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    • 1. Transformation to Value-Based Personalized Healthcare: Cancer as a Model
      William S. Dalton, PhD, MDPresident, CEO & Center DirectorMoffitt Cancer Center & Research InstituteTampa, Florida
    • 2. Total Cancer Care: A Personalized Approach to a Patient’s Health Journey
      Populations at Risk
      Intervention
      Diagnosis
      Survivorship
      Prognosis
      Relapsed Disease
      Treatment
      – Behavioral Research
      – Psychosocial & Palliative Care
      – Family Needs
      – Health Outcomes
      – Risk Factors
      – Genetics
      – Early Detection
      – Health Disparities
      – Recurrence Therapy
      – Drug Discovery
      – Adaptive Trial Design
      – Prevention
      – Lifestyle/Nutrition
      – Education
      – Genomics/Proteomics
      – Imaging Modalities
      – Nanotechnology
      – Primary Therapy
      • Multimodality
      • Target Based
      – Post Therapy
      • Surveillance
      – Clinical Trials Matching
      – Molecular Oncology
      – Biomarker Analysis
      (http://www.hhs.gov/myhealthcare/news/phc_2008_report.pdf; pg 243)
    • 3. The Necessary Components
      Clinically annotated bio-repository for tumor and normal specimens
      Partnership among researchers, clinicians, regulators, policy makers, and patients to design an integrated information network system
    • 4. The Approach for Cancer
      The Total Cancer Care Protocol
      • Can we follow you throughout your lifetime?
      • 5. Can we study your tumor using molecular technology?
      • 6. Can we recontact you?
    • Electronic Consenting System
      Consists of IRB Approved:
      • Introductory Video
      • 7. Consent Video by PI
      • 8. Informed Consent
      • 9. Signature Capture
      • 10. Demographics Survey
      Wireless touch- screen tablet
      Connects via secure interface and forwards HIPAA-compliant information to database
    • 11. Partners in the Fight Against Cancer
    • 12. Nexus Biostore
      • Four unit capacity of 2.4 Million samples
      • 13. Stores samples in a -80°C environment
      • 14. Handles samples in a -20°C environment
      • 15. Retrieves samples using NEXUS proprietary ‘Cool Transition’ technology
      • 16. Flexibility to accommodate a wide variety of samples, vessels and labware
      • 17. Automated 24/7 monitoring system in place
      • 18. Automated Inventory functionality provides real-time inventory tracking of stored biospecimens
    • As of August 16, 2011
      Total Cancer Care To Date
      Patients Consented
      78,615
      Tumors Collected
      28,146
      Tumors Profiled
      14,604
      8
      8
      8
      Confidential and Proprietary
    • 19. M2Gen Offices, Bio-repository 100,000 sqft in Tampa, FL
    • 20. The Approach
      Improved
      Medical
      Practice
      Create a delivery system that will integrate new technologies into the standard of care and develop evidence-based guidelines for the treatment of cancer.
    • 21. Four Portals to Total Cancer Care™
      Researcher View
      Next Generation Health and Research Informatics Platform
      • Cohort Identification
      • 22. Molecular Profiling
      • 23. Comparative Effectiveness
      Patient View
      • Personal Health Record
      • 24. Longitudinal Follow-up
      • 25. Personalized Search
      Administrators View
      • Operational Dashboards
      • 26. Quality & Safety Reporting
      • 27. Meaningful Use
      • 28. Decision Support
      • 29. Clinical Pathways
      • 30. Clinical Trial Matching
      • 31. Access for Affiliate Network
      Clinician View
    • 32. The HRI Platform Defined
      An integrated information platform that will create real-time relationships and associations from disparate data sources needed to create new knowledge for improved patient treatments, outcomes and prevention.
    • 33. Core
      Data Aggregation andStorage
      Source Systems
      Some representative examples of business level data domains
      Patient Cohort Examples
      Demographics
      Cancer Stage
      Diagnosis
      Treatment
      Labs
      Drugs
      Integrated Data Warehouse
      Data Factory Implementation
      Data
      Mapping
      Data
      Sourcing
      Data
      Profiling
      Data
      Modeling
      Data Linkage
      HRI Solution: Conceptual Architecture
      Front End
      Information Delivery
      Newly Diagnosed, Primary Pancreatic, having CEL File
      Cancer Registry
      LabVantage
      Capstone
      Primary Breast Cancer, Survival Time >30 months, Disease Stage 1-4, Diagnosed with Type 2 Diabetes, currently on Metaformin
      Female with myelodysplastic syndrome, currently taking vidaza as Ist course chemotherapy, initially diagnosed in 2007-2008
      CEL Files
      Galvanon
      3M
    • 34. HRI Demonstration   
    • 35. Number of patients in the HRI today & growing
    • 36. Patient data available – drill down capabilities to 5 levels of detailed data elements.
    • 37. Tissue specimen data available – drill down capabilities to 5 levels of detailed data elements.
