TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses.

ABSTRACT: Immunotherapy with immune checkpoint inhibitors (ICIs) is increasingly used to treat various tumor types. Determining patient responses to ICIs presents a significant clinical challenge. Although components of the tumor microenvironment (TME) are used to predict patient outcomes, comprehensive assessments of the TME are frequently overlooked. Using a top-down approach, the TME was divided into five layers-outcome, immune role, cell, cellular component, and gene. Using this structure, a neural network called TME-NET was developed to predict responses to ICIs. Model parameter weights and cell ablation studies were used to investigate the influence of TME components. The model was developed and evaluated using a pan-cancer cohort of 948 patients across four cancer types, with Area Under the C

SUBMITTER: Ding X 

PROVIDER: S-EPMC11337220 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature