Abstract IA021: Digital patient avatars predict cancer evolution and personalised therapeutic strategies

Abstract

Abstract Tumour heterogeneity manifested by genetic diversity and interchangeable cell states poses considerable challenges for the design of therapeutic strategies to combat cancer. While many strategies have been proposed to address this problem, using combination treatments and sequential delivery of therapies, the scope of combinations and sequencing order of therapies is enormous and is beyond the capacity of exhaustive testing through experimental models. We therefore propose using computational models to rapidly identify effective therapeutic interventions supported by convincing mechanistic explanations. Digital cancer avatars are computational programs representing oncogenic signalling networks and mapping molecular interactions to cell phenotype, which can be interrogated in silico. These computational programs specify how molecular interactions and perturbations produce cellular responses, such as cell proliferation, cell survival or cell death, and as such are well-suited to provide novel mechanistic insights into cellular behaviours as well as to identify therapeutic strategies for targeting them. In this talk, I will demonstrate the utility of bespoke computational models for triple-negative breast cancer, non-small cell lung cancer and melanoma to study how genomic changes drive tumour evolution and therapeutic response, and how these models are used to identify personalised combination treatments to overcome resistance. I will also present an in silico method applied to these models, to screen tens of millions combinations for effective sequential treatments by mapping the evolution of a tumour in response to a single-agent treatment and determining the optimal second-line treatment that will overcome resistance. Together these studies demonstrate the utility of digital avatars to improve our understanding of the mechanisms driving tumour progression and pave the way for personalised therapeutic strategies for improved cancer patient outcomes. Citation Format: Jasmin Fisher. Digital patient avatars predict cancer evolution and personalised therapeutic strategies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA021.