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Translational Informatics Rembrandt ISPY

Translational Informatics Rembrandt ISPY. RE pository of M olecular BRA in N eoplasia D a T a & I nvestigation of Serial S tudies to P redict Y our T herapeutic R esponse with I maging A nd mo L ecular analysis. Debilitating Brain Tumors. Poor Survival Rates

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Translational Informatics Rembrandt ISPY

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  1. Translational InformaticsRembrandtISPY REpositoryofMolecular BRAin Neoplasia DaTa & InvestigationofSerial Studiesto Predict Your Therapeutic Responsewith Imaging AndmoLecularanalysis

  2. Debilitating Brain Tumors

  3. Poor Survival Rates Median survival for GBM patients < 1 yr

  4. Let's Just do it

  5. Challenges • Few therapeutic advances in the last 3 decades • Histopathological classifications for the heterogeneous group of tumors known as gliomas are broad and do not predict for therapeutic outcome or prognosis • Standard therapies generally have minimal effect on long term survival What can we do to help?

  6. Expression array data SNPArray data Clinical data Proteomics data Rembrandt Knowledgebase Datawarehouse Better understanding Better treatments Concept Creation

  7. DNA RNA Tumor Core Punch Proteins NCI’s GMDI Study Blood Tumor Plasma

  8. Typical Rembrandt Usage Scenario • In brain tissue from patients diagnosed with the glioblastoma multiforme subtype of Astrocytoma, which genes in the EGF signaling pathway are over or under expressed in cancerous versus normal tissue? • What is the underlying evidence for such aberrant expression? For example, what are the platforms that were used to study gene expression and what are the respective expression values for the reporters for these genes? • How did EGFR up-regulation affect survival of patients within this study? • Of these groups of samples, which ones were obtained from patients that were males and were diagnosed between the ages of 25 and 40 yrs?

  9. Gene Expression Search Use cases

  10. Demo Application: http://rembrandt-db.nci.nih.gov Informational Site: http://rembrandt.nci.nih.gov

  11. Rembrandt Project Milestones Timeline Dec’04 Summer’05 March’05 Proof-of-concept release (version 0.5) Point release (version 0.51) Full release (version 1.0) • Analysis tools (LOH and Copy number) • Co-visualize mRNA expression and DNA copy number • Other data types (Tissue Array, Clinical) • Tighter integration with caCore • Search and retrieval portal • Information website • Support for Gene Expression, SNP Array and Clinical data • All Genes usecase • Inbox • Report Enhancements • Improve documentation on “help” buttons • Mac/Safari compatibility

  12. I-SPY Trial Investigationof SerialStudies to Predict Your Therapeutic Response with Imaging And moLecularanalysis Courtesy: Laura Esserman, Director, UCSF CF Buck Breast Care Center

  13. Motivation: ISPY Trial • Studies of Neoadjuvant therapy have shown us that the order of therapy is not important • Response to therapy, however, is critical in determining outcome • Response to Therapy is the most important predictor of survival after neoadjuvant chemotherapy • Hypotheses: Breast Cancer is heterogeneous • Molecular and Imaging markers, will predict response to therapy and determine outcome

  14. ISPY Trial Informatics • Main Informatics goals • Collect and share biomedical research study results with study members, the SPOREs community, and the NCI intramural/extramural community • Integrate biomedical research study results and analyze in support of translational research • Capture the biomarkers that predict response to therapy throughout the course of the cancer treatment cycle • Facilitate cross platform validation of markers • Accelerate our ability to find meaningful robust biomarkers

  15. I-SPY Informatics Support Tissue Sectioning Lab Clinical Lab MRI Lab Gene Expression Lab CGH Lab Experimentation QI Portal I-SPY Investigator Review I-SPY Study Portal Proteomics Lab Summer ’05 Jan ’05 P53 Mutation Lab Pre-Experimentation Post-Experimentation

  16. Quality Indicator (QI) Portal • The QI Portal will allow I-SPY users to: • Submit sample quality indicators (e.g. DNA or RNA) • Retrieve all quality indicators that have been submitted to the database

  17. DNA Quality Indicators • The DNA Quality Indicators page captures DNA and P53 Multiplex PCR information • DNA quality indicators are based on the 260/280 absorbance ratio • P53 Multiplex indicators are based on the PCR rating ISPY

  18. So, what are the building blocks of Rembrandt and ISPY application portals?

  19. Rembrandt & ISPY Leverage NCICB and caBIG Infrastructure Components • Aligns with caBIG principles: • Open source • Open access • Syntactic and Semantic interoperability • Federated data • NCICB Infrastructure • caARRAY gene expression data repositories and analysis tools • Cancer Genome Anatomy Project (CGAP) genomic tools • C3D Clinical Informatics System • caCORE Infrastructure (caBIO, EVS, caDSR) • caBIG Infrastructure being delivered by caBIG workspaces

  20. caIntegrator - High-level architecture

  21. Vision for caIntegrator Application Developer Application User Framework Web Application Portals caIntegrator Framework Domain Object Model I-SPY Middle Tier REMBRANDT Query/Reporting Services Data Access Objects Business Objects Custom Portal Data warehouse ? DW1 DW2

  22. Rembrandt Design OverviewSystem Architecture,Application Framework & Data Warehouse Objectives

  23. Rembrandt Business Objectives • Must support translation research use cases: • Build an infrastructure that provides users with the ability to perform true translational queries • Allow users to view the results by easily pivoting between the various dimensions: • Disease View • Patient / Sample View • Experiment Results/ Annotations View • Time Course View (future)

  24. Rembrandt Technical Objectives • Extensible • Tiered Architecture • Abstraction / Model View Controller • Support Strong Type Checking & Validations • Query Performance • Groundwork for a robust bioinformatics knowledgebase framework • Gap Analysis • Identify missing processes, work flow, data & infrastructure components

  25. Another Architecture Perspective

  26. Query & Retrieval Objects : Support Strong Type Checking & Validations • Such as Query, View, Criteria, Domain Element objects • Abstract presentation logic from the query helper objects • Provides ability to nest cross domain queries (AND/OR) • Is strong typed • Can validate itself

  27. Example: Criteria Objects • Criteria Objects • Consists of DomainElements • Provides Generic Cross Domain Filters • Each Criteria can validate itself • For e.g.: RegionCriteria • Consists of ChromosomeNumberDE, CytobandDE, BasePairPositionDEs for start & end positions. • Is used in both Gene Expression and Comparative Genomic domain queries

  28. Agnostication can result in Obfuscation… • Challenge: Making Rembrandt dB agnostic using a standard Object Relational Mapping (ORM) layer AND still create high performance queries. • Currently using Apache’s Object Relational Bridge (OJB) • All ORMs provide great abstraction but may not help produce the most efficient SQL. • Custom implementations or extending frameworks can become a maintenance nightmare.

  29. High Performance Query Processing • Multi-threaded Query Processing: • All queries are constructed and executed in parallel on separate threads from Java server side • Dimensional Result Set Processing • All result set dimensions are reconstituted in Java server side • For example: • The entire Chromosome 7 (1 and 15854551 bp) • We retrieves about 51,000 fact records plus all associated annotations and display results for all 51 samples in 20 sec.

  30. Multi-threaded Query Processing in Java

  31. Groundwork a robust bioinformatics knowledgebase framework • Challenge: Lay the foundation for a clinical genomic framework that… • Integrates Clinical data with Experimental data • Provide researchers with the ability to perform complex ad hoc querying and reporting across multiple domains. • Generic enough to support other similar clinical genomic studies such as I-SPY

  32. Rembrandt Data Warehouse Schema • Highly de-normalized, query optimized star schema • The Fact tables contain all the pre-calculated data points based on various scientific algorithms. • The dimension tables contain study relevant data points, such as clinical information, genomic annotation information, etc. • Lookup tables and mapping tables provide static general information, such as gender, etc.

  33. Rembrandt Data Warehouse Schema Dimensions (PROBESET_DIM, CLONE_DIM, DISEASE_DIM, etc) Fact Tables (DIFFERENTIAL_GENE_SFACT , DIFFERENTIAL_GENE_GFACT , ARRAY_GENO_ABN_FACT) Lookup/Mapping Tables

  34. Gap Analysis (Identify missing processes, work flow, data & infrastructure components) • Data Lookup Strategies • New use cases for caBIO, caArray, caGRID & EVS • Data Preprocessing & ETL needs

  35. NCICB Development team • Ram Bhattaru • Himanso Sahni • Kevin Rosso • Ryan Landy • James Luo • Alex Jiang • Dana Zhang • Dave Bauer • Subha Madhavan • Nick Xiao • Prashant Shah • NCICB Advising team • Sharon Settnek • Carl Schaefer • Mervi Heiskanen • Sue Dubman • Peter Covitz • Ken Buetow • NOB/CCR/NCI • Howard Fine • JC Zenklusen • Yuri Kotliarov • Tracy Lively • NINDS • Bob Finkelstein • UCSF • Laura Esserman • Meredith Buxton

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