Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Matteo Italia; Kenneth Y Wertheim; Sabine Taschner-Mandl; Dawn Walker; Fabio Dercole

doi:10.3390/cancers15071986

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

Cancers (Basel). 2023 Mar 26;15(7):1986. doi: 10.3390/cancers15071986.

Authors

Matteo Italia¹, Kenneth Y Wertheim^{2

3

4

5}, Sabine Taschner-Mandl⁶, Dawn Walker^{2

3}, Fabio Dercole¹

Affiliations

¹ Department of Electronic, Information, and Bioengineering, Politecnico di Milano, 20133 Milano, Italy.
² Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield S10 2TN, UK.
³ Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK.
⁴ Centre of Excellence for Data Science, Artificial Intelligence, and Modelling, University of Hull, Kingston upon Hull HU6 7RX, UK.
⁵ School of Computer Science, University of Hull, Kingston upon Hull HU6 7RX, UK.
⁶ St. Anna Children's Cancer Research Institute, 1090 Vienna, Austria.

Abstract

Neuroblastoma is the most common extra-cranial solid tumour in children. Despite multi-modal therapy, over half of the high-risk patients will succumb. One contributing factor is the one-size-fits-all nature of multi-modal therapy. For example, during the first step (induction chemotherapy), the standard regimen (rapid COJEC) administers fixed doses of chemotherapeutic agents in eight two-week cycles. Perhaps because of differences in resistance, this standard regimen results in highly heterogeneous outcomes in different tumours. In this study, we formulated a mathematical model comprising ordinary differential equations. The equations describe the clonal evolution within a neuroblastoma tumour being treated with vincristine and cyclophosphamide, which are used in the rapid COJEC regimen, including genetically conferred and phenotypic drug resistance. The equations also describe the agents' pharmacokinetics. We devised an optimisation algorithm to find the best chemotherapy schedules for tumours with different pre-treatment clonal compositions. The optimised chemotherapy schedules exploit the cytotoxic difference between the two drugs and intra-tumoural clonal competition to shrink the tumours as much as possible during induction chemotherapy and before surgical removal. They indicate that induction chemotherapy can be improved by finding and using personalised schedules. More broadly, we propose that the overall multi-modal therapy can be enhanced by employing targeted therapies against the mutations and oncogenic pathways enriched and activated by the chemotherapeutic agents. To translate the proposed personalised multi-modal therapy into clinical use, patient-specific model calibration and treatment optimisation are necessary. This entails a decision support system informed by emerging medical technologies such as multi-region sequencing and liquid biopsies. The results and tools presented in this paper could be the foundation of this decision support system.

Keywords: clonal evolution; drug delivery; drug resistance; evolutionary computation; mathematical model; neuroblastoma; ordinary differential equations; population dynamics; precision medicine.

Grants and funding

K.Y.W., S.T.M., and D.W. are grateful for funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826494 (PRIMAGE project). In addition, K.Y.W. is grateful for funding provided by the Insigneo Institute for in Silico Medicine to acquire foundational knowledge for this project. The authors want to thank Barbara Hero for her scientific advice, as well as Ritvik Mehra and Abigail Barlow for the preliminary results that inspired this project.