NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
Cancer Moonshot, Data sharing and the Genomic Data CommonsWarren Kibbe
Gave the inaugural Informatics Grand Rounds at City of Hope on September 8th. NIH Commons, Genomic Data Commons, NCI Cloud Pilots, Cancer Moonshot and rationale for changing incentives around data sharing all discussed.
Nci clinical genomics data sharing ncra sept 2016Warren Kibbe
Gave an update on the Cancer Research Data Ecosystem, the Genomic Data Commons, Cloud Pilots, incentives for data sharing in cancer research to the NCI Council of Research Advocates (NCRA) on Monday, September 26th, 2016
National Cancer Data Ecosystem and Data SharingWarren Kibbe
Grand Rounds at the Siteman Cancer Center at Washington University. Highlighting the Genomic Data Commons and the National Cancer Data Ecosystem defined by the Cancer Moonshot Blue Ribbon Panel
Converged IT Summit - NCI Data SharingWarren Kibbe
Cancer Moonshot, Data Sharing, Genomic Data Commons, NCI Cloud Pilots, Cancer Research Data Ecosystem, technology advances, chemotherapy advances, MATCH, NCI Cancer Moonshot Blue Ribbon Panel Recommendations
DOE-NCI Pilots presentation at the Frederick National Laboratory Advisory Com...Warren Kibbe
May 2016 FNLAC presentation of the DOE-NCI partnership around three pilots focused on existing projects in NCI and existing NSCI directives and activities in DOE.
NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
Cancer Moonshot, Data sharing and the Genomic Data CommonsWarren Kibbe
Gave the inaugural Informatics Grand Rounds at City of Hope on September 8th. NIH Commons, Genomic Data Commons, NCI Cloud Pilots, Cancer Moonshot and rationale for changing incentives around data sharing all discussed.
Nci clinical genomics data sharing ncra sept 2016Warren Kibbe
Gave an update on the Cancer Research Data Ecosystem, the Genomic Data Commons, Cloud Pilots, incentives for data sharing in cancer research to the NCI Council of Research Advocates (NCRA) on Monday, September 26th, 2016
National Cancer Data Ecosystem and Data SharingWarren Kibbe
Grand Rounds at the Siteman Cancer Center at Washington University. Highlighting the Genomic Data Commons and the National Cancer Data Ecosystem defined by the Cancer Moonshot Blue Ribbon Panel
Converged IT Summit - NCI Data SharingWarren Kibbe
Cancer Moonshot, Data Sharing, Genomic Data Commons, NCI Cloud Pilots, Cancer Research Data Ecosystem, technology advances, chemotherapy advances, MATCH, NCI Cancer Moonshot Blue Ribbon Panel Recommendations
DOE-NCI Pilots presentation at the Frederick National Laboratory Advisory Com...Warren Kibbe
May 2016 FNLAC presentation of the DOE-NCI partnership around three pilots focused on existing projects in NCI and existing NSCI directives and activities in DOE.
Day 2 Big Data panel at the NIH BD2K All Hands 2016 meetingWarren Kibbe
Big data in oncology and implications for open data, open science, rapid innovation, data reuse, reproducibility and data sharing. Cancer Moonshot, Precisions Medicine Initiative (PMI), the Genomic Data Commons, NCI Cloud Pilots, NCI-DOE Pilots, and the Cancer Research Data Ecosystem.
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
CI4CC Moonshot Blue Ribbon Panel Report 20161010Warren Kibbe
Presentation to the Fall CI4CC meeting in Utah. CI4CC Moonshot Blue Ribbon Panel Report. Highlights of Vice President Biden's Cancer Moonshot and the NCI Blue Ribbon Panel Recommendations.
May 2016 NCI Cancer Center Directors meeting. Data Sharing and the Cancer Genomic Data Commons (GDC). Focus is on cancer genomic and clinical phenotype data.
NCI Cancer Imaging Program - Cancer Research Data EcosystemWarren Kibbe
Given to the NCI Cancer Imaging Program monthly telecon on January 9th, 2017. NCI Genomic Data Commons, Beau Biden Cancer Moonshot Blue Ribbon Panel, Cancer Research Data Ecosystem and the role of imaging in precision medicine
SAMSI Precision Medicine Keynote, August 2018: Data: where Precision Oncology...Warren Kibbe
The promise of precision medicine in oncology is predicated on the availability of accurate, high quality data from the clinic and the laboratory. Likewise, a Learning Health System is one in which we use data to monitor that we are following guidelines and care pathways to deliver the best care and not revert to prior practices (regression testing for care!) and also provide real world evidence to determine effectiveness and identify populations that would benefit from novel therapies. Into this mix of clinical drivers are the rapidly changing capabilities in instrumentation, computing, computation, and the pervasive use of sensors and smart devices. I will highlight a few of the obvious and perhaps not as obvious opportunities in leveraging the increasingly digital landscape in healthcare and biomedical research as we move toward a national learning health system for cancer.
