# Presentation: 502812

## caIntegrator:Building a robust translational researchframework for cancer clinical studies

## Agenda

- Background, Vision and Scope
- Overview of caIntegrator and its goals
- Architecture
- Information Models
- Domain Model
- CDE & Vocabulary Usage
- Value Domain
- APIs
- Data Model
- Q & A

## Background

- _Rembrandt_
**RE**pository of **M**olecular **BRA**in **N**eoplasia **D**a**T**a
- Glioma molecular diagnostic Initiative
- Enable biologically-meaningful histopathological classification of tumors (based on patient’s genomic profiles)
- Facilitate cross platform validation of markers
- _I-SPY_
**I**nvestigation of **S**erial Studies to **P**redict **Y**our **T**herapeutic **R**esponse with **I**maging **A**nd mo**L**ecular analysis
- Stage III Breast Carcinoma therapeutic trial
- Capture the biomarkers that predict response to therapy throughout the course of the cancer treatment cycle
- Accelerate our ability to find meaningful robust biomarkers
- _CGEMS_
**C**ancer **Ge**netic **M**arkers of **S**usceptibility
- An NCI enterprise initiative to conduct whole genome association studies to identify genes giving rise to risk of prostate and breast cancer.
- The project is designed to capitalize on new knowledge of human genetic variation and technical advances in ultra-high-throughput genotyping.

## Project vision

- _Generic biomedical integrative informatics platform_
- To build a caBIG-compatible generic framework that allows retrieval and transformation of data from a variety of heterogeneous data sources that house microarray, genomic, tissue array, imaging and clinical data
- _User-centric, high-performance search, retrieval and analysis platform for translational data:_
- To build an analytical tool that allows Clinician /Scientists/Biostatisticians to conduct translational analysis of study specific data in a user friendly manner
- caIntegrator-derived applications:  Rembrandt, ISPY, CGEMS, DCEG/EAGLE, (Future: ALL CTEP/OCG study)

## Scope

- Study data is organized by concept instead of application e.g.
- Rembrandt / I-SPY/ CGEMS study data is organized by patient, sample, disease and molecular anomalies instead of by submission portals (C3D, caArray, caImage etc.)
- Focus is on the modeling, analysis, search and retrieval of clinical genomic data, not on daily operations or transaction processing
- Create a “hypothesis-generating/discovery” suite of tools for use by physician scientists, bioinformaticians and statisticians
- Create a suite of downloadable and configurable components for reuse by other similar translational projects
- Align with caBIG principles:
- Open source, Open access, Syntactic and Semantic interoperability and Federated access
- Inter-operate with NCICB and caBIG Infrastructure Components
- caCORE, caArray, C3D, WebGenome and other caBIG workspace delivered components

## Tumor suppressor gene in Glioma – EMP3

## caIntegrator usage scenarios

- An investigator is interested in looking at a Heatmap of gene expression in normal vs. tumor tissue samples annotated with corresponding survival rates from adenocarcinoma male cases, age 50 or younger, who have a mutation in the EGFR gene, ubiquitin level higher than median in serum, and had smoked more than 20 cigarettes per day for >10 years and have low depression scores? **EAGLE**
- Find the top differentially expressing genes between good vs. poor survival outcome groups from Brain tumor clinical study. **Rembrandt**
- Show a PCA graph for DCIS breast cancer samples and annotate the samples by the change in longest diameter from correlative MR imaging study. **I-SPY/Breast Spore**
- Select [pre-computed] genotype summaries from the Illumina HumanHapMap bead array, CGEMS SNP id, dbSNP id, genomic locus, and any gene features that include the locus for genotype summaries that show a significant difference [user-supplied P-value] between two populations [for example, normal and advanced disease] for a given study [for example, prostate cancer study]. A genotype summary for a probed locus contains the frequency for each genotype (AA/Aa/aa) in the population and a measure of significance. **CGEMS**

