Effect of Misreported Family History on Mendelian Mutation Prediction Models

People with familial history of disease often consult with genetic counselors about their chance of carrying mutations that increase disease risk. To aid them, genetic counselors use Mendelian models that predict whether the person carries deleterious mutations based on their reported family history. Such models rely on accurate reporting of each member\u27s diagnosis and age of diagnosis, but this information may be inaccurate. Commonly encountered errors in family history can significantly distort predictions, and thus can alter the clinical management of people undergoing counseling, screening, or genetic testing. We derive general results about the distortion in the carrier probability estimate caused by misreported diagnoses in relatives. We show that the Bayes Factor that channels all family history information has a convenient and intuitive interpretation. We focus on the ratio of the carrier odds given correct diagnosis vs. given misreported diagnosis to measure the impact of errors. We derive the general form of this ratio and approximate it in realistic cases. Misreported age of diagnosis usually causes less distortion than misreported diagnosis. This is the first systematic quantitative assessment of the effect of misreported family history on mutation prediction. We apply the results to the BRCAPRO model, which predicts the risk of carrying a mutation in the breast and ovarian cancer genes BRCA1 and BRCA2

BayesMendel: An R Environment for Mendelian Risk Prediction

Several important syndromes are caused by deleterious germline mutations of individual genes. In both clinical and research applications it is useful to evaluate the probability that an individual carries an inherited genetic variant of these genes, and to predict the risk of disease for that individual, using information on his/her family history. Mendelian risk prediction models accomplish these goals by integrating Mendelian principles and state-of-the art statistical models to describe phenotype/genotype relationships. Here we introduce an R library called BayesMendel that allows implementation of Mendelian models in research and counseling settings. BayesMendel is implemented in an object--oriented structure in the language R and distributed freely as an open source library. In its first release, it includes two major cancer syndromes: the breast-ovarian cancer syndrome and the hereditary non-polyposis colorectal cancer syndrome, along with up-to-date estimates of penetrance and prevalence for the corresponding genes. Input genetic parameters can be easily modified by users. BayesMendel can also serve as a generic tool for genetic epidemiologists to flexibly implement their own Mendelian models for novel syndromes and local subpopulations, without reprogramming complex statistical analyses and prediction tools

Nonparametric Adjustment for Measurement Error in Time to Event Data

Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved

Extending Mendelian Risk Prediction Models to Handle Misreported Family History

Mendelian risk prediction models calculate the probability of a proband being a mutation carrier based on family history and known mutation prevalence and penetrance. Family history in this setting, is self-reported and is often reported with error. Various studies in the literature have evaluated misreporting of family history. Using a validation data set which includes both error-prone self-reported family history and error-free validated family history, we propose a method to adjust for misreporting of family history. We estimate the measurement error process in a validation data set (from University of California at Irvine (UCI)) using nonparametric smoothed Kaplan-Meier estimators, and use Monte Carlo integration to implement the adjustment. In this paper, we extend BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancers, to adjust for misreporting in family history. We apply the extended model to data from the Cancer Genetics Network (CGN)

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA

Oral leukoplakia and risk of progression to oral cancer: A population-based cohort study

BACKGROUND: The optimal clinical management of oral precancer remains uncertain. We investigated the natural history of oral leukoplakia, the most common oral precancerous lesion, to estimate the relative and absolute risks of progression to cancer, the predictive accuracy of a clinician\u27s decision to biopsy a leukoplakia vis-à-vis progression, and histopathologic predictors of progression. METHODS: We conducted a retrospective cohort study (1996-2012) of patients with oral leukoplakia (n = 4886), identified using electronic medical records within Kaiser Permanente Northern California. Among patients with leukoplakia who received a biopsy (n = 1888), we conducted a case-cohort study to investigate histopathologic predictors of progression. Analyses included indirect standardization and unweighted or weighted Cox regression. RESULTS: Compared with the overall Kaiser Permanente Northern California population, oral cancer incidence was substantially elevated in oral leukoplakia patients (standardized incidence ratio = 40.8, 95% confidence interval [CI] = 34.8 to 47.6; n = 161 cancers over 22 582 person-years). Biopsied leukoplakias had a higher oral cancer risk compared with those that were not biopsied (adjusted hazard ratio = 2.38, 95% CI = 1.73 to 3.28). However, to identify a prevalent or incident oral cancer, the biopsy decision had low sensitivity (59.6%), low specificity (62.1%), and moderate positive-predictive value (5.1%). Risk of progression to oral cancer statistically significantly increased with the grade of dysplasia; 5-year competing risk-adjusted absolute risks were: leukoplakia overall = 3.3%, 95% CI = 2.7% to 3.9%; no dysplasia = 2.2%, 95% CI = 1.5% to 3.1%; mild-dysplasia = 11.9%, 95% CI = 7.1% to 18.1%; moderate-dysplasia = 8.7%, 95% CI = 3.2% to 17.9%; and severe dysplasia = 32.2%, 95% CI = 8.1%-60.0%. Yet 39.6% of cancers arose from biopsied leukoplakias without dysplasia. CONCLUSIONS: The modest accuracy of the decision to biopsy a leukoplakia vis-à-vis presence or eventual development of oral cancer highlights the need for routine biopsy of all leukoplakias regardless of visual or clinical impression. Leukoplakia patients, particularly those with dysplasia, need to be closely monitored for signs of early cancer

