Large NCI clinical trials and research projects have been generating data on cancer genomes at an unprecedented rate, elucidating the mechanisms of cancer initiation and evolution, as well as resistance to therapy. To fully utilize this comprehensive data resource, which has exceeded a petabyte (1015 bytes) of data, the scientific community needs deep, user-friendly interactive computer visualizations of the data. These will empower investigators to discover the molecular processes driving each patient's cancer, and to identify potential translations of this knowledge into new therapies, ultimately advancing both our knowledge of cancer mechanisms and patient outcomes. We propose to develop a web-based Data Hub and Viz Hub platform to allow researchers to visualize the richness of the NCI's cancer genomics data from a single web interface. Visualizations will be composed of a set of ?bio-centric? views developed by the bioinformatics community and made available in BioJS, an open- source repository of tools to represent biological data. This will establish a new paradigm of web-based biological data visualization development by way of sharable and reusable modular open source components. To initiate the Viz Hub platform, we will integrate seven popular 3rd-party bioinformatics visualizations into the existing UCSC Xena Browser utilizing a plug-and-play framework. We will then work closely with several clinical labs to develop two new translational visualizations for the next generation of genomic medicine. One will be a Longitudinal Omics Integrator, giving researchers a highlighted overview of a patient while drilling down into genomic and functional data collected throughout treatment. Another will help researchers investigate responses to various new types of immunotherapy, which promise to revolutionize cancer treatment. This Immuno-Tracker and Immunoediting Viewer will show how immunogenic neoantigens, T-cell receptors, and B-cell receptors change over disease progression and in response to treatment. The visualization needs of the scientific community, NCI's Genomic Data Analysis Network, and Disease Working groups will be fully supported through our system of public and restricted Data Hubs. Our high- performance Data Hubs will be easy to install on a diverse range of computing environments. Users will be able to integrate public and restricted data, from large consortia and individual researchers (including their own labs), seamlessly on our web-based Viz Hub. Our commitment to bioinformatics community standards, such as GA4GH and BioJS, ensures that our contributions will be interoperable. Our designs will be vetted by users through testing in tumor boards and at designated booths at international cancer meetings. This will ensure that our platform will effectively serve researchers, biologists and clinicians now and into the future of precision medicine.

Public Health Relevance

We propose to create a web-based system called Xena for user-friendly interactive computer visualization and exploration of cancer genomes and other big data from large NCI clinical trials and projects. With a strong focus on usability and application in personalized medicine, Xena will integrate visual representations of cancer data into a single easy-to-use interface, helping cancer researchers identify potential clinical treatments for each cancer patient, in part by rapidly comparing them to thousands of other patients. By making big data accessible and interpretable, our project will accelerate research into the mechanisms and treatment of cancer, the second-leading cause of death in the U.S.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA210974-04
Application #
9756153
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Yang, Liming
Project Start
2016-09-13
Project End
2021-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California Santa Cruz
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
125084723
City
Santa Cruz
State
CA
Country
United States
Zip Code
95064
Ding, Li; Bailey, Matthew H; Porta-Pardo, Eduard et al. (2018) Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 173:305-320.e10
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Corces, M Ryan; Granja, Jeffrey M; Shams, Shadi et al. (2018) The chromatin accessibility landscape of primary human cancers. Science 362:
Hoadley, Katherine A; Yau, Christina; Hinoue, Toshinori et al. (2018) Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell 173:291-304.e6
Cherniack, Andrew D; Shen, Hui; Walter, Vonn et al. (2017) Integrated Molecular Characterization of Uterine Carcinosarcoma. Cancer Cell 31:411-423
Robertson, A Gordon; Kim, Jaegil; Al-Ahmadie, Hikmat et al. (2017) Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171:540-556.e25
Cancer Genome Atlas Research Network. Electronic address: elizabeth.demicco@sinaihealthsystem.ca; Cancer Genome Atlas Research Network (2017) Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas. Cell 171:950-965.e28
Robertson, A Gordon; Shih, Juliann; Yau, Christina et al. (2017) Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. Cancer Cell 32:204-220.e15
Cancer Genome Atlas Research Network. Electronic address: andrew_aguirre@dfci.harvard.edu; Cancer Genome Atlas Research Network (2017) Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. Cancer Cell 32:185-203.e13
Cancer Genome Atlas Research Network; Albert Einstein College of Medicine; Analytical Biological Services et al. (2017) Integrated genomic and molecular characterization of cervical cancer. Nature 543:378-384

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