Description
Cancer is the second leading cause of death in the EU. More than 150.000 persons within EU-28 die of colorectal cancer (CRC) every year (more than 10% of all cancer-related deaths). For advanced-stage disease, where surgery is not possible, systemic therapy is used. A few decades ago, chemotherapy was the only option, with overall survival around one year. With chemotherapy, targeted therapies and immunotherapies, survival has now increased to roughly three years. Although new targeted therapies pose great opportunities, the challenge is to link such therapies to those patients that will best respond to them. Five-year survival is still well below 20%, clearly indicating the need for improved tools for patient stratification and personalised therapies. Our systems medicine approach uses computer models for personalised therapy design.
Boolean computer models that represent individual patients tumours will be used to predict their response to drug therapies, and in silico predictions will be compared to clinical outcome data available from cancer patients and to drug responses in patient-derived spheroid and organoid cultures. Discrepancies between observations and predictions will be analysed to understand why some models fail, and through targeted experiments we will improve Boolean models that better represent individual patients. Our improved modelling platform will take patient tumour data from ex-vivo grown material, produce s short list of promising therapies that subsequently will be tested on the ex-vivo grown material, to deliver a short list of patient-specific therapies to the clinician. We expect that our decision support platform will improve diagnostics, prognostics and therapy design for advanced stage cancer, improve prognosis for advanced stage cancer, and improve stratification of patients for clinical trials. We will assess the moral aspects of how a model-based decision support platform may affect physicians, patients and our health care model.
