High-resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer

Background: Characterisation of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridisation studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics. Results: We performed an oligo array based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI and therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high grade ER negative breast cancer, characterised by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes (“hotspots”). We show that many of these hotspots are located in the phosphatome, kinome and chromatinome and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (HR=2.3 (1.3-4) P = 0.003) and time to distant metastasis (HR=2.6 (1.4-5.1) P = 0.004) independently of NPI. Conclusions: We present strong evidence for the existence of a novel subtype of high-grade ER negative tumors that is characterised by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.

[1]  Kenny Q. Ye,et al.  Novel patterns of genome rearrangement and their association with survival in breast cancer. , 2006, Genome research.

[2]  L. Chin,et al.  High-resolution characterization of the pancreatic adenocarcinoma genome , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[4]  V. P. Collins,et al.  Differential expression of selected histone modifier genes in human solid cancers , 2006, BMC Genomics.

[5]  I. Ellis,et al.  A gene-expression signature to predict survival in breast cancer across independent data sets , 2007, Oncogene.

[6]  Carlos Caldas,et al.  Mutations truncating the EP300 acetylase in human cancers , 2000, Nature Genetics.

[7]  Joanna M. Sasin,et al.  Protein Tyrosine Phosphatases in the Human Genome , 2004, Cell.

[8]  Céline Rouveirol,et al.  Bioinformatics Original Paper Computation of Recurrent Minimal Genomic Alterations from Array-cgh Data , 2022 .

[9]  S. Chin,et al.  Human and mouse oligonucleotide-based array CGH , 2005, Nucleic acids research.

[10]  R. Hruban,et al.  Alterations in pancreatic, biliary, and breast carcinomas support MKK4 as a genetically targeted tumor suppressor gene. , 1998, Cancer research.

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

[12]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[13]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[14]  T. Hunter,et al.  The Protein Kinase Complement of the Human Genome , 2002, Science.

[15]  D. Conrad,et al.  Global variation in copy number in the human genome , 2006, Nature.

[16]  M. West,et al.  Gene expression predictors of breast cancer outcomes , 2003, The Lancet.

[17]  Bing Zhang,et al.  GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies , 2004, BMC Bioinformatics.

[18]  Christian A. Rees,et al.  Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Wonshik Han,et al.  Genomic alterations identified by array comparative genomic hybridization as prognostic markers in tamoxifen-treated estrogen receptor-positive breast cancer , 2006, BMC Cancer.

[20]  C Caldas,et al.  Using array-comparative genomic hybridization to define molecular portraits of primary breast cancers , 2007, Oncogene.

[21]  Z. Szallasi,et al.  A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers , 2006, Nature Genetics.

[22]  K. Aldape,et al.  Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. , 2005, Cancer research.

[23]  L. Chin,et al.  High-resolution genomic profiles of human lung cancer. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[24]  I. Ellis,et al.  A 1 Mb minimal amplicon at 8p11–12 in breast cancer identifies new candidate oncogenes , 2005, Oncogene.

[25]  Philip M. Long,et al.  Breast cancer classification and prognosis based on gene expression profiles from a population-based study , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[26]  C. Hellberg,et al.  Protein-tyrosine phosphatases and cancer , 2006, Nature Reviews Cancer.

[27]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[28]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[29]  C. Caldas,et al.  Mutation analysis of CBP and PCAF reveals rare inactivating mutations in cancer cell lines but not in primary tumours , 2002, British Journal of Cancer.

[30]  Hidetoshi Shimodaira,et al.  Pvclust: an R package for assessing the uncertainty in hierarchical clustering , 2006, Bioinform..

[31]  Joel Greshock,et al.  High resolution genomic analysis of sporadic breast cancer using array-based comparative genomic hybridization , 2005, Breast Cancer Research.

[32]  I. Ellis,et al.  An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer , 2007, Genome Biology.

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

[34]  M. Wigler,et al.  Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.

[35]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[36]  I. Ellis,et al.  A consensus prognostic gene expression classifier for ER positive breast cancer , 2006, Genome Biology.

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

[38]  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.

[39]  F. Speleman,et al.  GAB2 is a novel target of 11q amplification in AML/MDS , 2006, Genes, chromosomes & cancer.

[40]  Jane Fridlyand,et al.  Bioinformatics Original Paper a Comparison Study: Applying Segmentation to Array Cgh Data for Downstream Analyses , 2022 .

[41]  Ajay N. Jain,et al.  Breast tumor copy number aberration phenotypes and genomic instability , 2006, BMC Cancer.

[42]  Ajay N. Jain,et al.  Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. , 2006, Cancer cell.

[43]  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.

[44]  Olivier Poch,et al.  A new look towards BAC-based array CGH through a comprehensive comparison with oligo-based array CGH , 2007, BMC Genomics.

[45]  Barbara J. Trask,et al.  Array Comparative Genomic Hybridization Analysis of Genomic Alterations in Breast Cancer Subtypes , 2004, Cancer Research.

[46]  L. Chin,et al.  High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. , 2006, Cancer cell.

[47]  G. Parmigiani,et al.  The Consensus Coding Sequences of Human Breast and Colorectal Cancers , 2006, Science.

[48]  Luke Hughes-Davies,et al.  EMSY Links the BRCA2 Pathway to Sporadic Breast and Ovarian Cancer , 2003, Cell.

[49]  Robert Tibshirani,et al.  Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene‐expression subtypes of breast cancer , 2006, Genes, chromosomes & cancer.

[50]  Eytan Domany,et al.  Relationship of gene expression and chromosomal abnormalities in colorectal cancer. , 2006, Cancer research.