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What is Risk Prediction?
Andrew N. Freedman, PhDClinical and Translational Epidemiology Branch
National Cancer Institute
EGRP, DCCPS, NCIJune 11, 2014
“Prediction is very difficult, especially if it’s about the future”
Niels Bohr
Outline
Prediction Models for Cancer Risk, Susceptibility and Treatment Management
Background Model Development and Evaluation Applications Challenges
Prediction Models for Cancer Risk, Susceptibility and Treatment Management
Increased attention to statistical models WebsitesHandbooks Professional societiesCommercial companies offering genetic
risk profiling
Proliferation of Personal Genomic Tests
Prediction Models for Cancer Absolute Risk Assessment Models
Estimates the probability of developing cancer over a defined period of time
Genetic Susceptibility Risk ModelsEstimates the likelihood of detecting a mutation in a
cancer susceptibility gene in a given family or individual
Cancer Outcome Risk ModelsPrognostic- estimates the likelihood of a patient
outcome, regardless of treatmentPredictive- estimates response to treatment
Absolute Breast Cancer Risk Models
NCI BCRAT “Gail” Model: (Gail et al. JNCI 1989) CASH “Claus: Model: (Claus et al. AJHG 1991) Group Health (Taplin et al. Cancer 1991) DevCan (Feuer et al. JNCI 1993) NHS (Rosner et al. JNCI 1996) BRCAPRO (Parmigiani/Berry, AJHG 1998) Jonker et al (CEBP 2003) IBIS (Tyrer/Cuzick et al. Stat Med 2004) BOADICEA (Antoniou et al, BJC 2004)
Risk models for predicting BRCA1/2 cancer susceptibility genes
Couch et al. NEJM, 1997. Shattuck-Eidens et al. JAMA, 1997. Myriad (Frank et al. JCO, 1998/2002.) BRCAPRO: Berry et al. JNCI 1997 Parmigiani, AJHG, 1998. Hartge et al. AJHG, 1999. Ontario FHAT (Gilpin et al. Clin Genet 2000) Vahteristo et al. Br J Can 2001 de la Hoya et al. Int J Cancer 2002 Jonker et al (CEBP 2003) LAMBDA (Apicella et al. Breast Can Res 2003 BOADICEA (Antoniou et al, BJC 2004) Manchester (Evans DG et al. J Med Gen 2004/2005)
Sponsored byDivision of Cancer Control and Population Sciences
Division of Cancer Epidemiology and GeneticsOffice of Women’s Health
National Cancer Institute, National Institutes of Health, Department of Health and Human Services
Cancer Risk Prediction Models: A Workshop on Development, Evaluation,
and Application
Washington, D.C. May 20-21, 2004
Num
ber o
f Gra
nts
Fiscal Year
Number of Cancer Risk Prediction Grant Applications Submitted and Awarded
DCCPS (FY05 – FY13)
Cumulative number of cancer risk and susceptibility prediction models
• Large cohort and case-control datasets, consortiums, and registries
• RCTs with biospecimens• Development of EHRs and availability of
large administrative claims databases• Evidence for effective screening,
intervention, prevention and treatment strategies
Current Opportunities in Cancer Risk Prediction
Promising new genomic and other “omic” markers
New risk prediction methodologies and evaluation techniques
Progress in research for communicating risk, decision-making and decision aids
Modeling cost-effectiveness and burden of disease
Current Opportunities in Cancer Risk Prediction
Prediction Model Development Risk Factors
• Environmental• Demographic, reproductive, smoking, medications
• Inherited• Family history• High penetrance alleles• Low penetrance polymorphisms
• Clinical • Age, sex, blood pressure, cholesterol, enzyme levels,
inflammatory markers, previous screening, treatment, etc.• Molecular/Genomic markers
• Somatic alterations, tumor subtype, protein expression, methylation, metabolomics, etc.
• Interactions
Prediction Model Development
Data• Cohort, case-control, nested case-control,
family and clinical studies, population surveys, SEER, and clinical trials
• Expert opinion
Risk Calculation• Empirical, logistic regression, proportional
hazards, Bayesian analyses, log Incidence, Markov models/decision theory, artificial neural network, classification tree, etc.
