festival of genomics 2016 london: analyze genomes: real-world examples

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


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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

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


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Recap: we.analyzegenomes.com 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


Drug Response Analysis

Pathway Topology Analysis

Medical Knowledge Cockpit Oncolyzer

Clinical Trial Recruitment

Cohort Analysis

...

Indexed Sources

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


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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


Standardized Modeling and runtime environment for

analysis pipelines

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■  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


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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!


Automatic clinical trial matching build on text

analysis features

Unified access to structured and un-structured data

sources

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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

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■  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


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■  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


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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


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Real-time Data Analysis and Interactive Exploration

Drug Response Analysis Data Sources



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



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Showcase



17 Calculating Drug Response… Predict Drug Response



18 cetuximab might be more

beneficial for the current case

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


Comparison of waveform data with history of similar patients

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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


Assessment of patients preconditions for clinical trials

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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


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Unified access to international side effect data

On-the-fly extension of database schema to add side

effect databases

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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


Unified access to formerly disjoint oncological data sources

Flexible analysis on patient’s longitudinal data

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■  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



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■  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



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■  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



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■  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



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■  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



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Master’s Project “Global Medical Knowledge” Winter Semester 2015/2016



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Markus

The Team [email protected]

■  Lars Rückert

■  Friedrich Horschig

■  Benjamin Reißaus

■  Markus Dücker

Supervisors

■  Milena Kraus

■  Dr. Matthieu-P. Schapranow

■  Dr. Matthias Uflacker

■  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



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Challenges Distributed Heterogeneous Data Sources in Life Sciences



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■  Data resides in different physical locations

■  Data is stored in heterogeneous data formats

Our Approach Integrated Data Analysis Platform



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Rapid Prototype Web Application with Real Trial Data



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Rapid Prototype Graphical Data Exploration



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■  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



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■  Hasso Plattner Institute

■  Analyze Genomes Platform and Application Examples

■  Methodology & Technology

■  Current Student Projects

■  Discussion and Q&A

Agenda



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■  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!



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Interdisciplinary Design Thinking

Teams

You?

■  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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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 [email protected] http://we.analyzegenomes.com/



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