Metabolomic prediction of breast cancer treatment induced neurological and metabolic toxicities - Archive ouverte HAL
Journal Articles Clinical Cancer Research Year : 2024

Metabolomic prediction of breast cancer treatment induced neurological and metabolic toxicities

Max Piffoux
Jérémie Jacquemin
Mélanie Pétéra
Angélique Abila
Charlotte Joly
Bernard Lyan
Anne-Laure Martin
Sibille Everhard
Sandrine Boyault
Barbara Pistilli
Marion Fournier
Julie Havas
Carole Tarpin
Christine Lasset
Paul Cottu
Fabrice Andre
Ines Vaz-Luis
Youenn Drouet
Olivier Trédan

Abstract

Abstract Background: Long-term treatment-related toxicities, such as neurological and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities. Methods: Untargeted high-resolution metabolomic profiles of 992 patients with ER+/HER2- breast cancer from the prospective CANTO cohort were acquired (n=1935 metabolites). A residual-based modeling strategy with a discovery and validation cohort was used to benchmark machine learning algorithms, taking into account confounding variables. Results: Adaptive LASSO has a good predictive performance, has limited optimism bias, and allows the selection of metabolites of interest for future translational research. The addition of low-frequency metabolites and non-annotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurological and metabolic toxicity profiles. Conclusions: Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.

Domains

Cancer
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Dates and versions

hal-04671046 , version 1 (13-08-2024)

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Max Piffoux, Jérémie Jacquemin, Mélanie Pétéra, Stephanie Durand, Angélique Abila, et al.. Metabolomic prediction of breast cancer treatment induced neurological and metabolic toxicities. Clinical Cancer Research, 2024, ⟨10.1158/1078-0432.CCR-24-0195⟩. ⟨hal-04671046⟩
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