Estela Carrasco

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.

PURPOSE Young women with germline BRCA mutations have unique reproductive challenges. Pregnancy after breast cancer does not increase the risk of recurrence; however, very limited data are available in patients with BRCA mutations. This study investigated the impact of pregnancy on breast cancer outcomes in patients with germline BRCA mutations. PATIENTS AND METHODS This is an international, multicenter, hospital-based, retrospective cohort study. Eligible patients were diagnosed between January 2000 and December 2012 with invasive early breast cancer at age ≤ 40 years and harbored deleterious germline BRCA mutations. Primary end points were pregnancy rate, and disease-free survival (DFS) between patients with and without a pregnancy after breast cancer. Pregnancy outcomes and overall survival (OS) were secondary end points. Survival analyses were adjusted for guarantee-time bias controlling for known prognostic factors. RESULTS Of 1,252 patients with germline BRCA mutations ( BRCA1, 811 patients; BRCA2, 430 patients; BRCA1/2, 11 patients) included, 195 had at least 1 pregnancy after breast cancer (pregnancy rate at 10 years, 19%; 95% CI, 17% to 22%). Induced abortions and miscarriages occurred in 16 (8.2%) and 20 (10.3%) patients, respectively. Among the 150 patients who gave birth (76.9%; 170 babies), pregnancy complications and congenital anomalies occurred in 13 (11.6%) and 2 (1.8%) cases, respectively. Median follow-up from breast cancer diagnosis was 8.3 years. No differences in DFS (adjusted hazard ratio [HR], 0.87; 95% CI, 0.61 to 1.23; P = .41) or OS (adjusted HR, 0.88; 95% CI, 0.50 to 1.56; P = .66) were observed between the pregnancy and nonpregnancy cohorts. CONCLUSION Pregnancy after breast cancer in patients with germline BRCA mutations is safe without apparent worsening of maternal prognosis and is associated with favorable fetal outcomes. These results provide reassurance to patients with BRCA-mutated breast cancer interested in future fertility.

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon-intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11 and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy and NPV (99.44%, 99.44% and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65% and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high negative predictive value obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors.