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Exploring a mutation-based signature to predict the benefits of immune checkpoint inhibitors in oncogene-addicted subsets of non-small cell lung cancer: a retrospective study

Year: 2025
Type: article
Abstract: Despite the effectiveness of immune checkpoint blockade (ICB) therapy in cancer treatment, oncogene-addicted subsets of non-small cell lung cancer (NSCLC) typically derive limited benefits. Herein, we established and evaluated a mutation-based signature to predict ICB efficacy in patients with both non-mutant and oncogene-mutant NSCLC. The predictive power of the signature was evaluated and validated across advanced NSCLCs cases with or without targetable oncogene alterations, using four public and one in-house immunotherapy cohort. NSCLC cohorts from The Cancer Genome Atlas were used to explore the underlying mechanisms of the signature. Herein, we defined a gene mutation number (GMN) based on the signature and classified patients into high and low GNM groups (GMN-H and GMN-L, respectively). The GMN-H group exhibited significantly improved overall survival (OS) (hazard ratio [HR], 0.23; 95% confidence interval [CI]: 0.14–0.37; P < 0.001 in the discovery cohort) and progression-free survival (HR, 0.43; 95% CI: 0.28–0.65; P < 0.001 in the discovery cohort; HR, 0.41; 95% CI: 0.26–0.66; P = 0.0011 in the validation cohort) after ICB therapy. The survival advantage of GMN-H persisted in patients with KRAS mutations, whereas only trends toward improved survival outcomes were observed in those with EGFR, HER2, or MET alterations. Moreover, in the KRAS G12C - and KRAS G12V -mutant subgroups, patients with GMN-H exhibited a better OS. Compared with GMN-L, GMN-H was associated with higher levels of neoantigens, enrichment of WNT and MAPK pathways, and increased intra-tumoral CD8 + T cell infiltration. The mutation-based signature established herein represents a robust biomarker for identifying patients with advanced NSCLCs who are most likely to benefit from ICB therapy, including those with targetable oncogene alterations.
Language: English
Subfield: Oncology
Field: Medicine
Open Access status: gold
Estimated APC paid: $3,490
OpenAlex ID: W4416537980
FWCI:
Cites: 58
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