Robust prediction of patient outcomes with immune checkpoint blockade therapy for cancer using common clinical, pathologic, and genomic features
Chang, T.-G.; Cao, Y.; Sfreddo, H.; Dhruba, S. R.; Lee, S.-H.; Valero, C.; Yoo, S.-K.; Chowell, D.; Morris, L. G. T.; Ruppin, E.
Show abstract
Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patients responses remains elusive. We analyzed eight cohorts of 2881 ICB-treated patients across 18 solid tumor types, the largest dataset to date, examining diverse clinical, pathologic, and genomic features. We developed the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent, compact 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As our method is accurate, interpretable, and only utilizes a few readily measurable features, we anticipate it will help improve clinical decision-making practices in precision medicine to maximize patient benefit.
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