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Risk Assessment for Esophageal Adenocarcinoma

Risk Assessment for Esophageal Adenocarcinoma. June 2014 Thomas Vaughan Fred Hutchinson Cancer Research Center. Two Distinct Cancers. Incidence of Esophageal Adenocarcinoma. Arithmetic. Log. WM. BM. WF. BF. NCI SEER*Stat Database: 9 Registries released April 2013.

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Risk Assessment for Esophageal Adenocarcinoma

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  1. Risk AssessmentforEsophageal Adenocarcinoma June 2014 Thomas VaughanFred Hutchinson Cancer Research Center

  2. Two Distinct Cancers

  3. Incidence of Esophageal Adenocarcinoma Arithmetic Log WM BM WF BF NCI SEER*Stat Database: 9 Registries released April 2013

  4. Natural History of EA Normal 20%Chronic Reflux (GERD) 10% Metaplasia (Barrett’s*) HG Dysplasia 0.2 - 0.5% annualAdenocarcinoma *2-4 M persons in U.S. with Barrett’s

  5. Metaplastic Epithelium Wild & Hardie, Nat Can Rev, 2003

  6. Surveillance Rationale Survival Cancer detected during surveillance Local Distant SEER Limited Use Data, 9 registries, April 2007

  7. Napoleon’s Lost Army

  8. Esophageal AdenocarcinomaThe Lost Cases

  9. Summary of Risk Factors

  10. Risk Factors Act at Different Stages

  11. Male Sex 8.0 3 - 4 2 Hardikar, PLoS ONE 2013; Edelstein, AJG 2009; Corley, Gastro 2007

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

  13. Aspirin/NSAIDs 0.6 – 0.7 ~ 1 ~ 0.5 Liao, Gastro 2012; Vaughan, Lancet Oncology 2005

  14. Summary of Risk Factors and Stages of Action

  15. Precision Prevention ? Reid et al, Nat Can Rev, 2010

  16. Primary Care SettingVery Low Risk • History/Physical (lifestyle, family history, obesity) • Low cost clinical screening based on • Blood (e.g., H pylori, telomeres, inflammatory cytokines) • Non-endoscopic cytology markers (e.g., Cytosponge) • Risk prediction tool for education/referral

  17. Secondary Care SettingLow Risk • Reproducible tissue-based markers • Safe interventions (e.g., lifestyle) • Secondary care risk model to guide surveillance and interventions

  18. Secondary Care SettingModerate Risk • Cost-effective surveillance protocol • Cost-effective interventions (e.g., chemoprevention) • Stratification into highest risk category

  19. Discrimination and calibration of risk model for BE among persons with reflux symptoms. - Thrift, et al. Cancer Prevention Research 2012

  20. “Cytosponge” • Non-endoscopic collection of esophageal epithelial cells

  21. Risk PredictionBarrett’s –> EA • Goal: Develop risk prediction models based on sets of variables available in different contexts (increasing cost) 1. Questionnaire based e.g., age, sex, reflux sx, obesity measures, smoking history, nsaid use 2. Adding blood measures e.g., CRP, leptin, insulin, metabolite panel 3. Adding clinical endoscopy data e.g., Barrett’s segment length 4. 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<0.2 • Model is fit on each imputation and Rubin’s rules are used to combine the estimated coefficients and variances across imputations.

  22. Risk PredictionBarrett’s –> EA • AUC • Naïve 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 above • Above steps repeated for different contexts. Thrift, Janes, Onstad, et al., in process

  23. Challenges & Discussion Points • Defining priorities • What context deserves top priority? • What data are needed/available to address each context? • Biomarkers & other unproven predictors • Confronting limited data resources • Small numbers for some contexts • Missing data • How to validate in consortium setting where we already have most cases (e.g., rare disease) • Implementing decision aids • Most value for physicians • Most value for patients/population

  24. the end

  25. Comparison of multiple cardiovascular risk scores Rebekah Young rlyoung@uw.edu June 11, 2014

  26. Cardiovascular Risk Scores • A 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

  27. 1. Points-based TC 2. Points-based LDL 3. Equation-based TC 4. Equation-based LDL

  28. Framingham Risk Scores Age: 59 Total cholesterol (TC): 148 High-density lipoprotein (HDL): 35 Systolic blood pressure (SBP): 120 Antihypertensive medication use: Yes Smoker: Former Family history of heart disease(FH): No Diabetes (DM): No

  29. Framingham Risk Scores Age: 59 Total cholesterol (TC): 148 High-density lipoprotein (HDL): 35 Systolic blood pressure (SBP): 120 Antihypertensive medication use: Yes Smoker: Former Family history of heart disease(FH): No Diabetes (DM): No

  30. Framingham Risk Scores Age: 59 Total cholesterol (TC): 148 High-density lipoprotein (HDL): 35 Systolic blood pressure (SBP): 120 Antihypertensive medication use: Yes Smoker: Former Family history of heart disease(FH): No Diabetes (DM): No

  31. Framingham Risk Scores Age: 59 Total cholesterol (TC): 148 High-density lipoprotein (HDL): 35 Systolic blood pressure (SBP): 120 Antihypertensive medication use: Yes Smoker: Former Family history of heart disease(FH): No Diabetes (DM): No

  32. Framingham Risk Scores Age: 59 Total cholesterol (TC): 148 High-density lipoprotein (HDL): 35 Systolic blood pressure (SBP): 120 Antihypertensive medication use: Yes Smoker: Former Family history of heart disease(FH): No Diabetes (DM): No

  33. Multiple Cardiovascular Risk Scores • A cautionary tale about using “TheFramingham risk score” • Different target populations (e.g.,diabetics, different age ranges) • Different endpoints • Variation within same endpoint • Overlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD death • Issue with online calculators

  34. Multiple Cardiovascular Risk Scores • A cautionary tale about using “TheFramingham risk score” • Different target populations (e.g.,diabetics, different age ranges) • Different endpoints • Variation within same endpoint • Overlap in the compositeFRS-98-TC-Pt: Angina, MI, CHD death, coronary insufficiencyFRS-ATP3-Eq: MI, CHD death • Issue with online calculators

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