Ensemble model for neoadjuvant chemotherapy response prediction and treatment sensitivity in TNBC based on DNA replication stress signatures.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer. Although neoadjuvant chemotherapy (NACT) has some effectiveness in TNBC, a portion of patients still do not benefit from them. The critical role of DNA replication stress (DRS) in cancer therapy has been recognized, but its study in TNBC NACT remains relatively limited. Affymetrix microarray data were obtained from the GEO database for both training and test sets. These data were processed using the "affy" R package. The Boruta algorithm and SVM-RFE method were employed for key gene selection, and an integrated model based on multiple algorithms was developed to establish a risk score. Additionally, the tumor microenvironment (TME) was analyzed, and the correlation between risk score and drug sensitivity was explored, incorporating several drug databases. Through the analysis of TNBC patients' responses to NACT, we found a close correlation between DRS and TNBC treatment responses and identified eight key genes. The developed ensemble model (ENS) demonstrated high AUC values of 0.922, 0.886, and 0.858 across the three independent datasets, respectively, indicating its strong ability to accurately predict the effectiveness of NACT. The study also revealed that patients with higher risk score are more prone to recurrence and metastasis, and have a rich TME composition. Additionally, drug sensitivity analysis offers potentially effective personalized treatment options for high-risk TNBC. This study successfully constructed an ensemble model to predict TNBC patients' response to NACT. Additionally, it was discovered that the risk score held significant value in analyzing the correlation between TNBC patients' TME and drug sensitivity. These findings offer important new insights into personalized treatment strategies for TNBC.