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A Vision for a Cancer Research Knowledge System
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Transcript of A Vision for a Cancer Research Knowledge System
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A Vision for a Cancer Research Knowledge System
September 14, 2016
Warren Kibbe, PhDNCI Center for Biomedical Informatics
@wakibbe
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Changing the Conversation Around Data Sharing
• How do we find data, software, standards?• How can we make data, annotations, software, metadata accessible?• How do we reuse data standards• How do we make more data machine readable?
NIH Data Commons
Data commons co-locate data, storage and computing infrastructure, and commonly used tools for analyzing and sharing data to create an interoperable resource for the research community.
*Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson, A Case for Data Commons Towards Data Science as a Service, to appear. Source of image: Interior of one of Google’s Data Center, www.google.com/about/datacenters/.
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FAIR –Making data
Findable, Accessible,
Attributable, Interoperable,
Reusable,and provide Recognition
Force11 white paperhttps://www.force11.org/group/fairgroup/fairprinciples
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Cancer Research Data Ecosystem
Well characterized Research Data Sets Cancer Cohorts Patient Data
EHR, Lab Data, Imaging, PROs, Smart Devices,
Decision Support
Learning from everycancer patient
Active researchparticipation
Research informationdonor
Clinical ResearchObservational Studies
Proteogenomics Imaging DataClinical Trials
Discovery Patient-engaged Research
SurveillanceBig Data
Implementation Research
SEERGDC
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Cancer Research Data Commons Ecosystem
GenomicData Commons
Data Validation and Harmonization
ImagingData Commons
ProteomicsData Commons
Clinical Data Commons
(Cohorts / Indiv.)
SEER(Populations)
Data Contributors and Consumers
Researchers PatientsCliniciansInstitutions
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Data Commons:Co-locate data, storage and computing infrastructure, and commonly used tools for analyzing and sharing data to create an interoperable resource for the research community.
Genomic Data Commons& Cloud Pilots
FAIR Principles: Microattribution, nanopublications, tracking the use of data, annotation of data, use of algorithms, supports the data /software /metadata
life cycle to provide credit and analyze impact of data, software, analytics, algorithm, curation and
knowledge sharing
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GDC Content TCGA 11,353 cases TARGET 3,178 cases
Current
Foundation Medicine 18,000 cases Cancer studies in dbGAP ~4,000 cases
Coming soon
NCI-MATCH ~5,000 cases Clinical Trial Sequencing Program ~3,000 cases
Planned (1-3 years)
Cancer Driver Discovery Program ~5,000 cases Human Cancer Model Initiative ~1,000 cases APOLLO – VA-DoD ~8,000 cases
GenomicData Commons
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Towards a Cancer Knowledge System• Continue genomic investigations of cancer
• Need > 100,000 cases analyzed• Embrace all genomic platforms• Relationship of relapse and primary biopsies
• Incorporate associated clinical annotations• Clinical trial data• Observational, longitudinal standard-of-care data• N-of-1 clinical data
• Promote and curate biological investigations of cancer genetic variants
• Driver vs. passenger mutations• Multiple phenotypic assays• Alterations in regulatory pathways – proteomics• Mechanisms of therapeutic resistance• Functional genomic investigations
• Integrative models for high-dimensional data
GenomicData Commons
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Development of the NCI Genomic Data Commons (GDC)To Foster the Molecular Diagnosis and Treatment of Cancer
GDC
Bob Grossman PIUniv. of Chicago
Ontario Inst. Cancer Res.Leidos
Institute of MedicineTowards Precision Medicine
2011
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GDC
Utility of a Cancer Knowledge System
Identifylow-frequencycancer drivers
Define genomicdeterminants of response
to therapy
Compose clinical trialcohorts sharing
targeted genetic lesions
Cancerinformation
donors
GenomicData Commons
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NCI Cloud PilotsVisualization, Compute, Pipelines, Workspaces
GenomicData Commons
Genomic Data Commons / Cloud Pilot Ecosystem - Today
Data Submission, Harmonization, Visualization & Download
Broad FireCloud
SBG CGC
ISB CGC
Researchers
APIs
Web InterfaceResearchersContributors
Authentication & Authorization
thru eRA Commons and dbGaP
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GenomicData Commons
Genomic Data Commons / Cloud Pilot Ecosystem– Near Term
Data Submission, Harmonization, Visualization & Download
Broad FireCloud
ISB CGC
Researchers
APIs
Web InterfaceResearchersContributors
Authentication & Authorization
thru eRA Commons and dbGaP
SBG CGC
Commons @ AWS
Commons @ GCP
Commons @ Azure
New algorithms,tools, pipelines, visualizations
DockStore
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Extra Slides
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Blue Ribbon Panel Recommendations
• Network for Direct Patient Engagement• Cancer Immunotherapy Translational Science Network• Therapeutic Target Identification to Overcome Drug Resistance• A National Cancer Data Ecosystem for Sharing and Analysis• Fusion Oncoproteins in Childhood Cancers• Symptom Management Research• Prevention and Early Detection – Implementation of Evidence-based Approaches• Retrospective Analysis of Biospecimens from Patients Treated with Standard of Care• Generation of Human Tumor Atlases• Development of New Enabling Cancer Technologies
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Genomic Data Commons
• Unified knowledge base that promotes sharing of genomic and clinical data between researchers and facilitates precision medicine in oncology
• Contains standardized data from approximately 14,500 patients, derived from NCI programs, including:
- The Cancer Genome Atlas (TCGA)- Therapeutically Applicable Research to Generate Effective Treatment (TARGET)- Cancer Genome Characterization Initiative (CGCI)- The Cancer Line Encyclopedia (CCLE)
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GA4GH
GenomicsAndHealth.org
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Data Working GroupGA4GH
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PMI – Oncology, the GDC and the Cloud Pilots Goals
• Support precision medicine-focused clinical research• Enable researchers to deposit well-annotated (Interoperable)
genomic data sets with the GDC• Provide a single source (and single dbGaP access request!) to
Find and Access these data• Enable effective analysis and meta-analysis of these data
without requiring local downloads – data Reuse• Understand Contributions, Assess value through usage, and
give Attribution to all users
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PMI – Oncology, the GDC and the Cloud Pilots Goals
• Provide a data integration platform to allow multiple data types, multi-scalar data, temporal data from cancer models and patients through open APIs
• Work with the Global Alliance for Genomics and Health (GA4GH) to define the next generation of secure, flexible, meaningful, interoperable, lightweight interfaces – open APIs
• Engage the cancer research community in evaluating the open APIs for ease of use and effectiveness