Gail Gong

Clinical factors predicting successful electrophysiologic-pharmacologic study in patients with ventricular tachycardia

Data from 142 patients who had sustained ventricular tachycardia or ventricular fibrillation were analyzed to determine if clinical variables predict response to antiarrhythmic drugs at electrophysiologic study. Effective antiarrhythmic drugs were identified for 43 patients (30%). Ten of 25 variables analyzed were univariate predictors of drug response at the probability (p) level of less than 0.05. Stepwise logistic regression identified three variables independently predictive of drug response: fewer coronary arteries with 70% or greater stenosis (p less than 0.001), female sex (p less than 0.002) and fewer episodes of arrhythmia (p less than 0.03). A function incorporating these three variables was constructed to predict the probability of drug response, and ranges of the predictor function corresponding to high, intermediate and low probabilities of drug response were identified. Response rates in the high (greater than 50%), intermediate and low (less than 10%) probability ranges were 28 (58%) of 48, 10 (27%) of 37 and 5 (9%) of 57, respectively. Thus 40% of the patients who had a less than 10% likelihood of benefit from electrophysiologic-pharmacologic study were classified into the low probability range. When the predictor function was applied prospectively to 25 additional patients, response rates in the three probability ranges were 3 (50%) of 6, 1 (12%) of 8 and 0 (0%) of 11. These data show that analysis of clinical variables can be used to estimate the probability of benefit from electrophysiologic-pharmacologic study.

Segregation analysis of prostate cancer in 1719 white, African-American and Asian-American families in the United States and Canada

Some data suggest that brothers of prostate cancer patients have higher disease risk than their fathers, supporting an X-linked or recessive mode of inheritance. However, higher observed frequencies in brothers than fathers may merely reflect the strong temporal changes in US incidence rates. Objectives: (a) to evaluate the fit of X-linked, recessive, and dominant modes of inheritance to prostate cancer incidence, specific for calendar year, age, and race, in population-based samples of US and Canadian families; and (b) to evaluate a simple multifactorial model for familial aggregation of prostate cancer due to shared low-penetrance variants of many genes or shared lifestyle factors. Methods: The data consist of reported prostate cancer incidence in first-degree relatives of 1719 white, African-American, and Asian-American men with and without prostate cancer at ages < 70 years. Model parameters were estimated by maximizing a pseudo-likelihood function of the data, and goodness of model fit was assessed by evaluating discrepancies between observed and expected numbers of pairs of relatives with prostate cancer. Results: After adjusting for temporal trends in prostate cancer incidence rates we found that the X-linked model fit poorly, underpredicting the observed number of affected father–son pairs. This also was true of the recessive model, although the evidence for poor fit did not achieve statistical significance. In contrast, the dominant model provided adequate fit to the data. In this model the race-specific penetrance estimates for carriers of deleterious genotypes were similar among African-Americans and whites, but lower among Asian-Americans: risk by age 80 years for carriers born in 1900 was estimated as 75.3% for African-Americans and whites, and 44.4% for Asian-Americans. None of the Mendelian models fit the data better than did the simple multifactorial model. Conclusions: The good fit of the multifactorial model suggests that multiple genes, each having low penetrance, may be responsible for most inherited prostate cancer susceptibility, and that the contribution of rare highly penetrant mutations is small.

