The UCSC cancer genomics browser: update 2011

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple data sets can be viewed simultaneously as coordinated ‘heatmap tracks’ to compare across studies or different data modalities. Users can order, filter, aggregate, classify and display data interactively based on any given feature set including clinical features, annotated biological pathways and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within all available data sets. The browser hosts a growing body of publicly available cancer genomics data from a variety of cancer types, including data generated from the Cancer Genome Atlas project. Multiple consortiums use the browser on confidential prepublication data enabled by private installations. Many new features have been added, including the hgMicroscope tumor image viewer, hgSignature for real-time genomic signature evaluation on any browser track, and ‘PARADIGM’ pathway tracks to display integrative pathway activities. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser’s rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data.

[1]  T. Golub,et al.  Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma , 2005, Nature.

[2]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[3]  T. Hunter,et al.  Signaling—2000 and Beyond , 2000, Cell.

[4]  A. Levine p53, the Cellular Gatekeeper for Growth and Division , 1997, Cell.

[5]  David Haussler,et al.  The UCSC genome browser database: update 2007 , 2006, Nucleic Acids Res..

[6]  M. Ellis,et al.  ESR1 gene amplification in breast cancer: a common phenomenon? , 2008, Nature Genetics.

[7]  Joe W. Gray,et al.  Translating insights from the cancer genome into clinical practice , 2008, Nature.

[8]  R. Weinberg Oncogenes and tumor suppressor genes , 1988, CA: a cancer journal for clinicians.

[9]  L. Esserman Neoadjuvant chemotherapy for primary breast cancer: Lessons learned and opportunities to optimize therapy , 2004, Annals of surgical oncology.

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

[11]  Joel S. Parker,et al.  Adjustment of systematic microarray data biases , 2004, Bioinform..

[12]  W. Hahn,et al.  Modelling the molecular circuitry of cancer , 2002, Nature Reviews Cancer.

[13]  C. Chelala,et al.  Genome-wide DNA copy number analysis in pancreatic cancer using high-density single nucleotide polymorphism arrays , 2008, Oncogene.

[14]  Kenneth H. Buetow,et al.  PID: the Pathway Interaction Database , 2008, Nucleic Acids Res..

[15]  C. Sherr Cancer Cell Cycles , 1996, Science.

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

[17]  Ting Wang,et al.  The UCSC Cancer Genomics Browser , 2009, Nature Methods.

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

[19]  C. Sherr,et al.  Principles of Tumor Suppression , 2004, Cell.

[20]  K. Kinzler,et al.  Cancer genes and the pathways they control , 2004, Nature Medicine.

[21]  J. Fridlyand,et al.  Deletion of chromosome 11q predicts response to anthracycline-based chemotherapy in early breast cancer. , 2007, Cancer research.

[22]  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.

[23]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[24]  D. Busam,et al.  An Integrated Genomic Analysis of Human Glioblastoma Multiforme , 2008, Science.

[25]  Mary Goldman,et al.  The UCSC Genome Browser database: update 2011 , 2010, Nucleic Acids Res..

[26]  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.

[27]  Wen-Lin Kuo,et al.  A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. , 2006, Cancer cell.

[28]  J. Costello,et al.  Genome-epigenome interactions in cancer. , 2007, Human molecular genetics.

[29]  Eric S. Lander,et al.  Integrative Genomic Approaches Identify IKBKE as a Breast Cancer Oncogene , 2007, Cell.

[30]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[31]  J. Broach,et al.  Oncogenes and cell proliferation. , 1995, Current opinion in genetics & development.

[32]  Li Li,et al.  High‐resolution genomic and expression analyses of copy number alterations in breast tumors , 2008, Genes, chromosomes & cancer.

[33]  Lajos Pusztai,et al.  Gene-expression signatures in breast cancer. , 2009, The New England journal of medicine.

[34]  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.

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

[36]  Derek Y. Chiang,et al.  Characterizing the cancer genome in lung adenocarcinoma , 2007, Nature.

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

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

[39]  J. Broach,et al.  Oncogenes and cell proliferation. , 1995, Current opinion in genetics & development.

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

[41]  S. Tavaré,et al.  High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer , 2007, Genome Biology.