ChimeraScan: a tool for identifying chimeric transcription in sequencing data

RNA-seq Based Transcription Characterization of Fusion Breakpoints as a Potential Estimator for Its Oncogenic Potential

Based on high-throughput sequencing technology, the detection of gene fusions is no longer a big challenge but estimating the oncogenic potential of fusion genes remains challenging. Recent studies successfully applied machine learning methods and gene structural and functional features of fusion mutation to predict their oncogenic potentials. However, the transcription characterizations features of fusion genes have not yet been studied. In this study, based on the clonal evolution theory, we hypothesized that a fusion gene is more likely to be an oncogenic genomic alteration, if the neoplastic cells harboring this fusion mutation have larger clonal size than other neoplastic cells in a tumor. We proposed a novel method, called iFCR (internal Fusion Clone Ratio), given an estimation of oncogenic potential for fusion mutations. We have evaluated the iFCR method in three public cancer transcriptome sequencing datasets; the results demonstrated that the fusion mutations occurring in tumor samples have higher internal fusion clone ratio than normal samples. And the most frequent prostate cancer fusion mutation, TMPRSS2-ERG, appears to have a remarkably higher iFCR value in all three independent patients. The preliminary results suggest that the internal fusion clone ratio might potentially advantage current fusion mutation oncogenic potential prediction methods.

Loss of AXIN1 drives acquired resistance to WNT pathway blockade in colorectal cancer cells carrying RSPO3 fusions

In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging as a promising target. However, its low prevalence severely limits availability of preclinical models for in-depth characterization. Using a pipeline designed to suppress stroma-derived signal, we find that RSPO3 "outlier" expression in CRC samples highlights translocation and fusion transcript expression. Outlier search in 151 CRC cell lines identified VACO6 and SNU1411 cells as carriers of, respectively, a canonical PTPRK(e1)-RSPO3(e2) fusion and a novel PTPRK(e13)-RSPO3(e2) fusion. Both lines displayed marked in vitro and in vivo sensitivity to WNT blockade by the porcupine inhibitor LGK974, associated with transcriptional and morphological evidence of WNT pathway suppression. Long-term treatment of VACO6 cells with LGK974 led to the emergence of a resistant population carrying two frameshift deletions of the WNT pathway inhibitor AXIN1, with consequent protein loss. Suppression of AXIN1 in parental VACO6 cells by RNA interference conferred marked resistance to LGK974. These results provide the first mechanism of secondary resistance to WNT pathway inhibition.

Analysis of the whole transcriptome from gingivo-buccal squamous cell carcinoma reveals deregulated immune landscape and suggests targets for immunotherapy

Background

Gingivo-buccal squamous cell carcinoma (GBSCC) is one of the most common oral cavity cancers in India with less than 50% patients surviving past 5 years. Here, we report a whole transcriptome profile on a batch of GBSCC tumours with diverse tobacco usage habits. The study provides an entire landscape of altered expression with an emphasis on searching for targets with therapeutic potential.

Methods

Whole transcriptomes of 12 GBSCC tumours and adjacent normal tissues were sequenced and analysed to explore differential expression of genes. Expression changes were further compared with those in TCGA head and neck cohort (n = 263) data base and validated in an independent set of 10GBSCC samples.

Results

Differentially expressed genes (n = 2176) were used to cluster the patients based on their tobacco habits, resulting in 3 subgroups. Immune response was observed to be significantly aberrant, along with cell adhesion and lipid metabolism processes. Different modes of immune evasion were seen across 12 tumours with up-regulation or consistent expression of CD47, unlike other immune evasion genes such as PDL1, FUT4, CTLA4 and BTLA which were downregulated in a few samples. Variation in infiltrating immune cell signatures across tumours also indicates heterogeneity in immune evasion strategies. A few actionable genes such as ITGA4, TGFB1 and PTGS1/COX1 were over expressed in most samples.

Conclusion

This study found expression deregulation of key immune evasion genes, such as CD47 and PDL1, and reasserts their potential as effective immunotherapeutic targets for GBSCC, which requires further clinical studies. Present findings reiterate the idea of using transcriptome profiling to guide precision therapeutic strategies.

Validation of a Targeted RNA Sequencing Assay for Kinase Fusion Detection in Solid Tumors

Kinase gene fusions are important drivers of oncogenic transformation and can be inhibited with targeted therapies. Clinical grade diagnostics using RNA sequencing to detect gene rearrangements in solid tumors are limited, and the few that are available require prior knowledge of fusion break points. To address this, we have analytically validated a targeted RNA sequencing assay (OSU-SpARKFuse) for fusion detection that interrogates complete transcripts from 93 kinase and transcription factor genes. From a total of 74 positive and 36 negative control samples, OSU-SpARKFuse had 93.3% sensitivity and 100% specificity for fusion detection. Assessment of repeatability and reproducibility revealed 96.3% and 94.4% concordance between intrarun and interrun technical replicates, respectively. Application of this assay on prospective patient samples uncovered OLFM4 as a novel RET fusion partner in a small-bowel cancer and led to the discovery of a KLK2-FGFR2 fusion in a patient with prostate cancer who subsequently underwent treatment with a pan-fibroblast growth factor receptor inhibitor. Beyond fusion detection, OSU-SpARKFuse has built-in capabilities for discovery research, including gene expression analysis, detection of single-nucleotide variants, and identification of alternative splicing events.

Spatiotemporal genomic architecture informs precision oncology in glioblastoma

Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.

Recurrent rearrangements of the Myb/SANT-like DNA-binding domain containing 3 gene (MSANTD3) in salivary gland acinic cell carcinoma

Pathogenic gene fusions have been identified in several histologic types of salivary gland neoplasia, but not previously in acinic cell carcinoma (AcCC). To discover novel gene fusions, we performed whole-transcriptome sequencing surveys of three AcCC archival cases. In one specimen we identified a novel HTN3-MSANTD3 gene fusion, and in another a novel PRB3-ZNF217 gene fusion. The structure of both fusions was consistent with the promoter of the 5’ partner (HTN3 or PRB3), both highly expressed salivary gland genes, driving overexpression of full-length MSANTD3 or ZNF217. By fluorescence in situ hybridization of an expanded AcCC case series, we observed MSANTD3 rearrangements altogether in 3 of 20 evaluable cases (15%), but found no additional ZNF217 rearrangements. MSANTD3 encodes a previously uncharacterized Myb/SANT domain-containing protein. Immunohistochemical staining demonstrated diffuse nuclear MSANTD3 expression in 8 of 27 AcCC cases (30%), including the three cases with MSANTD3 rearrangement. MSANTD3 displayed heterogeneous expression in normal salivary ductal epithelium, as well as among other histologic types of salivary gland cancer though without evidence of translocation. In a broader survey, MSANTD3 showed variable expression across a wide range of normal and neoplastic human tissue specimens. In preliminary functional studies, engineered MSANTD3 overexpression in rodent salivary gland epithelial cells did not enhance cell proliferation, but led to significant upregulation of gene sets involved in protein synthesis. Our findings newly identify MSANTD3 rearrangement as a recurrent event in salivary gland AcCC, providing new insight into disease pathogenesis, and identifying a putative novel human oncogene.

Fibronectin and androgen receptor expression data in prostate cancer obtained from a RNA-sequencing bioinformatics analysis

Prostate cancer is the second most commonly diagnosed male cancer in the world. The molecular mechanisms underlying its development and progression are still unclear. Here we show analysis of a prostate cancer RNA-sequencing dataset that was originally generated by Ren et al. [3] from the prostate tumor and adjacent normal tissues of 14 patients. The data presented here was analyzed using our RNA-sequencing bioinformatics analysis pipeline implemented on the bioinformatics web platform, Galaxy. The relative expression of fibronectin (FN1) and the androgen receptor (AR) were calculated in fragments per kilobase of transcript per million mapped reads, and represented in FPKM unit. A subanalysis is also shown for data from three patients, that includes the relative expression of FN1 and AR and their fold change. For interpretation and discussion, please refer to the article, “miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin” [1] by Das et al.

FuGePrior: A novel gene fusion prioritization algorithm based on accurate fusion structure analysis in cancer RNA-seq samples

BACKGROUND

Latest Next Generation Sequencing technologies opened the way to a novel era of genomic studies, allowing to gain novel insights into multifactorial pathologies as cancer. In particular gene fusion detection and comprehension have been deeply enhanced by these methods. However, state of the art algorithms for gene fusion identification are still challenging. Indeed, they identify huge amounts of poorly overlapping candidates and all the reported fusions should be considered for in lab validation clearly overwhelming wet lab capabilities.

RESULTS

In this work we propose a novel methodological approach and tool named FuGePrior for the prioritization of gene fusions from paired-end RNA-Seq data. The proposed pipeline combines state of the art tools for chimeric transcript discovery and prioritization, a series of filtering and processing steps designed by considering modern literature on gene fusions and an analysis on functional reliability of gene fusion structure.

CONCLUSIONS

FuGePrior performance has been assessed on two publicly available paired-end RNA-Seq datasets: The first by Edgren and colleagues includes four breast cancer cell lines and a normal breast sample, whereas the second by Ren and colleagues comprises fourteen primary prostate cancer samples and their paired normal counterparts. FuGePrior results accounted for a reduction in the number of fusions output of chimeric transcript discovery tools that ranges from 65 to 75% depending on the considered breast cancer cell line and from 37 to 65% according to the prostate cancer sample under examination. Furthermore, since both datasets come with a partial validation we were able to assess the performance of FuGePrior in correctly prioritizing real gene fusions. Specifically, 25 out of 26 validated fusions in breast cancer dataset have been correctly labelled as reliable and biologically significant. Similarly, 2 out of 5 validated fusions in prostate dataset have been recognized as priority by FuGePrior tool.

Oncogenic BRAF fusions in mucosal melanomas activate the MAPK pathway and are sensitive to MEK/PI3K inhibition or MEK/CDK4/6 inhibition

Despite remarkable progress in cutaneous melanoma genomic profiling, the mutational landscape of primary mucosal melanomas (PMM) remains unclear. Forty-six PMMs underwent targeted exome sequencing of 111 cancer-associated genes. Seventy-six somatic nonsynonymous mutations in 42 genes were observed, and recurrent mutations were noted on eight genes, including TP53 (13%), NRAS (13%), SNX31 (9%), NF1 (9%), KIT (7%) and APC (7%). Mitogen-activated protein kinase (MAPK; 37%), cell cycle (20%) and phosphatidylinositol 3-kinase (PI3K)-mTOR (15%) pathways were frequently mutated. We biologically characterized a novel ZNF767-BRAF fusion found in a vemurafenib-refractory respiratory tract PMM, from which cell line harboring ZNF767-BRAF fusion were established for further molecular analyses. In an independent data set, NFIC-BRAF fusion was identified in an oral PMM case and TMEM178B-BRAF fusion and DGKI-BRAF fusion were identified in two malignant melanomas with a low mutational burden (number of mutation per megabase, 0.8 and 4, respectively). Subsequent analyses revealed that the ZNF767-BRAF fusion protein promotes RAF dimerization and activation of the MAPK pathway. We next tested the in vitro and in vivo efficacy of vemurafenib, trametinib, BKM120 or LEE011 alone and in combination. Trametinib effectively inhibited tumor cell growth in vitro, but the combination of trametinib and BKM120 or LEE011 yielded more than additive anti-tumor effects both in vitro and in vivo in a melanoma cells harboring the BRAF fusion. In conclusion, BRAF fusions define a new molecular subset of PMM that can be targeted therapeutically by the combination of a MEK inhibitor with PI3K or cyclin-dependent kinase 4/6 inhibitors.