    • 38. The Need for Linked Queries
      Patient 1
      Patient 2
      1-1-2009
      Lung Upper Lobe
      1-1-2010
      Lung
      Upper Lobe
      1-1-2010
      Adenocarcinoma
      NOS
      LINK
      6-30-2010
      Adenocarcinoma
      NOS
      6-30-2010
      Skin Trunk
    • 39. Venn Diagrams
    • 40. Four Portals to Total Cancer Care™
      Researcher View
      Next Generation Health and Research Informatics Platform
      • Cohort Identification
      • 41. Molecular Profiling
      • 42. Comparative Effectiveness
      Patient View
      • Personal Health Record
      • 43. Longitudinal Follow-up
      • 44. Personalized Search
      Administrators View
      • Operational Dashboards
      • 45. Quality & Safety Reporting
      • 46. Meaningful Use
      • 47. Decision Support
      • 48. Clinical Pathways
      • 49. Clinical Trial Matching
      • 50. Access for Affiliate Network
      Clinician View
    • 51. Stakeholders as Partners
      Researcher View
      Total Cancer Care Multi-Dimensional Data Warehouse
    • 52. How is Moffitt Benefiting from the RIE?
      Using the TCC Database to match patients to clinical trials
      Right treatment for the right patient using molecular markers for patient selection
      Development of Comparative Effectiveness Research Infrastructure
      What works best for whom
      Integration of molecular, clinical, biospecimen and patient self-report data
      Gene expression data, Exome sequencing data, SNP/CNV data for new diagnostics, prognostic response and new drug discovery
    • 53. Radiochemotherapy
      Validation of a Predictive Model of Clinical Response to Concurrent Radiochemotherapy
      Javier Torres-Roca, MD
      (R21 CA135620)
      Eschrich SA, et al., Int J Radiat Oncol Biol Phys, 2009
      • TCC database: validation of clinical response
      Figure 1
      Defining the pathway scale by mathematical modeling
      A linear regression algorithm is used to model the pathway/network scale in the radiosensitivity continuum. Biological variables (ras status, p53 status and TO) known to influence radiosensitivity along with gene expression are included in the model
    • 54. High-Throughput Sequencing
      Exome Sequencing
      361 breast and ovary biospecimens sequenced at BGI
      Whole exome sequencing (Agilent SureSelect 38MB kit )
      Raw and analyzed data currently available
      4,000 samples being sequenced at BGI
      ~1,400 genes
      500 lung, 400 kidney, 300 colon
      150 each: uterus, pancreas, ovary, endometrium
      100 each: heme malignancies, melanoma, breast
      50 each: stomach, esphagus, liver, cervix, soft tissue, rectum, anus
      650 undecided
      Whole genome sequencing: Melanoma
      13 match pairs at Wash U Genome Inst.
    • 55. Melanoma Comprehensive
      Research Center
      Melanoma whole genome sequencing
      • 15 melanomas and matched normal pairs chosen from TCC bio-repository
      • 56. Linked to TCC gene expression array and clinical follow-up databases
      • 57. Completed in only 2 months
      • 58. Further analysis by MCC Cancer Informatics Core
      • 59. Funded by MCC and a gift from Donald A. Adam
    • Immunology
      Classification into high and low NF-kB
      Correlation of NF-kB Signature
      with Ras Signature
      Ras Signature
      r=0.692 (p<0.001)
      NF-kB and K-ras Signatures in lung cancer
      Amer Beg, PhD
      Initial Study:
      400 Lung Patients
      TCC database validating signatures
      P50 CA121182
    • 60. Cancer Epidemiology
      Insulin-Like Growth Factor Axis & Colon Cancer Outcomes
      300 Patient Cohort Study
      Erin Siegel, PhD
      Outcomes:
      • Treatment Toxicity & response
      • 61. Quality of Life & symptoms
      • 62. Recurrence & survival
      Recruitment at Surgery
      • Tumor Tissue
      • 63. Gene expression Profile
      • 64. Pre-surgery blood
      • 65. New Patient Questionnaire
      • 66. Physical Activity
      • 67. Anthropometrics
      • 68. Quality of Life (QOL)
      Treatment Information
      Follow-up
      3M
      12M
      6M
      Toxicity & QOL
      • Blood draws
      • 69. Anthropometrics
      • 70. Questionnaires (health behaviors, symptoms & QOL)
      *Green = utilizing TCC infrastructure
      State of Florida, 09BN-13
    • 71. Health Outcomes & Behavior
      Patient Centered Outcomes Research (PCOR)
      David Fenstermacher, PhD
      • New information infrastructure to support PCOR or Comparative Effectiveness Research (CER)
      • 72. Metadata-driven data model
      • 73. Natural language processing algorithms
      • 74. Developed novel data dictionary and metadata tools
      • 75. Generated additional descriptive tool to understand differences in patient response and validation for exponential failure.
