The goal of the CCDI is to build a community committed to sharing pediatric cancer data to improve treatments, quality of life, and survivorship. The initiative aims to aggregate broad categories of clinical and research data, develop infrastructure for data sharing, and ensure policies support data access. Recommendations include establishing national strategies for molecular characterization and developing a federated data platform to enable learning from every child with cancer.
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
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
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
Data sharing drivers in precision oncology, biomedical research, and healthcare. Accelerating discovery, innovation, providing credit for all stakeholders - patients, researchers, care providers, payers.
As part of the 4th Annual Early Age Onset CRC Summit theNational Colorectal Cancer Roundtable (NCCRT) Family History and Early Onset Task Group hosted a Special Symposium focused on the importance of Family Health History for colorectal cancer, including advanced adenomas, and its importance in preventing colorectal cancer. The Symposium included presentations on the current challenges and opportunities surrounding ascertainment and documentation of actionable family health history information in primary care.
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
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.
Webinar: Increase research efficiency and enable collaboration with the IDBS ...IDBS
Streamline patient stratification together with omics and sample management
Find out how the solution enables research scientists and clinicians across Healthcare, Pharma and other Life Sciences organizations to create a comprehensive research platform that empowers high quality decision-making. It provides the building blocks to allow clinical researchers to capture and curate their data, to manage ontologies, and to integrate, search and visualize data from clinical, biobanks and omics data sources.
In this webinar you will see how to:
- improve sample management
- capture Data Provenance
- stratify patient populations
- explore omics data in the context of clinical phenotype
- facilitate a results sharing culture between departments and collaborators
To view the webinar: http://www.idbs.com/en/news-events/list-of-webinars/2014/03/increase-research-efficiency-and-enable-collaboration-with-the-idbs-translational-science-solution/
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.
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.
Overview of the Patient-Centered Outcomes Research Institute (PCORI), how PCORI views Patient-Centered Outcomes Research and how this is related to PCORI’s major funding mechanisms.
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.
As part of the 4th Annual Early Age Onset CRC Summit theNational Colorectal Cancer Roundtable (NCCRT) Family History and Early Onset Task Group hosted a Special Symposium focused on the importance of Family Health History for colorectal cancer, including advanced adenomas, and its importance in preventing colorectal cancer. The Symposium included presentations on the current challenges and opportunities surrounding ascertainment and documentation of actionable family health history information in primary care.
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
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.
Webinar: Increase research efficiency and enable collaboration with the IDBS ...IDBS
Streamline patient stratification together with omics and sample management
Find out how the solution enables research scientists and clinicians across Healthcare, Pharma and other Life Sciences organizations to create a comprehensive research platform that empowers high quality decision-making. It provides the building blocks to allow clinical researchers to capture and curate their data, to manage ontologies, and to integrate, search and visualize data from clinical, biobanks and omics data sources.
In this webinar you will see how to:
- improve sample management
- capture Data Provenance
- stratify patient populations
- explore omics data in the context of clinical phenotype
- facilitate a results sharing culture between departments and collaborators
To view the webinar: http://www.idbs.com/en/news-events/list-of-webinars/2014/03/increase-research-efficiency-and-enable-collaboration-with-the-idbs-translational-science-solution/
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.
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.
Overview of the Patient-Centered Outcomes Research Institute (PCORI), how PCORI views Patient-Centered Outcomes Research and how this is related to PCORI’s major funding mechanisms.
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
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.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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.
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!
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
1. Learning from Every Child :
The NCI Childhood Cancer
Data Initiative
November 18, 2022
Duke-NUS
@wakibbe
Warren A. Kibbe, Ph.D.
