- Open Access
Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer
© Abate et al.; licensee BioMed Central Ltd. 2014
- Received: 6 February 2014
- Accepted: 5 August 2014
- Published: 4 September 2014
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.
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.
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: firstname.lastname@example.org.
- Gene fusion
- Next-generation sequencing
- Machine learning
Gene fusions are the result of genetic aberrations (translocations, deletions, amplifications and inversions) involving the juxtaposition of two genes that can generate a single hybrid transcript. Since 1960, gene fusions have been known to play a major role in tumorgenesis. The BCR-ABL1 gene fusion, arising from the Philadelphia chromosome (t(9;22)(q34;q11)), was the first case of a translocation-induced gene fusion associated with the development of a cancer, namely chronic myelogenous leukemia . In this fusion, the N-terminus oligomerization domain of BCR and the tyrosine kinase domain in ABL1 are essential in promoting oncogenic activity . Among the gene fusions associated with tumor development, it is worth mentioning TMPRSS2-ERG, a gene fusion occurring in 40-80% of cases of prostate cancer ,  and fusions involving the ALK gene with different partners in various malignancies , such as NPM1-ALK in anaplastic large cell lymphoma (ALCL)  and ELM4-ALK in non-small-cell lung cancer .
Discovering the relationship between gene fusions and cancer is gaining significant momentum thanks to advances in next generation sequencing (NGS) technology, particularly RNA paired-end sequencing . Recently, the application of this technology allowed the discovery of new chromosomal rearrangements of the CIITA gene with various promiscuous partners in the lymphomagenesis of primary mediastinal B cell lymphomas . In Singh et al. , the analysis of RNA-Seq data led to the discovery of the highly oncogenic fusion protein FGFR3-TACC3 in 3% of patients diagnosed with glioblastoma multiforme (GBM). Even though FGFR3-TACC3 occurs at low frequency, the efficacy of FGFR inhibitors in the treatment of these tumors opens the door to personalized therapies for this deadly disease. Moreover, the FGFR3-TACC3 fusion has been found in other cancers such as bladder  and lung . These recent discoveries underscore the power of high throughput genomics for the identification of targetable gene fusions, opening the door to personalized cancer therapies.
Several bioinformatics tools are now established for the detection of candidate fusion events from paired-end RNA-Seq data. Generally, the detection of read pairs that discordantly map to two distinct genes generates a first set of gene fusion candidates. Subsequently, the exact fusion junction is determined for each candidate by searching for reads spanning the breakpoint, i.e. reads that partially map to both genes. FusionSeq  and deFuse  were the earliest examples of software based on this strategy. Detection tools differ in the type and number of cascading filters they apply to reduce the large number of false positive fusions. ChimeraScan  implements an algorithm based on trimming reads to increase fusion detection sensitivity. Bellerophontes  uses TopHat  and Cufflinks  to identify gene fusions involving truly expressed genes, and applies a set of modular cascading filters based on an accurate gene fusion model . A comprehensive comparison of fusion detection tools has recently been published .
The methods adopted by fusion detection tools to shrink the list of candidates lead to increased specificity but reduced sensitivity. As reported in the comparative analysis performed by Abateet al. , the heterogeneity of filtering strategies often yields poorly overlapping sets of candidate transcripts between algorithms. The union of all candidate fusions reported by different detection tools should be considered for further experimental validation, in order to maximize sensitivity. A problem arises however, since the number of putative gene fusions might be on the order of hundreds of candidates per RNA-Seq sample. This is largely due to the presence of read-through events, reverse transcriptase template switching artifacts, and different systematic errors in the analysis of the reads . The naïve approach of considering all candidates from all detection tools quickly overwhelms the capacity of experimental validation procedures, and highlights the need to focus on a reduced number of select biologically relevant fusions driving the oncogenic progression of disease.
The classification of gene fusions into driver and passenger events is a complex problem that has not been fully explored yet. To address this issue, several databases have collected hundreds of chromosomal translocations involved in cancer cases and reported in the biomedical literature. For instance, Mitelman , TICdb  and ChimerDB2.0  are manually curated repositories of known gene fusions along with detailed information such as chromosomal breakpoints, reported tissue types, and fusion sequences. New computational approaches to nominate biologically relevant fusions from high-throughput data have been proposed. ConSig assesses driver gene fusions by combining copy number variations (CNV), ontologies and interactomes based on the assumption that fusion events are more likely to arise from genes with similar biological functions . Wu et al. have proposed a network based approach relying on relative co-occurrence of protein domains and domain-domain interactions, and location of the gene fusion in a gene network . Recently, Oncofuse has improved the computational analysis with a machine learning approach based on a Naïve Bayes classifier applied to preserved domains after chromosomal rearrangement . Compared to earlier methods, Oncofuse introduces a new level of detail by considering only the domains that are maintained on the resulting fusion transcripts. The domain analysis should be extended, however, by taking into account all possible transcript isoforms as well as the reading frame, which plays a crucial role since frame-shifted fusions imply a loss of the 3’-gene domains. Moreover, Oncofuse relies on a Naïve Bayes classifier that makes a restrictive assumption on the class conditional independence of all features. Taking the FGFR3-TACC3 gene fusion as an example, however, the acquired coiled-coil domain of the TACC3 gene cooperates with tyrosine kinase functionality of FGFR3 to produce the dramatic oncogenic effect . This example illustrates the limitations of a model assumption that ignores interactions between functional protein domains.
In this paper we aim to discern oncogenic driver fusions from the background of passenger events and artifacts by combining 1) functional domain annotation based on accurate fusion sequence analysis and 2) a binary classification algorithm using gradient tree boosting. The implementation of this methodology is Pegasus, a new framework for the functional characterization of RNA-Seq gene fusion candidates and quantification of their oncogenic potential. Pegasus runs on top of multiple state of the art fusion detection tools in order to maximize detection sensitivity and consider the largest possible set of fusion candidates.
The main innovative steps introduced by Pegasus are as follows:
Common interface between several fusion detection tools.
Chimeric transcript sequence reconstruction: a key feature since fusion detection tools do not report whole transcript sequences.
Reading frame identification and accurate domain annotation, including both preserved and lost protein domains within the assembled chimeric transcript.
Prediction of fusion oncogenic potential: high performance ensemble learning technique trained on a feature space of protein domain annotations.
Automated workflow that would otherwise require massive effort if manually executed by the scientist.
We assess the trained Pegasus model’s prediction accuracy by applying it to a set of recently discovered gene fusions where it compares quite favorably with the current state of the art, Oncofuse. Beyond curated datasets, we report the results of Pegasus on real RNA-Seq data from three distinct patient cohorts: public GBM samples from TCGA, non-public GBM samples, and non-public ALCL samples. We successfully identify driver gene fusions in both cancer types and demonstrate the utility of coupling our algorithm with experimental analysis.
Enhanced overexpression of an oncogene is exemplified by the famous IgH-MYC fusion (Figure 1a), and is the main reason for our explicit annotation of oncogene status and interactions with known oncogenes in our feature space representation of fusion transcripts. In other cases, deregulating properties can be associated with the fused transcript, such as insertion of one or two nucleotides across the junction breakpoint introducing a shift of the reading frame. This scenario is illustrated in the PPP2RA-CHEK2 fusion  (Figure 1b) where the introduced frame-shifted sequence prevents the formation of the CHEK2 protein that is a known tumor suppressor gene. Here we see the motivation for our explicit annotation of tumor suppressor status and interactions with known tumor suppressors in the feature space, as well as the need for computing reading frame of each candidate fusion. Finally, fusion transcripts can also yield a completely new chimeric protein. BCR-ABL1  and NPM1-ALK  are well studied examples of such in-frame fusions. The new protein is generally larger than the kinase involved and causes an increase of the tyrosine kinase activity (Figure 1c). Moreover, in the recently discovered FGFR3-TACC3 gene fusion, the acquired coiled-coil domain of TACC3 gene drives the localization of the fusion protein to the mitotic spindle through a mechanism that is dependent on tyrosine kinase functionality  (Figure 1d). It seems that a reasonable feature space representation for predicting the oncogenic properties of such novel chimeric proteins should maintain knowledge of both preserved and lost functional domains in the partner genes.
