A discussion of Big Data for precision oncology. Mobile applications and eMERGE.

1. Precision
Oncology
and
Big
Data
Warren A. Kibbe, PhD
wakibbe@northwestern.edu
http://wiki.bioinformatics.northwestern.edu/index.php/Warren_Kibbe

2. Opportuni5es
• Big
Data
in
Cancer
– Mobility
and
pervasive
compu5ng
– Social
data
– NGS
– Imaging
(fMRI,
CT
scans)
• EHR
integra5on
– Analy5cs
based
on
clinical
data
– Decision
support

3. Challenges
• EHR
–
we
need
synop5c
and
seman5c
data
to
support
precision
medicine
• EHR
–
Truly
automated
and
useful
decision
support
• Handling
and
analyzing
big
data
• Appropriate,
open
access
to
pa5ent-­‐derived
big
data
• Incorpora5ng
social
data
• Mobile
compu5ng

4. GeMng
Big
• Big
Data
is
about
emergent
proper5es
• Big
Data
changes
the
sta5s5cal
paradigm
–
rather
than
modeling
whether
the
sample
is
representa5ve
of
the
popula5on,
you
have
all
the
data
from
the
popula5on
• How
do
we
combine
systems
biology
and
social
data
with
therapeu5cs
and
big
data
from
healthcare
providers
?

5. -­‐omics,
clinical,
nutri5on,
exposure
• Teasing
apart
the
factors
contribu5ng
to
risk
and
therapeu5c
efficacy
is
complicated!
• Sources
of
data
we
would
like
to
have
across
all
pa5ents:
Genomic
data
Treatment
Outcomes
Microbiome
data
Metabolomics
Exposure
data
Nutri8on
Labs
Behavior
Medical
History

6. -­‐omics,
clinical,
nutri5on,
exposure
• And
of
course
we
would
like
all
these
data
consistent
and
reliable!
Genomic
data
Treatment
Outcomes
Microbiome
data
Metabolomics
Exposure
data
Nutri8on
Labs
Behavior
Medical
History

7. -­‐omics,
clinical,
nutri5on,
exposure
• We
aren’t
there
yet!
• What
can
we
do
now?

8. Examples
of
current
solu5ons
• Mobile
ePROs,
either
at
home
or
in
the
clinic
• Care
diaries
on
tablets
–
response,
recovery
• Integra5on
of
NLP
and
phenotype
algorithms
at
the
point
of
care
• Integra5on
of
clinically
ac5onable
genomic
variants
into
EHR
(think
Hercep5n
and
HER2)
• Decision
support
for
infec5ous
diseases
based
on
social
network
and
GPS
–
not
just
for
MRSA

9. Mobile
compu5ng
• Measuring
depression,
pain
and
anxiety
directly
with
the
pa5ent
• Center
for
Behavioral
Interven5on
Technologies
at
Northwestern
– David
Mohr,
Director
and
PI

10. Mobilyze
(P20
MH090318
PI:
Mohr)
Burns,
M.
N.,
Mohr,
D.
C.
(2011).
J
Med
Internet
Res,
13(3),
e55.
Mobilyze
is
a
mobile
applica5on
aimed
at
trea5ng
major
depression
and
includes
• Didac5c
Content
(text,
video,
audio)
aimed
in
providing
educa5on
about
behavior
change
strategies
• Interac5ve
Tools
that
assist
in
implemen5ng
changes
• No5fica5ons
that
provide
reminders
• Feedback
that
provide
insight.
www.cbits.northwestern.edu

11. Context
Awareness
• Context
awareness
refers
to
the
idea
that
computers
can
both
sense,
and
react
based
on
their
environment.
• A
second
aim
of
the
Mobilyze
project
is
to
harness
sensor
data
from
user
phones
to
develop
models
that
can
detect
treatment
relevant
states
in
real
5me,
which
can
then
be
used
to
posi5vely
reinforce
treatment
congruent
behavior
and
provide
assistance
when
need
is
detected.
www.cbits.northwestern.edu

12. Context
Inference
System
The
Machine
Learner
is
“trained”
using
EMA
(queries)
www.cbits.northwestern.edu

13. Purple
Robot
• A
full
real-­‐5me
sensor
data
acquisi5on
placorm
for
collec5ng
informa5on
about
the
user
and
their
immediate
surroundings.
Purple
Robot
provides
– Full
access
to
the
Android
sensor
framework
(e.g.
the
accelerometer,
gyroscope,
pressure
sensor,
light
sensor,
etc.)
– Access
to
other
device
informa5on
(e.g.
badery
level,
running
soeware
&
apps,
and
hardware
informa5on).
– Op5ons
to
scan
for
external
devices
such
as
wireless
access
points
and
visible
Bluetooth
devices.
– Loca5on
sensors
that
use
the
built-­‐in
GPS
and
cellular
triangula5on
op5ons
to
map
the
user’s
loca5on.
– Local
environmental
data
sources
such
as
solar
event
5ming
(sunrise
&
sunset)
and
weather
condi5ons.
– Communica5on
paderns,
including
phone
logs
and
text-­‐
message
transcripts.
– Cryptographic
anonymiza5on
of
personally-­‐iden5fiable
informa5on
before
it
leaves
the
device.
• Purple
Robot
has
been
open
sourced.
• hdp://tech.cbits.northwestern.edu/purple-­‐robot/
www.cbits.northwestern.edu

14. EHR
Integra5on
• EHR
systems
are
rela5vely
closed
• Implemen5ng
two
way
integra5on
is
difficult
• Innova5on
using
EHRs
is
difficult
• Example:
Lightweight
coupling
of
electronic
pa5ent
reported
outcomes
(ePROs)

15. Mobile
Devices
in
the
Clinic
Andrew
Gawron,
MD,
PhD
Center
for
Healthcare
Studies
John
Pandolfino,
MD
Division
of
Gastroenterology
and
Hepatology
Northwestern
University

16. Outpa5ent
care
Inpa5ent
care
Procedures
Our
approach
16

17. Integration with eNOTIS
Informatics
Ø eNOTIS: Open source
web-based subject
registration system
Ø Meets federal guidelines
for electronic reporting
and addresses a
mandate that accrual
information be tracked,
validated, and reported.
https://enotis.northwestern.edu/login
https://github.com/NUBIC/eNOTIS
17

18. Delivery on an iPad
Ø eCapture is a web-based
system delivers forms for
administrator or patient
facing data collection and
it is linked to eNOTIS.
Ø Information, collected
through these systems,
can be linked with other
clinical information
available in the EHR
https://github.com/NUBIC/surveyor
18

19. Dashboard view by study
19

20. Results
450
>4000 ePROs collected in
1 year in 2 clinics
400
Number of Patients
350
300
250
200
150
100
50
0
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
April
May
Jun
Jul
Aug
Ø 482 patients recruited
v 434 patients completed at least one measure
Ø Mean age 48yrs
v 52.5% female, 87.7% white
20

21. Results: Time burden
Patient Reported Outcome
Measure
Disease Specific
GerdQ
Heartburn Symptom/Experience
Heartburn Vigilance/Awareness
Impaction Dysphagia Questionnaire
Visceral Sensitivity Index
Not Disease Specific
Discomfort Tolerance Scale
Anxiety Sensitivity Index
BSI-18
PANAS
Perceived Stress Scale
Ø Most
# of items
Patients
(N)
Median time, min
(IQR)
6
13
16
6
15
413
432
424
426
432
1.0 (1.6)
1.3 (1.7)
1.8 (1.8)
1.3 (1.8)
1.9 (2.0)
7
16
18
20
4
391
432
430
432
434
1.4 (1.4)
1.8 (1.7)
1.4 (1.4)
1.8 (1.5)
0.8 (0.8)
patients required ≤ 2 minutes for each ePRO measure
Ø Average time to complete all measures: ∼ 20 minutes
21

22. Results: Usability
N=93 patients
Ø ~90% of patients reported the system easy to use
22

23. Results: Satisfaction and Patient Recall
N=93 patients
Ø 95.7% would recommend the system to other patients
Ø 46.2% reported that the system helped them remember
symptom occurrence
Ø 35.5% said that it encouraged them to discuss medical
issues with their doctor
23

24. Decision
Support
and
EHRs
• eMERGE
project
–
NHGRI
funded
study
to
examine
the
validity
of
the
EHR
for
iden5fying
disease
cohorts
for
gene5c
studies
– Automated
disease
/
phenotype
algorithms
– Genera5on
of
SNP
variants
from
a
pa5ent
cohort
• Integra5on
with
EpicCare
the
phenotype
algorithms
• Integra5on
of
genomic
variants
with
care

25. eMERGE
I
Ques5ons
• Technical
– Is
the
informa5on
in
the
EHR?
– How
to
get
it
out?
– Does
it
work
across
ins5tu5ons?
• Ethics,
Legal,
Social
(ELSI)
– Recrui5ng
(Purposeful
/
Opportunis5c)
– Consen5ng
(Opt
in
/
Opt
out)
– Privacy
EHRs and Genomic Discovery
– Data
Use

26. oup Health Cooperative
Northwestern
Epic
Epic
Marshfield ClinicCerner
Internal
eClinicalWorks
Mt. Sinai/Columbi
Epic/Allscripts
Mayo Clinic
GE Centricity+
Cerner
Geisinger
Epic
Coordinati
Vanderbilt ng center
Internal + Epic

27. •
•
Rex
Chisholm
Maureen
Smith
•
•
•
Jennifer
Pacheco
• Will
Thompson
• Arun
Muthalagu
• Anna
Roberts
• Tony
Miqueli
•
• Geoff
Hayes
• Laura
Rasmussen-­‐Torvik
• Loren
Armstrong
• Doug
Scheener
•
•
•
•
Jus5n
Starren
Abel
Kho
Steve
Persell
Phil
Greenland
Bill
Lowe
Mark
Graves
Sharon
Aufox

28. Ph D e f
en ine
ot
yp
e
Multi-diciplinary
Team
Tra
n
Def slate
initi
Dat on to
a
Analyze
Data
A
A
e
atte s
d h
ida m m
alli ritiht
VVa goor
llg
Validated
Phenotype
Data
Warehouse
80+ Years of
Clinical Notes
y
Identif
ts
Subjec
Statistical
Modeling

29. Universal Omics
Complexity / Maturity

30. Primary
Actor
Clinician
Actionability
Disease
Risk:
Order
Test
Disease
Risk:
Change
Behavior
Pa8ent
Disease
Risk:
Watchful
Wai8ng
Low
Disease
Associa8on
Gene8c
Varia8on
of
Unknown
Clinical
Significance
(GVUCS)
Consumer
Researcher
Informa8cian

31. Not
Computable

32. EHR
Integra8on
–
Overview
Results
from
CLIA
cer5fied
lab
EHR
Ancillary
‘Omics
‘Omic
Repository
Complex
Data
Simple
Data
“Lab”
Results
Analy5cs
Option 1
Ac5onable
Adributes
Observa5ons
Option 2
External
DSS
EHR
DSS
Actionable
Results
Patient
Information

33. High-Throughput Sequence Data, Methylation,
Tissue Array, Tertiary Structure, etc.
SNP calls, Regulatory
Network Analysis, etc.
SNPs, Network Activation,
Indels, CNVs, Rearrangements, etc.
Informa8on
Filter for Actionable
Clinical Significance
Na8onal
DB
of
Clinically
Significant
Variants
Clinically Relevant
“Omic” Findings
Knowledge
Pa8ent
Specific
Clinical
and
Environmental
Data
Ac8on
Scien8fic
Literature
EHR
Integration
Personalized
Health Care
Na8onal
DB
of
‘Omic
CDS
Rules
Bedside
Bench
Raw
“Omics”
Data

34. Open
Ques5ons
• What
data
goes
“in
the
EHR”
and
what
data
is
stored
in
ancillary
systems.
• What
clinical
decision
support
is
internal
to
the
EHR
and
what
is
external?
• How
do
we
incorporate
mobile
data?
We
need
to
speed
up
our
ability
to
transform
findings
into
ac5onable
calls

35. Issues
• Appropriate
access
to
precision
oncology
data
–
big
data
in
cancer
needs
innova5on
• How
do
we
promote
pipeline
innova5on
–
data
handling,
mining,
analy5cs?
• How
do
we
build
true
healthcare
learning
systems,
where
every
pa5ent
contributes
to
our
knowledge
of
cancer?

36. CI4CC
Cancer
Informa5cs
For
Cancer
Centers
www.ci4cc.org

37. Thank
you
Warren
A.
Kibbe
wakibbe@northwestern.edu