Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Kemkar, Sharvari; Tao, Mengdi; Ghosh, Alokendra; Stamatakos, Georgios; Graf, Norbert; Poorey, Kunal; Balakrishnan, Uma; Trask, Nathaniel; Radhakrishnan, Ravi

doi:10.3389/fphys.2024.1473125

Title: Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Journal Article · 23 October 2024 · Frontiers in Physiology

DOI: https://doi.org/10.3389/fphys.2024.1473125 · OSTI ID:2474788

Kemkar, Sharvari ^[1]; Tao, Mengdi ^[1]; Ghosh, Alokendra ^[1]; Stamatakos, Georgios ^[2]; Graf, Norbert ^[3]; Poorey, Kunal ^[4]; Balakrishnan, Uma ^[5]; Trask, Nathaniel ^[1]; Radhakrishnan, Ravi ^[1]

Univ. of Pennsylvania, Philadelphia, PA (United States)
National Technical University of Athens, Zografos (Greece)
Saarland University, Homburg (Germany)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Univ. of Pennsylvania, Philadelphia, PA (United States)

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

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Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH)

Grant/Contract Number:: NA0003525

OSTI ID:: 2474788

Report Number(s):: SAND--2024-14608J

Journal Information:: Frontiers in Physiology, Journal Name: Frontiers in Physiology Vol. 15; ISSN 1664-042X

Publisher:: Frontiers Media S.A.Copyright Statement

Country of Publication:: United States

Language:: English

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