BRCA1 Variant Confers Intermediate Cancer Risk

Methods

Confirmation of Transcriptional Transactivation Activity

Using methods previously described, we first compared transcriptional transactivation activity of BRCA1 R1699Q in the 293T cell line with that of pathogenic variant, R1699W, at the same residue, and also to pathogenic control, A1708E, and confirmed our original findings that this variant displayed intermediate function compared with wild-type sequence and known pathogenic TAD variants (see supplementary figure S2).

Genetic Analyses

With ethical approval from the relevant institutional review boards, we then initiated large-scale genetic studies to assess if this intermediate function might translate to the risk of breast and ovarian cancer in families. Informed consent was obtained from all participants. Through collaboration facilitated in part by the ENIGMA consortium, we ascertained sufficient information from multiple clinical cancer genetics centres around the world (Table 1) to compare family history and risk profiles of families in which the R1699Q variant had been identified, with families with the known pathogenic mutation R1699W at the same residue. For an additional reference group, we also collected a set of pedigrees that had been clinically tested for BRCA1 and BRCA2 mutations from the same centres within the same time frame as the R1699Q and R1699W families, but for which no pathogenic mutation or any other unclassified variant had been found (BRCA-X). The time frame was determined by the centres to ensure that a similar criterion for testing was used. The proband in each instance was defined as the individual initially screened for BRCA1/2 mutations.

Family History Analysis

As a measure of how each family fit the characteristics of a BRCA1 mutation-positive family, we used the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk-prediction algorithm to calculate the probability that the proband from each family was a carrier of a BRCA1 mutation based on the pedigree structure and the phenotypes of individuals in the pedigree. BOADICEA uses a full pedigree likelihood approach, and incorporates ages at diagnosis of breast and ovarian cancer, presence of pancreatic and prostate cancer, the age at last follow-up for unaffected individuals, and the year of birth to account for cohort effects in penetrance. The model estimates the simultaneous effects of the high-risk genes BRCA1 and BRCA2 using the age-specific penetrance estimates derived from 22 population-based studies, while allowing for unknown genetic effects that explain the residual familial clustering of breast cancer. The residual familial clustering is explained by a polygenic component with variance that decreases linearly with age.

The estimated probabilities of the proband carrying a pathogenic BRCA1 mutation based on the BOADICEA prediction model, B_i, were then transformed in order to better fit a Gaussian distribution using a logit transformation b_i=logit(B_i)=ln(B_i/(1−B_i), so that standard statistical methods could be used. For each centre that contributed R1699Q/W families, we calculated the mean and SD of the probabilities calculated for the BRCA-X families from this centre. This distribution was used to create z-scores as Z_ij=(b_ij−Xj)/S_j, where b_ij is the logit of the BOADICEA-predicted probability of a BRCA1 mutation in the ith BRCA-X family in the jth centre, X_j and S_j are the sample mean and SD of the logit-transformed B_i from the jth centre. For families with the sequence variants of interest, R1699Q and R1699W, these Z_ij thus represent the position of family histories of probands carrying an R1699Q or R1699W variant within the distribution of families tested negative for BRCA1/2 mutations in the same centres and time frame. Letting Z_Qi be the standardised logit score of the ith R1699Q family- and Z_Wi represent the corresponding score for the ith R1699W family, and assuming further that the Z_Qi and Z_Wi are Normally distributed with means μ_Q and μ_W and variances σ _Q and σ _W respectively, these scores can then be used to test the following two hypotheses:

1. 
   The family histories of R1699Q probands are more BRCA1-like than those of matched BRCA-X. That is, we test the null hypothesis μ_Q=0 versus the alternative μ_Q>0 with a one-sample t test. Rejection of the null hypothesis indicates that the R1699Q families have proband/family histories more compatible with a pathogenic BRCA1 mutation than the centre-matched BRCA-X families.
   
2. 
   The family histories of R1699Q are less 'BRCA1-like' than those of R1699W mutations. This is tested by a two-sample t test of the null hypothesis μ_Q=μ_W against the one-sided alternative μ_Q<μ_W.
   

If both these null hypotheses are rejected, this indicates that R1699Q variants are, in some sense, intermediate in terms of their BRCA1 family history profile compared with BRCA-X and BRCA1 R1699W families.

Segregation Analyses

Risk was analysed more directly through analysis of cosegregation of the R1699Q/W genotypes in the relatives of probands presenting with R1699Q/W variants. Analyses included 30 R1699Q informative families with 111 total tested individuals and 19 R1699W families with 80 tested individuals. Risks were estimated by examining the likelihood of the genotypes of the family members (both, women affected with breast or ovarian cancer, and healthy women) as a function of BRCA1 penetrance, conditional on the proband's genotype and all pedigree phenotypes. The conditioning is needed to account for the fact that families were ascertained on the basis of the cancer phenotypes in the entire family, and the fact that the proband carried the variant. In this situation, most information about penetrance derives from the distribution of variant genotypes among unaffected women. Because there was insufficient additional genotyping in these families to reliably estimate age-specific risk ratios for each age group, we examined the risk associated with the R1699Q/W variants relative to those associated with the 'average pathogenic BRCA1 mutation', as found in much larger studies of predominantly truncating mutations. In these analyses, the age-specific HR (by decade) was assumed to be a constant multiple of the estimate of Antoniou et al, with cumulative penetrances re-estimated at each trial value of the multiplier. This allowed for a similar pattern of age-specific effects, as in BRCA1, but only required estimation of a single parameter. We also repeated the analyses allowing for separate penetrance multipliers for breast cancer and ovarian cancer to allow for the possibility that the functional effects of R1699Q or R1699W might be more relevant to cancer risk for one but not both these cancers. We varied the multiplier of the assumed standard penetrance of BRCA1 from 0.05 to 2, in increments of 0.01, in order to find the value that maximised the likelihood of the observed data (and to obtain CIs). If under a particular model, a given value of the penetrance implied risks of cancer in carriers lower in a given age group than in non-carriers, these were constrained to be the same as the non-carrier rates.

The analysis of penetrance was done using the LINKAGE package of programs to calculate pedigree likelihoods, and the other statistical analyses were performed using STATA V.11.0 (StatCorp, College Station, Texas, USA).