Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors

Abstract

In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.

The temporal evolution of cancer hallmarks

“…The hallmarks with the fewest shared genes were metabolism (59%, 251/429) and genome instability (41% 87/211). Intriguingly, less than half of the known cancer driver genes were associated with any specific hallmark (42%, 152/365) and our previous list of escape genes (18) shared almost 70%. Among the genes most represented in the ten hallmarks we found AKT, Ras, PIK3 and MAPK gene families (9 out of 10 hallmarks), and also other well characterised genes such as BRAF (7/10) and TP53 (8/10).…”

A transcriptome-wide meta-analysis reveals lack of cancer-cell intrinsic determinants of response to immune checkpoint blockade

“…As cancer cells are under negative selective pressure from the immune system, several mechanisms of immune escape by cancer cells have been proposed: (i) cancer cells down-regulate antigen presentation by deleting HLA alleles 46 , (ii) cancer cells deplete neoantigens to avoid detection 63 , (iii) oncogenic signaling may up-regulate PD-L1 expression and suppress T-cell recruitment 64 . However, the prevalence of these immune evasion mechanisms are still unclear.…”

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

“…Although deriving a predictive score was not the overarching aim of this work, the high predictive value of this approach underscores its value for cancer immunology and immunotherapy, and the importance of modeling the different facets of tumor heterogeneity. It is likely that the prediction of patients' response to ICB can be further improved by incorporating additional drivers of anticancer immune responses and mechanisms of immune evasion, like the presence of tertiary lymphoid structures 15,88 , antigen presentation defects 89,90 , neoantigen burden and "quality" [91][92][93] , as well as cancer and germline genetics 94 .…”