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Journal Article Ppublish 2 citations

PubMed: 39106085 DOI: 10.1158/1078-0432.CCR-24-0195

Metabolomic Prediction of Breast Cancer Treatment-Induced Neurologic and Metabolic Toxicities.

Clin Cancer Res 30(20):4654-4666

Authors: Max Piffoux, Jérémie Jacquemin, Mélanie Pétéra, Stéphanie Durand, Angélique Abila, Delphine Centeno, Charlotte Joly, Bernard Lyan, Anne-Laure Martin, Sibille Everhard, Sandrine Boyault, Barbara Pistilli, Marion Fournier, Philippe Rouanet, Julie Havas, Baptiste Sauterey, Mario Campone, Carole Tarpin, Marie-Ange Mouret-Reynier, Olivier Rigal, Thierry Petit, Christine Lasset, Aurélie Bertaut, Paul Cottu, Fabrice André, Ines Vaz-Luis, Estelle Pujos-Guillot, Youenn Drouet, Olivier Trédan

Affiliations

  • Medical Oncology Department, Centre Léon Bérard, Lyon, France.
  • Prevention and Public Health Department, Centre Léon Bérard, Lyon, France.
  • Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
  • Unicancer, Paris, France.
  • Cancer Genomic Platform, Department of Translational Research and Innovation, Centre de Recherche en Cancérologie de Lyon, INSERM U1052-CNRS UMR5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
  • Institut Gustave Roussy, Villejuif, France.
  • Institut Bergonié, Bordeaux, France.
  • Institut du Cancer de Montpellier - Val d'Aurelle, France.
  • Institut de Cancérologie de l'Ouest, Angers, France.
  • Institut de Cancérologie de l'Ouest, Nantes, France.
  • Institut Paoli-Calmettes, Marseille, France.
  • Centre Jean Perrin, Clermont-Ferrand, France.
  • Centre Henri Becquerel, Rouen, France.
  • Centre Paul Strauss, Strasbourg, France.
  • UMR CNRS 5558 LBBE, Claude Bernard Lyon 1 University, Villeurbanne, France.
  • Methodology and biostatistic unit, Centre Georges François Leclerc, Dijon, France.
  • Institut Curie, Paris, France.
  • Université Paris Cité, Paris, France.
  • CRCL UMR INSERM 1052-CNRS 5286, Lyon, France.

Abstract

PURPOSE

Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. We investigated the interest of metabolomic profiling to predict toxicities.

EXPERIMENTAL DESIGN

Untargeted high-resolution metabolomic profiles of 992 patients with estrogen receptor (ER)+/HER2- breast cancer from the prospective CANTO cohort were acquired (n = 1935 metabolites). A residual-based modeling strategy with discovery and validation cohorts was used to benchmark machine learning algorithms, taking into account confounding variables.

RESULTS

Adaptive Least Absolute Shrinkage and Selection (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 nonannotated metabolites increases the predictive power. Metabolomics adds extra performance to clinical variables to predict various neurologic 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.

MeSH

Breast Neoplasms, Neoplasms, Breast, Metabolomics, Therapeutics

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