How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?

1. The reality of moving towards precision
medicine
Elia Stupka

2. Beyond Personalized Medicine: Participation and Precision

3. Diseasomes and pathways are here to stay!
From common complex diseases to “multiple rare
diseases” and “biological pathway clusters”
From diseases to “diseasomes”
From risk factor to “risk pattern”
From clinical utility to “personal utility”

4. Diseasome driven example: IMI Project Aetionomy
IMI project involving several pharmas and a dozen academic sites to model
neurodegenerative disease and cross-disease models, e.g. synapse

5. Unique opportunities emerging from NGS scale-up
Cheap Data
Generation
thanks to
Accessible
–OMICS
Technologies
Vast Medical Data
Collection
thanks to
Patient
Engagement
technologies
Complementing Big
Data Approaches
with
High Throughput
Experimental
Validation
Tackling Complex
Disease:
Link Detailed
Subphenotypes to
Specific Biology

6. Challenges: beyond interpreting single genomes…

7. Big Data: Seeing vs Looking
I am looking for my biomarker in the right place
and with the right tool, I know it’s the right one…
With –OMICS
we can “see”
“everything”
Need to know
what you are
looking for!
Before knowing non-
coding RNAs existed
in the genome we
were not “seeing” them

8. Genome Layers: more than meets the eye
Coding gene RNA-Seq: microRNAs, lncRNAs, alt. splicing…
CAGE-Seq:TSSs
DNA Methylation
Chip-Seq
/Histone Code
Sequence Variants: SNPs, INDELs, CNVs, Structural Variants
Allele Specific Expression
Allele Specific Methylation

9. Moving from statistics to deep biology, and then back to
statistics
Move Away
from N=1000
Approach for
Research
Move towards:
Understand How THIS
Gene/Pathway in THIS Patient
is causing THIS Phenotype
AND ONLY THEN:
Go back to 10,000s of well
defined patients who can benefit
from drug targeted to specific
biology!

10. Moving towards understanding of single phenotypes
through combination of NGS, HCS, Comp Bio and Brains!

11. Example of deep single patient work

12. Getting the right target, a fitting example: our
collaboration with Circuit Therapeutics
• Thanks to this collaboration we are able to explore SINGLE CELLS IN SINGLE
CIRCUITS RESPONSIBLE FOR SPECIFIC BEHAVIOURAL PHENOTYPES
• Combining the power of NGS with the power of Optogenetics helps us shed light on
specific genes and their role in these circuits that would otherwise be lost in the
complexity of the brain
• We can compare „same“ cell types in different circuits

13. Mining the IGNOROME
• No differences in expression
between ignorome genes and well
known genes
• Main difference is how early they
were discovered, and moved onto
the „bandwagon“
• Let‘s get off the bandwagon!
• In our first dataset we have a few
dozen „ignorome“ genes!

14. How about putting patients at the CENTER of the whole
research and development process?
BI initiated a partnership with Patients Like me to develop a community for IPF: Idiopathic
Pulmonary Fibrosis, a rare and rapidly progressive diseae (from diagnosis to death: 3 years)
In the meantime, a few weeks ago, BI had the first drug ever approved for IPF on the market: Ofev
(Nintedanib)
This drug will lengthen significantly the life of IPF patients, but we can do much more, if we
understand the basic mechanisms that lead to fibrosis
The partnership is a unique, and much deeper way, to engage with patients, their symptoms, and
understand their progression

15. Over 2,500 patients enrolled!

16. In one year, more patients than any current trial

17. The Patient-Centric Data Onion
Online Virtual Community
self-reported information
Integration of clinical data
(HER + Clinics)
Collection of patient samples
and live wearable data
We can move from
online to the real
world, with samples
and wearables and
develop a much
deeper view of each
patient

18. Where can this take us?
Systems Modelling
of Disease
Progression, DAY by
DAY
Engaging very specific patients
for research projects integrating
clinical health records, imaging
data, genomics profiles.
Much clearer
patient stratification,
phenotype
sublcuster analysis
Clearer understand of REAL
LIFE experiences of the
disease, specific symptoms
which cause distress, unmet
medical needs, etc.

19. Moving towards precision medicine
PRECISE BIOLOGY
(e.g. Optogenetics)
PRECISE PATIENTS
(e.g. Patients Like Me)
PRECISION MEDICINE

20. The „Ecosystem“ beyond sequencing
Xten, GEL, etc provide
unprecedented Seq capacity
There are important efforts, public
and private, to make downstream
interpretation easier and cheaper
What about UPSTREAM? New
bottlenecks: biobanking, consent,
logistics, homogenous clinical data

21. The big buzzword: big data to transform healthcare…

22. WHAT: overview of the varied landscape of big data in
health
22
Source: Weber G, Mandl K, Kohane I. Finding the Missing Link for Big Biomedical Data. Journal of the American Medical Association, May
22, 2014. doi:10.1001/jama.2014.4228. Authored by the CTO of Harvard Medical School.

23. In 2010, GE was
risking to go out
of business, due
to its old business
model: sell
hardware and
repair it, risking to
become a
commodity
provider.
The money was
moving to IBM,
SAP and big-data
start-ups.These
companies were
proposing to
provide data and
analytics on data
from GE
equipment to
derive efficiency
and other benefits
In 2011 GE
initiated a
multibillion dollar
investment
towards the
industrial
internet: turn
every instrument
into into a cloud-
connected
communicating
device and
opening a CA
based big data
focused site
Now GE doesn‘t
sell jet engines, it
sells business
efficiency (more
miles flown, less
downtime) based
on cloud-enables
real-time decision
making.
The General Electrics Case Study
PDF

24. Business model change at GE
PHARMA:
• Use data and analytics to
prodive real-time
decision support for
targets, biomarkers,
clinical trials, and
markets
• Optimize and expand
customer, i.e. patient
outcomes
• Optimize assets (i.e.
patient-derived data) and
operations (i.e. where
and how knowledge is
utilized

25. GE was not agile,
responsive and
strategically
coherent in the
computational area
In 2011 all
IT/software/data
efforts were
scattered and
fragmented, with
12,000 software
professionals
working based on
local requirements,
frameworks,
technologies,
vendors, etc.
A new head was
brought in, a new
center built in
California, many
new, highly talented
people brought in,
agile with latest
technologies and big
data approaches.
150 people in 2013,
1,000 end of 2014.
Result: 1B$
incremental income
in 2014
Another example:
NEST, the digital
cloud-enabled
thermostat, bought
by Google for 3.2B$
The „only“
difference with a
normal thermostat
is that the data in
the cloud enables
companies to drive
efficiency, reduce
cost, increase profit.
Where did the GE investment go?

26. Knowledge, in 2015, an asset as valuable as compounds?
&

27. Patient profile
• Early signals from
wearable devices
• Genetics
• Who owns the data?
Apple, Google,
Patients Like Me,
23andMe?
Diagnosis
• Hospital general
diganosis, precise
biomarker driven
diagnosis?
• Who owns the data?
Illumina? Theranos
working directly
with Wallgreens as
one stop shop
Disease
Modelling
• Choice of
environmental,
lifestyle, natural, etc.
options. Eefficacy,
treatment switching
and treatment
combination,
• Who owns the data?
Patients Like Me?
Drug/Biomarker
• Drug approved,
for specific
subset of
patients
• Owned by
Pharma,
sometimes with
proprietary
biomarker
????
• Who can drive an
„ecosystem“ to
connect the dots
from early
symptoms to
precise
medicine?
• A 21st century
hospital? A
technology giant
like Apple or
Google? Or a
21st century
Pharma?
CONTEXT: key challenge in health: owning the interpretation
power to move from data to precise diagnosis to precise cure

28. Are we moving towards an App Store of Research Data?

29. Elia Stupka – elia.stupka@boehringer-ingelheim.com
Thank You!