Vice Chair & Professor of
Biostatistics and Bioinformatics
Duke University School of Medicine
warren.kibbe@duke.edu
2. 2
NCI Childhood Cancer Data Initiative (CCDI) –
Building A Community
Childhood and AYA cancers are rare diseases: only 4% of diagnosed cancers
In the US: About 16,000 cases for children from birth to 19 years of age
About 80,000 cases for young adults from 20 to 39 years of age
Acute need for datato support research
CCDI aims to build a
community focused on Pediatric and AYA Cancer
4. Personal & Professional Background
•PhD in Chemistry at Caltech, Postdoc in molecular genetics of
RAS at the Max-Planck-Institute for Biophysical Chemistry
•Cancer research for 25+ years - cancer informatics, data
science, healthcare – open science, open data advocate
•Feinberg School of Medicine at Northwestern for 15+ years
•Director NCI CBIIT 2013-2017; NCI CIO 2013-2017; Acting NCI
Deputy Director for Data Science 2016-2017
•At Duke since 2017
•Currently on IPA with NCI to co-lead the CCDI
•Lost three grandparents to cancer, father to cancer in 2019
6. The importance of Open Science
Calls for greater transparency and ‘open data access’ in clinical research continue
actively.
“Open science is the movement to make scientific research, data and
dissemination accessible to all levels of an inquiring society”*
Open Science Project**: “If we want open science to flourish, we should raise our
expectations to: Work. Finish. Publish. Release.”
FAIR Principles: Findability, Accessibility, Interoperability, and Reusability***
TRUST Principles: Transparency, Responsibility, User focus, Sustainability and
Technology****
* https://www.fosteropenscience.eu/resources
** http://openscience.org/
*** https://www.nature.com/articles/sdata201618
****https://www.nature.com/articles/s41597-020-0486-7
7. 7
The goal of the CCDI is to build a
community of pediatric cancer
researchers, advocates, families,
hospitals, and networks
committed to sharing data to
improve treatments, quality of
life, and survivorship of every
child with cancer.
8. “Focus on the critical need to collect, analyze, and share data better in order to maximize NCI’s ongoing
investment in pediatric and AYA cancers and survivorship. Tissue samples from patients with cancer in this age
group are critically limited and a valuable resource. The overall goal of the CCDI is not simply to generate more
data, but to build processes that transform data into knowledge that moves the field forward in meaningful
ways. The CCDI supports the wider pediatric cancer community’s goal of maximizing the stated goal of
“learning from every patient” so that ultimately those patients, survivors and their families can materially
benefit in terms of higher cure rates and improved long-term health outcomes.”
Aggregate and
generate broad
categories of data
Develop
infrastructure
Engage with experts
Empower patients
& families
Ensure appropriate
policy and funding
Develop strategy for
survivorship
Ensure diverse
patient
representation
Enable improved
patient outcomes
and treatment
Categories of Recommendations
BSA CCDI Working Group Report
24 recommendations
WG Report
https://deainfo.nci.nih.gov/advisory/bsa/sub-cmte/CCDI/CCDI%20BSA%20WG%20Report_Final%20061620.pdf
9. Ad Hoc Working Group in Support of the CCDI
Kevin Shannon
(Co-Chair)
UCSF
Otis Brawley
(Co-Chair)
Johns Hopkins
Peter Adamson
Sanofi
Tom Curran
Children’s
Mercy
James Downing
St. Jude
Julie Guillot
Leukemia &
Lymphoma
Society
Amanda
Haddock
Dragon Master
Foundation
Samuel
Volchenboum
U Chicago
John Maris
CHOP
Andrew Kung
MSKCC
Warren Kibbe
Duke
Andrea Hayes-
Jordan
UNC
Katherine Janeway
DFCI
Douglas Hawkins
Seattle Children’s
10. • Adapt treatments,
diagnostics, and
prevention
strategies as
knowledge is
gained
• Identify exceptional
responders
• Understand
molecular
landscape of rare
cancers
• Use germline for
predisposition
studies
• Aggregate, federate
• Data resource
inventory
• Identifiers for
biospecimens
• Develop NCCR
• Tools for translation
• Harmonize clinical
and research
terminologies
• AL and ML
approaches
• Simplify data access
• Bar coding for data
• Aggregate 6 broad
categories of data
• Existing molecular
• New “ideal” data
types
• Preclinical
• Off-trial patient
data
• Develop national
strategy for
molecular
characterization
• Cancer model data
Aggregate and
generate data
Develop
infrastructure
Enable improved
patient outcomes
and treatment
• Allow patients to
access their data
• Creative ways to “opt
in”
• Engage with all
stakeholders
Empower patients &
families
BSA CCDI Working Group Recommendations
11. • Make data
representative of full
spectrum of patients
• Consent diverse
populations
• Develop long-term
strategy for tracking
patients across
lifetime
• Set expectations for
data sharing
• Commit to data
sharing thru policy
and funding
• Convene experts for
molecular
characterization
• Convene experts for
NCCR
• Engage with all
stakeholders
Engage with experts
Ensure appropriate
policy and funding
Develop strategy for
survivorship
Ensure diverse
patient
representation
BSA CCDI Working Group Recommendations
12. Establish
Goals of
Initiative
Identify
Gaps,
Needs,
Research
Priorities
Develop
Structure to
Meet Goals,
Addressing
Gaps
Set
Milestones &
Assessment
Metrics
Begin FY22-FY24
Concept
Development
Process
State of
the Union
Kick-Off
Symposium
CCDI Structure
(BSA/NCAB)
CCDI Working Groups
& CCDI Leadership
BSA Working
Group Report
2019 - 2021
NCI: Leverage existing programs, funding mechanisms,
datasets for short-term solutions to implement vision
2022 - 2029
Issue Funding
Opportunities
Mix of funding based on NCI
strategic planning
CCDI Program Establishment
13. Clinical and Research Data Collection
(Molecular Characterization Initiative)
• Create national strategy for
molecular characterization that
allows us to learn from every
patient (clinical & research)
• Create and compile meaningful,
comprehensive data sets to
understand each type of cancer
and its effects over time
Federated Infrastructure (Data Platform)
• Establish a federated infrastructure to
manage and provide access to clinical
and research data
• Develop and enhance tools, methods
and pipelines for analyzing a variety of
data in different ways
Cohort & Survivorship
• Data from biomedical research and
clinical care is the foundation that
informs new treatments and
improves the lives of patients,
survivors and their families
• Adapt treatments, diagnostics,
prevention
CCDI is establishing a pediatric cancer data resource for the cancer research community;
teams must focus on coordinating across that long-term objective
Earlier phases of CCDI (2019 – 2021) – Build a strong base,
develop a data ecosystem
Later phases of CCDI (2022 –
2029) – Improve treatments;
evolve and expand ecosystem
CCDI – Learning from Every Child
14. $35.4 Million
$22.24 Million
Foundational Infrastructure
Data Portal
Data
Aggregation
Open Targets
Data Catalog
Clinical Data
Commons
Master Patient
Index
Submission
Pipeline
EHR Pilots
Data Model
Federated
Infrastructure
National Childhood Cancer Registry
$76.64 Million
Data Supplements, Training, Program Management $15.72 Million
Science
Clinical Pre-Clinical Cohort RPG Molecular
15. 15
Pediatric/AYA data from multiple sources
Improved understanding of why some cancers
develop resistance or don’t respond to treatment
Generation of new ideas for
intervention
Culture change towards improved
collaboration and data sharing
Development of new research
and analytical tools
CCDI
NCI - COG
Pediatric MATCH
16. Patient Data Touchpoints
Diagnosis
Confirmation / Refinement
Clinical presentation, biopsy, imaging
Molecular Sequencing,
proteomics, etc.
Decision-making / Clinical Care
Molecular Tumor Board, Review notes,
treatment decisions
Longitudinal Follow-up
Outcomes
Long-term outcomes
Short-term outcomes
Treatment
Treatments, procedures, adverse events
Secondary
Cancer
Recurrence
Progression
Data Touchpoint
Epidemiology / Population Sciences Data –
Familial data, environmental, registry, population studies, disease cohorts
Screening, Diagnosis, Treatment
17. Our ability to generate biomedical
data continues to grow in terms of
variety and volume
Current sources of data
molecular genome pathology imaging labs notes sensors
icons by the Noun Project
19. Diagnosis
Molecular Characterization
Clinical Decision,
Molecular Tumor Board
Treatment
Follow-on Cancer
Recurrence
Long-term follow-up
Outcomes
Federated
Data
Ecosystem
Cancer Data “Scorecard”
EHRs, LIMS, Radiology, Pathology Systems
Tumor Board Systems, Hospital
Systems, EHRs, Clinical Trial Matching
EHRs, CDMS, Adverse Events, CTMS
EHRs, LIMS, Radiology, Pathology Systems
EHRs, CDMS, CTMS
EHRs, Registries
EHRs, LIMS, Radiology, Pathology Systems
LIMS, Sequencing Systems
20. 20
• A national strategy, building on efforts including COG’s Project:EveryChild,
to offer appropriate clinical and molecular characterization to every child
with cancer that:
Enables discovery when these and other data are connected
Defines a minimum set of molecular diagnostics to be collected for
every pediatric and AYA cancer patient
Is accessible to all children with cancer, including those treated at
community-based institutions; provide access to underserved
pediatric cancer patients
Clinical sequencing of ~3,000 patients/year
Align with Rare Pediatric Tumor Cell Atlas
CCDI Childhood Molecular Characterization Initiative
21. 21
Childhood Molecular Characterization Initiative
• Expand access to comprehensive molecular sequencing as a step towards the
goal of reaching all children with pediatric cancer
• Develop NCI-recommended guidelines for clinical and molecular data
collection as part of standard of care
• Create a comprehensive, harmonized, and integrated database of clinical,
genomic, and phenotypic data for research
• First patient was enrolled in April 2022!
Clinical Service: Diagnostic clinical molecular
characterization services for patients who might
not otherwise have access to them
Data to be collected (CLIA certified)
• DNA: CLIA WES or NGS targeted panel
• Fusion panel
• Methylation: CLIA DNA Methylation array
• Clinical annotation
Research Discovery: Molecular characterization to learn
more about disease subtypes and rare cancers
Data to be collected (in additional to clinical/seq data on
selected populations)
• WGS/deep molecular (DNA) profiling
• RNAseq
• Longitudinal data
22. 22
CCDI Molecular Characterization Initiative
https://www.nih.gov/news-events/news-releases/nih-launches-program-offer-molecular-characterization-childhood-cancers
Launched March 21, 2022
First patient enrolled
April 2022
23. Clinical
Sequencing at IGM
(CLIA)
Targeted exome seq
Archer fusion
EPIC methylation
array
Clinical/demographic
PI: Elaine Mardis
PO: Malcolm Smith
Research-Grade
Characterization
(non-CLIA)
WGS
RNA-seq
Proteomics
Metabolomics
Other
Clinical
COG Project:
EveryChild
(CNS tumors,
sarcomas, or rare
cancers)
PI: Doug Hawkins/ Dx
Chairs
PO: Malcolm Smith
MyPART
Refractory or relapsed
cancer types (adult &
pediatric) with rare
cancers (DNA/RNA)
PIs: Brigitte
Widemann, Karlyne
Reilly
CCDI Molecular Characterization Initiative: Clinical Pipeline
CCDI Data
Ecosystem (AWS)
Deposit clinical
and genomic
data
Return of CLIA
genomic results
to patients and
providers
CCDI Molecular
Characterization
Pipeline
Protocol Enrollment
(Goal: determine best strategy
to treat & learn from each child)
