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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

Translational InformaticsRembrandtISPY

REpositoryofMolecular BRAin Neoplasia DaTa

&

InvestigationofSerial Studiesto Predict Your Therapeutic Responsewith Imaging AndmoLecularanalysis

slide3

Poor Survival Rates

Median survival for GBM patients < 1 yr

challenges
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?

rembrandt knowledgebase

Expression array data

SNPArray data

Clinical data

Proteomics data

Rembrandt Knowledgebase

Datawarehouse

Better understanding

Better treatments

Concept

Creation

nci s gmdi study

DNA

RNA

Tumor

Core Punch

Proteins

NCI’s GMDI Study

Blood

Tumor

Plasma

typical rembrandt usage scenario
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?
slide10

Demo

Application: http://rembrandt-db.nci.nih.gov

Informational Site: http://rembrandt.nci.nih.gov

rembrandt project milestones
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
i spy trial
I-SPY Trial

Investigationof

SerialStudies to

Predict

Your

Therapeutic

Response with

Imaging

And

moLecularanalysis

Courtesy: Laura Esserman, Director, UCSF CF Buck Breast Care Center

motivation ispy trial
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
ispy trial informatics
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
slide15

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

quality indicator qi portal
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
dna quality indicators
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

rembrandt ispy leverage ncicb and cabig infrastructure components
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
vision for caintegrator
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

rembrandt design overview system architecture application framework data warehouse objectives

Rembrandt Design OverviewSystem Architecture,Application Framework & Data Warehouse Objectives

rembrandt business objectives
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)
rembrandt technical objectives
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
query retrieval objects support strong type checking validations
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
example criteria objects
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
agnostication can result in obfuscation
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.
high performance query processing
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.
groundwork a robust bioinformatics knowledgebase framework
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
rembrandt data warehouse schema
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.
rembrandt data warehouse schema1
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

gap analysis identify missing processes work flow data infrastructure components
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
slide36
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