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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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Unsupervised hierarchical cluster analysis of tumour and normal mucosa spectra. The dendrogram and heatmap show the clustering of Tumour (T) and normal mucosa (NM) spectra using Euclidean correlation as the column distance measure with pair-wise average linkage as the clustering method. Row clustering (not shown) used Spearman's rank correlation as distance measure with pair-wise complete linkage as the clustering method. Specimens are colour-coded as green (NM) and red (T).
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Figure 1: Unsupervised hierarchical cluster analysis of tumour and normal mucosa spectra. The dendrogram and heatmap show the clustering of Tumour (T) and normal mucosa (NM) spectra using Euclidean correlation as the column distance measure with pair-wise average linkage as the clustering method. Row clustering (not shown) used Spearman's rank correlation as distance measure with pair-wise complete linkage as the clustering method. Specimens are colour-coded as green (NM) and red (T).

Mentions: Table 1 summarises the clinico-pathological data for the 36 CRC patients from whom specimens were obtained. In most cases, spectra of adequate quality from matching pairs of tumour and adjacent normal mucosa were obtained. However, some tissue protein preparations consistently yielded spectra of poor quality or that were poorly reproducible (see Methods section); these were excluded from the analysis. The resulting 64 spectra, representing 31 tumour and 33 normal mucosa specimens, generated a total of 265 protein peaks in the mass range 1800-16000Da. Illustrative examples of raw MALDI-TOF spectral profiles are shown in additional file 1. Although the overall intensity profile of individual protein peaks was very heterogeneous across different specimens, unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups (Figure 1) consistent with major differences in the tumour verses normal protein expression profiles.


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)

Unsupervised hierarchical cluster analysis of tumour and normal mucosa spectra. The dendrogram and heatmap show the clustering of Tumour (T) and normal mucosa (NM) spectra using Euclidean correlation as the column distance measure with pair-wise average linkage as the clustering method. Row clustering (not shown) used Spearman's rank correlation as distance measure with pair-wise complete linkage as the clustering method. Specimens are colour-coded as green (NM) and red (T).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Unsupervised hierarchical cluster analysis of tumour and normal mucosa spectra. The dendrogram and heatmap show the clustering of Tumour (T) and normal mucosa (NM) spectra using Euclidean correlation as the column distance measure with pair-wise average linkage as the clustering method. Row clustering (not shown) used Spearman's rank correlation as distance measure with pair-wise complete linkage as the clustering method. Specimens are colour-coded as green (NM) and red (T).
Mentions: Table 1 summarises the clinico-pathological data for the 36 CRC patients from whom specimens were obtained. In most cases, spectra of adequate quality from matching pairs of tumour and adjacent normal mucosa were obtained. However, some tissue protein preparations consistently yielded spectra of poor quality or that were poorly reproducible (see Methods section); these were excluded from the analysis. The resulting 64 spectra, representing 31 tumour and 33 normal mucosa specimens, generated a total of 265 protein peaks in the mass range 1800-16000Da. Illustrative examples of raw MALDI-TOF spectral profiles are shown in additional file 1. Although the overall intensity profile of individual protein peaks was very heterogeneous across different specimens, unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups (Figure 1) consistent with major differences in the tumour verses normal protein expression profiles.

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