A comprehensive exploration of immuno-genomic profiles for immune checkpoint blockade-based immunotherapy
Presenting author: Jing Yang, Department of Biostatistics, Vanderbilt University Medical Center
- Qi Liu, Department of Biostatistics, Vanderbilt University Medical Center
- Yu Shyr, Department of Biostatistics, Vanderbilt University Medical Center
Although great efforts have been devoted to identifying signatures predictive of response to immune checkpoint inhibitor (ICI) treatment, current biomarkers have poor generalizability and reproducibility across studies and cancer types. Integrating large-scale multi-omics studies holds great promise to identify robust biomarkers and understand immune-resistant mechanisms. Here, we performed the largest meta-analysis of 3,037 ICI-treated patients with genetic and/or transcriptomics profiles across 14 types of solid tumor. We identified and validated known and novel robust signatures associated with ICI outcome, including tumor mutational burden (TMB), IFNG and PD-1 expression, and most interestingly, interaction driving macrophages activation and T cells recruitment. Independent data from single-cell RNA sequencing and dynamic transcriptomic profiles on the ICI treatment further demonstrated enhanced circuits between macrophages and T cells contributing to ICI response. A multivariable model built from the robust signatures significantly outperformed predictions based on tumor mutational burden alone in three independent validation cohorts. All pre-processed data can be found at http://220.127.116.11:3838/Cancer-Immu/, an open-access data resource we developed.