A key molecular driver of tumor-infiltrating lymphocytes in invasive breast cancer identified by machine learning-based meta-mining

The immune system plays a crucial role at all stages of tumor development, including initiation, progression, and dissemination; however, the precise molecular mechanisms underlying tumor immunity remain unclear. In this study, we aimed to identify key targets associated with tumor-infiltrating lymphocytes (TILs) in early-stage breast cancer (BC) using a novel machine learning (ML) approach. We analyzed a cohort of 719 patients with early-stage BC from The Cancer Genome Atlas datasets, all of whom had available digital hematoxylin and eosin-stained whole slide images and tumor transcriptomic data. Stromal TIL grades (low, intermediate, and high) were evaluated based on the International Working Group criteria. Using artificial neural network ML methods, we identified 49 genes that exhibited differential expression across the stromal TIL grades. Cluster analysis of these genes resulted in the classification of patients into two distinct molecular subtypes (1 and 2), which were significantly associated with tumor aggressiveness and prognosis. Our findings highlight the potential of TIL-related gene sets in deciphering the intricate molecular networks that control tumor immunity in early-stage BC.