Background Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability, and tumor mutation burden (TMB) may improve benefit prediction. Methods Using treatment data and genomic and transcriptomic tumor tissue profiling from an observational trial (NCT03061305), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival (rwPFS) and overall survival (OS) prediction was validated in an independent cohort of trial patients. Results Here, by Cox modeling, we develop IRS—which combines TMB with CD274, PDCD1, ADAM12 and TOP2A quantitative expression—to predict pembrolizumab rwPFS (648 patients; 26 tumor types; IRS-High or -Low groups). In the 248 patient validation cohort (248 patients; 24 tumor types; non-pembrolizumab PD-[L]1 monotherapy treatment), median rwPFS and OS are significantly longer in IRS-High vs. IRS-Low patients (rwPFS adjusted hazard ratio [aHR] 0.52, p = 0.003; OS aHR 0.49, p = 0.005); TMB alone does not significantly predict PD-(L)1 rwPFS nor OS. In 146 patients treated with systemic therapy prior to pembrolizumab monotherapy, pembrolizumab rwPFS is only significantly longer than immediately preceding therapy rwPFS in IRS-High patients (interaction test p = 0.001). In propensity matched lung cancer patients treated with first-line pembrolizumab monotherapy or pembrolizumab+chemotherapy, monotherapy rwPFS is significantly shorter in IRS-Low patients, but is not significantly different in IRS-High patients. Across 24,463 molecularly-evaluable trial patients, 7.6% of patients outside of monotherapy PD-(L)1 approved tumor types are IRS-High/TMB-Low. Conclusions The validated, predictive, pan-tumor IRS model can expand PD-(L)1 monotherapy benefit outside currently approved indications.

Pembrolizumab is approved in many advanced solid tumor types, however predictive biomarkers and the proportion of pembrolizumab-benefiting patients vary. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability (MSI) status, and tumor mutation burden (TMB) may improve benefit prediction. Here, leveraging treatment data (time to next treatment [TTNT]) and comprehensive genomic and quantitative transcriptomic profiling on routine tumor tissue from 708 patients (24 tumor types) collected in an ongoing observational trial (NCT03061305), we report a multivariate, integrative predictor of pan-solid tumor pembrolizumab benefit. The Immune Response Score (IRS) model, which includes TMB and quantitative PD-1, PD-L2, ADAM12 and CD4 RNA expression, was confirmed as predictive through comparison of pembrolizumab TTNT with previous chemotherapy TTNT in a subset of 166 patients treated with both. Applying IRS to the entire NCT03061305 cohort (n=25,770 patients), 13.2-30.7% of patients (2.2-9.6% of patients outside of pembrolizumab approved tumor types [including TMB-High and MSI-High]) are predicted to benefit substantially from pembrolizumab. Hence, if prospectively validated, the IRS model may improve pembrolizumab benefit prediction across approved tumor types including patients outside of currently approved indications.