Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient

Joshua Southern, Guadalupe Gonzalez, Pia Borgas, Liam Poynter, Ivan Laponogov, Yoyo Zhong, Reza Mirnezami, Dennis Veselkov, Michael Bronstein, Kirill Veselkov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe’s culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy.

Original languageEnglish
Article number14862
JournalScientific Reports
Issue number1
StatePublished - Dec 2023
Externally publishedYes


FundersFunder number
Vodafone Foundation
UK Research and InnovationP/S023283/1, 10058099
European Commission101095359
European Research Council899932


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