ChimerDB 2.0--a knowledgebase for fusion genes updated

Detecting intragenic trans-splicing events from non-co-linearly spliced junctions by hybrid sequencing

Trans-spliced RNAs (ts-RNAs) are a type of non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting ts-RNAs is often interfered by experimental artifacts, circular RNAs (circRNAs) and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same gene, are often mistaken for circRNAs through analyses of RNA-seq data. Here we developed a bioinformatics pipeline (NCLscan-hybrid), which integrated short and long RNA-seq reads to minimize false positives and proposed out-of-circle and rolling-circle long reads to distinguish between intragenic ts-RNAs and circRNAs. Combining NCLscan-hybrid screening and multiple experimental validation steps successfully confirmed that four NCL events, which were previously regarded as circRNAs in databases, originated from trans-splicing. CRISPR-based endogenous genome modification experiments further showed that flanking intronic complementary sequences can significantly contribute to ts-RNA formation, providing an efficient/specific method to deplete ts-RNAs. We also experimentally validated that one ts-RNA (ts-ARFGEF1) played an important role for p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of ts-RNAs, expanding future studies for identification, biogenesis, and function of these important but understudied transcripts.

Targeted in silico characterization of fusion transcripts in tumor and normal tissues via FusionInspector

Here, we present FusionInspector for in silico characterization and interpretation of candidate fusion transcripts from RNA sequencing (RNA-seq) and exploration of their sequence and expression characteristics. We applied FusionInspector to thousands of tumor and normal transcriptomes and identified statistical and experimental features enriched among biologically impactful fusions. Through clustering and machine learning, we identified large collections of fusions potentially relevant to tumor and normal biological processes. We show that biologically relevant fusions are enriched for relatively high expression of the fusion transcript, imbalanced fusion allelic ratios, and canonical splicing patterns, and are deficient in sequence microhomologies between partner genes. We demonstrate that FusionInspector accurately validates fusion transcripts in silico and helps characterize numerous understudied fusions in tumor and normal tissue samples. FusionInspector is freely available as open source for screening, characterization, and visualization of candidate fusions via RNA-seq, and facilitates transparent explanation and interpretation of machine-learning predictions and their experimental sources.

Assessing the impacts of various factors on circular RNA reliability

Circular RNAs (circRNAs) are non-polyadenylated RNAs with a continuous loop structure characterized by a non-colinear back-splice junction (BSJ). Although millions of circRNA candidates have been identified, it remains a major challenge for determining circRNA reliability because of various types of false positives. Here, we systematically assess the impacts of numerous factors related to circRNA identification, conservation, biogenesis, and function on circRNA reliability by comparisons of circRNA expression from mock and the corresponding colinear/polyadenylated RNA-depleted datasets based on three different RNA treatment approaches. Eight important indicators of circRNA reliability are determined. The relative contribution to variability explained analyses reveal that the relative importance of these factors in affecting circRNA reliability in descending order is the conservation level of circRNA, full-length circular sequences, supporting BSJ read count, both BSJ donor and acceptor splice sites at the same colinear transcript isoforms, both BSJ donor and acceptor splice sites at the annotated exon boundaries, BSJs detected by multiple tools, supporting functional features, and both BSJ donor and acceptor splice sites undergoing alternative splicing. This study thus provides a useful guideline and an important resource for selecting high-confidence circRNAs for further investigations.

GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints

Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.

Identifying the oncogenic potential of gene fusions exploiting miRNAs

It is estimated that oncogenic gene fusions cause about 20% of human cancer morbidity. Identifying potentially oncogenic gene fusions may improve affected patients' diagnosis and treatment. Previous approaches to this issue included exploiting specific gene-related information, such as gene function and regulation. Here we propose a model that profits from the previous findings and includes the microRNAs in the oncogenic assessment. We present ChimerDriver, a tool to classify gene fusions as oncogenic or not oncogenic. ChimerDriver is based on a specifically designed neural network and trained on genetic and post-transcriptional information to obtain a reliable classification. The designed neural network integrates information related to transcription factors, gene ontologies, microRNAs and other detailed information related to the functions of the genes involved in the fusion and the gene fusion structure. As a result, the performances on the test set reached 0.83 f1-score and 96% recall. The comparison with state-of-the-art tools returned comparable or higher results. Moreover, ChimerDriver performed well in a real-world case where 21 out of 24 validated gene fusion samples were detected by the gene fusion detection tool Starfusion. ChimerDriver integrates transcriptional and post-transcriptional information in an ad-hoc designed neural network to effectively discriminate oncogenic gene fusions from passenger ones. ChimerDriver source code is freely available at https://github.com/martalovino/ChimerDriver.

3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia

3D genome alternations can dysregulate gene expression by rewiring enhancer-promoter interactions and lead to diseases. We report integrated analyses of 3D genome alterations and differential gene expressions in 18 newly diagnosed T-lineage acute lymphoblastic leukemia (T-ALL) patients and 4 healthy controls. 3D genome organizations at the levels of compartment, topologically associated domains and loop could hierarchically classify different subtypes of T-ALL according to T cell differentiation trajectory, similar to gene expressions-based classification. Thirty-four previously unrecognized translocations and 44 translocation-mediated neo-loops are mapped by Hi-C analysis. We find that neo-loops formed in the non-coding region of the genome could potentially regulate ectopic expressions of TLX3, TAL2 and HOXA transcription factors via enhancer hijacking. Importantly, both translocation-mediated neo-loops and NUP98-related fusions are associated with HOXA13 ectopic expressions. Patients with HOXA11-A13 expressions, but not other genes in the HOXA cluster, have immature immunophenotype and poor outcomes. Here, we highlight the potentially important roles of 3D genome alterations in the etiology and prognosis of T-ALL.

The non-coding genome of T-ALL has not been extensively studied. Here, the authors conduct RNA-seq, ATAC-seq and Hi-C seq analyses and find that T-ALL associated neo-loops may regulate key transcription factors including HOXA13; the aberrant expression of which is associated with poor prognosis.

Circulating Cell-Free DNA Combined to Magnetic Resonance Imaging for Early Detection of HCC in Patients with Liver Cirrhosis

Simple Summary

Liver cirrhosis can develop into malignant disease over time. Frequent monitoring would be advisable to detect the earliest signs of HCC progress resulting in a possible earlier treatment of the patient. Our study showed that the combination of genetic analysis of DNA freely circulating in the blood of the cirrhotic patients with MRI can represent a powerful strategy to timely identify suspect lesions, which can then be followed up more closely and thus potentially be treated earlier. In this way, personalized medicine can be applied to liver diseases such as cirrhosis.

Abstract

Liquid biopsy based on circulating cell-free DNA (cfDNA) is a promising non-invasive tool for the prognosis of hepatocellular cancer (HCC). In this exploratory study we investigated whether cfDNA and gene variants associated with HCC may be found in patients with liver cirrhosis (LC) and thus identify those at an increased risk for HCC. A cohort of 40 LC patients with no suspect neoplastic lesions was included in this study. Next generation sequencing (NGS) of cfDNA isolated from plasma was performed on a panel of 597 selected genes. Images of the patients who underwent MRI with hepatospecific contrast media during the study period were retrospectively re-evaluated (imaging was not part of the prospective study). cfDNA was detected in the plasma of 36 patients with LC. NGS-based analyses identified 20 variants in different combinations. Re-evaluation of the MRI images that were available for a proportion of the patients (n = 27) confirmed the absence of lesions in 8 cases carrying cfDNA without variants. In 6 of 19 patients with identified variants and MRI images available, MRI revealed a precursor lesion compatible with HCC and new lesions were discovered at follow-up in two patients. These precursor lesions were amenable for curative treatments. Mutation analysis revealed selective HCC related gene mutations in a subset of patients with LC, raising the suspect that these patients were at an increased risk for HCC development. MRI findings confirmed suspect nodular lesions of early stage HCC not detected with current standard screening procedures, which were only seen in patients carrying cfDNA variants. This opens a perspective for an HCC screening strategy combining both liquid biopsy and MRI in patients with LC.

The Search for Molecular Markers in a Gene-Orphan Case Study of a Pediatric Spinal Cord Pilocytic Astrocytoma

BACKGROUND/AIM

We herein presented a case of pediatric spinal cord pilocytic astrocytoma diagnosed on the basis of histopathological and clinical findings.

MATERIALS AND METHODS

Given the paucity of data on genetic features for this tumor, we performed exome, array CGH and RNA sequencing analysis from nucleic acids isolated from a unique and not repeatable very small amount of a formalin-fixed, paraffin-embedded (FFPE) specimen.

RESULTS

DNA mutation analysis, comparing tumor and normal lymphocyte peripheral DNA, evidenced few tumor-specific single nucleotide variants in DEFB119, MUC5B, NUDT1, LTBP3 and CPSF3L genes. Differently, tumor DNA was not characterized by for the main pilocytic astrocytoma gene variations, including BRAFV600E. An inframe trinucleotides insertion involving DLX6 or lnc DLX6-AS1 genes was scored in 44.9% of sequenced reads; the temporal profile of this variation on the expression of DLX-AS1 was investigated in patient's urine-derived exosomes, reporting no significant variation in the one-year molecular follow-up. Array CGH identified a tumor microdeletion at the 6q25.3 chromosomal region, spanning 1,01 Mb and comprising ZDHHC14, SNX9, TULP4 and SYTL3 genes. The expression of these genes did not change in urine-derived exosomes during the one-year investigation period. Finally, RNAseq did not reveal any of the common pilocytic BRAF-KIAA1549 genes fusion events.

CONCLUSION

To our knowledge, the present report is one of the first described gene-orphan case studies of a pediatric spinal cord pilocytic astrocytoma.

Targeted next-generation sequencing identifies the disruption of the SHANK3 and RYR2 genes in a patient carrying a de novo t(1;22)(q43;q13.3) associated with signs of Phelan-McDermid syndrome

Background

It has been known for more than 30 years that balanced translocations, especially if de novo, can associate with congenital malformations and / or neurodevelopmental disorders, following the disruption of a disease gene or its cis-regulatory elements at one or both breakpoints.

Case presentation

We describe a 10-year-old girl with a non-specific neurodevelopmental disorder characterized by moderate intellectual disability (ID), gross motor clumsiness, social and communication deficits. She carries a de novo reciprocal translocation between chromosomes 1q43 and 22q13.3, the latter suggesting the involvement of SHANK3. Indeed, its haploinsufficiency associates with Phelan-McDermid Syndrome, whose main symptoms are characterized by global developmental delay and absent or severely delayed expressive speech. A deep molecular approach, including next-generation sequencing of SHANK3 locus, allowed demonstrating the breakage of RYR2 and SHANK3 on the derivative chromosomes 1 and 22 respectively, and the formation of two fusion genes SHANK3-RYR2 and RYR2-SHANK3 with concomitant cryptic deletion of 3.6 and 4.1 kilobases at translocation junction of both derivatives chromosomes 22 and 1, respectively.

Conclusions

Although the interruption of SHANK3 accounts for the patient’s psychomotor retardation and autism-like behavior, we do not exclude that the interruption of RYR2 may also have a role on her disorder, or result in further pathogenicity in the future. Indeed, RYR2 that has a well-established role in the etiology of two autosomal dominant adulthood cardiac disorders (#600996 and #604772) is also expressed in the brain (cerebellum, hippocampus, and cerebral cortex) and about half of RYR2 mutation carriers present late onset primary generalized epilepsy without cardiac arrhythmogenic disorders. Moreover, RYR2 variants have also been sporadically reported in individuals with early onset schizophrenia or ID, and its constraint values suggest intolerance to loss-of-function. This study not only confirms the usefulness of the molecular mapping of de novo balanced rearrangements in symptomatic individuals, but also underscores the need for long-term clinical evaluation of the patients, for better evaluating the pathogenicity of the chromosomal breakpoints.

