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

Abstract

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 receptor 1 ligand (PD-L1) expression, and tumor mutational burden (TMB) from cohorts of LUAD public database and immunotherapeutic patients. Two classification groups (C1 and C2) in the training and two validation sets were identified for the efficacy of ICB via the DNN algorithm. Patients in C1 showed remarkably long OS and PFS to programmed death 1 (PD-1) inhibitors. The C1 group was significantly associated with increased expression of immune cell infiltration, immune checkpoints, activated T-effectors, and interferon gamma signature. C1 group also exhibited significantly higher TMB, neoantigens, transversion, or transition than the C2 group. This work provides novel insights that classification of DNNs using somatic mutations in LUAD could serve as a potentially predictive approach in developing a strategy for anti-PD-1/PD-L1 immunotherapy.