A Multifactorial Tumor and Immune Cell Profile Determines Response to Immune Checkpoint blockade in Melanoma

The complex crosstalk between tumor and immune cells during immune checkpoint blockade mandates the development of integrated models that interpret the anti-tumor immune response and predict clinical outcome. We have integrated genome-wide sequence and structural alterations with pre and on-therapy transcriptomic and T cell repertoire features for a cohort of 64 immunotherapy-naïve melanomas treated with anti-PD1 monotherapy or combined anti-PD1 and anti-CTLA4 therapy. While tumor mutation burden (TMB) was associated with improved response to therapy, expressed mutation burden was superior to TMB in predicting outcome. An increased pre-existing T cell density differentiated responding from non-responding tumors independent of therapy. Importantly, T cell repertoire reshaping determined by T cell receptor (TCR) clonotypic regressions and expansions reflected response to therapy such that a more dynamic repertoire predicted improved clinical outcome. Through whole-transcriptome analyses, we discovered differential abundance of B cell subsets in responding tumors, which highlights the importance of the interplay between pre-existing T and B cell immunity in shaping therapeutic response. Tumor temporal trajectories during therapy revealed distinct patterns of molecular response related to expressed mutation elimination or retention that accurately interpreted clinical response. High-dimensional genomic, transcriptomic and immune repertoire data were integrated by both a machine learning and a censored regression approach, resulting in a harmonized multi-modal predictor of response to immune checkpoint blockade. B cell abundance, expressed mutation load and tumor aneuploidy were combined to identify patients at high risk for recurrence, such that high risk patients had a significantly shorter progression-free survival (HR=9.18, 95% CI: 3.14-26.85, p=3.4e-06) especially in the anti-PD1/anti-CTLA4 group (HR=20.34, 95% CI: 4.08-101.32, p=1.68e-06).

• Type: Other
• Archiver: European Genome-Phenome Archive (EGA)