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The Future of Personalized Medicine

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Edgewater Technology Healthcare Executive Event
Houston, TX October 2, 2008
Ed Martinez
Moffit Cancer Center

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The Future of Personalized Medicine

  1. 1. A Cancer Center Vision: The development and state-wide adoption of a centralized scientific and clinical Data Warehouse The Future of Personalized Medicine Edward Martinez (Former VP & CIO of Moffitt Cancer Center) Case Study
  2. 2. What is Personalized Medicine? <ul><li>“The use of biomarkers or molecular signatures </li></ul><ul><li>to accelerate the development of new drugs </li></ul><ul><li>targeted to specific subpopulations of patients.” </li></ul><ul><li>-Former Director NCI </li></ul>
  3. 3. The Problem with Contemporary Cancer Care <ul><li>Cancer is a complex, heterogeneous disease </li></ul><ul><li>Many patients are treated to help an unknown few — one size fits all </li></ul><ul><li>Drug therapy is generally not tailored to the patient </li></ul><ul><li>Response rates low, toxicity high </li></ul><ul><li>Patients do not want to leave home for treatment </li></ul>
  4. 4. Current Approach to Phase II Trials is Hit or Miss <ul><li>Phase II trials test the efficacy (response rate) of a new drug for the first time </li></ul><ul><li>Patients recruited to trial on a first come first serve basis with no knowledge of target </li></ul><ul><li>Response to new drug “hit or miss” </li></ul><ul><li>50% of Phase II drugs retired </li></ul><ul><li>Millions lost; 2 steps backwards </li></ul>
  5. 5. The Solution: Total Cancer Care Total Cancer Care Personalized Cancer Care 2010 Study large populations… Identify cancer sub-populations… Develop therapies for sub-populations of individuals…
  6. 6. Moffitt’s Total Cancer Care? <ul><li>Perhaps the world’s largest translational research project </li></ul><ul><li>A means to collect, relate, and interpret clinical data and molecular data from thousands of patients across Florida </li></ul><ul><li>A mechanism to identify molecular signatures for diagnosis, prognosis, and prediction of response to therapy </li></ul><ul><li>A means to personalize cancer therapy by matching “pipeline” drugs to patients harboring molecular targets </li></ul><ul><li>A means to improve the quality of medicine </li></ul>
  7. 7. Hypothesis/Plan <ul><li>Identify drug-specific molecular targets (or surrogate for it) </li></ul><ul><li>Determine prevalence of targets </li></ul><ul><li>Enrich pipeline drug trials with patients harboring the target </li></ul><ul><li>Engage and invest in community and academic partners in clinical trials and quality improvement </li></ul><ul><li>Develop and disseminate evidence based therapy standards </li></ul><ul><li>Decrease time to drug registration </li></ul>
  8. 8. New Standard Of Care Personalized Molecular Medicine Signatures Central Data Warehouse 50,000 Patients (Primaries + Metastatic Biopsies) QOL, Clinical Tissue Survivorship Surveys Banking Pipeline Evidence IT Quality Therapeutic Based Improvement Solutions Trials Medicine TCC Affiliate MCC Consortium Network (Resections + Trials) (Metastatic Biopsies) The Building Blocks of Total Cancer Care
  9. 9. TCC Mission & Value <ul><li>Contribute to the cure of cancer </li></ul><ul><li>Identify the right drug for the right patient </li></ul><ul><li>Improve the performance of clinical trials </li></ul><ul><li>Measure and improve the quality of care </li></ul><ul><li>Bring clarity to and individual’s condition and treatment regimen </li></ul><ul><li>Raise the standard of care </li></ul>
  10. 10. Potential to capture 50% of cancer cases In second highest cancer burden State State-wide affiliations allow for sharing of clinical and genomic information
