AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers.

ABSTRACT:

Background

Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.

Methods

In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted

SUBMITTER: Gschwind A 

PROVIDER: S-EPMC11899402 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature