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Analyze Genomes: In-memory Apps supporting Precision Medicine

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This slide deck was presented on May 19, 2016 at Intel Tech Talks hosted by SAPPHIRE 2016 in Orlando, FL.

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Analyze Genomes: In-memory Apps supporting Precision Medicine

  1. 1. Analyze Genomes: In-Memory Apps Supporting Precision Medicine -- Real-world Experiences Dr. Matthieu-P. Schapranow SAPPHIRE, Orlando, USA May 19, 2016
  2. 2. ■  Online: Visit we.analyzegenomes.com for latest research results, slides, videos, tools, and publications ■  Offline: High-Performance In-Memory Genome Data Analysis: In-Memory Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014 ■  In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily! □  May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling Real-time Analysis of Big Medical Data □  May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research □  May 19 11.30am: In-Memory Apps Supporting Precision Medicine Where to find additional information? Schapranow, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 2
  3. 3. ■  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, SAPPHIRE, May 19, 2016 3 In-Memory Apps Supporting Precision Medicine
  4. 4. Schapranow, SAPPHIRE, May 19, 2016 Our Approach Analyze Genomes: Real-time Analysis of Big Medical Data 4 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 In-Memory Apps Supporting Precision Medicine Drug Response Analysis Pathway Topology Analysis Medical Knowledge CockpitOncolyzer Clinical Trial Recruitment Cohort Analysis ... Indexed Sources
  5. 5. 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, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 5
  6. 6. Schapranow, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 6
  7. 7. Schapranow, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 7
  8. 8. App Example: From Raw DNA to Variants ■  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 In-Memory Apps Supporting Precision Medicine Standardized Modeling and runtime environment for analysis pipelines 8 Schapranow, SAPPHIRE, May 19, 2016
  9. 9. Real-time Data Analysis and Interactive Exploration App Example: Identification of Optimal Chemotherapy Schapranow, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting 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) 9 Patient-specific Data Tumor-specific Data Compound Interaction Data ■  Honored by the 2015 PerMediCon Award
  10. 10. ■  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: Latest Medical Knowledge for Patients and Clinicians In-Memory Apps Supporting Precision Medicine 10 Schapranow, SAPPHIRE, May 19, 2016
  11. 11. Schapranow, SAPPHIRE, May 19, 2016 App Example: 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 In-Memory Apps Supporting Precision Medicine Unified access to multiple formerly disjoint data sources Pathway analysis of genetic variants with graph engine 11
  12. 12. Schapranow, SAPPHIRE, May 19, 2016■  Personalized clinical trials, e.g. by incorporating patient specifics ■  Classification of internal/external trials based on treating institute App Example: Latest International Clinical Trials In-Memory Apps Supporting Precision Medicine 12
  13. 13. Schapranow, SAPPHIRE, May 19, 2016 ■  Interactively explore relevant publications, e.g. PDFs ■  Improved ease of exploration, e.g. by highlighted medical terms and relevant concepts App Example: Latest International Publications In-Memory Apps Supporting Precision Medicine 13
  14. 14. ■  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, SAPPHIRE, May 19, 2016 14 In-Memory Apps Supporting Precision Medicine
  15. 15. ■  Online: Visit we.analyzegenomes.com for latest research results, slides, videos, tools, and publications ■  Offline: High-Performance In-Memory Genome Data Analysis: In-Memory Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014 ■  In Person: Join us for Intel Tech Talks at SAPPHIRE booth 625 daily! □  May 17 12.30pm: A Federated In-Memory Database Computing Platform Enabling Real-time Analysis of Big Medical Data □  May 18 12.30pm: In-Memory Apps for Next Generation Life Sciences Research □  May 19 11.30am: In-Memory Apps Supporting Precision Medicine Where to find additional information? Schapranow, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 15
  16. 16. 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, SAPPHIRE, May 19, 2016 In-Memory Apps Supporting Precision Medicine 16

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