The sex-based profiles of molecular biomarkers for immunotherapy responses of cancer patients
Year: 2025
Type: article
Abstract: Background Published clinical trials have yielded controversial findings regarding the effects of sex on the benefits of immune checkpoint inhibitors (ICIs). Sex-associated differences in the efficacy of immunotherapy remain an important, unresolved question. Methods We investigated sex-biased molecular profiles across a multitude of biomarkers linked to immunotherapy responses. Multiomics data from major solid tumors in The Cancer Genome Atlas, with sufficient sample sizes (≥50 patients of each sex), were analyzed. Ninety-five molecular markers characterizing 4 distinct aspects of the tumor immune system were summarized and compared. The inverse probability of weights algorithm was used to generate well-balanced sex subgroups. Results Our results showed that lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma, and liver hepatocellular carcinoma were the top 3 cancer types with extensive sex-biased biomarker profiles (31/95, 15/95, and 14/95, respectively). Notably, although both were categorized as non–small cell lung carcinoma, LUSC harbored significantly more sex-biased immunological features than those of lung adenocarcinoma (p < 0.01). We further explored the validity of this finding by analyzing ICI-responsive signatures and individual patient-level data for non–small cell lung carcinoma and found that sex had significant interaction effects on immunotherapy outcomes in LUSC (p interaction < 0.05), with women tending to derive greater benefits from ICIs than men. However, this difference was not apparent in the lung adenocarcinoma group (p interaction = 0.66), with men and women deriving comparable benefits. Conclusions We systematically characterized sex-biased profiles of key molecular biomarkers predicting immunotherapy responses across solid tumors, which could pave the way for individualized therapeutic approaches for men and women.
Authors: Jingjing Zhang, Shuang Wang, Ziwei Huang, Wenfei Li, Yupei Chen, Jun Ma, Jiawei Lv, Chenfei Wu
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4412070855