Risk AssessmentforEsophageal AdenocarcinomaJune 2014Thomas VaughanFred Hutchinson Cancer Research Center

Two Distinct CancersBlack:WhiteMale:FemaleIncidence TrendSquamous6:12:1Adenoca1:58:1Incidence of Esophageal Adenocarcinoma

Arithmetic

LogWMBMWFBFNCI SEER*Stat Database: 9 Registries released April 2013

5Natural History of EANormal 20% Chronic Reflux (GERD) 10% Metaplasia (Barretts*) HG Dysplasia0.2 - 0.5% annual Adenocarcinoma*2-4 M persons in U.S. with BarrettsMetaplastic EpitheliumWild & Hardie, Nat Can Rev, 2003

SurvivalLocalDistantCancer detected during surveillanceSEER Limited Use Data, 9 registries, April 2007 Surveillance RationaleNapoleons Lost Army

Esophageal AdenocarcinomaThe Lost Cases

Summary of Risk FactorsRisk FactorEA AssociationSex++++Race+++Reflux Symptoms++++Obesity+++Cigarettes++Genetics++H. pylori- -NSAIDs- -11

Risk Factors Act at Different Stages

Male Sex8.03 - 42Hardikar, PLoS ONE 2013; Edelstein, AJG 2009; Corley, Gastro 2007Genetic Contribution to Risk

h2g = 25% (p~10-7)h2g = 35% (p~10-9)minimal ?Ek, JNCI 2013Significant genetic contribution to BE and EAThe many common genes contributing to these disease are largely shared between EA and BE

Aspirin/NSAIDs0.6 0.7~ 1~ 0.5Liao, Gastro 2012; Vaughan, Lancet Oncology 2005Summary of Risk Factors and Stages of ActionRisk FactorBEBE -> EAEASex++++++++Race+++++Reflux Symptoms++++++Obesity++++++Cigarettes++++Genetics++(0)++H. pylori- - (0)- -NSAIDs(0)- -- -16

Reid et al, Nat Can Rev, 2010Precision Prevention ?Primary Care SettingVery Low Risk

History/Physical (lifestyle, family history, obesity)

Low cost clinical screening based onBlood (e.g., H pylori, telomeres, inflammatory cytokines)Non-endoscopic cytology markers (e.g., Cytosponge)

Risk prediction tool for education/referralSecondary Care SettingLow Risk

Reproducible tissue-based markersSafe interventions (e.g., lifestyle)Secondary care risk model to guide surveillance and interventionsSecondary Care SettingModerate Risk

Cost-effective surveillance protocolCost-effective interventions (e.g., chemoprevention)Stratification into highest risk category

Discrimination and calibration of risk model for BE among persons with reflux symptoms.

- Thrift, et al. Cancer Prevention Research 2012CytospongeNon-endoscopic collection of esophageal epithelial cells

Risk PredictionBarretts > EAGoal: Develop risk prediction models based on sets of variables available in different contexts (increasing cost)

1. Questionnaire basede.g., age, sex, reflux sx, obesity measures, smoking history, nsaid use2. Adding blood measurese.g., CRP, leptin, insulin, metabolite panel3. Adding clinical endoscopy datae.g., Barretts segment length4. Adding somatic genetic abnormalities (biopsy-based)e.g., specific mutations (p53) or genome-wide abnormalities

Steps:

Impute missing data (50 datasets)

Fit model using backward stepwise regression (also forward) with a threshold for staying in the model of p EAAUCNave AUC is calculated as average AUC of final model across imputations adjusted AUC is based on 100-fold cross-validation with a 2/3rds vs 1/3rds training/test split using the same backward stepwise regression procedure Calibration of model: still working on this but same ideas as aboveAbove steps repeated for different contexts.

Thrift, Janes, Onstad, et al., in process

Challenges & Discussion PointsDefining prioritiesWhat context deserves top priority?What data are needed/available to address each context?Biomarkers & other unproven predictorsConfronting limited data resourcesSmall numbers for some contextsMissing dataHow to validate in consortium setting where we already have most cases (e.g., rare disease)Implementing decision aidsMost value for physiciansMost value for patients/population

the endInflammation & Esophageal Cancer

Reid et al, Nat Can Rev, 201029

White Race4 - 53 - 4??

