ten cancer types in this decade due to their significant improvement of patient survival 1 . Yet, even in melanoma, the "poster child" for ICB success, 2 response rates are insufficient and alternative strategies are being explored, including combinations with chemotherapy, radiotherapy, targeted therapy, and other immune checkpoint inhibitors 2 . Despite the intensive efforts to enhance ICB efficacy, understand mechanisms of sensitivity/resistance, and discover predictive biomarkers, the determinants of ICB response are still poorly understood. High mutation and neoantigen burden in pretreated tumors, increased T cell infiltrates and induction of inflammatory pathways during treatment, and antigen presentation alterations have shown correlation with clinical benefit 3-18 . Although the expression of specific pathways has been associated with ICB response in particular patient cohorts 7,18,19 , uncovering broad predictive signatures based on gene expression profiles has been elusive due to difficulties in collecting high quality transcriptomic data from clinical sets.Recently, independent computational predictors were developed based on immune-related gene expression profiles, such as immune checkpoints, co-stimulatory molecules and T cell dysfunction and exclusion markers [20][21][22] . However, the utility of these approaches for patient stratification will require further validation.Mouse models have historically served as essential tools for plumbing mechanisms underlying tumor initiation, progression and drug response, and have enormous potential. However, their value for informin...