Oncovue jorge power point presentation.ppt(2)

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OncoVue® Breast Cancer Risk

Assessment Test

Rita MurryProgressive Medical EnterprisesEmpowerment through intelligent medicine

Research Background

• Research began in 1989 at Samuel Roberts Noble Foundation on inhibitors of cell growth

• Moved to the Oklahoma Medical Research Foundation in 1993

• Company spun-out in 1999

• Over 20,000 patient specimens analyzed in our genetic risk assessment studies

Personalized Medicine

• Cancer risk using genetics and clinical/personal history

• Cancer as a complex disease with multiple contributing genes

• Identification of cancer prevention drugs

• Cancer therapy targeted to genetically identifiable tumors

Overview

• Background • Model Development

– Study Population

– Genetic Polymorphisms

• Model Description• Validation and Clinical Utility• Testing Process• Case Study

BC is a Complex Disease

• Multiple pathways OMIM (>500 hits)• Excess estrogen and/or progesterone

(length of menstrual life, age of FLB, HRT, obesity, tall stature, etc.)

• Age

Traditional Risk Assessment

• Gail model (1989)– Age interval and lifetime risk

– AGEMEN, AGEFLB, NUMREL, NBIOP

– Recalibrated for prevention trials (1999)

• Claus model (1994) – Family history/age of onset in relatives

Genetic Risk Assessment

• BRACAnalysis® (1995)

– Familial breast and ovarian cancer

• Candidate Genes (1999)

• OncoVue® (2006)

– Multivariate Logistic Regression Model

– Candidate Genes and Personal Factors

• GWAS Markers (2007)

– 7 major SNPs/now a commercial test

Why a New Risk Model?

• Improve individualized breast cancer risk estimation by integrating genetic variation and personal factors

• Single Nucleotide Polymorphisms (SNPs)

• Personal History Measures (PHMs)• Risk estimation compared to the Gail

Model

Genotyping Strategy

Candidate genes vs. GWAS• Efficiently identify risk associations

• Minimize false discovery rate/misclassification of variants

• Functional polymorphisms likely to be causative polymorphism (not just in LD)

Gene Selection Criteria

• Biological/physiological relevance to cancer

• Known or predicted functional consequences

• Amino acid charge (coding regions), promoters (transcription), splice junctions/3'UTR (mRNA half life or translational control)

• Minor allele common across ethnicities (range 1-50%; median 30%; mean 28%)

125 SNPs- Many Pathways

• Steroid Hormone Receptors/Metabolism (25) • DNA Damage/Repair (30)• Xenobiotic/Conjugation/Detoxification (15)• Cell Cycle and Apoptosis (15)• Growth Factors and Signaling (12)• Immune Modulation/Cytokines (9)• Invasion/Metastasis (8)• Lipid Metabolism (5)• Free Radical Scavengers (3)• Angiogenesis (3)

Genotyping Platform

• Allele Specific Primer Extension (ASPE)- FlexMap beads

• Luminex benchtop flow cytometer• Multiplexed into 6 reactions• Double-strand sequence verified• < 5 ng DNA/multiplex• Genotyped over 20,000 individuals in

R&D

Case-Control Study

• OK, WA, CA, KS, FL, SC• Mammography screening clinics• Community breast cancer awareness

events• Enrolled unrelated individuals• Completed questionnaire on personal

medical history, family history of cancer and lifestyle

• Buccal cells in commercial mouthwash for DNA isolation

Study Population

• Demographic variables and allelic frequencies were similar across:

– Region of enrollment

– Clinic vs. Community

Model Building Strategy

• Combined all Caucasian women in study

• Assigned with a pseudo-random number generator into:

– Model Building (80%)

– Validation Set 1 (20%)

• Validation Set 2 African American women from same catchment areas

Sample Sets

African American Women Ages 30-69

Caucasian Women

Ages 30-69

Validation Set 1

Cases = 400

Controls = 793

Total = 1193

Valid Set 2

Cases = 164Controls = 417

Total = 581

Model Building

Cases = 1671

Controls = 3351

Total = 5022

Model Building Strategy

• Checked HWE in controls (117 passed)

• Systematically evaluate SNPs and PHM associations with case-control status

– Term individually

– Term*Term Interaction

– Term*Age Interaction

CYP11B2

• Key enzyme that ultimately converts 11-deoxycorticosterone to aldosterone

• C/C genotype associated with increased risk of type II diabetes

• Type II diabetes associated with increased risk of BC in postmenopausal women

CYP11B2 Age-Specific

0.5

1

1.5

2

30 40 50 60 70

Age (years)

OR

C/C (974)

C/T (2455)

Ref

R2 = 0.95 (35-65)

R2 = 0.89 (35-65)

