VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

Altered regulation and expression of genes by BET family of proteins in COPD patients

Correction: PLoS One 2018 12 (4): 0175997Background BET proteins (BRD2, BRD3, BRDT and BRD4) belong to the family of bromodomain containing proteins, which form a class of transcriptional co-regulators. BET proteins bind to acetylated lysine residues in the histones of nucleosomal chromatin and function either as co-activators or co-repressors of gene expression. An imbalance between HAT and HDAC activities resulting in hyperacetylation of histones has been identified in COPD. We hypothesized that pan-BET inhibitor (JQ1) treatment of BET protein interactions with hyperacety-lated sites in the chromatin will regulate excessive activation of pro-inflammatory genes in key inflammatory drivers of alveolar macrophages (AM) in COPD. Methods and findings Transcriptome analysis of AM from COPD patients indicated up-regulation of macrophage M1 type genes upon LPS stimulation. Pan-BET inhibitor JQ1 treatment attenuated expression of multiple genes, including pro-inflammatory cytokines and regulators of innate and adaptive immune cells. We demonstrated for the first time that JQ1 differentially modulated LPS-induced cytokine release from AM or peripheral blood mononuclear cells (PBMC) of COPD patients compared to PBMC of healthy controls. Using the BET regulated gene signature, we identified a subset of COPD patients, which we propose to benefit from BET inhibition. Conclusions This work demonstrates that the effects of pan-BET inhibition through JQ1 treatment of inflammatory cells differs between COPD patients and healthy controls, and the expression of BET protein regulated genes is altered in COPD. These findings provide evidence of histone hyperacetylation as a mechanism driving chronic inflammatory changes in COPD.Peer reviewe

Abstract 4186: syngenomic fingerprint: the biomic characterization of the mouse syngeneic tumor models

The pre-clinical assessment of immuno-oncology (IO) therapies can be enabled by the use of murine syngeneic tumors established in immuno-competent mice. With the aims of selecting relevant models and of minimizing animal experimentation by reducing the number of models tested, the full characterisation of syngeneic models at the transcriptomic and genomic level is a key objective for pre-clinical scientists. Model characterisation includes global aCGH, exon array analysis and FACS profiling alongside exome sequencing. The model data is undergoing hypothesis free and driven analyses which are already generating valuable insights. Comparison of in vivo tumor samples with their in vitro equivalents has highlighted enrichment for a number of immune pathways; as has the comparison of different tumor lines. The genomic, transcriptomic and ‘proteomic’ model data are being integrated to give a functional output which will act as a ‘Syngenomic Fingerprint’ for each model. The resulting Syngenomic fingerprints will help pre-clinical scientists to refine their in vivo plans through an improved understanding of the limits and advantages as well as the clinical relevance of some of our preclinical models. It is also supporting the targeted modification of models to better match specific human cancer types

An evaluation of the challenges to developing tumor BRCA1 and BRCA2 testing methodologies for clinical practice

Ovarian cancer patients with germline or somatic pathogenic variants benefit from treatment with poly ADP ribose polymerase (PARP) inhibitors. Tumor BRCA1/2 testing is more challenging than germline testing as the majority of samples are formalin-fixed paraffin embedded (FFPE), the tumor genome is complex, and the allelic fraction of somatic variants can be low. We collaborated with 10 laboratories testing BRCA1/2 in tumors to compare different approaches to identify clinically important variants within FFPE tumor DNA samples. This was not a proficiency study but an inter-laboratory comparison to identify common issues. Each laboratory received the same tumor DNA samples ranging in genotype, quantity, quality, and variant allele frequency (VAF). Each laboratory performed their preferred next-generation sequencing method to report on the variants. No false positive results were reported in this small study and the majority of methods detected the low VAF variants. A number of variants were not detected due to the bioinformatics analysis, variant classification, or insufficient DNA. The use of hybridization capture or short amplicon methods are recommended based on a bioinformatic assessment of the data. The study highlights the importance of establishing standards and standardization for tBRCA testing particularly when the test results dictate clinical decisions regarding life extending therapies

bcbio/bcbio-nextgen:

Fix vcf header bug: T/N SAMPLE lines are back - needed for import to SolveBio add strandedness: auto for -l A option in salmon report 10x more peaks in CHIP/ATAC-seq - use 0.05 qvalue fix misleading RNA-seq duplicated reads statistics: thanks @sib-bcf reorganize conda environments snpEff 5.0 strandedness: auto document WGBS pipeline steps make --local an option, not default in bismark alignment - too slow bcbioRNASeq update to 0.3.44 pureCN update to 2.0.1 octopus update to 0.7.