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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2010
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| Online Access: | http://hdl.handle.net/1721.1/59307 |
| _version_ | 1826194722867118080 |
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| author | Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Ganesan, Shridar DeLisi, Charles Bhanot, Gyan Dalgin, Gul S. Mesirov, Jill P. |
| author2 | Broad Institute of MIT and Harvard |
| author_facet | Broad Institute of MIT and Harvard Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Ganesan, Shridar DeLisi, Charles Bhanot, Gyan Dalgin, Gul S. Mesirov, Jill P. |
| author_sort | Alexe, Gabriela |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T10:01:08Z |
| format | Article |
| id | mit-1721.1/59307 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:01:08Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/593072022-09-30T18:19:10Z Portraits of breast cancer progression Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Ganesan, Shridar DeLisi, Charles Bhanot, Gyan Dalgin, Gul S. Mesirov, Jill P. Broad Institute of MIT and Harvard Koch Institute for Integrative Cancer Research at MIT Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Mesirov, Jill P. Evaluation studies Algorithms Artificial intelligence Breast neoplasms, diagnosis Breast neoplasms, metabolism Carcinoma, ductal, diagnosis Carcinoma, ductal, metabolism Diagnosis, computer-assisted methods Disease progression Female Gene expression profiling, methods Humans Neoplasm proteins, analysis Oligonucleotide array sequence analysis, methods Pattern recognition, automated methods Principal component analysis Reproducibility of results Sensitivity and specificity Neoplasm proteins Tumor markers, biological, analysis 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. 2010-10-14T12:28:56Z 2010-10-14T12:28:56Z 2007-08 2007-01 2010-09-03T16:07:05Z Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/59307 Dalgin, Gul S., et al. (2007). Portraits of breast cancer progression. BMC bioinformatics 8:291/1-16. 17683614 en http://dx.doi.org/10.1186/1471-2105-8-291 BMC Bioinformatics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Dalgin et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Evaluation studies Algorithms Artificial intelligence Breast neoplasms, diagnosis Breast neoplasms, metabolism Carcinoma, ductal, diagnosis Carcinoma, ductal, metabolism Diagnosis, computer-assisted methods Disease progression Female Gene expression profiling, methods Humans Neoplasm proteins, analysis Oligonucleotide array sequence analysis, methods Pattern recognition, automated methods Principal component analysis Reproducibility of results Sensitivity and specificity Neoplasm proteins Tumor markers, biological, analysis Alexe, Gabriela Scanfeld, Daniel Tamayo, Pablo Ganesan, Shridar DeLisi, Charles Bhanot, Gyan Dalgin, Gul S. Mesirov, Jill P. 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 | Evaluation studies Algorithms Artificial intelligence Breast neoplasms, diagnosis Breast neoplasms, metabolism Carcinoma, ductal, diagnosis Carcinoma, ductal, metabolism Diagnosis, computer-assisted methods Disease progression Female Gene expression profiling, methods Humans Neoplasm proteins, analysis Oligonucleotide array sequence analysis, methods Pattern recognition, automated methods Principal component analysis Reproducibility of results Sensitivity and specificity Neoplasm proteins Tumor markers, biological, analysis |
| url | http://hdl.handle.net/1721.1/59307 |
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