1.
Analyze Genomes Services for Precision Medicine
Dr. Matthieu-P. Schapranow
mHealth meets Diagnostics, Berlin
Jun 21, 2016

2.
■ Patients
□ Individual anamnesis, family history, and background
□ Require fast access to individualized therapy
■ Clinicians
□ Identify root and extent of disease using laboratory tests
□ Evaluate therapy alternatives, adapt existing therapy
■ Researchers
□ Conduct laboratory work, e.g. analyze patient samples
□ Create new research findings and come-up with treatment alternatives
The Setting
Actors in Oncology
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
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Analyze Genomes
Services for Precision
Medicine

3.
IT Challenges
Distributed Heterogeneous Data Sources
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Human genome/biological data
600GB per full genome
15PB+ in databases of leading institutes
Prescription data
1.5B records from 10,000 doctors and
10M Patients (100 GB)
Clinical trials
Currently more than 30k
recruiting on ClinicalTrials.gov
Human proteome
160M data points (2.4GB) per sample
>3TB raw proteome data in ProteomicsDB
PubMed database
>23M articles
Hospital information systems
Often more than 50GB
Medical sensor data
Scan of a single organ in 1s
creates 10GB of raw dataCancer patient records
>160k records at NCT
Analyze Genomes
Services for Precision
Medicine
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016

4.
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Our Approach
Analyze Genomes: Real-time Analysis of Big Medical Data
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In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Real-time
Analysis
App-spanning
User Profiles
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
Analyze Genomes
Services for Precision
Medicine
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

5.
Our Software Architecture
A Federated In-Memory Database System
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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Federated In-M em ory D atabase System
M aster Data and
Shared Algorithm s
Site A Site BCloud Provider
Cloud IM D B
Instance
Local IM DB
Instance
Sensitive D ata,
e.g. Patient Data
R
Local IM DB
Instance
Sensitive Data,
e.g. Patient D ata
R

6.
Our Methodology
Design Thinking
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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Combined column
and row store
Map/Reduce Single and
multi-tenancy
Lightweight
compression
Insert only
for time travel
Real-time
replication
Working on
integers
SQL interface on
columns and rows
Active/passive
data store
Minimal
projections
Group key Reduction of
software layers
Dynamic multi-
threading
Bulk load
of data
Object-
relational
mapping
Text retrieval
and extraction engine
No aggregate
tables
Data partitioning Any attribute
as index
No disk
On-the-fly
extensibility
Analytics on
historical data
Multi-core/
parallelization
Our Technology
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
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Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine

8.
In-Memory Database Technology
Use Case: Analysis of Genomic Data
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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Analysis of
Genomic Data
Alignment and Variant Calling
Analysis of Annotations in World-
wide DBs
Bound To CPU Performance Memory Capacity
Duration Hours – Days Weeks
HPI Minutes Real-time
In-Memory
Technology
Multi-Core Partitioning & Compression

9.
Use Case: Precision Medicine in Oncology
Identification of Best Treatment Option for Cancer Patient
■ Patient: 48 years, female, non-smoker, smoke-free environment
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
■ Markers: KRAS, EGFR, BRAF, NRAS, (ERBB2)
1. Surgery to remove tumor
2. Tumor sample is sent to laboratory to extract DNA
3. DNA is sequenced resulting in 750 GB of raw data per sample
4. Processing of raw data to perform analysis
5. Identification of relevant driver mutations using international medical knowledge
6. Informed decision making
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
9

10.
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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11.
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
11

12.
App Example: Integrating Processing and Real-time Analysis
of Genome Data in the Clinical Routine
■ Control center for processing of raw DNA data, such as
FASTQ, SAM, and VCF
■ Personal user profile guarantees privacy of uploaded
and processed data
■ Supports reproducible research process by storing all
relevant process parameters
■ Implements prioritized data processing and fair use, e.g.
per department or per institute
■ Supports additional service, such as data annotations,
billing, and sharing for all Analyze Genomes services
■ Honored by the 2014 European Life Science Award
Analyze Genomes
Services for Precision
Medicine
Standardized Modeling and
runtime environment for
analysis pipelines
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Schapranow, mHealth
meets Diagnostics, Jun
21, 2016

13.
■ Query-oriented search interface
■ Seamless integration of patient specifics, e.g. from EMR
■ Parallel search in international knowledge bases, e.g. for biomarkers, literature,
cellular pathway, and clinical trials
App Example:
Medical Knowledge Cockpit for Patients and Clinicians
Analyze Genomes
Services for Precision
Medicine
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Schapranow, mHealth
meets Diagnostics, Jun
21, 2016

14.
Real-time Data Analysis and
Interactive Exploration
App Example: Identifying Best Chemotherapy using
Drug Response Analysis
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
Smoking status,
tumor classification
and age
(1MB - 100MB)
Raw DNA data
and genetic variants
(100MB - 1TB)
Medication efficiency
and wet lab results
(10MB - 1GB)
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Patient-specific
Data
Tumor-specific
Data
Compound
Interaction Data

15.
Heart
Failure
Sleeping
disorder
Fibrosis
Blood
pressure
Blood
volume
Gene ex-
pression
Hyper-
trophyCalcium
meta-
bolism
Energy
meta-
bolism
Iron
deficiency
Vitamin-D
deficiency
Gender
Epi-
genetics
■ Integrated systems medicine based on
real-time analysis of healthcare data
■ Initial funding period: Mar ‘15 – Feb ‘18
■ Funded consortium partners:
Systems Medicine Model of Heart Failure (SMART)
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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16.
■ Interdisciplinary partners collaborate on enabling real-time healthcare research
■ Initial funding period: Aug 2015 – July 2018
■ Funded consortium partners:
□ AOK
German healthcare insurance company
□ data experts group
Technology operations
□ Hasso Plattner Institute
Real-time data analysis, in-memory database technology
□ Technology, Methods, and Infrastructure for Networked Medical Research
Legal and data protection
Smart Analysis Health Research Access (SAHRA)
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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■ For patients
□ Identify relevant clinical trials and medical experts
□ Become an informed patient
■ For clinicians
□ Identify pharmacokinetic correlations
□ Scan for similar patient cases, e.g. to evaluate therapy efficiency
■ For researchers
□ Enable real-time analysis of medical data, e.g. assess pathways
to identify impact of detected variants
□ Combined mining in structured and unstructured data, e.g. publications,
diagnosis, and EMR data
What to Take Home?
Test it Yourself: AnalyzeGenomes.com
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
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Analyze Genomes
Services for Precision
Medicine

18.
Keep in contact with us!
Dr. Matthieu-P. Schapranow
Program Manager E-Health & Life Sciences
Hasso Plattner Institute
August-Bebel-Str. 88
14482 Potsdam, Germany
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, mHealth
meets Diagnostics, Jun
21, 2016
Analyze Genomes
Services for Precision
Medicine
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