This document discusses how SAP's HANA in-memory database platform can be applied to advance personalized medicine by enabling real-time analysis of large datasets from various sources, including biological, clinical, and lifestyle data. Key points:
- SAP HANA allows fast loading and analysis of large genomic and other biomedical datasets in real-time to accelerate healthcare research
- SAP is collaborating with Stanford University to apply HANA to genome-wide analysis from human population studies to further global health
- A genome analysis application built on HANA could support computational pipelines from sequencing to variant calling to annotation and integration of various data types
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Applying innovative commercial technology to deliver on the promise of personalized medicine
1. Applying
innova,ve
commercial
technology
to
deliver
on
the
promise
of
personalized
medicine
Enakshi
Singh,
M.Sc.
SAP
Product
Management
SAP
HANA
Pla6orm
for
Healthcare
Public
2. SAP powers analytics that
SAP customers represent
86% of Global Fortune
500 companies.
74% of the world’s
transaction revenue
touches an SAP system.
Our customers
produce more than
82% of the world’s
medical devices.
track more than 6 billion
U.S. stock trades per day to
identify fraud and protect
investors.
Our customers
distribute more than
76% of the world’s
healthcare products.
Our customers
produce more than
77% of the world’s
beer.
Public
3.
But
can
you
apply
the
latest
technology
developments
and
capabili>es
to
further
healthcare
research?
Dr. Hasso Plattner,
Founder and Chairman of SAP
Supervisory Board
Public
4. Our
Vision:
Enable
Personalized
Medicine
Lifestyle
Data
SAP
HANA
Pla@orm
For
Healthcare
Clinical
Data
(EMRs)
Biological
Data
(-‐Omics
Data
+
Annota>ons)
Can
we
interpret
all
pa>ent
data
during
a
pa>ent’s
visit?
Real-‐>me
Data
Capture
&
Analysis
All
Relevant
Medical
Informa>on
Public
5. Vision:
SAP
HANA
Healthcare
Pla6orm
Informa>on
and
Feedback
within
the
Window
of
Opportunity
Pa>ents
Doctors
Insurers
Researchers
Real-‐Time
Data
Capture
and
Analysis
SAP
HANA
Healthcare
Pla6orm
Biological
Data
(e.g.
genomics)
Clinical
Data
(e.g.
EMRs)
Lifestyle
Data
(Care
Circles)
Lifestyle
Data
...
All
Relevant
Medical
Informa>on
Public
6. In-‐Memory
Technology
Building
Blocks
●
●
●
●
Read Event
Read Event
Repositories
Repositories
Combined
column
and
row
store
+
up to 2.000
up to 2.000
requests
requests
per second
per second
Minimal
projec>ons
Any
aVribute
as
index
Bulk
load
Discovery Service
Discovery Service
Mul>-‐core/
paralleliza>on
SAP HANA
SAP HANA
No
aggregate
tables
On-‐the-‐fly
extensibility
Map
reduce
P
P
t
A
A
Lightweight
Compression
Par>>oning
SQL
Analy>cs
on
historical
data
Single
and
mul>-‐tenancy
Object
to
rela>onal
mapping
Group
Key
SQL
interface
on
columns
&
rows
Reduc>on
of
layers
x
x
P
Ac>ve/passive
data
store
Dynamic
mul>-‐
threading
within
nodes
+
+
+
up to 8.000 read
up to 8.000 read
event notifications
event notifications
per second
per second
Insert
only
for
>me
travel
+
+
+
+
A
Verification
Verification
Services
Services
T
Text
Retrieval
and
Extrac>on
No
disk
Public
7. ●
●
●
●
Read Event
Read Event
Repositories
Repositories
Verification
Verification
Services
Services
SAP
HANA
Database
Technology
up to 8.000 read
up to 8.000 read
event notifications
event notifications
per second
per second
up to 2.000
up to 2.000
requests
requests
per second
per second
Discovery Service
Discovery Service
T
SAP HANA
SAP HANA
Bulk
load
P A
A
P
Fast
inser,on
of
large
genomic
datasets
or
other
relevant
datasets
Text
Retrieval
and
Extrac>on
Search
doctor’s
notes,
diagnoses,
etc.
(unstructured
data)
Lightweight
Compression
Fit
big
data
in
main
memory
while
allowing
fast
retrieval
SQL
+
+
+
Mul>-‐core/
paralleliza>on
Speedup
of
relevant
queries
across
many
nodes
SQL
interface
on
columns
&
rows
Easily
connect
with
other
tools
(e.g.
Rstudio)
On-‐the-‐fly
extensibility
Adap,ng
to
new
format
requirements
without
going
offline
(e.g.
changing
VCF
files)
Public
9. SAP
and
Stanford
Human
Popula>on
Genomics
and
Global
Health
“
”
"We
have
been
thrilled
to
work
with
SAP
and
HPI
on
a
collabora,on
to
accelerate
DNA
sequence
analysis.
In
our
pilot
projects,
we
are
seeing
drama,c
speedups
in
compu,ng
on
human
genome
varia,on
data
from
many
samples.
We
are
dreaming
of
what
will
soon
be
possible
as
we
integrate
phenotype,
genomics,
proteomics,
and
expose
data
to
empower
complex
trait
mapping
using
millions
of
health
records.”
-‐
Professor
Carlos
D.
Bustamante
at
the
Stanford
University
School
of
Medicine
Dr. Carlos D.
Bustamante
Analyze
genome
wide
paVerns
of
varia>on
within
and
between
species
to
address
fundamental
ques>ons
in
biology,
anthropology,
and
medicine:
implica,ons
for
global
health
and
disease
SAP
HANA
to
contribute
to
all
parts
of
computa>onal
pipeline
Sequencing
Pa>ent
Samples
Alignment
Raw
DNA
Reads
Mapped
Genome
Variant
Calling
Annota,on
and
Analysis
Discovered
Variants
Follow-‐up
and
Valida>on
Public
10. SAP
HANA
for
Healthcare:
Genome
Analysis
Applica>on
Computa>onal
Pipeline
e.g.
Bioinforma>cian
Sequencing
Service/Lab
e.g.
Biologist
Sequencing
Pa>ent
Samples
•
Alignment
Raw
DNA
Reads
Computa>onal
Analysis
e.g.
Clinicians
AND
Researchers
Variant
Calling
Mapped
Genome
Annota,on
and
Analysis
Discovered
Variants
Follow-‐up
and
Valida>on
All
steps
a`er
sequencing
will
be
supported
within
the
genome
analysis
applica>on
(from
raw
DNA
reads
to
annota>on
&
analysis)
-
Real-‐>me
analyses
of
large-‐scale
genome
variant
datasets
-
Implement
read
alignment,
variant
calling
&
all
other
relevant
“-‐omic”
algorithms
in
HANA
database
-
Create
data
models
to
have
readily
available
latest
annota>ons
(literature/metadata)
from
open-‐source
(and
poten>ally
commercial)
biological
databases
Public
11.
SAP
and
NCT
•
Medical
Explorer
For
ALL
Pa>ent
Data
The National Center for Tumor Diseases (NCT) Heidelberg provides patient
care, cancer research and cancer prevention united under one roof.
• Integrated content from various
sources of structured (tumor
documentation, medical
records, clinical trials) and
unstructured (doctor letters,
treatment guidelines, trial
reports, publications) nature.
• Medical records from 150,000
patients and 3,600,000
interactions, and a selection of
doctor letters from 120 doctors
A
search
for
lung
carcinoma
pa>ents
(only
NSCLC)
with
an
overexpressed
Epidermal
Growth
Factor
Receptor
(EGFR)
known
to
be
a
target
for
Tyrosine
Kinase
Inhibitors
(TKIs)
which
inhibit
the
EGFR
pathway.
In
this
example,
the
informa>on
on
EGFR
is
gathered
by
a
seman>c
text
analysis
of
the
pa>ents’
doctor
leVers.
Public
12. Co-‐Innova>on:
SAP
&
PHEMI
Health
Systems
The
speed
of
SAP
HANA
and
the
Privacy
by
Design
infrastructure
of
PHEMI
Health
Systems
will
allow
healthcare
professionals
to
analyze
vast
amounts
of
data
thereby
enabling:
• Lower
costs
by
quickly
discovering
what
works
and
what
doesn’t.
• Improve
pa,ent
care
by
analyzing
a
pa>ent’s
DNA
sequence
and
using
that
informa>on
to
decide
on
the
op>mal
drug,
thereby
personalizing
the
pa>ent’s
treatment.
• Reduce
wait
,mes
by
improving
hospital
clinic
efficiency
and
forecas>ng
popula>on
health
to
determine
where
and
when
to
increase
health
care
capacity
• Provide
privacy
of
sensi>ve
pa>ent
informa>on,
an
impera>ve
in
healthcare.
Public
13. New
Ways
of
Real-‐Time
Collabora>ve
Personalized
Medicine
Public
14. Thank
you
Contact
informa>on:
hana-‐healthcare-‐pla6orm@sap.com
Enakshi
Singh
Enakshi.Singh@sap.com