Discovery and validation of breast cancer subtypes

BackgroundPrevious studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+.ResultsBased upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability.ConclusionAs a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.

[1]  David Botstein,et al.  The Stanford Microarray Database , 2001, Nucleic Acids Res..

[2]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[3]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Howard Y. Chang,et al.  Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[5]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  David Botstein,et al.  Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. , 2004, Molecular biology of the cell.

[7]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Schenker,et al.  hAG-2 and hAG-3, human homologues of genes involved in differentiation, are associated with oestrogen receptor-positive breast tumours and interact with metastasis gene C4.4a and dystroglycan , 2003, British Journal of Cancer.

[9]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[10]  T. Hastie,et al.  Optimization and evaluation of T7 based RNA linear amplification protocols for cDNA microarray analysis , 2002, BMC Genomics.

[11]  Discovery and validation of breast cancer subtypes , 2007, BMC Genomics.

[12]  Helena R. Chang,et al.  Histopathologic Characteristics Predicting HER‐2/neu Amplification in Breast Cancer , 2005, The breast journal.

[13]  Amy V Kapp,et al.  Are clusters found in one dataset present in another dataset? , 2007, Biostatistics.

[14]  G Leclercq,et al.  About GATA3, HNF3A, and XBP1, three genes co-expressed with the oestrogen receptor-α gene (ESR1) in breast cancer , 2004, Molecular and Cellular Endocrinology.

[15]  Geoffrey J. McLachlan,et al.  A mixture model-based approach to the clustering of microarray expression data , 2002, Bioinform..