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Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information.

Mörén L, Bergenheim AT, Ghasimi S, Brännström T, Johansson M, Antti H - Metabolites (2015)

Bottom Line: However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas.Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)).Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)).

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Umeå University, SE 901 87 Umeå, Sweden. lina.moren@umu.se.

ABSTRACT
Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

No MeSH data available.


Related in: MedlinePlus

Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences associated with diagnosis and tumor grade. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of glioblastoma and oligodendroglioma in tissue (blue line) and serum (red line) with area under the ROC curve (AUROCC) values of 0.881 (0.791–0.970) and 0.826 (0.722–0.929), respectively (left). The scatter plots show the class differences between glioblastoma and oligodendroglioma based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model discriminating between World Health Organization (WHO) grade II and III in oligodendroglioma in tissue (blue line) and serum (red line) with AUROCC values of 0.833 (0.557–1.000) and 0.946 (0.858–1.000), respectively (left). The scatter plots show the class differences between grade II and grade III based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
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metabolites-05-00502-f001: Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences associated with diagnosis and tumor grade. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of glioblastoma and oligodendroglioma in tissue (blue line) and serum (red line) with area under the ROC curve (AUROCC) values of 0.881 (0.791–0.970) and 0.826 (0.722–0.929), respectively (left). The scatter plots show the class differences between glioblastoma and oligodendroglioma based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model discriminating between World Health Organization (WHO) grade II and III in oligodendroglioma in tissue (blue line) and serum (red line) with AUROCC values of 0.833 (0.557–1.000) and 0.946 (0.858–1.000), respectively (left). The scatter plots show the class differences between grade II and grade III based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).

Mentions: Comparing metabolic profiles from tissue between GBM and oligodendrogliomas revealed 12 significantly differentiating features (w*average ± 2 standard deviations (SD)). In serum, 13 metabolic features were significantly differentially expressed. Investigation of the significance of the detected metabolic patterns by means of orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that it was possible to distinguish between glioblastomas and oligodendrogliomas in both tumor and serum (A = 1 + 0 + 0, R2X = 0.39, R2Y = 0.379, Q2 = 0.341, p = 2.46 × 10−8 and A = 1 + 0 + 0, R2X = 0.25 R2Y = 0.251 Q2 = 0.223, p = 1.3 × 10−5) (Figure 1). Detected features with a suggested identity from spectral library comparison (fragmentation pattern and retention index) responsible for discriminating between the diagnoses in tumor and serum are listed in Table 1. In summary, higher levels of mannitol and phenylalanine where found in GBMs compared to oligodendrogliomas in tissue while 2-hydroxyglutaric acid, 4-Aminobutyric acid (GABA), creatinine, glycerol-2-phosphate, glycerol-3-phosphate, ribitol and myo-inositol showed higher levels in oligodendrogliomas as compared to GBM. In serum, cysteine was found at higher levels in GBMs, while lysine and 2-oxoisocaproic acid showed higher levels in oligodendrogliomas.


Metabolomic Screening of Tumor Tissue and Serum in Glioma Patients Reveals Diagnostic and Prognostic Information.

Mörén L, Bergenheim AT, Ghasimi S, Brännström T, Johansson M, Antti H - Metabolites (2015)

Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences associated with diagnosis and tumor grade. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of glioblastoma and oligodendroglioma in tissue (blue line) and serum (red line) with area under the ROC curve (AUROCC) values of 0.881 (0.791–0.970) and 0.826 (0.722–0.929), respectively (left). The scatter plots show the class differences between glioblastoma and oligodendroglioma based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model discriminating between World Health Organization (WHO) grade II and III in oligodendroglioma in tissue (blue line) and serum (red line) with AUROCC values of 0.833 (0.557–1.000) and 0.946 (0.858–1.000), respectively (left). The scatter plots show the class differences between grade II and grade III based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588809&req=5

