Sining Chen

Meta-analysis of BRCA1 and BRCA2 penetrance

PURPOSE Genetic counseling is now routinely offered to individuals at high risk of carrying a BRCA1 or BRCA2 mutation. Risk prediction provided by the counselor requires reliable estimates of the mutation penetrance. Such penetrance has been investigated by studies worldwide. The reported estimates vary. To facilitate clinical management and counseling of the at-risk population, we address this issue through a meta-analysis. METHODS We conducted a literature search on PubMed and selected studies that had nonoverlapping patient data, contained genotyping information, used statistical methods that account for the ascertainment, and reported risks in a useable format. We subsequently combined the published estimates using the DerSimonian and Laird random effects modeling approach. RESULTS Ten studies were eligible under the selection criteria. Between-study heterogeneity was observed. Study population, mutation type, design, and estimation methods did not seem to be systematic sources of heterogeneity. Meta-analytic mean cumulative cancer risks for mutation carriers at age 70 years were as follows: breast cancer risk of 57% (95% CI, 47% to 66%) for BRCA1 and 49% (95% CI, 40% to 57%) for BRCA2 mutation carriers; and ovarian cancer risk of 40% (95% CI, 35% to 46%) for BRCA1 and 18% (95% CI, 13% to 23%) for BRCA2 mutation carriers. We also report the prospective risks of developing cancer for currently asymptomatic carriers. CONCLUSION This article provides a set of risk estimates for BRCA1 and BRCA2 mutation carriers that can be used by counselors and clinicians who are interested in advising patients based on a comprehensive set of studies rather than one specific study.

Accumulation of Driver and Passenger Mutations During Tumor Progression

Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development—providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small—0.004 ± 0.0004—and has major implications for experimental cancer research.

Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense Mutations

Large-scale sequencing of cancer genomes has uncovered thousands of DNA alterations, but the functional relevance of the majority of these mutations to tumorigenesis is unknown. We have developed a computational method, called Cancer-specific High-throughput Annotation of Somatic Mutations (CHASM), to identify and prioritize those missense mutations most likely to generate functional changes that enhance tumor cell proliferation. The method has high sensitivity and specificity when discriminating between known driver missense mutations and randomly generated missense mutations (area under receiver operating characteristic curve, >0.91; area under Precision-Recall curve, >0.79). CHASM substantially outperformed previously described missense mutation function prediction methods at discriminating known oncogenic mutations in P53 and the tyrosine kinase epidermal growth factor receptor. We applied the method to 607 missense mutations found in a recent glioblastoma multiforme sequencing study. Based on a model that assumed the glioblastoma multiforme mutations are a mixture of drivers and passengers, we estimate that 8% of these mutations are drivers, causally contributing to tumorigenesis.

Characterization of BRCA1 and BRCA2 Mutations in a Large United States Sample

PURPOSE An accurate evaluation of the penetrance of BRCA1 and BRCA2 mutations is essential to the identification and clinical management of families at high risk of breast and ovarian cancer. Existing studies have focused on Ashkenazi Jews (AJ) or on families from outside the United States. In this article, we consider the US population using the largest US-based cohort to date of both AJ and non-AJ families. METHODS We collected 676 AJ families and 1,272 families of other ethnicities through the Cancer Genetics Network. Two hundred eighty-two AJ families were population based, whereas the remainder was collected through counseling clinics. We used a retrospective likelihood approach to correct for bias induced by oversampling of participants with a positive family history. Our approach takes full advantage of detailed family history information and the Mendelian transmission of mutated alleles in the family. RESULTS In the US population, the estimated cumulative breast cancer risk at age 70 years was 0.46 (95% CI, 0.39 to 0.54) in BRCA1 carriers and 0.43 (95% CI, 0.36 to 0.51) in BRCA2 carriers, whereas ovarian cancer risk was 0.39 (95% CI, 0.30 to 0.50) in BRCA1 carriers and 0.22 (95% CI, 0.14 to 0.32) in BRCA2 carriers. We also reported the prospective risks of developing cancer for cancer-free carriers in 10-year age intervals. We noted a rapid decrease in the relative risk of breast cancer with age and derived its implication for genetic counseling. CONCLUSION The penetrance of BRCA mutations in the United States is largely consistent with previous studies on Western populations given the large CIs on existing estimates. However, the absolute cumulative risks are on the lower end of the spectrum.

