dalton presentation

William S. Dalton, PhD, MDPresident, CEO & Center DirectorMoffitt Cancer Center & Research InstituteTampa, Florida

Transformation to Value-Based Personalized Healthcare: Cancer

as a Model

– Risk Factors– Genetics– Early Detection– Health Disparities

– Genomics/Proteomics– Imaging Modalities– Nanotechnology

– Molecular Oncology– Biomarker Analysis

– Primary Therapy • Multimodality • Target Based– Post Therapy • Surveillance– Clinical Trials Matching

– Recurrence Therapy– Drug Discovery– Adaptive Trial Design

– Behavioral Research– Psychosocial & Palliative Care– Family Needs– Health Outcomes

– Prevention– Lifestyle/Nutrition– Education

Intervention

Diagnosis

Prognosis

Treatment

Relapsed Disease

Survivorship Populations at Risk

Total Cancer Care: A Personalized Approach to a Patient’s Health Journey

(http://www.hhs.gov/myhealthcare/news/phc_2008_report.pdf; pg 243)

http://www.hhs.gov/myhealthcare/news/phc_2008_report.pdf

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?• Can we recontact you?

Wireless touch- screen tablet

Connects via secure interface and forwards HIPAA-compliant information to database

Consists of IRB Approved:

• Introductory Video

• Consent Video by PI

• Informed Consent

• Signature Capture

• Demographics Survey

Electronic Consenting System

Partners in the Fight Against Cancer

Expansion of consortium sites will encourage information exchange

Nexus Biostore

• Four unit capacity of 2.4 Million samples• Stores samples in a -80°C environment• Handles samples in a -20°C environment• Retrieves samples using NEXUS

proprietary ‘Cool Transition’ technology

• Flexibility to accommodate a wide variety of samples, vessels and labware

• Automated 24/7 monitoring system in place

• Automated Inventory functionality provides real-time inventory tracking of stored biospecimens

88Confidential and Proprietary

8

As of August 16, 2011As of August 16, 2011

Tumors Collected 28,146

Patients Consented78,615

Tumors Profiled14,604

Total Cancer Care To Date

M2Gen Offices, Bio-repository 100,000 sq ft in Tampa, FL

The Approach

Create a delivery system that will integrate new technologies into the standard of care and develop evidence-based guidelines for the

treatment of cancer.

Data Information Knowledge Wisdom

Improved

Medical

Practice

Four Portals to Total Cancer Care™

Next Generation Health and Research

Informatics Platform

Researcher View

Patient View

Administrators View

Clinician View

• Cohort Identification• Molecular Profiling• Comparative Effectiveness

• Personal Health Record• Longitudinal Follow-up• Personalized Search

• Operational Dashboards• Quality & Safety Reporting• Meaningful Use

• Decision Support• Clinical Pathways• Clinical Trial Matching• Access for Affiliate Network

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.

HRI Solution: Conceptual Architecture

Cancer Registry

LabVantage

Capstone

CEL Files

3M

Source Systems

Integrated Data Warehouse

Data Aggregation and Storage

Demographics Cancer Stage Diagnosis

Treatment Drugs Labs

Some representative examples of business level data domains

Data Factory Implementation

Data Profiling

Data Mapping

Data ModelingData Linkage

Data Sourcing

Patient Cohort Examples

Newly Diagnosed, Primary Pancreatic, having CEL File

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

Galvanon

Core Front End

Information Delivery

HRI Demonstration

Number of patients in the HRI today & growing

Patient data available – drill down capabilities to 5 levels of detailed data elements.

Tissue specimen data available – drill down capabilities to 5 levels of detailed data elements.

The Need for Linked Queries

Patient 1

1-1-2009Lung Upper Lobe

6-30-2010Adenocarcinoma

NOS

Patient 2

1-1-2010Lung

Upper Lobe

1-1-2010Adenocarcinoma

NOS

6-30-2010Skin Trunk

LINK

Venn Diagrams




Researcher View

Patient View

Administrators View

Clinician View

• Cohort Identification• Molecular Profiling• Comparative Effectiveness


• Operational Dashboards• Quality & Safety Reporting• Meaningful Use


Stakeholders as Partners

Total Cancer Care Multi-

Dimensional Data

Warehouse

Researcher View

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

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

Figure 1Defining 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

Radiochemotherapy

TCC database: validation of clinical response

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.

