Dataset Information

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

ABSTRACT:

Background

We 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.

Methods

Serum 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.

Results

Serum 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.

Conclusions

This 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.

SUBMITTER: Risi E

PROVIDER: S-EPMC9676351 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

Risi Emanuela E Lisanti Camilla C Vignoli Alessia A Biagioni Chiara C Paderi Agnese A Cappadona Silvia S Monte Francesca Del FD Moretti Erica E Sanna Giuseppina G Livraghi Luca L Malorni Luca L Benelli Matteo M Puglisi Fabio F Luchinat Claudio C Tenori Leonardo L Biganzoli Laura L

Translational oncology 20221117

<h4>Background</h4>We 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.<h4>Methods</h4>Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonan ...[more]

PMID: 36403505

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Project description:In cancer patients, hypercoagulability is a common finding. It has been associated with an increased risk of venous thromboembolism, but also to tumor proliferation and progression. In this prospective study of a large cohort of breast cancer patients, we aimed to evaluate whether pre-chemotherapy abnormalities in hemostatic biomarkers levels: (i) are associated with breast cancer-specific clinico-pathological features; and (ii) can predict for disease recurrence. D-dimer, fibrinogen, prothrombin fragment 1+2, and FVIIa/antithrombin levels were measured in 701 early-stage resected breast cancer patients candidate to adjuvant chemotherapy and prospectively enrolled in the HYPERCAN study. Significant prognostic parameters for disease recurrence were identified by Cox regression multivariate analysis and used for generating a risk assessment model. Pre-chemotherapy D-dimer, fibrinogen, and pro-thrombin fragment 1+2 levels were significantly associated with tumor size and lymph node metastasis. After 3.4 years of follow up, 71 patients experienced a recurrence. Cox multivariate analysis identified prothrombin fragment 1+2, tumor size, and Luminal B HER2-negative or triple negative molecular subtypes as independent risk factors for disease recurrence. Based on these variables, we generated a risk assessment model that significantly differentiated patients at low- and high-risk of recurrence (cumulative incidence: 6.2 vs 20.7%; Hazard Ratio=3.5; P<0.001). Our prospective clinical and laboratory data from the HYPERCAN study were crucial for generating a scoring model for assessing risk of disease recurrence in resected breast cancer patients, candidate to systemic chemotherapy. This finding stimulates future investigations addressing the role of plasma prothrombin fragment 1+2 in the management of breast cancer patients to provide the rationale for new therapeutic strategies. (The HYPERCAN study is registered at clinicaltrials.gov identifier 02622815).

Dataset Information

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

Background

Methods

Results

Conclusions

Publications

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

Similar Datasets

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