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Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.

Liao CC, Ward N, Marsh S, Arulampalam T, Norton JD - BMC Cancer (2010)

Bottom Line: Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks.A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ UK.

ABSTRACT

Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS).

Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method.

Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P < or = 0.01, false discovery rate < or = 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%). Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).

Conclusions: Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival.

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Heat map profile of marker peaks discriminating tumour from normal mucosa. The expression profiles and m/z values of the top 73 ranked peaks identified by Comparative Gene Marker Selection [28] (P = ≤ 0.01, FDR = ≤ 0.05) are depicted for all 64 tissue specimens.
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Figure 3: Heat map profile of marker peaks discriminating tumour from normal mucosa. The expression profiles and m/z values of the top 73 ranked peaks identified by Comparative Gene Marker Selection [28] (P = ≤ 0.01, FDR = ≤ 0.05) are depicted for all 64 tissue specimens.

Mentions: To quantitatively evaluate the differences between the protein expression profiles of tumour verses normal tissue, the Comparative Gene Marker Selection algorithm [28] was applied to the spectral data-set to determine the level of significance of difference between tumour and normal for each protein peak. Figure 2 shows the frequency distribution (occurrences) of protein peak P values (Feature P) that were binned in increments of 0.05. Above P = 0.05, the representation of protein peaks was fairly evenly distributed. However, nearly 100 peaks gave a P value < 0.05, indicating that a sizable fraction of proteins detected by MALDI-TOF mass spectrometry discriminate between tumour and normal colonic tissue. Applying a threshold of P ≤ 0.01, FDR ≤ 0.05, the expression profile of a total of 73 protein peaks was significantly different between tumour and normal tissue with 57 being up-regulated in normal tissue and 16 being up-regulated in tumour tissue. Figure 3 shows a heat-map profile of these 'marker peaks' and additional file 2 summarises their statistical features.


Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.

Liao CC, Ward N, Marsh S, Arulampalam T, Norton JD - BMC Cancer (2010)

Heat map profile of marker peaks discriminating tumour from normal mucosa. The expression profiles and m/z values of the top 73 ranked peaks identified by Comparative Gene Marker Selection [28] (P = ≤ 0.01, FDR = ≤ 0.05) are depicted for all 64 tissue specimens.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2927547&req=5

Figure 3: Heat map profile of marker peaks discriminating tumour from normal mucosa. The expression profiles and m/z values of the top 73 ranked peaks identified by Comparative Gene Marker Selection [28] (P = ≤ 0.01, FDR = ≤ 0.05) are depicted for all 64 tissue specimens.
Mentions: To quantitatively evaluate the differences between the protein expression profiles of tumour verses normal tissue, the Comparative Gene Marker Selection algorithm [28] was applied to the spectral data-set to determine the level of significance of difference between tumour and normal for each protein peak. Figure 2 shows the frequency distribution (occurrences) of protein peak P values (Feature P) that were binned in increments of 0.05. Above P = 0.05, the representation of protein peaks was fairly evenly distributed. However, nearly 100 peaks gave a P value < 0.05, indicating that a sizable fraction of proteins detected by MALDI-TOF mass spectrometry discriminate between tumour and normal colonic tissue. Applying a threshold of P ≤ 0.01, FDR ≤ 0.05, the expression profile of a total of 73 protein peaks was significantly different between tumour and normal tissue with 57 being up-regulated in normal tissue and 16 being up-regulated in tumour tissue. Figure 3 shows a heat-map profile of these 'marker peaks' and additional file 2 summarises their statistical features.

Bottom Line: Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks.A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ UK.

ABSTRACT

Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS).

Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method.

Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P < or = 0.01, false discovery rate < or = 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%). Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).

Conclusions: Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival.

Show MeSH
Related in: MedlinePlus