Disease-specific genomic analysis: identifying the signature of pathologic biology

MOTIVATION Genomic high-throughput technology generates massive data, providing opportunities to understand countless facets of the functioning genome. It also raises profound issues in identifying data relevant to the biology being studied. RESULTS We introduce a method for the analysis of pathologic biology that unravels the disease characteristics of high dimensional data. The method, disease-specific genomic analysis (DSGA), is intended to precede standard techniques like clustering or class prediction, and enhance their performance and ability to detect disease. DSGA measures the extent to which the disease deviates from a continuous range of normal phenotypes, and isolates the aberrant component of data. In several microarray cancer datasets, we show that DSGA outperforms standard methods. We then use DSGA to highlight a novel subdivision of an important class of genes in breast cancer, the estrogen receptor (ER) cluster. We also identify new markers distinguishing ductal and lobular breast cancers. Although our examples focus on microarrays, DSGA generalizes to any high dimensional genomic/proteomic data.

[1]  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.

[2]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[3]  Ash A. Alizadeh,et al.  'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns , 2000, Genome Biology.

[4]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[5]  P. Rudland,et al.  Significance of the metastasis-inducing protein AGR2 for outcome in hormonally treated breast cancer patients , 2006, British Journal of Cancer.

[6]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Arul M Chinnaiyan,et al.  Genes regulated by estrogen in breast tumor cells in vitro are similarly regulated in vivo in tumor xenografts and human breast tumors , 2006, Genome Biology.

[8]  Debashis Ghosh,et al.  Mixture models for assessing differential expression in complex tissues using microarray data , 2004, Bioinform..

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

[10]  Kamesh Munagala,et al.  Cancer characterization and feature set extraction by discriminative margin clustering , 2004, BMC Bioinformatics.

[11]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

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

[13]  D. Botstein,et al.  Gene expression patterns in human liver cancers. , 2002, Molecular biology of the cell.

[14]  W. Krzanowski,et al.  Cross-Validatory Choice of the Number of Components From a Principal Component Analysis , 1982 .

[15]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[16]  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.

[17]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[18]  D. Botstein,et al.  Variation in gene expression patterns in human gastric cancers. , 2003, Molecular biology of the cell.

[19]  G. S. Johnson,et al.  An Information-Intensive Approach to the Molecular Pharmacology of Cancer , 1997, Science.

[20]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[21]  W J Krzanowski,et al.  Cross-Validation for Choosing the Number of Important Components in Principal Component Analysis. , 1995, Multivariate behavioral research.

[22]  Alok J. Saldanha,et al.  Java Treeview - extensible visualization of microarray data , 2004, Bioinform..

[23]  George Stephanopoulos,et al.  Mapping physiological states from microarray expression measurements , 2002, Bioinform..

[24]  Céline Lefebvre,et al.  From the Cover: Location analysis of estrogen receptor alpha target promoters reveals that FOXA1 defines a domain of the estrogen response. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  J. Foekens,et al.  Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  Zhiyuan Hu,et al.  Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[27]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Céline Lefebvre,et al.  Location analysis of estrogen receptor target promoters reveals that FOXA 1 defines a domain of the estrogen response , 2005 .

[29]  R. Tibshirani,et al.  Prediction by Supervised Principal Components , 2006 .

[30]  C. Perou,et al.  Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. , 2005, Cancer research.

[31]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[33]  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.

[34]  J. Foekens,et al.  Laser microdissection and microarray analysis of breast tumors reveal ER-α related genes and pathways , 2006, Oncogene.

[35]  R. Strausberg,et al.  Mutation of GATA3 in human breast tumors , 2004, Oncogene.

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

[37]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[38]  M Vingron,et al.  Identification and Classification of Differentially Expressed Genes in Renal Cell Carcinoma by Expression Profiling on a Global Human 31 , 500-Element cDNA Array , 2001 .

[39]  Carsten O. Peterson,et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.