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Pathology and Imaging In Biomarker Development

Explore the importance of biomarkers in pathology and imaging for cancer diagnostics and treatment. Learn about different types of biomarkers, the Biomarker Consortium, and how Imaging Informatics and Digital Imaging can enhance research. Discover caTISSUE Suite and MR Spectroscopy in prostate cancer.

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Pathology and Imaging In Biomarker Development

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  1. Pathology and Imaging InBiomarker Development C. Carl Jaffe, MD, FACC Cancer Imaging Program National Cancer Institute

  2. Biomarker NIH Workshop definition (2001): a characteristic that is objectively measured … as an indicator of normal biologic or pathogenic processes or pharmacological responses to a therapeutic intervention

  3. Linguistic distinctions • biomarker • prognostic • predictive • ‘qualified’ biomarker • ‘surrogate’ marker

  4. Types of Biomarkers • Prognostic -- portend disease outcome at time of diagnosis without reference to any specific therapy • Predictive -- predict outcome of a particular therapy • Monitoring-- measure response to treatment and early detect disease progression or relapse

  5. Predictive vs Prognostic • Predictive markers can be used to make decisions about specific treatments • are essential for adaptive trial design • a predictive marker may not be prognostic if it does not predict outcome in untreated patients • Prognostic markers may not be predictive • i.e. doesn’t interact with particular treatment

  6. FDG-PET prediction of overall survivalafter chemo in patients with NSCLC Weber WA et al. J Clin Oncol 2003.

  7. FDG-PET Monitoring Response to Gleevec in GIST Baseline 24 hrs 7 days 2 mos 5.5 mos Dana-Farber Cancer Institute

  8. “Surrogate” biomarker • Biomarker used in place of definitive endpoint • May be observed earlier than definitive endpoint

  9. Context: Current Oncology Drugs Failure rate and development costs are high: >80% of drugs entering clinical development fail to get marketing approval 50% of new drugs reaching Phase III trials fail Development costs per drug from discovery through Phase III has been estimated at $0.8–1.7 billion requiring 8–10 years of time For new molecularly targeted oncology drugs, there are specific development issues Very promising oncology drugs may be effective only in selected cancer patients or risk groups Inhibition of critical signal transduction pathways may lead to collateral toxicity

  10. Biomarker Consortium OBQI - public-private partnerships • coordinated by Foundation for the NIH through the Biomarker Consortium, - a larger public-private partnership to promote discovery, development, qualification, and regulatory acceptance of biomarkers; • make research results and data arising under consortium projects publicly available • develop safe, innovative, and effective medicines and diagnostics to improve medical care, and improve public health.

  11. In this context – How might Imaging Informatics and Digital Imaging help? • Image storage and transmission • Distributed network communication • Database biospecimens • Integrate the broader healthcare record and enterprise • Enable performance auditing

  12. caBIG objectives software suite that provides a means of capturing, storing and sharing medical images. confederated archive for images and related data connected interoperably Imaging Clinical Research Pathology Molecular Biology

  13. caTISSUE 050107 Suite An enhanced application for biospecimen management

  14. caTissue Suite • Enhanced Collection Protocol Definition • Pre-define specimen processing schemes • Define multiple study arms and time points • Facilitated Specimen Accession • Pre-defined specimen and specimen-related data creation • Collection Protocol Consent Tracking • Pathology Annotation (CAE) • CAP protocol pathology annotation for major organ systems • caTIES-like Pathology Report Annotation • Custom Annotation (Dynamic Extensions) • Advanced Query “Wizard” • Create and save complex, pre-defined or parameterized searches • Specimen Requisition and Request Tracking

  15. Enhanced Protocol Definition Summary View 1. Specimens expected at selected collection point 3. Expected aliquots of derivative specimen 2. Expected derivative of selected specimen Storage Definition

  16. Pathology Annotation Pathology annotation forms for major organ systems Pre-defined pathology annotation forms Pathology annotation for case (SCG)

  17. caTissue Suite v1.0 • Demonstration Site: http://catissuecore.wustl.edu • Application release: 4/15/2008 • What’s next – • Usability enhancements • Security and control for multi-bank user environment • Improved custom form generation • Temporal queries • Other enhancements based on user feedback

  18. MR Spectroscopy: Prostate Figure 2 In vivo 1.5T 300mg lactate choline GPC, PC taurine, mI, Etn Glu, Gln lipids, leu, Ile, Val Glu, Gln PA, Glu, Gln mI mI alanine creatine, lysine, PCr PA mI creatine lactate sI taurine PA, PEtn UCSF Ex-vivo 11.4T 7mg

  19. National Cancer Institute Imaging Archive • repository for oncology image data including ongoing and former clinical trials, reference image collections and phantom data • Image visualization, interpretation and mark-up tool • A project to develop free and open source software for acquisition, archival and flexible distribution of images and related data via: • Internet portal • caGRID • DICOM Query Retrieve • API

  20. How Does It Fit The “Big” Picture? • caBIG modules: • caTissue: manage users, authentication/authorization, specimen registration, search, and specimen distribution. • caMicroscope: image viewer, data services, and image streaming. • caMicrosocpe • Will host the data service as a caGrid service • Uses GridFTP to stream large images

  21. What are the unresolved challenges ?

  22. Annotation is a challenge

  23. Vocabularies and Common Data Elements/Standards and Interoperability CAVITARY MASS Finding: mass Mass ID: 1 Margins: spiculated Length: 2.3cm Width: 1.2cm Cavitary: Y Calcified: N Spatial relationships: Abuts pleural surface; invades aorta AIM: Image Annotation and Structured Data Capture

  24. Common problem: Lack of a radiology Lexicon/Ontology • Limited radiology terminology in Snomed CT (Systematized Nomenclature of Medicine Clinical Terms) or UMLS (Unified Medical Language System) • Current general medical lexicons only include about 20% of terms used in radiology reports • Don’t have consensus on acquisition parameters such as MRI sequences including GRASS, ROAST, etc. to describe acquisition standards

  25. What is Data Compatibility? Lesson 5: Making a Tool caBIG™ Compatible • caBIG™ compatibility is about using standards to ensure interoperability among tools – so that data can be exchanged and understood between systems.

  26. TRANSFORMING PATHOLOGY:Emerging technology driving practice innovation

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