Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study

Tenori, Leonardo; Oakman, Catherine; Morris, Patrick G.; Gralka, Ewa; Turner, Natalie; Cappadona, Silvia; Fornier, Monica; Hudis, C.; Norton, Larry; Luchinat, Claudio; Leo, Angelo Di

doi:10.1016/j.molonc.2014.07.012

Cited by 85 publications

(86 citation statements)

References 32 publications

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“…Three other studies [48,49,50] discriminated early versus metastatic cancer [48,50] and time to progression in clinical trials including BC patients receiving anti-HER2 treatment [49]. All three studies used NMR and serum samples.…”

Section: Resultsmentioning

confidence: 99%

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Metabolomics Biomarkers for Breast Cancer

Günther

2015

Pathobiology

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Metabolomics represents a more recent addition to the range of omics tools, which are increasingly used in clinical applications. By measuring the composition of small molecules in tissues, blood or urine, it provides a sensitive molecular readout often associated with disease and its states, especially in cancer. Changes in metabolism related to cancer are increasingly well understood and are seen as a major hallmark of cancer. This review covers metabolomics used in human breast cancers, with a focus on its application in clinical diagnostics. There are clear indications that metabolomics could be a useful addition to currently established clinical diagnostic tools for breast cancer, including the possibility to detect cancer and to predict treatment responses and survival rates from blood and tissue samples.

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Section: Resultsmentioning

confidence: 99%

“…Overall survival was predicted with a sensitivity of 73% and specificity of 84%. In a further study, Tenori et al [50] analyzed NMR samples predominantly from ER- metastatic BC and predicted relapse with 90% sensitivity and 67% specificity, with lower His and higher Gluc and lipid levels in relapsing patients.…”

Section: Resultsmentioning

confidence: 99%

Metabolomics Biomarkers for Breast Cancer

Günther

2015

Pathobiology

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“…Unlike previous clinical metabolomics investigations [26, 27] this study focused on a homogeneous population of BC patients. We have observed that patients with higher serum concentrations of Spd and lower concentrations of Trp would be more likely to benefit from trastuzumab–paclitaxel neoadjuvant treatment.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, two previous investigations analysed the predictive value of basal serum metabolites on the outcome of BC patients in the metastatic and neoadjuvant settings [26, 27] but their results were contradictory. One of these studies consisted of a pilot multicentric investigation that attempted to correlate the serum metabolomics profile with patient outcome and treatments toxicity [26]. However, this study failed to identify any correlations between the patients' metabolomics profile and the therapies effect.…”

Section: Introductionmentioning

confidence: 99%

Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer

et al. 2016

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Defining biomarkers that predict therapeutic effects and adverse events is a crucial mandate to guide patient selection for personalized cancer treatments. In the present study, we applied a pharmacometabolomics approach to identify biomarkers potentially associated with pathological complete response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer patients. Based on histological response the 34 patients enrolled in the study were subdivided into two groups: good responders (n = 15) and poor responders (n = 19). The pre-treatment serum targeted metabolomics profile of all patients were analyzed by liquid chromatography tandem mass spectrometry and the differences in the metabolomics profile between the two groups was investigated by multivariate partial least squares discrimination analysis. The most relevant metabolites that differentiate the two groups of patients were spermidine and tryptophan. The Good responders showed higher levels of spermidine and lower amounts of tryptophan compared with the poor responders (p < 0.001, q < 0.05). The serum level of these two metabolites identified patients who achieved a pathological complete response with a sensitivity of 90% [0.79–1.00] and a specificity of 0.87% [0.67–1.00]. These preliminary results support the role played by the individual patients' metabolism in determining the response to cancer treatments and may be a useful tool to select patients that are more likely to benefit from the trastuzumab-paclitaxel treatment.

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“…This hypothesis was tested in a follow-up study using serum from a biobank of estrogen receptor–negative (ER − ) patients from the Memorial Sloan Kettering Cancer Center (New York, NY), for whom clinical outcome (relapse at 5 years) was known (8). A model was built in which EBC patients were assigned a metabolomic risk score [Random Forest (RF) risk score], which was a function of the likelihood that they would be misclassified as metastatic based on their serum NMR spectra.…”

Section: Introductionmentioning

confidence: 99%

Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

Hart

Vignoli

Tenori

et al. 2017

Clinical Cancer Research

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Purpose Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)–negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, pre-menopausal women with EBC who were enrolled in an international phase III trial. Experimental Design Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or ≥6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic. Results In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score. Conclusions In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors.

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Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study

Abstract: The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER-negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour-derived prognostic tool.

Cited by 85 publications

References 32 publications

Metabolomics Biomarkers for Breast Cancer

Metabolomics Biomarkers for Breast Cancer

Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer

Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

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