CCDI Funded
PI: Jaime Guidry Auvil, Tony Kerlavage
PO: Subhashini Jagu
Specific to
Enrollment
Other NCI Studies
Patient enrollment, CLIA
tissue processing,
clinical data collection,
mechanism to return
patient results
Additional
Sources of Tissue
Samples & Clinical
Data (NEW)
The pipeline began accepting samples from PEC CNS tumors in April 2022
24. Clinical
Sequencing at IGM
(CLIA)
Targeted exome seq
Archer fusion
EPIC methylation
array
Clinical/demographic
Research-Grade
Characterization
(non-CLIA)
WGS
RNA-seq
Proteomics
Metabolomics
Other
Clinical
Kids First X01s
CBTN (CNS tumors;
PI: Adam Resnick),
Sarcomas (PI:
Adam Shlein)
PO: Malcolm Smith
PDX Models
PI: PiVOT
Consortium
PO: Malcolm Smith
Pediatric MATCH
Dx/Normal tumors
PI: Will Parsons/Katie
Janeway
PO: Nita Seibel
Childhood
Cancer Survivor
Study (CCSS)
(Secondary Cancers)
PI: Greg Armstrong
PO: Nita Seibel
CCDI Molecular Characterization: Research Pipeline
CCDI Data
Ecosystem (AWS)
Deposit clinical
and genomic
data
Return of CLIA
genomic results
to patients and
providers
CCDI Molecular
Characterization
Pipeline
Protocol Enrollment/
Sample Collection
(Tissue & Clinical
Data)
**CCDI Funded
PI: Broad Institute,
Hudson Alpha/St. Jude
PO: Malcolm Smith
CCDI Funded
PI: Jaime Guidry Auvil, Tony Kerlavage
PO: Subhashini Jagu
The pipeline released the first bolus of data for research use in November 2022
25. Clinical Sequencing
(CLIA)
Targeted exome seq
Archer fusion
EPIC methylation array
Clinical/demographic
Research-Grade
Characterization
(non-CLIA)
WGS
RNA-seq
Proteomics
Metabolomics
Other
Clinical
CCDI Protocol
Pediatric Cancer Molecular & Clinical Characterization: CCDI Protocol
CCDI Data
Ecosystem
Deposit clinical
and omic data
Return of CLIA
genomic results
to patients and
providers
Patient remains
on CCDI
Protocol; may be
enrolled in sub-
studies or
clinical trials
COG
Project:
EveryChild
MyPART
Other NCI
Studies
Additional
Sources
Kids First
X01s
PDX Models
Pediatric
MATCH
CCSS
Longitudinal follow-up
for select patient
cohorts
CCDI Clinical
Characterization
Pipeline
Comprehensive clinical characterization
Etiology, family history, medical history
Clinical, PRO, imaging
Pathology
NIH Rare Tumor clinics
National tumor Board
Genetic counseling
Core set clinical characterization
Future State/Vision:
• A single study acts as an entry point clinical and research-
grade sequencing
• The study collects a comprehensive baseline and longitudinal
dataset for all patients and an enriched dataset
26. 26
• Gather data from every child diagnosed with cancer in the United States
• It will:
Capture the cancer care trajectory of children and AYAs, including care
provided outside of COG and other networks, to identify gaps and
disparities in care and outcome
Track biospecimen availability
Provide access to data from underserved patients
Provide for consistent research consent
Allow for long-term follow up of childhood cancer patients
• A critical component of this effort will be the National Childhood Cancer
Registry (NCCR)
CCDI National Childhood Cancer Cohort
28. The National Childhood Cancer Registry (NCCR)
28
• NCCR is a centralized infrastructure that brings together existing data on
all cancer patients ages 0 to 39
• Core data are derived from cancer registries
• Data from registries are extended and expanded to include additional relevant
information such as
• Detailed treatment
• Genomic characterization of the tumor
• Risk of recurrence
• Risk of subsequent primary cancers
• With a goal to capture the complete trajectory of care from diagnosis
throughout the patient’s life
• Currently includes patients representing 77% of all childhood
cancers with 21 state cancer registries participating
29. What is a
cancer
registry?
