Using age of onset to distinguish between subforms of breast cancer

Frequency of Pathogenic Germline Variants in CDH1, BRCA2, CHEK2, PALB2, BRCA1, and TP53 in Sporadic Lobular Breast Cancer

BACKGROUND

Invasive lobular breast cancer (ILC) accounts for approximately 15% of invasive breast carcinomas and is commonly associated with lobular carcinoma in situ (LCIS). Both have been shown to have higher familial risks than the more common ductal cancers. However, there are little data on the prevalence of the known high and moderate penetrance breast cancer predisposition genes in ILC. The aim of this study was to assess the frequency of germline variants in CDH1, BRCA2, BRCA1, CHEK2, PALB2, and TP53 in sporadic ILC and LCIS diagnosed in women ages ≤60 years.

METHODS

Access Array technology (Fluidigm) was used to amplify all exons of CDH1, BRCA2, BRCA1, TP53, CHEK2, and PALB2 using a custom-made targeted sequencing panel in 1,434 cases of ILC and 368 cases of pure LCIS together with 1,611 controls.

RESULTS

Case-control analysis revealed an excess of pathogenic variants in BRCA2, CHEK2, PALB2, and CDH1 in women with ILC. CHEK2 was the only gene that showed an association with pure LCIS [OR = 9.90; 95% confidence interval (CI), 3.42-28.66, P = 1.4 × 10^-5] with a larger effect size seen in LCIS compared with ILC (OR = 4.31; 95% CI, 1.61-11.58, P = 1.7 × 10^-3).

CONCLUSIONS

Eleven percent of patients with ILC ages ≤40 years carried germline variants in known breast cancer susceptibility genes.

IMPACT

Women with ILC ages ≤40 years should be offered genetic screening using a panel of genes that includes BRCA2, CHEK2, PALB2, and CDH1.

Study of breast cancer incidence in patients of lymphangioleiomyomatosis

Molecular evidence has linked the pathophysiology of lymphangioleiomyomatosis (LAM) to that of metastatic breast cancer. Following on this observation, we assessed the association between LAM and subsequent breast cancer. An epidemiological study was carried out using three LAM country cohorts, from Japan, Spain, and the United Kingdom. The number of incident breast cancer cases observed in these cohorts was compared with the number expected on the basis of the country-specific incidence rates for the period 2000–2014. Immunohistochemical studies and exome sequence analysis were performed in two and one tumors, respectively. All cohorts revealed breast cancer standardized incidence ratios (SIRs) ≥ 2.25. The combined analysis of all cases or restricted to pre-menopausal age groups revealed significantly higher incidence of breast cancer: SIR = 2.81, 95 % confidence interval (CI) = 1.32–5.57, P = 0.009; and SIR = 4.88, 95 % CI = 2.29–9.99, P = 0.0007, respectively. Immunohistochemical analyses showed positivity for known markers of lung metastatic potential. This study suggests the existence of increased breast cancer risk among LAM patients. Prospective studies may be warranted to corroborate this result, which may be particularly relevant for pre-menopausal women with LAM.

Exploring the link between germline and somatic genetic alterations in breast carcinogenesis

Recent genome-wide association studies (GWASs) have identified candidate genes contributing to cancer risk through low-penetrance mutations. Many of these genes were unexpected and, intriguingly, included well-known players in carcinogenesis at the somatic level. To assess the hypothesis of a germline-somatic link in carcinogenesis, we evaluated the distribution of somatic gene labels within the ordered results of a breast cancer risk GWAS. This analysis suggested frequent influence on risk of genetic variation in loci encoding for "driver kinases" (i.e., kinases encoded by genes that showed higher somatic mutation rates than expected by chance and, therefore, whose deregulation may contribute to cancer development and/or progression). Assessment of these predictions using a population-based case-control study in Poland replicated the association for rs3732568 in EPHB1 (odds ratio (OR) = 0.79; 95% confidence interval (CI): 0.63-0.98; P(trend) = 0.031). Analyses by early age at diagnosis and by estrogen receptor α (ERα) tumor status indicated potential associations for rs6852678 in CDKL2 (OR = 0.32, 95% CI: 0.10-1.00; P(recessive) = 0.044) and rs10878640 in DYRK2 (OR = 2.39, 95% CI: 1.32-4.30; P(dominant) = 0.003), and for rs12765929, rs9836340, rs4707795 in BMPR1A, EPHA3 and EPHA7, respectively (ERα tumor status P(interaction)<0.05). The identification of three novel candidates as EPH receptor genes might indicate a link between perturbed compartmentalization of early neoplastic lesions and breast cancer risk and progression. Together, these data may lay the foundations for replication in additional populations and could potentially increase our knowledge of the underlying molecular mechanisms of breast carcinogenesis.

