In-memory Applications for Oncology

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In-memory Applications for Oncology

  1. 1. In-Memory Applications Revolutionizing Oncology Research Dr. Matthieu-P. Schapranow Dokuz Eylul University, Izmir, Türkiye August 12, 2014
  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 Dr. Schapranow, HPI, Aug 12, 2014 2 In-Memory Applications Revolutionizing Oncology Research
  3. 3. ■  Motivation: Can we enable clinicians to take their therapy decisions: □  Incorporating all available specifics about each individual patient, □  Referencing latest lab results and worldwide medical knowledge, and □  Interactively during their ward round? Our Motivation Make Precision Medicine Come Routine in Real Life In-Memory Applications Revolutionizing Oncology Research 3 Dr. Schapranow, HPI, Aug 12, 2014
  4. 4. IT Challenges Distributed Heterogeneous Data Sources 4 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 In-Memory Applications Revolutionizing Oncology Research Dr. Schapranow, HPI, Aug 12, 2014
  5. 5. Our Methodology Design Thinking Methodology Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 5
  6. 6. Our Methodology Design Thinking Methodology Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 6 Desirability ■  Leveraging directed customer services ■  Portfolio of integrated services for clinicians, researchers, and patients ■  Include latest research results, e.g. most effective therapies Viability ■  Enable personalized medicine also in far-off regions and developing countries ■  Share data via the Internet to get feedback from word-wide experts (cost-saving) ■  Combine research data (publications, annotations, genome data) from international databases in a single knowledge base Feasibility ■  HiSeq 2500 enables high-coverage whole genome sequencing in 20h ■  IMDB enables allele frequency determination of 12B records within <1s ■  Detection of 1 relevant annotation out of 80M <1s ■  Cloud-based data processing services reduce TCO
  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 Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research
  8. 8. Dr. Schapranow, HPI, Aug 12, 2014 Our Approach Analyze Genomes: Real-time Analysis of Big Medical Data 8 Drug Response Analysis Pathway Topology Analysis Medical Knowledge Cockpit Oncolyzer Clinical Trial Assessment Cohort Analysis In-Memory Database Extensions for Life Sciences Data Exchange, App Store Access Control, Data Protection Fair Use Statistical Tools Combined and Linked Data Genome Data Cellular Pathways Genome Metadata Resarch Publications Pipeline and Analysis Models Real-time Analysis App-spanning User Profiles Drugs and Interactions ... In-Memory Applications Revolutionizing Oncology Research
  9. 9. 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 In-Memory Applications Revolutionizing Oncology Research Standardized Modeling and runtime environment for analysis pipelines 9 Dr. Schapranow, HPI, Aug 12, 2014
  10. 10. Individual Analysis Pipelines Standardized Modeling and Runtime Environment ■  Easy-to-use graphical modeling of analysis pipelines, e.g. BPMN-based pipelines for genome data processing ■  Runtime environment for analysis models integrating IMDB tools as well as any operating processes ■  Optimized for high-throughput processing, i.e. parallelization across CPU cores as well as distributed computing across computer systems ■  Implements fail-safe and recoverability using IMDB In-Memory Applications Revolutionizing Oncology Research Standardized Modeling and runtime environment for analysis pipelines 10 Dr. Schapranow, HPI, Aug 12, 2014
  11. 11. Interactive Genome Browser ■  Genome Browser enables interactive comparison of multiple genomes ■  Combined knowledge by integrating latest international annotations and literature, e.g. from NCBI, dbSNP, and UCSC ■  Detailed exploration of genome locations and existing associations ■  Ranked variants, e.g. accordingly to known diseases ■  Links always back to primary data sources to guarantee validity of discovered findings In-Memory Applications Revolutionizing Oncology Research Matching of genetic variants and relevant annotations Unified access to multiple formerly disjoint data sources 11 Dr. Schapranow, HPI, Aug 12, 2014