Prediction Model Evaluation Calibration
• Ability of a model to predict incidence of a disease in a group of individuals
Discriminatory Accuracy• Measures a model’s ability to discriminate at the
individual level among those who develop disease from those who do not
Clinical Usefulness• Decision-Analytic Approach, Decision Curves
Internal Validity• Data-splitting, cross validation, bootstrapping
External Validity• New independent sample
Gail Breast Cancer Risk Assessment Model (http://cancer.gov/bcrisktool)
Risk Factor Category Relative Risk of IBC in next 5 years
Age at menarche, y > 14 12-13 12
1.00 1.101.21
No. of breast biopsies Age at counseling, 50 y old
0 1 2
1.001.702.88
Age at counseling, 50 y old 0 1 2
1.001.271.62
Age at first live birthNumber of first-degree relatives with breast cancer
< 20 years 0 1 2
1.002.616.80
20-24 years 0 1 2
1.242.685.78
25-29 years or nulliparous 0 1 2
1.552.764.91
30 0 1 2
1.932.834.17
Baseline 5-year risk of invasive breast cancer in percentages, by age and race
Baseline 5-year risk, %
Age in years Black White Hispanic
20-24 0.003 0.003 0.006
25-29 0.025 0.022 0.021
30-34 0.076 0.077 0.057
35-39 0.165 0.191 0.126
40-44 0.285 0.366 0.235
45-49 0.343 0.540 0.378
50-54 0.376 0.640 0.456
55-59 0.474 0.788 0.537
60-64 0.581 0.969 0.623
65-69 0.592 1.135 0.727
70-74 0.656 1.209 0.824
75-79 0.761 1.285 0.798
80-84 0.876 1.280 0.730
Example: Breast Cancer Risk Projection Using the Gail Model A 42-year-old white women
Began menstruating at age 12 years, RR=1.10
No children and no affected first-degree relatives, RR=1.55
One previous benign breast biopsy, RR=1.70
Overall RR = 1.10 X 1.55 X 1.70= 2.90 Projected 5-year risk of invasive breast
cancer = 2.90 X 0.366 = 1.06%
Prediction Model Applications
Planning intervention trials Estimating the population burden of
disease Identifying individuals at high risk
Designing prevention strategies
Clinical decision-making
Prediction Model Applications
Planning intervention trials Estimating the population burden of
disease Identifying individuals at high risk
Designing prevention strategies
Clinical decision-making
Determining Trial Eligibility For Breast Cancer Chemoprevention Trials
age 35 years or older, and
a 5-year risk of invasive breast cancer of at least 1.67%
based on the Gail Breast Cancer Risk Assessment Model.
1Five-year projected risk of invasive breast cancer IBC is greater than or equal to 1.67%.
Estimates of the total number of U.S. women eligible for tamoxifen chemoprevention Trial, by race and age
0
10
20
30
40
50
60
White
Black
Hispanic
35-39 40-49 50-59 60-69 70-79
Age
Pe
rce
nt
Prediction Model Applications
Planning intervention trials Estimating the population burden of
disease Identifying individuals at high risk
Designing prevention strategies
Clinical decision-making
Graubard et al. CEBP 2010;19:2430-6
Tamoxifen Chemoprevention Eligibility and Positive Benefit/risk Index
0
10
20
30
40
50
60
35-39 40-49 50-59 60-69 70-78
% white womeneligible for tamoxifen
% white women with apositive benefit/riskindex for tamoxifen
AgeAge
Pe
rce
nt
Freedman et al. JNCI 2003;95:526-32
2.4 million women who could benefit from tamoxifen
Prediction Model Applications
Planning intervention trials Estimating the population burden of
disease Identifying individuals at high risk
Designing prevention strategies
Clinical decision-making
National Comprehensive Cancer Network (NCCN)Guidelines for Breast Screening
Women > 35 y with 5-year risk for IBC > 1.7%Annual mammogram+ clinical breast exam
every 6-12 mConsider risk reduction strategies
Women who have a lifetime risk >20% as defined by models that are largely dependent on family historyAnnual mammogram+ clinical breast exam
every 6-12 mConsider risk reduction strategiesConsider annual breast MRI
Bevers et al. JNCCN 2009;7:1060-96.
Distribution of risk by allele number.
Dunlop M G et al. Gut 2013;62:871-881
Copyright © BMJ Publishing Group Ltd & British Society of Gastroenterology. All rights reserved.
Prediction Model Applications
Planning intervention trials Estimating the population burden of
disease Identifying individuals at high risk
Designing prevention strategies
Clinical decision-making
Tamoxifen Effects on Events
LIFE-THREATENING RR (95%CI) INVASIVE BREAST CA 0.51 (.39-.66) HIP FRACTURE 0.55 (.25-1.1) ENDOMETRIAL CA
<50 2.5 (1.4-5.0)50+ 4.0 (1.7-11)
STROKE 1.6 (0.9-2.8) PUL. EMBOLUS 3.0 (1.2-9.3)
OTHER SEVERE EVENTS IN SITU BREAST CA 0.50 (0.33-0.77) DEEP VEIN THROMBOSIS 1.60 (0.91-2.86)
Calculating Tamoxifen Benefit/Risk Index
Net number of life-threatening events prevented
(the total number of invasive breast cancers + hip fractures -
the total number of endometrial cancers, strokes, and pulmonary embolisms)
+ half the net number of serious events prevented (the number of in situ breast cancers - the number of deep vein
thromboses)
over a 5-year period.
Gail et al. JNCI 1999;91:1829-46
10,000 40 year old white women with a uterus, with a 5-year risk of invasive breast cancer of 2%.
