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Computational modelling and functional validation platform for personalised colorectal cancer

clinical therapy decision support

In the subproject “Computational modelling and functional validation platform for personalised colorectal cancer clinical therapy decision support” we develop 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.

About the project

Background ONCOLOGICS projectWhile DNA-based patient biomarkers are increasingly used for cancer diagnostic purposes and therapy design it has proven difficult to infer from genomic alterations alone what the overall effect of a drug will be [Le Tourneau 2019]. Systems medicine approaches make use of multiple data types (genomic, transcriptomic, epigenomic, proteomic, etc), leveraging prior knowledge (e.g. molecular causalities underlying signalling) and computational systems model simulations. This enables systems approaches to propose personalised treatment by going beyond single, static genomic biomarkers (such as KRAS mutations in colon cancer), and rather considering the behaviour of a cancer signalling network as a whole, including the system's dynamic response.Advanced computational algorithms for simulation of cancer disease and its treatment have largely been confined to basic research in order to study cancer model systems (cell lines, xenografts etc) but with notable efforts in the last few years to bring such approaches closer to the clinic [Eduati 2020, Fröhlich 2018],. Time is now ripe to mobilise and develop computer models to efficiently deal with one of the most significant challenges today in preclinical screening: thecombinatorial explosion that results from considering large numbers of targeted drugs, administered in sets. Sets of drugs can have synergistic effects in therapy, allowing lowering individual dosage and thereby reducing side effects. However, a relatively small set of 150 drugs, for example, corresponds to more than 10.000 possible pairwise combinations and over half a million 3-way combinations, without even considering dosage, timing and sequence of treatment.To exhaustively analyse this colossal combinatorial space, experiments must be moved from drug screening assays to in silico approaches before validation in adequate experimental systems like e.g. patient tumour derived spheroids, organoids, or xenograft mice.



The aim of ONCOLOGICS is to deliver an integrated computational and experimental platform that uses patient-specific logic models and ex-vivo grown tumour material to select from a vast number of possible drug treatment regimens a subset that has predicted highest efficacy for an individual patient..


To achieve this central aim, the 5 objectives of ONCOLOGICS are

1. Test logic model-based patient-specific drug response predictions against available patient outcome data (WP1);

2. Enhance logic model performance through a systems medicine cycle (WP2)

3. Use enhanced models to predict responses of all actionable drugs and possible drug targets and validate in CRC patient-derived spheroid and organoid cultures (WP2)

4. Standardize data annotation and analysis of clinical and preclinical data and implement robust and secure GDPR- and FAIR-compliant data management system (WP3

5. Assess how ONCOLOGICS’ results may impact the role and self-image of physicians, the patient-physician relationships and the Scandinavian public health care model and suggest how to respond (WP4). 


Data management plan