LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features.

Autor: Chang TG; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA., Cao Y; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA., Sfreddo HJ; Department of Surgery and Cancer Immunogenomics Research Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Dhruba SR; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA., Lee SH; Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, South Korea., Valero C; Department of Surgery and Cancer Immunogenomics Research Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Yoo SK; The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Chowell D; The Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Morris LGT; Department of Surgery and Cancer Immunogenomics Research Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org., Ruppin E; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA. eytan.ruppin@nih.gov.
Jazyk: angličtina
Zdroj: Nature cancer [Nat Cancer] 2024 Aug; Vol. 5 (8), pp. 1158-1175. Date of Electronic Publication: 2024 Jun 03.
DOI: 10.1038/s43018-024-00772-7
Abstrakt: Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response 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 precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .
(© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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