• Cancer registry data provide the base for the
NCCR
• Registries:
• collect, store, and manage data on every person with
cancer from diagnosis until death
• are population based (capture all cancers within a
defined geographic area)
• have the legal authority to capture data on every
cancer
• require health care providers to report information on
cancer patients to state registries
29
30. The National
Childhood
Cancer
Registry
Components
30
Routine linkages will be performed centrally with
external data sources including:
• Complete registry abstracts for each cancer
case (1995-2019+)
• Survival data from National Death Index (NDI)
& State vital records
• Residential history data
• essential to enable data to be linked over
time for treatment, recurrence,
subsequent cancers or adverse effects
• Virtual Pooled Registry (VPR)
• A national system of virtual connections
for all state registries the supports
• de-duplication (many pediatric
patients receive care across state
boundaries) and
• allows capture of subsequent cancers
as patients age
31. New NCCR data sources
Data sources currently used
• Birth
Records,
Blood spots)
Selected
state vital
records
•Long-term
follow-up
center, AACCR
Children’s
Oncology
Group
SEER Cancer
Registries
Leveraging Existing
SEER*DMS servers
holding PII for
Secure Data
Platform
Selected State
Cancer Registries
(including TX, TN, PA, IL, NJ,
OH, FL)
Instance of DMS*Lite
For each registry to hold
PII for linkages
NCCR
Database***
Combines
de-identified
data submitted
from
participating
registries plus
linked data
from additional
sources
Conceptual Framework: National Childhood Cancer Registry
***NCCR Database– holds de-identified childhood cancer
patient data submitted from participating registries.
Infrastructure to support research on childhood cancers 31
32. Why are the data from NCCR important: Providing a Report Card -
Leukemia as an example*
*https://seer.cancer.gov/statistics/nccr/
• Registries provide de-identified
data on every patient with cancer
in a defined geographic region
• These data allow us to
• Monitor progress (report
card) of clinical care on
outcomes
• Help us identify groups of
patients who may not be
experiencing the benefit of
the new treatments by age,
racial and ethnic subgroups,
or geographic area
• Similar trends are being developed
for survival and mortality
32
34. 34
• Designed to federate data from multiple children’s cancer institutions
and community-based and NCI-supported childhood/AYA data
resources, featuring:
Patient-level data from all available sources
Easy access to data to enable deep analytics
Supports interoperability among existing data resources and with tools
and other resources for use by researchers
Provide a central portal to find and analyze childhood/AYA cancer data
CCDI Childhood Cancer Data Platform
35. 35
Establish a Federated Pediatric Cancer Data Ecosystem:
Underlying data science
infrastructure
Enhanced cloud-computing
Services linking clinical, image, &
molecular data
Standards & tools for data
interoperability
Data repositories (e.g. Pediatric
Preclinical Data Commons)
Linked data (National Childhood
Cancer Registry) Genomics Data
Proteomics
Data
Cancer Models
Molecular
Imaging Data
Clinical
Trials
Treatments
Patient Outcomes
Cohort Studies
Electronic
Health Records
Preclinical
Data
Immuno-oncology
Data
Demographics
Clinical
Characterization
Discovery Science Clinical Studies/Care
36. Childhood Cancer Data Catalog
CCDI Annual Symposium
A guide to finding childhood cancer programs, registries, and repositories
Slides thanks to Dr. Patrick Dunn, Frederick National Laboratory
38. 38
ACatalog of Data Resources for Childhood Cancer Research
A guide to
relevant
research
programs,
clinical trials,
data sets and
repositories to
support the
RACE for
Children Act
*
https://datacatalog.ccdi.cancer.gov
41. 41
engage with CCDI
• Visit the CCDI website to learn more
(on the NCI website)
• Review the BSA CCDI Working Group
final report
• Receive email updates from NCI on the
CCDI
• Contact the CCDI with questions about
engagement, ongoing activities, or
funding opportunities
• Look for information about webinars
starting in 2022
The Childhood Cancer Data Initiative (CCDI) improves treatments, quality of life, and survivorship by learning from every child.
As I believe many of you know, CCDI was first proposed in the 2019 State of the Union address to provide a total of $500M over 10 years to the NCI to support childhood and AYA cancers. With limited funds, it was decided to focus on data sharing to maximize the benefit and power of the critical data being generated to study childhood, adolescent and young adult malignancies.
