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Pathology and Imaging InBiomarker Development
C. Carl Jaffe, MD, FACCCancer Imaging ProgramNational Cancer Institute
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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
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Linguistic distinctions
• biomarker• prognostic• predictive
• ‘qualified’ biomarker
• ‘surrogate’ marker
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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
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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
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FDG-PET prediction of overall survival after chemo in patients with NSCLC
Weber WA et al. J Clin Oncol 2003.
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FDG-PET Monitoring Response to Gleevec in GIST
Baseline 24 hrs 7 days 2 mos 5.5 mos
Dana-Farber Cancer Institute
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“Surrogate” biomarker
• Biomarker used in place of definitive endpoint
• May be observed earlier than definitive endpoint
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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 failDevelopment 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
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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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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
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caBIG objectives
Clinical Research
PathologyMolecular Biology
Imaging
software suite that provides a means of capturing, storing and sharing medical images.
confederated archive for images and related data connected interoperably
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An enhanced application for biospecimen management
050107
caTISSUESuite
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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
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Enhanced Protocol DefinitionSummary View
1. Specimens expected at selected collection point
2. Expected derivative of selected specimen
3. Expected aliquots of derivative specimen
Storage Definition
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Pathology AnnotationPathology annotation forms for major organ systems
Pathology annotation for case (SCG)
Pre-defined pathology annotation forms
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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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Figure 2
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MR Spectroscopy: Prostate
UCSF
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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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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
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What are the unresolved challenges ?
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Annotation is a challenge
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CAVITARY MASS
Finding: massMass ID: 1
Margins: spiculatedLength: 2.3cmWidth: 1.2cmCavitary: YCalcified: N
Spatial relationships: Abuts pleural surface;
invades aorta
AIM: Image Annotation and Structured Data Capture
Vocabularies and Common Data Elements/Standards and Interoperability
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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
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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
TRANSFORMING PATHOLOGY:Emerging technology driving practice
innovation