The Cancer Genome Atlas (TCGA) project holds promise for a comprehensive understanding of human cancer through the application of genomic technologies. However, current cancer genomic analytical and visualization technologies still have many limitations that will likely prevent investigators from taking full advantage of this resource. The proposed UCSC-Buck Institute Genome Data Analysis Center will support an integrative analysis of TCGA data for all surveyed cancer types throughout the project. The major components of the pipeline are a pathway-centric multi-layer machine learning tool called Biolntegrator, a genome rearrangement detector for next-gen sequencing data, and the tightly coupled UCSC browser tool suite.
We aim to detect cancer-associated molecular alterations and the biological pathways that are perturbed by them in tumor samples. Samples will then be classified into clinically relevant categories based on pathway perturbations rather than perturbations of individual genes, which we believe will be more robust, biologically meaningful and clinically accurate. Using Biolntegrator and the associated tools, we will further integrate TCGA data with datasets from external studies, including cell line studies, animal studies and clinical trials, to identify (1) cancer-associated molecular alterations;(2) dysregulated pathways and signatures useful in clinical diagnosis, prognosis, and drug response prediction;and (3) gene targets for the development of novel therapeutics. These results will provide the basis for a refined patient stratification in therapy and will generate new hypotheses for translational research. The tightly coupled UCSC browser suite, which will be enhanced to accommodate the needs of the TCGA project, includes the UCSC Cancer Genomics Browser for visualizing TCGA cancer genomics, clinical data, and analysis results;the UCSC Tumor Browser for displaying tumor genome rearrangements and other tumor mutations;and the UCSC Human Genome Browser for integrating the data with human genome annotations and information gleaned from other projects such as ENCODE and the NIH Epigenomics Roadmap Initiative. The browser resource, hosting this rapidly growing body of cancer genomics data, will enable investigators to perform interactive in-silico experiments to test new hypotheses derived from the TCGA data. Collectively, these proposed tools will enable cancer researchers to better explore the breadth and depth of the TCGA resources and to further characterize molecular pathways that influence cellular dynamics and stability in cancer. Ultimately, insights gained by applying these tools will advance our knowledge of human cancer biology and stimulate the discovery of new prognostic and diagnostic markers, leading to new therapeutic and preventative strategies.

Public Health Relevance

The UCSC-Buck Institute Cancer Genome Data Analysis Center aims to analyze the TCGA project data to identify (1) cancer-associated molecular alterations;(2) dysregulated pathway signatures that can be used in clinical diagnosis, prognosis, and drug response prediction;and (3) candidate gene targets for the development of novel therapeutics. Insights learned from this endeavor will advance the knowledge of cancer and human biology, and will enhance cancer treatment and prevention by personalizing it to the genetic background of the patient and the mutations present in the tumor.

Agency
National Institute of Health (NIH)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
3U24CA143858-05S1
Application #
8827877
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Yang, Liming
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California Santa Cruz
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Santa Cruz
State
CA
Country
United States
Zip Code
95064
Cancer Genome Atlas Research Network; Linehan, W Marston; Spellman, Paul T et al. (2016) Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. N Engl J Med 374:135-45
Si, H; Lu, H; Yang, X et al. (2016) TNF-α modulates genome-wide redistribution of ΔNp63α/TAp73 and NF-κB cREL interactive binding on TP53 and AP-1 motifs to promote an oncogenic gene program in squamous cancer. Oncogene 35:5781-5794
Zheng, Siyuan; Cherniack, Andrew D; Dewal, Ninad et al. (2016) Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma. Cancer Cell 29:723-36
Sokolov, Artem; Carlin, Daniel E; Paull, Evan O et al. (2016) Pathway-Based Genomics Prediction using Generalized Elastic Net. PLoS Comput Biol 12:e1004790
Camargo, M Constanza; Bowlby, Reanne; Chu, Andy et al. (2016) Validation and calibration of next-generation sequencing to identify Epstein-Barr virus-positive gastric cancer in The Cancer Genome Atlas. Gastric Cancer 19:676-81
Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M et al. (2016) Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164:550-63
Drake, Justin M; Paull, Evan O; Graham, Nicholas A et al. (2016) Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell 166:1041-54
Cancer Genome Atlas Network (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517:576-82
Ewing, Adam D; Houlahan, Kathleen E; Hu, Yin et al. (2015) Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods 12:623-30
Yau, Christina; Meyer, Laurence; Benz, Stephen et al. (2015) FOXM1 cistrome predicts breast cancer metastatic outcome better than FOXM1 expression levels or tumor proliferation index. Breast Cancer Res Treat 154:23-32

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