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
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.
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
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
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.
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
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.
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
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
The Business of Genomic Testing by James CrawfordKnome_Inc
View this webinar at: http://www.knome.com/webinar-business-of-genomic-testing. This presentation discusses the findings of a College of American Pathologists survey of “early adopters” of NGS recently published in "Genetics in Medicine". The study objective was to identify the reasons for health systems to bring next-generation sequencing into their clinical laboratories and to understand the process by which such decisions were made. A standardized open-ended interview was conducted with the laboratory medical directors and/or department of pathology chairs of 13 different academic institutions in 10 different states.
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.
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.
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.
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
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
The Business of Genomic Testing by James CrawfordKnome_Inc
View this webinar at: http://www.knome.com/webinar-business-of-genomic-testing. This presentation discusses the findings of a College of American Pathologists survey of “early adopters” of NGS recently published in "Genetics in Medicine". The study objective was to identify the reasons for health systems to bring next-generation sequencing into their clinical laboratories and to understand the process by which such decisions were made. A standardized open-ended interview was conducted with the laboratory medical directors and/or department of pathology chairs of 13 different academic institutions in 10 different states.
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.
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.
American Association for Cancer Research Annual Meeting 2022
Analysis of images of routinely acquired tissue specimens promise to provide biomarkers that can be used to predict disease outcome and steer treatment, improve diagnostic reproducibility, and reveal new insights to further advance current human understanding of disease. The advent of AI and ubiquitous high-end computing are making it possible to carry out accurate whole slide image morphological and molecular tissue analyses at cellular and subcellular resolutions. AI methods are can enable exploration and discovery of novel diagnostic biomarkers grounded in prognostically predictive spatial and molecular patterns as well as quantitative assessments of predictive value and reproducibility of traditional morphological patterns employed in anatomic pathology. AI methods may be adapted to help steer treatment through integrative analysis of clinical information along with Pathology, Radiology and molecular data.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
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
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.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
Presentation at Pathology Visions 2017 - https://digitalpathologyassociation.org/2017-pathology-visions-agenda
I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Characterization of the c...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: Characterization of the cell phenotypes involved in metastasis
Characterization of the cell phenotypes involved in metastasis: Using tranSMART to enable high-throughput heterogeneous data integration and analysis
Brian Athey, University of Michigan
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.
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!
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.
Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
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.
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!
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
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
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.
1. Cancer Biology, HTAN, Cancer
Trajectories, and Precision Oncology
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe
#PrecisionOncology
#DataSharing
#CancerBiology
#SingleCellTechniques
#DataHarmonization
Some slides from
Ethan Cerami @HTAN,
Fred Streitz @ LLNL
Thank you!
2. Personal & Professional Background
• PhD in Chemistry at Caltech, Postdoc in
molecular genetics of RAS
• Cancer research for 20+ years - cancer
informatics, data science, healthcare
• Faculty in the 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
• Lost three grandparents to cancer, father to
cancer in 2019
4. As of January 2020, there
were an estimated
17,200,000
cancer survivors in the U. S.
From https://cancercontrol.cancer.gov/ocs/statistics/statistics.html ,
based on Bluethmann SM, Mariotto AB, Rowland, JH. Anticipating the
''Silver Tsunami'': Prevalence Trajectories and Comorbidity Burden among
Older Cancer Survivors in the United States. Cancer Epidemiol Biomarkers
Prev. (2016) 25:1029-1036
5. In 2030, there will be an
estimated
22,000,000
cancer survivors in the U. S.
From https://doi.org/10.3322/caac.21565 Miller KD, Nogueira L, et al,
Cancer Treatment and Survivorship Statistics, 2019. CA Cancer J Clin
(2019) 0:1-23
Survival, incidence, and all-cause mortality rates were assumed to be
constant from 2016 through 2030.
6. Cancer Survivors
• Financial toxicity
• Neuropathy
• Lymphodema
• Cardiotoxicity
• Sexual dysfunction
• Chronic fatigue
• Cognitive
impairment
• Disfigurement
• Self-image
• Risk of recurrence
Side effects of cancer treatment
Just to name a few side effects
7. 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.