As we know, cancers in children and AYAs are rare — representing approximately 4% of cancers diagnosed annually in the United States, which poses many research challenges.
The CCDI is being leveraged to building a wider community centered around childhood cancer care and research data; one that can collectively improve treatments, quality of life, and survivorship by learning from every child.
Specifically, this means collecting high-quality data, consistently, from every patient diagnosed, particularly those with ultra-rare forms of cancer. And that has to happen whether they are treated at major academic medical facilities, or in their local communities.
Further, those data have to be shared, and made accessible in rapid, comprehensive fashion to ensure that every child can benefit.
The long-term vision for NCI, and for pediatric/AYA cancer communities, is to enhance data sharing, accessibility, and usability in a way that is most broadly beneficial. We want this initiative to serve as an example for how the larger adult research and participant communities can maximize their data value to benefit cancer research.
As NCI has done with other major initiatives, Dr. Sharpless convened this working group supporting CCDI to advise the Institute regarding opportunities to bring together or generate data describing cancers in children and young adults, and to identify and address gaps in data sharing to make it work better for patients, survivors and families.
These members with diverse expertise in the pediatric research and care communities further sought to define the pressing scientific questions that would provide the opportunity to deliver significant changes in outcomes for kids with cancer when answered.
NCI has long had a number of programs dedicated to research in childhood and AYA cancers and survivorship; several that have been ongoing for many years and several more programs that were launched more recently in the context of these Acts, the Cancer Moonshot and now CCDI efforts.
CCDI is an important initiative of the NCI
It allows us the opportunity to look across the investments NCI has already made to address childhood cancer, and think about how those can be linked and leveraged to increase progress
We are confident that using CCDI to bring these datasets together in a thoughtful and meaningful way will begin to provide a firm foundation for the insights and innovation that this data-focused initiative can produce for many years to come.
While these efforts are in some ways on independent paths, NCI is moving them forward together – parallel efforts that complement and strengthen each other, and that will naturally intersect in order to make each effort as scientifically strong as possible, to maximize their success, and their impact for children with cancer.
While efforts are underway to develop the National Childhood Cancer Registry and the Pediatric Preclinical Data Commons here at the NCI, we recognize that CCDI activities started this year and moving forward will need to incorporate mechanisms to more actively federate with additional clinical and research data resources both within and outside of NCI.
Our vision for establishing a wider ecosystem includes many types of data from discovery science, to patient care, to population and surveillance studies that can be queried in a meaningful way by a variety of stakeholders in the pediatric/AYA cancer communities.
Components of this federated pediatric cancer data ecosystem include an essential underlying data science infrastructure, enhanced cloud-computing platforms, Services that link disparate information (including clinical, image & molecular characterization data) stored in data repositories and registries.
Successful implementation of this CCDI infrastructure requires development and promotion of standards & tools for data interoperability, as well as a clear plan for sustainability & data governance to ensure long-term health and function of the ecosystem.
Many resources are available now for clinical trials, data, and analysis
More resources are being actively developed
Finding relevant resources can be challenging
Data Catalog is a NCI sponsored guide for exploring the
CCDI is using each critical piece of data to help the research and clinical care communities complete the puzzle and provide the bigger picture that is needed to understand each individual cancer and improve lives for children and their families.
Focusing CCDI activities on building sound infrastructure to manage and share clinical care and research data, developing and refining tools to analyze and use those data effectively, ensuring our research efforts create comprehensive and meaningful datasets that lead to impactful discoveries, and seeking to collect and use data that can be translated into novel therapies, will provide the pediatric/AYA cancer communities with the answers needed to make progress.
The Childhood Cancer Data Initiative is committed to joining with the full community to improve treatments, quality of life, and survivorship by learning from every child.
Planning efforts for these pending opportunities are currently underway and we expect to have more concrete information coming out over the next few months. So please do continue to check the CCDI website for updates.