This presentation covers the "Analyze Genomes: Real-world Examples" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.

1. Analyze Genomes: Real-world Examples
Dr. Matthieu-P. Schapranow
Festival of Genomics, London, U.K.
Jan 19, 2016

2. What are the Trends?
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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https://www.google.com/trends/explore#q=Big data%2C Life sciences%2C Precision medicine&cmpt=q @ Nov 9, 2015
Life Sciences
Big Data
Precision Medicine

3. 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
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, Festival of
Genomics, Jan 19, 2016
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Real-world Examples
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Genomics, Jan 19, 2016
Recap: we.analyzegenomes.com
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:
Real-world Examples
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

7. 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, Festival of
Genomics, Jan 19, 2016
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Real-world Examples
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8. Cloud-based Services for Processing of DNA Data
■ 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:
Real-world Examples
Standardized Modeling and
runtime environment for
analysis pipelines
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Schapranow, Festival of
Genomics, Jan 19, 2016

9. ■ 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
Medical Knowledge Cockpit for Patients and Clinicians
Linking Patient Specifics with International Knowledge
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016

10. Medical Knowledge Cockpit for Patients and Clinicians
■ Search for affected genes in distributed and
heterogeneous data sources
■ Immediate exploration of relevant information, such as
□ Gene descriptions,
□ Molecular impact and related pathways,
□ Scientific publications, and
□ Suitable clinical trials.
■ No manual searching for hours or days:
In-memory technology translates searching into
interactive finding!
Analyze Genomes:
Real-world Examples
Automatic clinical trial
matching build on text
analysis features
Unified access to structured
and un-structured data
sources
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Schapranow, Festival of
Genomics, Jan 19, 2016

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Genomics, Jan 19, 2016
Medical Knowledge Cockpit for Patients and Clinicians
Pathway Topology Analysis
■ Search in pathways is limited to “is a certain
element contained” today
■ Integrated >1,5k pathways from international
sources, e.g. KEGG, HumanCyc, and WikiPathways,
into HANA
■ Implemented graph-based topology exploration and
ranking based on patient specifics
■ Enables interactive identification of possible
dysfunctions affecting the course of a therapy
before its start Analyze Genomes:
Real-world Examples
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
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Genomics, Jan 19, 2016
■ Interactively explore relevant publications, e.g. PDFs
■ Improved ease of exploration, e.g. by highlighted medical terms and relevant
concepts
Medical Knowledge Cockpit for Patients and Clinicians
Publications
Analyze Genomes:
Real-world Examples
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13. ■ In-place preview of relevant data, such as publications and publication meta data
■ Incorporating individual filter settings, e.g. additional search terms
Medical Knowledge Cockpit for Patients and Clinicians
Publications
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016

14. Schapranow, Festival of
Genomics, Jan 19, 2016■ Personalized clinical trials, e.g. by incorporating patient specifics
■ Classification of internal/external trials based on treating institute
Medical Knowledge Cockpit for Patients and Clinicians
Latest Clinical Trials
Analyze Genomes:
Real-world Examples
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15. Real-time Data Analysis and
Interactive Exploration
Drug Response Analysis
Data Sources
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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

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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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17. Showcase
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Genomics, Jan 19, 2016
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Real-world Examples
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Calculating Drug Response…Predict Drug Response

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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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cetuximab might be more
beneficial for the current case

19. Real-time Processing of Event Data from Medical Sensors
■ Processing of sensor data, e.g. from Intensive Care
Units (ICUs) or wearable sensor devices (quantify self)
■ Multi-modal real-time analysis to detect indicators for
severe events, such as heart attacks or strokes
■ Incorporates machine-learning algorithms to detect
severe events and to
inform clinical
personnel in time
■ Successfully tested
with 100 Hz event
rate, i.e. sufficient
for ICU use
Analyze Genomes:
Real-world Examples
Comparison of waveform data
with history of similar patients
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Genomics, Jan 19, 2016
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20. Real-time Assessment of Clinical Trial Candidates
■ Switch from trial-centric to patient-centric clinical trials
■ Real-time matching and clustering of patients and
clinical trial inclusion/exclusion criteria
■ No manual pre-screening of patients for months:
In-memory technology enables interactive pre-
screening process
■ Reassessment of already screened or already
participating patient reduces recruitment costs
Analyze Genomes:
Real-world Examples
Assessment of patients
preconditions for clinical trials
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Schapranow, Festival of
Genomics, Jan 19, 2016

21. Drug Safety
Statistical Analysis of Drug Side Effects Data
■ Combines confirmed side effect data from different
data sources
■ Interactive statistical analysis, e.g. apriori rules, to
discover still unknown interactions
■ Integrates personal prescription data and directly
report side effects
■ Work together with your doctor to prevent interaction
with already prescribed drugs
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016
Unified access to
international side effect data
On-the-fly extension of
database schema to add side
effect databases
+++

