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Title: The%20TMAJ%20Software%20Project%20and%20Database:


1
The TMAJ Software Project and Database
  • Angelo M. De Marzo MD PhD
  • James Morgan BS

November 12, 2007
2
Introduction
  • Many putative new disease target genes with
    diagnostic, prognostic, and therapeutic
    applications
  • Validation requires many samples
  • Quantitative RT-PCR or protein arrays have
    disadvantages
  • Genes may be expressed in multiple different cell
    types
  • In situ analyses ideal but generally slow
  • Tissue microarrays address some of these problems

3
TMA Technology
Tissue microarray technology for high-throughput
molecular profiling of cancer Kallioniemi O
et.al. Human Molecular Genetics, 2001, vol. 10,
No. 7
4
Slide from Mark A. Rubin, M.D, Dana Farber
5
Donor Block Sampling
Transfer to Recipient Block
6
Tissue Microarray Advantages
  • High throughput
  • Expands tissue use
  • Uniform reaction conditions
  • Built-in controls
  • Economize use of reagents
  • Facilitates data recording and linking to
    clinical data

7
JHU Tissue Microarray 400 cores, 0.6 mm
8
Tissue Microarray 400 cores, 0.6 mm each HE
of 4 µm section
9
Digital Image Acquisition
  • Can use conventional microscopes
  • Record data in spreadsheet diagnoses and
    interpretations
  • Or the data can be recorded on paper for later
    entry into a spreadsheet or database
  • Major Problem
  • Easy to loose track of the x and y coordinates of
    given spots

10
Tissue Microarray Image Acquisition
ACIS II, DAKO
Aperio ScanScope
11
Need for Data Management
200 TMAs from Johns Hopkins TMA lab
12
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13
What is TMAJ?
  • TMA-J is a set of open source software tools and
    backend database structure to facilitate
    management and analysis of tissue microarrays and
    associated pathology and image data

14
What Does TMAJ Do?
The software applications provide a platform for
  • Entering pathology data
  • Managing users and permissions
  • Designing TMAs
  • Viewing and scoring TMA (and other) images online
  • Side-by-side viewing of serial TMA images from
    slides stained for different biomarkers
  • Publishing large numbers of TMA images and
    datasets on the Internet

15
What Does TMAJ Do?
  • The Database Tracts
  • Clinical information about patients
  • Pathology specimens and associated data
  • Pathology tissue blocks
  • Tissue Microarray cores
  • TMA Blocks
  • TMA Slides
  • TMA core images
  • TMA image scoring data manual or semi-automated

16
Primary Goals of System
  • Address security issues
  • Remove or isolate patient identifiers
  • Manage multiple organ systems
  • Develop web based interface
  • Scalable to accommodate large number of
    simultaneous users
  • Storage of large sets of images with diagnoses
  • Data structure compatible with emerging standards
    for easy data exchange
  • CaBIG compatibility (to be defined)
  • The tissue microarray data exchange
    specification
  • Berman et al., (http//www.pubmedcentral.nih.gov/a
    rticlerender.fcgi?artid165444)

17
Database Design
18
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19
Patients, Specimens, Blocks
The Patients, Specimens, Blocks, and Tissue
Diagnosis tables all form a one-to-many
relationship.
20
ArrayBlocks
The Schema of ArrayBlock-related tables. 
21
Security Protecting Patient Information
  • Database stored on a secure server
  • Identifiable patient information in encrypted
    tables (Approved by the IRB)
  • Researchers have no access to patient identifiers
  • Creates virtual separate entities clinical
    database and research database

22
DATA
Tissue Microarrays 701 Specimens 29,860 Tissue
Blocks 34,783 ArrayCores 90,051 ArraySlides
7532 ArrayImages 248,746
230 users, 41 Institutions, updated Nov. 4, 2008
23
Specimens in TMAJ
24
Tissue Blocks in TMAJ
25
Applications Java from Sun Microsystems
  • Java Web Start Software
  • Java Web Start software provides a
    browser-independent architecture for deploying
    Java technology-based applications to the client
    desktop
  • Each application runs on a dedicated Java Virtual
    Machine (JVM)

26
Applications Screenshots
27
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28
Specimens Application
  • This application allows for detailed input of
    data on individual specimens and
    donor-tissue-blocks. 

29
Security Options Specimens
  • Users may only access specimens to which they
    have permission.
  • Admins may assign a user permission to a specimen
    by using the Users-Specimens tab in the
    Administrator application.

30
Images Application
31
Image Application Filtering
  • The table shows information about every image
    (identified by x and y) in an ArraySlide.
  • Images identified as Prostate Carcinoma are
    highlighted in red.

32
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33
Images Application Viewing 2 Stains
34
T. Cornish, MD PhD, J. Morgan Image Analysis
Software in Process (v 1.0)
35
Publishing TMA Images and Scoring Data Over the
Internet
  • Roughly modeled after Stanford Microarray
    Database
  • Concept
  • Once a study is published by a journal, all TMA
    diagnoses, image, scoring and non-protected
    clinical data can be published as supplemental
    data to the Internet for public online viewing or
    down loading
  • TMAJ Images now linked to Proteinpedia database
  • (http//humanproteinpedia.org) by Akhilesh Pandy,
    MD PhD.

36
For More Information
  • http//tmaj.pathology.jhmi.edu
  • To see published images
  • login to tmaj as a guest and then click the
    Images button.
  • Username guest
  • Password guest

37
Institutions Using TMAJ
  • Johns Hopkins University
  • Harvard Dana Farber Cancer Institute
  • Cleveland Clinic
  • University of Texas Southwestern
  • Vanderbilt University

38
Dynamic Fields in TMAJ
  • What are Dynamic Fields, why are they important,
    and how are they managed in TMAJ?

39
Dynamic Fields
  • Different organ systems will have different
    recorded data. For example the Gleason score is
    only relevant to the prostate.
  • Dynamic fields allow TMAJ to keep track of
    different data for different organ systems.
  • TMAJ can have dynamic fields added at any time
    through the GUI. Database access is not needed
    and the code does not need to be recompiled.

40
Dynamic Fields GUI
? The user is prompted to choose a specimen type.
When users add a new specimen, they are prompted
to choose a Specimen Type. In this case they
choose the Radical Prostatectomy type. After
the type is selected, we see fields that are
common for every specimen (SurgPathNumber and
Date SpecimenTaken), as well as fields that are
only relevant for a Radical Prostatectomy
(GleasonSum, HasSeminalVesicle). Note the
dynamic fields are in italics.
41
Changing Meta Data
Above we see a Type called Prostate Atrophy
with several fields such as HistologicType and
Prostate_Zone. The Prostate_Zone has several
allowed choices such as Central Zone and
Peripheral Zone. These values can be added,
modified, or deleted by using the buttons on
right.
42
One Approach A Key-Values Table
  • A Key Values table would only have 3 fields A
    Key (such a Prostate Weight), a value, and a
    foreign key that links the record back to the
    main table (such as the Specimens table).
  • We did not use this approach because it does not
    keep track of the meta-data. Meta-Data is data
    that describes data, and in this case it would be
    the type (Prostate), the field for the type
    (Prostate Weight), and any allowed choices.

43
Dynamic Data for Specimens
  • Fields common to all Specimens are stored in the
    Specimens Table
  • The SpecimenTypes, SpecimenFields, and
    SpecimenEnums are the Meta Data
  • The SpecimenTypes contains values such as
    Prostate, Bladder, Kidney, and Lung
  • The SpecimenFields lists the field names for each
    Specimen Type. A Prostate SpecimenType may have
    a Gleason Score or Prostate Weight field.
  • The SpecimenEnums table give a list of valid
    choices for each SpecimenField.

44
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45
TMAJ Frida Integration
  • The image analysis software package Frida has
    been integrated with TMAJ

46
Using Image Analysis in TMAJ
  • New Image Analysis Sessions are created for a
    scanned array-slide

47
Viewing Image Analysis Results
  • Image Analysis Results may be viewed side-by-side
    with a regular scoring session

48
Acknowledgements
Tissue Microarray Lab Marc Halushka MD
PhD Helen Fedor BS Marcella Southerland BS Qizhi
Zheng MD James Morgan BS Kristen Lecksell BS
De Marzo Lab Jessica Hicks BS Toby Cornish MD
PhD
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