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Analyze Genomes: Real-world Examples
Dr. Matthieu-P. Schapranow
Festival of Genomics, London, U.K.
Jan 19, 2016
What are the Trends?
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
2
https://www.goo...
Use Case: Precision Medicine in Oncology
Identification of Best Treatment Option for Cancer Patient
■  Patient: 48 years, f...
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
4
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
5
Schapranow, Festival of
Genomics, Jan 19, 2016
Recap: we.analyzegenomes.com
Real-time Analysis of Big Medical Data
6
In-Me...
Use Case: Precision Medicine in Oncology
Identification of Best Treatment Option for Cancer Patient
■  Patient: 48 years, f...
Cloud-based Services for Processing of DNA Data
■  Control center for processing of raw DNA data, such as
FASTQ, SAM, and ...
■  Query-oriented search interface
■  Seamless integration of patient specifics, e.g. from EMR
■  Parallel search in intern...
Medical Knowledge Cockpit for Patients and Clinicians
■  Search for affected genes in distributed and
heterogeneous data s...
Schapranow, Festival of
Genomics, Jan 19, 2016
Medical Knowledge Cockpit for Patients and Clinicians
Pathway Topology Anal...
Schapranow, Festival of
Genomics, Jan 19, 2016
■  Interactively explore relevant publications, e.g. PDFs
■  Improved ease ...
■  In-place preview of relevant data, such as publications and publication meta data
■  Incorporating individual filter set...
Schapranow, Festival of
Genomics, Jan 19, 2016■  Personalized clinical trials, e.g. by incorporating patient specifics
■  C...
Real-time Data Analysis and
Interactive Exploration
Drug Response Analysis
Data Sources
Schapranow, Festival of
Genomics, ...
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
16
Showcase
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
17
Calculating Drug Response…...
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
18
cetuximab might be more
beneficial ...
Real-time Processing of Event Data from Medical Sensors
■  Processing of sensor data, e.g. from Intensive Care
Units (ICUs...
Real-time Assessment of Clinical Trial Candidates
■  Switch from trial-centric to patient-centric clinical trials
■  Real-...
Drug Safety
Statistical Analysis of Drug Side Effects Data
■  Combines confirmed side effect data from different
data sourc...
Schapranow, Festival of
Genomics, Jan 19, 2016
From University to Market
Oncolyzer
■  Research initiative for exchanging r...
■  Combines patient’s
longitudinal time series data
with individual analysis
results
■  Real-time analysis across
hospital...
■  Allows real-time analysis on
complete patient cohort
■  Supports identification of
clinical trial participants
based on ...
■  Shows all patients the logged-
in clinician is assigned for
■  Provides overview about most
recent results and treatmen...
■  Displays time series data, e.g.
temperature or BMI
■  Allows graphical exploration of
time series data
From University ...
■  Flexible combination of
medical data
■  Enables interactive and
graphical exploration
■  Easy to use even without
speci...
Master’s Project “Global Medical Knowledge”

Winter Semester 2015/2016
Schapranow, Festival of
Genomics, Jan 19, 2016
Anal...
■  Motivation:
□  Combine individual patient-specific, heart-associated data
□  Support real-time data analysis
□  Support ...
Challenges
Distributed Heterogeneous Data Sources in Life Sciences
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze ...
Our Approach
Integrated Data Analysis Platform
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world ...
Rapid Prototype
Web Application with Real Trial Data
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-...
Rapid Prototype
Graphical Data Exploration
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Exam...
■  Create web app for individual user roles
□  Interview all domain experts involved in data acquisition process
□  Extend...
■  Hasso Plattner Institute
■  Analyze Genomes Platform and Application Examples
■  Methodology & Technology
■  Current St...
■  Global Medical Knowledge (Master’s project)
■  Markers for cardiovascular diseases to
assess treatment options (DHZB)
■...
■  For patients
□  Identify relevant clinical trials and medical experts
□  Become an informed patient
■  For clinicians
□...
Keep in contact with us!
Hasso Plattner Institute
Enterprise Platform & Integration Concepts (EPIC)
Program Manager E-Heal...
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Festival of Genomics 2016 London: Analyze Genomes: Real-world Examples

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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.

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Festival of Genomics 2016 London: Analyze Genomes: Real-world Examples

  1. 1. Analyze Genomes: Real-world Examples Dr. Matthieu-P. Schapranow Festival of Genomics, London, U.K. Jan 19, 2016
  2. 2. What are the Trends? Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 2 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. 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 Analyze Genomes: Real-world Examples 3
  4. 4. Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 4
  5. 5. Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 5
  6. 6. Schapranow, Festival of Genomics, Jan 19, 2016 Recap: we.analyzegenomes.com Real-time Analysis of Big Medical Data 6 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. 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 Analyze Genomes: Real-world Examples 7
  8. 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 8 Schapranow, Festival of Genomics, Jan 19, 2016
  9. 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 9 Schapranow, Festival of Genomics, Jan 19, 2016
  10. 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 10 Schapranow, Festival of Genomics, Jan 19, 2016
  11. 11. Schapranow, Festival of 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 11
  12. 12. Schapranow, Festival of 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 12
  13. 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 13 Schapranow, Festival of Genomics, Jan 19, 2016
  14. 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 14
  15. 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) 15 Patient-specific Data Tumor-specific Data Compound Interaction Data
  16. 16. Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 16
  17. 17. Showcase Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 17 Calculating Drug Response…Predict Drug Response
  18. 18. Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 18 cetuximab might be more beneficial for the current case
  19. 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 19 Schapranow, Festival of Genomics, Jan 19, 2016 t
  20. 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 20 Schapranow, Festival of Genomics, Jan 19, 2016
  21. 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 21 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 +++
  22. 22. Schapranow, Festival of 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 22 t
  23. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 23
  24. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 24
  25. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 25
  26. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 26
  27. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 27
  28. 28. Master’s Project “Global Medical Knowledge”
 Winter Semester 2015/2016 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 28 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. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 29
  30. 30. Challenges Distributed Heterogeneous Data Sources in Life Sciences Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 30 ■  Data resides in different physical locations ■  Data is stored in heterogeneous data formats
  31. 31. Our Approach Integrated Data Analysis Platform Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 31
  32. 32. Rapid Prototype Web Application with Real Trial Data Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 32
  33. 33. Rapid Prototype Graphical Data Exploration Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 33
  34. 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 Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 34
  35. 35. ■  Hasso Plattner Institute ■  Analyze Genomes Platform and Application Examples ■  Methodology & Technology ■  Current Student Projects ■  Discussion and Q&A Agenda Schapranow, Festival of Genomics, Jan 19, 2016 Analyze Genomes: Real-world Examples 35
  36. 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 36 Interdisciplinary Design Thinking Teams You?
  37. 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 Schapranow, Festival of Genomics, Jan 19, 2016 37 Analyze Genomes: Real-world Examples
  38. 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 Analyze Genomes: Real-world Examples 38

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