Clinical attribute-based risk matrix for prognostication in immunotherapy-treated non-small cell lung cancer

Abstract

Biomarkers used to guide immunotherapy in non-small cell lung cancer (NSCLC) are invasive, variably available, and insufficient to capture clinical and regional heterogeneity, leaving many patients inadequately stratified for prognosis. We develop a clinical attribute–based framework that leverages routinely collected variables to estimate survival risk, even when baseline data are incomplete. We analyze 17 051 patients with NSCLC treated with immunotherapy, assembled from published cohorts and tertiary centers across multiple regions, and evaluate seventeen common clinical attributes using a two-stage survival modeling strategy with external validation in Western and East Asian populations. Clinical attributes exhibit context-dependent prognostic effects across variable-defined strata and survival endpoints, with performance status and disease stage emerging as the most consistent determinants of survival risk prediction, alongside region-specific modifiers. Here we show that this “start-anywhere” clinical risk matrix provides robust, population-aware prognostic stratification and complements biomarkers for assessing immunotherapy outcomes in patients with NSCLC. Current biomarkers used to guide immunotherapy in non-small cell lung cancer (NSCLC) do not adequately stratify patients for prognosis. Here, the authors show that CLARITY-ICI Matrix, a clinically deployable, missing-data-tolerant framework, enables population-aware stratification of survival risk in immunotherapy-treated NSCLC using routinely available clinical attributes and complements biomarkers.