Extension of the Cohort Analysis for Genetic Epidemiology (CAGE) Program to Assess Excess Risk of Cancer Mei Liu

What is CAGE? A UNIX-based program A UNIX-based program Uses age-, sex- and calendar years-specific incidence rates to calculate standardized cancer incidence ratios (Observed/Expected) Uses age-, sex- and calendar years-specific incidence rates to calculate standardized cancer incidence ratios (Observed/Expected) assuming that the observed number of malignancies in the population follow a Poisson distribution assuming that the observed number of malignancies in the population follow a Poisson distribution Requires three files Requires three files Registry data file: cancer registry data Registry data file: cancer registry data Cohort/pedigree data file (.dat file) Cohort/pedigree data file (.dat file) Input file (.inp file) Input file (.inp file)

What Can CAGE Do? Assessing familial aggregation of cancer using pedigree data Assessing familial aggregation of cancer using pedigree data Comparison of primary cancer risk and risk of second primary cancers within cohorts of cancer patients Comparison of primary cancer risk and risk of second primary cancers within cohorts of cancer patients

CAGE Registry Database Connecticut Tumor Registry (CTR) Connecticut Tumor Registry (CTR) Connecticut cancer rates from 1931 to 1985 Connecticut cancer rates from 1931 to 1985 The oldest database available in the Surveillance, Epidemiology and End Results (SEER) program The oldest database available in the Surveillance, Epidemiology and End Results (SEER) program The original CAGE database The original CAGE database

CAGE Registry Database Update of CAGE registry databases Update of CAGE registry databases Updated the CTR data to include incidence rates through 1995 stratified by gender Updated the CTR data to include incidence rates through 1995 stratified by gender Constructed three databases to include all SEER registries data from 1973 to 2000, stratified by race/ethnicity (White, Black, Hispanic) and gender Constructed three databases to include all SEER registries data from 1973 to 2000, stratified by race/ethnicity (White, Black, Hispanic) and gender

Limitation of SEER Hispanic Data The available SEER Hispanic data only contains incidence rates from 1991 to 2000 The available SEER Hispanic data only contains incidence rates from 1991 to 2000 An imputation method was developed, based on cancer incidence for Whites, to impute incidence for Hispanics An imputation method was developed, based on cancer incidence for Whites, to impute incidence for Hispanics

Imputation of Hispanic Cancer Rates

Simulation Study Simulation study to test the imputation scheme Simulation study to test the imputation scheme The null hypothesis: Our proposed imputation scheme The null hypothesis: Our proposed imputation scheme To evaluate type I error To evaluate type I error  5000 replicates of cohorts were simulated  1200 subjects each cohort with simulated birth year, age and gender information  Cancer status determined by the imputed cancer rates To evaluate the power to detect the excess risk To evaluate the power to detect the excess risk  1000 replicates of cohorts were simulated under the assumptions SIR = 1.5 and SIR = 2.0 respectively

Type I Error And Power  level Type I error Power SIR = 1.5 SIR = %100%100% 0.010%99.8%100% Empirical type I error and power at  = 0.05 and 0.01 level

Evaluation of the Imputation Question: Question: What happens if the imputed rates are incorrect? What happens if the imputed rates are incorrect? Flat Rate

Evaluation of the Imputation Question: Question: What happens if the imputed rates are incorrect? What happens if the imputed rates are incorrect? Decreasing Rate

Evaluation of the Imputation Question: Question: What happens if the imputed rates are incorrect? What happens if the imputed rates are incorrect? Increasing Rate

Evaluation of the Imputation Test of three alternative hypotheses Test of three alternative hypotheses  level Excess risk rates (%) Flat rate Decreasing 0.8% rate Increasing

Evaluation of the Imputation Is there a difference in using White only versus Black only versus White & Black rates to impute Hispanic rates? Is there a difference in using White only versus Black only versus White & Black rates to impute Hispanic rates?  level Excess risk rates (%) Blacks Whites & Blacks

Application to Real Data Brain Tumor Study Brain Tumor Study Study purpose Study purpose  To investigate whether a brain tumor patient’s first- degree relatives (FDRs) have increased risk of cancer Study participants Study participants  1648 glioma patients (1476 Whites,58 Blacks,114 Hispanics) who registered at The UT MD Anderson Cancer Center from June 1992 and June 2006  first-degree relatives (8858 Whites, 347 Blacks, 888 Hispanics)

Brain Tumor Study WhitesBlacksHispanics SIR(95% CI) All Cancers 1.21 ( ) 1.43 ( ) 0.96 ( ) Brain Tumor 2.14 ( ) 8.81 ( ) 0 Lung 0.75 (0.62 – 0.90) 0.92 ( ) 0.85 ( ) Melanoma 2.02 (1.58 – 2.54) 00 SIRs and 95% CIs for first-degree relatives by race

Conclusions CAGE is flexible and relatively easy to use CAGE is flexible and relatively easy to use Our updated CAGE program makes it possible to perform the race-specific analyses Our updated CAGE program makes it possible to perform the race-specific analyses Limitation of CAGE Limitation of CAGE Intra-family correlation, family size Intra-family correlation, family size  If you also have family data for healthy controls, can use GEE model to adjust for intra-family correlation and family size

References Lustbader ED, McLaughlin L: CAGE: Cohort Analysis for Genetic Epidemiology. Philadelphia, PA, Fox Chase Cancer Center, 1995 Lustbader ED, McLaughlin L: CAGE: Cohort Analysis for Genetic Epidemiology. Philadelphia, PA, Fox Chase Cancer Center, 1995 SEER- Scientific Systems. National Cancer Institution, SEER- Scientific Systems. National Cancer Institution,

Acknowledgements Dr. Carol J. Etzel Dr. Carol J. Etzel Dr. Michael E. Scheurer Dr. Michael E. Scheurer Dr. Melissa L. Bondy Dr. Melissa L. Bondy Dr. Sara Strom Dr. Sara Strom Dr. Chris Amos Dr. Chris Amos