A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer (original) (raw)
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Molecular Oncology, 2012
Background: Metabolomics, a global study of metabolites and small molecules, is a novel expanding field. In this pilot study, metabolomics has been applied to serum samples from women with metastatic breast cancer to explore outcomes and response to treatment. Patients and methods: Pre-treatment and serial on-treatment serum samples were available from an international clinical trial in which 579 women with metastatic breast cancer were randomized to paclitaxel plus either a targeted anti-HER2 treatment (lapatinib) or placebo. Serum metabolomic profiles were obtained using 600 MHz nuclear magnetic resonance spectroscopy. Profiles were compared with time to progression, overall survival and treatment toxicity. Results: Pre-and on-treatment serum samples were assessed for over 500 patients. Unbiased metabolomic profiles in the biologically unselected overall trial population did not correlate with outcome or toxicity. In a subgroup of patients with HER2-positive disease treated with paclitaxel plus lapatinib, metabolomic profiles from patients in the upper and lower thirds of the dataset showed significant differences for time to progression (N ¼ 22, predictive accuracy ¼ 89.6%) and overall survival (N ¼ 16, predictive accuracy ¼ 78.0%). Conclusions: In metastatic breast cancer, metabolomics may play a role in sub selecting patients with HER2 positive disease with greater sensitivity to paclitaxel plus lapatinib.
Journal of Pharmaceutical and Biomedical Analysis
Doxorubicin, cisplatin, and tamoxifen are part of many chemotherapeutic regimens. However, studies investigating the effect of chemotherapy on the metabolism of breast cancer cells are still limited. We used 1 H high-resolution magic angle spinning (HR-MAS) NMR spectroscopy to study the metabolic profile of human breast cancer MDA-MB-231 cells either untreated (control) or treated with tamoxifen, cisplatin, and doxorubicin. 1 H HR-MAS NMR single pulse spectra evidenced signals from all mobile cell compounds, including fatty acids (membranes), water-soluble proteins, and metabolites. NMR spectra showed that phosphocholine (i.e., a biomarker of breast cancer malignant transformation) signals were stronger in control than in treated cells, but significantly decreased upon treatment with tamoxifen/cisplatin. NMR spectra acquired with Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence were interpreted only qualitatively because signal areas were attenuated according to their transverse relaxation times (T 2). The CPMG method was used to identify soluble metabolites such as organic acids, amino acids, choline and derivatives, taurine, guanidine acetate, tyrosine, and phenylalanine. The fatty acid variations observed by single pulse as well as the lactate, acetate, glycine, and phosphocholine variations observed through CPMG 1 H HR-MAS NMR have potential to characterize both responder and non-responder tumors in a molecular level. Additionally, we emphasized that comparable tumors (i.e., with the same origin, in this case breast cancer) may respond totally differently to chemotherapy. Our observations reinforce the theory that alterations in cellular metabolism may contribute to the development of a malignant phenotype and cell resistance.
Metabolites, 2019
Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC...
Cancers
Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We...
ACS Omega
Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using highfield 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89−1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.
NMR in Biomedicine, 2010
Absolute quantitative measures of breast cancer tissue metabolites can increase our understanding of biological processes. Electronic REference To access In vivo Concentrations (ERETIC) was applied to high resolution magic angle spinning MR spectroscopy (HR MAS MRS) to quantify metabolites in intact breast cancer samples. The ERETIC signal was calibrated using solutions of creatine and TSP. The largest relative errors of the ERETIC method were 8.4%, compared to 4.4% for the HR MAS MRS method using TSP as a standard. The same MR experimental procedure was applied to intact tissue samples from breast cancer patients with clinically defined good (n ¼ 13) and poor (n ¼ 16) prognosis. All samples were examined by histopathology for relative content of different tissue types and proliferation index (MIB-1) after MR analysis. The resulting spectra were analyzed by quantification of tissue metabolites (b-glucose, lactate, glycine, myo-inositol, taurine, glycerophosphocholine, phosphocholine, choline and creatine), by peak area ratios and by principal component analysis. We found a trend toward lower concentrations of glycine in patients with good prognosis (1.1 mmol/g) compared to patients with poor prognosis (1.9 mmol/g, p ¼ 0.067). Tissue metabolite concentrations (except for b-glucose) were also found to correlate to the fraction of tumor, connective, fat or glandular tissue by Pearson correlation analysis. Tissue concentrations of b-glucose correlated to proliferation index (MIB-1) with a negative correlation factor (S0.45, p ¼ 0.015), consistent with increased energy demand in proliferating tumor cells. By analyzing several metabolites simultaneously, either in ratios or by metabolic profiles analyzed by PCA, we found that tissue metabolites correlate to patients' prognoses and health status five years after surgery. This study shows that the diagnostic and prognostic potential in MR metabolite analysis of breast cancer tissue is greater when combining multiple metabolites (MR Metabolomics).
