CityAge: The Data Effect Vancouver

1. Moving from Big Data to
Better Models of Disease
and Drug Response
Joel Dudley, PhD
Director of Biomedical Informatics &
Assistant Professor of Genetics and Genomic Sciences,
Mount Sinai School of Medicine
Icahn School of Medicine at
Mount Sinai
@IcahnIns(tute

2. Mount Sinai Health System Facts
7
Member hospital campuses
>3,500
Hospital beds
>3,100,000
Patient visits
>6,000
Physicians

4. Mount Sinai is attracting key talent to
thrive in a Big Data world
Demeter

5. There are rarely smoking guns in
human disease biology

6. There are rarely smoking guns in
human disease biology

7. We must embrace complexity to fully
That promise to enable the construction of molecular and that define
understand human physiologynetworks disease
the biological processes that comprise living systems
ENVIRONMENT
Non-coding RNA network
ENVIRONMENT
HEART
protein network
GI TRACT
KIDNEY
metabolite network
IMMUNE SYSTEM
VASCULATURE
transcriptional network
ENVIRONMENT
ENVIRONMENT
BRAIN

8. We must embrace complexity to fully
understand human physiology and disease
“A complex adaptive system has three characteristics. The
first is that the system consists of a number of
heterogeneous agents, and each of those agents makes
decisions about how to behave. The most important
dimension here is that those decisions will evolve over
time. The second characteristic is that the agents interact
with one another. That interaction leads to the third—
something that scientists call emergence: In a very real
way, the whole becomes greater than the sum of the parts.
The key issue is that you can’t really understand the whole
system by simply looking at its individual parts”
.
- Michael J. Mauboussin (investment banker)

9. Although our ability to embrace complexity
will bump up against our want to tell stories
Zeus, the sky god; when he is angry he throws
lightening bolts out of the sky
Ptolemaic astronomy: the earth is
the center of the universe
The earth is flat
Biological processes are driven by simple linearly
ordered pathways (e.g. TGF-beta signaling)

10. Integrating and modeling the digital universe of information

11. We need to be able to leverage the digital universe
of information to solve complex problems
1.8
ZETTABYTES
(1.8 trillion gigabytes) of information
will be created and replicated in 2011and growing fast (it has grown by a factor of 9 in just five years)
Last 2011
IDC
Digital
Ucrackedsponsored
bzettabyte
year WE niverse
Study
the 1 y
EMC

12. Being masters of really big data is now critical
for biomedical research (TB→PB→EB→ZB)
Organisms
Tissues
Single
cells
Single
cell,
real-­‐2me,
con2nuous?

13. Real time observation systems add complex
but powerful new dimensions to NGS
Inter Pulse Distance (IPD)

14. We measure more than we know

15. Exploring the transcriptional landscape of human disease
20k+
Genes
~300
Diseases
and
Condi2ons
Blue:
gene
goes
down
in
disease
Yellow:
gene
goes
up
in
disease

16. Building molecular taxonomies of human
disease
Figure 2. Significant disease-disease similarities. (A) Hierarchical clustering of the disease correlations. The distance between two diseases wa
Suthram S, Dudley J et al. Network-based elucidation of human disease similarities reveals common
defined to be (1-correlation coefficient) of the two diseases. The tree was constructed using the average method of hierarchical clustering. The re
functional a p-value of 0.01 and for pluripotent disease correlations below this line are considered (2010)
line corresponds to modules enrichedFDR of 10.37% and, drug targets. PLoS Computational Biology significant. The different color
represent the various categories of significant disease correlations. (B) The network of all the 138 significant disease correlations. The colo

17. Data Driven Approach to Connect Drugs
and Disease Using Molecular Profiles
Sirota, M., Dudley, J. T., et al. (2011). Discovery and Preclinical Validation of Drug Indications Using
Compendia of Public Gene Expression Data. Science Translational Medicine, 3(96).

18. Topiramate Reduces IBD Severity in a TNBS Rodent
Model of IBD
• TNBS chemically
induced rat model of
IBD
• Animals treated with
80mg/kg topiramate
oral after sensitization
• Prednisolone positive
control (approved for
IBD in humans)
Dudley, J. T., Sirota, M., et al. (2011). Computational Repositioning of the Anticonvulsant Topiramate
for Inflammatory Bowel Disease. Science Translational Medicine, 3(96).

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24. Molecular networks act as sensors and mediators
of complex and adaptive cellular physiology

25. What we are about: Integrating big data across many
domains to build predictive models that improve how we
diagnose and treat disease
Population
Predictive Network Model
Sample
acquisition
Slide
courtesy
of
Eric
Schadt

27. Causal network models generate testable
predictions from in silico experiments
Ultimately want to drive decision making in drug discovery
Novel phosphatase
under development at
Merck for T2D
Grit
Sh3gl2
Prr7
PPM1L
C6
Insulin
Fat
Mass
Irx3
Glra2
Atp1a3
Slc38a1
Glucose
Tcf7l2
Predictions derived from the predictive models
Slide
courtesy
of
Eric
Schadt
Increases fat mass
Negatively impacts
Hypertension genes
Lowers glucose
BAD
GOOD
Raises insulin

28. Predictions are great, but only meaningful if they are validated
GLUCOSE
LOWERED
GOOD
Grit
Sh3gl2
Prr7
PPM1L
C6
Insulin
Fat
Mass
Irx3
Glra2
Atp1a3
BAD
Slc38a1
Glucose
FAT MASS
INCREASED
Tcf7l2
BLOOD PRESSURE
INCREASED
BAD
Slide
courtesy
of
Eric
Schadt

29. Validation of network model
But wait, the network also shows PPM1L and PPARG
prediction in a patient population
(target of Avandia) in a causal relationship
PPARG
PPM1L
Network Predicts:
- Avandia will lower glucose
- Avandia will make you fat
- Avandia will increase
cardiovascular risk
Validation 2 years later:

30. Leveraging NGS and Predictive Network
Models to Drive Personalized Cancer Therapy
Humancell
systemscreening
Pa1ent2specific
mutantflymodels
Pa1ent2specific
xenogra7models
Soma4c
varia4on
Clinical'
CD8'epitope'
predic4on
Tumor'RNA'
Network'
integra4on
Tumor'DNA'
Germline'DNA'
Chemo'
genomic
Public'data'
integra4on
Cancer'Pa)ent'
Profiling
Pa)ent0Specific'
Analyses
Interpreta)on''Screens'
Informed'by'Pa)ent0
Specific'Tumor'Network
Personalized'Report''
Treatment'Op)ons'
Delivered'to'Clinician

31. Personalized multiscale tumor
networks to diagnose and treat cancers
Tumor$biopsy$+$normal
Genomics Core Facility
(Illumina, PacBio, Ion)
RNA$+$DNA
= key driver
Key
driver
targeted
therapy
Patient-specific subnetwork
Predictive network model of cancer

32. Personalized multiscale tumor
networks to diagnose and treat cancers
Tumor$biopsy$+$normal
Genomics Core Facility
(Illumina, PacBio, Ion)
RNA$+$DNA
= key driver
Pa2ent
network
targeted
therapy
Patient-specific subnetwork
Predictive network model of cancer

33. Personalized multiscale networks to
model dynamics of complex disease
DNA
Cell'specific-RNA
Cytokines
Clinical-labs
Physiometrics
0:
min
00
Th1
Th17
0:05 min
0:10
min

34. How to capture all of the clinical data exhaust?
CPOE
EMR
Billing
Telemetry

35. Data driven translational medicine
pipeline at Mount Sinai
BioBank
Research.and.
Clinical.Queries;
Experiment.
CreaAon;.etc.
PaAent.
Traffic
Sequencing.
Facility
Clinical.Labs
Clinical.Data
AcAonable.
Feedback
EMR
(EPIC)
Data.
Warehouse
Disease.Model.
ConstrucAon.and.
PredicAon.
GeneraAon
Primary.Data
HighF
Performance.
CompuAng

36. Multiscale analysis of patient networks
enables precision medicine
=
Genomic
Environment
Clinical

37. Multiscale measures of patients becoming
available through the Mount Sinai Biobank
Diagnoses
DNA
RNA
Drugs
Microbiome
Immune
Labs
Procedures

38. Image credit:
Li Li (ISMMS)

39. Many possible topological analyses can be driven using Mt.
Sinai genotype/phenotype data
Topological network generated using SNP
data separates race
Low
enr
ich
Hig
. di
he
abe
tes
nric
h. d
iab
ete
s
DMSEA
DMSAA
DMSHA
DMSHA,
diabetes enriched

40. The
personal
biosensor
wave
is
forming

41. Printable
tattoo
biosensor

43. Key challenge: incorporate data-driven models
into clinical decision support at the point-of-care
PRAC TICE
CLIPMERGE platform
Rules for actionable
gene/drug pairs
CRAE
Genome-informed CDS
Electronic
health
record
CLIPMERGE
database
This patient has been prescribed clopidogrel
(Plavix®) and is a CYP2C19-poor metabolizer
(*2/*2) according to genomic testing. Poor
metabolizer status is associated with significantly
diminished antiplatelet response to clopidogrel and
increased risk for adverse cardiovascular events
following percutaneous coronary intervention (PCI).
If no contraindication, consider alternative medication
from order set below. Click here to learn more.
Longitudinal clinical data
Clinical genotype data
Mount Sinai
Genetic Testing Laboratory
OK
Reference material
If no contraindication, consider prescribing an alternative
medication. Click the medication name for further information
including indications, dosage and contraindications.
®
PRASUGREL (Effient )
®
TICAGRELOR (Brilinta )
OK
CLIPMERGE PGx saliva sample
from consented BIOMe participant
Drug information
Figure 1 A platform for the implementation of genome-informed clinical decision support (CDS). Saliva samples from BioMe patients sent to the Mount Sinai
Genetic Testing Laboratory are subjected to clinical pharmacogenomic testing. Valid genotypes are released to the CLIPMERGE database, which also contains
longitudinal clinical data extracted from the electronic health record (EHR). These data are assessed by the clinical risk assessment engine (CRAE), which contains
prespecified rules relating actionable genotype–drug pairs to genome-informed advice messages. If a rule is fulfilled, decision support is delivered in real time
via the EHR. A mockup of CDS for a clopidogrel (Plavix) poor metabolizer is shown, consisting of a text segment, a reference link, and an order set with suggested
alternative medications.
Erwin Bottinger
useful genomic information, regardless of how it is generated.
Omri Gottesman
DEVELOPMENT AND EVALUATION OF CDS CONTENT

44. New from Oxford University Press
•
•
PERSONAL
GENOMICS
Disease risk modeling
•
EXPLORING
Visualization
Pharmacogenomics
•
DNA-to-physiology
•
Gene-by-environment
•
More!
JOEL T. DUDLEY KONRAD J. KARCZEWSKI
Foreword by George M. Church
Foreword
by
George
Church
http://exploringpersonalgenomics.org

45. Thank you for your attention
Email: joel.dudley@mssm.edu
Twitter: @jdudley
Web:
research.mssm.edu/dudley/
Icahn School of Medicine at
Mount Sinai