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    • 1. Transformation to Value-Based Personalized Healthcare: Cancer as a Model<br />William S. Dalton, PhD, MDPresident, CEO & Center DirectorMoffitt Cancer Center & Research InstituteTampa, Florida<br />
    • 2. Total Cancer Care: A Personalized Approach to a Patient’s Health Journey<br />Populations at Risk<br />Intervention<br />Diagnosis<br />Survivorship<br />Prognosis<br />Relapsed Disease<br />Treatment<br />– Behavioral Research<br />– Psychosocial & Palliative Care<br />– Family Needs<br />– Health Outcomes<br />– Risk Factors<br />– Genetics<br />– Early Detection<br />– Health Disparities<br />– Recurrence Therapy<br />– Drug Discovery<br />– Adaptive Trial Design<br />– Prevention<br />– Lifestyle/Nutrition<br />– Education<br />– Genomics/Proteomics<br />– Imaging Modalities<br />– Nanotechnology<br />– Primary Therapy <br /> • Multimodality<br /> • Target Based<br />– Post Therapy <br /> • Surveillance<br />– Clinical Trials Matching<br />– Molecular Oncology<br />– Biomarker Analysis<br />(http://www.hhs.gov/myhealthcare/news/phc_2008_report.pdf; pg 243) <br />
    • 3. The Necessary Components<br />Clinically annotated bio-repository for tumor and normal specimens<br />Partnership among researchers, clinicians, regulators, policy makers, and patients to design an integrated information network system<br />
    • 4. The Approach for Cancer<br />The Total Cancer Care Protocol<br /><ul><li>Can we follow you throughout your lifetime?
    • 5. Can we study your tumor using molecular technology?
    • 6. Can we recontact you? </li></li></ul><li>Electronic Consenting System<br />Consists of IRB Approved:<br /><ul><li>Introductory Video
    • 7. Consent Video by PI
    • 8. Informed Consent
    • 9. Signature Capture
    • 10. Demographics Survey</li></ul>Wireless touch- screen tablet<br />Connects via secure interface and forwards HIPAA-compliant information to database<br />
    • 11. Partners in the Fight Against Cancer<br />
    • 12. Nexus Biostore<br /><ul><li>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</li></li></ul><li>As of August 16, 2011<br />Total Cancer Care To Date<br />Patients Consented<br />78,615<br />Tumors Collected <br />28,146<br />Tumors Profiled<br />14,604<br />8<br />8<br />8<br />Confidential and Proprietary<br />
    • 19. M2Gen Offices, Bio-repository 100,000 sqft in Tampa, FL<br />
    • 20. The Approach<br />Improved<br />Medical <br />Practice<br />Create a delivery system that will integrate new technologies into the standard of care and develop evidence-based guidelines for the treatment of cancer. <br />
    • 21. Four Portals to Total Cancer Care™<br />Researcher View<br />Next Generation Health and Research Informatics Platform<br /><ul><li> Cohort Identification
    • 22. Molecular Profiling
    • 23. Comparative Effectiveness</li></ul>Patient View<br /><ul><li> Personal Health Record
    • 24. Longitudinal Follow-up
    • 25. Personalized Search</li></ul>Administrators View<br /><ul><li> Operational Dashboards
    • 26. Quality & Safety Reporting
    • 27. Meaningful Use
    • 28. Decision Support
    • 29. Clinical Pathways
    • 30. Clinical Trial Matching
    • 31. Access for Affiliate Network</li></ul>Clinician View<br />
    • 32. The HRI Platform Defined<br />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. <br />
    • 33. Core <br />Data Aggregation andStorage<br />Source Systems<br />Some representative examples of business level data domains<br />Patient Cohort Examples<br />Demographics<br />Cancer Stage<br />Diagnosis<br />Treatment<br />Labs<br />Drugs<br />Integrated Data Warehouse<br />Data Factory Implementation<br />Data <br />Mapping<br />Data <br />Sourcing<br />Data <br />Profiling<br />Data <br />Modeling<br />Data Linkage<br />HRI Solution: Conceptual Architecture<br />Front End<br />Information Delivery<br />Newly Diagnosed, Primary Pancreatic, having CEL File <br />Cancer Registry<br />LabVantage<br />Capstone<br />Primary Breast Cancer, Survival Time >30 months, Disease Stage 1-4, Diagnosed with Type 2 Diabetes, currently on Metaformin <br />Female with myelodysplastic syndrome, currently taking vidaza as Ist course chemotherapy, initially diagnosed in 2007-2008<br />CEL Files<br />Galvanon<br />3M<br />