Activating mutations and translocations in the guanine exchange factor VAV1 in peripheral T-cell lymphomas

Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of non-Hodgkin lymphomas frequently associated with poor prognosis and for which genetic mechanisms of transformation remain incompletely understood. Using RNA sequencing and targeted sequencing, here we identify a recurrent in-frame deletion (VAV1 Δ778-786) generated by a focal deletion-driven alternative splicing mechanism as well as novel VAV1 gene fusions (VAV1-THAP4, VAV1-MYO1F, and VAV1-S100A7) in PTCL. Mechanistically these genetic lesions result in increased activation of VAV1 catalytic-dependent (MAPK, JNK) and non-catalytic-dependent (nuclear factor of activated T cells, NFAT) VAV1 effector pathways. These results support a driver oncogenic role for VAV1 signaling in the pathogenesis of PTCL.

ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data

Background

Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.

Results

Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved.

Conclusions

ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3404-9) contains supplementary material, which is available to authorized users.

Integrated genomic analyses identify frequent gene fusion events and VHL inactivation in gastrointestinal stromal tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. We sequenced nine exomes and transcriptomes, and two genomes of GISTs for integrated analyses. We detected 306 somatic variants in nine GISTs and recurrent protein-altering mutations in 29 genes. Transcriptome sequencing revealed 328 gene fusions, and the most frequently involved fusion events were associated with IGF2 fused to several partner genes including CCND1, FUS, and LASP1. We additionally identified three recurrent read-through fusion transcripts: POLA2-CDC42EP2, C8orf42-FBXO25, and STX16-NPEPL1. Notably, we found intragenic deletions in one of three exons of the VHL gene and increased mRNAs of VEGF, PDGF-β, and IGF-1/2 in 56% of GISTs, suggesting a mechanistic link between VHL inactivation and overexpression of hypoxia-inducible factor target genes in the absence of hypoxia. We also identified copy number gain and increased mRNA expression of AMACR, CRIM1, SKP2, and CACNA1E. Mapping of copy number and gene expression results to the KEGG pathways revealed activation of the JAK-STAT pathway in small intestinal GISTs and the MAPK pathway in wild-type GISTs. These observations will allow us to determine the genetic basis of GISTs and will facilitate further investigation to develop new therapeutic options.

Transcriptome and long noncoding RNA sequencing of three extracellular vesicle subtypes released from the human colon cancer LIM1863 cell line

Previously we reported that LIM1863 colorectal cancer (CRC) cells secrete three distinct extracellular vesicle subtypes – two subpopulations of exosomes (apical EpCAM-Exos and basolateral A33-Exos) and shed microvesicles (sMVs) – with distinct protein and miRNA signatures. Here, we extend our omics approach to understand the fundamental role of LIM1863-derived EVs by performing a comprehensive analysis of their mRNAs and long non-coding RNAs (lncRNAs) using RNA-Seq. We show that 2,389 mRNAs, 317 pseudogene transcripts, 1,028 lncRNAs and 206 short non-coding RNAs selectively distributed to (i.e., are enriched in) LIM1863 EVs, relative to the parent cell. An Ensembl/UniProtKB analysis revealed 1,937 mRNAs encode canonical proteins, 348 isoforms (including splice-variant proteins), and 119 ‘missing proteins’ (i.e., annotated in Ensembl but not UniProtKB). Further dissection of our protein/RNA data revealed that 6/151 observed RNA binding proteins have the potential to interact with ~75% of EV-enriched RNAs. Intriguingly, the co-existence of U1 and U2 ribonucleoproteins and their cognate snRNAs in LIM1863 EVs suggests a possible association of CRC EVs with recipient cell splicing events. Our data reveal several potential lncRNA CRC biomarkers and novel splicing/fusion genes that, collectively, will advance our understanding of EV biology in CRC and accelerate the development of EV-based diagnostics and therapeutics.

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.

Three-dimensional modelling identifies novel genetic dependencies associated with breast cancer progression in the isogenic MCF10 model

