Multifactorial Analysis of Prognostic and Predictive Biomarkers in Triple Negative Breast Cancer Patients

Triple-negative breast cancer (TNBC) represents one of the most aggressive subtypes of breast cancer, characterized by the absence of key molecular targets including estrogen receptors (ER), progesterone receptors (PR), and HER2. This molecular profile significantly limits treatment modalities, establishing chemotherapy as the definitive treatment. The high rates of recurrences and metastasis, along with the lack of specific targeted therapies, make TNBC a major clinical challenge. This article evaluates critical prognostic and predictive biomarkers of TNBC, including BRCA1/BRCA2 gene mutations, PD-L1 expression, tumor-infiltrating lymphocytes (TILs), circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA). These markers are pivotal for outcome prediction and treatment optimization. Moreover, a transformative approach to TNBC treatment is represented by personalized medicine based on molecular profiling supported by artificial intelligence (AI). The integration of artificial intelligence (AI) facilitates the analysis of substantial data sets, the accurate prediction of clinical outcomes, and the formulation of customized treatment strategies for individual patients. Thus, this article analyzes current data concerning prognostic and predictive markers of TNBC, with a particular emphasis on their clinical utility and the potential for personalized therapy.