    • 34. HRI Demonstration   <br />
    • 35. Number of patients in the HRI today & growing<br />
    • 36. Patient data available – drill down capabilities to 5 levels of detailed data elements.<br />
    • 37. Tissue specimen data available – drill down capabilities to 5 levels of detailed data elements.<br />
    • 38. The Need for Linked Queries<br />Patient 1<br />Patient 2<br />1-1-2009<br />Lung Upper Lobe<br />1-1-2010<br />Lung <br />Upper Lobe<br />1-1-2010<br />Adenocarcinoma<br />NOS<br />LINK<br />6-30-2010<br />Adenocarcinoma<br />NOS<br />6-30-2010<br />Skin Trunk<br />
    • 39. Venn Diagrams<br />
    • 40. Four Portals to Total Cancer Care™<br />Researcher View<br />Next Generation Health and Research Informatics Platform<br /><ul><li> Cohort Identification
    • 41. Molecular Profiling
    • 42. Comparative Effectiveness</li></ul>Patient View<br /><ul><li> Personal Health Record
    • 43. Longitudinal Follow-up
    • 44. Personalized Search</li></ul>Administrators View<br /><ul><li> Operational Dashboards
    • 45. Quality & Safety Reporting
    • 46. Meaningful Use
    • 47. Decision Support
    • 48. Clinical Pathways
    • 49. Clinical Trial Matching
    • 50. Access for Affiliate Network</li></ul>Clinician View<br />
    • 51. Stakeholders as Partners<br />Researcher View<br />Total Cancer Care Multi-Dimensional Data Warehouse<br />
    • 52. How is Moffitt Benefiting from the RIE?<br />Using the TCC Database to match patients to clinical trials<br />Right treatment for the right patient using molecular markers for patient selection<br />Development of Comparative Effectiveness Research Infrastructure<br />What works best for whom<br />Integration of molecular, clinical, biospecimen and patient self-report data<br />Gene expression data, Exome sequencing data, SNP/CNV data for new diagnostics, prognostic response and new drug discovery<br />
    • 53. Radiochemotherapy<br />Validation of a Predictive Model of Clinical Response to Concurrent Radiochemotherapy<br />Javier Torres-Roca, MD<br />(R21 CA135620) <br />Eschrich SA, et al., Int J Radiat Oncol Biol Phys, 2009<br /><ul><li>TCC database: validation of clinical response</li></ul>Figure 1<br />Defining the pathway scale by mathematical modeling<br />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<br />
    • 54. High-Throughput Sequencing<br />Exome Sequencing<br />361 breast and ovary biospecimens sequenced at BGI<br />Whole exome sequencing (Agilent SureSelect 38MB kit )<br />Raw and analyzed data currently available<br />4,000 samples being sequenced at BGI<br />~1,400 genes<br />500 lung, 400 kidney, 300 colon<br />150 each: uterus, pancreas, ovary, endometrium<br />100 each: heme malignancies, melanoma, breast<br />50 each: stomach, esphagus, liver, cervix, soft tissue, rectum, anus<br />650 undecided<br />Whole genome sequencing: Melanoma<br />13 match pairs at Wash U Genome Inst.<br />
    • 55. Melanoma Comprehensive <br />Research Center<br />Melanoma whole genome sequencing<br /><ul><li>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</li></li></ul><li>Immunology<br />Classification into high and low NF-kB<br />Correlation of NF-kB Signature<br />with Ras Signature<br />Ras Signature<br />r=0.692 (p<0.001)<br />NF-kB and K-ras Signatures in lung cancer<br />Amer Beg, PhD<br />Initial Study:<br />400 Lung Patients<br />TCC database validating signatures<br />P50 CA121182<br />
    • 60. Cancer Epidemiology<br />Insulin-Like Growth Factor Axis & Colon Cancer Outcomes<br />300 Patient Cohort Study<br />Erin Siegel, PhD<br />Outcomes:<br /><ul><li>Treatment Toxicity & response
    • 61. Quality of Life & symptoms
    • 62. Recurrence & survival</li></ul>Recruitment at Surgery<br /><ul><li>Tumor Tissue
    • 63. Gene expression Profile
    • 64. Pre-surgery blood
    • 65. New Patient Questionnaire
    • 66. Physical Activity
    • 67. Anthropometrics