  11. 11. Strategic Approach <ul><li>Use layered Plug ‘n’ Play approach </li></ul><ul><li>Build once and extend for other protocols </li></ul><ul><li>Enable rapid implementation </li></ul><ul><li>Develop seamless interconnection (clinical & research data sets) </li></ul><ul><li>Leverage existing systems, databases, applications and infrastructure </li></ul><ul><li>Enhance or expand systems as necessary </li></ul><ul><li>Incorporate industry standards (e.g. ca-BIG, HL7, CDISC) </li></ul><ul><li>Apply what we have learned from past experience </li></ul>
  12. 12. Data Warehouse Strategy <ul><li>Design “Centralized” data Warehouse to answer questions for researchers, clinicians, bio-informatics personnel… </li></ul><ul><li>Develop seamless interconnection (clinical & research data sets) </li></ul>
  13. 13. Data Warehouse Design Objectives <ul><li>Architecture </li></ul><ul><ul><li>Built for rapid inflow of data </li></ul></ul><ul><ul><li>Data delivery accomplished via data marts, cubes… </li></ul></ul><ul><li>Built to accept heterogeneous data sources and observations </li></ul><ul><li>Dynamically accounts for new data elements being added in source systems </li></ul><ul><li>Provides unified storage for questions that would span multiple operational systems </li></ul><ul><li>Association of research and clinical data from the enterprise </li></ul><ul><li>Data can be exported in multiple formats and shared with other systems as required </li></ul>
  14. 14. Operational Data Flow Challenges Tissue Tracker Cerner Surginet OnCore HPV Colon 400 Capstone Galvanon MDDB Cancer Registry Capstone Micro Array LabVantage SurgiNet PathNet PharmNet Staging Area Data is extracted from source systems and delivered here ETL ETL ETL DR ETL DM ETL Data Warehouse Data is transformed into Standard Structures Tissue Analysis Person Tissue Events EMR Micro Array Data Mining Genetic Analysis EMR Analysis Event Analysis
  15. 15. Person-Centric Model <ul><li>Survey Information </li></ul><ul><li>Demographics </li></ul><ul><li>History </li></ul><ul><li>Screening </li></ul><ul><li>Risk Assessment </li></ul><ul><li>Tissue Samples </li></ul><ul><li>Location </li></ul><ul><li>Gross/Tissue Dx </li></ul><ul><li>Preparation </li></ul><ul><li>Type </li></ul><ul><li>MicroArray Data </li></ul><ul><li>Gene Expression </li></ul><ul><li>Experiment Analysis </li></ul><ul><li>Sequence Verified Annotation </li></ul><ul><li>Electronic Medical Record </li></ul><ul><li>Treatment </li></ul><ul><li>Medications </li></ul><ul><li>Lab results </li></ul><ul><li>Clinical Trial Data </li></ul><ul><li>Protocols </li></ul><ul><li>Studies </li></ul><ul><li>Participation </li></ul><ul><li>Surgical Data </li></ul><ul><li>Procedures </li></ul><ul><li>Timelines </li></ul><ul><li>Cancer Registry </li></ul><ul><li>Follow-up information </li></ul><ul><li>Diagnosis </li></ul><ul><li>Vital Status </li></ul><ul><li>Operational Data </li></ul><ul><li>Billing </li></ul><ul><li>Scheduling </li></ul><ul><li>Visit information </li></ul>Person Linkage of Person to other data through a unique identifier Independent of MRN, SS#, or any other known identifier
  16. 16. Data Elements – Sample Person Biospecimen EMR Trials Age Gender Race Ethnicity Source Category Gross Diagnosis Preparation Type Assay Results Diagnosis Lab Results Medications Surgeries Scheduling Physician Protocols Studies Phases Participation Consent Therapy Event Type Hierarchy Attribute Result Date Association Chip Type Array Type Probe set Probe Signal Level Genes Present QC Metrics Form Observation Answer Language Skip Pattern MRN SSN Name Birth Date Address Date of Death Phone Events Questionnaire MicroArray PHI
  17. 17. Pre- Warehouse <ul><li>What patients have received chemotherapy? </li></ul><ul><ul><li>Cerner </li></ul></ul><ul><li>How many lung tissue samples do we have? </li></ul><ul><ul><li>Tissue Tracker </li></ul></ul><ul><li>Who is in this demographic? </li></ul><ul><ul><li>Galvanon </li></ul></ul><ul><li>What gene’s are present or absent in this sample? </li></ul><ul><ul><li>Micro Array Data </li></ul></ul><ul><li>Who is currently on this Clinical Trial? </li></ul><ul><ul><li>OnCore </li></ul></ul>Queries are limited to a single system. Getting results that span more than one system requires extensive time and effort, and in some cases may not even be possible.