Network modeling links breast cancer susceptibility and centrosome dysfunction

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

Multivariate logistic regression for familial aggregation in age at disease onset

Familial aggregation studies seek to identify diseases that cluster in families. These studies are often carried out as a first step in the search for hereditary factors affecting the risk of disease. It is necessary to account for age at disease onset to avoid potential misclassification of family members who are disease-free at the time of study participation or who die before developing disease. This is especially true for late-onset diseases, such as prostate cancer or Alzheimer's disease. We propose a discrete time model that accounts for the age at disease onset and allows the familial association to vary with age and to be modified by covariates, such as pedigree relationship. The parameters of the model have interpretations as conditional log-odds and log-odds ratios, which can be viewed as discrete time conditional cross hazard ratios. These interpretations are appealing for cancer risk assessment. Properties of this model are explored in simulation studies, and the method is applied to a large family study of cancer conducted by the National Cancer Institute-sponsored Cancer Genetics Network (CGN).

Mixture cure model with an application to interval mapping of quantitative trait loci

When censored time-to-event data are used to map quantitative trait loci (QTL), the existence of nonsusceptible subjects entails extra challenges. If the heterogeneous susceptibility is ignored or inappropriately handled, we may either fail to detect the responsible genetic factors or find spuriously significant locations. In this article, an interval mapping method based on parametric mixture cure models is proposed, which takes into consideration of nonsusceptible subjects. The proposed model can be used to detect the QTL that are responsible for differential susceptibility and/or time-to-event trait distribution. In particular, we propose a likelihood-based testing procedure with genome-wide significance levels calculated using a resampling method. The performance of the proposed method and the importance of considering the heterogeneous susceptibility are demonstrated by simulation studies and an application to survival data from an experiment on mice infected with Listeria monocytogenes.

Semiparametric variance-component models for linkage and association analyses of censored trait data

Variance-component (VC) models are widely used for linkage and association mapping of quantitative trait loci in general human pedigrees. Traditional VC methods assume that the trait values within a family follow a multivariate normal distribution and are fully observed. These assumptions are violated if the trait data contain censored observations. When the trait pertains to age at onset of disease, censoring is inevitable because of loss to follow-up and limited study duration. Censoring also arises when the trait assay cannot detect values below (or above) certain thresholds. The latent trait values tend to have a complex distribution. Applying traditional VC methods to censored trait data would inflate type I error and reduce power. We present valid and powerful methods for the linkage and association analyses of censored trait data. Our methods are based on a novel class of semiparametric VC models, which allows an arbitrary distribution for the latent trait values. We construct appropriate likelihood for the observed data, which may contain left or right censored observations. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We develop stable and efficient numerical algorithms to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed methods outperform the existing ones in practical situations. We provide an application to the age at onset of alcohol dependence data from the Collaborative Study on the Genetics of Alcoholism. A computer program is freely available.

The role of GABRA2 in risk for conduct disorder and alcohol and drug dependence across developmental stages

We use findings from the behavior genetics literature about how genetic factors (latently) influence alcohol dependence and related disorders to develop and test hypotheses about the risk associated with a specific gene, GABRA2, across different developmental stages. This gene has previously been associated with adult alcohol dependence in the Collaborative Study of the Genetics of Alcoholism (COGA) sample [Edenberg, H. J., Dick, D. M., Xuei, X., Tian, H., Almasy, L., Bauer, L. O., Crowe, R., Goate, A., Hesselbrock, V., Jones, K. A., Kwon, J., Li, T. K., Nurnberger Jr., J. I., O'Connor, S. J., Reich, T., Rice, J., Schuckit, M., Porjesz, B., Foroud, T., and Begleiter, H. (2004). Am. J. Hum. Genet. 74:705-714] and other studies [Covault, J., Gelernter, J., Hesselbrock, V., Nellissery, M., and Kranzler, H. R. (2004). Am. J. Med. Genet. B Neuropsychiatr. Genet. 129B:104-109; Lappalainen, J., Krupitsky, E., Remizov, M., Pchelina, S., Taraskina, A., Zvartau, E., Somberg, L. K., Covault, J., Kranzler, H. R., Krystal, J., and Gelernter, J. (2005). Alcohol. Clin. Exp. Res. 29:493-498]. In a sample of children and adolescents ascertained as part of the COGA project, we find that GABRA2 is significantly associated with childhood conduct disorder symptoms, but not with childhood alcohol dependence symptoms. A consistent elevation in risk for alcohol dependence associated with GABRA2 is not evident until the mid-20s and then remains throughout adulthood. GABRA2 is also associated with other drug dependence in our sample, both in adolescence and adulthood.

Random-effects Cox proportional hazards model: general variance components methods for time-to-event data

Proportional hazards regression models are commonly used to study factors associated with time-to-event data. Because many complex genetic diseases exhibit variation in age at onset, it is important to have the capability to perform survival analyses on data collected from individuals whose observations are correlated due to shared genes or environment. While there are widely accepted methods for variance components analysis for simple quantitative traits, a parallel methodology for survival data has not been available. This manuscript outlines a method to perform variance component analyses under general random effects proportional hazards models. This method is based on a Laplace approximation, and makes computation for correlated time-to-event data feasible. The correlated frailty models described here can be used to perform genetic analyses, and other analyses with structured random effects, on age-at-onset data in a manner analogous to standard variance components methods for quantitative traits. We illustrate the use of the method by examining the heritability of breast cancer in a large familial cohort study. We also perform variance components linkage analyses on data simulated for the Twelfth Genetic Analysis Workshop (GAW12), and further examine the performance of this method for linkage analysis in a simulation study. The breast cancer analyses support significant heritability of disease age-at-onset that is of moderate size. The variance component linkage analyses successfully identify the location of the disease genes that were simulated to have a direct impact on age-at-onset. The methods outlined here make it possible to perform general variance components analyses on time-to-event endpoints, even on large data sets, in a computationally efficient manner.

Practical application of residuals from survival models in quantitative trait linkage analysis

A number of familial diseases have an age-of-onset component, which can be considered as a censored quantitative trait. However, few software resources are available for the use of time-to-event endpoints in linkage analysis. The purpose of this analysis was to examine the use of martingale residuals from Cox survival models as quantitative traits for familial diseases with variable age at onset. We used these residuals as quantitative traits in variance components linkage scans for the 50 replicates of the general population simulated data for chromosomes 6 and 7. The region on chromosome 6 containing markers D06G034 and D06G035 demonstrated evidence for linkage, consistent with the underlying genetic model. This analysis demonstrates the applicability of using martingale residuals as a quantitative trait in linkage analyses of diseases that depend on age of onset.

Mixture models for linkage analysis of affected sibling pairs and covariates

To determine the genetic etiology of complex diseases, a common study design is to recruit affected sib/relative pairs (ASP/ARP) and evaluate their genome-wide distribution of identical by descent (IBD) sharing using a set of highly polymorphic markers. Other attributes or environmental exposures of the ASP/ARP, which are thought to affect liability to disease, are sometimes collected. Conceivably, these covariates could refine the linkage analysis. Most published methods for ASP/ARP linkage with covariates can be conceptualized as logistic models in which IBD status of the ASP is predicted by pair-specific covariates. We develop a different approach to the problem of ASP analysis in the presence of covariates, one that extends naturally to ARP under certain conditions. For ASP linkage analysis, we formulate a mixture model in which a disease mutation is segregating in only a fraction alpha of the sibships, with 1 - alpha sibships being unlinked. Covariate information is used to predict membership within groups; in this report, the two groups correspond to the linked and unlinked sibships. For an ASP with covariate(s) Z = z and multilocus genotype X = x, the mixture model is alpha(z)g(x; lambda) + [1 - alpha(z)]g(0)(x), in which g(0)(x) follows the distribution of genotypes under the null IBD distribution and g(x; lambda) allows for increased IBD sharing. Two mixture models are developed. The pre-clustering model uses covariate information to form probabilistic clusters and then tests for excess IBD sharing independent of the covariates. The Cov-IBD model determines probabilistic group membership by joint consideration of covariate and IBD values. Simulations show that incorporating covariates into linkage analysis can enhance power substantially. A feature of our conceptualization of ASP linkage analysis, with covariates, is that it is apparent how data analysis might evaluate covariates prior to the linkage analysis, thus avoiding the loss of power described by Leal and Ott [2000] when data are stratified.

