Breast cancer (BC) is a complex disease with high prevalence in the EU. 75% of the tumors are estrogen receptor-positive (ER+), and are treated with endocrine therapies (ET). MESI-STRAT will develop new concepts for knowledge-based stratification of patients into subgroups with different ET resistance mechanisms. We will establish predictive models for (1) patient stratification prior and during ET; (2) recurrence risk assessment when ending ET; (3) marker panel development to guide targeted therapies for ET-resistant patients; (4) novel ET resistance mechanism-based therapy design. The unique collection of matched BC tissue, serum, and >10 years follow-up from the patient organization PATH is essential for the longitudinal analysis of ET resistance and relapse. Our team of oncologists, modelers, bioinformaticians and experimentalists will develop new computational models in combination with network analyses and pharmacogenomics, to integrate multi-omics data and explore metabolic and signaling (MESI) networks driving ET resistance. Metabolite marker panels measured in biological fluids will enable patient stratification, resistance monitoring and clinical decision-making. This is a new concept as BC metabolism is poorly explored for diagnostics and therapy. Upon successful validation in preclinical models, the predictive marker panels and related treatments will be jointly investigated by our clinical and industrial partners in clinical studies. Our 3 SMEs will closely co-develop the research, and directly exploit the MESI-STRAT results. BC accounts for the highest cancer-related health-care costs in the EU. Our stratification concepts will increase cost effectiveness and the patients’ quality of life by (1) avoiding ineffective therapies, (2) marker detection in body fluids without surgical interventions, and (3) reducing clinical trial cohorts by improved stratification. This will accelerate the translation of MESI-STRAT results into medical use.