Development of a prognostic model for breast cancer patients based on intratumoral tumor-infiltrating lymphocytes using machine learning algorithms.

ABSTRACT:

Background

Breast cancer remains a formidable global health challenge, with tumor-infiltrating lymphocytes (TILs) serving as pivotal biomarkers associated with disease progression, therapeutic response, and survival. While research typically focused on stromal TILs (sTILs), we hypothesize that intratumoral TILs (iTILs), which are in direct contact with tumor cells, have a more profound role in the immune-tumor interactions. In light of this, we have developed an iTIL-centric model for breast cancer patient stratification and prognostic prediction.

Methods

We sourced RNA-seq data and clinical profiles of breast cancer patients from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) to form our training dataset. Testi

SUBMITTER: Wu X 

PROVIDER: S-EPMC12078914 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature