FusionFinder: a software tool to identify expressed gene fusion candidates from RNA-Seq data

Single-cell gene fusion detection by scFusion

Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing

Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.

Safety and Efficacy of Personalized Cancer Vaccines in Combination With Immune Checkpoint Inhibitors in Cancer Treatment

Cancer immunotherapy can induce sustained responses in patients with cancers in a broad range of tissues, however, these treatments require the optimized combined therapeutic strategies. Despite immune checkpoint inhibitors (ICIs) have lasting clinical benefit, researchers are trying to combine them with other treatment modalities, and among them the combination with personalized cancer vaccines is attractive. Neoantigens, arising from mutations in cancer cells, can elicit strong immune response without central tolerance and out-target effects, which is a truly personalized method. Growing studies show that the combination can elevate the antitumor efficacy with acceptable safety and minimal additional toxicity compared with single agent vaccine or ICI. Herein, we have searched these preclinical and clinical trials and summarized safety and efficacy of personalized cancer vaccines combined with ICIs in several malignancies. Meanwhile, we discuss the rationale of the combination and future challenges.

Landscape of transcriptome variations uncovering known and novel driver events in colorectal carcinoma

We focused on an integrated view of genomic changes in Colorectal cancer (CRC) and distant normal colon tissue (NTC) to test the effectiveness of expression profiling on identification of molecular targets. We performed transcriptome on 16 primary coupled CRC and NTC tissues. We identified pathways and networks related to pathophysiology of CRC and selected potential therapeutic targets. CRC cells have multiple ways to reprogram its transcriptome: a functional enrichment analysis in 285 genes, 25% mutated, showed that they control the major cellular processes known to promote tumorigenesis. Among the genes showing alternative splicing, cell cycle related genes were upregulated (CCND1, CDC25B, MCM2, MCM3), while genes involved in fatty acid metabolism (ACAAA2, ACADS, ACAT1, ACOX, CPT1A, HMGCS2) were downregulated. Overall 148 genes showed differential splicing identifying 17 new isoforms. Most of them are involved in the pathogenesis of CRC, although the functions of these variants remain unknown. We identified 2 in-frame fusion events, KRT19-KRT18 and EEF1A1-HSP90AB1, encoding for chemical proteins in two CRC patients. We draw a functional interactome map involving integrated multiple genomic features in CRC. Finally, we underline that two functional cell programs are prevalently deregulated and absolutely crucial to determinate and sustain CRC phenotype.

Fusion genes: A promising tool combating against cancer

The driving roles of fusion genes during tumorigenesis have been recognized for decades, with efficacies demonstrated in clinical diagnosis and targeted therapy. With advances in sequencing technologies and computational biology, a surge in the identification of fusion genes has been witnessed during the past decade. The discovery and presence of splicing based fusions in normal tissues have challenged our canonical conceptions on fusion genes and offered us novel medical opportunities. The specificity of fusion genes to neoplastic tissues and their diverse functionalities during carcinogenesis foster them as promising tools in the battle against cancer. It is time to re-visit and comb through our cutting-edge knowledge on fusion genes to accelerate clinical translation of these internal markers. Urged as such, we are encouraged to categorize fusion events according to mechanisms leading to their generation, oncological consequences and clinical implications, offer insights on fusion occurrence across tumors from the system level, highlight feasible practices in fusion-related pharmaceutical development, and identify understudied yet important niches that may lead future research trend in this field.

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.

Systematic chemical and molecular profiling of MLL-rearranged infant acute lymphoblastic leukemia reveals efficacy of romidepsin

To address the poor prognosis of mixed lineage leukemia (MLL)-rearranged infant acute lymphoblastic leukemia (iALL), we generated a panel of cell lines from primary patient samples and investigated cytotoxic responses to contemporary and novel Food and Drug Administration-approved chemotherapeutics. To characterize representation of primary disease within cell lines, molecular features were compared using RNA-sequencing and cytogenetics. High-throughput screening revealed variable efficacy of currently used drugs, however identified consistent efficacy of three novel drug classes: proteasome inhibitors, histone deacetylase inhibitors and cyclin-dependent kinase inhibitors. Gene expression of drug targets was highly reproducible comparing iALL cell lines to matched primary specimens. Histone deacetylase inhibitors, including romidepsin (ROM), enhanced the activity of a key component of iALL therapy, cytarabine (ARAC) in vitro and combined administration of ROM and ARAC to xenografted mice further reduced leukemia burden. Molecular studies showed that ROM reduces expression of cytidine deaminase, an enzyme involved in ARAC deactivation, and enhances the DNA damage-response to ARAC. In conclusion, we present a valuable resource for drug discovery, including the first systematic analysis of transcriptome reproducibility in vitro, and have identified ROM as a promising therapeutic for MLL-rearranged iALL.

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.

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.

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.

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.

Cancer Neoantigens and Applications for Immunotherapy

Recent advances in immune checkpoint blockade therapy have revolutionized the treatment of cancer. Tumor-specific antigens that are generated by somatic mutation, neoantigens, can influence patient response to immunotherapy and contribute to tumor shrinkage. Recent evidence demonstrating the success of checkpoint blockade immunotherapy in boosting T-cell reactivity against patient-specific neoantigens constitutes a strong rationale for the development of personalized vaccines against these nonself peptides. With the decreasing cost of next-generation sequencing, peptide manufacturing, and improvement of in silico prediction of peptide immunogenicity, it is increasingly important to evaluate the potential use of neoantigens in both diagnosis and treatment. Specifically, these neoantigens could be useful both as predictors of immune checkpoint blockade therapy response and/or incorporated in therapeutic vaccination strategies.

Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing

We developed an innovative hybrid sequencing approach, IDP-fusion, to detect fusion genes, determine fusion sites and identify and quantify fusion isoforms. IDP-fusion is the first method to study gene fusion events by integrating Third Generation Sequencing long reads and Second Generation Sequencing short reads. We applied IDP-fusion to PacBio data and Illumina data from the MCF-7 breast cancer cells. Compared with the existing tools, IDP-fusion detects fusion genes at higher precision and a very low false positive rate. The results show that IDP-fusion will be useful for unraveling the complexity of multiple fusion splices and fusion isoforms within tumorigenesis-relevant fusion genes.

Detecting and targetting oncogenic fusion proteins in the genomic era

The advent of widespread cancer genome sequencing has accelerated our understanding of the molecular aberrations underlying malignant disease at an unprecedented rate. Coupling the large number of bioinformatic methods developed to locate genomic breakpoints with increased sequence read length and a deeper understanding of coding region function has enabled rapid identification of novel actionable oncogenic fusion genes. Using examples of kinase fusions found in liquid and solid tumours, this review highlights major concepts that have arisen in our understanding of cancer pathogenesis through the study of fusion proteins. We provide an overview of recently developed methods to identify potential fusion proteins from next-generation sequencing data, describe the validation of their oncogenic potential and discuss the role of targetted therapies in treating cancers driven by fusion oncoproteins.

Fusion genes with ALK as recurrent partner in ependymoma-like gliomas: a new brain tumor entity?

BACKGROUND

We have previously characterized 19 ependymal tumors using Giemsa banding and high-resolution comparative genomic hybridization. The aim of this study was to analyze these tumors searching for fusion genes.

METHODS

RNA sequencing was performed in 12 samples. Potential fusion transcripts were assessed by seed count and structural chromosomal aberrations. Transcripts of interest were validated using fluorescence in situ hybridization and PCR followed by direct sequencing.

RESULTS

RNA sequencing identified rearrangements of the anaplastic lymphoma kinase gene (ALK) in 2 samples. Both tumors harbored structural aberrations involving the ALK locus 2p23. Tumor 1 had an unbalanced t(2;14)(p23;q22) translocation which led to the fusion gene KTN1-ALK. Tumor 2 had an interstitial del(2)(p16p23) deletion causing the fusion of CCDC88A and ALK. In both samples, the breakpoint of ALK was located between exons 19 and 20. Both patients were infants and both tumors were supratentorial. The tumors were well demarcated from surrounding tissue and had both ependymal and astrocytic features but were diagnosed and treated as ependymomas.

CONCLUSIONS

By combining karyotyping and RNA sequencing, we identified the 2 first ever reported ALK rearrangements in CNS tumors. Such rearrangements may represent the hallmark of a new entity of pediatric glioma characterized by both ependymal and astrocytic features. Our findings are of particular importance because crizotinib, a selective ALK inhibitor, has demonstrated effect in patients with lung cancer harboring ALK rearrangements. Thus, ALK emerges as an interesting therapeutic target in patients with ependymal tumors carrying ALK fusions.

Novel KAT6B-KANSL1 fusion gene identified by RNA sequencing in retroperitoneal leiomyoma with t(10;17)(q22;q21)

Retroperitoneal leiomyoma is a rare type of benign smooth muscle tumor almost exclusively found in women and with histopathological features similar to uterine leiomyomas. The pathogenesis of retroperitoneal leiomyoma is unclear and next to nothing is known about the cytogenetics and molecular genetics of the tumor. Here we present the first cytogenetically analyzed retroperitoneal leiomyoma. It had a t(10;17)(q22;q21) as the sole chromosomal abnormality. Using RNA-Sequencing and the ‘grep’ command to search the fastq files of the sequence data we found that the translocation resulted in fusion of the genes KAT6B (10q22) with KANSL1 (17q21). RT-PCR together with direct (Sanger) sequencing verified the presence of a KAT6B-KANSL1 fusion transcript. No reciprocal KANSL1-KAT6B transcript was amplified suggesting that it was either absent or unexpressed. The KAT6B-KANSL1 fusion transcript consists of exons 1 to 3 of KAT6B and exons 11 to 15 of KANSL1, is 3667 bp long, has a 1398 bp long open reading frame, and codes for a 466 amino acid residue protein. The corresponding KAT6B-KANSL1 protein contains the NEMM domain (including the linker histone H1/H5, domain H15) of KAT6B and the PEHE domain of KANSL1. The function of the fusion protein might be regulation of transcription with an affinity for chromatin (linker histone H1/H5) and interaction with the HAT domain of KAT8 (PEHE domain). The tumor expressed HMGA2 and HMGA1 even though 12q14-15 and 6p looked normal by G-banding analysis. The tumor also expressed MED12 in the absence of exon 2 mutations. Overall, the data show that the examined retroperitoneal leiomyoma resembles a subset of uterine leiomyomas in terms of histology and genetics.

Tandem RNA chimeras contribute to transcriptome diversity in human population and are associated with intronic genetic variants

Chimeric RNAs originating from two or more different genes are known to exist not only in cancer, but also in normal tissues, where they can play a role in human evolution. However, the exact mechanism of their formation is unknown. Here, we use RNA sequencing data from 462 healthy individuals representing 5 human populations to systematically identify and in depth characterize 81 RNA tandem chimeric transcripts, 13 of which are novel. We observe that 6 out of these 81 chimeras have been regarded as cancer-specific. Moreover, we show that a prevalence of long introns at the fusion breakpoint is associated with the chimeric transcripts formation. We also find that tandem RNA chimeras have lower abundances as compared to their partner genes. Finally, by combining our results with genomic data from the same individuals we uncover intronic genetic variants associated with the chimeric RNA formation. Taken together our findings provide an important insight into the chimeric transcripts formation and open new avenues of research into the role of intronic genetic variants in post-transcriptional processing events.

Is an observed non-co-linear RNA product spliced in trans, in cis or just in vitro?

Global transcriptome investigations often result in the detection of an enormous number of transcripts composed of non-co-linear sequence fragments. Such 'aberrant' transcript products may arise from post-transcriptional events or genetic rearrangements, or may otherwise be false positives (sequencing/alignment errors or in vitro artifacts). Moreover, post-transcriptionally non-co-linear ('PtNcl') transcripts can arise from trans-splicing or back-splicing in cis (to generate so-called 'circular RNA'). Here, we collected previously-predicted human non-co-linear RNA candidates, and designed a validation procedure integrating in silico filters with multiple experimental validation steps to examine their authenticity. We showed that >50% of the tested candidates were in vitro artifacts, even though some had been previously validated by RT-PCR. After excluding the possibility of genetic rearrangements, we distinguished between trans-spliced and circular RNAs, and confirmed that these two splicing forms can share the same non-co-linear junction. Importantly, the experimentally-confirmed PtNcl RNA events and their corresponding PtNcl splicing types (i.e. trans-splicing, circular RNA, or both sharing the same junction) were all expressed in rhesus macaque, and some were even expressed in mouse. Our study thus describes an essential procedure for confirming PtNcl transcripts, and provides further insight into the evolutionary role of PtNcl RNA events, opening up this important, but understudied, class of post-transcriptional events for comprehensive characterization.

Assessing the hodgepodge of non-mapped reads in bacterial transcriptomes: real or artifactual RNA chimeras?

BACKGROUND

RNA sequencing methods have already altered our view of the extent and complexity of bacterial and eukaryotic transcriptomes, revealing rare transcript isoforms (circular RNAs, RNA chimeras) that could play an important role in their biology.

RESULTS

We performed an analysis of chimera formation by four different computational approaches, including a custom designed pipeline, to study the transcriptomes of M. pneumoniae and P. aeruginosa, as well as mixtures of both. We found that rare transcript isoforms detected by conventional pipelines of analysis could be artifacts of the experimental procedure used in the library preparation, and that they are protocol-dependent.

CONCLUSION

By using a customized pipeline we show that optimal library preparation protocol and the pipeline to analyze the results are crucial to identify real chimeric RNAs.

The "grep" command but not FusionMap, FusionFinder or ChimeraScan captures the CIC-DUX4 fusion gene from whole transcriptome sequencing data on a small round cell tumor with t(4;19)(q35;q13)

Whole transcriptome sequencing was used to study a small round cell tumor in which a t(4;19)(q35;q13) was part of the complex karyotype but where the initial reverse transcriptase PCR (RT-PCR) examination did not detect a CIC-DUX4 fusion transcript previously described as the crucial gene-level outcome of this specific translocation. The RNA sequencing data were analysed using the FusionMap, FusionFinder, and ChimeraScan programs which are specifically designed to identify fusion genes. FusionMap, FusionFinder, and ChimeraScan identified 1017, 102, and 101 fusion transcripts, respectively, but CIC-DUX4 was not among them. Since the RNA sequencing data are in the fastq text-based format, we searched the files using the "grep" command-line utility. The "grep" command searches the text for specific expressions and displays, by default, the lines where matches occur. The "specific expression" was a sequence of 20 nucleotides from the coding part of the last exon 20 of CIC (Reference Sequence: NM_015125.3) chosen since all the so far reported CIC breakpoints have occurred here. Fifteen chimeric CIC-DUX4 cDNA sequences were captured and the fusion between the CIC and DUX4 genes was mapped precisely. New primer combinations were constructed based on these findings and were used together with a polymerase suitable for amplification of GC-rich DNA templates to amplify CIC-DUX4 cDNA fragments which had the same fusion point found with "grep". In conclusion, FusionMap, FusionFinder, and ChimeraScan generated a plethora of fusion transcripts but did not detect the biologically important CIC-DUX4 chimeric transcript; they are generally useful but evidently suffer from imperfect both sensitivity and specificity. The "grep" command is an excellent tool to capture chimeric transcripts from RNA sequencing data when the pathological and/or cytogenetic information strongly indicates the presence of a specific fusion gene.

Sequential combination of karyotyping and RNA-sequencing in the search for cancer-specific fusion genes

Cancer-specific fusion genes are often caused by cytogenetically visible chromosomal rearrangements such as translocations, inversions, deletions or insertions, they can be the targets of molecular therapy, they play a key role in the accurate diagnosis and classification of neoplasms, and they are of prognostic impact. The identification of novel fusion genes in various neoplasms therefore not only has obvious research importance, but is also potentially of major clinical significance. The "traditional" methodology to detect them began with cytogenetic analysis to find the chromosomal rearrangement, followed by utilization of fluorescence in situ hybridization techniques to find the probe which spans the chromosomal breakpoint, and finally molecular cloning to localize the breakpoint more precisely and identify the genes fused by the chromosomal rearrangement. Although laborious, the above-mentioned sequential approach is robust and reliable and a number of fusion genes have been cloned by such means. Next generation sequencing (NGS), mainly RNA sequencing (RNA-Seq), has opened up new possibilities to detect fusion genes even when cytogenetic aberrations are cryptic or information about them is unknown. However, NGS suffers from the shortcoming of identifying as "fusion genes" also many technical, biological and, perhaps in particular, clinical "false positives," thus making the assessment of which fusions are important and which are noise extremely difficult. The best way to overcome this risk of information overflow is, whenever reliable cytogenetic information is at hand, to compare karyotyping and sequencing data and concentrate exclusively on those suggested fusion genes that are found in chromosomal breakpoints. This article is part of a Directed Issue entitled: Rare Cancers.

Comparison between karyotyping-FISH-reverse transcription PCR and RNA-sequencing-fusion gene identification programs in the detection of KAT6A-CREBBP in acute myeloid leukemia

An acute myeloid leukemia was suspected of having a t(8;16)(p11;p13) resulting in a KAT6A-CREBBP fusion because the bone marrow was packed with monoblasts showing marked erythrophagocytosis. The diagnostic karyotype was 46,XY,add(1)(p13),t(8;21)(p11;q22),der(16)t(1;16)(p13;p13)[9]/46,XY[1]; thus, no direct confirmation of the suspicion could be given although both 8p11 and 16p13 seemed to be rearranged. The leukemic cells were examined in two ways to find out whether a cryptic KAT6A-CREBBP was present. The first was the “conventional” approach: G-banding was followed by fluorescence in situ hybridization (FISH) and reverse transcription PCR (RT-PCR). The second was RNA-Seq followed by data analysis using FusionMap and FusionFinder programs with special emphasis on candidates located in the 1p13, 8p11, 16p13, and 21q22 breakpoints. FISH analysis indicated the presence of a KAT6A/CREBBP chimera. RT-PCR followed by Sanger sequencing of the amplified product showed that a chimeric KAT6A-CREBBP transcript was present in the patients bone marrow. Surprisingly, however, KATA6A-CREBBP was not among the 874 and 35 fusion transcripts identified by the FusionMap and FusionFinder programs, respectively, although 11 sequences of the raw RNA-sequencing data were KATA6A-CREBBP fragments. This illustrates that although many fusion transcripts can be found by RNA-Seq combined with FusionMap and FusionFinder, the pathogenetically essential fusion is not always picked up by the bioinformatic algorithms behind these programs. The present study not only illustrates potential pitfalls of current data analysis programs of whole transcriptome sequences which make them less useful as stand-alone techniques, but also that leukemia diagnosis still relies on integration of clinical, hematologic, and genetic disease features of which the former two by no means have become superfluous.

Fusion transcript discovery in formalin-fixed paraffin-embedded human breast cancer tissues reveals a link to tumor progression

The identification of gene fusions promises to play an important role in personalized cancer treatment decisions. Many rare gene fusion events have been identified in fresh frozen solid tumors from common cancers employing next-generation sequencing technology. However the ability to detect transcripts from gene fusions in RNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor tissues, which exist in very large sample repositories for which disease outcome is known, is still limited due to the low complexity of FFPE libraries and the lack of appropriate bioinformatics methods. We sought to develop a bioinformatics method, named gFuse, to detect fusion transcripts in FFPE tumor tissues. An integrated, cohort based strategy has been used in gFuse to examine single-end 50 base pair (bp) reads generated from FFPE RNA-Sequencing (RNA-Seq) datasets employing two breast cancer cohorts of 136 and 76 patients. In total, 118 fusion events were detected transcriptome-wide at base-pair resolution across the 212 samples. We selected 77 candidate fusions based on their biological relevance to cancer and supported 61% of these using TaqMan assays. Direct sequencing of 19 of the fusion sequences identified by TaqMan confirmed them. Three unique fused gene pairs were recurrent across the 212 patients with 6, 3, 2 individuals harboring these fusions respectively. We show here that a high frequency of fusion transcripts detected at the whole transcriptome level correlates with poor outcome (P<0.0005) in human breast cancer patients. This study demonstrates the ability to detect fusion transcripts as biomarkers from archival FFPE tissues, and the potential prognostic value of the fusion transcripts detected.

Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data

BACKGROUND

RNA-seq has spurred important gene fusion discoveries in a number of different cancers, including lung, prostate, breast, brain, thyroid and bladder carcinomas. Gene fusion discovery can potentially lead to the development of novel treatments that target the underlying genetic abnormalities.

