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Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment.

Wibom C, Pettersson F, Sjöström M, Henriksson R, Johansson M, Bergenheim AT - Br. J. Cancer (2006)

Bottom Line: The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation.The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes.In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology, University Hospital, Umeå, Sweden.

ABSTRACT
Radiotherapy is one of the mainstays of glioblastoma (GBM) treatment. This study aims to investigate and characterise differences in protein expression patterns in brain tumour tissue following radiotherapy, in order to gain a more detailed understanding of the biological effects. Rat BT4C glioma cells were implanted into the brain of two groups of 12 BDIX-rats. One group received radiotherapy (12 Gy single fraction). Protein expression in normal and tumour brain tissue, collected at four different time points after irradiation, were analysed using surface enhanced laser desorption/ionisation - time of flight - mass spectrometry (SELDI-TOF-MS). Mass spectrometric data were analysed by principal component analysis (PCA) and partial least squares (PLS). Using these multivariate projection methods we detected differences between tumours and normal tissue, radiation treatment-induced changes and temporal effects. 77 peaks whose intensity significantly changed after radiotherapy were discovered. The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation. The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes. In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

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Related in: MedlinePlus

Partial least squares model based on low mass range (2.5–20 kDa) data from the supernatant fraction of three normal tissue (grey) and three tumour tissue samples (black). All samples are from the untreated control group, killed 24 days after implantation and analysed in triplicate. (A) Partial least squares scores t1 vs t2. Q2Y(cum)=0.96 for the first two components. (B) First dimension PLS loadings (w1) plotted against mass variables. (C) Low mass range, mean spectrum derived from all nine spectra generated from the normal tissue samples. (D) Low mass range, mean spectrum derived from all nine spectra generated from the tumour tissue samples. The shading of the bars under the spectra in (C) and (D) indicates the sign and magnitude of the w1 values for each m/z variable: black corresponds to high positive w1 values (i.e. parts of the spectra where the tumour samples' spectra display substantially higher relative intensities than the normal samples' spectra), white corresponds to high negative w1 values (i.e. parts of the spectra where the normal samples' spectra have substantially higher relative intensities) and grey corresponds to low w1 values. The colour coding is the same in the blown-up region of the spectra (inset), but scaled according to the minimum and maximum w1 values associated with that region.
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fig2: Partial least squares model based on low mass range (2.5–20 kDa) data from the supernatant fraction of three normal tissue (grey) and three tumour tissue samples (black). All samples are from the untreated control group, killed 24 days after implantation and analysed in triplicate. (A) Partial least squares scores t1 vs t2. Q2Y(cum)=0.96 for the first two components. (B) First dimension PLS loadings (w1) plotted against mass variables. (C) Low mass range, mean spectrum derived from all nine spectra generated from the normal tissue samples. (D) Low mass range, mean spectrum derived from all nine spectra generated from the tumour tissue samples. The shading of the bars under the spectra in (C) and (D) indicates the sign and magnitude of the w1 values for each m/z variable: black corresponds to high positive w1 values (i.e. parts of the spectra where the tumour samples' spectra display substantially higher relative intensities than the normal samples' spectra), white corresponds to high negative w1 values (i.e. parts of the spectra where the normal samples' spectra have substantially higher relative intensities) and grey corresponds to low w1 values. The colour coding is the same in the blown-up region of the spectra (inset), but scaled according to the minimum and maximum w1 values associated with that region.

Mentions: Figure 2C and D show the mean spectra for the low mass region from both groups, derived from nine individual spectra from the supernatant fraction from untreated rats killed 24 days after implantation. Application of PLS-DA to the data yielded a complete separation of the two groups in the first dimension (Figure 2A). In Figure 2B the PLS-DA loadings (w1) are plotted for each variable on the m/z axis, to illustrate which variables, that is parts of the spectra, are the most important in the separation of the two groups. Variables on the m/z axis with a relatively high absolute w1 value were more important for the separation than those with a relatively low absolute value, meaning that high absolute values in w1 corresponded to m/z variables discriminating between the two groups.


Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment.