NMR in Biomedicine, 2012
e,f and Ingrid S. Gribbestad a Purpose -This study aimed to evaluate whether MR metabolic profiling can be used for prediction of long-term survival and monitoring of treatment response in locally advanced breast cancer patients during neoadjuvant chemotherapy (NAC). Methods: High resolution magic angle spinning (HR MAS) MR spectra of pre-and post-treatment biopsies from 33 patients were acquired. Tissue concentrations of choline-containing metabolites (tCho), glycine and taurine were assessed using electronic reference to access in vivo concentration (ERETIC) of the signal and receiver operating characteristic (ROC) curves was used to define their potential to predict patient survival and treatment response. The metabolite profiles obtained by HR MAS spectroscopy were related to long-term survival and treatment response by genetic algorithm partial least squares discriminant analysis (GA PLS-DA). Results -Different pre-treatment MR metabolic profiles characterized by higher levels of tCho and lower levels of lactate were observed in patients with long-term survival (≥5years, survivors) compared to patients who died of cancer recurrence (<5years, non-survivors). A significant decrease in glycerophosphocholine (GPC) post-treatment was associated with long-term survival (p=0.046) and partial response (p=0.014) to NAC. Long-term survival was best predicted by GPC using ROC analyses (sens. 66.7%, spec. 62.5%), while taurine had the best predictive value of treatment response (sens. 72.7%, spec. 63.2%). GA PLS-DA multivariate classification models successfully discriminated between survivors and non-survivors, resulting in 82.7% and 90.2% cross-validation (CV) classification accuracy, pre-and post-treatment, respectively. Classification of treatment response using GA PLS-DA was not successful for this patient cohort. Conclusions -Our results demonstrate that HR MAS MR metabolic profiles consisting of important metabolic characteristics of breast cancer tumors could potentially assist the classification and prediction of long-term survival in locally advanced breast cancer patients, in addition to being used as an adjunct for evaluation of treatment response to NAC.
Metabolites
Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were furt...
Cancers, 2021
The lipid tumour demand may shape the host metabolism adapting the circulating lipids composition to its growth and progression needs. This study aims to exploit the straightforward 1H-NMR lipoproteins analysis to investigate the alterations of the circulating lipoproteins’ fractions in HER2-positive breast cancer and their modulations induced by treatments. The baseline 1H-NMR plasma lipoproteins profiles were measured in 43 HER2-positive breast cancer patients and compared with those of 28 healthy women. In a subset of 32 patients, longitudinal measurements were also performed along neoadjuvant chemotherapy, after surgery, adjuvant treatment, and during the two-year follow-up. Differences between groups were assessed by multivariate PLS-DA and by univariate analyses. The diagnostic power of lipoproteins subfractions was assessed by ROC curve, while lipoproteins time changes along interventions were investigated by ANOVA analysis. The PLS-DA model distinguished HER2-positive breast...
NMR in Biomedicine, 2003
In-vitro NMR spectroscopic examinations of tissue extracts can be combined with appropriate patternrecognition and visualization techniques in order to monitor characteristic metabolic differences between tissue classes. In the present study, such techniques are applied to a set of 88 breast-tissue samples with the intention of identifying typical differences between various tissue classes. The set contains 49 breast-tumor samples of various tumor grades and 39 samples of healthy tissue. The metabolite compositions of the tissue extracts were investigated using a dual extraction technique and high-resolution 1 H-NMR spectroscopy. The spectra of the hydrophilic and the lipophilic compounds were assigned to three groups according to different malignancy grades of the respective tissue samples. The group characteristics were analyzed using the k-nearest-neighbor method and self-organizing-map visualizations. The results show an increase of UDP-hexose, phosphocholine and phosphoethanolamine concentrations according to the tumor grade. Higher concentrations of taurine were detected in the malignant samples. Myo-inositol and glucose content were elevated in control samples compared with malignant tissue. Both compounds also characterized different subgroups in the pool of unaffected tissue samples depending upon fat content or fibrosis. Several lipid metabolites showed a characteristic elevation with high malignancy.