RESULTS

In this study, we provide comprehensive view of gene fusion landscape in 185 glioblastoma multiforme patients from two independent cohorts. Fusions occur in approximately 30-50% of GBM patient samples. In the Ivy Center cohort of 24 patients, 33% of samples harbored fusions that were validated by qPCR and Sanger sequencing. We were able to identify high-confidence gene fusions from RNA-seq data in 53% of the samples in a TCGA cohort of 161 patients. We identified 13 cases (8%) with fusions retaining a tyrosine kinase domain in the TCGA cohort and one case in the Ivy Center cohort. Ours is the first study to describe recurrent fusions involving non-coding genes. Genomic locations 7p11 and 12q14-15 harbor majority of the fusions. Fusions on 7p11 are formed in focally amplified EGFR locus whereas 12q14-15 fusions are formed by complex genomic rearrangements. All the fusions detected in this study can be further visualized and analyzed using our website: http://ivygap.swedish.org/fusions.

CONCLUSIONS

Our study highlights the prevalence of gene fusions as one of the major genomic abnormalities in GBM. The majority of the fusions are private fusions, and a minority of these recur with low frequency. A small subset of patients with fusions of receptor tyrosine kinases can benefit from existing FDA approved drugs and drugs available in various clinical trials. Due to the low frequency and rarity of clinically relevant fusions, RNA-seq of GBM patient samples will be a vital tool for the identification of patient-specific fusions that can drive personalized therapy.

Integrative transcriptome sequencing identifies trans-splicing events with important roles in human embryonic stem cell pluripotency

Trans-splicing is a post-transcriptional event that joins exons from separate pre-mRNAs. Detection of trans-splicing is usually severely hampered by experimental artifacts and genetic rearrangements. Here, we develop a new computational pipeline, TSscan, which integrates different types of high-throughput long-/short-read transcriptome sequencing of different human embryonic stem cell (hESC) lines to effectively minimize false positives while detecting trans-splicing. Combining TSscan screening with multiple experimental validation steps revealed that most chimeric RNA products were platform-dependent experimental artifacts of RNA sequencing. We successfully identified and confirmed four trans-spliced RNAs, including the first reported trans-spliced large intergenic noncoding RNA (“tsRMST”). We showed that these trans-spliced RNAs were all highly expressed in human pluripotent stem cells and differentially expressed during hESC differentiation. Our results further indicated that tsRMST can contribute to pluripotency maintenance of hESCs by suppressing lineage-specific gene expression through the recruitment of NANOG and the PRC2 complex factor, SUZ12. Taken together, our findings provide important insights into the role of trans-splicing in pluripotency maintenance of hESCs and help to facilitate future studies into trans-splicing, opening up this important but understudied class of post-transcriptional events for comprehensive characterization.

Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions

MOTIVATION

Gene fusions resulting from chromosomal aberrations are an important cause of cancer. The complexity of genomic changes in certain cancer types has hampered the identification of gene fusions by molecular cytogenetic methods, especially in carcinomas. This is changing with the advent of next-generation sequencing, which is detecting a substantial number of new fusion transcripts in individual cancer genomes. However, this poses the challenge of identifying those fusions with greater oncogenic potential amid a background of 'passenger' fusion sequences.

RESULTS

In the present work, we have used some recently identified genomic hallmarks of oncogenic fusion genes to develop a pipeline for the classification of fusion sequences, namely, Oncofuse. The pipeline predicts the oncogenic potential of novel fusion genes, calculating the probability that a fusion sequence behaves as 'driver' of the oncogenic process based on features present in known oncogenic fusions. Cross-validation and extensive validation tests on independent datasets suggest a robust behavior with good precision and recall rates. We believe that Oncofuse could become a useful tool to guide experimental validation studies of novel fusion sequences found during next-generation sequencing analysis of cancer transcriptomes.

AVAILABILITY AND IMPLEMENTATION

Oncofuse is a naive Bayes Network Classifier trained and tested using Weka machine learning package. The pipeline is executed by running a Java/Groovy script, available for download at www.unav.es/genetica/oncofuse.html.