Ramifications across many aspects of health care
8. Take homes
• The Human Tumor Atlas Network
(HTAN) is a deep dive in understanding
cell diversity, interplay with biological
systems, population diversity, and
disease progression
• Single cell techniques are changing our
view of biology
• Data science is everywhere
• Understanding the patient context, the
patient trajectory, is key
9. Technology Today
• Devices, smart phones, smart
homes, smart cars, smart cities
• Real World Evidence
• Built Environment Data
• Precision Medicine – Learning Health
• Single-cell measurements
• Data Science
Technology now allows us to measure our world, our
environment, our interactions, and ourselves at scale
10. Fundamental Changes
• Data generation is not the bottleneck
• Most data are now ‘digital first’
• Old statistical models assuming variable
independence are inadequate – systems
and pathways are not independent!
• Project management is critical in scaling
population science
Well-defined experiments are still key
12. Understanding Patient Trajectories
We are now measuring
a few more points
along the path.
ICGCmed, HTAN, Kids
First, BeatAML all have
longitudinal collection,
multi-modal
measurements
17. Health vs Disease
• What is ’normal’?
• Systematic and measurement error
• Biological heterogeneity
• Population Health
18. The promise
of precision
medicine:
How can we
meaningfull
y group
patients? signs and
symptoms,
demographics,
exposure, diet,
traits, etc.
Slide from Melissa Haendel
19.
20. Cryo-EM
• Able to get atomic resolution of
flexible molecules, like membrane-
bound proteins
21. Single cell techniques
• Sequencing
• Proteomics
• Metabolomics
• Microenvironment
https://arxiv.org/abs/1704.01379
Growing ability to focus
on dynamics!
24. Multi-modal experimental
data, image reconstruction,
analytics
Adaptive spatial
resolution
Adaptive time
stepping
High-fidelity subgrid modeling
Experiments
on nanodisc
CryoEM
imaging
X-ray/neutron
scattering
Protein structure
databases
Adaptive sampling molecular dynamics
simulation codes
Unsupervised deep
feature learning
Uncertainty quantification
Mechanistic
network models
RAS activation
experiments
(FNLCR)
Phase field
model
Coarse-
grain MD
Classical
MD
Machine learning guided
dynamic validation
Granular RAS
membrane
interaction
simulations
Atomic resolution
RAS-RAF interaction
RAS Activation
Predictive simulation
and analysis of RAS
Phase Field model of
lipid membrane
Cancer Moonshot Pilot 2
25. Lipid content: RAS/HVR binding by SPR, alpha
assays in nanodiscs, liposomes, imaging in GVUs,
lipidomics, SANS (possibly with contrast variation)
RAS/HVR mobility & dynamics: single particle
tracking, FCS, CG simulations of farnesylated
HVR and RAS on nanodiscs and membranes,
use to constrain phase field coupling
RAF-membrane affinity: SPR in liposomes,
biophysical measurements, MD
simulations to identify regions of interest
that interact with membrane
RAS/HVR-membrane binding: SPR in liposomes,
biophysical measurements, SANS (with contrast
variation), AA and free-energy calculations of RAS/HVR
binding to constrain CG parameters, free energies to
inform phase field
HVR structure/dynamics: crystallization,
CD, MD of HVR in multi-component lipid
platform to inform mobility in phase field
model
RAS activity & structure: GTPase, GTP off-rate,
crystallization, NMR, cryo-EM?, SANS, AA MD
simulations constrain CG parameters
RAS-RBD structure: crystallization, NMR, AA
simulations to constrain CG parameters
RBD-CRD and CRAF structure: crystallization, NMR,
cryo-EM, CG simulations validated against AA
simulations
RAS-RBD binding: SPR, ITC, alpha assays in
nanodiscs, TIRF, SANS (possibly with contrast
variation), compare with AA simulations and
constrain CG simualtions
RAF activation: dimerization, phosphorylation state(s), long time-scale CG simulations
and kinetic estimation, multi-scale simulations multi-scale simulations of RAS/RAF
dynamics on membrane
RAS/HVR multimeric state: BRET,
step photobleaching, PALM, AA and
CG MD of KRAS/HV R on
nanodisc and multi-component lipid
platform
Experimental data to inform modelSimulations to build model
Farnesyl dynamics: solid state NMR,
AA and CG simulations of farnesyl in
membranes and lipid bilayers
informs phase field model
Lipid domains: Confocal microscopy
RAS/HVR localization in GUVs, Calibrate
coarse-grained (CG) simulations with all-
atom (AA) Simulations, Calculate free
energies of domains
Close collaboration of experimentalists and
theorists to build predictive model
27. Team Science is critical
Clinical Trials
Biostatists
Bioinformatics
Clinical Care
Clinical Research
EHRs, Imaging, Lab Systems
Data Science, ML, BD, HPC
Analytics and Visualization
Open Data enhances collaboration and team science!
