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Metabolomic Characterization of Ovarian Epithelial Carcinomas by HRMAS-NMR Spectroscopy.

Ben Sellem D, Elbayed K, Neuville A, Moussallieh FM, Lang-Averous G, Piotto M, Bellocq JP, Namer IJ - J Oncol (2011)

Bottom Line: Results.Conclusions.Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients.

View Article: PubMed Central - PubMed

Affiliation: Biophysics and Nuclear Medicine Department, University Hospitals of Strasbourg, 67098 Strasbourg, France.

ABSTRACT
Objectives. The objectives of the present study are to determine if a metabolomic study by HRMAS-NMR can (i) discriminate between different histological types of epithelial ovarian carcinomas and healthy ovarian tissue, (ii) generate statistical models capable of classifying borderline tumors and (iii) establish a potential relationship with patient's survival or response to chemotherapy. Methods. 36 human epithelial ovarian tumor biopsies and 3 healthy ovarian tissues were studied using (1)H HRMAS NMR spectroscopy and multivariate statistical analysis. Results. The results presented in this study demonstrate that the three histological types of epithelial ovarian carcinomas present an effective metabolic pattern difference. Furthermore, a metabolic signature specific of serous (N-acetyl-aspartate) and mucinous (N-acetyl-lysine) carcinomas was found. The statistical models generated in this study are able to predict borderline tumors characterized by an intermediate metabolic pattern similar to the normal ovarian tissue. Finally and importantly, the statistical model of serous carcinomas provided good predictions of both patient's survival rates and the patient's response to chemotherapy. Conclusions. Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients.

No MeSH data available.


Related in: MedlinePlus

Score plot of the first two principal components (PC1, PC2) from PLS-DA model obtained when comparing: (a) Healthy ovarian tissues (filled triangle) versus the 3 epithelial carcinomas: mucinous (open diamond), endometrioid (open square) and serous (open triangle). Model parameters: R2Y = 0.75, Q2 = 0.50.  (b) Healthy ovarian tissues (full triangle) versus endometrioid carcinomas (open square). Model parameters:  R2Y = 0.96, Q2 = 0.45.  (c) Healthy ovarian tissues (full triangle) versus mucinous carcinomas (open diamond). Model parameters: R2Y = 0.94, Q2 = 0.69. (d) Healthy ovarian tissues (full triangle) versus high Silverberg score (grade III) of serous carcinomas (open triangle). Model parameters:  R2Y = 0.91, Q2 = 0.68. In these models, the predicted borderline cases are represented by filled circles (c, d) and the predicted low Silverberg score (grade I-II) serous carcinomas by open circles (d).
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fig2: Score plot of the first two principal components (PC1, PC2) from PLS-DA model obtained when comparing: (a) Healthy ovarian tissues (filled triangle) versus the 3 epithelial carcinomas: mucinous (open diamond), endometrioid (open square) and serous (open triangle). Model parameters: R2Y = 0.75, Q2 = 0.50. (b) Healthy ovarian tissues (full triangle) versus endometrioid carcinomas (open square). Model parameters: R2Y = 0.96, Q2 = 0.45. (c) Healthy ovarian tissues (full triangle) versus mucinous carcinomas (open diamond). Model parameters: R2Y = 0.94, Q2 = 0.69. (d) Healthy ovarian tissues (full triangle) versus high Silverberg score (grade III) of serous carcinomas (open triangle). Model parameters: R2Y = 0.91, Q2 = 0.68. In these models, the predicted borderline cases are represented by filled circles (c, d) and the predicted low Silverberg score (grade I-II) serous carcinomas by open circles (d).

Mentions: The 1D 1H CPMG HRMAS spectra are characterized by a high resolution and a low level of lipid signals which allowed the identification of a total of 38 different metabolites (Table 1). Typical 1D HRMAS spectra of healthy ovarian tissue and of the three different histological types of epithelial ovarian carcinoma tissues are presented in Figure 1 along with a partial metabolite assignment. Only the 4.7–0.5 ppm region used in the subsequent statistical analysis is shown. The PLS-DA analysis applied to the all-ovarian biopsies generated a two component PLS-DA model characterized by a faithful representation of the Y data (R2Y = 0.75) and by a good cumulative confidence criterion of prediction (Q2 = 0.50). These results demonstrate an effective difference in the metabolic pattern of healthy tissues and the three histological types (Figure 2(a)).