metabolites-05-00502-f001: Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences associated with diagnosis and tumor grade. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of glioblastoma and oligodendroglioma in tissue (blue line) and serum (red line) with area under the ROC curve (AUROCC) values of 0.881 (0.791–0.970) and 0.826 (0.722–0.929), respectively (left). The scatter plots show the class differences between glioblastoma and oligodendroglioma based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model discriminating between World Health Organization (WHO) grade II and III in oligodendroglioma in tissue (blue line) and serum (red line) with AUROCC values of 0.833 (0.557–1.000) and 0.946 (0.858–1.000), respectively (left). The scatter plots show the class differences between grade II and grade III based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
Mentions: Comparing metabolic profiles from tissue between GBM and oligodendrogliomas revealed 12 significantly differentiating features (w*average ± 2 standard deviations (SD)). In serum, 13 metabolic features were significantly differentially expressed. Investigation of the significance of the detected metabolic patterns by means of orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that it was possible to distinguish between glioblastomas and oligodendrogliomas in both tumor and serum (A = 1 + 0 + 0, R2X = 0.39, R2Y = 0.379, Q2 = 0.341, p = 2.46 × 10−8 and A = 1 + 0 + 0, R2X = 0.25 R2Y = 0.251 Q2 = 0.223, p = 1.3 × 10−5) (Figure 1). Detected features with a suggested identity from spectral library comparison (fragmentation pattern and retention index) responsible for discriminating between the diagnoses in tumor and serum are listed in Table 1. In summary, higher levels of mannitol and phenylalanine where found in GBMs compared to oligodendrogliomas in tissue while 2-hydroxyglutaric acid, 4-Aminobutyric acid (GABA), creatinine, glycerol-2-phosphate, glycerol-3-phosphate, ribitol and myo-inositol showed higher levels in oligodendrogliomas as compared to GBM. In serum, cysteine was found at higher levels in GBMs, while lysine and 2-oxoisocaproic acid showed higher levels in oligodendrogliomas.

Bottom Line: However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas.Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)).Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)).

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Umeå University, SE 901 87 Umeå, Sweden. lina.moren@umu.se.

ABSTRACT
Glioma grading and classification, today based on histological features, is not always easy to interpret and diagnosis partly relies on the personal experience of the neuropathologists. The most important feature of the classification is the aimed correlation between tumor grade and prognosis. However, in the clinical reality, large variations exist in the survival of patients concerning both glioblastomas and low-grade gliomas. Thus, there is a need for biomarkers for a more reliable classification of glioma tumors as well as for prognosis. We analyzed relative metabolite concentrations in serum samples from 96 fasting glioma patients and 81 corresponding tumor samples with different diagnosis (glioblastoma, oligodendroglioma) and grade (World Health Organization (WHO) grade II, III and IV) using gas chromatography-time of flight mass spectrometry (GC-TOFMS). The acquired data was analyzed and evaluated by pattern recognition based on chemometric bioinformatics tools. We detected feature patterns in the metabolomics data in both tumor and serum that distinguished glioblastomas from oligodendrogliomas (p(tumor) = 2.46 × 10(-8), p(serum) = 1.3 × 10(-5)) and oligodendroglioma grade II from oligodendroglioma grade III (p(tumor) = 0.01, p(serum) = 0.0008). Interestingly, we also found patterns in both tumor and serum with individual metabolite features that were both elevated and decreased in patients that lived long after being diagnosed with glioblastoma compared to those who died shortly after diagnosis (p(tum)(o)(r) = 0.006, p(serum) = 0.004; AUROCC(tumor) = 0.846 (0.647-1.000), AUROCC(serum) = 0.958 (0.870-1.000)). Metabolic patterns could also distinguish long and short survival in patients diagnosed with oligodendroglioma (p(tumor) = 0.01, p(serum) = 0.001; AUROCC(tumor) = 1 (1.000-1.000), AUROCC(serum) = 1 (1.000-1.000)). In summary, we found different metabolic feature patterns in tumor tissue and serum for glioma diagnosis, grade and survival, which indicates that, following further verification, metabolomic profiling of glioma tissue as well as serum may be a valuable tool in the search for latent biomarkers for future characterization of malignant glioma.

No MeSH data available.


Related in: MedlinePlus