  18. 18. Post Warehouse <ul><li>Find Caucasian females who have Breast Cancer, have received either chemotherapy or radiation and the number of tumor tissue samples we have </li></ul><ul><ul><li>(Data Warehouse) </li></ul></ul><ul><li>For patients with lung cancer, show top 10 administered drugs and gene expression profile associated with those tumors for patients with survival greater than 24 months </li></ul><ul><ul><li>(Data Warehouse) </li></ul></ul><ul><li>To qualify for a new clinical trial, show patients who have colon cancer, have smoked, have received a particular drug and have tissues samples available to study. </li></ul><ul><ul><li>(Data Warehouse) </li></ul></ul>Queries span multiple systems and provide a holistic view of data
  19. 19. Same Data – User Categories Nurse Bio-Informatics Scientist Data Manager Clinician Researcher
  20. 20. Classes of Data Users Executive User Functional User Power User Highly Aggregated More Detail Complete Raw Data <ul><ul><ul><li>Aggregated Data </li></ul></ul></ul><ul><ul><ul><li>Limited Drill Down </li></ul></ul></ul><ul><ul><ul><li>Graphical Display, Dashboards, Interactive </li></ul></ul></ul><ul><ul><ul><li>Standard & Ad hoc Reporting </li></ul></ul></ul><ul><ul><ul><ul><li>Parameter-Driven by Users at Run-Time </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Sorting, Selection, Filtering, Drill-Down </li></ul></ul></ul></ul><ul><ul><ul><li>Utilizing Standard Functions (e.g. calculations) </li></ul></ul></ul><ul><ul><ul><li>Direct Access to Detailed Raw Data </li></ul></ul></ul><ul><ul><ul><ul><li>“ Just give me the data in SAS” </li></ul></ul></ul></ul><ul><ul><ul><li>Access-controlled Views </li></ul></ul></ul>
  21. 21. Vision: Aligned Data Sources Cerner OPTX Tissue Bank Lawson Cancer Registry CT Admin Affiliate Systems Sunquest Research Systems Other Databases Legacy Database
  22. 22. Vision: Disparate Sources  CUI Extract, Transform, Load Common Data Semantics, Standards Interactive Presentation Lab or Medical Instruments IVR Hand-Held Databases Other Systems DB
  23. 23. Logical View: Multi-Dimensional Data • Data on each Clinical Event • by Program • by Trial • by Site • by Investigator • by Patient Clinical Data Warehouse Facts Dimensions Integrated Clinical Data Mart Facts Dimensions Detailed Clinical Data Facts Treatment Descriptors Trial Descriptors Site Descriptors Patient Descriptors Physician / Investigator Descriptors <ul><li>Therapeutic Area </li></ul><ul><li>Compound </li></ul><ul><li>Protocol </li></ul>• Tx Group • Con Meds • Prior Diagnoses • Outcomes • Phase • Protocol • Type • Location • Contract Status • IRB • Location • Contract Status • Type • Fee Structure <ul><li>Sponsor </li></ul>• Demographics
  24. 24. Logical View: Shared Meta-Data Clinical Outcomes Facts Patient Descriptors Location Descriptors Protocol Descriptors Investigator Descriptors Date / Time Descriptors Data Collection Facts Patient Satisfaction Facts Trial Enrollment Facts Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions
  25. 25. Logical View: Process-Driven View Per Protocol Per Study Per Site Per Patient Patient Screened Patient Randomized Data Set Received CRFs Received Data Set Coded Data Set Queries CRFs Certified Per Visit Patient Dropped Data Set Certified Safety Review Patient on Listing Patient Certified Patient Withdrawn
  26. 26. Data Pipeline Extraction / Capture Mapping Transformation OpTx Trial Metrics Affiliate Systems Cancer Registry Cerner (lists, code maps, dictionaries) Subject Area Data Marts Master Reference Data Analytics / Dashboards Standard Reports Live (ad hoc) reports Alerts Source Data Data Preparation Data Provisioning Applications Data Cubes 01 02 M F IF(sum(a,b) . . .