Identification of Chimeric RNAs Using RNA-Seq Reads and Protein-Protein Interactions of Translated Chimeras

Chimeric RNA moieties typically consist of exons from two genes expressed from different genomic locations and produced by chromosomal translocations, trans-splicing or transcription errors. Recent advances in next-generation sequencing procedures have opened new horizons for identification of novel chimeric transcripts in various diseases in a personalized manner. Here we describe the detailed computational procedures to identify chimeric transcripts using RNA-seq reads. Moreover, we elaborate on the domain-domain co-occurrence method to detect alterations in chimeric protein-protein interaction (ChiPPI) networks produced by chimeric RNA that are translated to chimeric proteins.

Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

BACKGROUND

Accurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.

RESULTS

We benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.

CONCLUSION

The lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Circulating DNA as prognostic biomarker in patients with advanced hepatocellular carcinoma: a translational exploratory study from the SORAMIC trial

BACKGROUND

Liquid biopsy based on cell-free DNA circulating in plasma has shown solid results as a non-invasive biomarker. In the present study we evaluated the utility of circulating free DNA (cfDNA) and the sub-type tumor DNA (ctDNA) in hepatocellular cancer (HCC) patients to assess therapy response and clinical outcome.

METHODS

A cohort of 13 patients recruited in the context of the SORAMIC trial with unresectable, advanced HCC and different etiological and clinicopathological characteristics was included in this exploratory study. Plasma samples were collected between liver micro-intervention and beginning of sorafenib-based systemic therapy and then in correspondence of three additional follow-ups. DNA was isolated from plasma and next generation sequencing (NGS) was performed on a panel of 597 selected cancer-relevant genes.

RESULTS

cfDNA levels showed a significant correlation with the presence of metastases and survival. In addition cfDNA kinetic over time revealed a trend with the clinical history of the patients, supporting its use as a biomarker to monitor therapy. NGS-based analysis on ctDNA identified 28 variants, detectable in different combinations at the different time points. Among the variants, HNF1A, BAX and CYP2B6 genes showed the highest mutation frequency and a significant association with the patients' clinicopathological characteristics, suggesting a possible role as driver genes in this specific clinical setting.

CONCLUSIONS

Taken together, the results support the prognostic value of cfDNA/ctDNA in advanced HCC patients with the potential to predict therapy response. These findings support the clinical utility of liquid biopsy in advanced HCC improving individualized therapy and possible earlier identification of treatment responders.

PITDB: a database of translated genomic elements

PITDB is a freely available database of translated genomic elements (TGEs) that have been observed in PIT (proteomics informed by transcriptomics) experiments. In PIT, a sample is analyzed using both RNA-seq transcriptomics and proteomic mass spectrometry. Transcripts assembled from RNA-seq reads are used to create a library of sample-specific amino acid sequences against which the acquired mass spectra are searched, permitting detection of any TGE, not just those in canonical proteome databases. At the time of writing, PITDB contains over 74 000 distinct TGEs from four species, supported by more than 600 000 peptide spectrum matches. The database, accessible via http://pitdb.org, provides supporting evidence for each TGE, often from multiple experiments and an indication of the confidence in the TGE’s observation and its type, ranging from known protein (exact match to a UniProt protein sequence), through multiple types of protein variant including various splice isoforms, to a putative novel molecule. PITDB’s modern web interface allows TGEs to be viewed individually or by species or experiment, and downloaded for further analysis. PITDB is for bench scientists seeking to share their PIT results, for researchers investigating novel genome products in model organisms and for those wishing to construct proteomes for lesser studied species.

LncRNAs and PRC2: Coupled Partners in Embryonic Stem Cells

The power of embryonic stem cells (ESCs) lies in their ability to self-renew and differentiate. Behind these two unique capabilities is a fine-tuned molecular network that shapes the genetic, epigenetic, and epitranscriptomic ESC plasticity. Although RNA has been shown to be functionally important in only a small minority of long non-coding RNA genes, a growing body of evidence has highlighted the pivotal and intricate role of lncRNAs in chromatin remodeling. Due to their multifaceted nature, lncRNAs interact with DNA, RNA, and proteins, and are emerging as new modulators of extensive gene expression programs through their participation in ESC-specific regulatory circuitries. Here, we review the tight cooperation between lncRNAs and Polycomb repressive complex 2 (PRC2), which is intimately involved in determining and maintaining the ESC epigenetic landscape. The lncRNA-PRC2 partnership is fundamental in securing the fully pluripotent state of ESCs, which must be primed to differentiate properly. We also reflect on the advantages brought to this field of research by the advent of single-cell analysis.

FusionScan: accurate prediction of fusion genes from RNA-Seq data

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

Integrative transcriptome sequencing reveals extensive alternative trans-splicing and cis-backsplicing in human cells

Transcriptionally non-co-linear (NCL) transcripts can originate from trans-splicing (trans-spliced RNA; 'tsRNA') or cis-backsplicing (circular RNA; 'circRNA'). While numerous circRNAs have been detected in various species, tsRNAs remain largely uninvestigated. Here, we utilize integrative transcriptome sequencing of poly(A)- and non-poly(A)-selected RNA-seq data from diverse human cell lines to distinguish between tsRNAs and circRNAs. We identified 24,498 NCL events and found that a considerable proportion (20-35%) of them arise from both tsRNAs and circRNAs, representing extensive alternative trans-splicing and cis-backsplicing in human cells. We show that sequence generalities of exon circularization are also observed in tsRNAs. Recapitulation of NCL RNAs further shows that inverted Alu repeats can simultaneously promote the formation of tsRNAs and circRNAs. However, tsRNAs and circRNAs exhibit quite different, or even opposite, expression patterns, in terms of correlation with the expression of their co-linear counterparts, expression breadth/abundance, transcript stability, and subcellular localization preference. These results indicate that tsRNAs and circRNAs may play different regulatory roles and analysis of NCL events should take the joint effects of different NCL-splicing types and joint effects of multiple NCL events into consideration. This study describes the first transcriptome-wide analysis of trans-splicing and cis-backsplicing, expanding our understanding of the complexity of the human transcriptome.

Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study

Assessing the impact of kinase in gene fusion is essential for both identifying driver fusion genes (FGs) and developing molecular targeted therapies. Kinase domain retention is a crucial factor in kinase fusion genes (KFGs), but such a systematic investigation has not been done yet. To this end, we analyzed kinase domain retention (KDR) status in chimeric protein sequences of 914 KFGs covering 312 kinases across 13 major cancer types. Based on 171 kinase domain-retained KFGs including 101 kinases, we studied their recurrence, kinase groups, fusion partners, exon-based expression depth, short DNA motifs around the break points and networks. Our results, such as more KDR than 5'-kinase fusion genes, combinatorial effects between 3'-KDR kinases and their 5'-partners and a signal transduction-specific DNA sequence motif in the break point intronic sequences, supported positive selection on 3'-kinase fusion genes in cancer. We introduced a degree-of-frequency (DoF) score to measure the possible number of KFGs of a kinase. Interestingly, kinases with high DoF scores tended to undergo strong gene expression alteration at the break points. Furthermore, our KDR gene fusion network analysis revealed six of the seven kinases with the highest DoF scores (ALK, BRAF, MET, NTRK1, NTRK3 and RET) were all observed in thyroid carcinoma. Finally, we summarized common features of 'effective' (highly recurrent) kinases in gene fusions such as expression alteration at break point, redundant usage in multiple cancer types and 3'-location tendency. Collectively, our findings are useful for prioritizing driver kinases and FGs and provided insights into KFGs' clinical implications.

Regional perturbation of gene transcription is associated with intrachromosomal rearrangements and gene fusion transcripts in high grade ovarian cancer

Genomic rearrangements are a hallmark of cancer biology and progression, allowing cells to rapidly transform through alterations in regulatory structures, changes in expression patterns, reprogramming of signaling pathways, and creation of novel transcripts via gene fusion events. Though functional gene fusions encoding oncogenic proteins are the most dramatic outcomes of genomic rearrangements, we investigated the relationship between rearrangements evidenced by fusion transcripts and local expression changes in cancer using transcriptome data alone. 9,953 gene fusion predictions from 418 primary serious ovarian cancer tumors were analyzed, identifying depletions of gene fusion breakpoints within coding regions of fused genes as well as an N-terminal enrichment of breakpoints within fused genes. We identified 48 genes with significant fusion-associated upregulation and furthermore demonstrate that significant regional overexpression of intact genes in patient transcriptomes occurs within 1 megabase of 78 novel gene fusions that function as central markers of these regions. We reveal that cancer transcriptomes select for gene fusions that preserve protein and protein domain coding potential. The association of gene fusion transcripts with neighboring gene overexpression supports rearrangements as mechanism through which cancer cells remodel their transcriptomes and identifies a new way to utilize gene fusions as indicators of regional expression changes in diseased cells with only transcriptomic data.

Proteogenomic Characterization of Human Early-Onset Gastric Cancer

We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations. Phosphoproteome data elucidated signaling pathways associated with somatic mutations based on mutation-phosphorylation correlations. Moreover, correlations between mRNA and protein abundances provided potential oncogenes and tumor suppressors associated with patient survival. Furthermore, integrated clustering of mRNA, protein, phosphorylation, and N-glycosylation data identified four subtypes of diffuse GCs. Distinguishing these subtypes was possible by proteomic data. Four subtypes were associated with proliferation, immune response, metabolism, and invasion, respectively; and associations of the subtypes with immune- and invasion-related pathways were identified mainly by phosphorylation and N-glycosylation data. Therefore, our proteogenomic analysis provides additional information beyond genomic analyses, which can improve understanding of cancer biology and patient stratification in diffuse GCs.

NCLcomparator: systematically post-screening non-co-linear transcripts (circular, trans-spliced, or fusion RNAs) identified from various detectors

BACKGROUND

Non-co-linear (NCL) transcripts consist of exonic sequences that are topologically inconsistent with the reference genome in an intragenic fashion (circular or intragenic trans-spliced RNAs) or in an intergenic fashion (fusion or intergenic trans-spliced RNAs). On the basis of RNA-seq data, numerous NCL event detectors have been developed and detected thousands of NCL events in diverse species. However, there are great discrepancies in the identification results among detectors, indicating a considerable proportion of false positives in the detected NCL events. Although several helpful guidelines for evaluating the performance of NCL event detectors have been provided, a systematic guideline for measurement of NCL events identified by existing tools has not been available.

RESULTS

We develop a software, NCLcomparator, for systematically post-screening the intragenic or intergenic NCL events identified by various NCL detectors. NCLcomparator first examine whether the input NCL events are potentially false positives derived from ambiguous alignments (i.e., the NCL events have an alternative co-linear explanation or multiple matches against the reference genome). To evaluate the reliability of the identified NCL events, we define the NCL score (NCLscore) based on the variation in the number of supporting NCL junction reads identified by the tools examined. Of the input NCL events, we show that the ambiguous alignment-derived events have relatively lower NCLscore values than the other events, indicating that an NCL event with a higher NCLscore has a higher level of reliability. To help selecting highly expressed NCL events, NCLcomparator also provides a series of useful measurements such as the expression levels of the detected NCL events and their corresponding host genes and the junction usage of the co-linear splice junctions at both NCL donor and acceptor sites.

CONCLUSION

NCLcomparator provides useful guidelines, with the input of identified NCL events from various detectors and the corresponding paired-end RNA-seq data only, to help users selecting potentially high-confidence NCL events for further functional investigation. The software thus helps to facilitate future studies into NCL events, shedding light on the fundamental biology of this important but understudied class of transcripts. NCLcomparator is freely accessible at https://github.com/TreesLab/NCLcomparator .

