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

5. Can we study your tumor using molecular technology?

7. Consent Video by PI

8. Informed Consent

9. Signature Capture

10. Demographics SurveyWireless touch- screen tablet Connects via secure interface and forwards HIPAA-compliant information to database

11. Partners in the Fight Against Cancer

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

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.

22. Molecular Profiling

24. Longitudinal Follow-up

26. Quality & Safety Reporting

27. Meaningful Use

28. Decision Support

29. Clinical Pathways

30. Clinical Trial Matching

31. Access for Affiliate NetworkClinician 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

41. Molecular Profiling

43. Longitudinal Follow-up

45. Quality & Safety Reporting

46. Meaningful Use

47. Decision Support

48. Clinical Pathways

49. Clinical Trial Matching

50. Access for Affiliate NetworkClinician 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

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.

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

61. Quality of Life & symptoms

63. Gene expression Profile

64. Pre-surgery blood

65. New Patient Questionnaire

66. Physical Activity

67. Anthropometrics

69. Anthropometrics

70. Questionnaires (health behaviors, symptoms & QOL)*Green = utilizing TCC infrastructure State of Florida, 09BN-13

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)

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

83. Longitudinal Follow-up

91. Clinical Pathways

92. Clinical Trial Matching

93. Access for Affiliate NetworkClinician View

95. Clinical outcomes studies

96. Comparative effectiveness data

97. Comprehensive disease models

99. Pathways Approach Clinical Priorities in Pathway development Efficacy Toxicity Cost Comprehensive Clinical Coverage

100. Fixing Clinical Trials?

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

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

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

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%)

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.

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

130. Rapid Learning Information System for Cancer Care & Research

131. Thank You