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Project description

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). The MESI-STRAT consortium will develop new models for knowledge-based stratification of patients into subgroups with different ET resistance mechanisms. We will establish predictive pipelines for (1) patient stratification prior and during ET; (2) recurrence risk assessment when ending ET; (3) marker panels to guide established 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.

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