Project Timeline

12/29/10Samples enterLibrary Construction

1/04/111st case enteredsequencing pipeline

3/04/11All sequencingcomplete

• 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

Completed in only 2 months

Further analysis by MCC Cancer Informatics Core

Funded by MCC and a gift from Donald A. Adam

Melanoma Comprehensive Research Center

http://genome.wustl.edu/

Classification into high and low NF-kB

P50 CA121182

NF-kB and K-ras Signatures in lung cancerAmer Beg, PhD

Initial Study:• 400 Lung

Patients

• TCC database validating signatures

Correlation of NF-kB Signaturewith Ras Signature

Ras Signaturer=0.692 (p<0.001)

Immunology

Insulin-Like Growth Factor Axis & Colon Cancer Outcomes 300 Patient Cohort StudyErin Siegel, PhD

State of Florida, 09BN-13

Blood draws Anthropometrics Questionnaires (health

behaviors, symptoms & QOL)

Toxicity & QOL

Recruitment at Surgery Tumor Tissue Gene expression Profile Pre-surgery blood New Patient

Questionnaire Physical Activity Anthropometrics Quality of Life (QOL)

Treatment Information

Outcomes: Treatment Toxicity & response Quality of Life & symptoms Recurrence & survival

Follow-up

3M 6M 12M

*Green = utilizing TCC infrastructure

Cancer Epidemiology

• New information infrastructure to support PCOR or Comparative Effectiveness Research (CER) • Metadata-driven data model • Natural language processing algorithms • 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)

Patient Centered Outcomes Research (PCOR) David Fenstermacher, PhD

UC2 CA148332 (NCI Grand Opportunity grant)

Health Outcomes & Behavior

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• Enrolled 37 patients in 4 months• Time from LOI submission to last patient treated just over

10 months• OEWG/IOM expectation for N01 trial activation is 210 days

Clinical Trial Matching




Patient View





Clinician View


Clinical Pathways: Decision Support

• Decision support tools available at point-of-care that leverage:– Clinical outcomes studies– Comparative effectiveness data– Comprehensive disease models– Evidence-based clinical pathways

Clinical Pathways

Pathways Approach

• Clinical Priorities in Pathway development1. Efficacy

2. Toxicity

3. Cost

• Comprehensive Clinical Coverage

Work-up Disease Free Monitoring

Surgical Workflow Recurrence

Neoadjuvant Care Progression-Free Monitoring

Adjuvant Care Progression

Initial Rx/Induction 1st & Later Line Metastatic Therapy

Consolidation/Maintenance Molecular Diagnostics

Fixing Clinical Trials?

Current Clinical Trial Challenges

• Trial activation too slow• Trial accrual too slow• Patients do not want to leave home• 80% of cancer care delivered locally• Novel investigational trials performed in

Academic Medical Centers• Trials are searching for patients

Current Clinical Trial Challenges

• Cancer patients enrolled: 2-3 % in community and 10-12 % in cancer centers

• Early phase trials’ response rates too low• Early enrollers on Phase I trials are under-

treated• Small incremental benefits in large later phase

trials• Regulatory burden is increasing

Clinical Trials Vision

• Develop a consortium network for clinical trials (practices and hospitals)

• Obtain molecular data from patients’ tumors• Maintain real-time clinical data on patients • Match drugs to patients using molecular and

clinical data• Faster and smaller trials with increased response

rates

Trials designed for and directed to patients

Trials searching for patients

TODAY

TOMORROW

Paradigm Shift

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

One Tumor Type

Y 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

Assumptions Reducing Sample Size

Diminishing# of Patients

Trial-Ready

Potential “Positive” Factors:

Starting sample sizeY

Availability of biopsy* (-20%)

Adequacy of biopsy (-20%)

Assay failure (-5%)

Death/Morbid/Toxicity (-20%)

Temporal Readiness within 1 yr of the Bx (TTP < 1yr) (-20%)

Performance Status or inadequate Labs (-20%)

Prevalence of Mutation (-60%)

Pt/MD Choice of Rx (-30%)

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.

Ultimate Goal of New Trials

Biomarker-driven trials at Moffitt

• Phase 3 RRM1/ERCC1 directed chemo in advanced NSCLC (completed)

• Phase 2 R115777 in elderly AML with specific 2-gene ratio (active)

• Phase 2 Notch inhibitor in mCRC (completed)

• Planned TCC consortium trials

• CY 2011 Pharma Trials

Dalton, Fenstermacher, et al, Clin Cancer Res; 16 (24) December 15, 2010

Designing a New Research & Healthcare Network Model

ResearchInformationExchanges

ResearchInformationExchanges

Hospitals & Healthcare Networks

Personal Health Records

Researchers Centers& Networks

Genomic Data &

AnnotationServices

Genomic Data &

AnnotationServices

Insurers

ResearchersPatients

Offices & Clinics

Rapid Learning Information System for Cancer Care & Research

Thank You