Landscape of pathogenic variations in a panel of 34 genes and cancer risk estimation from 5131 HBOC families.

PURPOSE: Integration of gene panels in the diagnosis of hereditary breast and ovarian cancer (HBOC) requires a careful evaluation of the risk associated with pathogenic or likely pathogenic variants (PVs) detected in each gene. Here we analyzed 34 genes in 5131 suspected HBOC index cases by next-generation sequencing. METHODS: Using the Exome Aggregation Consortium data sets plus 571 individuals from the French Exome Project, we simulated the probability that an individual from the Exome Aggregation Consortium carries a PV and compared it to the estimated frequency within the HBOC population. RESULTS: Odds ratio conferred by PVs within BRCA1, BRCA2, PALB2, RAD51C, RAD51D, ATM, BRIP1, CHEK2, and MSH6 were estimated at 13.22 [10.01-17.22], 8.61 [6.78-10.82], 8.22 [4.91-13.05], 4.54 [2.55-7.48], 5.23 [1.46-13.17], 3.20 [2.14-4.53], 2.49 [1.42-3.97], 1.67 [1.18-2.27], and 2.50 [1.12-4.67], respectively. PVs within RAD51C, RAD51D, and BRIP1 were associated with ovarian cancer family history (OR = 11.36 [5.78-19.59], 12.44 [2.94-33.30] and 3.82 [1.66-7.11]). PALB2 PVs were associated with bilateral breast cancer (OR = 16.17 [5.48-34.10]) and BARD1 PVs with triple-negative breast cancer (OR = 11.27 [3.37-25.01]). Burden tests performed in both patients and the French Exome Project population confirmed the association of PVs of BRCA1, BRCA2, PALB2, and RAD51C with HBOC. CONCLUSION: Our results validate the integration of PALB2, RAD51C, and RAD51D in the diagnosis of HBOC and suggest that the other genes are involved in an oligogenic determinism.

Detecting splicing patterns in genes involved in hereditary breast and ovarian cancer.

Interpretation of variants of unknown significance (VUS) is a major challenge for laboratories performing molecular diagnosis of hereditary breast and ovarian cancer (HBOC), especially considering that many genes are now known to be involved in this syndrome. One important way these VUS can have a functional impact is through their effects on RNA splicing. Here we present a custom RNA-Seq assay plus bioinformatics and biostatistics pipeline to analyse specifically alternative and abnormal splicing junctions in 11 targeted HBOC genes. Our pipeline identified 14 new alternative splices in BRCA1 and BRCA2 in addition to detecting the majority of known alternative spliced transcripts therein. We provide here the first global splicing pattern analysis for the other nine genes, which will enable a comprehensive interpretation of splicing defects caused by VUS in HBOC. Previously known splicing alterations were consistently detected, occasionally with a more complex splicing pattern than expected. We also found that splicing in the 11 genes is similar in blood and breast tissue, supporting the utility and simplicity of blood splicing assays. Our pipeline is ready to be integrated into standard molecular diagnosis for HBOC, but it could equally be adapted for an integrative analysis of any multigene disorder.

OutLyzer: software for extracting low-allele-frequency tumor mutations from sequencing background noise in clinical practice.

Highlighting tumoral mutations is a key step in oncology for personalizing care. Considering the genetic heterogeneity in a tumor, software used for detecting mutations should clearly distinguish real tumor events of interest that could be predictive markers for personalized medicine from false positives. OutLyzer is a new variant-caller designed for the specific and sensitive detection of mutations for research and diagnostic purposes. It is based on statistic and local evaluation of sequencing background noise to highlight potential true positive variants. 130 previously genotyped patients were sequenced after enrichment by capturing the exons of 22 genes. Sequencing data were analyzed by HaplotypeCaller, LofreqStar, Varscan2 and OutLyzer. OutLyzer had the best sensitivity and specificity with a fixed limit of detection for all tools of 1% for SNVs and 2% for Indels. OutLyzer is a useful tool for detecting mutations of interest in tumors including low allele-frequency mutations, and could be adopted in standard practice for delivering targeted therapies in cancer treatment.

Genetic profiles of cervical tumors by high-throughput sequencing for personalized medical care.

Cancer treatment is facing major evolution since the advent of targeted therapies. Building genetic profiles could predict sensitivity or resistance to these therapies and highlight disease-specific abnormalities, supporting personalized patient care. In the context of biomedical research and clinical diagnosis, our laboratory has developed an oncogenic panel comprised of 226 genes and a dedicated bioinformatic pipeline to explore somatic mutations in cervical carcinomas, using high-throughput sequencing. Twenty-nine tumors were sequenced for exons within 226 genes. The automated pipeline used includes a database and a filtration system dedicated to identifying mutations of interest and excluding false positive and germline mutations. One-hundred and seventy-six total mutational events were found among the 29 tumors. Our cervical tumor mutational landscape shows that most mutations are found in PIK3CA (E545K, E542K) and KRAS (G12D, G13D) and others in FBXW7 (R465C, R505G, R479Q). Mutations have also been found in ALK (V1149L, A1266T) and EGFR (T259M). These results showed that 48% of patients display at least one deleterious mutation in genes that have been already targeted by the Food and Drug Administration approved therapies. Considering deleterious mutations, 59% of patients could be eligible for clinical trials. Sequencing hundreds of genes in a clinical context has become feasible, in terms of time and cost. In the near future, such an analysis could be a part of a battery of examinations along the diagnosis and treatment of cancer, helping to detect sensitivity or resistance to targeted therapies and allow advancements towards personalized oncology.

Next-generation sequencing for the diagnosis of hereditary breast and ovarian cancer using genomic capture targeting multiple candidate genes.

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.

Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea.

Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38-39 Mb genomes include 11,860-14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared to <1% of B. cinerea. The arsenal of genes associated with necrotrophic processes is similar between the species, including genes involved in plant cell wall degradation and oxalic acid production. Analysis of secondary metabolism gene clusters revealed an expansion in number and diversity of B. cinerea-specific secondary metabolites relative to S. sclerotiorum. The potential diversity in secondary metabolism might be involved in adaptation to specific ecological niches. Comparative genome analysis revealed the basis of differing sexual mating compatibility systems between S. sclerotiorum and B. cinerea. The organization of the mating-type loci differs, and their structures provide evidence for the evolution of heterothallism from homothallism. These data shed light on the evolutionary and mechanistic bases of the genetically complex traits of necrotrophic pathogenicity and sexual mating. This resource should facilitate the functional studies designed to better understand what makes these fungi such successful and persistent pathogens of agronomic crops.