The initiation and progression of breast cancer from the transformation of the normal epithelium to ductal carcinoma in situ (DCIS) and invasive disease is a complex process involving the acquisition of genetic alterations and changes in gene expression, alongside microenvironmental and recognized histological alterations. Here, we sought to comprehensively characterise the genomic and transcriptomic features of the MCF10 isogenic model of breast cancer progression, and to functionally validate potential driver alterations in three-dimensional (3D) spheroids that may provide insights into breast cancer progression, and identify targetable alterations in conditions more similar to those encountered in vivo. We performed whole genome, exome and RNA sequencing of the MCF10 progression series to catalogue the copy number and mutational and transcriptomic landscapes associated with progression. We identified a number of predicted driver mutations (including PIK3CA and TP53) that were acquired during transformation of non-malignant MCF10A cells to their malignant counterparts that are also present in analysed primary breast cancers from The Cancer Genome Atlas (TCGA). Acquisition of genomic alterations identified MYC amplification and previously undescribed RAB3GAP1-HRAS and UBA2-PDCD2L expressed in-frame fusion genes in malignant cells. Comparison of pathway aberrations associated with progression showed that, when cells are grown as 3D spheroids, they show perturbations of cancer-relevant pathways. Functional interrogation of the dependency on predicted driver events identified alterations in HRAS, PIK3CA and TP53 that selectively decreased cell growth and were associated with progression from preinvasive to invasive disease only when cells were grown as spheroids. Our results have identified changes in the genomic repertoire in cell lines representative of the stages of breast cancer progression, and demonstrate that genetic dependencies can be uncovered when cells are grown in conditions more like those in vivo. The MCF10 progression series therefore represents a good model with which to dissect potential biomarkers and to evaluate therapeutic targets involved in the progression of breast cancer. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Identifying fusion transcripts using next generation sequencing

Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website.

Identification of mutations, gene expression changes and fusion transcripts by whole transcriptome RNAseq in docetaxel resistant prostate cancer cells

Docetaxel has been the standard first-line therapy in metastatic castration resistant prostate cancer. The survival benefit is, however, limited by either primary or acquired resistance. In this study, Du145 prostate cancer cells were converted to docetaxel-resistant cells Du145-R and Du145-RB by in vitro culturing. Next generation RNAseq was employed to analyze these cell lines. Forty-two genes were identified to have acquired mutations after the resistance development, of which thirty-four were found to have mutations in published sequencing studies using prostate cancer samples from patients. Fourteen novel and 2 previously known fusion genes were inferred from the RNA-seq data, and 13 of these were validated by RT-PCR and/or re-sequencing. Four in-frame fusion transcripts could be transcribed into fusion proteins in stably transfected HEK293 cells, including MYH9-EIF3D and LDLR-RPL31P11, which were specific identified or up-regulated in the docetaxel resistant DU145 cells. A panel of 615 gene transcripts was identified to have significantly changed expression profile in the docetaxel resistant cells. These transcriptional changes have potential for further study as predictive biomarkers and as targets of docetaxel treatment.

Impact of RNA degradation on fusion detection by RNA-seq

Background

RNA-seq is a well-established method for studying the transcriptome. Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5′ end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables involved in this process.

Results

Using both artificially and naturally degraded samples, we found that there is a reduced ability to detect fusions as the distance of the breakpoint from the 3′ end of the gene increases. The median transcript coverage decreases exponentially as a function of the distance from the 3′ end and there is a linear relationship between the coverage decay rate and the RNA integrity number (RIN). Based on these findings we developed plots that show the probability of detecting a gene fusion (“sensitivity”) as a function of the distance of the fusion breakpoint from the 3′ end.

Conclusions

This study developed a strategy to assess the impact that RNA degradation has on the ability to detect gene fusions by RNA-seq.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3161-9) contains supplementary material, which is available to authorized users.

FN1-EGF gene fusions are recurrent in calcifying aponeurotic fibroma

Calcifying aponeurotic fibroma (CAF) is a soft tissue neoplasm with a predilection for the hands and feet in children and adolescents. Its molecular basis is unknown. We used chromosome banding analysis, fluorescence in situ hybridization (FISH), mRNA sequencing (RNA-seq), RT-PCR, and immunohistochemistry to characterize a series of CAFs. An insertion ins(2;4)(q35;q25q?) was identified in the index case. Fusion of the FN1 and EGF genes, mapping to the breakpoint regions on chromosomes 2 and 4, respectively, was detected by RNA-seq and confirmed by RT-PCR in the index case and two additional cases. FISH on five additional tumours identified FN1-EGF fusions in all cases. CAFs analysed by RT-PCR showed that FN1 exon 23, 27 or 42 was fused to EGF exon 17 or 19. High-level expression of the entire FN1 gene in CAF suggests that strong FN1 promoter activity drives inappropriate expression of the biologically active portion of EGF, which was detected immunohistochemically in 8/9 cases. The FN1-EGF fusion, which has not been observed in any other neoplasm, appears to be the main driver mutation in CAF. Although further functional studies are required to understand the exact pathogenesis of CAF, the composition of the chimera suggests an autocrine/paracrine mechanism of transformation. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

RNA sequencing (RNAseq) is a versatile method that can be utilized to detect and characterize gene expression, mutations, gene fusions, and noncoding RNAs. Standard RNAseq requires 30 - 100 million sequencing reads and can include multiple RNA products such as mRNA and noncoding RNAs. We demonstrate how targeted RNAseq (capture) permits a focused study on selected RNA products using a desktop sequencer. RNAseq capture can characterize unannotated, low, or transiently expressed transcripts that may otherwise be missed using traditional RNAseq methods. Here we describe the extraction of RNA from cell lines, ribosomal RNA depletion, cDNA synthesis, preparation of barcoded libraries, hybridization and capture of targeted transcripts and multiplex sequencing on a desktop sequencer. We also outline the computational analysis pipeline, which includes quality control assessment, alignment, fusion detection, gene expression quantification and identification of single nucleotide variants. This assay allows for targeted transcript sequencing to characterize gene expression, gene fusions, and mutations.

