ArticleTranslational oncology2023
Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.
Article in Translational oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 5 citations in OpenAlex.
- An integrated computational, in vitro and metabolomics approach to the discovery of novel aromatase inhibitors with anti-breast cancer activities.Scientific reports · 2025Article
- The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors.GeroScience · 2024Article
- EDITORIAL : Special Edition on Geriatric Oncology.Translational oncology · 2024Article
- The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review.Current issues in molecular biology · 2023Review
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16 authors at 3 institutions in 1 country.
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Abstract
backgroundWe previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population.
methodsSerum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves.
resultsSerum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk.
conclusionsThis analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features.
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