31. Human Tumor Atlas Network
Creating integrative, predictive models that cross time and
spatial resolution scales
31
10 sites
Precancerous lesions Cancerous lesions
Molecular characterization Deep phenotyping
Single cell transcriptome Single cell DNA
Subcellular imaging Cellular and tissue imaging
Proteomics Metabolomics
Microbiome Biolayers and fluidics
32. Health vs Disease
• What is ’normal’?
• Systematic and measurement error
• Biological heterogeneity
• Population Health
41. Policy Working Group
Chairs: Justin Guinney (Sage Bionetworks (DCC)), Bruce Johnson (DFCI), Aviv Regev (Broad (HTAPP))
Clinical / Biospecimen Working Group
Chairs: Warren Kibbe (Duke), Dan Merrick (UC-Denver (BU Research Center)), Asaf Rotem (DFCI (HTAPP))
Molecular Characterization Working Group
Chairs: Peter Sorger (HMS), Orit Rozenblatt-Rosen (Broad), Ken Lau (Vanderbilt)
Data Analysis Working Group
Chairs: Li Ding (WUSTL), Dana Pe’er (MSKCC), Kai Tan (CHOP)
Human Tumor Atlas Network (HTAN)
42. RFC Draft
RFC Draft
DCC
Data
Release
Tissue Repository and
cross-network projects
Tissue Processing
Guidelines
(1-2 protocols)
AugustJanuary 2020
Clinical Biospecimen Work Group time-line
RFC Draft
Open for Comments
RFC Release
Biospecimen
Metadata, tier 1
Use-cases
Standardized Repository of Reference Specimens (SRRS)
• Management Committee Policy
o SRRS Tissue Requirements
o Tissue Request Process
• Assessment of diversity, profile of HTAN centers
• Specimens survey
• Proposals for projects
• Diversity survey across HTAN
Open for Comments
Clinical Data Elements
tier 2 (closed)
Open for
Comments
RFC Release
• HTAN Identifiers
• Clinical Data Elements, tier 1
RFC
Release
RFC Draft Biospecimen Metadata, tier 2
Clinical Data Elements, tier 3
47. Enabling Understanding
• A brief tour of computational biology
• Duke Human Tumor Atlas Project
DCIS and breast precursor lesions
– Shelley Hwang, Duke
– Rob West, Stanford
48.
49.
50.
51.
52.
53. • So what does that look like when we
do single cell analysis??
• --stay tuned!
55. DCC is focused on four “buckets” of work
1. RFCs & Metadata (Vesteinn)
2. Data Ingress (Justin)
3. Imaging Plans (Niki)
4. Data Release Portal (Ethan)
56. HTAN Metadata
Describes
● Clinical attributes
● Properties of biospecimens
● Information on data files
● Relationships between and among the above
Will be used to locate data in HTAN
● Data Release Portal
● Synapse
61. Three EASY steps for data deposition
1
Upload your data to Synapse
● Contact your DCC liaison prior to upload:
1. Indicate desired cloud storage (AWS, GCS)
2. DCC initializes your Synapse project and storage location
62. Feature Highlight: Data Ingress Step 3
3
Annotate and submit your metadata: initiates data
validation and sharing
Metadata can be
filled in and stored
offline
63. HTAN images
Multichannel, H&E, radiology
Data
ingress and
metadata
Minerva
Access control via Synapse login
Image
validation
Proposed HTAN imaging
plan
Imaging metadata RFC
Brian White (Sage) to lead DCC imaging efforts.
