A Graph Curvature-Based Pipeline for Discovering Immune Checkpoint Response Biomarkers

Abstract

Immune checkpoint inhibitors (ICIs), also called immune checkpoint blockers, are a promising category of targeted therapy for solid tumors. Predicting which patients will respond to ICI therapy remains an open problem under active investigation. This paper adds to this effort by developing a modular pipeline for the discovery of biomarkers from tumor RNA-sequencing data. We contextualize gene expression measurements using a protein-protein interaction (PPI) network and use a notion of graph curvature to find (pairs of) genes in the PPI that could serve as potential biomarkers. Our candidate biomarkers are evaluated using an extensive literature search and transfer learning experiments. We also provide a harmonized collection of drug-specific candidate markers found through rank aggregation that we believe merit further study.