Identification of an immune classifier for predicting the prognosis and therapeutic response in triple-negative breast cancer

Abstract

Objectives: Triple-negative breast cancer (TNBC) poses a significant challenge due to the lack of reliable prognostic gene signatures and an understanding of its immune behavior. Methods: We analyzed clinical information and mRNA expression data from 162 TNBC patients in TCGA-BRCA and 320 patients in METABRIC-BRCA. Utilizing weighted gene coexpression network analysis, we pinpointed 34 TNBC immune genes linked to survival. The least absolute shrinkage and selection operator Cox regression method identified key TNBC immune candidates for prognosis prediction. We calculated chemotherapy sensitivity scores using the “pRRophetic” package in R software and assessed immunotherapy response using the Tumor Immune Dysfunction and Exclusion algorithm. Results: In this study, 34 survival-related TNBC immune gene expression profiles were identified. A least absolute shrinkage and selection operator-Cox regression model was used and 15 candidates were prioritized, with a concomitant establishment of a robust risk immune classifier. The high-risk TNBC immune groups showed increased sensitivity to therapeutic agents like RO-3306, Tamoxifen, Sunitinib, JNK Inhibitor VIII, XMD11-85h, BX-912, and Tivozanib. An analysis of the Search Tool for Interaction of Chemicals database revealed the associations between the high-risk group and signaling pathways, such as those involving Rap1, Ras, and PI3K-Akt. The low-risk group showed a higher immunotherapy response rate, as observed through the tumor immune dysfunction and exclusion analysis in the TCGA-TNBC and METABRIC-TNBC cohorts. Conclusion: This study provides insights into the immune complexities of TNBC, paving the way for novel diagnostic approaches and precision treatment methods that exploit its immunological intricacies, thus offering hope for improved management and outcomes of this challenging disease.