64. OMERO Minerva Digital Slide Archive
● Tool for interpreting and interacting with
complex images (CyCIF, IHC, H&E)
● Image viewing via OpenSeadragon
● Guided analysis approach → stories
● Enables fast sharing of large image data
stored on Amazon S3
● Open source → opportunity for joint
development
● Image and metadata management /
storage
● Image viewing
● Analytics
● Large, active user community
● (Web) client / server architecture
● Centralized repository
Capabilities of OMERO, Minerva, and
DSA
● Web-based visualization for medical
imaging data (radiology, pathology)
● Image viewing with DICOM plugins,
OpenSeadragon, OpenLayers, Leaflet
● Support for multiple storage types (S3,
GCS, NFS, local)
● Tools support web-based image
annotation/markup
● Visualization of computer generated
annotations (e.g. show 100,000+ cell
boundaries)
65. Bird’s Eye View
Atlas A
Atlas B
Atlas B
Atlas Overview
Data Overview
Publications
Biospecimen/Clinical Data
Derived Data
Primary NGS Data
Content set by atlas
Imaging Data
Auto created via Synapse
Links to NIH Controlled
Access Repo
Legend
69. Project GENIE has already released 80,000 cancer genomes. Kudos to Drs. Charles Sawyers
at MSKCC and Shawn Sweeney at AACR and so many people for making this happen.
http://cancerdiscovery.aacrjournals.org/content/7/8/818
Project GENIE
http://www.cbioportal.org/genie/login.jsp
73. Data Harmonization
• The process of semantic and
syntactic mapping of data to a set of
definitions, predefined data
elements, data model.
• Validation and Harmonization of
primary and secondary data is crucial
to enable analysis and reuse
74. Spanning the Semantic Chasm of Despair
Building a Translational Bridge
CD2H
Thanks to Melissa Haendel
75. Applying Machine Learning
• Using sensors, IoT devices to
understand and intervene
individually at a national scale before
an acute episode
– Opportunities in prevention, monitoring
for adverse events in patients being
given therapy, behavior and improving
survivorship
Editor's Notes
We now have tools to let us both understand social determinants of health and build ‘early warning systems’ to identify people who are have acute medical issues
Biochemical/biophysical properties of fully processed KRAS4b
RAS on nanodiscs
Lipid composition of RAS:membrane interaction
RAS:RAF binding in the context of nanodisc membranes
Structure of RAS on membranes
Crystallography of KRAS, and KRAS:effector complexes
NMR of KRAS bound to nanodiscs
X-ray/neutron scattering of KRAS on nanodiscs
Cryo-EM imaging of KRAS protein complexes (+effectors)
Dynamics of RAS in membranes
Supported bilayers in vitro
Live cell imaging with single molecule tracking
Adaptive spatial resolution (e.g., sub-grid modeling)
Propagating both coarse-grained and classical (atomistic) MD information, we aim to maintain the highest fidelity possible at the point of interactions while capturing long distance effects
Multiple time scales
By judiciously switching between spatial scales we enable investigation of timescales that are orders of magnitude longer than possible with fine-scale simulation alone.
Automated hypothesis generation and dynamic validation
We will efficiently and accurately explore, e.g., possible interaction sequences by coupling Machine Learning techniques with large-scale predictive simulation.
Extreme scale simulation
Requried novel computational algorithms and techniques will be developed for use on Sierra-class architectures, and will be designed for exascale.
Deep learning algorithms
Powerful pattern recognition tools will accelerate our predictive simulation capability by giving rapidly identifying, e.g., the time or region where a sub-grid model is needed or by logically exploring an intractably large decision tree.
Uncertainty quantification
Application of our extensive capability will be tested in the new (highly uncertain) world of biology and healthcare, leading to new insights and the development of new methods
Scalable statistical inference tools
The continued convergence of data analytics and predictive simulation as we approach exascale will require statistical tools that scale far beyond what is current, requiring the development of new strategies.
We now have tools to let us both understand social determinants of health and build ‘early warning systems’ to identify people who are have acute medical issues
Answering some important scientific questions need diverse teams, team science, and project management!
Colleagues at MD Anderson
Important to make use of standards that other consortia have developed
Cell Atlas Curation WG
Common core elements from meta-data use cases
Resources & best practicesschema.org: Open metadata standard for the web developed by Google, Microsoft, Yahoo, Yandex, and W3C