Background: High-risk melanoma pts treated with ipi had a more favorable outcome than those treated with interferon-α2b (IFN) in phase III adjuvant trial E1609. We hypothesized that further profiling of circulating immune cells and cytokines would inform underlying mechanisms associated with immune response vs. resistance to CTLA4 blockade. Methods: Multiparameter flow cytometry was used to compare surface protein expression on PBMC collected at baseline, focusing on Treg, MDSC and effector T cells (N = 210 ipi, N = 119 IFN). Multiplex Luminex assays were used to test baseline serum for concentrations of candidate cytokines, chemokines and growth factors. A survival analysis tool, DRPPM-PATH-SURVEIOR, was then used to perform a univariate screening of these analytes using a Cox proportional hazards (PH) model. A median cut-point was derived for each marker, and pts were split into above or below median cohorts. The dichotomized scores were then processed through a Cox regression analysis. Features were ranked based on p-values calculated by a likelihood test for overall survival (OS) and recurrence-free survival (RFS). A Lasso Cox regression model was further developed using Glmnet by optimizing lambda at minimum mean cross-validated error. Pts were randomly and evenly split into training and testing datasets. Results: Initial univariate Cox PH regression model screening identified 6 cellular and 2 serum protein biomarkers associated with OS and RFS. Enriched populations of CTLA4⁺ Treg (CD3⁺/CD4⁺/CD25^hi+/CD152⁺) and monocytic (M)-MDSC (Lin-CD33⁺/HLA-DR^lo+/CD14⁺/CD15⁺) were identified as being associated with poor outcome, while enriched populations of CXCR3⁺/CD4⁺ T cells, CXCR3⁺/CD8⁺ T cells, CTLA4⁺/INFg⁺/CD8⁺ T cells and CD39⁺ Treg (CD3⁺/CD4⁺/CD25^hi+/CD39⁺) were associated with better outcome. Higher levels of CCL3 and CXCL11 chemokines in serum were associated with improved OS and RFS. To develop a prognostic model predictive of ipi treatment benefits, we focused on top ranked biomarkers and generated and tested a Lasso Cox regression model based on CTLA4⁺ Treg, M-MDSC and CXCR3⁺/CD8⁺ T cell populations using a training subset (N = 105 ipi pts). This model was predictive of pt OS and RFS in the testing subset (N = 105) with C-index = 0.67. To examine broader applicability of our Lasso Cox model, we analyzed an additional 119 pts treated on the IFN control arm. By incorporating treatment arm as an interaction term with our 3 biomarker Lasso Cox score, our model was determined to be predictive of risk in pts treated with ipi but not IFN. Conclusions: Our analysis confirms the predictive value of a 3 biomarker Lasso Cox score predicated on CTLA4⁺ Treg, M-MDSC and CXCR3⁺/CD8⁺ T cell content in patient blood at baseline as it relates to adjuvant ipi treatment outcomes.