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Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10−8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 14% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7×10−8, HR = 1.14, 95% CI: 1.09–1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4×10−8, HR = 1.27, 95% CI: 1.17–1.38) and 4q32.3 (rs4691139, P = 3.4×10−8, HR = 1.20, 95% CI: 1.17–1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific association. The 17q21.31 locus was also associated with ovarian cancer risk in 8,211 BRCA2 carriers (P = 2×10−4). These loci may lead to an improved understanding of the etiology of breast and ovarian tumors in BRCA1 carriers. Based on the joint distribution of the known BRCA1 breast cancer risk-modifying loci, we estimated that the breast cancer lifetime risks for the 5% of BRCA1 carriers at lowest risk are 28%–50% compared to 81%–100% for the 5% at highest risk. Similarly, based on the known ovarian cancer risk-modifying loci, the 5% of BRCA1 carriers at lowest risk have an estimated lifetime risk of developing ovarian cancer of 28% or lower, whereas the 5% at highest risk will have a risk of 63% or higher. Such differences in risk may have important implications for risk prediction and clinical management for BRCA1 carriers.

To optimize the molecular diagnosis of hereditary breast and ovarian cancer (HBOC), we developed a next-generation sequencing (NGS)-based screening based on the capture of a panel of genes involved, or suspected to be involved in HBOC, on pooling of indexed DNA and on paired-end sequencing in an Illumina GAIIx platform, followed by confirmation by Sanger sequencing or MLPA/QMPSF. The bioinformatic pipeline included CASAVA, NextGENe, CNVseq and Alamut-HT. We validated this procedure by the analysis of 59 patients' DNAs harbouring SNVs, indels or large genomic rearrangements of BRCA1 or BRCA2. We also conducted a blind study in 168 patients comparing NGS versus Sanger sequencing or MLPA analyses of BRCA1 and BRCA2. All mutations detected by conventional procedures were detected by NGS. We then screened, using three different versions of the capture set, a large series of 708 consecutive patients. We detected in these patients 69 germline deleterious alterations within BRCA1 and BRCA2, and 4 TP53 mutations in 468 patients also tested for this gene. We also found 36 variations inducing either a premature codon stop or a splicing defect among other genes: 5/708 in CHEK2, 3/708 in RAD51C, 1/708 in RAD50, 7/708 in PALB2, 3/708 in MRE11A, 5/708 in ATM, 3/708 in NBS1, 1/708 in CDH1, 3/468 in MSH2, 2/468 in PMS2, 1/708 in BARD1, 1/468 in PMS1 and 1/468 in MLH3. These results demonstrate the efficiency of NGS in performing molecular diagnosis of HBOC. Detection of mutations within other genes than BRCA1 and BRCA2 highlights the genetic heterogeneity of HBOC. In BRCA1 and BRCA2 mutation carriers, the cumulative risk of breast cancer at 70 years has been estimated to 65 and 45%, respectively, and the risk of ovarian cancer to 39 and 10%, respectively. 3 The identification of a deleterious BRCA1/BRCA2 mutation within a family is crucial for the medical follow-up, as mutation carriers should be offered annual MRI or, alternatively, prophylactic mastectomy and prophylactic salpingooophorectomy. Furthermore, in a breast cancer patient, the detection of a germline BRCA1 or BRCA2 mutation may have important therapeutic consequences: complete mastectomy instead of partial mastectomy and, in the future, the prescription of specific targeted therapies, such as PARP inhibitors. 4,5 Considering the medical consequences of the identification of a germline BRCA1 or BRCA2 mutation and the frequency of mutation carriers, which has

To identify new personal biomarkers for the improved diagnosis, prognosis and biological follow-up of human papillomavirus (HPV)-associated carcinomas, we developed a generic and comprehensive Capture-HPV method followed by Next Generation Sequencing (NGS). Starting from biopsies or circulating DNA samples, this Capture-NGS approach rapidly identifies the HPV genotype, HPV status (integrated, episomal or absence), the viral-host DNA junctions and the associated genome rearrangements. This analysis of 72 cervical carcinomas identified five HPV signatures. The first two signatures contain two hybrid chromosomal–HPV junctions whose orientations are co-linear (2J-COL) or non-linear (2J-NL), revealing two modes of viral integration associated with chromosomal deletion or amplification events, respectively. The third and fourth signatures exhibit 3–12 hybrid junctions, either clustered in one locus (MJ-CL) or scattered at distinct loci (MJ-SC) while the fifth signature consists of episomal HPV genomes (EPI). Cross analyses between the HPV signatures and the clinical and virological data reveal unexpected biased representation with respect to the HPV genotype, patient age and disease outcome, suggesting functional relevance(s) of this new classification. Overall, our findings establish a facile and comprehensive rational approach for the molecular detection of any HPV-associated carcinoma and definitive personalised sequence information to develop sensitive and specific biomarkers for each patient.

Background The purpose of this study was to estimate precise age-specific tubo-ovarian carcinoma (TOC) and breast cancer (BC) risks for carriers of pathogenic variants in RAD51C and RAD51D. Methods We analyzed data from 6178 families, 125 with pathogenic variants in RAD51C, and 6690 families, 60 with pathogenic variants in RAD51D. TOC and BC relative and cumulative risks were estimated using complex segregation analysis to model the cancer inheritance patterns in families while adjusting for the mode of ascertainment of each family. All statistical tests were two-sided. Results Pathogenic variants in both RAD51C and RAD51D were associated with TOC (RAD51C: relative risk [RR] = 7.55, 95% confidence interval [CI] = 5.60 to 10.19; P = 5 × 10-40; RAD51D: RR = 7.60, 95% CI = 5.61 to 10.30; P = 5 × 10-39) and BC (RAD51C: RR = 1.99, 95% CI = 1.39 to 2.85; P = 1.55 × 10-4; RAD51D: RR = 1.83, 95% CI = 1.24 to 2.72; P = .002). For both RAD51C and RAD51D, there was a suggestion that the TOC relative risks increased with age until around age 60 years and decreased thereafter. The estimated cumulative risks of developing TOC to age 80 years were 11% (95% CI = 6% to 21%) for RAD51C and 13% (95% CI = 7% to 23%) for RAD51D pathogenic variant carriers. The estimated cumulative risks of developing BC to 80 years were 21% (95% CI = 15% to 29%) for RAD51C and 20% (95% CI = 14% to 28%) for RAD51D pathogenic variant carriers. Both TOC and BC risks for RAD51C and RAD51D pathogenic variant carriers varied by cancer family history and could be as high as 32–36% for TOC, for carriers with two first-degree relatives diagnosed with TOC, or 44–46% for BC, for carriers with two first-degree relatives diagnosed with BC. Conclusions These estimates will facilitate the genetic counseling of RAD51C and RAD51D pathogenic variant carriers and justify the incorporation of RAD51C and RAD51D into cancer risk prediction models.

It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict transcript analyses to the most appropriate cases. Web-based tools designed to provide such predictions are available. We evaluated the performance of six of these tools (Splice Site Prediction by Neural Network [NNSplice], Splice-Site Finder [SSF], MaxEntScan [MES], Automated Splice-Site Analyses [ASSA], Exonic Splicing Enhancer [ESE] Finder, and Relative Enhancer and Silencer Classification by Unanimous Enrichment [RESCUE]-ESE) using 39 unrelated retinoblastoma patients carrying different RB1 variants (31 intronic and eight exonic). These 39 patients were screened for abnormal splicing using puromycin-treated cell lines and the results were compared to the predictions. As expected, 17 variants impacting canonical AG/GT splice sites were correctly predicted as deleterious. A total of 22 variations occurring at loosely defined positions (+/-60 nucleotides from an AG/GT site) led to a splice defect in 19 cases and 16 of them were classified as deleterious by at least one tool (84% sensitivity). In other words, three variants escaped in silico detection and the remaining three were correctly predicted as neutral. Overall our results suggest that a combination of complementary in silico tools is necessary to guide molecular geneticists (balance between the time and cost required by RNA analysis and the risk of missing a deleterious mutation) because the weaknesses of one in silico tool may be overcome by the results of another tool.

Genetic analysis identifies the HMMR gene as a modifier of the breast cancer risk associated with BRCA1 gene mutation, while cell biological analysis of the protein product suggests a function in regulating development of the mammary gland.