Unraveling the complex biology of cancer using data science and single cell technologies in the Human Tumor Atlas Network (HTAN)

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

3. Topics
• Survivorship
• HTAN
• Precision Oncology

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

11. Understanding Patient Trajectories
We are all guilty of
looking under the lamp
post. A single measure
of a tumor is
convenient, but not
biology.

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

13. Understanding Patient Trajectories
But we really need to be able see the whole road. Continuous sensors, smart homes, smart cities

14. Biology is so much more than DNA
https://xkcd.com/1605/

16. Biological Scales
Molecular to Systems Biology
DNA
Large molecule
Sequence
encodes
identity of protein
Humans: 23 pairs
of
DNA molecules
over 4 billion
nucleotides per
pair
Protein
109 10-12 -10-4
Complexes
30,000 proteins
with variants
proteins,
cofactors,
metabolites,
2nd msgrs
10-6 -103
Organelles,
cells
10-3 -104
Tissues
1 -108
Organs
1 -109
10-9- 10-4 10-9 -10-8
10-8 -10-7 10-7 -10-5
10-6 -10-2 10-3 -1
Size scale (meters)
Time scale (seconds)
compartments
structures
function
signaling,
networks
emergent
properties
© 2004 WAKibbe, Northwestern University

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

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!

22. Example Basic Science Problem

23. NCI RAS Initiative +
NCI-DOE Joint Initiative
https://science.energy.gov/~/media/ascr/ascac/pdf/meetings/201809/ASCR2018_streitz_nomovies.pdf Fred Streitz

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

26. Machine Learning and Data Analytics requires
computing horsepower

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!

28. NCI RAS Initiative +
NCI-DOE Joint Initiative
https://science.energy.gov/~/media/ascr/ascac/pdf/meetings/201809/ASCR2018_streitz_nomovies.pdf Fred Streitz

29. NCI RAS Initiative +
NCI-DOE Joint Initiative
https://science.energy.gov/~/media/ascr/ascac/pdf/meetings/201809/ASCR2018_streitz_nomovies.pdf Fred Streitz

30. Another Cancer Moonshot Project

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

35. Tumor progression

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

43. protocols.io
https://www.protocols.io/view/single-cell-rna-seq-expression-analysis-pbidike/abstract
https://www.protocols.io/download?s3url=https://s3.amazonaws.com/pr-journal/vq9fw26.pdf&ofn=Preparation%20of%20Single-
Cell%20RNA-Seq%20Libraries%20for%20Next%20Generation%20Sequencing.pdf

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

53. • So what does that look like when we
do single cell analysis??
• --stay tuned!

54. HTAN Data Coordinating Center

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

57. Cross-consortium initiative to standardize meta-
data

58. The Process for Developing a Metadata
Standard: RFCs
1. Existing
Standards
2. Working
Group input
3. Request for
comments (RFC)
4. HTAN
standard

59. RFC Status
● Closed: Clinical 1,
Biospecimen 1, Imaging 1&2,
scRNAseq 1, bulk RNAseq 1-
3, scATACseq 1
● Open: Clinical 2, scRNAseq
2-4, WES 1-3
● Drafting: Biospecimen 2,
scATACseq 2, ...

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

66. Architecture
SynapseCloud Buckets Data Release Portal
Content Management
System
Single
Cell
Viewe
r
...Imaging
Viewer

67. HTAN beta

68. Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe

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

70. NCI Genomic Data Commons

71. NCI Genomic Data Commons

72. NCI Genomic Data Commons

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