ONCOLOGICS

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.