An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma.

ABSTRACT:

Background

Programmed death ligand 1 (PD-L1) and tumor mutation burden (TMB) have been developed as biomarkers for the treatment of immune checkpoint inhibitors (ICIs). However, some patients who are TMB-high or PD-L1-high remained resistant to ICIs therapy. Therefore, a more clinically applicable and effective model for predicting the efficacy of ICIs is urgently needed.

Methods

In this study, genomic data for 466 patients with melanoma treated with ICIs from seven independent cohorts were collected and used as training and validation cohorts (training cohort n = 300, validation cohort1 n = 61, validation cohort2 n = 105). Ten machine learning classifiers, including Random Forest classifier, Stochastic Gradient Descent (SGD) classifier and Linear Support Vector Classifier (

SUBMITTER: Pan C 

PROVIDER: S-EPMC9887306 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature