A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin

Kolokotroni, Eleni and Abler, Daniel and Ghosh, Alokendra and Tzamali, Eleftheria and Grogan, James and Georgiadi, Eleni and Büchler, Philippe and Radhakrishnan, Ravi and Byrne, Helen and Sakkalis, Vangelis and Nikiforaki, Katerina and Karatzanis, Ioannis and McFarlane, Nigel J. B. and Kaba, Djibril and Dong, Feng and Bohle, Rainer M. and Meese, Eckart and Graf, Norbert and Stamatakos, Georgios (2024) A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin. Journal of Personalized Medicine, 14 (5). 475. ISSN 2075-4426 (https://doi.org/10.3390/jpm14050475)

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Abstract

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.

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