ArticleClinical cancer research : an official journal of the American Association for Cancer Research2024
Metabolomic Prediction of Breast Cancer Treatment-Induced Neurologic and Metabolic Toxicities.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Relationship between BMI and chemotherapy-induced peripheral neuropathy in cancer patients: a dose-response meta-analysis.World journal of surgical oncology · 2025Pooled it
- Integrated workflow for univariate and multivariate evaluation of batch correction reliability.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Applications of Metabolomics to the Clinical Management of Breast Cancer: New Perspectives for Diagnosis, Treatment and Prognosis.International journal of molecular sciences · 2026Review
- Profiles of neuropsychiatric toxicity associated with different endocrine therapies for breast cancer: a global pharmacovigilance study based on FAERS and VigiAccess.Frontiers in pharmacology · 2026Article
- Metabolomic profiling for predicting breast cancer treatment toxicities.Translational cancer research · 2025Article
- Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detection.Medicina (Kaunas, Lithuania) · 2025Article
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29 authors.
Funding
Abstract
purposeLong-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
designUntargeted 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.
resultsAdaptive 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.
conclusionsUntargeted high-resolution metabolomics allows better toxicity prediction by considering environmental exposure, metabolites linked to microbiota, and low-frequency metabolites.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.