Drinking excessive alcohol can contribute to the risk of developing breast cancer. Alcohol contains a lot of calories that can lead to excess weight, which in turn can increase our risk of breast cancer.
Day 2 Big Data panel at the NIH BD2K All Hands 2016 meetingWarren Kibbe
Big data in oncology and implications for open data, open science, rapid innovation, data reuse, reproducibility and data sharing. Cancer Moonshot, Precisions Medicine Initiative (PMI), the Genomic Data Commons, NCI Cloud Pilots, NCI-DOE Pilots, and the Cancer Research Data Ecosystem.
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
CI4CC Moonshot Blue Ribbon Panel Report 20161010Warren Kibbe
Presentation to the Fall CI4CC meeting in Utah. CI4CC Moonshot Blue Ribbon Panel Report. Highlights of Vice President Biden's Cancer Moonshot and the NCI Blue Ribbon Panel Recommendations.
May 2016 NCI Cancer Center Directors meeting. Data Sharing and the Cancer Genomic Data Commons (GDC). Focus is on cancer genomic and clinical phenotype data.
NCI Cancer Imaging Program - Cancer Research Data EcosystemWarren Kibbe
Given to the NCI Cancer Imaging Program monthly telecon on January 9th, 2017. NCI Genomic Data Commons, Beau Biden Cancer Moonshot Blue Ribbon Panel, Cancer Research Data Ecosystem and the role of imaging in precision medicine
SAMSI Precision Medicine Keynote, August 2018: Data: where Precision Oncology...Warren Kibbe
The promise of precision medicine in oncology is predicated on the availability of accurate, high quality data from the clinic and the laboratory. Likewise, a Learning Health System is one in which we use data to monitor that we are following guidelines and care pathways to deliver the best care and not revert to prior practices (regression testing for care!) and also provide real world evidence to determine effectiveness and identify populations that would benefit from novel therapies. Into this mix of clinical drivers are the rapidly changing capabilities in instrumentation, computing, computation, and the pervasive use of sensors and smart devices. I will highlight a few of the obvious and perhaps not as obvious opportunities in leveraging the increasingly digital landscape in healthcare and biomedical research as we move toward a national learning health system for cancer.
Drinking excessive alcohol can contribute to the risk of developing breast cancer. Alcohol contains a lot of calories that can lead to excess weight, which in turn can increase our risk of breast cancer.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
The scientific discovery of the induced pluripotent stem cell (iPSC) technology using adult stem cel is rather recent, in 2006. Now a new era of personalized medical treatments in clearer to perceived and accelerating worldwide with motivation groups and individuals in medical intervention, science & financical circles , more specifically in next decade.
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
The Human Variome Database in Australia in 2014 - Graham TaylorHuman Variome Project
There are a number of genetics and genomics initiatives underway in Australia, including the Australian node of the Human Variome Project (HVPA), as well as many active research collaborations including familial cancer, endocrine disease, and developmental delay. Most of these projects work with disease-specific databases on a research basis, with the risk that such archives may be ephemeral. HVPA is the only database that is directly integrated with accredited clinical reporting of variants. As such it is designed to capture variants that have passed scrutiny as diagnostically robust, and have therefore already been curated by qualified staff. Registered users access the HVPA database via a secure Internet portal.
I will describe three recent developments of the HVPA database and portal: the upgraded search interface, linkage to other datasets via BioGrid using hash-based de-identified case matching, and the introduction of a genome wide database using LOVD3. Finally I will discuss the future direction of the HVPA and the questions of utility, quality control and sustainability of genetic variation databases.
Search interface
The search interface has to provide useful tools for clinicians and lab scientists so that the HPVA project offers them direct benefits and incentivises them to participate. Following a request for feedback from users, a series of improvements were implemented, initially on a demonstration server and then on the live server following review by the Steering Committee. The highest priorities were for more information about numbers of times particular variants were
recorded, the ability to search by range and to filter by pathogenicity. There was also interest in enabling direct uploading of VCF files and the automated calculation of pathogenicity scores. Many of these features are now implemented and examples will be presented.
Linkage to other datasets
We have implemented the hash key algorithm and work is in progress with BioGrid to link variation data to clinical data sets.
Genome wide database
We have established an HVPA LOVD3 database and are working with the Human Genetics Society of Australasia on a pilot study to sequence the exomes of two trios and review the data using this database.
Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Mo...Jerry Lee
Special Seminar at the 8th Taiwan Biosignatures Workshop to share overall work of NCI's Center for Strategic Scientific Initiatives since 2003 as well as CSSI's influence on select projects initiated by the 2016 WH Cancer Moonshot Task Force that include Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) network, International Cancer Proteogenome Consortium, and the Blood Profiling Atlas in Cancer (BloodPAC) commons.
Accelerating the benefits of genomics worldwideJoaquin Dopazo
Grand Challenges in Genomics
A Joint NHGRI and Wellcome Trust Strategic Meeting
25 and 26 February 2019
https://www.wellcomeevents.org/WELLCOME/media/uploaded/EVWELLCOME/event_661/Draft_agenda_for_WT_December_2018.pdf
Join lecture: Nicky Mulder, Han Brunner and Joaquin Dopazo
The given presentation outlines services of the cloud platform "Analyze Genomes" enabling precision medicine. It was presented on the mHealth meets Diagnostics symposium in Berlin on Jun 21, 2016.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Similar to NCI Cancer Genomic Data Commons for NCAB September 2016 (20)
Overview of the NIH-funded RADx-UP - Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) Coordination and Data Collection Center (CDCC) with a focus on the Common Data Elements used to gather data across the RADx-UP Consortium for COVID-19 testing.
RADx-UP CDCC presentation for the NIH Disaster Interest GroupWarren Kibbe
Presentation on the RADx-Underserved Populations Coordination and Data Collection Center with an emphasis on how it will help understand and reduce the disparities associated with the COVDI-19 pandemic
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Seminar for Dr. Min Zhang's Purdue Bioinformatics Seminar Series. Touched on learning health systems, the Gen3 Data Commons, the NCI Genomic Data Commons, Data Harmonization, FAIR, and open science.
Drivers for data sharing in funding of biomedical research. Importance of data sharing on open science, innovation, reproducibility that is enabled by digital technologies and data science.
Data in precision oncology SAMSI Precision Medicine Meeting mar 2019Warren Kibbe
Talk at the March 14-15 2019 SAMSI Advances in Precision and Personalized Medicine held as part of the Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED) at NCSU, Raleigh, NC
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
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CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
Best Ayurvedic medicine for Gas and IndigestionSwastikAyurveda
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
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New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
NCI Cancer Genomic Data Commons for NCAB September 2016
1. Genomic Data Commons
NCI Cloud Pilots
Spetember 7th, 2016
Louis Staudt, MD, PhD
Warren Kibbe, PhD
@wakibbe
2. 2
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/.
4. 4
NIH Genomic Data Sharing Policy
https://gds.nih.gov/
Went into effect January 25, 2015
NCI guidance:
http://www.cancer.gov/grants-training/grants-
management/nci-policies/genomic-data
Requires public sharing of genomic data sets
5. 5
The Cancer Genomic Data Commons
(GDC) is an existing effort to
standardize and simplify submission of
genomic data to NCI and follow the
principles of FAIR – Findable,
Accessible, Interoperable, Reusable.
The GDC is part of the NIH Big Data to
Knowledge (BD2K) initiative and an
example of the NIH Commons
Genomic Data Commons
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
6. 6
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)
9. 9
Genomic Data Commons (GDC)
was highlighted in the June 29th Cancer Moonshot
Summit at Howard University in the US
Foundation Medicine announced the release of 18,000
genomic profiles to the GDC at the Cancer Moonshot
Summit
10. NCI Genomic Data Commons
The GDC went live with approximately 4.1 PB of data.
This includes: 2.6 PB of legacy data;
and 1.5 PB of “harmonized” data.
577,878 files about 14194 cases (patients), in 42 cancer types,
across 29 primary sites.
10 major data types, ranging from Raw Sequencing Data, Raw
Microarray Data, to Copy Number Variation, Simple Nucleotide
Variation and Gene Expression.
Data are derived from 17 different experimental strategies, with the
major ones being RNA-Seq, WXS, WGS, miRNA-Seq, Genotyping
Array and Expression Array.
16. Development of the NCI Genomic Data Commons (GDC)
To Foster the Molecular Diagnosis and Treatment of Cancer
GDC
Bob Grossman PI
Univ. of Chicago
Ontario Inst. Cancer Res.