Performance of BRCA1/2 Mutation Prediction Models in Asian Americans

PURPOSE There are established differences in breast cancer epidemiology between Asian and white individuals, but little is known about hereditary breast cancer in Asian populations. Although increasing numbers of Asian individuals are clinically tested for BRCA1/2 mutations, it is not known whether computer models that predict mutations work accurately in Asian individuals. We compared the performance in Asian and white individuals of two widely used BRCA1/2 mutation prediction models, BRCAPRO and Myriad II. PATIENTS AND METHODS We evaluated BRCAPRO and Myriad II in 200 Asian individuals and a matched control group of 200 white individuals who were tested for BRCA1/2 mutations at four cancer genetics clinics, by comparing numbers of observed versus predicted mutation carriers and by evaluating area under the receiver operating characteristic curve (AUC) for each model. RESULTS BRCAPRO and Myriad II accurately predicted the number of white BRCA1/2 mutation carriers (25 observed v 24 predicted by BRCAPRO; 25 predicted by Myriad II, P > or = .69), but underpredicted Asian carriers by two-fold (49 observed v 25 predicted by BRCAPRO; 26 predicted by Myriad II; P < or = 3 x 10(-7)). For BRCAPRO, this racial difference reflects substantial underprediction of Asian BRCA2 mutation carriers (26 observed v 4 predicted; P = 1 x 10(-30)); for Myriad II, separate mutation predictions were not available. For both models, AUCs were nonsignificantly lower in Asian than white individuals, suggesting less accurate discrimination between Asian carriers and noncarriers. CONCLUSION Both BRCAPRO and Myriad II underestimated the proportion of BRCA1/2 mutation carriers, and discriminated carriers from noncarriers less well, in Asian compared with white individuals.

Performance of Prediction Models for BRCA Mutation Carriage in Three Racial/Ethnic Groups: Findings from the Northern California Breast Cancer Family Registry

Purpose: Patients with early-onset breast and/or ovarian cancer frequently wish to know if they inherited a mutation in one of the cancer susceptibility genes, BRCA1 or BRCA2. Accurate carrier prediction models are needed to target costly testing. Two widely used models, BRCAPRO and BOADICEA, were developed using data from non-Hispanic Whites (NHW), but their accuracies have not been evaluated in other racial/ethnic populations. Methods: We evaluated the BRCAPRO and BOADICEA models in a population-based series of African American, Hispanic, and NHW breast cancer patients tested for BRCA1 and BRCA2 mutations. We assessed model calibration by evaluating observed versus predicted mutations and attribute diagrams, and model discrimination using areas under the receiver operating characteristic curves. Results: Both models were well-calibrated within each racial/ethnic group, with some exceptions. BOADICEA overpredicted mutations in African Americans and older NHWs, and BRCAPRO underpredicted in Hispanics. In all racial/ethnic groups, the models overpredicted in cases whose personal and family histories indicated >80% probability of carriage. The two models showed similar discrimination in each racial/ethnic group, discriminating least well in Hispanics. For example, BRCAPROs areas under the receiver operating characteristic curves were 83% (95% confidence interval, 63-93%) for NHWs, compared with 74% (59-85%) for African Americans and 58% (45-70%) for Hispanics. Conclusions: The poor performance of the model for Hispanics may be due to model misspecification in this racial/ethnic group. However, it may also reflect racial/ethnic differences in the distributions of personal and family histories among breast cancer cases in the Northern California population. (Cancer Epidemiol Biomarkers Prev 2009;18(4):1084–91)

Breast Cancer Risk for Noncarriers of Family-Specific BRCA1 and BRCA2 Mutations: Findings From the Breast Cancer Family Registry

PURPOSE Women with germline BRCA1 and BRCA2 mutations have five- to 20-fold increased risks of developing breast and ovarian cancer. A recent study claimed that women testing negative for their family-specific BRCA1 or BRCA2 mutation (noncarriers) have a five-fold increased risk of breast cancer. We estimated breast cancer risks for noncarriers by using a population-based sample of patients with breast cancer and their female first-degree relatives (FDRs). PATIENTS AND METHODS Patients were women with breast cancer and their FDRs enrolled in the population-based component of the Breast Cancer Family Registry; patients with breast cancer were tested for BRCA1 and BRCA2 mutations, as were FDRs of identified mutation carriers. We used segregation analysis to fit a model that accommodates familial correlation in breast cancer risk due to unobserved shared risk factors. RESULTS We studied 3,047 families; 160 had BRCA1 and 132 had BRCA2 mutations. There was no evidence of increased breast cancer risk for noncarriers of identified mutations compared with FDRs from families without BRCA1 or BRCA2 mutations: relative risk was 0.39 (95% CI, 0.04 to 3.81). Residual breast cancer correlation within families was strong, suggesting substantial risk heterogeneity in women without BRCA1 or BRCA2 mutations, with some 3.4% of them accounting for roughly one third of breast cancer cases. CONCLUSION These results support the practice of advising noncarriers that they do not have any increase in breast cancer risk attributable to the family-specific BRCA1 or BRCA2 mutation.