Proteogenomics from a bioinformatics angle: A growing field

Proteogenomics is a research area that combines areas as proteomics and genomics in a multi-omics setup using both mass spectrometry and high-throughput sequencing technologies. Currently, the main goals of the field are to aid genome annotation or to unravel the proteome complexity. Mass spectrometry based identifications of matching or homologues peptides can further refine gene models. Also, the identification of novel proteoforms is also made possible based on detection of novel translation initiation sites (cognate or near-cognate), novel transcript isoforms, sequence variation or novel (small) open reading frames in intergenic or un-translated genic regions by analyzing high-throughput sequencing data from RNAseq or ribosome profiling experiments. Other proteogenomics studies using a combination of proteomics and genomics techniques focus on antibody sequencing, the identification of immunogenic peptides or venom peptides. Over the years, a growing amount of bioinformatics tools and databases became available to help streamlining these cross-omics studies. Some of these solutions only help in specific steps of the proteogenomics studies, e.g. building custom sequence databases (based on next generation sequencing output) for mass spectrometry fragmentation spectrum matching. Over the last few years a handful integrative tools also became available that can execute complete proteogenomics analyses. Some of these are presented as stand-alone solutions, whereas others are implemented in a web-based framework such as Galaxy. In this review we aimed at sketching a comprehensive overview of all the bioinformatics solutions that are available for this growing research area. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:584-599, 2017.

The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes

This year's Database Issue of Nucleic Acids Research contains 152 papers that include descriptions of 54 new databases and update papers on 98 databases, of which 16 have not been previously featured in NAR As always, these databases cover a broad range of molecular biology subjects, including genome structure, gene expression and its regulation, proteins, protein domains, and protein-protein interactions. Following the recent trend, an increasing number of new and established databases deal with the issues of human health, from cancer-causing mutations to drugs and drug targets. In accordance with this trend, three recently compiled databases that have been selected by NAR reviewers and editors as 'breakthrough' contributions, denovo-db, the Monarch Initiative, and Open Targets, cover human de novo gene variants, disease-related phenotypes in model organisms, and a bioinformatics platform for therapeutic target identification and validation, respectively. We expect these databases to attract the attention of numerous researchers working in various areas of genetics and genomics. Looking back at the past 12 years, we present here the 'golden set' of databases that have consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database. The Database Issue is freely available online at the https://academic.oup.com/nar web site. An updated version of the NAR Molecular Biology Database Collection is available at http://www.oxfordjournals.org/nar/database/a/.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification

INTRODUCTION

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Cytogenetic Resources and Information

The main databases devoted stricto sensu to cancer cytogenetics are the "Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer" ( http://cgap.nci.nih.gov/Chromosomes/Mitelman ), the "Atlas of Genetics and Cytogenetics in Oncology and Haematology" ( http://atlasgeneticsoncology.org ), and COSMIC ( http://cancer.sanger.ac.uk/cosmic ).However, being a complex multistep process, cancer cytogenetics are broadened to "cytogenomics," with complementary resources on: general databases (nucleic acid and protein sequences databases; cartography browsers: GenBank, RefSeq, UCSC, Ensembl, UniProtKB, and Entrez Gene), cancer genomic portals associated with recent international integrated programs, such as TCGA or ICGC, other fusion genes databases, array CGH databases, copy number variation databases, and mutation databases. Other resources such as the International System for Human Cytogenomic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), and the Human Gene Nomenclature Database (HGNC) allow a common language.Data within the scientific/medical community should be freely available. However, most of the institutional stakeholders are now gradually disengaging, and well-known databases are forced to beg or to disappear (which may happen!).

ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922 chimeric transcripts along with 11 714 cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the ‘Full Collection’. In addition, for every chimera, we have added a predicted chimeric protein–protein interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922 chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.

RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method

While gene fusions have been increasingly detected by next-generation sequencing (NGS) technologies based methods in human cancers, these methods have limitations in identifying driver fusions. In addition, the existing methods to identify driver gene fusions ignored the specificity among different cancers or only considered their local rather than global topology features in networks. Here, we proposed a novel network-based method, called RWCFusion, to identify phenotype-specific cancer driver gene fusions. To evaluate its performance, we used leave-one-out cross-validation in 35 cancers and achieved a high AUC value 0.925 for overall cancers and an average 0.929 for signal cancer. Furthermore, we classified 35 cancers into two classes: haematological and solid, of which the haematological got a highly AUC which is up to 0.968. Finally, we applied RWCFusion to breast cancer and found that top 13 gene fusions, such as BCAS3-BCAS4, NOTCH-NUP214, MED13-BCAS3 and CARM-SMARCA4, have been previously proved to be drivers for breast cancer. Additionally, 8 among the top 10 of the remaining candidate gene fusions, such as SULF2-ZNF217, MED1-ACSF2, and ACACA-STAC2, were inferred to be potential driver gene fusions of breast cancer by us.

CoReCG: a comprehensive database of genes associated with colon-rectal cancer

Cancer of large intestine is commonly referred as colorectal cancer, which is also the third most frequently prevailing neoplasm across the globe. Though, much of work is being carried out to understand the mechanism of carcinogenesis and advancement of this disease but, fewer studies has been performed to collate the scattered information of alterations in tumorigenic cells like genes, mutations, expression changes, epigenetic alteration or post translation modification, genetic heterogeneity. Earlier findings were mostly focused on understanding etiology of colorectal carcinogenesis but less emphasis were given for the comprehensive review of the existing findings of individual studies which can provide better diagnostics based on the suggested markers in discrete studies.

Colon Rectal Cancer Gene Database (CoReCG), contains 2056 colon-rectal cancer genes information involved in distinct colorectal cancer stages sourced from published literature with an effective knowledge based information retrieval system. Additionally, interactive web interface enriched with various browsing sections, augmented with advance search facility for querying the database is provided for user friendly browsing, online tools for sequence similarity searches and knowledge based schema ensures a researcher friendly information retrieval mechanism.