11-deoxycorticosterone to aldosterone, Type II Diabetes

Ralph, D. et al. CANCER, 2007, 1940-48

Final Terms

4

Model Building Steps

1Univariate 2 p-value took

top 25%

2Forward stepwise selection modeling

3

p-value to enter ≤ 0.1

p-value to retain ≤0.05

Bootstrap 5000X for SE

Strategy for Iterative Analyses

Performed individually on:

• Entire dataset Ages 30-69– Winners incorporated into model

• Stratified Ages 30-49, 50-69

– Winners incorporated into model

• Stratified by PHMs (First Degree Relative Status)

OncoVue®

SNPs SNP*AGE PERSONAL HISTORY MEASURES

30 - 69

SNP*AGE PERSONAL HISTORY

MEASURES*AGE

30 - 49 w/o

FDR

SNP*AGESNPs30 - 49

w/1 FDR

30 50 60

49

70

69

40

Total of 22 SNPs in 19 Genes and 5 PHMs

All Ages

Term Age Interaction

Individual Terms

CYP1A1

ACACA (IVS17)

IGF2

NUMREL

ACACA (5’UTR)

VDR

XRCC2

AGEFLB

NBIOP

MSH6

CYP11B2

ESR1

ERCC5

Genes, SNPs and Function

BRCA1 Interaction

• ACACA (IVS17)

• ACACA (5’UTR)

• ACACA (PIII)

DNA Repair• MSH6

• RAD51L3

• XPC

• ERCC5

• XRCC2

Steroid Hormone Metabolism

• COMT

• CYP11B2

• CYP19

• CYP1A1

• CYP1B1 (N453S)

• CYP1B1 (R48G)

• ESR1

• VDR

Genes, SNPs and Function

Cell Cycle/Apoptosis

• KLK10

• TNFSF6

Detoxification

• EPHX

• SOD2

• INS

• IGF2

Growth Factors

Risk Model Performance

• Evaluate with informative measures that reflect improvement in individual risk estimation

• Not a traditional diagnostic test

• Gail Model is current clinical tool for risk estimation for the majority of women

Positive Likelihood Ratio (PLR)

PLR = Elevated Cases/All Cases Elevated Controls/All Controls

• Not sensitive to population characteristics or disease prevalence

• Completely Random = 1.0• Increase represents improvement• Elevated risk threshold ≥12%• 1.5X SEER average risk for ages 30-69

OncoVue® Performance

• Model Building (1671 Ca/3351 Co)• Validation 1 (400 Ca/793 Co)• Validation 2 (164 Ca/417 Co)• External Blinded Validation (169 Ca/177 Co)

– Marin County California 1997-1999

– Removed Ca/Co status

– Provided buccal cells and PHM

– Genotyped and scored for OncoVue

– Returned for evaluation by UCSF

Fold Improvement-12% Threshold

PLR Sample Set

OncoVue® Gail Model

Fold Improvement (95% CI)

p-value

Model Building 2.1 1.2 1.8 (1.4, 2.2) <0.0001

Validation 1 2.4 1.5 1.7 (1.1, 2.5) 0.024

Validation 2 3.2 1.4 2.2 (1.1, 5.3) 0.034

Marin County 2.2 0.90 2.4 (1.1, 5.6) 0.036 *PLR = Positive likelihood ratio, CI = Confidence Interval

Fold Improvement

• Trend in fold improvement increases at higher cut-off thresholds

• At 20% the fold improvement

– Model Building= 3.0 (p<0.0001)

– Validation Set 1 = 2.1 (p=0.07)

– Validation Set 2 = no controls >20%

PLR for BRCA1

For ages 30-69 - assuming a RR=8.0 for BRCA1 carrier and 8% SEER average risk

Cases Controls

BRCA+ 410 590 = 1000

BRCA - 80 920 = 1000

490 1510

PLR = 410/490 ÷ 590/1510 = 2.1 (1.9, 2.3)

Increase in Cases Identified at Elevated Risk

Gail Model OncoVue®

Sample Set Cases Controls Cases Controls

No. of Additional Detected

Cases

Percent more Cases

over Gail

Model Building 454 760 577 760 123 27%

Validation 1 118 161 135 161 17 14%

Validation 2 32 56 42 56 10 31%

Marin County 37 43 56 43 19 51%

Blinded Validation

• OncoVue showed a 2.4-fold statistically significant (p=0.036) improvement over the GM alone

• Additional 51% of cases accurately assigned elevated risk

• GM underestimated risk for those individuals actually at highest risk of developing breast cancer

OncoVue Summary

• First DNA-based test for estimating age-specific risk of developing sporadic breast cancer

– Applicable to the majority of women– Not a test for familial breast cancer risk (Myriad)