Transcriptome analysis revealed chimeric RNAs, single nucleotide polymorphisms and allele-specific expression in porcine prenatal skeletal muscle

Prenatal skeletal muscle development genetically determines postnatal muscle characteristics such as growth and meat quality in pigs. However, the molecular mechanisms underlying prenatal skeletal muscle development remain unclear. Here, we performed the first genome-wide analysis of chimeric RNAs, single nuclear polymorphisms (SNPs) and allele-specific expression (ASE) in prenatal skeletal muscle in pigs. We identified 14,810 protein coding genes and 163 high-confidence chimeric RNAs expressed in prenatal skeletal muscle. More than 94.5% of the chimeric RNAs obeyed the canonical GT/AG splice rule and were trans-splicing events. Ten and two RNAs were aligned to human and mouse chimeric transcripts, respectively. We detected 106,457 high-quality SNPs (6,955 novel), which were mostly (89.09%) located within QTLs for production traits. The high proportion of non-exonic SNPs revealed the incomplete annotation status of the current swine reference genome. ASE analysis revealed that 11,300 heterozygous SNPs showed allelic imbalance, whereas 131 ASE variants were located in the chimeric RNAs. Moreover, 4 ASE variants were associated with various economically relevant traits of pigs. Taken together, our data provide a source for studies of chimeric RNAs and biomarkers for pig breeding, while illuminating the complex transcriptional events underlying prenatal skeletal muscle development in mammals.

Integrated genetic and pharmacologic interrogation of rare cancers

Identifying therapeutic targets in rare cancers remains challenging due to the paucity of established models to perform preclinical studies. As a proof-of-concept, we developed a patient-derived cancer cell line, CLF-PED-015-T, from a paediatric patient with a rare undifferentiated sarcoma. Here, we confirm that this cell line recapitulates the histology and harbours the majority of the somatic genetic alterations found in a metastatic lesion isolated at first relapse. We then perform pooled CRISPR-Cas9 and RNAi loss-of-function screens and a small-molecule screen focused on druggable cancer targets. Integrating these three complementary and orthogonal methods, we identify CDK4 and XPO1 as potential therapeutic targets in this cancer, which has no known alterations in these genes. These observations establish an approach that integrates new patient-derived models, functional genomics and chemical screens to facilitate the discovery of targets in rare cancers.

Clonal evolution of glioblastoma under therapy

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. To better understand how GBM evolves, we analyzed longitudinal genomic and transcriptomic data from 114 patients. The analysis shows a highly branched evolutionary pattern in which 63% of patients experience expression-based subtype changes. The branching pattern, together with estimates of evolutionary rate, suggests that relapse-associated clones typically existed years before diagnosis. Fifteen percent of tumors present hypermutation at relapse in highly expressed genes, with a clear mutational signature. We find that 11% of recurrence tumors harbor mutations in LTBP4, which encodes a protein binding to TGF-β. Silencing LTBP4 in GBM cells leads to suppression of TGF-β activity and decreased cell proliferation. In recurrent GBM with wild-type IDH1, high LTBP4 expression is associated with worse prognosis, highlighting the TGF-β pathway as a potential therapeutic target in GBM.

Identification of ETV6-RUNX1-like and DUX4-rearranged subtypes in paediatric B-cell precursor acute lymphoblastic leukaemia

Fusion genes are potent driver mutations in cancer. In this study, we delineate the fusion gene landscape in a consecutive series of 195 paediatric B-cell precursor acute lymphoblastic leukaemia (BCP ALL). Using RNA sequencing, we find in-frame fusion genes in 127 (65%) cases, including 27 novel fusions. We describe a subtype characterized by recurrent IGH-DUX4 or ERG-DUX4 fusions, representing 4% of cases, leading to overexpression of DUX4 and frequently co-occurring with intragenic ERG deletions. Furthermore, we identify a subtype characterized by an ETV6-RUNX1-like gene-expression profile and coexisting ETV6 and IKZF1 alterations. Thus, this study provides a detailed overview of fusion genes in paediatric BCP ALL and adds new pathogenetic insights, which may improve risk stratification and provide therapeutic options for this disease.

SePIA: RNA and small RNA sequence processing, integration, and analysis

Background

Large-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types.

Results

We developed SePIA (Sequence Processing, Integration, and Analysis), a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow’s extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance.

Conclusion

SePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA’s modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at http://anduril.org/sepia.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0099-z) contains supplementary material, which is available to authorized users.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

Identification of Alternative Splicing and Fusion Transcripts in Non-Small Cell Lung Cancer by RNA Sequencing

Background

Lung cancer is the most common cause of cancer related death. Alterations in gene sequence, structure, and expression have an important role in the pathogenesis of lung cancer. Fusion genes and alternative splicing of cancer-related genes have the potential to be oncogenic. In the current study, we performed RNA-sequencing (RNA-seq) to investigate potential fusion genes and alternative splicing in non-small cell lung cancer.

Methods

RNA was isolated from lung tissues obtained from 86 subjects with lung cancer. The RNA samples from lung cancer and normal tissues were processed with RNA-seq using the HiSeq 2000 system. Fusion genes were evaluated using Defuse and ChimeraScan. Candidate fusion transcripts were validated by Sanger sequencing. Alternative splicing was analyzed using multivariate analysis of transcript sequencing and validated using quantitative real time polymerase chain reaction.

Results

RNA-seq data identified oncogenic fusion genes EML4-ALK and SLC34A2-ROS1 in three of 86 normal-cancer paired samples. Nine distinct fusion transcripts were selected using DeFuse and ChimeraScan; of which, four fusion transcripts were validated by Sanger sequencing. In 33 squamous cell carcinoma, 29 tumor specific skipped exon events and six mutually exclusive exon events were identified. ITGB4 and PYCR1 were top genes that showed significant tumor specific splice variants.

Conclusion

In conclusion, RNA-seq data identified novel potential fusion transcripts and splice variants. Further evaluation of their functional significance in the pathogenesis of lung cancer is required.

Transcriptome sequencing identifies ETV6-NTRK3 as a gene fusion involved in GIST

Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract. The vast majority of GISTs are driven by oncogenic activation of KIT, PDGFRA or, less commonly, BRAF. Loss of succinate dehydrogenase complex activity has been identified in subsets of KIT/PDGFRA/BRAF-mutation negative tumours, yet a significant fraction of GISTs are devoid of any of such alterations. To address the pathobiology of these 'quadruple-negative' GISTs, we sought to explore the possible involvement of fusion genes. To this end we performed transcriptome sequencing on five KIT/PDGFRA/BRAF-mutation negative, SDH-proficient tumours. Intriguingly, the analysis unveiled the presence of an ETV6-NTRK3 gene fusion. The screening by FISH of 26 additional cases, including KIT/PDGFRA-mutated GISTs, failed to detect other ETV6 rearrangements beside the index case. This was a 'quadruple-negative' GIST located in the rectum, an uncommon primary site for GIST development (∼4% of all GISTs). The fusion transcript identified encompasses exon 4 of ETV6 and exon 14 of NTRK3 and therefore differs from the canonical ETV6-NTRK3 chimera of infantile fibrosarcomas. However, it retains the ability to induce IRS1 phosphorylation, activate the IGF1R downstream signalling pathway and to be targeted by IGF1R and ALK inhibitors. Thus, the ETV6-NTRK3 fusion might identify a subset of GISTs with peculiar clinicopathological characteristics which could be eligible for such therapies. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Recurrent SKIL-activating rearrangements in ETS-negative prostate cancer

Prostate cancer is the third most common cause of male cancer death in developed countries, and one of the most comprehensively characterized human cancers. Roughly 60% of prostate cancers harbor gene fusions that juxtapose ETS-family transcription factors with androgen regulated promoters. A second subtype, characterized by SPINK1 overexpression, accounts for 15% of prostate cancers. Here we report the discovery of a new prostate cancer subtype characterized by rearrangements juxtaposing the SMAD inhibitor SKIL with androgen regulated promoters, leading to increased SKIL expression. SKIL fusions were found in 6 of 540 (1.1%) prostate cancers and 1 of 27 (3.7%) cell lines and xenografts. 6 of 7 SKIL-positive cancers were negative for ETS overexpression, suggesting mutual exclusivity with ETS fusions. SKIL knockdown led to growth arrest in PC-3 and LNCaP cell line models of prostate cancer, and its overexpression led to increased invasiveness in RWPE-1 cells. The role of SKIL as a prostate cancer oncogene lends support to recent studies on the role of TGF-β signaling as a rate-limiting step in prostate cancer progression. Our findings highlight SKIL as an oncogene and potential therapeutic target in 1-2% of prostate cancers, amounting to an estimated 10,000 cancer diagnoses per year worldwide.

Clinical application of genomic profiling to find druggable targets for adolescent and young adult (AYA) cancer patients with metastasis

Background

Although adolescent and young adult (AYA) cancers are characterized by biological features and clinical outcomes distinct from those of other age groups, the molecular profile of AYA cancers has not been well defined. In this study, we analyzed cancer genomes from rare types of metastatic AYA cancers to identify driving and/or druggable genetic alterations.

Methods

Prospectively collected AYA tumor samples from seven different patients were analyzed using three different genomics platforms (whole-exome sequencing, whole-transcriptome sequencing or OncoScan™). Using well-known bioinformatics tools (bwa, Picard, GATK, MuTect, and Somatic Indel Detector) and our annotation approach with open access databases (DAVID and DGIdb), we processed sequencing data and identified driving genetic alterations and their druggability.

Results

The mutation frequencies of AYA cancers were lower than those of other adult cancers (median = 0.56), except for a germ cell tumor with hypermutation. We identified patient-specific genetic alterations in candidate driving genes: RASA2 and NF1 (prostate cancer), TP53 and CDKN2C (olfactory neuroblastoma), FAT1, NOTCH1, and SMAD4 (head and neck cancer), KRAS (urachal carcinoma), EML4-ALK (lung cancer), and MDM2 and PTEN (liposarcoma). We then suggested potential drugs for each patient according to his or her altered genes and related pathways. By comparing candidate driving genes between AYA cancers and those from all age groups for the same type of cancer, we identified different driving genes in prostate cancer and a germ cell tumor in AYAs compared with all age groups, whereas three common alterations (TP53, FAT1, and NOTCH1) in head and neck cancer were identified in both groups.

Conclusion

We identified the patient-specific genetic alterations and druggability of seven rare types of AYA cancers using three genomics platforms. Additionally, genetic alterations in cancers from AYA and those from all age groups varied by cancer type.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2209-1) contains supplementary material, which is available to authorized users.

New somatic mutations and WNK1-B4GALNT3 gene fusion in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most frequent thyroid malignant neoplasia. Oncogene activation occurs in more than 70% of the cases. Indeed, about 40% of PTCs harbor mutations in BRAF gene, whereas RET rearrangements (RET/PTC oncogenes) are present in about 20% of cases. Finally, RAS mutations and TRK rearrangements account for about 5% each of these malignancies. We used RNA-Sequencing to identify fusion transcripts and mutations in cancer driver genes in a cohort of 18 PTC patients. Furthermore, we used targeted DNA sequencing to validate identified mutations. We extended the screening to 50 PTC patients and 30 healthy individuals. Using this approach we identified new missense mutations in CBL, NOTCH1, PIK3R4 and SMARCA4 genes. We found somatic mutations in DICER1, MET and VHL genes, previously found mutated in other tumors, but not described in PTC. We identified a new chimeric transcript generated by the fusion of WNK1 and B4GALNT3 genes, correlated with B4GALNT3 overexpression. Our data confirmed PTC genetic heterogeneity, revealing that gene expression correlates more with the mutation pattern than with tumor staging. Overall, this study provides new data about mutational landscape of this neoplasia, suggesting potential pharmacological adjuvant therapies against Notch signaling and chromatin remodeling enzymes.

NCLscan: accurate identification of non-co-linear transcripts (fusion, trans-splicing and circular RNA) with a good balance between sensitivity and precision

Analysis of RNA-seq data often detects numerous 'non-co-linear' (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts (trans-spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method ('NCLscan'), which utilized a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans-spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans-spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome.

Novel fusion transcripts in bladder cancer identified by RNA-seq

Urothelial carcinoma (UC) is the most common type of bladder cancer and is the second most frequently diagnosed genitourinary tumor. The identification of fusion genes in bladder cancer might provide new perspectives for its classification and significance. In this study, we present a thorough search on three UC samples for novel fusion transcripts in bladder cancer using high-throughput RNA sequencing. We used stringent requirements for 819 fusion candidates and nominated 10 candidate fusion transcripts. Among them four novel fusion genes SEPT9/CYHR, IGF1R/TTC23, SYT8/TNNI2 and CASZ1/DFFA were validated and characterized in 48 formalin-fixed paraffin-embedded (FFPE) specimens of bladder cancer. Chromosomal rearrangements of regions 17q25, 15q26.3 and 1p36.22 resulting in the fusion transcripts SEPT9/CYHR, IGF1R/TTC23 and CASZ1/DFFA, appeared to be rare or unique events because they were not detected in the 48 UC samples. In contrast, the SYT8/TNNI2 fusion transcript resulting from transcription-induced chimerism by read-through mechanisms was a rather common and tumor-specific event occurring in 37.5% (18/48) of the UC specimens. Further investigation of functional and clinical relevance of novel fusion genes remains to be elucidated to reveal their role in bladder carcinogenesis.

Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data

RNA-Seq made possible the global identification of fusion transcripts, i.e. "chimeric RNAs". Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users, and developers to have an unbiased assessment of the performance of existing fusion detection tools. Toward this goal, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. We have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data.

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Identification of Patients with Recurrent Glioblastoma Who May Benefit from Combined Bevacizumab and CCNU Therapy: A Report from the BELOB Trial

The results from the randomized phase II BELOB trial provided evidence for a potential benefit of bevacizumab (beva), a humanized monoclonal antibody against circulating VEGF-A, when added to CCNU chemotherapy in patients with recurrent glioblastoma (GBM). In this study, we performed gene expression profiling (DASL and RNA-seq) of formalin-fixed, paraffin-embedded tumor material from participants of the BELOB trial to identify patients with recurrent GBM who benefitted most from beva+CCNU treatment. We demonstrate that tumors assigned to the IGS-18 or "classical" subtype and treated with beva+CCNU showed a significant benefit in progression-free survival and a trend toward benefit in overall survival, whereas other subtypes did not exhibit such benefit. In particular, expression of FMO4 and OSBPL3 was associated with treatment response. Importantly, the improved outcome in the beva+CCNU treatment arm was not explained by an uneven distribution of prognostically favorable subtypes as all molecular glioma subtypes were evenly distributed along the different study arms. The RNA-seq analysis also highlighted genetic alterations, including mutations, gene fusions, and copy number changes, within this well-defined cohort of tumors that may serve as useful predictive or prognostic biomarkers of patient outcome. Further validation of the identified molecular markers may enable the future stratification of recurrent GBM patients into appropriate treatment regimens.

Reproducible, Scalable Fusion Gene Detection from RNA-Seq

Chromosomal rearrangements resulting in the creation of novel gene products, termed fusion genes, have been identified as driving events in the development of multiple types of cancer. As these gene products typically do not exist in normal cells, they represent valuable prognostic and therapeutic targets. Advances in next-generation sequencing and computational approaches have greatly improved our ability to detect and identify fusion genes. Nevertheless, these approaches require significant computational resources. Here we describe an approach which leverages cloud computing technologies to perform fusion gene detection from RNA sequencing data at any scale. We additionally highlight methods to enhance reproducibility of bioinformatics analyses which may be applied to any next-generation sequencing experiment.

Transcriptome Sequencing for the Detection of Chimeric Transcripts

The occurrence of chimeric transcripts has been reported in many cancer cells and seen as potential biomarkers and therapeutic targets. Modern high-throughput sequencing technologies offer a way to investigate individual chimeric transcripts and the systematic information of associated gene expressions about underlying genome structural variations and genomic interactions. The detection methods of finding chimeric transcripts from massive amount of short read sequence data are discussed here. Both assembly-based and alignment-based methods are used for the investigation of chimeric transcripts.

Uterine adenosarcomas are mesenchymal neoplasms

Uterine adenosarcomas (UAs) are biphasic lesions composed of a malignant mesenchymal (ie stromal) component and an epithelial component. UAs are generally low-grade and have a favourable prognosis, but may display sarcomatous overgrowth (SO), which is associated with a worse outcome. We hypothesized that, akin to breast fibroepithelial lesions, UAs are mesenchymal neoplasms in which clonal somatic genetic alterations are restricted to the mesenchymal component. To characterize the somatic genetic alterations in UAs and to test this hypothesis, we subjected 20 UAs to a combination of whole-exome (n = 6), targeted capture (n = 13) massively parallel sequencing (MPS) and/or RNA sequencing (n = 6). Only three genes, FGFR2, KMT2C and DICER1, were recurrently mutated, all in 2/19 cases; however, 26% (5/19) and 21% (4/19) of UAs harboured MDM2/CDK4/HMGA2 and TERT gene amplification, respectively, and two cases harboured fusion genes involving NCOA family members. Using a combination of laser-capture microdissection and in situ techniques, we demonstrated that the somatic genetic alterations detected by MPS were restricted to the mesenchymal component. Furthermore, mitochondrial DNA sequencing of microdissected samples revealed that epithelial and mesenchymal components of UAs were clonally unrelated. In conclusion, here we provide evidence that UAs are genetically heterogeneous lesions and mesenchymal neoplasms.

Landscape of gene fusions in epithelial cancers: seq and ye shall find

Enabled by high-throughput sequencing approaches, epithelial cancers across a range of tissue types are seen to harbor gene fusions as integral to their landscape of somatic aberrations. Although many gene fusions are found at high frequency in several rare solid cancers, apart from fusions involving the ETS family of transcription factors which have been seen in approximately 50% of prostate cancers, several other common solid cancers have been shown to harbor recurrent gene fusions at low frequencies. On the other hand, many gene fusions involving oncogenes, such as those encoding ALK, RAF or FGFR kinase families, have been detected across multiple different epithelial carcinomas. Tumor-specific gene fusions can serve as diagnostic biomarkers or help define molecular subtypes of tumors; for example, gene fusions involving oncogenes such as ERG, ETV1, TFE3, NUT, POU5F1, NFIB, PLAG1, and PAX8 are diagnostically useful. Tumors with fusions involving therapeutically targetable genes such as ALK, RET, BRAF, RAF1, FGFR1-4, and NOTCH1-3 have immediate implications for precision medicine across tissue types. Thus, ongoing cancer genomic and transcriptomic analyses for clinical sequencing need to delineate the landscape of gene fusions. Prioritization of potential oncogenic "drivers" from "passenger" fusions, and functional characterization of potentially actionable gene fusions across diverse tissue types, will help translate these findings into clinical applications. Here, we review recent advances in gene fusion discovery and the prospects for medicine.

FuMa: reporting overlap in RNA-seq detected fusion genes

A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools.

AVAILABILITY AND IMPLEMENTATION

The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/

CONTACT

y.hoogstrate@erasmusmc.nl or a.stubbs@erasmusmc.nl

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High throughput de novo RNA sequencing elucidates novel responses in Penicillium chrysogenum under microgravity

In this study, the transcriptional alterations in Penicillium chrysogenum under simulated microgravity conditions were analyzed for the first time using an RNA-Seq method. The increasing plethora of eukaryotic microbial flora inside the spaceship demands the basic understanding of fungal biology in the absence of gravity vector. Penicillium species are second most dominant fungal contaminant in International Space Station. Penicillium chrysogenum an industrially important organism also has the potential to emerge as an opportunistic pathogen for the astronauts during the long-term space missions. But till date, the cellular mechanisms underlying the survival and adaptation of Penicillium chrysogenum to microgravity conditions are not clearly elucidated. A reference genome for Penicillium chrysogenum is not yet available in the NCBI database. Hence, we performed comparative de novo transcriptome analysis of Penicillium chrysogenum grown under microgravity versus normal gravity. In addition, the changes due to microgravity are documented at the molecular level. Increased response to the environmental stimulus, changes in the cell wall component ABC transporter/MFS transporters are noteworthy. Interestingly, sustained increase in the expression of Acyl-coenzyme A: isopenicillin N acyltransferase (Acyltransferase) under microgravity revealed the significance of gravity in the penicillin production which could be exploited industrially.

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection.

Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.

Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

Integrated genomics approach to identify biologically relevant alterations in fewer samples

BACKGROUND

Several statistical tools have been developed to identify genes mutated at rates significantly higher than background, indicative of positive selection, involving large sample cohort studies. However, studies involving smaller sample sizes are inherently restrictive due to their limited statistical power to identify low frequency genetic variations.

RESULTS

We performed an integrated characterization of copy number, mutation and expression analyses of four head and neck cancer cell lines - NT8e, OT9, AW13516 and AW8507 - by applying a filtering strategy to prioritize for genes affected by two or more alterations within or across the cell lines. Besides identifying TP53, PTEN, HRAS and MET as major altered HNSCC hallmark genes, this analysis uncovered 34 novel candidate genes altered. Of these, we find a heterozygous truncating mutation in Nuclear receptor binding protein, NRBP1 pseudokinase gene, identical to as reported in other cancers, is oncogenic when ectopically expressed in NIH-3 T3 cells. Knockdown of NRBP1 in an oral carcinoma cell line bearing NRBP1 mutation inhibit transformation and survival of the cells.

CONCLUSIONS

In overall, we present the first comprehensive genomic characterization of four head and neck cancer cell lines established from Indian patients. We also demonstrate the ability of integrated analysis to uncover biologically important genetic variation in studies involving fewer or rare clinical specimens.

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.