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

2. CIP NCIA 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. CIP NCIA Linguistic distinctions biomarker prognostic predictive ‘qualified’ biomarker ‘surrogate’ marker

4. CIP NCIA 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. CIP NCIA 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. CIP NCIA FDG-PET prediction of overall survival after chemo in patients with NSCLC Weber WA et al. J Clin Oncol 2003.

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

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

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10. 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

11. CIP NCIA 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.

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14. 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

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

16. CIP NCIA An enhanced application for biospecimen management 050107 caTISSUE Suite

17. CIP NCIA 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

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

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

20. CIP NCIA 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

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

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28. 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

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31. 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

32. CIP NCIA What are the unresolved challenges ?

33. CIP NCIA Annotation is a challenge

34. CIP NCIA 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 Vocabularies and Common Data Elements/Standards and Interoperability

35. CIP NCIA 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

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

38. TRANSFORMING PATHOLOGY: Emerging technology driving practice innovation