Limits...
Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression.

Brozkova K, Budinska E, Bouchal P, Hernychova L, Knoflickova D, Valik D, Vyzula R, Vojtesek B, Nenutil R - Breast Cancer Res. (2008)

Bottom Line: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools.Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14.This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Masaryk Memorial Cancer Institute, Zluty kopec 7, 656 53 Brno, Czech Republic.

ABSTRACT

Introduction: Microarray-based gene expression profiling represents a major breakthrough for understanding the molecular complexity of breast cancer. cDNA expression profiles cannot detect changes in activities that arise from post-translational modifications, however, and therefore do not provide a complete picture of all biologically important changes that occur in tumors. Additional opportunities to identify and/or validate molecular signatures of breast carcinomas are provided by proteomic approaches. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools.

Methods: Whole tissue lysates of 105 breast carcinomas were analyzed on IMAC 30 ProteinChip Arrays (Bio-Rad, Hercules, CA, USA) using the ProteinChip Reader Model PBS IIc (Bio-Rad) and Ciphergen ProteinChip software (Bio-Rad, Hercules, CA, USA). Cluster analysis of protein spectra was performed to identify protein patterns potentially related to established clinicopathological variables and/or tumor markers.

Results: Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14. These tumor groups resembled closely luminal types A and B, basal and HER2-like carcinomas.

Conclusion: Our results show similar clustering of tumors to those provided by cDNA expression profiles of breast carcinomas. This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study. As SELDI-TOF MS provides different information from cDNA expression profiles, the results suggest the technique's potential to supplement and expand our knowledge of breast cancer, to identify novel biomarkers and to produce clinically useful classifications of breast carcinomas.

Show MeSH

Related in: MedlinePlus

Graphical representation of Spearman correlation matrix of 130 surface-enhanced laser desorption/ionization time-of-flight mass spectrometry peaks. Red color intensity, positive correlation; green color intensity, negative correlation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2481497&req=5

Figure 1: Graphical representation of Spearman correlation matrix of 130 surface-enhanced laser desorption/ionization time-of-flight mass spectrometry peaks. Red color intensity, positive correlation; green color intensity, negative correlation.

Mentions: IMAC 30 (immobilized metal affinity capture) ProteinChip arrays were used to analyze tissue lysates from 105 breast cancer patients. After data processing by Biomarker Wizard software, a total of 130 peaks were selected. Information about the peaks is presented in Additional file 1. The normalized linear intensities of peaks analyzed by hierarchical clustering revealed subgroups of peaks and of patients. The graphical representation of Spearman correlation matrix of peaks is shown in Figure 1. These data clearly demonstrate the groups of peaks that are highly positively correlated, indicating coexpression of these peaks in individual tumors. The hierarchical clustering combines peaks into two, three, or six potential groups, respectively, according to their level of positive correlation. In each of these categories we can find groups of adjacent correlated peaks, as apparent in Figure 1. The highest correlation can be found between peaks from 80 to 82, peaks from 99 to 105, 117 and 118, and peaks from 121 to 124, which form the first group of six categorizations. Descriptive statistics for peaks classified into the six groups are summarized in Table 2. Note that the groups are listed according to decreasing minimal correlation within the group. Using the Spearman correlation matrix to derive the distance matrix for hierarchical clustering, the categorization of patients was most strongly affected by groups of highly correlated peaks.


Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression.

Brozkova K, Budinska E, Bouchal P, Hernychova L, Knoflickova D, Valik D, Vyzula R, Vojtesek B, Nenutil R - Breast Cancer Res. (2008)

Graphical representation of Spearman correlation matrix of 130 surface-enhanced laser desorption/ionization time-of-flight mass spectrometry peaks. Red color intensity, positive correlation; green color intensity, negative correlation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2481497&req=5

Figure 1: Graphical representation of Spearman correlation matrix of 130 surface-enhanced laser desorption/ionization time-of-flight mass spectrometry peaks. Red color intensity, positive correlation; green color intensity, negative correlation.
Mentions: IMAC 30 (immobilized metal affinity capture) ProteinChip arrays were used to analyze tissue lysates from 105 breast cancer patients. After data processing by Biomarker Wizard software, a total of 130 peaks were selected. Information about the peaks is presented in Additional file 1. The normalized linear intensities of peaks analyzed by hierarchical clustering revealed subgroups of peaks and of patients. The graphical representation of Spearman correlation matrix of peaks is shown in Figure 1. These data clearly demonstrate the groups of peaks that are highly positively correlated, indicating coexpression of these peaks in individual tumors. The hierarchical clustering combines peaks into two, three, or six potential groups, respectively, according to their level of positive correlation. In each of these categories we can find groups of adjacent correlated peaks, as apparent in Figure 1. The highest correlation can be found between peaks from 80 to 82, peaks from 99 to 105, 117 and 118, and peaks from 121 to 124, which form the first group of six categorizations. Descriptive statistics for peaks classified into the six groups are summarized in Table 2. Note that the groups are listed according to decreasing minimal correlation within the group. Using the Spearman correlation matrix to derive the distance matrix for hierarchical clustering, the categorization of patients was most strongly affected by groups of highly correlated peaks.

Bottom Line: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools.Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14.This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study.

View Article: PubMed Central - HTML - PubMed

Affiliation: Masaryk Memorial Cancer Institute, Zluty kopec 7, 656 53 Brno, Czech Republic.

ABSTRACT

Introduction: Microarray-based gene expression profiling represents a major breakthrough for understanding the molecular complexity of breast cancer. cDNA expression profiles cannot detect changes in activities that arise from post-translational modifications, however, and therefore do not provide a complete picture of all biologically important changes that occur in tumors. Additional opportunities to identify and/or validate molecular signatures of breast carcinomas are provided by proteomic approaches. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools.

Methods: Whole tissue lysates of 105 breast carcinomas were analyzed on IMAC 30 ProteinChip Arrays (Bio-Rad, Hercules, CA, USA) using the ProteinChip Reader Model PBS IIc (Bio-Rad) and Ciphergen ProteinChip software (Bio-Rad, Hercules, CA, USA). Cluster analysis of protein spectra was performed to identify protein patterns potentially related to established clinicopathological variables and/or tumor markers.

Results: Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14. These tumor groups resembled closely luminal types A and B, basal and HER2-like carcinomas.

Conclusion: Our results show similar clustering of tumors to those provided by cDNA expression profiles of breast carcinomas. This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study. As SELDI-TOF MS provides different information from cDNA expression profiles, the results suggest the technique's potential to supplement and expand our knowledge of breast cancer, to identify novel biomarkers and to produce clinically useful classifications of breast carcinomas.

Show MeSH
Related in: MedlinePlus