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Genomics, Jan 19, 2016
From University to Market
Oncolyzer
■ Research initiative for exchanging relevant
tumor data to improve personalized treatment
■ Real-time analysis of tumor data in seconds
instead of hours
■ Information available at your fingertips: In-
memory technology on mobile devices, e.g. iPad
■ Interdisciplinary cooperation between clinicians,
clinical researchers, and software engineers
■ Honored with the 2012 Innovation Award of the
German Capitol Region
Analyze Genomes:
Real-world Examples
Unified access to formerly disjoint
oncological data sources
Flexible analysis on patient’s
longitudinal data
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t

23. ■ Combines patient’s
longitudinal time series data
with individual analysis
results
■ Real-time analysis across
hospital-wide data using
always latest data when
details screen is accessed
■ http://analyzegenomes.com/
apps/oncolyzer-mobile-app/
From University to Market
Oncolyzer: Patient Details Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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24. ■ Allows real-time analysis on
complete patient cohort
■ Supports identification of
clinical trial participants
based on their individual
anamnesis
■ Flexible filters and various
chart types allow graphical
exploration of data on
mobile devices
From University to Market
Oncolyzer: Patient Analysis Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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25. ■ Shows all patients the logged-
in clinician is assigned for
■ Provides overview about most
recent results and treatments
for each patient
■ http://global.sap.com/
germany/solutions/
technology/enterprise-
mobility/healthcare-apps/
mobile-patient-record-app.epx
From University to Market
SAP EMR: Patient Overview Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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26. ■ Displays time series data, e.g.
temperature or BMI
■ Allows graphical exploration of
time series data
From University to Market
SAP EMR: Patient Detail Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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27. ■ Flexible combination of
medical data
■ Enables interactive and
graphical exploration
■ Easy to use even without
specific IT background
From University to Market
SAP Medical Research Insights
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Genomics, Jan 19, 2016
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Real-world Examples
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28. Master’s Project “Global Medical Knowledge”
Winter Semester 2015/2016
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Markus
The Team mpws2015hp@hpi.de
■ Lars Rückert
■ Friedrich Horschig
■ Benjamin Reißaus
■ Markus Dücker
Supervisors
■ Milena Kraus
■ Dr. Matthieu-P. Schapranow
■ Dr. Matthias Uflacker

29. ■ Motivation:
□ Combine individual patient-specific, heart-associated data
□ Support real-time data analysis
□ Support discovery of predictive markers
■ Contribution
□ Collect data from multiple sources
□ Integrate data into single in-memory database system
□ Support graphical data analysis
Motivation and Contribution
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Genomics, Jan 19, 2016
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Real-world Examples
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30. Challenges
Distributed Heterogeneous Data Sources in Life Sciences
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Genomics, Jan 19, 2016
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Real-world Examples
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■ Data resides in different physical locations
■ Data is stored in heterogeneous data formats

31. Our Approach
Integrated Data Analysis Platform
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Genomics, Jan 19, 2016
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Real-world Examples
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32. Rapid Prototype
Web Application with Real Trial Data
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Genomics, Jan 19, 2016
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Real-world Examples
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33. Rapid Prototype
Graphical Data Exploration
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Genomics, Jan 19, 2016
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Real-world Examples
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34. ■ Create web app for individual user roles
□ Interview all domain experts involved in data acquisition process
□ Extend web application to individual needs
■ Extend analysis capabilities
□ Graphical data exploration
□ User-specific visualization
Outlook & Next Steps
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Genomics, Jan 19, 2016
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Real-world Examples
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35. ■ Hasso Plattner Institute
■ Analyze Genomes Platform and Application Examples
■ Methodology & Technology
■ Current Student Projects
■ Discussion and Q&A
Agenda
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Genomics, Jan 19, 2016
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Real-world Examples
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36. ■ Global Medical Knowledge (Master’s project)
■ Markers for cardiovascular diseases to
assess treatment options (DHZB)
■ Combine health data to improve health care
research (AOK)
■ Pharmacogenetics (Bayer)
■ Generously supported by
Join us for upcoming projects!
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Interdisciplinary
Design Thinking
Teams
You?

37. ■ 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
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Genomics, Jan 19, 2016
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Analyze Genomes:
Real-world Examples

38. Keep in contact with us!
Hasso Plattner Institute
Enterprise Platform & Integration Concepts (EPIC)
Program Manager E-Health
Dr. Matthieu-P. Schapranow
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, Festival of
Genomics, Jan 19, 2016
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Real-world Examples
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