Colorectal cancer gene database (CoReCG) is expected to be a single point source for identification of colorectal cancer-related genes, thereby helping with the improvement of classification, diagnosis and treatment of human cancers.

Database URL: lms.snu.edu.in/corecg

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.

Evolutionary Insights into RNA trans-Splicing in Vertebrates

Pre-RNA splicing is an essential step in generating mature mRNA. RNA trans-splicing combines two separate pre-mRNA molecules to form a chimeric non-co-linear RNA, which may exert a function distinct from its original molecules. Trans-spliced RNAs may encode novel proteins or serve as noncoding or regulatory RNAs. These novel RNAs not only increase the complexity of the proteome but also provide new regulatory mechanisms for gene expression. An increasing amount of evidence indicates that trans-splicing occurs frequently in both physiological and pathological processes. In addition, mRNA reprogramming based on trans-splicing has been successfully applied in RNA-based therapies for human genetic diseases. Nevertheless, clarifying the extent and evolution of trans-splicing in vertebrates and developing detection methods for trans-splicing remain challenging. In this review, we summarize previous research, highlight recent advances in trans-splicing, and discuss possible splicing mechanisms and functions from an evolutionary viewpoint.

Fusion genes in malignant neoplastic disorders of haematopoietic system

OBJECTIVES

The new World Health Organization's (WHO) classification of haematopoietic and lymphoid tissue neoplasms incorporating the recurrent fusion genes as the defining criteria for different haematopoietic malignant phenotypes is reviewed. The recurrent fusion genes incorporated in the new WHO's classification and other chromosomal rearrangements of haematopoietic and lymphoid tissue neoplasms are reviewed.

METHODOLOGY

Cytokines and transcription factors in haematopoiesis and leukaemic mechanisms are described. Genetic features and clinical implications due to the encoded chimeric neoproteins causing malignant haematopoietic disorders are reviewed.

RESULTS AND DISCUSSION

Multiple translocation partner genes are well known for leukaemia such as MYC, MLL, RARA, ALK, and RUNX1. With the advent of more sophisticated diagnostic tools and bioinformatics algorithms, an exponential growth in fusion genes discoveries is likely to increase.

CONCLUSION

Demonstration of fusion genes and their specific translocation breakpoints in malignant haematological disorders are crucial for understanding the molecular pathogenesis and clinical phenotype of cancer, determining prognostic indexes and therapeutic responses, and monitoring residual disease and relapse status.

Proteogenomics for understanding oncology: recent advances and future prospects

The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.

Exon-centric regulation of ATM expression is population-dependent and amenable to antisense modification by pseudoexon targeting

ATM is an important cancer susceptibility gene that encodes a critical apical kinase of the DNA damage response (DDR) pathway. We show that a key nonsense-mediated RNA decay switch exon (NSE) in ATM is repressed by U2AF, PUF60 and hnRNPA1. The NSE activation was haplotype-specific and was most promoted by cytosine at rs609621 in the NSE 3' splice-site (3'ss), which is predominant in high cancer risk populations. NSE levels were deregulated in leukemias and were influenced by the identity of U2AF35 residue 34. We also identify splice-switching oligonucleotides (SSOs) that exploit competition of adjacent pseudoexons to modulate NSE levels. The U2AF-regulated exon usage in the ATM signalling pathway was centred on the MRN/ATM-CHEK2-CDC25-cdc2/cyclin-B axis and preferentially involved transcripts implicated in cancer-associated gene fusions and chromosomal translocations. These results reveal important links between 3'ss control and ATM-dependent responses to double-strand DNA breaks, demonstrate functional plasticity of intronic variants and illustrate versatility of intronic SSOs that target pseudo-3'ss to modify gene expression.

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.

Transcriptome Profiling of Pediatric Core Binding Factor AML

The t(8;21) and Inv(16) translocations disrupt the normal function of core binding factors alpha (CBFA) and beta (CBFB), respectively. These translocations represent two of the most common genomic abnormalities in acute myeloid leukemia (AML) patients, occurring in approximately 25% pediatric and 15% of adult with this malignancy. Both translocations are associated with favorable clinical outcomes after intensive chemotherapy, and given the perceived mechanistic similarities, patients with these translocations are frequently referred to as having CBF-AML. It remains uncertain as to whether, collectively, these translocations are mechanistically the same or impact different pathways in subtle ways that have both biological and clinical significance. Therefore, we used transcriptome sequencing (RNA-seq) to investigate the similarities and differences in genes and pathways between these subtypes of pediatric AMLs. Diagnostic RNA from patients with t(8;21) (N = 17), Inv(16) (N = 14), and normal karyotype (NK, N = 33) were subjected to RNA-seq. Analyses compared the transcriptomes across these three cytogenetic subtypes, using the NK cohort as the control. A total of 1291 genes in t(8;21) and 474 genes in Inv(16) were differentially expressed relative to the NK controls, with 198 genes differentially expressed in both subtypes. The majority of these genes (175/198; binomial test p-value < 10⁻³⁰) are consistent in expression changes among the two subtypes suggesting the expression profiles are more similar between the CBF cohorts than in the NK cohort. Our analysis also revealed alternative splicing events (ASEs) differentially expressed across subtypes, with 337 t(8;21)-specific and 407 Inv(16)-specific ASEs detected, the majority of which were acetylated proteins (p = 1.5x10^-51 and p = 1.8x10^-54 for the two subsets). In addition to known fusions, we identified and verified 16 de novo fusions in 43 patients, including three fusions involving NUP98 in six patients. Clustering of differentially expressed genes indicated that the homeobox (HOX) gene family, including two transcription factors (MEIS1 and NKX2-3) were down-regulated in CBF compared to NK samples. This finding supports existing data that the dysregulation of HOX genes play a central role in biology CBF-AML hematopoiesis. These data provide comprehensive transcriptome profiling of CBF-AML and delineate genes and pathways that are differentially expressed, providing insights into the shared biology as well as differences in the two CBF subsets.

FARE-CAFE: a database of functional and regulatory elements of cancer-associated fusion events

Chromosomal translocation (CT) is of enormous clinical interest because this disorder is associated with various major solid tumors and leukemia. A tumor-specific fusion gene event may occur when a translocation joins two separate genes. Currently, various CT databases provide information about fusion genes and their genomic elements. However, no database of the roles of fusion genes, in terms of essential functional and regulatory elements in oncogenesis, is available. FARE-CAFE is a unique combination of CTs, fusion proteins, protein domains, domain–domain interactions, protein–protein interactions, transcription factors and microRNAs, with subsequent experimental information, which cannot be found in any other CT database. Genomic DNA information including, for example, manually collected exact locations of the first and second break points, sequences and karyotypes of fusion genes are included. FARE-CAFE will substantially facilitate the cancer biologist’s mission of elucidating the pathogenesis of various types of cancer. This database will ultimately help to develop ‘novel’ therapeutic approaches.

Database URL:http://ppi.bioinfo.asia.edu.tw/FARE-CAFE

Decoding of exon splicing patterns in the human RUNX1-RUNX1T1 fusion gene

The t(8;21) translocation is the most widespread genetic defect found in human acute myeloid leukemia. This translocation results in the RUNX1-RUNX1T1 fusion gene that produces a wide variety of alternative transcripts and influences the course of the disease. The rules of combinatorics and splicing of exons in the RUNX1-RUNX1T1 transcripts are not known. To address this issue, we developed an exon graph model of the fusion gene organization and evaluated its local exon combinatorics by the exon combinatorial index (ECI). Here we show that the local exon combinatorics of the RUNX1-RUNX1T1 gene follows a power-law behavior and (i) the vast majority of exons has a low ECI, (ii) only a small part is represented by "exons-hubs" of splicing with very high ECI values, and (iii) it is scale-free and very sensitive to targeted skipping of "exons-hubs". Stochasticity of the splicing machinery and preferred usage of exons in alternative splicing can explain such behavior of the system. Stochasticity may explain up to 12% of the ECI variance and results in a number of non-coding and unproductive transcripts that can be considered as a noise. Half-life of these transcripts is increased due to the deregulation of some key genes of the nonsense-mediated decay system in leukemia cells. On the other hand, preferred usage of exons may explain up to 75% of the ECI variability. Our analysis revealed a set of splicing-related cis-regulatory motifs that can explain "attractiveness" of exons in alternative splicing but only when they are considered together. Cis-regulatory motifs are guides for splicing trans-factors and we observed a leukemia-specific profile of expression of the splicing genes in t(8;21)-positive blasts. Altogether, our results show that alternative splicing of the RUNX1-RUNX1T1 transcripts follows strict rules and that the power-law component of the fusion gene organization confers a high flexibility to this process.

Biogenesis, identification, and function of exonic circular RNAs

Circular RNAs (circRNAs) arise during post-transcriptional processes, in which a single-stranded RNA molecule forms a circle through covalent binding. Previously, circRNA products were often regarded to be splicing intermediates, by-products, or products of aberrant splicing. But recently, rapid advances in high-throughput RNA sequencing (RNA-seq) for global investigation of nonco-linear (NCL) RNAs, which comprised sequence segments that are topologically inconsistent with the reference genome, leads to renewed interest in this type of NCL RNA (i.e., circRNA), especially exonic circRNAs (ecircRNAs). Although the biogenesis and function of ecircRNAs are mostly unknown, some ecircRNAs are abundant, highly expressed, or evolutionarily conserved. Some ecircRNAs have been shown to affect microRNA regulation, and probably play roles in regulating parental gene transcription, cell proliferation, and RNA-binding proteins, indicating their functional potential for development as diagnostic tools. To date, thousands of ecircRNAs have been identified in multiple tissues/cell types from diverse species, through analyses of RNA-seq data. However, the detection of ecircRNA candidates involves several major challenges, including discrimination between ecircRNAs and other types of NCL RNAs (e.g., trans-spliced RNAs and genetic rearrangements); removal of sequencing errors, alignment errors, and in vitro artifacts; and the reconciliation of heterogeneous results arising from the use of different bioinformatics methods or sequencing data generated under different treatments. Such challenges may severely hamper the understanding of ecircRNAs. Herein, we review the biogenesis, identification, properties, and function of ecircRNAs, and discuss some unanswered questions regarding ecircRNAs. We also evaluate the accuracy (in terms of sensitivity and precision) of some well-known circRNA-detecting methods.

An Efficient Method for Identifying Gene Fusions by Targeted RNA Sequencing from Fresh Frozen and FFPE Samples

Fusion genes are known to be key drivers of tumor growth in several types of cancer. Traditionally, detecting fusion genes has been a difficult task based on fluorescent in situ hybridization to detect chromosomal abnormalities. More recently, RNA sequencing has enabled an increased pace of fusion gene identification. However, RNA-Seq is inefficient for the identification of fusion genes due to the high number of sequencing reads needed to detect the small number of fusion transcripts present in cells of interest. Here we describe a method, Single Primer Enrichment Technology (SPET), for targeted RNA sequencing that is customizable to any target genes, is simple to use, and efficiently detects gene fusions. Using SPET to target 5701 exons of 401 known cancer fusion genes for sequencing, we were able to identify known and previously unreported gene fusions from both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue RNA in both normal tissue and cancer cells.

