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
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.
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
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.
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.
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
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.
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.
FDA NGS and Big Data Conference September 2014Warren Kibbe
Presentation for the FDA NGS and Big Data Conference September 2014 held on the NIH campus. NCI initiatives, including Cancer Genomics Data Commons, NCI Cloud Pilots, big data issues for cancer
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
Keynote at NVIDIA GPU Technology Conference in D.C.Jerry Lee
Presentation at NVIDIA GPU Technology Conference in D.C. on how the Cancer Moonshot Task Force under Vice President Biden is using AI to help end cancer as we know it. Dr. Lee will discuss global efforts to empower A.I. and deep learning for oncology with larger and more accessible datasets.
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.
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
US Federal Cancer Moonshot- One Year LaterJerry Lee
Presentation from former Cancer Moonshot Data and Technology Track Co-chairs Jerry S.H. Lee, PhD (NCI, former OVP) and Dimitri Kusnezov, PhD (DOE) to update on efforts that will help realize the Data/Tech Track's vision of a national learning healthcare system for cancer. These include NCI/DOE pilots, DOE/VA pilot, NCI GDC, DoD/VA/NCI APOLLO, NCI/GSK ATOM, and BloodPAC.
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.
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
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.
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.
FDA NGS and Big Data Conference September 2014Warren Kibbe
Presentation for the FDA NGS and Big Data Conference September 2014 held on the NIH campus. NCI initiatives, including Cancer Genomics Data Commons, NCI Cloud Pilots, big data issues for cancer
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
Keynote at NVIDIA GPU Technology Conference in D.C.Jerry Lee
Presentation at NVIDIA GPU Technology Conference in D.C. on how the Cancer Moonshot Task Force under Vice President Biden is using AI to help end cancer as we know it. Dr. Lee will discuss global efforts to empower A.I. and deep learning for oncology with larger and more accessible datasets.
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.
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
US Federal Cancer Moonshot- One Year LaterJerry Lee
Presentation from former Cancer Moonshot Data and Technology Track Co-chairs Jerry S.H. Lee, PhD (NCI, former OVP) and Dimitri Kusnezov, PhD (DOE) to update on efforts that will help realize the Data/Tech Track's vision of a national learning healthcare system for cancer. These include NCI/DOE pilots, DOE/VA pilot, NCI GDC, DoD/VA/NCI APOLLO, NCI/GSK ATOM, and BloodPAC.
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
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.
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.
Presentation "The Impact of All Data on Healthcare"
Keith Perry
Associate VP & Deputy CIO
UT MD Anderson Cancer Center
With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint.
Learning Objectives:
∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge
∙ Provide examples of an analytical direction that supports evolution in medicine
∙ Expose the challenges facing the industry with respect to ~omits
Advancing Convergence and Innovation in Cancer ResearchJerry Lee
Describes NCI's Center for Strategic Scientific Initiatives activities (2005 - 2017) as well as data and technology activities of the 2016 White House Cancer Moonshot Task Force (2016 - 2017).
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
Opportunities in technology and connected health for population science Warren Kibbe
AACR Modernizing Population Science in the Digital Age MEG meeting.Keynote on Opportunities in technology and connected health for population science from February 2019
Focus is on the cancer data science and informatics community, a sad farewell to our friend and colleague Paul Fearn, kudos to Frank Manion, Mia Levy, and Samir Courdy. A little bit of overall change in cancer therapies, informatics, technology, and of course data science. A few highlights from publications as well!
Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
Ethical, Legal, and Social Implications of ELSI Learning Health Systems 2017 Conference, University of Michigan. Learning from the experience and outcomes of every cancer patient
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
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Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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.
Precision Medicine in the Age of NCI MATCH and the Beau Biden Cancer Moonshot
1. Precision Medicine in the Age
of NCI MATCH and the Beau
Biden Cancer Moonshot
Warren Kibbe, Ph.D.