Wibom C, Pettersson F, Sjöström M, Henriksson R, Johansson M, Bergenheim AT - Br. J. Cancer (2006)

Partial least squares model based on low mass range (2.5–20 kDa) data from the supernatant fraction of three normal tissue (grey) and three tumour tissue samples (black). All samples are from the untreated control group, killed 24 days after implantation and analysed in triplicate. (A) Partial least squares scores t1 vs t2. Q2Y(cum)=0.96 for the first two components. (B) First dimension PLS loadings (w1) plotted against mass variables. (C) Low mass range, mean spectrum derived from all nine spectra generated from the normal tissue samples. (D) Low mass range, mean spectrum derived from all nine spectra generated from the tumour tissue samples. The shading of the bars under the spectra in (C) and (D) indicates the sign and magnitude of the w1 values for each m/z variable: black corresponds to high positive w1 values (i.e. parts of the spectra where the tumour samples' spectra display substantially higher relative intensities than the normal samples' spectra), white corresponds to high negative w1 values (i.e. parts of the spectra where the normal samples' spectra have substantially higher relative intensities) and grey corresponds to low w1 values. The colour coding is the same in the blown-up region of the spectra (inset), but scaled according to the minimum and maximum w1 values associated with that region.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2361353&req=5

fig2: Partial least squares model based on low mass range (2.5–20 kDa) data from the supernatant fraction of three normal tissue (grey) and three tumour tissue samples (black). All samples are from the untreated control group, killed 24 days after implantation and analysed in triplicate. (A) Partial least squares scores t1 vs t2. Q2Y(cum)=0.96 for the first two components. (B) First dimension PLS loadings (w1) plotted against mass variables. (C) Low mass range, mean spectrum derived from all nine spectra generated from the normal tissue samples. (D) Low mass range, mean spectrum derived from all nine spectra generated from the tumour tissue samples. The shading of the bars under the spectra in (C) and (D) indicates the sign and magnitude of the w1 values for each m/z variable: black corresponds to high positive w1 values (i.e. parts of the spectra where the tumour samples' spectra display substantially higher relative intensities than the normal samples' spectra), white corresponds to high negative w1 values (i.e. parts of the spectra where the normal samples' spectra have substantially higher relative intensities) and grey corresponds to low w1 values. The colour coding is the same in the blown-up region of the spectra (inset), but scaled according to the minimum and maximum w1 values associated with that region.
Mentions: Figure 2C and D show the mean spectra for the low mass region from both groups, derived from nine individual spectra from the supernatant fraction from untreated rats killed 24 days after implantation. Application of PLS-DA to the data yielded a complete separation of the two groups in the first dimension (Figure 2A). In Figure 2B the PLS-DA loadings (w1) are plotted for each variable on the m/z axis, to illustrate which variables, that is parts of the spectra, are the most important in the separation of the two groups. Variables on the m/z axis with a relatively high absolute w1 value were more important for the separation than those with a relatively low absolute value, meaning that high absolute values in w1 corresponded to m/z variables discriminating between the two groups.

Bottom Line: The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation.The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes.In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Oncology, University Hospital, Umeå, Sweden.

ABSTRACT
Radiotherapy is one of the mainstays of glioblastoma (GBM) treatment. This study aims to investigate and characterise differences in protein expression patterns in brain tumour tissue following radiotherapy, in order to gain a more detailed understanding of the biological effects. Rat BT4C glioma cells were implanted into the brain of two groups of 12 BDIX-rats. One group received radiotherapy (12 Gy single fraction). Protein expression in normal and tumour brain tissue, collected at four different time points after irradiation, were analysed using surface enhanced laser desorption/ionisation - time of flight - mass spectrometry (SELDI-TOF-MS). Mass spectrometric data were analysed by principal component analysis (PCA) and partial least squares (PLS). Using these multivariate projection methods we detected differences between tumours and normal tissue, radiation treatment-induced changes and temporal effects. 77 peaks whose intensity significantly changed after radiotherapy were discovered. The prompt changes in the protein expression following irradiation might help elucidate biological events induced by radiation. The combination of SELDI-TOF-MS with PCA and PLS seems to be well suited for studying these changes. In a further perspective these findings may prove to be useful in the development of new GBM treatment approaches.

Show MeSH
Related in: MedlinePlus