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Radiation therapy with phenotypic medicine: towards N-of-1 personalization

British Journal of Cancer (2024)Cite this article

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Abstract

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient’s own small datasets. With PM, clinicians may guide patients’ RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

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Fig. 1: Clinical implementation with PM.
Fig. 2: A proposed RT workflow with the incorporation of PM.
Fig. 3: Simulated treatment regimens to illustrate how PM modulates radiation doses.

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Funding

DH gratefully acknowledges funding from the following: Institute for Digital Medicine Translational Research Programme (grant number A-0001319-00-00), Yong Loo Lin School of Medicine, NUS; AI Singapore Programme (award number: AISG GC 2019 002), Singapore National Research Foundation; Open Fund‐Large Collaborative Grant (grant number MOH‐OFLCG18May‐0028), National Medical Research Council, Ministry of Health; Tier 1 FRC Grant (grant number R‐397‐000‐333‐114), Ministry of Education; Next-Generation Brain-Computer-Brain Platform – A Holistic Solution for the Restoration & Enhancement of Brain Functions (NOURISH) project from the RIE2020 Advanced Manufacturing And Engineering (Ame) Programmatic Fund [grant number A20G8b0102/A-0002199-02-00]; Micron Foundation and Sun Life Singapore.

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Author notes

  1. These authors contributed equally: Li Ming Chong, Peter Wang.

Authors and Affiliations

  1. Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore

    Li Ming Chong, Peter Wang, Anh T. L. Truong, Lester Wen Jeit Tan, Shi Bei Tan, Kirthika Senthil Kumar, Agata Blasiak & Dean Ho

  2. The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore

    Li Ming Chong, Peter Wang, V. Vien Lee, Smrithi Vijayakumar, Anh T. L. Truong, Lester Wen Jeit Tan, Shi Bei Tan, Kirthika Senthil Kumar, Agata Blasiak & Dean Ho

  3. The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore

    Li Ming Chong, Peter Wang, Anh T. L. Truong, Lester Wen Jeit Tan, Shi Bei Tan, Agata Blasiak & Dean Ho

  4. Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, 168583, Singapore

    Hong Qi Tan & Fu Qiang Wang

  5. Department of Radiation Oncology, National University Cancer Institute, Singapore, 119074, Singapore

    Teri Danielle You Ying Yeoh & Balamurugan A. Vellayappan

  6. Department of Radiation Oncology, Westmead Hospital, Sydney, NSW, Australia

    Eric Hau

  7. Department of Radiation Oncology, Blacktown Haematology and Cancer Care Centre, Sydney, NSW, Australia

    Eric Hau

  8. Westmead Medical School, The University of Sydney, Sydney, NSW, Australia

    Eric Hau

  9. Centre for Cancer Research, Westmead Institute of Medical Research, Sydney, NSW, Australia

    Eric Hau

  10. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore

    Balamurugan A. Vellayappan

  11. Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore

    Agata Blasiak & Dean Ho

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Contributions

LMC, PW, AB, and DH have led the paper, conceived the ideas, and provided the main framework. LMC, and PW drafted the main manuscript equally. VVL, and SV are behavioral and implementation scientists, who have provided part of the writing on behavioral and implementation sciences pertaining to phenotypic medicine and its challenges. TDYY, FQW, EH, and BAV are radiation oncologists, and HQT is a senior medical physicist who have given vital inputs such as clinical feasibility and treatment designs, and provided part of the writing. KSK, SBT, ATLT, and LWJT are data scientists who have hands-on experience with past clinical trials using Phenotypic Medicine platforms. They have provided inputs on the technological feasibility and challenges with Phenotypic Medicine. Funding for this paper has come from the grant under DH.

Corresponding authors

Correspondence to Balamurugan A. Vellayappan, Agata Blasiak or Dean Ho.

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Competing interests

DH, AB, KSK, SBT, ATLT and LWJT are co-inventors of previously filed pending patents on artificial intelligence-based therapy development. DH is a shareholder of KYAN Therapeutics, which has licensed intellectual property pertaining to AI-based oncology drug development. The findings from this study are being made available for public benefit, and no intellectual property rights arising from the work reported here are being pursued. The remaining authors declare no competing interests.

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Chong, L.M., Wang, P., Lee, V.V. et al. Radiation therapy with phenotypic medicine: towards N-of-1 personalization. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02653-3

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