Gene

Blockade

Biology

Immune checkpoint

Receptor

Computational biology

Genetics

Genome

Internal medicine

Genomics

Oncology

Medicine

Genomic sequencing

Immunotherapy

Cancer

Cancer Genetics

Background: High tumor mutation burden (TMB) and total mutation count (TMC) can be predictive of better response to immune checkpoint blockade (ICB). Nevertheless, TMB and TMC are limited by variation across cancers and inconsistent definitions due to different profiling methods (targeted vs whole genome sequencing). Our objective was to identify genomic alterations (GAs) associated with ICB response and builds a novel genomic signature predictive of ICB response, independent of TMB/TMC. Methods: This was a pan-cancer next generation sequencing (NGS)-association study using January 2014-May 2016 data from AACR Project Genomics Evidence Neo-plasia Information Exchange (GENIE). Participants included 6619 patients with metastatic or un-resectable cancer across 9 cancer types (including 1572 ICB-treated patients). GA data was collected using next-generation sequencing (NGS) assays and downloaded from cbioportal.org. Predictive analyses for ICB response were performed to develop the signature (ImmGA). Results: GAs in 16 genes were associated with improved OS in ICB-treated patients (p < 0.005). 13 GAs were associated with an OS benefit in ICB-treated patients (Pinteraction < 0.05); these genes composed the ImmGA signature. High ImmGA score (≥2 alterations out of 13 predictive GAs) was associated with better OS in ICB-treated patients (AHR:0.67, 95%CI [0.6–0.75], p = 1.4e−12), even after accounting for TMC (Pinteraction = 8e−16). High ImmGA was associated with better OS in ICB-treated patients across most cancers and across different ICB treatment modalities. Conclusion: A novel signature predictive of ICB response (ImmGA) was developed from 13 GAs. Further investigation of the utility of ImmGA for treatment and trial selection is warranted.

Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE)