The goal of this multidisciplinary project is to comprehensively characterise high-grade serous ovarian cancer (HGS-OvCa) at single-cell level, identify the best combination of drug combination to kill HGS-OvCa populations and commercialise a predictive biomarker kit for finding the right therapeutic regimen to the right patient.
This project takes an advantage on prospectively and longitudinally collected fresh sample specimens from multiple anatomic sites of HGS-OvCa patients with metastatic disease. Fluorescence activated cell sorting and recently developed mass cytometry are used to identify subpopulations in HGS-OvCa tumors. This is followed by single-cell analysis at genetic and transcriptomics levels, and ex vivo drug screening experiments. These data will be used to establish network models to predict the most effective combinatorial treatments. The key results will be validated with existing HGS-OvCa data together with prospective and retrospective cohorts and in vivo models. The clinically most actionable treatment suggestions from our
modelling efforts will be translated to HGS-OvCa patient care.
Ovarian cancer kills more than 40,000 women in Europe every year due to lack of effective and long-lasting therapeutic regimens. HERCULES presents an innovative strategy to suggest effective treatments that lead to a marked decrease in ovarian cancer deaths and reduce the number of expensive but inefficient treatments. Our approach paves the way to move beyond the current trial-and-error clinical assessment of drug combinations toward more systematic prediction of the most effective drug combinations for each patient. The proposed approach will be a major breakthrough in systems medicine and will benefit individual ovarian cancer patients and the health-care system through more effective treatments, and the
diagnostic and pharmaceutical industry through tools for better stratified clinical trials, and novel treatment and diagnostic modalities.
Fields of science
- natural sciencescomputer and information sciencesdata sciencedata analysis
- natural sciencescomputer and information sciencesdatabases
- medical and health sciencesclinical medicinesurgery
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistance
- medical and health sciencesclinical medicinecancer
Call for proposal
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Funding SchemeRIA - Research and Innovation action