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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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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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.
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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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DOI: https://doi.org/10.1038/s41416-024-02653-3