DefinePK

Abstract

Triple-negative breast cancer (TNBC) is an aggressive and clinically heterogeneous breast cancer subtype lacking estrogen receptor, progesterone receptor, and HER2 expression, and is associated with poor prognosis due to its high recurrence rate and limited therapeutic options. Early detection remains a critical unmet need, as current diagnostic tools such as imaging and tissue biopsy are limited by sensitivity, invasiveness, and inability to capture tumor heterogeneity. Integrative multi-omics profiling, encompassing genomics, transcriptomics, epigenomics, proteomics, and metabolomics, offers a powerful strategy to uncover molecular mechanisms underlying TNBC and to identify biomarkers that may improve early detection and patient stratification. This study applies a comprehensive multi-omics approach, combining high-resolution tissue profiling with minimally invasive liquid biopsy analyses of circulating tumor DNA, extracellular vesicles, and plasma-derived proteins and metabolites. Advanced computational frameworks and machine-learning integration are employed to derive robust, biologically meaningful biomarker signatures. The proposed strategy aims to (i) identify and validate multi-layered biomarker panels distinguishing early-stage TNBC from benign and other breast cancer subtypes, (ii) evaluate their detectability in accessible biospecimens, and (iii) assess reproducibility across independent cohorts. By bridging molecular insights with clinically applicable assays, this integrative approach has the potential to enable earlier diagnosis, improve patient outcomes, and guide precision oncology strategies for TNBC.