Reflux Symptoms5 (recurrent)6 (long duration)8 (high frequency)2 - 3??Cook, (provisionnal PLoS ONE); Ronkainen, Gastro 2005; Zagari, GUT 2008

Abdominal Obesity2.52.01.3Singh, CGH 2013; Hardikar, PLoS ONE 2013; Kubo, GUT 2013

Cigarette Smoking2.01.81.5Cook, JNCI 2010 ; Singh, CGH 2013; Hardikar, PLoS ONE 2013

Helicobacter pylori0.4 - 0.5~0.5??Islami, Can Prev Res 2008, Fishbach, Helicobacter 2012Specific Genes ImplicatedLevine, Nature Genetics 2013; Su, Nature Genetics 2012LocationGeneNotes19p13CRTC1Oncogenic activity9q22BARX1Transcription factor important in esophageal specification3p14FOXP1Regulates esophageal development16q24FOXF1Tumor suppressor Targeted Metabolomics

Buas, Raftery, et al. in preparationClassification: GERD vs. HGD/EA

Buas, Raftery, et al. in preparationPLS-DA w/ LOO cross-validationBEACON World - 2014

Background Esophageal CancerMajor world health problemAlmost million new cases per year6th most common cancer in menTreatment usually ineffectivemedian survival < 1 year5 yr survival: 4% (1975) to 14.2% (1995)*Large geographic variationfrom 4 to >60 per 100,000high incidence central Asian cancer -belt*Jemal, Cancer, 2004

Population Size40Nave ROC Questionnaire and Blood

Specialized CareHigh Risk

Validate medical and/or surgical interventions appropriate for highest risk group

Comparison of multiple cardiovascular risk scoresRebekah Youngrlyoung@uw.eduJune 11, 2014

Cardiovascular Risk ScoresA risk assessment tool to predict a person's chance of having a heart-related event over some period of time (usually 10 years)Risk scores are used by physicians to initiate discussion of preventive medication

1. Points-based TC2. Points-based LDL3. Equation-based TC4. Equation-based LDL

Risk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderRisk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderATP3-FRS-CHD 5. FRS-ATP3-Eq 6. FRS-ATP3-Pt>20 yearsMI, CHD deathAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, Anti-hypertension medicationRisk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderATP3-FRS-CHD 5. FRS-ATP3-Eq 6. FRS-ATP3-Pt>20 yearsMI, CHD deathAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, Anti-hypertension medicationFRS-CVD 7. FRS-CHD-TC-Eq 8. FRS-CHD-BMI-Eq 9. FRS-CHD-TC-Pt 10. FRS-CHD-BMI-Pt30 74 yearsAngina, MI, CHD death, stroke, TIA, peripheral vascular disease, heart failureAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Anti-hypertension medicationRisk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderATP3-FRS-CHD 5. FRS-ATP3-Eq 6. FRS-ATP3-Pt>20 yearsMI, CHD deathAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, Anti-hypertension medicationFRS-CVD 7. FRS-CHD-TC-Eq 8. FRS-CHD-BMI-Eq 9. FRS-CHD-TC-Pt 10. FRS-CHD-BMI-Pt30 74 yearsAngina, MI, CHD death, stroke, TIA, peripheral vascular disease, heart failureAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Anti-hypertension medicationReynolds 11. RRS-FRS(?)50 80 yearsMI, CHD death, stroke, coronary revascularizationAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, hs-CRP, Family History, HbA1cRisk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderATP3-FRS-CHD 5. FRS-ATP3-Eq 6. FRS-ATP3-Pt>20 yearsMI, CHD deathAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, Anti-hypertension medicationFRS-CVD 7. FRS-CHD-TC-Eq 8. FRS-CHD-BMI-Eq 9. FRS-CHD-TC-Pt 10. FRS-CHD-BMI-Pt30 74 yearsAngina, MI, CHD death, stroke, TIA, peripheral vascular disease, heart failureAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Anti-hypertension medicationReynolds 11. RRS-FRS(?)50 80 yearsMI, CHD death, stroke, coronary revascularizationAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, hs-CRP, Family History, HbA1cAHA-ACC-ASCVD 12. AHA-ACC-FRS(?)40 79 yearsMI, CHD death, strokeAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Race, Anti-hypertension MedicationFramingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No58Framingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No 1. FRS-98-TC-Eq10%59Framingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9%60Framingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8%61Framingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%62Multiple Cardiovascular Risk Scores 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%A cautionary tale about using TheFramingham risk scoreDifferent target populations (e.g.,diabetics, different age ranges)Different endpointsVariation within same endpointOverlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD deathIssue with online calculators

