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

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.

Fig. 1. Clinical implementation with PM.

a A list of clinical trials performed using small data AI-derived PM. Therapeutic doses are guided by patients’ own response profiles during treatment. b Considerations and examples for strategy for successful implementation of CDSS, including PM.

Fig. 2. A proposed RT workflow with the incorporation of PM.

The blue boxes denote the existing conventional workflow, while the green boxes denote how PM can be incorporated and modify the existing workflow to guide individualized fractionation regimens by the medical physics and radiation oncology departments. The red boxes denote involvement with the phlebotomy lab if response blood markers such as ctDNA are involved.

Fig. 3. Simulated treatment regimens to illustrate how PM modulates radiation doses.

a Conventional RT for NSCLC. b, c PM such as CURATE.AI-guided dosing for NSCLC with the appropriate dose-response data pairs. For calibration, 3 unique dose-response data pairs are required for calibration. The five green blocks of various shades represent different levels of doses administered with the response measured at the end of each week. Possible unique radiation/osimertinib treatment dose-response profiles. The individual initial dose-response profiles are generated through calibrations and evolve with subsequent data inputs for (d) patient A and (e) patient B during the efficacy-driven phase. The doses are obtained from the response surfaces corresponds to the intended response outcome.