ArticleMetabolomics : Official journal of the Metabolomic Society2025
A predictive model for neoadjuvant therapy response in breast cancer.
Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Integrative proteomics and metabolomics advance early cancer detection and targeted therapy with emerging technologies and clinical applications.Discover oncology · 2026Review
- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025Review
- Predicting prognosis of patients with triple‑negative breast cancer undergoing neoadjuvant chemotherapy based on inflammatory status at different time points: A propensity score matching analysis.Oncology letters · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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Abstract
Neoadjuvant therapy is a standard treatment for breast cancer, but its effectiveness varies among patients. This highlights the importance of developing accurate predictive models. Our study uses metabolomics and machine learning to predict the response to neoadjuvant therapy in breast cancer patients.
objectiveTo develop and validate predictive models using machine learning and circulating metabolites for forecasting responses to neoadjuvant therapy among breast cancer patients, enhancing personalized treatment strategies.
methodsBased on pathological analysis after neoadjuvant chemotherapy and surgery, this retrospective study analyzed 30 young women breast cancer patients from a single institution, categorized as responders or non-responders. Utilizing liquid chromatography-tandem mass spectrometry, we investigated the plasma metabolome, explicitly targeting 40 metabolites, to identify relevant biomarkers linked to therapy response, using machine learning to generate a predictive model and validate the results.
resultsEighteen significant biomarkers were identified, including specific acylcarnitines and amino acids. The most effective predictive model demonstrated a remarkable accuracy of 90.7% and an Area Under the Curve (AUC) of 0.999 at 95% confidence, illustrating its potential utility as a web-based application for future patient management. This model's reliability underscores the significant role of circulating metabolites in predicting therapy outcomes.
conclusionOur study's findings highlight the crucial role of metabolomics in advancing personalized medicine for breast cancer treatment by effectively identifying metabolite biomarkers correlated with neoadjuvant therapy response. This approach signifies a critical step towards tailoring treatment plans based on individual metabolic profiles, ultimately improving patient outcomes in breast cancer care.
Indexed as
Identifiers
39979511What Socratic holds
Registered trials
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 Socratic graph.