63Multiple Cardiovascular Risk Scores 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%A cautionary tale about using TheFramingham risk scoreDifferent target populations (e.g.,diabetics, different age ranges)Different endpointsVariation within same endpointOverlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD deathIssue with online calculators

64Multiple Cardiovascular Risk Scores 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%A cautionary tale about using TheFramingham risk scoreDifferent target populations (e.g.,diabetics, different age ranges)Different endpointsVariation within same endpointOverlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD deathIssue with online calculators

65Multiple Cardiovascular Risk Scores 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%A cautionary tale about using TheFramingham risk scoreDifferent target populations (e.g.,diabetics, different age ranges)Different endpointsVariation within same endpointOverlap in the compositeFRS-98-TC-Pt: MI,CHD death, Angina, Coronary insufficiencyFRS-ATP3-Eq: MI, CHD deathIssue with online calculators

66Multiple Cardiovascular Risk Scores 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%A cautionary tale about using TheFramingham risk scoreDifferent target populations (e.g.,diabetics, different age ranges)Different endpointsVariation within same endpointOverlap in the compositeFRS-98-TC-Pt: MI,CHD death, Angina, Coronary insufficiencyFRS-ATP3-Eq: MI, CHD deathOnline calculators

67

http://www.mayoclinic.org/heart-disease-risk/itt-20084942Created by Mayo Foundation for Medical Education and Research using content from Framingham Heart Study Cardiovascular Disease 10-Year BMI-Based Risk Score Calculator, Framingham Heart Study General Cardiovascular Disease 30-Year Lipid-Based and BMI-Based Calculators, and ACC/AHA Pooled Cohort Equations CV Risk Calculator. Thank-you!!!

Questions & Comments

Rebekah Youngrlyoung@uw.edu

69Risk ScoreTarget Age GroupCardiovascular EventsVariables IncludedFRS-CHD 1. FRS-98-TC-Eq 2. FRS-98-HDL-Eq 3. FRS-98-TC-Pt 4. FRS-98-HDL-Pt30 74 yearsAngina, MI, CHD death, coronary insufficiencyAge, Total Cholesterol, HDL-C, SBP & DBP, Diabetes status, Smoking, GenderATP3-FRS-CHD 5. FRS-ATP3-Eq 6. FRS-ATP3-Pt>20 yearsMI, CHD deathAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, Anti-hypertension medicationFRS-CVD 7. FRS-CHD-TC-Eq 8. FRS-CHD-BMI-Eq 9. FRS-CHD-TC-Pt 10. FRS-CHD-BMI-Pt30 74 yearsAngina, MI, CHD death, stroke, TIA, peripheral vascular disease, heart failureAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Anti-hypertension medicationReynolds 11. RRS-FRS(?)50 80 yearsMI, CHD death, stroke, coronary revascularizationAge, Total Cholesterol, HDL-C, SBP, Smoking, Gender, hs-CRP, Family History, HbA1cAHA-ACC-ASCVD 12. AHA-ACC-FRS(?)40 79 yearsMI, CHD death, strokeAge, Total Cholesterol, HDL-C, BP, Diabetes status, Smoking, Gender, Race, Anti-hypertension MedicationFramingham Risk Scores

Age: 59Total cholesterol (TC): 148High-density lipoprotein (HDL): 35Systolic blood pressure (SBP): 120Antihypertensive medication use: YesSmoker: FormerFamily history of heart disease(FH): NoDiabetes (DM): No 1. FRS-98-TC-Eq10% 2. FRS-98-LDL-Eq5% 3. FRS-98-TC-Pt4% 4. FRS-98-LDL-Pt9% 5. FRS-ATP3-Eq10% 6. FRS-ATP3-Pt8% 7. FRS-CHD-TC-Eq18% 8. FRS-CHD-BMI-Eq24% 9. FRS-CHD-TC-Pt16% 10. FRS-CHD-BMI-Pt22% 11. RRS-(FRS?)7% 12. AHA-ACC-FRS(?)13%71Development and Validation of an HIV Risk Assessment Tool for African Women

