Post on 09-Jan-2017
Analyze Genomes Services for Precision Medicine
Dr. Matthieu-P. Schapranow mHealth meets Diagnostics, Berlin
Jun 21, 2016
■ 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
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Analyze Genomes Services for Precision Medicine
IT Challenges Distributed Heterogeneous Data Sources
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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 data Cancer patient records
>160k records at NCT Analyze Genomes Services for Precision Medicine
Schapranow, mHealth meets Diagnostics, Jun 21, 2016
Schapranow, mHealth meets Diagnostics, Jun 21, 2016
Our Approach Analyze Genomes: 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 Services for Precision Medicine
Drug Response Analysis
Pathway Topology Analysis
Medical Knowledge Cockpit Oncolyzer
Clinical Trial Recruitment
Cohort Analysis
...
Indexed Sources
Our Software Architecture A Federated In-Memory Database System
Schapranow, mHealth meets Diagnostics, Jun 21, 2016
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Federated In-M em ory D atabase System
M aster D ata andS hared A lgorithm s
S ite A S ite BC loud Provider
C loud IM D BInstance
Local IM D BInstance
S ensitive D ata,e.g . P atient D ata
R
Local IM D BInstance
Sensitive D ata,e .g. P atien t D ata
R
Our Methodology Design Thinking
Schapranow, mHealth meets Diagnostics, Jun 21, 2016
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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
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Schapranow, mHealth meets Diagnostics, Jun 21, 2016
Analyze Genomes Services for Precision Medicine
In-Memory Database Technology Use Case: Analysis of Genomic Data
Schapranow, mHealth meets Diagnostics, Jun 21, 2016
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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
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
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Schapranow, mHealth meets Diagnostics, Jun 21, 2016
Analyze Genomes Services for Precision Medicine
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Schapranow, mHealth meets Diagnostics, Jun 21, 2016
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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
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Schapranow, mHealth meets Diagnostics, Jun 21, 2016
■ 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
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Schapranow, mHealth meets Diagnostics, Jun 21, 2016
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)
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Patient-specific Data
Tumor-specific Data
Compound Interaction Data
Heart Failure
Sleeping disorder
Fibrosis
Blood pressure
Blood volume
Gene ex-pression
Hyper-trophy Calcium
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)
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■ 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)
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■ 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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Analyze Genomes Services for Precision Medicine
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
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