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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 between long and short survival time. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with glioblastomas in tissue (blue line) and serum (red line) with ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (left). The scatter plots show class differences between short survival time and long survival time 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 for the discrimination of short survival time compared to long survival time in patients with oligodendrogliomas in tissue (blue line) and serum (red line). AUROCC values for survival in oligodendroglioma were calculated to 1 (1.000–1.000) for both tissue and serum (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
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metabolites-05-00502-f002: 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 between long and short survival time. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with glioblastomas in tissue (blue line) and serum (red line) with ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (left). The scatter plots show class differences between short survival time and long survival time 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 for the discrimination of short survival time compared to long survival time in patients with oligodendrogliomas in tissue (blue line) and serum (red line). AUROCC values for survival in oligodendroglioma were calculated to 1 (1.000–1.000) for both tissue and serum (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).

Mentions: In GBM, the metabolic profiles of tumor samples from patients surviving long after diagnosis (≥3 years) were compared to patients that died shortly after diagnosis (≤4 months). Based on seven resolved features passing the significance criteria, OPLS-DA provided a significant separation associated with time of survival (A = 1 + 0 + 0, R2X = 0.669, R2Y = 0.474, Q2 = 0.427, p = 0.006) (Figure 2). Interpretation of the model revealed that glycerol-3-phoshate, myo-inositol, ribitol and fructose increased in level with long survival. Furthermore, we detected a significant association with survival time in serum samples from the same patients in an OPLS-DA model based on five resolved features (A = 1 + 0 + 0, R2X = 0.536, R2Y = 0.572, Q2 = 0.478, p = 0.004; Figure 2). Unfortunately, it was not possible to obtain a suggested identity for any of the affected features found in serum. The same comparison was carried out in patients with oligodendroglioma. In tumor tissue, eight resolved features passed the significance criteria and provided a significant metabolic pattern (OPLS-DA model) in relation to time of survival (A = 1 + 0 + 0, R2X = 0.56, R2Y = 0.796, Q2 = 0.767, p = 0.01; Figure 2). High levels of ribitol, myo-inositol and spermidine were associated with long survival, while high levels of glycine, aminomalonic acid and highly likely an unidentified sterol were associated with short survival time. A complete separation with respect to survival time could also be seen in serum (Figure 2). The final OPLS-DA model was based on 13 resolved features, which together formed a significant metabolic pattern (A = 1 + 0 + 0, R2X = 0.521, R2Y = 0.909, Q2 = 0.855, p = 0.001). In this model, myo-inositol and hexadecenoic acid were associated with long survival time. All features with a putative identity significant in oligodendroglioma survival can be viewed in Table 1. AUCROCC analyses of the extracted metabolic patterns visualized as the OPLS-DA score values following a seven-fold cross-validation procedure for GBM in tumor and serum gave ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (Figure 2). In oligodendroglioma, the corresponding ROC values reached the value 1 (1.000–1.000) in both tumor and serum.


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 between long and short survival time. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with glioblastomas in tissue (blue line) and serum (red line) with ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (left). The scatter plots show class differences between short survival time and long survival time 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 for the discrimination of short survival time compared to long survival time in patients with oligodendrogliomas in tissue (blue line) and serum (red line). AUROCC values for survival in oligodendroglioma were calculated to 1 (1.000–1.000) for both tissue and serum (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
© Copyright Policy
Related In: Results  -  Collection

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

metabolites-05-00502-f002: 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 between long and short survival time. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with glioblastomas in tissue (blue line) and serum (red line) with ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (left). The scatter plots show class differences between short survival time and long survival time 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 for the discrimination of short survival time compared to long survival time in patients with oligodendrogliomas in tissue (blue line) and serum (red line). AUROCC values for survival in oligodendroglioma were calculated to 1 (1.000–1.000) for both tissue and serum (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).
Mentions: In GBM, the metabolic profiles of tumor samples from patients surviving long after diagnosis (≥3 years) were compared to patients that died shortly after diagnosis (≤4 months). Based on seven resolved features passing the significance criteria, OPLS-DA provided a significant separation associated with time of survival (A = 1 + 0 + 0, R2X = 0.669, R2Y = 0.474, Q2 = 0.427, p = 0.006) (Figure 2). Interpretation of the model revealed that glycerol-3-phoshate, myo-inositol, ribitol and fructose increased in level with long survival. Furthermore, we detected a significant association with survival time in serum samples from the same patients in an OPLS-DA model based on five resolved features (A = 1 + 0 + 0, R2X = 0.536, R2Y = 0.572, Q2 = 0.478, p = 0.004; Figure 2). Unfortunately, it was not possible to obtain a suggested identity for any of the affected features found in serum. The same comparison was carried out in patients with oligodendroglioma. In tumor tissue, eight resolved features passed the significance criteria and provided a significant metabolic pattern (OPLS-DA model) in relation to time of survival (A = 1 + 0 + 0, R2X = 0.56, R2Y = 0.796, Q2 = 0.767, p = 0.01; Figure 2). High levels of ribitol, myo-inositol and spermidine were associated with long survival, while high levels of glycine, aminomalonic acid and highly likely an unidentified sterol were associated with short survival time. A complete separation with respect to survival time could also be seen in serum (Figure 2). The final OPLS-DA model was based on 13 resolved features, which together formed a significant metabolic pattern (A = 1 + 0 + 0, R2X = 0.521, R2Y = 0.909, Q2 = 0.855, p = 0.001). In this model, myo-inositol and hexadecenoic acid were associated with long survival time. All features with a putative identity significant in oligodendroglioma survival can be viewed in Table 1. AUCROCC analyses of the extracted metabolic patterns visualized as the OPLS-DA score values following a seven-fold cross-validation procedure for GBM in tumor and serum gave ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (Figure 2). In oligodendroglioma, the corresponding ROC values reached the value 1 (1.000–1.000) in both tumor and serum.

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