Personalized therapy has revolutionized cancer care during the past decades, but still suffers from frequent drug resistance and relapse. There is an urgent clinical need to comprehend drug resistance mechanisms and timely track them to prevent relapse. While outgrowth of novel or existing clones with DNA mutations and copy number changes is a well known cause of therapy failure, emerging evidence supports a role for non-genetic/epigenetic heterogeneity and plasticity in the selective process to escape the pressure of therapy. A prominent example of this phenotypic plasticity is epithelial-to-mesenchymal transition (EMT) in response to chemotherapy. The major aim of this project is to develop a computational pipeline to monitor epigenetic switches occurring in tumor cells during EMT using blood plasma circulating-free DNA (cfDNA).The project will primarily focus on triple-negative breast cancer (TNBC), a type of breast cancer for which epigenetic switches leading to chemotherapy resistance have been described. The project will first generate an epigenetic atlas of the EMT cell states occurring in TNBC using both bulk and single-cell technologies. Subsequently, the project will optimize a computational deconvolution pipeline to accurately estimate cell state fractions using cfDNA. Finally, validation will be conducted on both a mouse model and a cohort of TNBC patients as a starting point to transition the pipeline into clinical practice for personalized cancer care.