Censored survival data with misclassified covariates: a case study of breast-cancer mortality.

A number of alternative models are used to examine the relationship of survival among breast-cancer patients to the time since diagnosis and to the stage of the disease at diagnosis. The data concern 2,495 women aged 55-64 diagnosed with breast cancer in the San Francisco Bay area of California. In particular, the authors examine the extent to which the bad fit of simple models for breast-cancer survival is due to measurement error in the covariates.

Segregation analysis of case-control data using generalized estimating equations.

Generalized estimating equations (GEEs) (Liang and Zeger, 1986, Biometrika 73, 13-22) are used to fit genetic models to binary disease data for families of subjects in case-control studies. The GEEs include model specification of both the disease probabilities and the two-way (and possibly three-way) correlation coefficients of the family disease data. These quantities are modelled as nonlinear functions of unobserved genotypes, observed environmental covariates, and the unknown parameters; the functions reflect the method used to ascertain the family data. Goodness of fit is tested by allowing more flexible forms for the correlation coefficients, regressing them against covariates specific to the relevant pair (or triple) of family members. The approach is applied to family data obtained from simulated and real case-control studies. This semiparametric approach is less dependent on unverifiable assumptions and more computationally tractable than other methods for segregation analysis.

Cutaneous melanoma at Lawrence Livermore National Laboratory: comparison with rates in two San Francisco Bay Area counties

During the period 1974 through 1985, employees at Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States) were diagnosed with cutaneous malignant melanoma at approximately three times the rate of the surrounding community. We investigated two explanations for this excess: the first explanation examined was that the recorded incidence of the neighboring community underestimates actual incidence. We estimated the amount of excess attributable to underreporting by using data from a survey conducted among San Francisco Bay Area clinicians and pathologists to determine previously unrecorded occurrences. We found that underreporting has negligible impact on melanoma incidence. The second explanation examined was that heightened medical awareness of the disease among LLNL employees and their physicians has led to greater detection. We found that LLNL melanomas are thinner than those from the surrounding community and that no excess was observed if we limited our attention to thicker, more invasive melanomas.

Testing Covariance Structure in Multivariate Models: Application to Family Disease Data

Abstract Recent interest in modeling multivariate responses for members of groups has emphasized the need for testing goodness of fit. Here we describe a way to test the covariance structure of a multivariate distribution parameterized by a vector θ. The idea is to extend this distribution, the “null” distribution, to a more general distribution that depends on θ, an additional scalar γ, and a specific quadratic function of the response vector chosen to capture features of an alternative covariance structure. When γ = 0, the more general distribution reduces to the null one. Standard likelihood theory yields a score test for γ = 0; that is, a test of fit of the null distribution. The score statistic is the standardized difference between observed and expected values of the quadratic function, where the expectation is taken with respect to the null distribution, with θ replaced by its maximum likelihood estimate. Applying the methods to case-control data on familial cancers of the ovary and breast, we illu...

Estimating genetic influence on disease from population-based case-control data: application to cancers of the breast and ovary

We describe genetic mixture models and goodness-of-fit statistics for evaluating the joint effects of genetic and environmental factors on the risk of chronic diseases. We focus particularly on situations wherein the gene(s) of interest play roles in several diseases, and death due to one disease can censor the occurrence of others. We use the methods to investigate the risks of cancers of the breast and ovary associated with germline mutations of BRCA1, using data pooled from three population-based U.S. case-control studies of ovarian cancer. We evaluate the goodness-of-fit of the genetic models by comparing the predicted numbers of diseased mother-daughter and sister-sister pairs to the numbers observed. We also use simulations to examine the performance of estimates obtained from such complex mixture models, and the contribution of control families to the precision of parameter estimates.