• Single integrated statistical model or algorithm• Developed in a model building set of 5000+

cases/controls and initially validated in two independent populations1

• Subsequently have completed a blinded validation in high risk population from Marin County, CA2

1 Jupe, E. et al. Proc. AACR 2008; 49: 451.

2 Dalessandri, K. et al. Presentation # 502, San Antonio Breast Cancer Symposium, December 2008.

OncoVue Summary

• Outperforms other genetics-based testing for sporadic breast cancer that were developed using different strategies1,2

• Presented at San Antonio Breast Cancer Symposium in 2008 and was subject of got widespread AP press release and press conference

• Paper chosen for late-breaking presentation at 2009 SABCS

1 Jupe, E. et al. Presentation # 3177, San Antonio Breast Cancer Symposium, December 2009.

2 Dalessandri, K. et al. Presentation # 3057, San Antonio Breast Cancer Symposium, December 2009.

Current Clinical Utilization

• 33 Comprehensive Breast Care Centers in 24 states (Breast surgeons, Oncologists, Mammography)

• Important tool to drive decision making and stratify risk and is applicable to most patients

• Over 2,600 clinical test results delivered

• Risk perception and behavior studies found that “knowledge is power”

• Reimbursement under existing CPT Codes

Testing Process is Simple

Clinical Decision Tool

• Decision support tool to guide screening and prevention options

• Identification of women that are candidates for preventative anti-estrogen therapies (tamoxifen, raloxifene) following guidelines from American Society of Clinical Oncology (ASCO)

• Identification of women who may benefit from supplemental MRI screening following guidelines from the American Cancer Society (ACS)

Other Clinical Utility

• Decisions regarding the use of hormone replacement therapy (HRT) in post-menopausal patients

• Potential value in identifying higher risk patients that need more frequent screening than the recent controversial recommendations from the US Preventive Services Task Force (USPSTF)

• Although it is not a diagnostic test for disease – high risk score has led to additional testing that brings about early diagnosis

OncoVue Risk and Early Diagnosis

• 50 year old woman presented for routine screening• Mammogram appeared normal• High Oncovue risk with 5 year = 4.9• More sophisticated imaging found 2mm tumor

Changing Health Care Model

Diagnosis Detection Treatment

Monitor Therapy

Prevention or Earliest Possible Detection

Genetic Predisposition Testing

Intervention

Outcome?

Change Treatment

Work in Progress• The Next Generation of OncoVue

– SBIR Phase I (miRNA polymorphisms)– OCAST-OARS (miRNA targets)– OCAST-OARS (Copy Number Variation)– AVON Foundation – Collaboration with Marin

County Health Department (breast density)

• Risk tests for other cancers – Ovarian

Acknowledgments

InterGenetics• Sharmila Manjeshwar, PhD• Daniele DeFreese, MS• Bobby Gramling, MS• Thomas Pugh, MS• Laura Blaylock, BS

Statistical Consultants• Christopher Aston, PhD• Dr. Lue Ping Zhou, PhD• Nicholas Knowlton, MS

OUHSC• John Mulvihill, MD

University of California San Francisco• Kathie Dalessandri, MD• Margaret R. Wrensch, PhD • John K. Wiencke, PhD• Dr. Rei Miike, PhD

Buck Institute for Age Research

• Christopher C. Benz, MD

Zero Breast Cancer• Georgianna Farren, MD

Acknowledgments

Marin County Health Department• Mark Powell, MD, MPH• Lee Ann Prebil, PhD • Rochelle Ereman, MS, MPH

Acknowledgments

Funding Sources• NIH – SBIR

• US Army BCRP

• Oklahoma Center for the Advancement of Science and Technology

• American Cancer Society

• Swisher Family Trust

• Presbyterian Health Foundation

• Oklahoma Life Sciences Fund

Clinical and Scientific Advisory Board

• John Mulvihill, M.D.– Professor of Pediatrics; Chief of the Human Genetics Section;

Kimberley V. Talley Chair, Children’s Medical Research Institute, University of Oklahoma Health Sciences Center

• Debra Mitchell, M.D.– Medical Director, Breast Imaging of Oklahoma; Former Imaging

Director, University of Oklahoma Breast Institute; Adjunct Associate Professor, Dept. Radiological Sciences, University of Oklahoma Health Sciences Center

• Christopher Aston, Ph.D. – Biostatistics/Bioinformatics Core Director, General Clinical

Research Center; Associate Professor, Dept. Pediatrics and Genetics, University of Oklahoma Health Sciences Center

• Linda Thompson, Ph.D.– Member, Immunobiology and Cancer Program, Oklahoma Medical

Research Foundation; Adjunct Professor, Department of Microbiology and Immunology, University of Oklahoma Health Science Center

Academic Collaborators and Research Participants