Jen Balkus, PhD, MPHStaff Scientist VIDDAssociate Director Microbicide Trials Network Statistical and Data Management Center

Risk Prediction Seminar - DiscussionJune 11, 2014

The ProblemWomen account for more than half of all new HIV infections globally, with the greatest incidence occurring in African womenSeveral recently completed biomedical HIV prevention trials in women have reported high incidence ratesAs great as 10% at some study sites Improving our understanding of predictors of HIV acquisition in African women is urgently needed in order to:Identify opportunities for intervention Inform the scale-up of targeted HIV prevention activities One Potential SolutionAn HIV risk score for African womenCould be used in multiple settings:To improve clinical trial recruitment efficiency by enrolling women identified as high-riskCommunity and counseling (VCT) settingsProgram and policy settings to inform scale-up of novel interventionsEmpiric HIV risk scores have recently been developed for several populationsAfrican HIV serodiscordant couples Men who have sex with men (MSM) in the United States Example risk score. Kahle et al. JAIDS (2013)

The Plan Derive an HIV risk score using data from women enrolled in MTN-003 (the VOICE trial)5,029 women enrolled from Uganda, South Africa and ZimbabweBaseline factors associated with HIV acquisition in VOICE Baseline characteristicCategoryHIVIncidence*HR(95% CI)Age (years)< 25 8.11 1.78(1.36, 2.32) 25 3.38 RefMarried or living with husband/primary partnerNo7.851.91(1.29, 2.84)Yes1.85RefPartner provides financial or material supportNo8.731.34(1.03, 1.75)Yes5.10RefPrimary partner has other partnersYes5.541.67(1.10, 2.53)Dont know6.551.68(1.23, 2.30)No3.70RefAny curable STI** Yes10.121.57(1.23, 2.00)No4.64RefHSV-2 seropositiveYes7.051.71(1.35, 2.16)No4.54RefAlcohol use per week in the past 3 months2 times6.461.72(0.99, 2.99) 1 time7.161.30(0.99, 1.71)None5.22Ref*Per 100 person-years; **Chlamydia, gonorrhea, trichomonas or syphilis Nair et al. CROI (2014) The PlanPredict HIV risk within 1 year of enrollment using baseline characteristics that can easily be assessed in a clinical or research settingCox proportional hazards models to assess univariate predictorsFully stepwise multivariable model constructed based on lowest Akaike Information Criterion (AIC)Risk score generated by dividing the coefficient for the predictor in the final model by the lowest coefficient among all predictors in the model and rounding to the nearest integerPredictive ability of the total score and each predictor will be assessed by calculating the time-varying area under the curve (AUC)Final score will be internally validated using 10-fold cross-validation and the AUC for the final model will be compared with the mean AUC of the 10 different models

The PlanValidate the risk score using date from 4 recently or soon to be completed HIV prevention trials in African womenProposed external validation datasets for development of an HIV risk score for African womenStudy characteristicsHPTN 035MDP301FEM-PrEPASPIRE (MTN-020)Number of women enrolled3,0999,3852,1203,476*Number of HIV infections(HIV incidence)184(3.5 per 100 p-yrs)335(4.5 per 100 p-yrs)68(4.8 per 100 p-yrs)Not available; study is ongoingHIV testing frequencyQuarterly12, 24, 40 and 52 weeks MonthlyMonthlyYears conducted2005-20082005-20082009-20112012-presentParticipating countriesMalawi, South Africa, Uganda, Zambia, ZimbabweSouth Africa, Tanzania, Uganda, ZambiaKenya, South Africa, TanzaniaMalawi, South Africa, Uganda, ZimbabweIntervention(s) evaluated0.5% PRO2000 gel;BufferGel2% PRO200 gel;0.5% PRO2000 gelOral Tenofovir/EmtricitabineDapivirine vaginal ring*Enrollment will be completed by third quarter 2014.ChallengesVarying follow-up time due to administrative censoring 3 of 5 arms were stopped early due to futility: Oral tenofovir arm, vaginal tenofovir gel arm and vaginal placebo gel arm

Studies proposed for external validation have similar but not identical questions at baseline

Measured and unmeasured differences by countryToo much variability?Limit analyses to South Africa?