Leidos
Institute of Medicine
Towards Precision Medicine
2011
17. GDC Infrastructure and Functionality
Data
Submitters
Open
Access
Users
Controlled
Access
Users
eRA
Commons
& dbGaP
Open Access
Data
Metadata+Data
Storage
Reporting
System
Harmonization
GDC Users GDC System Components
Data
Submission
Data Security
System
APIsDigital ID
System
Controlled
Access Data
20. Recovery
rate
(% true
positives) A0F0
SomaticSniper 81.1% 76.5%
VarScan 93.9% 84.3%
MuSE 93.1% 87.3%
All Three 96.4% 91.2%
GDC variant calling
pipelines
Wash U
Baylor
Broad
GDC Data Harmonization
Multiple pipelines needed to recover all variants
21. GDC Content
GDC
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 ~3,000 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
~56,000 cases
22. What Makes GDC Special?
Stores raw genomic data, allowing continuous reanalysis as
computation methods and genome annotations improve
NCI commitment to maintain long-term storage of cancer
genomic data in the GDC with free access to researchers
Utilizes shared bioinformatic pipelines to facilitate cross-study
comparisons and integrated analysis of multiple data types
Maintains harmonized clinical data in a highly structured and
extensible schema
Enables researchers to comply with the NIH Genomic Data
Sharing policy as well as journal requirements for data sharing
GDC
The explanatory power of data in the GDC will grow over time as
it accrues more cases => GDC will promote precision
oncology
23. Other Cancer Data Sharing Efforts
Signature Efforts Data
BRCA Challenge
Somatic variant sharing
Isolated genetic variants
No raw sequencing data
Precision medicine questions
Somatic variant sharing
Panel gene resequencing
Clinical response
Clinical trial
Public-private partnerships
Comprehensive genomics
Detailed clinical
phenotype data
Clinical trial access
Clinical/genomic data
aggregation
EHR data
Clinical sequencing
Clinical oncology standards
EHR data
Clinical sequencing
24. GDC
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
25. GDC
Utility of a Cancer Knowledge System
Identify
low-frequency
cancer drivers
Define genomic
determinants of response
to therapy
Compose clinical trial
cohorts sharing
targeted genetic lesions
Cancer
information
donor
26. 26
Support the Precision Medicine Initiative
• Expand data model to include
other data (e.g. imaging and
proteomics)
• Allow easy publication of
persistent links to data,
annotations, algorithms, tools,
workflows
• Measure usage and impact
• Change incentives for public
contributions
The Genomic Data Commons and Cloud Pilots
27. 27
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
28. 28
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
29. Cancer data ecosystem
Well characterized
research data sets Cancer cohorts Patient data
EHR, lab data, imaging,
PROs, smart devices,
decision support
Learning from every
cancer patient
Active research
participation
Research information
donor
Clinical Research
Observational studies
Proteogenomics
Imaging data
Clinical trials
Discovery
Patient engaged
Research
Surveillance
Big Data
Implementation research
SEER
30. GDC Acknowledgements
NCI Center for Cancer Genomics Univ. of Chicago
Bob Grossman
Allison Heath
Mike Ford
Zhenyu Zhang
Ontario Institute for Cancer Research
Lou Staudt
Zhining Wang
Martin Ferguson
JC Zenklusen
Daniela Gerhard
Deb Steverson
Vincent Ferretti
'Francois Gerthoffert
JunJun Zhang
Leidos Biomedical Research
Mark Jensen
Sharon Gaheen
Himanso Sahni
NCI NCI CBIIT
Tony Kerlavage
Tanya Davidsen
31. CGC Pilot Team Principal Investigators
• Gad Getz, Ph.D - Broad Institute - http://firecloud.org
• Ilya Shmulevich, Ph.D - ISB - http://cgc.systemsbiology.net/
• Deniz Kural, Ph.D - Seven Bridges – http://www.cancergenomicscloud.org
NCI Project Officer & CORs
• Anthony Kerlavage, Ph.D –Project Officer
• Juli Klemm, Ph.D – COR, Broad Institute
• Tanja Davidsen, Ph.D – COR, Institute for Systems Biology
• Ishwar Chandramouliswaran, MS, MBA – COR, Seven Bridges Genomics
GDC Principal Investigator
• Robert Grossman, Ph.D - University of Chicago
• Allison Heath, Ph.D - University of Chicago
• Vincent Ferretti, Ph.D - Ontario Institute for Cancer Research
Cancer Genomics Project Teams
NCI Leadership Team
• Doug Lowy, M.D.
• Lou Staudt, M.D., Ph.D.
• Stephen Chanock, M.D.
• George Komatsoulis, Ph.D.
• Warren Kibbe, Ph.D.
Center for Cancer Genomics Partners
• JC Zenklusen, Ph.D.
• Daniela Gerhard, Ph.D.
• Zhining Wang, Ph.D.
• Liming Yang, Ph.D.
• Martin Ferguson, Ph.D.