Transformation to Value-Based Personalized Healthcare: Cancer as a Model William S. Dalton, PhD, MDPresident, CEO & Center DirectorMoffitt Cancer Center & Research InstituteTampa, Florida
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)
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
The Approach for Cancer The Total Cancer Care Protocol
Can we follow you throughout your lifetime?
Can we study your tumor using molecular technology?
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.
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
Stakeholders as Partners Researcher View Total Cancer Care Multi-Dimensional Data Warehouse
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
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
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.
Melanoma Comprehensive Research Center Melanoma whole genome sequencing
15 melanomas and matched normal pairs chosen from TCC bio-repository
Linked to TCC gene expression array and clinical follow-up databases
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
Cancer Epidemiology Insulin-Like Growth Factor Axis & Colon Cancer Outcomes 300 Patient Cohort Study Erin Siegel, PhD Outcomes:
Developed novel data dictionary and metadata tools
Generated additional descriptive tool to understand differences in patient response and validation for exponential failure.
CER analyses to guide developing CER infrastructure
3 CER studies on myelodysplastic syndrome completed(Alan List, et al., submitted in Blood)
UC2 CA148332 (NCI Grand Opportunity grant)
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
Re-contacted Moffitt TCC patients using general eligibility criteria
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
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.
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
Rapid Learning Information System for Cancer Care & Research