Optimizing T cell inflamed signature through a combination biomarker approach for predicting immunotherapy response in NSCLC.

The introduction of anti-PD-1/PD-L1 therapies revolutionized treatment for advanced non-small cell lung cancer (NSCLC), yet response rates remain modest, underscoring the need for predictive biomarkers. While a T cell inflamed gene expression profile (GEP) has predicted anti-PD-1 response in various cancers, it failed in a large NSCLC cohort from the Stand Up To Cancer-Mark (SU2C-MARK) Foundation. Re-analysis revealed that while the T cell inflamed GEP alone was not predictive, its performance improved significantly when combined with gene signatures of myeloid cell markers. These additional signatures, however, showed negative contributions to prediction, hinting at immune alterations affecting therapy. Based on this, we proposed a combination biomarker approach that integrates the T cell inflamed GEP with immune-altered signatures, derived from the SU2C-MARK cohort using a machine-learning approach, as novel biomarkers. These signatures consisted of genes highly expressed in myeloid and stromal cells. We then assessed the predictive ability of these combined biomarkers in six independent cancer cohorts treated with anti-PD-1. The combined biomarkers demonstrated enhanced performance in NSCLC and gastric cancer cohorts, but not in melanoma cohorts. Our study introduces new biomarkers for predicting anti-PD-(L)1 response in NSCLC and offers mechanistic insights into treatment efficacy.