Prediction of Clinical Outcomes and Immunotherapy Response in Breast Cancer Based on T Cell-Mediated Tumor...

Immune checkpoint inhibitors (ICIs) are becoming promising treatments for individuals with breast cancer (BRCA), yet only a limited number of patients show a favorable response to ICI therapy. Consequently, it is essential to identify candidate patients who would gain the most benefit from these medications. Unfortunately, there is a deficiency of validated biomarkers that can predict the response to immunotherapy and overall survival. Since the core principle of ICI therapy is T cell-mediated tumor killing (TTK), our objective was to identify unique prognostic biomarkers of TTK to predict survival outcomes and responses to immune-based treatment in BRCA patients.

This study used transcriptomic data from BRCA patients, using the TCGA and GSE20685 cohorts as the training and external validation sets, respectively. First, weighted gene co-expression network analysis (WGCNA) and differential expression analysis were employed to identify key genes associated with TTK, followed by the construction of a prognostic risk model using the univariate cox and LASSO regression analyses. Concurrently, TIMER, MCPCounter, and CIBERSORT methods were employed to analyze immune infiltration differences across risk groups, with drug sensitivity analysis integrated to predict potential therapeutic agents. Furthermore, single-cell RNA sequencing (scRNA-seq) analysis clarified the expression profiles of key genes across distinct cell subpopulations, and cell functional experiments validated their potential biological functions in BRCA cells.

This study identified key genes associated with TTK and constructed a risk model comprising HOXC13, KDELR2, POP1, PGK1, and ZIC2. Results demonstrated a significantly poorer prognosis in the high-risk group, with ROC curves indicating robust predictive performance validated in both training and validation cohorts. Immune infiltration analysis revealed increased infiltration of B cells, macrophages, CD4+ T cells, and Tregs in the high-risk group. Drug-sensitivity analysis demonstrated significant negative correlations between risk scores and IC50 values across multiple drugs. Single-cell analysis revealed high KDELR2 expression in fibroblasts and PGK1 expression in epithelial cells. Functional experiments further confirmed that silencing HOXC13 significantly suppressed proliferation, migration, and invasion in BRCA cells.

This study revealed the expression patterns of multiple key genes associated with TTK in BRCA and their potential regulatory roles in the immune microenvironment, suggesting that immune cell infiltration may be an important factor affecting the prognosis of BRCA.

The index related to TTK appears to be a valuable biomarker for effectively assessing survival and forecasting the success of therapy in patients with BRCA. This risk metric can enable timely and targeted early interventions for patients, thus promoting advancements in personalized medicine and enhancing the research in precise immuno-oncology.