ROCK: a resource for integrative breast cancer data analysis

Given the steady increase in breast cancer rates in both the developed and developing world, there has been a concerted research effort undertaken worldwide to understand the molecular mechanisms underpinning the disease. The data generated from numerous clinical trials and experimental studies shed light on different aspects of the disease. We present a new version of the ROCK database (rock.icr.ac.uk), which integrates such diverse data types allowing unique analyses of published breast cancer experimental data. We have added several new data types and analysis modules to ROCK, which allow the user to interactively query and research the huge amounts of available experimental data and perform complex correlations across studies and data types such as gene expression, genomic copy number aberrations, micro RNA expression, RNA interference, survival analysis, clinical annotation and signalling protein networks. We present the recent and major functional updates and enhancements to the ROCK resource, including new analysis modules and microRNA and NGS data integration, and illustrate how ROCK can be used to confirm known experimental results as well as generate novel leads and new experimental hypotheses using the Wnt signalling cell surface receptor FZD7 and the Myc oncogene. ROCK provides a unique breast cancer analysis platform of integrated experimental datasets at the genomic, transcriptomic and proteomic level. This paper presents how ROCK has transitioned from being simply a database to an interactive resource useful to the broader breast cancer research community in our effort to facilitate research into the underlying molecular mechanisms of breast cancer.

[1]  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.

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

[3]  Hailong Wu,et al.  p53 represses c-Myc through induction of the tumor suppressor miR-145 , 2009, Proceedings of the National Academy of Sciences.

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

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

[6]  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.

[7]  Daniel Rios,et al.  Ensembl 2011 , 2010, Nucleic Acids Res..

[8]  K. Henrick,et al.  Inference of macromolecular assemblies from crystalline state. , 2007, Journal of molecular biology.

[9]  Gianluca Bontempi,et al.  Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen , 2008, BMC Genomics.

[10]  Sandhya Rani,et al.  Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..

[11]  Robert D. Finn,et al.  InterPro in 2011: new developments in the family and domain prediction database , 2011, Nucleic acids research.

[12]  Sam Griffiths-Jones,et al.  The microRNA Registry , 2004, Nucleic Acids Res..

[13]  M. Teitell,et al.  Expression of sprouty2 inhibits B-cell proliferation and is epigenetically silenced in mouse and human B-cell lymphomas. , 2009, Blood.

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

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

[16]  Ana Kozomara,et al.  miRBase: integrating microRNA annotation and deep-sequencing data , 2010, Nucleic Acids Res..

[17]  Borisas Bursteinas,et al.  ROCK: a breast cancer functional genomics resource , 2010, Breast Cancer Research and Treatment.

[18]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[19]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[20]  Anjali J. Koppal,et al.  Supplementary data: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites , 2010 .

[21]  Bissan Al-Lazikani,et al.  canSAR: an integrated cancer public translational research and drug discovery resource , 2011, Nucleic Acids Res..

[22]  John W M Martens,et al.  Four miRNAs associated with aggressiveness of lymph node-negative, estrogen receptor-positive human breast cancer , 2008, Proceedings of the National Academy of Sciences.

[23]  Edith A Perez,et al.  MicroRNA signatures: clinical biomarkers for the diagnosis and treatment of breast cancer. , 2011, Trends in molecular medicine.

[24]  M. Zvelebil,et al.  Transcriptome analysis of embryonic mammary cells reveals insights into mammary lineage establishment , 2011, Breast Cancer Research.

[25]  Rafael C. Jimenez,et al.  The IntAct molecular interaction database in 2012 , 2011, Nucleic Acids Res..

[26]  Janet Kelso,et al.  Transcription Factors Are Targeted by Differentially Expressed miRNAs in Primates , 2012, Genome biology and evolution.

[27]  A. Ashworth,et al.  A high-resolution integrated analysis of genetic and expression profiles of breast cancer cell lines , 2009, Breast Cancer Research and Treatment.

[28]  S A Forbes,et al.  The Catalogue of Somatic Mutations in Cancer (COSMIC) , 2008, Current protocols in human genetics.

[29]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[30]  A. Ashworth,et al.  Integrative molecular and functional profiling of ERBB2-amplified breast cancers identifies new genetic dependencies , 2014, Oncogene.

[31]  Yi-Hsuan Chen,et al.  miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes , 2007, Nucleic Acids Res..

[32]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

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

[34]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[35]  A. Lal,et al.  MicroRNAs and their target gene networks in breast cancer , 2010, Breast Cancer Research.

[36]  J. Bergh,et al.  Identification of molecular apocrine breast tumours by microarray analysis , 2005, Breast Cancer Research.

[37]  John N Weinstein,et al.  A stromal gene signature associated with inflammatory breast cancer , 2008, International journal of cancer.

[38]  J. Ross,et al.  Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  S. Sleijfer,et al.  MicroRNA-30c expression level is an independent predictor of clinical benefit of endocrine therapy in advanced estrogen receptor positive breast cancer , 2011, Breast Cancer Research and Treatment.

[40]  A. Ashworth,et al.  Tiling Path Genomic Profiling of Grade 3 Invasive Ductal Breast Cancers , 2009, Clinical Cancer Research.

[41]  Leming Shi,et al.  Effect of training-sample size and classification difficulty on the accuracy of genomic predictors , 2010, Breast Cancer Research.

[42]  Doron Betel,et al.  The microRNA.org resource: targets and expression , 2007, Nucleic Acids Res..

[43]  K. Zhang,et al.  FZD7 has a critical role in cell proliferation in triple negative breast cancer , 2011, Oncogene.

[44]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[45]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[46]  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.

[47]  Elizabeth Iorns,et al.  Integrated Functional, Gene Expression and Genomic Analysis for the Identification of Cancer Targets , 2009, PloS one.

[48]  E. Prochownik,et al.  MYC oncogenes and human neoplastic disease , 1999, Oncogene.

[49]  Maria Victoria Schneider,et al.  MINT: a Molecular INTeraction database. , 2002, FEBS letters.

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

[51]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

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

[53]  P. Sebastiani,et al.  Gene expression in histologically normal epithelium from breast cancer patients and from cancer-free prophylactic mastectomy patients shares a similar profile , 2010, British Journal of Cancer.

[54]  Johannes Goll,et al.  Protein interaction data curation: the International Molecular Exchange (IMEx) consortium , 2012, Nature Methods.

[55]  T. Barrette,et al.  Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. , 2007, Neoplasia.

[56]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[57]  Alan Mackay,et al.  Functional viability profiles of breast cancer. , 2011, Cancer discovery.

[58]  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.