  12. 12. Analysis of Patient Cohorts ■  In a patient cohort, a subset does not respond to therapy – why? ■  Clustering using various statistical algorithms, such as k-means or hierarchical clustering ■  Calculation of all locus combinations in which at least 5% of all TCGA participants have mutations: 200ms for top 20 combinations ■  Individual clusters are calculated in parallel directly within the database ■  K-means algorithm: 50ms (PAL) vs. 500ms (R) In-Memory Applications Revolutionizing Oncology Research Fast clustering directly performed within the in- memory database 12 Dr. Schapranow, HPI, Aug 12, 2014
  13. 13. Dr. Schapranow, HPI, Aug 12, 2014 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 In-Memory Applications Revolutionizing Oncology Research Unified access to formerly disjoint oncological data sources Flexible analysis on patient’s longitudinal data 13 t
  14. 14. ■  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://epic.hpi.uni- potsdam.de/Home/ HanaOncolyzer Oncolyzer Patient Details Screen Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 14
  15. 15. ■  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 Oncolyzer Patient Analysis Screen Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 15
  16. 16. ■  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 SAP EMR Patient Overview Screen Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 16
  17. 17. ■  Displays time series data, e.g. temperature or BMI ■  Allows graphical exploration of time series data SAP EMR Patient Detail Screen Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 17
  18. 18. Dr. Schapranow, HPI, Aug 12, 2014 SAP Medical Research Insights ■  Clinical data from different sources and departments are combined within a single in- memory database system to form a unified biobank ■  Combine complex filter criteria to identify adequate patient samples, e.g. for clinical research or trials ■  Breakthrough for managing and analysis of biobank data in a systematic way In-Memory Applications Revolutionizing Oncology Research Unified access to formerly disjoint medical and biological data sources Flexible Analysis on historical data 18 t
  19. 19. Perform Manual Data Exploration And Analysis Drug Response Analysis Data Sources and Matching In-Memory Applications Revolutionizing Oncology Research Collect Patient Data Sequence Tumor Conduct Xenograft Experiments Metadata e.g. smoking status, tumor classification and age Genome Data e.g. raw DNA data and genetic variants Experiment Results e.g. medication effectivity obtained from wet laboratory 19 Dr. Schapranow, HPI, Aug 12, 2014
  20. 20. Drug Response Analysis Interactive Data Exploration ■  Drug response depends on individual genetic variants of tumors ■  Challenge: Identification of relevant genetic variants and their impact on drug response is a ongoing research activity, e.g. Xenograft models ■  Exploration of experiment results is time- consuming and Excel-driven ■  In-memory technology enables interactive exploration of experiment data to leverage new scientific insights In-Memory Applications Revolutionizing Oncology Research Interactive analysis of correlations between drugs and genetic variants 20 Dr. Schapranow, HPI, Aug 12, 2014
  21. 21. Interactive Clinical Trial Recruitment ■  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 In-Memory Applications Revolutionizing Oncology Research Assessment of patients preconditions for clinical trials 21 Dr. Schapranow, HPI, Aug 12, 2014
  22. 22. ■  For patients □  Identify relevant clinical trials and medical experts □  Start most appropriate therapy as early as possible ■  For clinicians □  Preventive diagnostics to identify risk patients early □  Indicate pharmacokinetic correlations □  Scan for similar patient cases, e.g. to evaluate therapy ■  For researchers □  Enable real-time analysis of medical data and its assessment, e.g. assess pathways to identify impact of detected variants □  Combined free-text search in publications, diagnosis, and EMR data, i.e. structured and unstructured data What to take home? Test-drive it yourself: http://we.AnalyzeGenomes.com Dr. Schapranow, HPI, Aug 12, 2014 22 In-Memory Applications Revolutionizing Oncology Research
  23. 23. 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/ Dr. Schapranow, HPI, Aug 12, 2014 In-Memory Applications Revolutionizing Oncology Research 23

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