Aspirin, ibuprofen, and reduced risk of advanced colorectal adenoma incidence and recurrence and colorectal cancer in the PLCO Cancer Screening Trial

BACKGROUND: Studying the differential impact of aspirin and other nonsteroidal anti-inflammatory drugs across the stages of colorectal neoplasia from early adenoma to cancer is critical for understanding the benefits of these widely used drugs. METHODS: With 13 years of follow-up, the authors prospectively evaluated the association between aspirin and ibuprofen use and incident distal adenoma (1221 cases), recurrent adenoma (862 cases), and incident colorectal cancer (CRC; 2826 cases) among men and women in the population-based Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. With multivariable-adjusted models, odds ratio (ORs) and 95% confidence intervals (CIs) for adenoma incidence and recurrence and hazard ratios (HRs) and 95% CIs for incident CRC were determined. RESULTS: The authors observed a significantly reduced risk of incident adenoma with ibuprofen use (≥30 vs \u3c4 pills per month: OR, 0.76 [95% CI, 0.60-0.95]; P(trend) = .04), particularly advanced adenoma (OR, 0.48 [95% CI, 0.28-0.83]; P(trend) = .005). Among those with a previous adenoma detected through screening, aspirin use was associated with a decreased risk of advanced recurrent adenoma (≥30 vs \u3c4 pills per month: OR, 0.56 [95% CI, 0.36-0.87]; P(trend) = 0.006). Both aspirin (HR, 0.88 [95% CI, 0.81-0.96]; P(trend) \u3c.0001) and ibuprofen use (HR, 0.81 [95% CI, 0.70-0.93); P(trend) = 0.003) ≥30 versus \u3c4 pills per month were significantly associated with reduced CRC risk. CONCLUSIONS: In this large prospective study with long-term follow-up, a beneficial role for not only aspirin, but also ibuprofen, in preventing advanced adenoma and curbing progression to recurrence and cancer among older adults was observed

Effectiveness of VIA, Pap, and HPV DNA Testing in a Cervical Cancer Screening Program in a Peri-Urban Community in Andhra Pradesh, India

BACKGROUND: While many studies have compared the efficacy of Pap cytology, visual inspection with acetic acid (VIA) and human papillomavirus (HPV) DNA assays for the detection cervical intraepithelial neoplasia and cancer, few have evaluated the program effectiveness. METHODS AND FINDINGS: A population-based sample of 5603 women from Medchal Mandal in Andhra Pradesh, India were invited to participate in a study comparing Pap cytology, VIA, and HPV DNA screening for the detection of CIN3+. Participation in primary screening and all subsequent follow-up visits was rigorously tracked. A 20% random sample of all women screened, in addition to all women with a positive screening test result underwent colposcopy with directed biopsy for final diagnosis. Sensitivity, specificity, positive and negative predictive values were adjusted for verification bias. HPV testing had a higher sensitivity (100%) and specificity (90.6%) compared to Pap cytology (sensitivity  =  78.2%; specificity = 86.0%) and VIA (sensitivity = 31.6%; specificity = 87.5%). Since 58% of the sample refused involvement and another 28% refused colposcopy or biopsy, we estimated that potentially 87.6% of the total underlying cases of CIN3 and cancer may have been missed due to program failures. CONCLUSIONS: We conclude that despite our use of available resources, infrastructure, and guidelines for cervical cancer screening implementation in resource limited areas, community participation and non-compliance remain the major obstacles to successful reduction in cervical cancer mortality in this Indian population. HPV DNA testing was both more sensitive and specific than Pap cytology and VIA. The use of a less invasive and more user-friendly primary screening strategy (such as self-collected swabs for HPV DNA testing) may be required to achieve the coverage necessary for effective reduction in cervical cancer mortality