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Can the tumor transcriptomic profile predict immunotherapy efficacy?
14 novembre 2025
By Lila Rouland | Published on November 14, 2025 | 2 min readThe emergence of immunotherapies, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of many cancers. However, only a minority of patients derive durable benefit. Current biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI) have significant limitations i
By Lila Rouland | Published on November 14, 2025 | 2 min read
The emergence of immunotherapies, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of many cancers. However, only a minority of patients derive durable benefit. Current biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI) have significant limitations in sensitivity and specificity. To overcome these challenges, large-scale transcriptomic approaches offer a new opportunity to precisely characterize tumor immune phenotypes. This study proposes and evaluates pan-cancer transcriptomic classifiers capable of predicting response to ICIs, based on the integrative analysis of multiple databases and clinical trials.
The emergence of immunotherapies, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of many cancers. However, only a minority of patients derive durable benefit. Current biomarkers such as PD-L1 expression, tumor mutational burden (TMB), and microsatellite instability (MSI) have significant limitations in sensitivity and specificity. To overcome these challenges, large-scale transcriptomic approaches offer a new opportunity to precisely characterize tumor immune phenotypes. This study proposes and evaluates pan-cancer transcriptomic classifiers capable of predicting response to ICIs, based on the integrative analysis of multiple databases and clinical trials.
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Scientific reference
Patel N, Nebozhyn M, Singal G, et al. Tumor transcriptome-wide expression classifiers to predict response to immune checkpoint inhibitors. Nature Communications. 2023;14:5321.
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