In silico oncology: a mechanistic multiscale model of clinical prostate cancer response to external radiation therapy as the core of a digital (virtual) twin. Sensitivity analysis and a clinical adaptation approach

Georgios S. Stamatakos ^1* Eleni Kolokotroni ¹ Foteini Panagiotidou ¹ Stamatia Tsampa ¹ Christos Kyroudis ¹ Simon Spohn ^2,3,4 Anca-Ligia Grosu ^2,3 Dimos Baltas ^3,5 Constantinos Zamboglou ^2,3,6 Ilias Sachpazidis ^3,5

• ¹ In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
• ² Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Baden-Wurttemberg, Germany
• ³ Partner Site Freiburg, German Cancer Consortium, German Cancer Research Center (DKFZ), Heidelberg, Baden-Württemberg, Germany
• ⁴ Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
• ⁵ Division of Medical Physics, Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Baden-Wurttemberg, Germany
• ⁶ German Oncology Center, European University Cyprus, Engomi, Nicosia, Cyprus

Prostate cancer (PCa) is the most frequent diagnosed malignancy in male patients in Europe and radiation therapy (RT) is a main treatment option. However, current RT concepts for PCa have an imminent need to be rectified in order to modify the radiotherapeutic strategy by considering (i) the personal PCa biology in terms of radio resistance and (ii) the individual preferences of each patient. To this end, a mechanistic multiscale model of prostate tumor response to external radiotherapeutic schemes, based on a discrete entity and discrete event simulation approach has been developed. Following technical verification, an adaptation to clinical data approach is delineated. Multiscale data has been provided by the University of Freiburg. Additionally, a sensitivity analysis has been performed. The impact of model parameters such as cell cycle duration, dormant phase duration, apoptosis rate of living and progenitor cells, fraction of dormant stem and progenitor cells that reenter cell cycle, number of mitoses performed by progenitor cells before becoming differentiated, fraction of stem cells that perform symmetric division, fraction of cells entering the dormant phase following mitosis, alpha and beta parameters of the linear quadratic model and oxygen enhancement ratio has been studied. The model has been shown to be particularly sensitive to the apoptosis rate of living stem and progenitor cells, the fraction of dormant stem and progenitor cells that reenter cell cycle, the fraction of stem cells that perform symmetric division and the fraction of cells entering the dormant phase following mitosis. A qualitative agreement of the model behavior with experimental and clinical knowledge has set the basis for the next steps towards its thorough clinical validation and its eventual certification and clinical translation. The paper showcases the feasibility, the fundamental design and the qualitative behavior of the model in the context of in silico experimentation. Further data is being collected in order to enhance the model parametrization and conduct extensive clinical validation. The envisaged digital twin or OncoSimulator, a concept and technologically integrated system that our team has previously developed for other cancer types, is expected to support both patient personalized treatment and in silico clinical trials.