Precision Medicine Driving Progress in Triple Negative Breast Cancer: Biomarkers and AI-Enabled Approaches

Triple-negative breast cancer (TNBC) remains one of the most clinically challenging breast cancer subtypes, defined by the absence of estrogen receptor, progesterone receptor, and HER2 expression and characterized by marked inter- and intra-tumoral heterogeneity. Although recent advances—including immunotherapy and antibody–drug conjugates—have improved outcomes for some patients, durable benefit is still unevenly distributed, resistance is common, and optimal patient selection remains an urgent unmet need. Precision medicine in TNBC therefore depends on the discovery, validation, and clinical implementation of robust biomarkers, coupled with analytic frameworks capable of integrating multi-dimensional data at scale, across both early-stage and advanced/metastatic disease settings.This Research Topic will highlight how biomarker science and AI-enabled approaches are accelerating progress across the TNBC continuum, with a specific focus on both early-stage and advanced/metastatic clinical settings, from risk stratification and early detection to therapy selection, response monitoring, and mechanisms of resistance. We welcome contributions spanning molecular, cellular, and microenvironmental determinants of TNBC biology, including genomic and epigenomic alterations, transcriptional and proteomic programs, DNA damage repair deficiency, immune contexture, spatial organization, and clonal evolution. In early-stage TNBC, areas of particular interest include, but are not limited to, baseline biomarkers and biomarkers in residual disease after neoadjuvant therapy. In advanced/metastatic TNBC, relevant topics include, but are not limited to, biomarkers in the primary tumor as well as biomarkers across distinct metastatic sites. Particular interest includes predictive and pharmacodynamic biomarkers for immune checkpoint blockade, targeted agents, and antibody–drug conjugates, as well as biomarkers that inform rational combination strategies and treatment sequencing.A central focus is the integration of advanced computational methods—including machine learning and multimodal AI—to translate complex data into clinically actionable insight. Relevant areas include AI-driven discovery of predictive signatures; modeling of response and toxicity; digital pathology and radiomics for non-invasive phenotyping; spatial and single-cell analytics to resolve heterogeneity; and approaches that combine imaging, pathology, omics, and clinical variables. Studies addressing dataset shift, bias, interpretability, uncertainty, and reproducibility are encouraged, as are frameworks for prospective validation and deployment in real-world clinical workflows.The Topic also seeks work that bridges biology to clinical decision-making: development and harmonization of assays; trial design innovations such as biomarker-enriched, adaptive, and platform trials; ctDNA and minimal residual disease strategies; and longitudinal monitoring to capture resistance trajectories across disease stages. Submissions that emphasize rigorous validation, cross-cohort generalizability, and clinically meaningful endpoints will be particularly valued.By bringing together biomarker discovery and AI-enabled precision oncology, this Research Topic aims to define actionable TNBC subgroups, improve patient selection for existing and emerging therapies, and accelerate the translation of data-rich insights into improved outcomes for patients with TNBC in both early-stage and advanced/metastatic settings.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Uzma Saddia Asghar, Tiago Veiras Collares, Kay Yeung, Prem S Subramaniam, ANNA ADAM-ARTIGUES Source: https://www.frontiersin.org/research-topics/78927/precision-medicine-driving-progress-in-triple-negative-breast-cancer-biomarkers-and-ai-enabled-approaches