PancPRO: Risk Assessment for Individuals With a Family History of Pancreatic Cancer

PURPOSE The rapid fatality of pancreatic cancer is, in large part, the result of an advanced stage of diagnosis for the majority of patients. Identification of individuals at high risk of developing pancreatic cancer is a first step towards the early detection of this disease. Individuals who may harbor a major pancreatic cancer susceptibility gene are one such high-risk group. The goal of this study was to develop and validate PancPRO, a Mendelian model for pancreatic cancer risk prediction in individuals with familial pancreatic cancer, to identify high-risk individuals. METHODS PancPRO was built by extending the Bayesian modeling framework developed for BRCAPRO, trained using published data, and validated using independent prospective data on 961 families enrolled onto the National Familial Pancreas Tumor Registry, including 26 individuals who developed incident pancreatic cancer during follow-up. RESULTS We developed a risk prediction model, PancPRO, and free software for the estimation of pancreatic cancer susceptibility gene carrier probabilities and absolute pancreatic cancer risk. Model validation demonstrated an observed to predicted pancreatic cancer ratio of 0.83 (95% CI, 0.52 to 1.20) and high discriminatory ability, with an area under the receiver operating characteristic curve of 0.75 (95% CI, 0.68 to 0.81) for PancPRO. CONCLUSION PancPRO is the first risk prediction model for pancreatic cancer. When we validated our model using the largest registry of familial pancreatic cancer, our model provided accurate risk assessment. Our findings highlight the importance of detailed family history for clinical cancer risk assessment and demonstrate that accurate genetic risk assessment is possible even when the causative genes are not known.

