乳腺癌治疗引起的神经和代谢毒性的代谢组学预测。

IF 10 1区 医学 Q1 ONCOLOGY Clinical Cancer Research Pub Date : 2024-10-15 DOI:10.1158/1078-0432.CCR-24-0195
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. 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\":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}
引用次数: 0

摘要

背景:长期治疗相关毒性,如神经和代谢毒性,是乳腺癌的主要问题。我们研究了代谢组学图谱对预测毒性的兴趣:方法:我们从前瞻性CANTO队列中获取了992名ER+/HER2-乳腺癌患者的非靶向高分辨率代谢组图谱(代谢物数=1935)。在考虑混杂变量的情况下,采用基于残差的建模策略,以发现队列和验证队列作为机器学习算法的基准:结果:自适应 LASSO 具有良好的预测性能,乐观偏差有限,可以为未来的转化研究选择感兴趣的代谢物。增加低频代谢物和非注释代谢物可提高预测能力。代谢组学为临床变量增加了额外的性能,以预测各种神经和代谢毒性特征:非靶向高分辨率代谢组学通过考虑环境暴露、与微生物群相关的代谢物和低频代谢物,可以更好地预测毒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Clinical Cancer Research
Clinical Cancer Research 医学-肿瘤学
CiteScore
20.10
自引率
1.70%
发文量
1207
审稿时长
2.1 months
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.
期刊最新文献
A Phase 2 study of acimtamig (AFM13) in patients with CD30-positive, relapsed or refractory peripheral T-cell lymphomas Phase II Trial of Induction Chemotherapy for Advanced Sinonasal Squamous Cell Carcinoma Homoharringtonine Added to Venetoclax and Azacitidine Improves Outcome and Mitigates Genetic Impact in Relapsed/Refractory AML: A Multi-center Cohort Study Phase Ib clinical and pharmacodynamic study of the TIE2 kinase inhibitor rebastinib with paclitaxel or eribulin in HER2-negative metastatic breast cancer Targeting T-cell costimulation to the surface of tumor cells
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1