Metabolomic Characterization of Ovarian Epithelial Carcinomas by HRMAS-NMR Spectroscopy.

Ben Sellem D, Elbayed K, Neuville A, Moussallieh FM, Lang-Averous G, Piotto M, Bellocq JP, Namer IJ - J Oncol (2011)

Score plot of the first two principal components (PC1, PC2) from PLS-DA model obtained when comparing: (a) Healthy ovarian tissues (filled triangle) versus the 3 epithelial carcinomas: mucinous (open diamond), endometrioid (open square) and serous (open triangle). Model parameters: R2Y = 0.75, Q2 = 0.50.  (b) Healthy ovarian tissues (full triangle) versus endometrioid carcinomas (open square). Model parameters:  R2Y = 0.96, Q2 = 0.45.  (c) Healthy ovarian tissues (full triangle) versus mucinous carcinomas (open diamond). Model parameters: R2Y = 0.94, Q2 = 0.69. (d) Healthy ovarian tissues (full triangle) versus high Silverberg score (grade III) of serous carcinomas (open triangle). Model parameters:  R2Y = 0.91, Q2 = 0.68. In these models, the predicted borderline cases are represented by filled circles (c, d) and the predicted low Silverberg score (grade I-II) serous carcinomas by open circles (d).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3090613&req=5

fig2: Score plot of the first two principal components (PC1, PC2) from PLS-DA model obtained when comparing: (a) Healthy ovarian tissues (filled triangle) versus the 3 epithelial carcinomas: mucinous (open diamond), endometrioid (open square) and serous (open triangle). Model parameters: R2Y = 0.75, Q2 = 0.50. (b) Healthy ovarian tissues (full triangle) versus endometrioid carcinomas (open square). Model parameters: R2Y = 0.96, Q2 = 0.45. (c) Healthy ovarian tissues (full triangle) versus mucinous carcinomas (open diamond). Model parameters: R2Y = 0.94, Q2 = 0.69. (d) Healthy ovarian tissues (full triangle) versus high Silverberg score (grade III) of serous carcinomas (open triangle). Model parameters: R2Y = 0.91, Q2 = 0.68. In these models, the predicted borderline cases are represented by filled circles (c, d) and the predicted low Silverberg score (grade I-II) serous carcinomas by open circles (d).
Mentions: The 1D 1H CPMG HRMAS spectra are characterized by a high resolution and a low level of lipid signals which allowed the identification of a total of 38 different metabolites (Table 1). Typical 1D HRMAS spectra of healthy ovarian tissue and of the three different histological types of epithelial ovarian carcinoma tissues are presented in Figure 1 along with a partial metabolite assignment. Only the 4.7–0.5 ppm region used in the subsequent statistical analysis is shown. The PLS-DA analysis applied to the all-ovarian biopsies generated a two component PLS-DA model characterized by a faithful representation of the Y data (R2Y = 0.75) and by a good cumulative confidence criterion of prediction (Q2 = 0.50). These results demonstrate an effective difference in the metabolic pattern of healthy tissues and the three histological types (Figure 2(a)).

Bottom Line: Results.Conclusions.Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients.

View Article: PubMed Central - PubMed

Affiliation: Biophysics and Nuclear Medicine Department, University Hospitals of Strasbourg, 67098 Strasbourg, France.

ABSTRACT
Objectives. The objectives of the present study are to determine if a metabolomic study by HRMAS-NMR can (i) discriminate between different histological types of epithelial ovarian carcinomas and healthy ovarian tissue, (ii) generate statistical models capable of classifying borderline tumors and (iii) establish a potential relationship with patient's survival or response to chemotherapy. Methods. 36 human epithelial ovarian tumor biopsies and 3 healthy ovarian tissues were studied using (1)H HRMAS NMR spectroscopy and multivariate statistical analysis. Results. The results presented in this study demonstrate that the three histological types of epithelial ovarian carcinomas present an effective metabolic pattern difference. Furthermore, a metabolic signature specific of serous (N-acetyl-aspartate) and mucinous (N-acetyl-lysine) carcinomas was found. The statistical models generated in this study are able to predict borderline tumors characterized by an intermediate metabolic pattern similar to the normal ovarian tissue. Finally and importantly, the statistical model of serous carcinomas provided good predictions of both patient's survival rates and the patient's response to chemotherapy. Conclusions. Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients.

No MeSH data available.


Related in: MedlinePlus