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Analyze Genomes Services for Precision Medicine

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The given presentation outlines services of the cloud platform "Analyze Genomes" enabling precision medicine. It was presented on the mHealth meets Diagnostics symposium in Berlin on Jun 21, 2016.

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Analyze Genomes Services for Precision Medicine

  1. 1. Analyze Genomes Services for Precision Medicine Dr. Matthieu-P. Schapranow mHealth meets Diagnostics, Berlin Jun 21, 2016
  2. 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 2 Analyze Genomes Services for Precision Medicine
  3. 3. IT Challenges Distributed Heterogeneous Data Sources 3 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. 4. Schapranow, mHealth meets Diagnostics, Jun 21, 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 Analyze Genomes Services for Precision Medicine Drug Response Analysis Pathway Topology Analysis Medical Knowledge CockpitOncolyzer Clinical Trial Recruitment Cohort Analysis ... Indexed Sources
  5. 5. Our Software Architecture A Federated In-Memory Database System Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine 5 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. 6. Our Methodology Design Thinking Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine 6
  7. 7. 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 7 Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine
  8. 8. In-Memory Database Technology Use Case: Analysis of Genomic Data Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine 8 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. 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. 10. Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine 10
  11. 11. Schapranow, mHealth meets Diagnostics, Jun 21, 2016 Analyze Genomes Services for Precision Medicine 11
  12. 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 12 Schapranow, mHealth meets Diagnostics, Jun 21, 2016
  13. 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 13 Schapranow, mHealth meets Diagnostics, Jun 21, 2016
  14. 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) 14 Patient-specific Data Tumor-specific Data Compound Interaction Data
  15. 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 15
  16. 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 16
  17. 17. ■  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 17 Analyze Genomes Services for Precision Medicine
  18. 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 18

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