# Presentation: 086541

## A caBIG Success Story 

***caTRIP from Soup to Nuts***

- Patrick McConnell
- Duke Comprehensive Cancer Centerpatrick.mcconnell@duke.edu

## Who is involved in caTRIP?

**Duke Bioinformatics**
- Jamie Cuticchia (PI)
- Patrick McConnell (lead architect)
**Duke Information Systems**
- Bob Annechiarico (PM)
- Wilma Stanley (developer)
- Mark Peedin (developer)
- Mohamad Farid (DBA)
- Jeff Allred (IT manager)
**Duke Pathology**
- Raj Dash (domain expert)
- Chris Hubbard (developer)
**Duke Oncology**
- Kelley Marcom (domain expert)
- Gretchen Kimmick (domain expert)
- Kimberly Blackwell (domain expert)
- Lee Wilke (domain expert)
**Duke CALGB**
- Kimberly Johnson (DataMart liaison)

**SemanticBits**
- Ram Chilukuri (lead developer)
- Srini Akkala (developer)
- Sanjeev Agarwal (developer)
**5 AM Solutions**
- Bill Mason (developer)
**NCI**
- Julie Klemm (ICR WS lead)
- Carl Shaefer (NCI rep)
- Subha Madhavan (caIntegrator PM)
**BAH**
- Curtis Lockshin
- Mehul Shah (tech support)

## Agenda

**caTRIP overview**
- Translational informatics, goals, data systems, application
**Development process**
- UPF, use cases, requirements, modeling, implementation, documentation
**A success story?**
- Compatibility, end users, data sharing, security
**Discussion/questions**

## OverviewWhat is translational research?

**Bench-to-Bedside**
**Wikipedia (the source of all knowledge):***Translational medicine is a branch of medical research that attempts to more directly connect basic research to patient care.** *
**Basic research occurs in the lab**
**Patient care occurs in the clinic**
**Translational research broadened...***Translational medicine can also have a much broader definition, referring to the development and application of new technologies in a patient driven environment - where the emphasis is on early patient testing and evaluation.**...facilitate the interaction between basic research clinical medicine, particularly in clinical trials.*

## OverviewInitial focus

**Our initial focus will be on connecting existing data systems, including basic science data, to enhance patient care **
**Initial problem scenario: *****outcomes analysis***
- Use data from existing patients to inform the treatment of another patient
- Leverage clinical, pathology, tissue, and basic science data
**Scenario:**Patient A enters the clinic.  What treatments were applied with success on other patients with similar characteristics (race, sex, symptoms, pathology results, adverse events, biomarkers).

## OverviewBroadened focus: scientific use cases

**Find available tumor tissue**
- What are all the tissue specimens from her2/neu positive patients that have a primary tumor in the breast and are BRCA1 positive?
**Find factors of survival**
- What are all the ER positive patients that have survived breast cancer after radiation treatment?
**Find patients for trials**
- What are all the patients that are triple negative (ER, PR, and HER2/NEU negative)?
**Determine the distribution of disease factors over time**
- Does a change in pathology biomarkers over time contribute to recurrence or death?
**Determine correlation of factors pre and post surgery**
- Does a change in ER or PR status before and after surgery correlate with other factors?
**Find pathology reports of interest**
- Show me all of the pathology reports for Her2/Neu positive patients with a lobular carcinoma.

## OverviewConnecting disparate data systems

**Tumor Registry**Diagnosis, Treatment, Recurrence, Follow-up

**CAE**Pathology Biomarkers

**caTissue CORE**Tissue Bank

**caTIES**Pathology Reports

**caIntegrator**SNP Data

- caTRIP

- caTRIP

- caTRIP

- caTRIP

**MRN**

## OverviewArchitecture overview

- MAW3

- Tumor Registry

- Illumina

- caTIES

- CAE

- caTissueCORE

- TR

- CGEMSSNP

-      Duke

- Domain Grid Services

- Distributed Query Engine

- query

- discover

- GUI

- IndexService

- IdPService

- Core Grid Services

- Domain Controller

- authenticate

- caTissue CORE

- caTIES

- CAE

- TR

- caIntegrator

- GridGrouper

- authorize

## Development processOverview of Unified Process Framework

**Development process**Overview of Unified Process Framework

**Inception**
- Gathering use cases
- Specifying requirements
**Elaboration**
- Create domain model
- Develop baseline architecture
- Tackle risky/difficult use cases
**Construction**
- Implement remaining use cases
**Transition**
- Generate end user and other documentation
- Deploy production application at adopter sites

## Development processDrafting use cases

**Captures the functional requirements of a system**
**Describes scenarios and actors **
**Communication link between developers and domain experts**

## Development processSpecifying requirements

**Formal description of what the system should do**
**Non-functional requirements describe how it does something (e.g. performance)**

| Req. ID | Requirement |
| --- | --- |
| SEC-5 | caTRIP will use CSM APIs for user authentication; that is, to verify a user’s credentials and allow access to the application and authorization services. |
| SEC-10 | A Java Key Store will be needed at the caTRIP client side |
| SEC-15 | CA Certificate Chain to certify the caTRIP query engine service and the caTRIP user. |
| SEC-20 | User’s private key management system. |
| SEC-25 | Each caTRIP user shall be authenticated against a specific trusted Identity Provider (IDP) registered with Dorian in the caGrid in order to execute the Federated Query. The caTRIP GUI should provide a means for users to login. |
| SEC-30 | In Phase one of the caTRIP project, the login will be based on two fields; username and password. |
| SEC-35 | The login infrastructure may provide “Remember Me” feature in which the username and password are stored on the user’s client machine. |

## Development processCreating a domain model

** **

**Participant**

**Diagnosis**

**Treatment**

**Follow up and ****Recurrence**

**Collaborative ****Staging**

## Development processImplementing the application

## Development processGenerating documentation

## A success story?caBIG compatibility

**Challenge**
- Silver-compatibility is in some ways (and for good reason) stringent
- Grid technologies were still in development (caGrid 1.0 is now released)
**caTRIP is a silver-compatible application (in theory)**
- Compatibility submission package completed
- Going through review now for silver-compatible data services
**caTRIP leverages caCORE technologies**
- Common Security Module (CSM) provides authorization
- caCORE-SDK provides tooling to create Java classes from UML (XMI), XML schemas, and castor mappings
**caTRIP leverages caGrid technologies**
- Index Service provides advertisement and discovery
- Authentication Service provides
- Dorian helps provide authentication
- GTS provides trust fabrics

## A success story?Engaging end users

**Challenge**
- It is difficult to engage oncologists/clinicians: time and interest
- It is critical to engage domain experts
**Engaged Raj Dash**
- Pathologist at Duke with technical expertise and domain expertise
- Manager of the tissue bank
**Engaged Breast Oncology Clinic**
- Weekly meeting of oncologists that discuss cases
- Helped define use cases
- Provided feedback on application usability and features

## A success story?Data sharing

**Challenge**
- It is difficult to deidentify datasets owned by different groups, even in a single institution
- Existing processes involve a middleman and are not scalable
**Sharing identifiable data**
- IRB approval to build system
- IRB approval by individuals to access data
**Sharing deidentified data**
*Traditional method*: data manager deidentifies an entire dataset then throws away the key
*Proposed method*: trusted service provider (TSP) deidentifies discreet values

## A success story?Distributed deidentification

- Trusted Service Provider

| PHI                     | DEID |
| --- | --- |
| MRN1                     | ABC123 |
| MRN2 | DEF456 |
|  |  |
| . . .                     | . . . |

- MRN3

- GHI789

- MRN3

- GHI789

- Randomly generated

- Has IRB approval to see identifiable data

- Has IRB approval to see identifiable data

- Has IRB approval to store identifiable data

- Secure connection

| PHI                     | DEID |
| --- | --- |
| MRN1                     | ABC123 |
| MRN2 | DEF456 |
| MRN3  | GHI789 |
| . . .                     | . . . |

## A success story?Security

**Challenge**
- Security in a distributed grid environment is a difficult problem
- The caGrid security infrastructure is still very new (caGrid 1.0 is released)

- caGrid Authentication Service

- SAML Assertion

- User Credentials

- Dorian

- Duke Authentication Plugin

- Duke Domain ControllerNT Security

- Grid Data Service

- User Grid Certificate

- CSM

- backenddata

- authentication

- authorization

- GridGrouper

- Trust Fabric

## A success story?Useful to the community?

**Clinical scenario for demonstration**
- A patient enters the clinic and is diagnosed with a lobular carcinoma
- The Her2/Neu biomarker test comes back positive
- Clinical question: ***What are the treatments and outcomes of other patients with similar characteristics?***
- Query for diagnosis date, treatment, treatment date, survival, recurrence, and BRCA1 and BRCA2 status
- Look for treatments given with success and correlation between BRCA status in case test should be ordered
**Research scenario for demonstration**
- Scientific question: ***Is there a correlation between Nottingham Score and Her2/Neu status for patients diagnosed with lobular carcinoma?***
- Query for Nottingham Score of patients with Her2/Neu status of positive/negative
- The average Nottingham Score for each category is calculated
- Investigate gene biomarkers that correlate with a Her2/Neu status of negative and survival
- Query caTRIP for all available tissue and to order for microarray experiments