Identification of cancer gene fusions based on advanced analysis of the human genome or transcriptome

Many gene fusions have been recognized as important diagnostic and/or prognostic markers in human malignancies. In recent years, novel gene fusions have been identified in cases without prior knowledge of the genetic background. Accompanied by a powerful computational data analysis method, new genome-wide screening approaches were used to detect cryptic genomic aberrations. This review focused on advanced genomewide screening approaches in fusion gene identification, such as microarray-based approaches, next-generation sequencing, and NanoString nCounter gene expression system. The fundamental rationale and strategy for fusion gene identification using each biotech platform are also discussed.

State of art fusion-finder algorithms are suitable to detect transcription-induced chimeras in normal tissues?

Background

RNA-seq has the potential to discover genes created by chromosomal rearrangements. Fusion genes, also known as "chimeras", are formed by the breakage and re-joining of two different chromosomes. It is known that chimeras have been implicated in the development of cancer. Few publications in the past showed the presence of fusion events also in normal tissue, but with very limited overlaps between their results. More recently, two fusion genes in normal tissues were detected using both RNA-seq and protein data.

Due to heterogeneous results in identifying chimeras in normal tissue, we decided to evaluate the efficacy of state of the art fusion finders in detecting chimeras in RNA-seq data from normal tissues.

Results

We compared the performance of six fusion-finder tools: FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse and TopHat-fusion. To evaluate the sensitivity we used a synthetic dataset of fusion-products, called positive dataset; in these experiments FusionMap, FusionFinder, MapSplice, and TopHat-fusion are able to detect more than 78% of fusion genes. All tools were error prone with high variability among the tools, identifying some fusion genes not present in the synthetic dataset. To better investigate the false discovery chimera detection rate, synthetic datasets free of fusion-products, called negative datasets, were used. The negative datasets have different read lengths and quality scores, which allow detecting dependency of the tools on both these features. FusionMap, FusionFinder, mapSplice, deFuse and TopHat-fusion were error-prone. Only FusionHunter results were free of false positive. FusionMap gave the best compromise in terms of specificity in the negative dataset and of sensitivity in the positive dataset.

Conclusions

We have observed a dependency of the tools on read length, quality score and on the number of reads supporting each chimera. Thus, it is important to carefully select the software on the basis of the structure of the RNA-seq data under analysis. Furthermore, the sensitivity of chimera detection tools does not seem to be sufficient to provide results consistent with those obtained in normal tissues on the basis of fusion events extracted from published data.

State-of-the-art fusion-finder algorithms sensitivity and specificity

Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way. Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions. Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

Novel BRD4-NUT fusion isoforms increase the pathogenic complexity in NUT midline carcinoma

Nuclear protein in testis (NUT)-midline carcinoma (NMC) is a rare, aggressive disease typically presenting with a single t(15;19) translocation that results in the generation of a bromodomain-containing protein 4 (BRD4)-NUT fusion. PER-624 is a cell line generated from an NMC patient with an unusually complex karyotype that gave no initial indication of the involvement of the NUT locus. Analysis of PER-624 next-generation transcriptome sequencing (RNA-Seq) using the algorithm FusionFinder identified a novel transcript in which Exon 15 of BRD4 was fused to Exon 2 of NUT, therefore differing from all published NMC fusion transcripts. The three additional exons contained in the PER-624 fusion encode a series of polyproline repeats, with one predicted to form a helix. In the NMC cell line PER-403, we identified the 'standard' NMC fusion and two novel isoforms. Knockdown by small interfering RNA in either cell line resulted in decreased proliferation, increased cell size and expression of cytokeratins consistent with epithelial differentiation. These data demonstrate that the novel BRD4-NUT fusion in PER-624 encodes a functional protein that is central to the oncogenic mechanism in these cells. Genomic PCR indicated that in both PER-624 and PER-403, the translocation fuses an intron of BRD4 to a region upstream of the NUT coding sequence. Thus, the generation of BRD4-NUT fusion transcripts through post-translocation RNA-splicing appears to be a common feature of these carcinomas that has not previously been appreciated, with the mechanism facilitating the expression of alternative isoforms of the fusion. Finally, ectopic expression of wild-type NUT, a protein normally restricted to the testis, could be demonstrated in PER-403, indicating additional pathways for aberrant cell signaling in NMC. This study contributes to our understanding of the genetic diversity of NMC, an important step towards finding therapeutic targets for a disease that is refractory to current treatments.