Acting Deputy Director
National Cancer Center
Rockville, MD
2. Precision Medicine in the Age of NCI
MATCH and the Beau Biden Cancer
Moonshot
Warren Kibbe, PhD
warren.kibbe@nih.gov
@wakibbe
April 28th, 2017
3. 3
Outline
1. Motivation
2. Data & Computation in
Biomedicine
3. NCI MATCH
4. Cancer Moonshot
5. Data Commons
Thanks to many folks for slides, but especially Dr. Jerry Lee
4. 4
In 2016 there were an estimated
1,700,000 new cancer cases
and
600,000 cancer deaths
- American Cancer Society
Cancer remains the second most common cause of
death in the U.S.
- Centers for Disease Control and Prevention
5. 5
In 2016 there were an estimated
15,500,000
cancer survivors in the U. S.
6. 6
Understanding Cancer
Precision medicine will lead to fundamental
understanding of the complex interplay between
genetics, epigenetics, nutrition, environment and clinical
presentation and direct effective, evidence-based
prevention and treatment.
7. 7
(10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
Cancer is a grand challenge
Deep biological understanding
Advances in scientific methods
Advances in instrumentation
Advances in technology
Data and computation
Mathematical models
Cancer Research and Care generate
detailed data that is critical to
create a learning health system for cancer
Requires:
8. 8
(10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
12. 12
Keeping in mind cellular dynamics
On average across 375
tumor samples, ONLY
33% of RNA expression
differences correlated
with protein abundance
Zhang B et al, Proteogenomic characterization of human colon and rectal cancer, Nature, 2014, July 20.
13. 13
" there is great potential for new insights to come
from the combined analysis of cancer proteomic
and genomic data, as proteomic data can now
reproducibly provide information about protein
levels and activities that are difficult or impossible
to infer from genomic data alone ”
Douglas R. Lowy, MD
Acting Director of the National Cancer Institute, National Institutes of Health
5/25/2016
17. 17
18
Application of Cancer Genomics is changing
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match
18. NCI MATCH
•Conduct across 2400 NCI-supported sites
•Pay for on-study and at progression biopsies
•Screen 5000 patients to complete
30 phase II trials; target 25% ‘rare’ tumors;
1CR, PR, SD, and PD as defined by RECIST
2Stable disease is assessed relative to tumor status at re-initiation of study agent
3Rebiopsy; if additional mutations, offer new targeted therapy
,2
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match
19. 19
MATCH Assay: Workflow for 12-14 Day Turnaround
Tissue Fixation
Path Review
Nucleic Acid
Extraction
Library/Template Prep
Sequencing , QC
Checks
Clinical
Laboratory
aMOI
Verification
Biopsy Received at Quality Control Center
1 DAY
1 DAY
1 DAY
1 DAY
3 DAYS
10-12 days
Tumor content >70%
Centralized Data
Analysis
DNA/RNA yields >20 ng
Library yield >20 pM
Test fragments
Total read
Reads per BC
Coverage
NTC, Positive, Negative
Controls
aMOIs Identified
Rules Engine
Treatment
Selection
3-5 DAYS
20. 20
MATCH Observations
MATCH is open nationwide at ~1500 NCORP and NCTN sites
Accrual has been 100-150 patients / week
Match rate was initially ~8% for first 8 arms
After reopening May 30, 2016 rate has been ~20% for 20+ arms
Processing has been holding to 12-14 days average
Interest has been high
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match
21. 21
Precision Oncology
It isn’t just about matching patients to therapy, it is also about avoiding
therapies that will not work.
Biology is complex, and we still have a lot of basic biology to
understand
Genomics+imaging+clinical labs is the first wave of precision oncology
25. 25
Biology and Medicine are now data
intensive enterprises
Scale is rapidly changing
Technology, data, computing and IT are
pervasive in the lab, the clinic, in the
home, and across the population
29. 29
Expert Systems vs Machine Learning
In 1945, the British philosopher Gilbert Ryle
identified two kinds of knowledge— factual,
propositional knowledge that can be ordered into
rules—“knowing that.” versus implicit,
experiential, skill-based—“knowing how.”