## Data Integration via caIntegrator

## caIntegrator Architecture Guiding Principles 

- Build a framework with the infrastructural components needed to develop enterprise level translational applications such as Rembrandt, I-SPY and CGEMS
- Guiding principles :
- Driven by user requirements
- User-friendly for a wide range of audience (physician scientists, programmers, statisticians)
- Standards-based and pattern-driven
- Extensible and scalable
- Reuse/extend existing open-source technologies
- caBIG silver-level compatibility

## caIntegrator Architecture

**Analysis** **server**

**PRESENTATION**
**TIER**

**BUSINESS TIER**

**DATABASE/**
**ANALYSIS **
**TIER**

**Service Layer**

**WEB**
**SERVER**
- (**JBoss /Tomcat**)

**Report Generation**
**(XML/XSL) **

**Asynchronous Updates **
**(AJAX)**

**Presentation Cache (ECHACHE)**

**Query Builder**
**(Struts)**

**Findings Factory**

**Business Cache (ECHACHE)**

**Security Manager (CSM)**

**DTOs**

**Analysis Server Client Manager**
** ****(JMS Node)**

***JMS***
***(Asynchronous)***

**BIOAssay Service**

**Multi-Threaded Query Service (OJB/Hibernate)**

**BioAssay DTOs**

***App state***

**R**

- R-Binary

**Object Query Service**

**Study**
**Query Service**

**Analytical Query Service**

***future***

## caIntegrator Architecture Highlights

- N – tiered Architecture (J2EE)
- Rich user friendly web tier (Struts, XML/XSL, AJAX)
- Clinical-Genomics Service layer that handles both fine and coarse grained, strongly typed objects
- Scalable Run Time Analysis Service (JMS/R-Server/ R-Binary)
- High Performance Query Service (Multi-threaded Query processing/ Hybrid Star Schema)
- Remote interface with WebGenome (EJB)

## UI components: Technologies Employed

- UI components: *Technologies Employed*

**Business**
** ****Tier**

**JFreeChart**

**Struts**

**DWR (AJAX)**

**CSV/ BRB**

**Cache**

**Findings **
**Factory**

## UI components: Technologies Employed

- JFreeChart: charting API
- Kaplan-Meier Survival Plots
- Gene Expression Histogram/Box & Whiskers Plot
- Clinical Scatter Plots
- PCA Plots
- DWR: AJAX Transactions
- Query Inbox
- Form dynamic population
- Report Building
- Flash: generate SWF tutorials
- Dom4J
- Manages XML for presentation based on findings

## Clinical Genomic Object Model v2.0

**Study **

**Findings**

**Annotations**

## CG-OM Highlights

- Based on Rembrandt, I-SPY and CGEMS use cases.
- “Finding objects” model the in-silico transformation and analyses performed on the clinical and experimental datasets.
- Study package groups study-related objects such as Study, StudyParticipant, Agent and Histology, Specimen etc.
- The CinicalFinding object encapsulates the clinical measurements, observations and assessments.
- Annotation objects such as GeneBiomarker, ProteinBiomarker and SNPAnnotation help provide context to the various Findings.
- Use of controlled vocabulary and common data elements enable standard representation of data
- Semantically interoperable with other models in caBIG (caBIO, CT-OM, caTissue, MAGE-OM)

## CG-OM Findings

**Findings:**
- Clinical Finding
- Copy Number
- Copy Number
- LOH
- FISH
- Gene Expression
- DifferentialFoldChange
- Protein
- Cell Lysate
- IHC
- Variation
- Somatic Mutation
- Germ line Mutation
- Analysis
- Class Comparison
- PCA
- SNP

**Study:**
- Study
- StudyParticipant
- Specimen
- Agent
- Histology
- TimeCourse
- Activity
- SubstanceAdminstration

**Annotation:**
- GeneBiomarker
- ProteinBiomarker
- GeneReporterAnnotation
- SNPAnnotation
- BioAssayArrayType
- Pathway
- GenBankAccession
- SNPPanel

## CG-OM Example: Genotype Finding

## Common Data Elements

## Standards Usage

- CDE Standards Usage such as
- Karnofsky Performance Status Score
- Lansky Performance Status Score
- Patient Race Category
- Patient Ethnic Group Category
- Agent , Chemo, Radiation
- HUGO Gene Symbol, Uniprot ID, Entrez Gene ID
- Vocabulary Usage such as
- WHO Disease Classification for Gliomas
- Gene Ontology
- Race categories

## Value Domain

## PIM to PSM: Enumerated Value Domains to enums

## caIntegrator Interfaces

- Object Query Service
- Study Query Service
- Analytical Query Service

## caIntegrator API Use Cases

- Support for both Fine Grained and Coarse Grained access
- Objects may be Transient
- Requires Runtime Analysis
- Query Performance is High Priority
- Typical Result Set may return 10K-100K records

## caIntegrator Solution

- Object Query Service
- Use the caCORE SDK to generate the fine grain “caCORE-like” API
- Study Query Service
- Handles coarse gain API that utilizes Data Transfer Objects (DTOs)
- Study service layer uses HQL to assemble request
- Both Object Query Service & Study Query Service  are built on common Domain Objects and map to the same Hibernate mappings
- Analytical Query Service
- Support for Run Time Analysis Objects

## caIntegrator Clinical Genomic Services

## Leveraging caCORE SDK

- Design CG-OM
- Model annotated via the Semantic Integrator Workbench (SIW).
- Metadata registered using the UML Loader.
- Object Query Service
- Use the caCORE Code Generator to generate a caCORE-like API

## Enhancements to caCORE toolkit

- Generation of “POJO” beans for in-container applications
- Support for Java 1.5 Enums
- Support for abstract classes in the code generator
- Support for “Marker” or “Tagged” Interfaces
- Share Hibernate mappings between caCORE “Domain” objects and “POJO” beans

## Object Query Service: FISHFinding example

## Study Query Service: CGEMS DTO

## CGEMS code example

## Study Query Service Example

## Analytical Query Service: caIntegrator Findings Service

- Translational Service API serves “Findings” based on clinical genomic (CG) criteria (queryDTO)
- Consumes Genomic and clinical DTOs and returns Finding DTO’s that encapsulate DO’s
- DTO’s comprise of data from CG-OM objects

## Class Comparison Analysis

## Analytical Query Service Example

## Analytical Server Architecture

_**Higher-order analysis components**_
- R tools for HC, Class prediction, PCA (more will be added as the project proceeds)
- Other open-source/caBIG ICR data analysis tools

## Analytical Server Architecture 

- Rembrandt/I-SPY
- Front End

- Middle Tier

**Analysis Server **
**Client Manager**

- Analysis Node

- Analysis Node

- Analysis Node

## Analysis Node Architecture

_**Analysis Node**_

- JMS Message Handler

-  _JMS Message Handler_: receives analysis requests from the
-   AnalysisRequestQueue and sends analysis results to the
-   AnalysisResultQueue.
-  _ThreadPoolExecutor_: uses the Java 1.5 ThreadPoolExecutor class to handle
-   thread execution and job queuing. **(See:java.util.concurrent.ThreadPoolExecutor)**
-  _Compute Thread_: Responsible for performing the actual statistical
-   computation. Receives an analysis request as input and produces an
-   analysis response.

- Thread Pool Executor

- Compute Thread(s)

## Data Model 

- Modified Snowflake and Relational Schema
- Additional Fact tables for gene expression, copy number, genotype data
- CSM related tables and institutional based access
- Clinical data tables

## caIntegrator data model

**CSM**

**Clinical**

**Genomic Fact**
**CN:~35 M**
**GE:~20M**

**Dimensions**

## caIntegrator User Access

- Use Case: Provide role based read only access (Super, Institution Level, Public (default))
- Utilize CSM 3.0.1 for
- Authentication via LDAP
- User Provisioning Tool (UPT) for assigning groups and roles to users
- Authorization:
- Current CSM authorization design applies access control measures after each query has been executed.
- Therefore, caIntegrator data processing tier will add applicable datasets constrains to each query as we have high data thru-put and high performance needs.

## Questions?