Semiparametric estimation of major gene effects for age of onset

Analysis of age of onset is a key factor in the segregation and linkage analysis of some complex genetic traits. Previous work in the genetics literature has used parametric distributional assumptions on age of onset. In this paper, a Cox model with latent major gene effects is used: a semiparametric model with unspecified baseline hazard. A Monte Carlo EM procedure is used to obtain maximum likelihood estimates. Markov chain Monte Carlo is used to realize genotypic configurations from the posterior distribution given the current model and the observed data, and these genotypic configurations are used to estimate the expectations in the EM algorithm. Simulated data sets indicate that the parameters can be estimated well, and one real data set shows the practical applicability of the proposed method.

Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history

BACKGROUND

Heritable mutations of the breast cancer gene BRCA1 are rare, occurring in fewer than 1% of women in the general population, and therefore account for a small proportion of cases of breast and ovarian cancers. Nevertheless, the presence of such mutations is highly predictive of the development of these cancers.

PURPOSE

We developed and applied a mathematic model for calculating the probability that a woman with a family history of breast and/or ovarian cancer carries a mutation of BRCA1.

METHODS AND RESULTS

As a basis for the model, we use Mendelian genetics and apply Bayes' theorem to information on the family history of these diseases. Of importance are the exact relationships of all family members, including both affected and unaffected members, and ages at diagnosis of the affected members and current ages of the unaffected members. We used available estimates of BRCA1 mutation frequencies in the general population and age-specific incidence rates of breast and ovarian cancers in carriers and noncarriers of mutations to estimate the probability that a particular member of the family carries a mutation. This probability is based on cancer statuses of all first- and second-degree relatives. We first describe the model by considering single individuals: a woman diagnosed with breast and/or ovarian cancer and also a woman free of cancer. We next considered two artificial and two actual family histories and addressed the sensitivity of our calculations to various assumptions. Particular relationships of family members with and without cancer can have a substantial impact on the probability of carrying a susceptibility gene. Ages at diagnosis of affected family members and their types of cancer are also important. A woman with two primary cancers can have a probability of carrying a mutation in excess of 80%, even with no other information about family history. The number and relationships of unaffected members, along with their current ages or ages at death, are critical determinants of one's carrier probability. An affected woman with several cancers in her family can have a probability of carrying a mutation that ranges from close to 100% to less than 5%.

CONCLUSION

Our model gives informative and specific probabilities that a particular woman carries a mutation.

IMPLICATIONS

This model focuses on mutations in BRCA1 and assumes that all other breast cancer is sporadic. With the cloning of BRCA2, we now know that this assumption is incorrect. We have adjusted the model to include BRCA2, but the use of this version must await publication of penetrance data for BRCA2, including those for male breast cancer that are apparently associated with BRCA2 but not with BRCA1. The current model is, nevertheless, appropriate and useful. Of principal importance is its potential and that of improved versions for aiding women and their health care providers in assessing the need for genetic testing.

The genetics of breast and ovarian cancer

A number of genes are known to be involved in inherited susceptibility to breast and/or ovarian cancer. In the context of high-risk families the most important genes are BRCA1 on chromosome 17q, which is associated with a high penetrance of both breast and ovarian cancer, and BRCA2 on chromosome 13q, which causes a high risk of breast cancer but a lower risk of ovarian cancer. Other high-risk cancer genes that confer increased risks of breast or ovarian cancer in addition to other cancers include the hereditary non-polyposis colorectal cancer genes and the TP53 gene, which causes breast cancer as part of the Li-Fraumeni syndrome. The predisposing mutations in these genes are relatively rare in the population. More common genes which are associated with an increased, but lower, risk of breast cancer are the ataxiatelangiectasia gene and the HRAS1 gene. This paper reviews recent progress in mapping and cloning of these susceptibility genes, and provides estimates of the cancer risks associated with each gene and the frequency of predisposing mutations.

Cancer risks in A-T heterozygotes

It is well established that ataxia-telangiectasia (A-T) patients suffer a grossly elevated risk of cancer, particularly lymphoma and leukaemia, but the possibility of an excess cancer risk of cancer in heterozygotes carriers of A-T mutations is more controversial. A number of studies indicate that female relatives of A-T patients suffer excess risk of breast cancer; based on an overview of all currently available data the estimated relative risk of breast cancer to A-T heterozygotes is 3.9-fold (95% CI 2.1-7.2). There is some suggestion that relative risk declines with age. In contrast, there is no consistent evidence of a risk from any other cancer; the estimated risk from all studies is 1.9 (95% CI 1.5-2.5) but some studies show a larger effect whilst others show no excess risk. On the basis of these results and the likely frequency of the A-T gene, A-T heterozygotes would account for between 1 and 13% of breast cancer cases, with 3.8% being the best estimate. However, unless the breast cancer risk has been seriously underestimated, the A-T gene will make little contribution to familial breast cancer.

Genetic epidemiology of breast cancer

It has been recognized for some time that a family history of breast cancer is associated rather strongly with a woman's own risk of developing the disease. Recent segregation analyses of population-based data on familial patterns provide evidence for a rare autosomal dominant allele that increases a carrier's susceptibility to breast cancer. The estimated proportion of breast cancer patients who carry this allele declines sharply with age at diagnosis. Empirical estimates of the risk associated with particular patterns of family history of breast cancer indicate the following: (1) having any first-degree relative with breast cancer increases a woman's risk of breast cancer 1.5-3-fold, depending on age, (2) having multiple first degree relatives affected is associated with particularly elevated risks, (3) having a second-degree relative affected increases the risk by approximately 50%, (4) affected family members on the maternal side and the paternal side contribute similarly to the risk, (5) a family history of breast cancer is associated with bilateral disease, and (6) breast cancer in males is associated with breast cancer in female relatives in much the same way as is breast cancer in women. Ovarian cancer clearly has been shown to be associated with breast cancer in families, and genetic linkage has provided strong evidence for a breast-ovarian cancer gene located somewhere on chromosome 17q. At the population level, having a first degree relative with ovarian cancer may be at least as predictive of a woman's risk for developing breast cancer as is having a second-degree relative with breast cancer. Considerably weaker evidence points to a possible familial relationship between breast and endometrial cancer and between breast cancer in women and prostatic cancer in males. The clinical applications of the genetic epidemiology of breast cancer are complicated by uncertainty as to the efficacy of mammographic screening in women under the age of 50. For the vast majority of women with a positive family history, the epidemiologic evidence does provide the basis for offering considerable reassurance in that risks are not extremely high. For that rather small subgroup at exceptionally high risk, realistic estimates of the magnitude of absolute risk over the next 10-20 years may be more informative and less alarming than lifetime probabilities.

Screening and genetic counselling for relatives of patients with breast cancer in a family cancer clinic

Family history is the major risk factor in the aetiology of breast cancer. Breast screening is currently available to women from the age of 50 to 64 through the National Breast Screening Programme. There is, however, an equivalent risk of developing breast cancer below 50 for first degree relatives of women diagnosed with breast cancer premenopausally. We have estimated the risk of breast cancer for relatives of women affected at different ages and used these to establish a family cancer clinic offering breast screening based on individual risk. In three years we have seen 851 patients. Compliance for annual radiology was in excess of 83% over this period and of five cancers detected one had a lump at presentation, two developed interval breast lumps, and two were asymptomatic.