Machine Learning is based on ‘learning how’.
Expert systems, or rule based machines, are
based on ‘knowing that’.
30. 30
Human Cognition
Three kinds of learning:
Learning that – rule-based knowledge
Learning how – experiential knowledge
Learning why – integrative, explanatory knowledge
34. 34
The Beau Biden Cancer Moonshot
• Accelerate progress in cancer,
including prevention & screening
• From cutting edge basic research to
wider uptake of standard of care
• Encourage greater cooperation
and collaboration
• Within and between academia,
government, and private sector
• Enhance data sharing
Blue Ribbon Panel recommendations (Oct ‘16); Implementation Working Groups established (Jan ‘17)
cancer.gov/brp
35. 35
Relationship Between Bypass Budget and Blue Ribbon Panel Report
• Bypass Budget addresses NCI’s
entire research portfolio
• Lays out the plan for NCI’s
continued investment in cancer
research
• Cancer Moonshot is a unique
opportunity to enhance cancer
research in specific areas that are
poised for acceleration
• The BRP report made 10 bold, yet
feasible, recommendations that
will fast-track initiatives if infused
with Moonshot funding
36. 36
A Few Beau Biden Cancer Moonshot Milestones
• Announced by Former President Obama at the State of the Union January 12, 2016
• Blue Ribbon Panel convened at AACR, April 18, 2016
• Genomic Data Commons went public June 6, 2016
• Vice President’s Cancer Moonshot Summit – June 29, 2016
• Rethinking Clinical Trial Search – Open API at https://clinicaltrialsapi.cancer.gov
• Blue Ribbon Panel recommendations – accepted by the National Cancer Advisory Board on
September 7th, 2016
• Cancer Moonshot Task Force and BRP recommendations sent to President on October 17th,
2016 https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/milestones
and released at https://cancer.gov/brp
• 21st Century Cures Act funding the Beau Biden Cancer Moonshot passed 94-5 by the Senate
on December 8 and signed by Former President Obama December 13, 2016.
37. 37
• 28 Members
• Clinicians, researchers, advocates, pharma and tech industries
• Three face-to-face meetings to identify “Moonshot”
recommendations
• 7 Working Groups
• Clinical trials, enhanced data sharing, cancer immunology, tumor
evolution, implementation science, pediatric cancer, precision
prevention and early detection
• Met weekly for 6 weeks to generate 2-3
recommendations/working group
• More than 150 people were part of the working group
Blue Ribbon Panel: Members & Working Groups
39. 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 4D Human Tumor Atlas
• Development of New Enabling Cancer Technologies
40. 40
Beau Biden Cancer Moonshot Funding Opportunities
40
https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/funding
14 RFAs as of April 21, 2017
42. 42
Cancer Data Sharing
& Data Commons
• Support open science
• Support data reusability
• Aligned with Cancer Moonshot
• Part of Precision Medicine
• Reduce Health Disparities
• Improve patient access to clinical
trials
• Toward a learning National Cancer
Data Ecosystem
Reduce the risk, improve early detection, outcomes and survivorship in cancer
43. 43
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
NCI Genomic Data Commons
National Cancer Data Ecosystem
Data Commons co-locate data, storage and computing infrastructure, and
frequently 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/.
44. Cancer Research Data Ecosystem – Cancer Moonshot BRP
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
SEERGDC
46. 46
GDC as an example of a new
architecture for storing and sharing
cancer data
47. 47
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, Attributable,
Interoperable, Reusable, and Provide
Recognition.
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
Force11 white paper
https://www.force11.org/group/fairgroup/fairprinciples
48. NCI Genomic Data Commons
The GDC went live on June 6th, 2016 with approximately 4.1 PB of data.
577,878 files about 14194 cases (patients), in 42 cancer types, across 29 primary
disease sites, 400 clinical data elements
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.
