Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction. This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of previously published method. The main focus of the paper, however, is to use the context specific gene regulatory networks to classify the tumors into clinically relevant sub-groups and providing candidates for a finer sub-grouping of the previously known intrinsic tumors with focus on basal-like tumors. Further analysis of pathway enrichment of the key contexts provides an understanding of the biological mechanism for identified subtypes of breast cancers.

[1]  H. Ishwaran,et al.  Lung metastasis genes couple breast tumor size and metastatic spread , 2007, Proceedings of the National Academy of Sciences.

[2]  J. Bergh,et al.  Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.

[3]  Michael L. Gatza,et al.  A pathway-based classification of human breast cancer , 2010, Proceedings of the National Academy of Sciences.

[4]  Steve Horvath,et al.  Higher levels of GATA3 predict better survival in women with breast cancer. , 2010, Human pathology.

[5]  A. Nobel,et al.  Concordance among Gene-Expression – Based Predictors for Breast Cancer , 2011 .

[6]  Ling Yin,et al.  Notch signaling: emerging molecular targets for cancer therapy. , 2010, Biochemical pharmacology.

[7]  M Monden,et al.  Selection of mRNA markers for detection of lymph node micrometastases in breast cancer patients. , 2000, Oncology reports.

[8]  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.

[9]  H. Kölbl,et al.  The humoral immune system has a key prognostic impact in node-negative breast cancer. , 2008, Cancer research.

[10]  Tanja Cufer,et al.  The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy , 2009, Breast Cancer Research and Treatment.

[11]  Andy J. Minn,et al.  Genes that mediate breast cancer metastasis to lung , 2005, Nature.

[12]  Seungchan Kim,et al.  Mining molecular contexts of cancer via in-silico conditioning. , 2007, Computational systems bioinformatics. Computational Systems Bioinformatics Conference.

[13]  P. Bonnier,et al.  Comparison of the prognosis indication of VEGFR-1 and VEGFR-2 and Tie2 receptor expression in breast carcinoma. , 2005, International journal of oncology.

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

[15]  L. Holmberg,et al.  Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts , 2005, Breast Cancer Research.

[16]  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.

[17]  L. Placanica,et al.  Characterization of an atypical gamma-secretase complex from hematopoietic origin. , 2010, Biochemistry.

[18]  Seungchan Kim,et al.  Clustering Context-Specific Gene Regulatory Networks , 2010, Pacific Symposium on Biocomputing.

[19]  M. Brun,et al.  Conditioning-Based Modeling of Contextual Genomic Regulation , 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  Clifford A. Meyer,et al.  Genome-wide analysis of estrogen receptor binding sites , 2006, Nature Genetics.

[21]  A Prat,et al.  Notch-associated expression profiles in basal-like and claudin-low breast cancer molecular subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  John D. Storey.  A direct approach to false discovery rates , 2002 .

[23]  S. Dongen.  A cluster algorithm for graphs , 2000 .

[24]  Joshy George,et al.  Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. , 2006, Cancer research.

[25]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  P. Jaccard.  Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines , 1901 .

[27]  Michael L. Bittner,et al.  Context-Specific Gene Regulations in Cancer Gene Expression Data , 2009, Pacific Symposium on Biocomputing.

[28]  Jason I. Herschkowitz,et al.  Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer , 2010, Breast Cancer Research.

[29]  S. Dongen.  Graph clustering by flow simulation , 2000 .