Validity of Models for Predicting BRCA1 and BRCA2 Mutations

Context A computer software program from the University of Texas Southwestern Medical Center (CaGene) has multiple prediction models to estimate BRCA1 and BRCA2 mutation probability, which guides decisions on whether to test for a mutation. A comprehensive quantitative evaluation of how well the prediction models discriminate between persons who carry the mutation and those who do not is lacking. Contribution The authors used 7 prediction models to estimate the probability of a BRCA1/BRCA2 mutation in 3342 families. All 7 models were able to discriminate between mutation-positive and mutation-negative people. Implications The probability that someone carries the BRCA1/BRCA2 mutation should not be considered in isolation when decisions are being made about genetic testing. Other factors should be discussed with the patient and factored into the decision-making process. The Editors Deleterious mutations of BRCA1 (MIM 113705) and BRCA2 (MIM 600185) increase the risk for breast and ovarian cancer (13). Whereas deleterious variants are relatively rare in the general population, they are common among families with multiple occurrences of breast or ovarian cancer (46). When counseling a woman facing decisions about genotyping for BRCA1 and BRCA2, it is important to accurately evaluate the probability that she carries a deleterious mutation (pretest mutation probability) and the probability that a mutation will be found if she is genotyped (which depends on the accuracy of mutation testing). Reliable, evidence-based, individualized counseling strategies can enhance informed decision making, both about whether to pursue BRCA1/BRCA2 testing and what to do with the results (79). The demand for assessment of complex family histories of cancer has led to widespread use of statistical models to estimate mutation probabilities (2, 1018). Model-based predictions are currently used in counseling about genetic testing, are included in materials distributed to women considering genetic testing (1821), are used for determining eligibility for screening and prevention studies (22), and are factored into coverage decisions by insurers (23). More than a dozen models exist. They use different statistical methods and source populations, pedigree features, and predicted outcomes. In clinical practice, different models applied to the same person can give a wide range of probabilities that a BRCA1/BRCA2 mutation is present. This degree of variability raises concerns about whether some models are more accurate than others and calls for a careful independent comparative evaluation of the predictive performance of existing models. We assessed the validity of commonly used models for estimating mutation probabilities of BRCA1 and BRCA2 in individuals identified through the Cancer Genetics Network. We assembled a large set of families with history of breast cancer, ovarian cancer, or both. We used standardized computational methods across contributing institutions to evaluate 7 models. Our main goal was to measure how well these models discriminated between mutation carriers and noncarriers. Methods Study Overview We conducted a cross-sectional, multicenter analysis. For each family in the study, we identified an individual (the counselee) for whom we collected genetic test results for BRCA1, BRCA2, or both; genotyping methods; pretest estimations of mutation probability using each model; and additional information about family history of cancer. We used genetic test results as the gold standard for judging the sensitivity and specificity of the various models. We evaluated all models on every counselee, except where noted. Data Collection Table 1 summarizes the salient data (2432). Sources include 3 population-based studies and 8 data sets of individuals seen in clinics for women at high risk for a BRCA mutation. In the population-based studies, the participants reflected the demographic characteristics of a defined subpopulation (for example, all breast cancer cases in Orange County in the University of California, Irvine [UCI], study [31]). In contrast, patients from high-risk clinics had been referred because of a family history of cancer or were self-referred because of an interest in genetic testing (inclusion criteria varied across clinics). Table 1. Demographic Characteristics of Counselees and Sample Size, by Center Each center calculated all of the model probabilities for its own families. We designated the first genotyped person in each family as the counselee and computed predictions by using the genetic counseling software CaGene (University of Texas Southwestern Medical Center, Dallas, Texas) (24). The software version was customized and distributed to participating sites to ensure uniform procedures across all sites. Data entry and computation of model predictions were performed at the sites. This decentralized approach for data entry and probability calculations allowed site investigators to use pedigree information that models required but that centers could not export to a central site because of privacy concerns. In addition to model predictions, a subset of centers also exported the data required for the models to the National Cancer Institutes (NCI) Cancer Genetics Network Data Coordinating Center. The study population includes 3342 families. The institutional review boards at each participating institution approved the study protocol. All included counselees gave consent for using their data for research according to local institutional review board requirements. The Cancer Genetics Network steering committee reviewed the study design. Genetic Testing Appendix Table 1 summarizes genotyping methods by center and provides a brief description of each method. Determining whether a person carries a deleterious mutation of BRCA1 or BRCA2 is technically demanding because of the large size of these genes, the wide spectrum of mutations, and the presence of mutations whose clinical significance is unknown (3335). Commercial testing uses sequencing to search for unknown mutations or to probe for mutations that are commonly found among Ashkenazi Jewish persons. Research settings, particularly in the time in which the study was conducted, have used less expensive and less sensitive techniques (Appendix Table 1). Although sequencing is the most sensitive of the techniques used in our study, recent evidence highlights how it can miss certain mutations, such as large deletions or intronic mutations (3, 36). Therefore, the set of individuals carrying a mutation (the carriers) is not the same as the set of individuals who test positive for a mutation (the positive cases). Thus, Table 1 underestimates the true number of carriers; the size of the error varies according to the method of genotyping. Appendix Table 1. Number of Counselees, by Genotyping Method for Each Gene and Center Models We studied 7 models: BRCAPRO, the family history assessment tool (FHAT), Finnish, Myriad, NCI, University of Pennsylvania (Penn), and Yale University (Yale). Appendix Table 2 summarizes the characteristics, input variables, and output of the models. Three broad categories of models have been proposed: empirical (Finnish, Myriad, NCI, and Penn), mendelian (BRCAPRO and Yale), and expert-based (FHAT). The first step in developing an empirical model is to summarize the salient aspects of a family history in some predictor variables. The second step is to apply statistical learning techniques, such as logistic regression, to describe the relationship between these variables and the genotyping results (the dependent variable). Mendelian models represent the known modes of inheritance of deleterious genetic variants by established mathematical relationships between phenotypes (in this case, cancer status of family members) and genotypes (14, 3741). The mendelian model inputs include cancer incidence curves (penetrance) for both carriers and noncarriers and the prevalence of deleterious variants. Expert-based models calculate scores that summarize degree of risk, using algorithms constructed on the basis of clinical judgment. For example, FHAT (16) uses a 17-question interview to produce a quantitative score (score range, 0 to 45) representing the severity of family history. Appendix Table 2. Input Variables and Features of Each Model Empirical models calculate the probability of a positive test result for a mutation in the counselee (that is, the result of genetic testing), whereas mendelian models directly estimate the probability of carrying a mutation (the true mutation status of the counselee) (37). The 2 types of predictions are therefore not directly comparable, a fact often overlooked in counseling practice. Because genotyping methods are highly specific for the BRCA1 and BRCA2 genes (that is, they have a very low false-positive rate), multiplying the genotype probability by the genotyping sensitivity gives the probability of finding a mutation. Therefore, to compare an empirical model probability of a BRCA mutation with a mendelian model probability, one must know the sensitivity of the genotyping method of the study used to develop the empirical model. Expert-based scores do not have a direct probabilistic interpretation. In our analyses, we rescaled the FHAT score by dividing by its maximum value of 45. The Penn model (11) estimates the probability of a positive BRCA1 test result in any family member. We adapted it to provide the probability of a positive test in the counselee. We assigned affected counselees the same mutation probability as the family. We assigned unaffected counselees one half the family probability if the closest affected relative of the counselee is a first-degree relative and one quarter of the family probability if the closest relative is a second-degree relative. We used a version of the BRCAPRO (13, 14) model based on the genetic variables described by Iversen and colleagues (42). We defined the Yale model by postulating a si

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.

Random Effects Models in a Meta-Analysis of the Accuracy of Two Diagnostic Tests Without a Gold Standard

In studies of the accuracy of diagnostic tests, it is common that both the diagnostic test itself and the reference test are imperfect. This is the case for the microsatellite instability test, which is routinely used as a prescreening procedure to identify individuals with Lynch syndrome, the most common hereditary colorectal cancer syndrome. The microsatellite instability test is known to have imperfect sensitivity and specificity. Meanwhile, the reference test, mutation analysis, is also imperfect. We evaluate this test via a random effects meta-analysis of 17 studies. Study-specific random effects account for between-study heterogeneity in mutation prevalence, test sensitivities and specificities under a nonlinear mixed effects model and a Bayesian hierarchical model. Using model selection techniques, we explore a range of random effects models to identify a best-fitting model. We also evaluate sensitivity to the conditional independence assumption between the microsatellite instability test and the mutation analysis by allowing for correlation between them. Finally, we use simulations to illustrate the importance of including appropriate random effects and the impact of overfitting, underfitting, and misfitting on model performance. Our approach can be used to estimate the accuracy of two imperfect diagnostic tests from a meta-analysis of multiple studies or a multicenter study when the prevalence of disease, test sensitivities and/or specificities may be heterogeneous among studies or centers.

Incorporating tumor immunohistochemical markers in BRCA1 and BRCA2 carrier prediction.

Recent studies have demonstrated that histopathologic features of inherited breast cancers due to BRCA1 gene mutations often differ from those of sporadic breast cancer and from breast cancers caused by germline BRCA2 mutations. Invasive breast carcinomas in individuals with germline BRCA1 gene mutations tend to be of higher grade, either basal-type or basal-like, estrogen receptor (ER) negative, progesterone receptor (PR) negative, HER2-Neu (ERBB-2) negative, cytokeratin 5/6 (CK5/6) positive, CK14 positive. Incorporating this information into our Mendelian risk prediction model, BRCAPRO may allow for improved estimation of BRCA1 and BRCA2 carrier risk.

Multiple Diseases in Carrier Probability Estimation: Accounting for Surviving All Cancers Other than Breast and Ovary in BRCAPRO

Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 families from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPROs concordance index from 0.758 to 0.762 (p=0.046), improves its positive predictive value from 35 to 39 per cent (p<10(-6)) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability<10 per cent.