Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images.

ABSTRACT: Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy,

SUBMITTER: Patkar S 

PROVIDER: S-EPMC11659524 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature