It is a well-known challenge for society, the health service, and unfortunately also for individuals, that many patients receive cancer treatment that only works for the few. This entails high personal costs - survival, quality of life, side effects - and significant societal costs - expensive medicines used without benefit, lost life years).
Together with outstanding research institutions at the Institute Curie (Paris), Charité (Berlin), Barcelona Supercomupting Center (Barcelona), ProtAtOnce (Athens) and Uppsala University, NTNU launches ONCOLOGICS, a consortium that will find personalized treatments using computer simulation and testing drugs on patient-derived cancer cells to provide clinical decision support with individual drugs or combinations of drugs. Computer simulation is key because it enables testing of numerous drugs in computer simulations. A characteristic of cancer cells is that they have tuned the signal traffic to attenuate signals that will normally inhibit cell growth, and promote signals favoring cell growth. Modern cancer medicines work by interfering with cancer signaling traffic, and give us an opportunity to control signal traffic in cancer cells. By simulating cancer-intrinsic signaling and the effect of medication on this, one can identify the most promising treatments that are then tested in the laboratory on the patient's own cells.
We have defined maps of signalling traffic in colon cancer cells, and we are currently working on optimizing these for measurements of signaling traffic from individual patients. We will use computer models to study the causes of mismatches between computer model predictions and observations from cancer cells in the laboratory, and then update the computer models based on what we learn along the way, and subsequently going back and forth between simulations and oservations. Systems medicine seeks to understand cancer as a result of interactions within cells and between different cell types.
Based on the agreement between the computer models and the observations in the laboratory, we will constantly improve our model understanding of cancer. Our goal is to improve cancer therapy.
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 a short list of promising therapies that subsequently will be tested on the ex-vivo grown material, to deliver patient-specific therapy suggestions to the clinician.
We will assess the ethical aspects of how a model-based decision support platform may affect physicians, patients and our health care model. 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.