  27. 27. Enterprise Data Strategy Requirements: Scientific, Business, Functional, Technical, Data Implementation Plan Technology Architecture Vision
  28. 28. Data Exchange Interfaces
  29. 29. Data Warehouse Architecture
  30. 30. Challenges <ul><li>Initial investment required ($?) </li></ul><ul><li>Ability to support dynamically evolving requirements </li></ul><ul><li>Data integration effort across Moffitt and its affiliates </li></ul><ul><li>Process improvements required to support normalized data gathering efforts </li></ul><ul><li>Quality of existing data </li></ul><ul><li>Partnering with the right solution providers </li></ul><ul><li>Assessment of Affiliate technology capabilities to support strategic direction </li></ul>
  31. 31. Touch Screen Technology
  32. 32. Multi-Dimensional Clinical Data
  33. 33. Multi-Dimensional Clinical Data
  34. 34. Integrating Genomic and Clinical Data
  35. 35. Data Governance Data Stewards Data Owners Data Governance and Monitoring Committee (DGMC) Data Users IT Executive Sponsors
  36. 36. Data Governance Strategy <ul><li>Fundamental Aspects of Data Generation & Data Consumption </li></ul><ul><ul><li>Data is an Enterprise Asset </li></ul></ul><ul><ul><li>Authoritative Data Sources </li></ul></ul><ul><ul><li>Data Quality </li></ul></ul><ul><ul><li>Data Life Cycle </li></ul></ul><ul><ul><li>Data Ownership </li></ul></ul><ul><ul><li>Level of Aggregation </li></ul></ul><ul><ul><li>Protected Health Information (PHI) </li></ul></ul><ul><ul><li>Freezing of Data Sets & Versions of Data Release </li></ul></ul><ul><ul><li>Protection Against Misinterpretation </li></ul></ul>
  37. 37. Phased Implementation OCT 2005 FEB 2006 100 days 6 months Ongoing Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access AUG 2006 Release 2 Surgery Billing Intake Survey Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access Microarray Data Warehouse V1.0 Specimen Bank Clinical Trials EMR Data Access AUG 2006 Release 3 Clinical Trials Data Access LIMS Partners Affiliates Release 2 Surgery Billing Intake Survey Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access Microarray Data Warehouse V1.0 Specimen Bank Clinical Trials EMR Data Access Data Warehouse V1.1 <ul><li>EMR: </li></ul><ul><ul><li>Registration </li></ul></ul><ul><ul><li>Surgical Scheduling </li></ul></ul><ul><ul><li>Medications Summary </li></ul></ul><ul><ul><li>Formulary </li></ul></ul>Cancer Registry AUG 2006
  38. 38. Three Portals to Data Patient View Clinician View Researcher View TCC Multi-Dimensional Data Warehouse View my lab Clinical Data Gene Expression Results online • Data guides Rx Clinical Data Signatures
  39. 39. Building on the Foundation Genomic Data Biospecimen Data Quality P4P Costs Risk Factors Integrated Data System
  40. 40. How Pharmacogenomics Can Streamline Clinical Trials & Build Pharma Partnerships Traditional Clinical TCC Trial Trials Responders Only Broad Patient Population 10-12 Years 3-5 Years
  41. 41. Trial Enrichment 300 patients profiled in database <ul><li>New Phase II opened in Florida Network •30 patients identified & matched to new Phase II trial </li></ul><ul><li>Trial completed in record time </li></ul><ul><li>Drug has significant chance for POS, less toxicity </li></ul>
  42. 42. PLASMA EXPRESSION URINE PROTEOMICS PROFILNG PROTEOMICS BIOINFORMATICS ( PREOP/postop) CONSULTING COMMERCIAL OPPORTUNITIES CPG CGH/LOH METHYLATION AGILENT/ (AGILENT AFFY SNP CHIP BASED) M 2 GEN TCC DATA WAREHOUSE TISSUE SINGLE CELL PHOSPHO- SEQUENCING PROTEOMICS (SOMATIC (SHOTGUN) MUTATIONS) PARAFFIN Molecular RTPCR Imaging TMA HIGH THROUGHPUT TECHNOLOGIES IN HOUSE VS. OUTSOURCE
  43. 43. Lessons Learned to Date <ul><li>QI projects are feasible at affiliate sites </li></ul><ul><li>Patients of all ages can participate in web based surveys </li></ul><ul><li>Local physician leadership and buy in key </li></ul><ul><li>Centralized TCC consenting likely more effective than distributed model </li></ul><ul><li>Don’t affect physicians workload or patient flow </li></ul><ul><li>Develop value for patients, physicians, hospitals, funding partners </li></ul>
  44. 44. Quotes from: <ul><li>The Council of Scientific Advisors, 2003 </li></ul><ul><li>“ This extraordinary initiative serves dual purposes: utilizing clinical expertise of the Moffitt Cancer Center for the good of cancer patients throughout Florida; and creating a network that, if properly utilized, could improve the quality of care and provide enormous opportunities for health outcomes research including research in quality of care and cost-effectiveness.” </li></ul><ul><li>David Brailer, MD – President’s Consultant on Health Information Systems, 2004 </li></ul><ul><li>“ This needs to happen, and it needs to happen (in Florida).” </li></ul><ul><li>Anne Barker, MD, Deputy Director of NCI, 2005 </li></ul><ul><li>“ You have just seen a glimpse into the future of medicine, </li></ul><ul><li>and the way it needs to be.” </li></ul>
  45. 45. Edward Martinez [email_address] 813-420-4703

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