Detection of EGFR Mutation Distribution and Transcriptional Variants in IDH-Wildtype High-Grade Gliomas Using a Next-Generation Sequencing Oncopanel

\Purpose: To detect the epidermal growth factor receptor gene (EGFR) mutation profile and transcriptional variants in high-grade gliomas (HGGs), we sequenced EGFR and evaluated the EGFR splicing profile using a next-generation sequencing (NGS) oncopanel. Methods: We analyzed 124 HGGs—10 grade Ⅲ IDH-wildtype anaplastic astrocytomas (AAs) and 114 grade Ⅳ IDH-wildtype glioblastomas (GBMs). Results: The EGFR mutations were observed in 6.0% of grade Ⅳ GBMs and in 33% of grade Ⅲ AAs. Four cases harbored missense mutations in the EGFR kinase domain (L747A, S768I, V774M, and T790M). A total of 25% of the GBMs showed EGFR amplification. Moreover, 27% of the EGFR mutations occurred in the kinase domain. EGFRvⅢ positivity was detected in 8.0% of EGFR-amplified GBMs. We identified two other EGFR variants in GBM cases with deletions of exons 6–7 (Δe 6-7) (one case) and exons 2–14 (Δe 2-14) (two cases). Interestingly, in one case, the initial EGFRvIII mutation transformed into an EGFR Δe 2-14 mutation during recurrence. The frequency of EGFR alterations in our cohort was lower but the frequency of EGFR mutations in the kinase domain in our cohort was higher than that in The Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center cohorts. Conclusions: We suggested that the EGFR gene profiles of GBM differ among cohorts and identified rare EGFR variants with longitudinal and temporal transformations of EGFRvⅢ.

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types.

Detection of EGFR Mutation Distribution and Transcriptional Variants in IDH-wildtype High-grade Gliomas using a Next-generation Sequencing Oncopanel

Purpose: To detect the epidermal growth factor receptor gene (EGFR) mutation profile and transcriptional variants in high-grade gliomas (HGGs), we sequenced EGFR and evaluated the EGFR splicing profile using a next-generation sequencing (NGS) oncopanel. Methods: We analyzed 124 HGGs—10 grade Ⅲ IDH-wildtype anaplastic astrocytomas (AAs) and 114 grade Ⅳ IDH-wildtype glioblastomas (GBMs). Results: The EGFR mutations were observed in 6.0% of grade Ⅳ GBMs and in 33% of grade Ⅲ AAs. Four cases harbored missense mutations in the EGFR kinase domain (L747A, S768I, V774M, and T790M). A total of 25% of the GBMs showed EGFR amplification. Moreover, 27% of the EGFR mutations occurred in the kinase domain. EGFRvⅢ positivity was detected in 8.0% of EGFR-amplified GBMs. We identified two other EGFR variants in GBM cases with deletions of exons 6–7 (Δe 6-7) (one case) and exons 2–14 (Δe 2-14) (two cases). Interestingly, in one case, the initial EGFRvIII mutation transformed into an EGFR Δe 2-14 mutation during recurrence. The frequency of EGFR alterations in our cohort was lower but the frequency of EGFR mutations in the kinase domain in our cohort was higher than that in The Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center cohorts. Conclusions: We suggested that the EGFR gene profiles of GBM differ among cohorts and identified rare EGFR variants with longitudinal and temporal transformations of EGFRvⅢ.

MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Genomic rearrangements can modify gene function by altering transcript sequences, and have been shown to be drivers in both cancer and rare diseases. Although there are now many methods to detect structural variants from Whole Genome Sequencing (WGS), RNA sequencing (RNA-seq) remains under-utilised as a technology for the detection of gene altering structural variants. Calling fusion genes from RNA-seq data is well established, but other transcriptional variants such as fusions with novel sequence, tandem duplications, large insertions and deletions, and novel splicing are difficult to detect using existing approaches.To identify all types of variants in transcriptomes, we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference free approach, which combines de novo assembly of transcripts with differential expression analysis, to identify up-regulated novel variants in a case sample.We validated MINTIE on simulated and real data sets and compared it with eight other approaches for finding novel transcriptional variants. We found MINTIE was able to detect all defined variant classes at high rates (>70%) while no other method was able to achieve this.We applied MINTIE to RNA-seq data from a cohort of acute lymphoblastic leukemia (ALL) patient samples and identified several novel clinically relevant variants, including an unpartnered recurrent fusion involving the tumour suppressor gene RB1, and variants in ALL-associated genes: tandem duplications in IKZF1 and PAX5, and novel splicing in ETV6. We further demonstrate the utility of MINTIE to identify rare disease variants using RNA-seq, including the discovery of an inter-chromosomal translocation in the DMD gene in a patient with muscular dystrophy. We posit that MINTIE will be able to identify new disease variants across a range of cancers and other disease types.

ArtiFuse—computational validation of fusion gene detection tools without relying on simulated reads

Motivation Gene fusions are an important class of transcriptional variants that can influence cancer development and can be predicted from RNA sequencing (RNA-seq) data by multiple existing tools. However, the real-world performance of these tools is unclear due to the lack of known positive and negative events, especially with regard to fusion genes in individual samples. Often simulated reads are used, but these cannot account for all technical biases in RNA-seq data generated from real samples. Results Here, we present ArtiFuse, a novel approach that simulates fusion genes by sequence modification to the genomic reference, and therefore, can be applied to any RNA-seq dataset without the need for any simulated reads. We demonstrate our approach on eight RNA-seq datasets for three fusion gene prediction tools: average recall values peak for all three tools between 0.4 and 0.56 for high-quality and high-coverage datasets. As ArtiFuse affords total control over involved genes and breakpoint position, we also assessed performance with regard to gene-related properties, showing a drop-in recall value for low-expressed genes in high-coverage samples and genes with co-expressed paralogues. Overall tool performance assessed from ArtiFusions is lower compared to previously reported estimates on simulated reads. Due to the use of real RNA-seq datasets, we believe that ArtiFuse provides a more realistic benchmark that can be used to develop more accurate fusion gene prediction tools for application in clinical settings. Availability and implementation ArtiFuse is implemented in Python. The source code and documentation are available at https://github.com/TRON-Bioinformatics/ArtiFusion. Supplementary information Supplementary data are available at Bioinformatics online.

Spotlight on the transglutaminase 2 gene: a focus on genomic and transcriptional aspects

The type 2 isoenzyme is the most widely expressed transglutaminase in mammals displaying several intra- and extracellular activities depending on its location (protein modification, modulation of gene expression, membrane signalling and stabilization of cellular interactions with the extracellular matrix) in relation to cell death, survival and differentiation. In contrast with the appreciable knowledge about the regulation of the enzymatic activities, much less is known concerning its inducible expression, which is altered in inflammatory and neoplastic diseases. In this context, we first summarize the gene's basic features including single-nucleotide polymorphism characterization, epigenetic DNA methylation and identification of regulatory regions and of transcription factor-binding sites at the gene promoter, which could concur to direct gene expression. Further aspects related to alternative splicing events and to ncRNAs (microRNAs and lncRNAs) are involved in the modulation of its expression. Notably, this important gene displays transcriptional variants relevant for the protein's function with the occurrence of at least seven transcripts which support the synthesis of five isoforms with modified catalytic activities. The different expression of the TG2 (type 2 transglutaminase) variants might be useful for dictating the multiple biological features of the protein and their alterations in pathology, as well as from a therapeutic perspective.