Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants.

BACKGROUND: Branch points (BPs) map within short motifs upstream of acceptor splice sites (3'ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3'ss. RESULTS: We used a large set of constitutive and alternative human 3'ss collected from Ensembl (n = 264,787 3'ss) and from in-house RNAseq experiments (n = 51,986 3'ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3'ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. CONCLUSIONS: Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3'ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.

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.

Validation of the Clinical Use of GIScar, an Academic-developed Genomic Instability Score Predicting Sensitivity to Maintenance Olaparib for Ovarian Cancer.

PURPOSE: The optimal application of maintenance PARP inhibitor therapy for ovarian cancer requires accessible, robust, and rapid testing of homologous recombination deficiency (HRD). However, in many countries, access to HRD testing is problematic and the failure rate is high. We developed an academic HRD test to support treatment decision-making. EXPERIMENTAL DESIGN: Genomic Instability Scar (GIScar) was developed through targeted sequencing of a 127-gene panel to determine HRD status. GIScar was trained from a noninterventional study with 250 prospectively collected ovarian tumor samples. GIScar was validated on 469 DNA tumor samples from the PAOLA-1 trial evaluating maintenance olaparib for newly diagnosed ovarian cancer, and its predictive value was compared with Myriad Genetics MyChoice (MGMC). RESULTS: GIScar showed significant correlation with MGMC HRD classification (kappa statistics: 0.780). From PAOLA-1 samples, more HRD-positive tumors were identified by GIScar (258) than MGMC (242), with a lower proportion of inconclusive results (1% vs. 9%, respectively). The HRs for progression-free survival (PFS) with olaparib versus placebo were 0.45 [95% confidence interval (CI), 0.33-0.62] in GIScar-identified HRD-positive BRCA-mutated tumors, 0.50 (95% CI, 0.31-0.80) in HRD-positive BRCA-wild-type tumors, and 1.02 (95% CI, 0.74-1.40) in HRD-negative tumors. Tumors identified as HRD positive by GIScar but HRD negative by MGMC had better PFS with olaparib (HR, 0.23; 95% CI, 0.07-0.72). CONCLUSIONS: GIScar is a valuable diagnostic tool, reliably detecting HRD and predicting sensitivity to olaparib for ovarian cancer. GIScar showed high analytic concordance with MGMC test and fewer inconclusive results. GIScar is easily implemented into diagnostic laboratories with a rapid turnaround.

Two novel variants in the 3'UTR of the BRCA1 gene in familial breast and/or ovarian cancer.

For the majority of breast and/or ovarian cancer patients tested for BRCA1/2 genes, mutation screening of the coding regions remains negative. MicroRNAs which negatively regulate mRNA translation by binding to 3' untranslated region (3'UTR) are implicated in cancer. Genetic changes in the 3'UTR of several genes were reported to be associated with higher susceptibility to particular tumor types. The aim of this study was to analyze the BRCA1 3'UTR in patients tested negative for BRCA1/2 deleterious mutations, in order to find variants implicated in the decrease of BRCA1 expression through modification of miRNA binding. Genotyping analyses were performed on genomic DNA of 70 BRCA negatives index cases, selected among patients with breast or ovarian cancer, less than 50 years old, with a strong family history. The co-occurrence of the identified variants with deleterious BRCA1 mutations was then determined in a control population of 210 patients. A luciferase gene reporter assay was used to investigate the impact of the variants on the BRCA1 gene expression. Two novel variants, c.*750A>G and c.*1286C>A, were identified in the 3'UTR of BRCA1 gene, in two patients. The former was found three times in the control population, whereas the latter was absent. The used functional assay did not reveal any effect on the luciferase expression. This study reveals a weak genomic variability in the 3'UTR of the BRCA1 gene. All together, the results led us to classify the variant c.*750A>G as probably neutral, the variant c.*1286C>A remaining unclassified.

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.

BRCA1 allele-specific expression in genetic predisposed breast/ovarian cancer.

Germline allele specific expression (ASE), resulting in a lowered expression of one of the BRCA1 alleles, has been described as a possible predisposition marker in Hereditary Breast or Ovarian Cancer (HBOC), usable for molecular diagnosis in HBOC. The main objective of this prospective case-control study was to compare the proportion of ASE between controls without familial history of breast or ovarian cancer, and HBOC cases without BRCA1 or BRCA2 deleterious mutation. BRCA1 ASE evaluated on three SNPs among controls and HBOC patients without deleterious mutation were assessed by pyrosequencing. The allelic ratios and the proportion of ASE were compared between controls and cases using a Student's t test and a Fisher exact test, respectively. The linearity and reproducibility of the ASE dosage was demonstrated with R2 > 0.99 and a coefficient of variation below 10 %, and ASE was detected in two positive controls harbouring BRCA1 truncated mutations. In the heterozygote population, composed of 99/264 controls (37.5 %) and 96/227 patients (42.3 %), we detected a 5 % ASE without truncated mutations, in each population. We failed to detect any significant difference of ASE between controls and patients. So far, BRCA1 Allelic specific expression is not usable in routine diagnosis as a possible predisposition marker in HBOC patients except for the detection of truncated mutations.

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.