Department of Mathematics, Statistics and Computer Science, St Olaf College, Northfield, Minnesota, USA.
Citations 24
Inherited Mutations in 17 Breast Cancer Susceptibility Genes Among a Large Triple-Negative Breast Cancer Cohort Unselected for Family History of Breast Cancer
Purpose Recent advances in DNA sequencing have led to the development of breast cancer susceptibility gene panels for germline genetic testing of patients. We assessed the frequency of mutations in 17 predisposition genes, including BRCA1 and BRCA2, in a large cohort of patients with triple-negative breast cancer (TNBC) unselected for family history of breast or ovarian cancer to determine the utility of germline genetic testing for those with TNBC. Patients and Methods Patients with TNBC (N = 1,824) unselected for family history of breast or ovarian cancer were recruited through 12 studies, and germline DNA was sequenced to identify mutations. Results Deleterious mutations were identified in 14.6% of all patients. Of these, 11.2% had mutations in the BRCA1 (8.5%) and BRCA2 (2.7%) genes. Deleterious mutations in 15 other predisposition genes were detected in 3.7% of patients, with the majority observed in genes involved in homologous recombination, including PALB2 (1.2%) and BARD1, RAD51D, RAD51C, and BRIP1 (0.3% to 0.5%). Patients with TNBC with mutations were diagnosed at an earlier age (P < .001) and had higher-grade tumors (P = .01) than those without mutations. Conclusion Deleterious mutations in predisposition genes are present at high frequency in patients with TNBC unselected for family history of cancer. Mutation prevalence estimates suggest that patients with TNBC, regardless of age at diagnosis or family history of cancer, should be considered for germline genetic testing of BRCA1 and BRCA2. Although mutations in other predisposition genes are observed among patients with TNBC, better cancer risk estimates are needed before these mutations are used for clinical risk assessment in relatives.
RVboost: RNA-seq variants prioritization using a boosting method
Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.
Murine Leukemia Virus Uses NXF1 for Nuclear Export of Spliced and Unspliced Viral Transcripts
ABSTRACT Intron-containing mRNAs are subject to restricted nuclear export in higher eukaryotes. Retroviral replication requires the nucleocytoplasmic transport of both spliced and unspliced RNA transcripts, and RNA export mechanisms of gammaretroviruses are poorly characterized. Here, we report the involvement of the nuclear export receptor NXF1/TAP in the nuclear export of gammaretroviral RNA transcripts. We identified a conserved cis -acting element in the pol gene of gammaretroviruses, including murine leukemia virus (MLV) and xenotropic murine leukemia virus (XMRV), named the CAE (cytoplasmic accumulation element). The CAE enhanced the cytoplasmic accumulation of viral RNA transcripts and the expression of viral proteins without significantly affecting the stability, splicing, or translation efficiency of the transcripts. Insertion of the CAE sequence also facilitated Rev-independent HIV Gag expression. We found that the CAE sequence interacted with NXF1, whereas disruption of NXF1 ablated CAE function. Thus, the CAE sequence mediates the cytoplasmic accumulation of gammaretroviral transcripts in an NXF1-dependent manner. Disruption of NXF1 expression impaired cytoplasmic accumulations of both spliced and unspliced RNA transcripts of XMRV and MLV, resulting in their nuclear retention or degradation. Thus, our results demonstrate that gammaretroviruses use NXF1 for the cytoplasmic accumulation of both spliced and nonspliced viral RNA transcripts. IMPORTANCE Murine leukemia virus (MLV) has been studied as one of the classic models of retrovirology. Although unspliced host messenger RNAs are rarely exported from the nucleus, MLV actively exports unspliced viral RNAs to the cytoplasm. Despite extensive studies, how MLV achieves this difficult task has remained a mystery. Here, we have studied the RNA export mechanism of MLV and found that (i) the genome contains a sequence which supports the efficient nuclear export of viral RNAs, (ii) the cellular factor NXF1 is involved in the nuclear export of both spliced and unspliced viral RNAs, and, finally, (iii) depletion of NXF1 results in nuclear retention or degradation of viral RNAs. Our study provides a novel insight into MLV nuclear export.
Novel TRAF1‐ALK fusion identified by deep RNA sequencing of anaplastic large cell lymphoma