Recurrent BCAM-AKT2 fusion gene leads to a constitutively activated AKT2 fusion kinase in high-grade serous ovarian carcinoma

High-grade serous ovarian cancer (HGSC) is among the most lethal forms of cancer in women. Excessive genomic rearrangements, which are expected to create fusion oncogenes, are the hallmark of this cancer. Here we report a cancer-specific gene fusion between BCAM, a membrane adhesion molecule, and AKT2, a key kinase in the PI3K signaling pathway. This fusion is present in 7% of the 60 patient cancers tested, a significant frequency considering the highly heterogeneous nature of this malignancy. Further, we provide direct evidence that BCAM-AKT2 is translated into an in-frame fusion protein in the patient's tumor. The resulting AKT2 fusion kinase is membrane-associated, constitutively phosphorylated, and activated as a functional kinase in cells. Unlike endogenous AKT2, whose activity is tightly regulated by external stimuli, BCAM-AKT2 escapes the regulation from external stimuli. Moreover, a BCAM-AKT2 fusion gene generated via chromosomal translocation using the CRISPR/Cas9 system leads to focus formation in both OVCAR8 and HEK-293T cell lines, suggesting that BCAM-AKT2 is oncogenic. Together, the results indicate that BCAM-AKT2 expression is a new mechanism of AKT2 kinase activation in HGSC. BCAM-AKT2 is the only fusion gene in HGSC that is proven to translate an aberrant yet functional kinase fusion protein with oncogenic properties. This recurrent genomic alteration is a potential therapeutic target and marker of a clinically relevant subtype for tailored therapy of HGSC.

Onco-proteogenomics: cancer proteomics joins forces with genomics

The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.

The landscape and therapeutic relevance of cancer-associated transcript fusions

Transcript fusions as a result of chromosomal rearrangements have been a focus of attention in cancer as they provide attractive therapeutic targets. To identify novel fusion transcripts with the potential to be exploited therapeutically, we analyzed RNA sequencing, DNA copy number and gene mutation data from 4,366 primary tumor samples. To avoid false positives, we implemented stringent quality criteria that included filtering of fusions detected in RNAseq data from 364 normal tissue samples. Our analysis identified 7,887 high confidence fusion transcripts across 13 tumor types. Our fusion prediction was validated by evidence of a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole genome sequencing data was available. Cancers with higher levels of genomic instability showed a corresponding increase in fusion transcript frequency, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations, suggesting an important role for tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4,366 samples (7.4%). Potentially druggable kinase fusions involving ALK, ROS, RET, NTRK, and FGFR gene families were detected in bladder carcinoma (3.3%), glioblastoma (4.4%), head and neck cancer (1.0%), low grade glioma (1.5%), lung adenocarcinoma (1.6%), lung squamous cell carcinoma (2.3%), and thyroid carcinoma (8.7%), suggesting a potential for application of kinase inhibitors across tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes were detected in 111 samples (2.5%) and may additionally be considered as therapeutic targets. In summary, we described the landscape of transcript fusions detected across a large number of tumor samples and revealed fusion events with clinical relevance that have not been previously recognized. Our results support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated.

ChiTaRS 2.1--an improved database of the chimeric transcripts and RNA-seq data with novel sense-antisense chimeric RNA transcripts

Chimeric RNAs that comprise two or more different transcripts have been identified in many cancers and among the Expressed Sequence Tags (ESTs) isolated from different organisms; they might represent functional proteins and produce different disease phenotypes. The ChiTaRS 2.1 database of chimeric transcripts and RNA-Seq data (http://chitars.bioinfo.cnio.es/) is the second version of the ChiTaRS database and includes improvements in content and functionality. Chimeras from eight organisms have been collated including novel sense–antisense (SAS) chimeras resulting from the slippage of the sense and anti-sense intragenic regions. The new database version collects more than 29 000 chimeric transcripts and indicates the expression and tissue specificity for 333 entries confirmed by RNA-seq reads mapping the chimeric junction sites. User interface allows for rapid and easy analysis of evolutionary conservation of fusions, literature references and experimental data supporting fusions in different organisms. More than 1428 cancer breakpoints have been automatically collected from public databases and manually verified to identify their correct cross-references, genomic sequences and junction sites. As a result, the ChiTaRS 2.1 collection of chimeras from eight organisms and human cancer breakpoints extends our understanding of the evolution of chimeric transcripts in eukaryotes as well as their functional role in carcinogenic processes.

Transcription-mediated chimeric RNAs in prostate cancer: time to revisit old hypothesis?

Chromosomal rearrangements and fusion genes play important roles in tumor development and progression. Four high-frequency prostate cancer-specific fusion genes were recently reported in Chinese cases. We attempted to confirm one of the fusion genes, USP9Y-TTTY15, by reverse transcription PCR, but detected the presence of the USP9Y-TTTY15 fusion transcript in cancer samples, nonmalignant prostate tissues, and normal tissues from other organs, demonstrating that it is a transcription-induced chimeric RNA, which is commonly produced in normal tissues. In 105 prostate cancer samples and case-matched adjacent nonmalignant tissues, we determined the expression level of USP9Y-TTTY15 and a previously reported transcription-induced chimeric RNA, SLC45A3-ELK4. The expression levels of both chimeric RNAs vary greatly in cancer and normal cells. USP9Y-TTTY15 expression is neither higher in cancer than adjacent normal tissues, nor correlated with features of advanced prostate cancer. Although the expression level of SLC45A3-ELK4 is higher in cancer than normal cells, and a dramatic increase in its expression from normal to cancer cells is correlated with advanced disease, its expression level in cancer samples alone is not correlated with any clinical parameters. These data show that both chimeric RNAs contribute less to prostate carcinogenesis than previously reported.

Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer

Background

The extraordinary success of imatinib in the treatment of BCR-ABL1 associated cancers underscores the need to identify novel functional gene fusions in cancer. RNA sequencing offers a genome-wide view of expressed transcripts, uncovering biologically functional gene fusions. Although several bioinformatics tools are already available for the detection of putative fusion transcripts, candidate event lists are plagued with non-functional read-through events, reverse transcriptase template switching events, incorrect mapping, and other systematic errors. Such lists lack any indication of oncogenic relevance, and they are too large for exhaustive experimental validation.

Results

We have designed and implemented a pipeline, Pegasus, for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.

Conclusion

We show the effectiveness of Pegasus in predicting new driver fusions in 176 RNA-Seq samples of glioblastoma multiforme (GBM) and 23 cases of anaplastic large cell lymphoma (ALCL). Contact: fa2306@columbia.edu.