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
{"title":"乳腺癌治疗引起的神经和代谢毒性的代谢组学预测。","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","doi":"10.1158/1078-0432.CCR-24-0195","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Experimental design: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.</p>","PeriodicalId":10279,"journal":{"name":"Clinical Cancer Research","volume":null,"pages":null},"PeriodicalIF":10.0000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metabolomic Prediction of Breast Cancer Treatment-Induced Neurologic and Metabolic Toxicities.\",\"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\",\"doi\":\"10.1158/1078-0432.CCR-24-0195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Long-term treatment-related toxicities, such as neurologic and metabolic toxicities, are major issues in breast cancer. 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Metabolomics adds extra performance to clinical variables to predict various neurologic and metabolic toxicity profiles.</p><p><strong>Conclusions: </strong>Untargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.</p>\",\"PeriodicalId\":10279,\"journal\":{\"name\":\"Clinical Cancer Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.0000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/1078-0432.CCR-24-0195\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1078-0432.CCR-24-0195","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Metabolomic Prediction of Breast Cancer Treatment-Induced Neurologic and Metabolic Toxicities.
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.
期刊介绍：
Clinical Cancer Research is a journal focusing on groundbreaking research in cancer, specifically in the areas where the laboratory and the clinic intersect. Our primary interest lies in clinical trials that investigate novel treatments, accompanied by research on pharmacology, molecular alterations, and biomarkers that can predict response or resistance to these treatments. Furthermore, we prioritize laboratory and animal studies that explore new drugs and targeted agents with the potential to advance to clinical trials. We also encourage research on targetable mechanisms of cancer development, progression, and metastasis.
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