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Portraits of breast cancer progression

BACKGROUND: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids t...

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Autores principales: Dalgin, Gul S, Alexe, Gabriela, Scanfeld, Daniel, Tamayo, Pablo, Mesirov, Jill P, Ganesan, Shridar, DeLisi, Charles, Bhanot, Gyan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978212/
https://www.ncbi.nlm.nih.gov/pubmed/17683614
http://dx.doi.org/10.1186/1471-2105-8-291
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author Dalgin, Gul S
Alexe, Gabriela
Scanfeld, Daniel
Tamayo, Pablo
Mesirov, Jill P
Ganesan, Shridar
DeLisi, Charles
Bhanot, Gyan
author_facet Dalgin, Gul S
Alexe, Gabriela
Scanfeld, Daniel
Tamayo, Pablo
Mesirov, Jill P
Ganesan, Shridar
DeLisi, Charles
Bhanot, Gyan
author_sort Dalgin, Gul S
collection PubMed
description BACKGROUND: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems. RESULTS: We illustrate the method on a public microarray dataset from 36 breast cancer patients of whom 31 were diagnosed with at least two of three pathological stages of disease (atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Our method identifies an optimum set of genes and divides the samples into stable clusters which correlate with clinical classification into Luminal, Basal-like and Her2+ subtypes. Our analysis reveals a hierarchical portrait of breast cancer progression and identifies genes and pathways for each stage, grade and subtype. An intriguing observation is that the disease phenotype is distinguishable in ADH and progresses along distinct pathways for each subtype. The genetic signature for disease heterogeneity across subtypes is greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes have distinct progression pathways. Our method identifies six disease subtype and one normal clusters. The first split separates the normal samples from the cancer samples. Next, the cancer cluster splits into low grade (pathological grades 1 and 2) and high grade (pathological grades 2 and 3) while the normal cluster is unchanged. Further, the low grade cluster splits into two subclusters and the high grade cluster into four. The final six disease clusters are mapped into one Luminal A, three Luminal B, one Basal-like and one Her2+. CONCLUSION: We confirm that the cancer phenotype can be identified in early stage because the genes altered in this stage progressively alter further as the disease progresses through DCIS into IDC. We identify six subtypes of disease which have distinct genetic signatures and remain separated in the clustering hierarchy. Our findings suggest that the heterogeneity of disease across subtypes is higher than the heterogeneity of the disease progression within a subtype, indicating that the subtypes are in fact distinct diseases.
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spelling pubmed-19782122007-09-18 Portraits of breast cancer progression Dalgin, Gul S Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Mesirov, Jill P Ganesan, Shridar DeLisi, Charles Bhanot, Gyan BMC Bioinformatics Research Article BACKGROUND: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems. RESULTS: We illustrate the method on a public microarray dataset from 36 breast cancer patients of whom 31 were diagnosed with at least two of three pathological stages of disease (atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Our method identifies an optimum set of genes and divides the samples into stable clusters which correlate with clinical classification into Luminal, Basal-like and Her2+ subtypes. Our analysis reveals a hierarchical portrait of breast cancer progression and identifies genes and pathways for each stage, grade and subtype. An intriguing observation is that the disease phenotype is distinguishable in ADH and progresses along distinct pathways for each subtype. The genetic signature for disease heterogeneity across subtypes is greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes have distinct progression pathways. Our method identifies six disease subtype and one normal clusters. The first split separates the normal samples from the cancer samples. Next, the cancer cluster splits into low grade (pathological grades 1 and 2) and high grade (pathological grades 2 and 3) while the normal cluster is unchanged. Further, the low grade cluster splits into two subclusters and the high grade cluster into four. The final six disease clusters are mapped into one Luminal A, three Luminal B, one Basal-like and one Her2+. CONCLUSION: We confirm that the cancer phenotype can be identified in early stage because the genes altered in this stage progressively alter further as the disease progresses through DCIS into IDC. We identify six subtypes of disease which have distinct genetic signatures and remain separated in the clustering hierarchy. Our findings suggest that the heterogeneity of disease across subtypes is higher than the heterogeneity of the disease progression within a subtype, indicating that the subtypes are in fact distinct diseases. BioMed Central 2007-08-06 /pmc/articles/PMC1978212/ /pubmed/17683614 http://dx.doi.org/10.1186/1471-2105-8-291 Text en Copyright © 2007 Dalgin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Dalgin, Gul S
Alexe, Gabriela
Scanfeld, Daniel
Tamayo, Pablo
Mesirov, Jill P
Ganesan, Shridar
DeLisi, Charles
Bhanot, Gyan
Portraits of breast cancer progression
title Portraits of breast cancer progression
title_full Portraits of breast cancer progression
title_fullStr Portraits of breast cancer progression
title_full_unstemmed Portraits of breast cancer progression
title_short Portraits of breast cancer progression
title_sort portraits of breast cancer progression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978212/
https://www.ncbi.nlm.nih.gov/pubmed/17683614
http://dx.doi.org/10.1186/1471-2105-8-291
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