Immunometabolism signature derived from on-treatment tumor specimens predicts immune checkpoint blockade response in metastatic melanoma.

ABSTRACT: Gene signatures have been developed to predict the immune checkpoint blockade (ICB) response and prognosis in patients with melanoma. However, most of these signatures are obtained from pre-treatment biopsy samples, and there is no predictive combination of immune and metabolic signatures from patients receiving treatment. In this study, using the Elastic Net Regression (ENLR) algorithm, we built an immunometabolism signature using on-treatment (IMME-ON) tumor specimens based on clinical information and transcriptome data from patients with metastatic melanoma after anti-PD1 and or anti-CTLA4 treatment. The IMME-ON signature was validated in three independent datasets of metastatic melanoma, achieving area under the curve (AUC) values of 0.79-0.86. We also combined all the test samples and

SUBMITTER: Chen S 

PROVIDER: S-EPMC12214063 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature