LORIS: A LOgistic Regression-based Immunotherapy-response Score

This is a repository of scripts for reproducing the paper titled "LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic, and genomic features" by Chang et al. (Nature Cancer 2024). Briefly, in this work, Chang et al. developed a new clinical score called the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent and concise 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 multiple 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 the method is accurate, interpretable, and only utilizes a few readily measurable features, it may help improve clinical decision-making practices in precision medicine to maximize patient benefit.