Foundation Medicine announced the release of 18,000 genomic profiles to the GDC
at the Cancer Moonshot Summit, June 29th, 2016
The Multiple Myeloma Research Foundation announced it would be releasing its
CoMMpass study of more than 1000 cases of Multiple Myeloma on Sept 29th, 2016.
49. 49
NCI Cancer Genomics Cloud Pilots
Democratize access to
NCI-generated genomic
and related data, and to
create a cost-effective
way to provide scalable
computational capacity
to the cancer research
community.
Cloud Pilots provide:
• Access to large genomic data sets without need to download
• Access to popular pipelines and visualization tools
• Ability for researchers to bring their own tools and pipelines to the data
• Ability for researchers to bring their own data and analyze in combination with existing genomic
data
• Workspaces, for researchers to save and share their data and results of analyses
50. 50
• PI: Gad Getz
• Google Cloud
• Firehose in the cloud including Broad best practices workflows
•http://firecloud.org
Broad Institute
• PI: Ilya Shmulevich
• Google Cloud
• Leverage Google infrastructure; Novel query and visualization
•http://cgc.systemsbiology.net/
Institute for
Systems Biology
• PI: Deniz Kural
• Amazon Web Services
• Interactive data exploration; > 30 public pipelines
•http://www.cancergenomicscloud.org
Seven Bridges
Genomics
Three NCI Genomics Cloud Pilots
Selection
Design/Build
I
Design/Build
II
Evaluation Extension
Sept 2016Jan 2016April 2015Sept 2014
Jan 2014
51. SBG CGC
Broad FireCloud ISB CGC
Researchers
APIs
Web Interface
APIs
Web Interface
Data Submission
& Harmonization
Genomics Cloud Pilots:
Visualization, Compute,
Pipelines, WorkspacesAuthentication
& Authorization thru
eRA Commons & dbGaP
GDC
GDC / Cloud Pilots Framework: Today
Genomic Data
Commons:
Harmonization,
Visualization,
& Download
52. Researchers
Web Interface Web Interface
Data Submission
& Harmonization
Authentication
& Authorization thru
eRA Commons & dbGaP
GDC
GDC / Cloud Pilots Framework: Near Future
Broad FireCloud
ISB CGC
SBG CGC
GDC@AWS
GDC@GCP
GDC@Azure DockStore
Analysis resources
APIs APIs
53. The NCI Cancer Research Data Commons Vision:
A virtual, expandable infrastructure
GDC
Clinical
Functional
Cancer Models
Imaging
Population
Proteomics
NCI Cancer Research
Data Commons
GDC
Researcher
s
Patients
Clinician
s
Authentication
& Authorization
Multiple Cloud-based Commons Nodes
• Interoperable through data standards and common identifiers
• Data are validated & harmonized using pre-defined processes as agreed by the community
• Secure access to controlled data to protect patient privacy
54. Node A
Cloud X
NCI Cancer Research Data Commons:
An Individual Node
Cloud Y
Data
Contributors
Data
Submission
Data
Mirroring
55. 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
56.
57.
58. Discovery of Cancer Drivers With 2% Prevalence
Lung adeno.
+ 2,900
Colorectal
+ 1,200
Ovarian
+ 500
Lawrence et al, Nature 2014
Power Calculation for Cancer Driver Discovery
Need to resequence >100,000 tumors to
identify all cancer drivers at >2% prevalence
59. Workspace –
isolated environment for collaborative analysis
Data + Methods → Results
sample data and
metadata (e.g.
BAMs, tissue type)
algorithms
(e.g. mutation
calling)
Wiring logic
(e.g. use the exome
capture BAM)
executions and results
(e.g. run mutation caller v41
on this exact bam and track
results)
Slide courtesy of Broad Institute
60. 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
61. 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.
62.
63.
64. 64
Integrated data sets, interoperable
resources, harmonized data are
necessary for and enable
biologically informed cancer
computational predictive models
67. 67
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