Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma.

ABSTRACT: Although several biomarkers have been proposed to predict the response of patients with lung adenocarcinoma (LUAD) to immune checkpoint blockade (ICB) therapy, existing challenges such as test platform uniformity, cutoff value definition, and low frequencies restrict their effective clinical application. Here, we attempted to use deep neural networks (DNNs) based on somatic mutations to predict the clinical benefit of ICB to LUAD patients undergoing immunotherapy. We used DNNs to train and validate the predictive model in three cohorts. Kaplan-Meier estimates determined the overall survival (OS) and progression-free survival (PFS) between specific subgroups. Then, we performed a relevant analysis on the multiple-dimension data types including immune cell infiltration, programmed death rece

SUBMITTER: Peng J 

PROVIDER: S-EPMC7051190 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature