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The TMAJ Software Project and Database:

TMAJ software provides a platform for managing and analyzing tissue microarrays, pathology data, and associated images. It offers features such as data entry, user management, TMA design, image viewing and scoring, and publishing datasets online.

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The TMAJ Software Project and Database:

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  1. The TMAJ Software Project and Database: Angelo M. De Marzo MD PhD James Morgan BS November 12, 2007

  2. Introduction • Many putativenew 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 – H&E 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. 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

  13. 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

  14. 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

  15. 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/articlerender.fcgi?artid=165444)

  16. Database Design

  17. Patients, Specimens, & Blocks The Patients, Specimens, Blocks, and Tissue Diagnosis tables all form a one-to-many relationship.

  18. ArrayBlocks The Schema of ArrayBlock-related tables. 

  19. 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”

  20. 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

  21. Specimens in TMAJ

  22. Tissue Blocks in TMAJ

  23. 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)

  24. Applications & Screenshots

  25. Specimens Application • This application allows for detailed input of data on individual specimens and donor-tissue-blocks. 

  26. 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.

  27. Images Application

  28. 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.

  29. Images Application: Viewing 2 Stains

  30. T. Cornish, MD PhD, J. Morgan – Image Analysis Software in Process (v 1.0)

  31. 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.

  32. 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

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

  34. Dynamic Fields in TMAJ What are Dynamic Fields, why are they important, and how are they managed in TMAJ?

  35. 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.

  36. 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.

  37. 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.

  38. 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.

  39. 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.

  40. TMAJ & Frida Integration • The image analysis software package Frida has been integrated with TMAJ

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

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

  43. 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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