Computational Horizons in Cancer (CHIC)

Abstract

To provide an introduction to in silico oncology and more generally in silico medicine through the CHIC project; to outline the clinical drive, the clinical orientation and the envisaged clinical translation of this emergent and promising interdisciplinary domain; to introduce cancer multi-modeller multiscale hypermodels including the Oncosimulator; to outline pertinent repositories, technologies and clinically relevant scenarios aiming at treatment individualization and in silico clinical trials; to provide clinical adaptation and partial clinical validation outcomes regarding both the hypermodels and the technological infrastructure developed by CHIC. The purpose of the four year EU-US large scale integrating project CHIC (“CHIC - Computational Horizons in Cancer: Developing Meta- and Hyper- Multiscale Models and Respositories for In Silico Oncology”) (http://www.chic-vph.eu/) funded by the European Commission (Grant Agreement No 600841) was to develop, clinically adapt and partly clinically validate meta- and hyper-multiscale models and repositories for in silico oncology. A host of clinical, experimental, mathematical, computational and software engineering strategies, methods and techniques have been devised and/or utilized in order to both develop and test multiscale hypermodels. A hypermodel is a complex mathematical and computational model consisting of more than one elementary component model. Each component model or “hypomodel” simulates a crucial biological mechanism of tumour growth and response to treatment. Hypomodels are connected together in several ways dictated by the current biological and clinical knowledge. Both mechanistic and machine learning based hypermodels have been developed driven by clinically relevant questions formulated by the clinical partners of the CHIC consortium. The overarching idea of the project was to exploit the accumulated quantitative experimental and clinical knowledge concerning several spatiotemporal scales of cancer biocomplexity in order to produce treatment response predictions as precise as possible based on the patient’s individual multiscale data (e.g. imaging, histological, molecular, and clinical data). To this end several candidate treatment schemes can be simulated using detailed hypermodels fed with the actual multiscale data of the patient. The treatment scheme performing best in silico will serve as the optimal suggestion to the clinician to consider for their final treatment strategy decision. Most hypomodels or component models have been developed by different leading cancer modelling groups participating in the CHIC project scattered across EU and US. A clinician friendly technological platform for hypermodel creation and execution (CRAF) has also been developed and successfully tested. Four paradigmatic cancer types have been considered: nephroblastoma, non small cell lung cancer, glioblastoma (treated with immunotharepy in conjunction with radiotherapy and chemotherapy) and prostate cancer. Both the hypermodels and the technological platforms developed by CHIC have been documented, disseminated and demonstrated in real time and in detail to the appointed independent scientific evaluators of the European Commission. The overall project outcome has been assessed twice as Excellent and worth further translational development and multifaceted exploitation. Clinical adaptation, partial clinical validation and clinical demonstration of the hypermodels and the technologies developed have demonstrated the great translational potential of cancer hypemodelling and in silico oncology at large. However, in order for the cancer hypermodels to enter clinical practice as clinical decision support systems (CDS) or as platforms for the conduction of in silico trials regarding new treatment schemes and schedules, prospective clinical trials are necessary. The latter will definitely assess the clinical value of the hypermodels developed. This is the next step towards clinical translation and is currently under exploration within the context of clinical trials such as the SIOP ones.