LIFE-THREATENING EXPECTED PREVENTED INVASIVE BREAST CA 200 97 HIP FRACTURE 2 1 ENDOMETRIAL CA 10 -16 STROKE 22 - 13 PUL. EMBOLUS 7 -15
net 54OTHER SEVERE EVENTS IN SITU BREAST CA 106 53 DEEP VEIN THROMBOSIS 24 -15 net 38
net 38 Net Risk Benefit Index = 1x54 + 0.5x38 = 73
1Five-year projected risk of invasive breast cancer IBC is greater than or equal to 1.67%.
Benefit/risk indices for tamoxifen chemoprevention by level of 5-year projected risk of invasive breast cancer among white women with and without a uterus, by age group
70-79 60-69 50-59 40-49 30-39
White Women With A Uterus White Women Without A Uterus
7.0
6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
70-79 60-69 50-59 40-49 30-39 5-y projected risk of IBC, %
Benefits outweigh risks
Risks outweigh benefits
Benefit/risk indices for tamoxifen and raloxifene chemoprevention by level of 5-year projected risk for invasive breast cancer (IBC) for white non-Hispanic women with a uterus, by age group.
Update with raloxifene b/r slide
Freedman et al. JCO 2011;29:2327-33.
Paik, S. et al. J Clin Oncol; 24:3726-3734 2006
Kaplan-Meier plots for distant recurrence comparing treatment with tamoxifen (Tam) alone versus treatment with tamoxifen plus
chemotherapy (Tam + chemo)
Oncotype Dx: Computation of the Recurrence ScoreTM
• Determine the expression of 21 individual genes• Multiply the individual expression profile by a coefficient contained in an algorithm• Calculate the final Recurrence Score
Breast Oncotype Dx- Patient Report
The patient report includesRecurrence Score (RS)Average 10-year distant
recurrence rate for that RSGraph of 10-year recurrence
risk as a function of RS in tamoxifen-treated patients
The report is sent toTreating physicianSubmitting pathologist
Advantages of clinical prediction models compared with simple risk classification in oncology practice
Risk Prediction Models (RPM) can improve predictive accuracy Can incorporate multiple variables
RPM allow incorporation of novel predictors Multiple biomarkers
RPM can aid in the choice of cut-points for decision-making Allows examination of risk/recurrence as continuous
RPM can aid in individualized decision-making
Vickers AJ, CA Cancer J Clin. 2011
Challenges
“Too many models, not enough independent validation” (Vickers AJ, CA Cancer J Clin. 2011)
oWhat current models require validation?
oWhat quantitative criteria should be used to assess the performance of risk models for various purposes?
Challenges
Clinical Usefulness
o Can we improve the accuracy of cancer screening risk prediction models at the individual level with the addition of risk/genetic factors?
o What level of accuracy is needed to be useful in clinical decision-making and what are the best ways to measure it?
Challenges
Poor integration into clinical practice
o How should cancer risk prediction models be disseminated to health care providers (e.g. EHRs), patients, and the public?
o How do we best convey risk and uncertainty to health care providers, patients, and the public?
o How can they be used effectively to improve cancer education, risk communication and the development of decision aids?
Graphical Representations of Risk
National Breast Cancer Centrehttp://www.nbcc.org.au/risk/understandingrisk.html
Harvard Universityhttp://www.diseaseriskindex.harvard.edu/update/english
National Cancer Institute
Website Resources
NCI’s Cancer Risk Prediction Resources http://epi.grants.cancer.gov/cancer_risk_prediction/
Bibliography of Risk Prediction ModelsFunding OpportunitiesWorkshop and WebinarsFunded Projects
Cancer Prognostic Resources: A catalog of Interactive Cancer Prognostic Toolshttp://cancercalculators.org/index.aspx
Cancer Intervention and Surveillance Modeling Networkhttp://cisnet.cancer.gov/
END
Ezaz G et al. J Am Heart Assoc 2014 Feb 28;3(1):e000472. doi: 10.1161/JAHA.113.000472.
Risk prediction model for heart failure and cardiomyopathy after adjuvant trastuzumab therapy for breast cancer
Too many models, not enough independent validation
Obtain data to develop and validate more accurate and useful risk models
Extend existing models with data sources that include diverse racial and ethnic groups
Develop risk models with cancer treatment outcomes and non-cancer outcomes
Future Research
Clinical and Public Health Usefulness
Develop risk models that can help identify which population subgroups would benefit (or not benefit) from specific screening and risk reducing interventions
Develop risk models that can help identify which patient subgroups would benefit (or not benefit) from specific cancer therapies weighing both treatment response and toxicity
Future Research
Poor integration into clinical practice
Promote effective cancer risk communication and decision making
Incorporate patient preferences into models for use in clinical decision-making
Create simple, user-friendly models for healthcare providers to facilitate decision-making and referrals
Future Research
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