    • 68. Quality of Life (QOL)</li></ul>Treatment Information<br />Follow-up<br />3M<br />12M<br />6M<br />Toxicity & QOL<br /><ul><li>Blood draws
    • 69. Anthropometrics
    • 70. Questionnaires (health behaviors, symptoms & QOL)</li></ul>*Green = utilizing TCC infrastructure<br />State of Florida, 09BN-13<br />
    • 71. Health Outcomes & Behavior<br />Patient Centered Outcomes Research (PCOR)<br />David Fenstermacher, PhD<br /><ul><li>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)</li></ul>UC2 CA148332 (NCI Grand Opportunity grant)<br />
    • 78. Clinical Trial Matching<br />Using TCC Warehouse to Accrue Patients Jonathan R. Strosberg, MD<br />Phase 2 trial of single agent Roche gamma secretase inhibitor in metastatic CRC (PI, Jonathan Strosberg, MD) <br /><ul><li>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</li></li></ul><li>Four Portals to Total Cancer Care™<br />Next Generation Health and Research Informatics Platform<br />Patient View<br /><ul><li> Personal Health Record
    • 83. Longitudinal Follow-up
    • 84. Personalized Search</li></li></ul><li>
    • 85.
    • 86.
    • 87.
    • 88.
    • 89.
    • 90. Four Portals to Total Cancer Care™<br />Next Generation Health and Research Informatics Platform<br /><ul><li> Decision Support
    • 91. Clinical Pathways
    • 92. Clinical Trial Matching
    • 93. Access for Affiliate Network</li></ul>Clinician View<br />
    • 94. Clinical Pathways: Decision Support<br /><ul><li>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</li></li></ul><li>Clinical Pathways<br />
    • 99. Pathways Approach<br />Clinical Priorities in Pathway development<br />Efficacy<br />Toxicity<br />Cost<br />Comprehensive Clinical Coverage<br />
    • 100. Fixing Clinical Trials? <br />
    • 101. Current Clinical Trial Challenges<br /><ul><li>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</li></li></ul><li>Current Clinical Trial Challenges<br /><ul><li>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</li></li></ul><li>Clinical Trials Vision<br /><ul><li>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</li></li></ul><li>Paradigm Shift<br />TODAY<br />Trials searching for patients<br />TOMORROW<br />Trials designed for and directed to patients<br />
    • 115. 344<br /> 275<br />220<br />209<br />167<br />134<br />107<br /> 42<br /> 30<br />Molecular Mapping to Produce Dynamic Pool of Trial-Ready Patients<br />(Many Mapped to Pre-Selectively Enroll a Few)<br />Newly Diagnosed Metastatic/Locally-Advanced Patients<br />Assumptions Reducing Sample Size<br /><ul><li>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%)</li></ul>One Tumor Type<br />Diminishing<br /># of Patients<br />Potential “Positive” Factors:<br /><ul><li>Could also limit to specific diseases (such as colon, lung, breast, pancreas) to ensure proper final mix</li></ul>* Could allow primary biopsies for brain, prostate, pancreas, ovary, bladder, pancreas where distant metastases hard to access;<br />* Could also assume physicians might consider a “diagnostic” Bx in a situation when otherwise they might pass<br />Trial-Ready<br />
    • 124. Ultimate Goal of New Trials<br />To incorporate molecular characteristics <br />of the tumor, as well as the patient’s genetic <br />background, into an individualized treatment <br />plan to maximize clinical benefit to the <br />patient from specific anti-tumor agents. <br />
    • 125. Biomarker-driven trials at Moffitt<br /><ul><li>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 </li></li></ul><li>Designing a New Research & Healthcare Network Model<br />Hospitals & Healthcare Networks<br />Offices & Clinics<br />Insurers<br />Research<br />Information<br />Exchanges<br />Personal <br />Health <br />Records<br />Researchers Centers<br />& Networks<br />Genomic Data &<br />Annotation<br />Services<br />Patients<br />Researchers<br />Dalton, Fenstermacher, et al, Clin Cancer Res; 16 (24) December 15, 2010<br />
    • 130. Rapid Learning Information System for Cancer Care & Research<br />
    • 131. Thank You<br />

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