      • 76. CER analyses to guide developing CER infrastructure
      • 77. 3 CER studies on myelodysplastic syndrome completed(Alan List, et al., submitted in Blood)
      UC2 CA148332 (NCI Grand Opportunity grant)
    • 78. Clinical Trial Matching
      Using TCC Warehouse to Accrue Patients Jonathan R. Strosberg, MD
      Phase 2 trial of single agent Roche gamma secretase inhibitor in metastatic CRC (PI, Jonathan Strosberg, MD)
      • Trial (NCI 8537) supported by CTEP N01 contract
      • 79. Re-contacted Moffitt TCC patients using general eligibility criteria
      • 80. Enrolled 37 patients in 4 months
      • 81. Time from LOI submission to last patient treated just over 10 months
      • 82. OEWG/IOM expectation for N01 trial activation is 210 days
    • Four Portals to Total Cancer Care™
      Next Generation Health and Research Informatics Platform
      Patient View
      • Personal Health Record
      • 83. Longitudinal Follow-up
      • 84. Personalized Search
    • 85.
    • 86.
    • 87.
    • 88.
    • 89.
    • 90. Four Portals to Total Cancer Care™
      Next Generation Health and Research Informatics Platform
      • Decision Support
      • 91. Clinical Pathways
      • 92. Clinical Trial Matching
      • 93. Access for Affiliate Network
      Clinician View
    • 94. Clinical Pathways: Decision Support
      • Decision support tools available at point-of-care that leverage:
      • 95. Clinical outcomes studies
      • 96. Comparative effectiveness data
      • 97. Comprehensive disease models
      • 98. Evidence-based clinical pathways
    • Clinical Pathways
    • 99. Pathways Approach
      Clinical Priorities in Pathway development
      Efficacy
      Toxicity
      Cost
      Comprehensive Clinical Coverage
    • 100. Fixing Clinical Trials?
    • 101. Current Clinical Trial Challenges
      • Trial activation too slow
      • 102. Trial accrual too slow
      • 103. Patients do not want to leave home
      • 104. 80% of cancer care delivered locally
      • 105. Novel investigational trials performed in Academic Medical Centers
      • 106. Trials are searching for patients
    • Current Clinical Trial Challenges
      • Cancer patients enrolled: 2-3 % in community and 10-12 % in cancer centers
      • 107. Early phase trials’ response rates too low
      • 108. Early enrollers on Phase I trials are under-treated
      • 109. Small incremental benefits in large later phase trials
      • 110. Regulatory burden is increasing
    • Clinical Trials Vision
      • Develop a consortium network for clinical trials (practices and hospitals)
      • 111. Obtain molecular data from patients’ tumors
      • 112. Maintain real-time clinical data on patients
      • 113. Match drugs to patients using molecular and clinical data
      • 114. Faster and smaller trials with increased response rates
    • Paradigm Shift
      TODAY
      Trials searching for patients
      TOMORROW
      Trials designed for and directed to patients
    • 115. 344
      275
      220
      209
      167
      134
      107
      42
      30
      Molecular Mapping to Produce Dynamic Pool of Trial-Ready Patients
      (Many Mapped to Pre-Selectively Enroll a Few)
      Newly Diagnosed Metastatic/Locally-Advanced Patients
      Assumptions Reducing Sample Size
      • Starting sample sizeY
      • 116. Availability of biopsy* (-20%)
      • 117. Adequacy of biopsy (-20%)
      • 118. Assay failure (-5%)
      • 119. Death/Morbid/Toxicity (-20%)
      • 120. Temporal Readiness within 1 yr of the Bx (TTP < 1yr) (-20%)
      • 121. Performance Status or inadequate Labs (-20%)
      • 122. Prevalence of Mutation (-60%)
      • 123. Pt/MD Choice of Rx (-30%)
      One Tumor Type
      Diminishing
      # of Patients
      Potential “Positive” Factors:
      • Could also limit to specific diseases (such as colon, lung, breast, pancreas) to ensure proper final mix
      * Could allow primary biopsies for brain, prostate, pancreas, ovary, bladder, pancreas where distant metastases hard to access;
      * Could also assume physicians might consider a “diagnostic” Bx in a situation when otherwise they might pass
      Trial-Ready
    • 124. Ultimate Goal of New Trials
      To incorporate molecular characteristics
      of the tumor, as well as the patient’s genetic
      background, into an individualized treatment
      plan to maximize clinical benefit to the
      patient from specific anti-tumor agents.
    • 125. Biomarker-driven trials at Moffitt
      • Phase 3 RRM1/ERCC1 directed chemo in advanced NSCLC (completed)
      • 126. Phase 2 R115777 in elderly AML with specific 2-gene ratio (active)
      • 127. Phase 2 Notch inhibitor in mCRC (completed)
      • 128. Planned TCC consortium trials
      • 129. CY 2011 Pharma Trials
    • Designing a New Research & Healthcare Network Model
      Hospitals & Healthcare Networks
      Offices & Clinics
      Insurers
      Research
      Information
      Exchanges
      Personal
      Health
      Records
      Researchers Centers
      & Networks
      Genomic Data &
      Annotation
      Services
      Patients
      Researchers
      Dalton, Fenstermacher, et al, Clin Cancer Res; 16 (24) December 15, 2010
    • 130. Rapid Learning Information System for Cancer Care & Research
    • 131. Thank You

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