Fusion detection tools integration
The Fusion Detection Tools Integration is the repository of the entire set of fusion candidates detected by any of the fusion detection tools. Several fusion detection algorithms are supported in Pegasus: Bellerophontes, deFuse, and ChimeraScan. Each tool adopts a private formalism for reporting fusion information with different levels of detail. However, some chimeric fusion features are common to all the fusion detection tool reports (e.g. genes involved in the fusion, genomic breakpoint coordinates, number of reads encompassing and spanning the fusion breakpoint, etc.). Thus, the internal database structure of Pegasus provides a unique point of access for all the information needed to fully describe a gene fusion candidate. Furthermore, experimental analysis might involve the comparison of several RNA-Seq samples per case study. To this end, the common repository embedded in Pegasus provides an organized overview of all the fusions occurring in the entire sample set. This feature allows comparison and the recurrence analysis of the fusion candidates within both the same experimental dataset (samples of the same disease) and within different experimental datasets (samples across different diseases).
Chimeric transcript sequence reconstruction and domain annotation
We adopt the annotation file from ENSEMBL database . Since several distinct isoforms might be available for a specific gene, Pegasus considers the combinations of all possible isoforms reported in the annotations of those genes involved in the fusion (Figure 3). The chimeric transcript sequence is therefore reconstructed combining the 5’ gene isoform sequence (from the isoform start codon to the genomic breakpoint) and the 3’ gene isoform sequence (from the genomic breakpoint to the isoform stop codon). Different gene isoforms allow for different protein domains to be retained or disrupted during the fusion. If this scenario occurs, Pegasus considers the union of all possible domains that are retained and lost and as input features for downstream classification. Furthermore, the fusion breakpoint can fall in either the coding region (exon-exon junction boundaries), or in non-coding regions (exon-intron or intron-intron junction boundaries). Pegasus takes the latter scenario into account and if the fusion breakpoint falls in an intron, the intronic sequence is retained.
The annotation of the preserved and lost protein domains is essential in order to capture the oncogenic potential of a translated chimeric transcript. The nucleotide fusion sequence assembled in the previous step is translated into an amino acid sequence. Subsequently, the UniProt web service  is queried for all available annotations of the putative protein encoded by the two genes involved in the fusion (Figure 4b). Leveraging the reading frame information and fusion breakpoint, Pegasus determines the conserved and lost domains associated with both 5’ and 3’ genes. It is worth emphasizing that both conserved and the lost domains are valuable features of a fusion transcript, with the former more likely to discriminate oncogene related fusions and the latter more likely to discriminate tumor suppressor related fusions.
The domain annotation permits the creation of a detailed feature space for the fusion transcripts, a prerequisite step for posing the ensuing machine learning task. In Pegasus the feature space is composed of:
Binary information about reading frame and breakpoint region (if the breakpoint falls in coding regions, introns and UTRs);
Presence or absence of ~1000 protein domains from UniProt. Our selection was based on the domains occurring in the training set from ChimerDB2.0.
Number of oncogenic or tumor suppressor domains, as defined by association with the keywords “tumor suppressor” or “oncogene” in the UniProt database.
Number of protein-protein oncogenic interacting domains. We check if one or more domains of the fusion interact with both oncogenic and tumor suppressor domains.
Driver fusion prediction as a binary classification task
Fit a regression tree to r im producing regions R jm , j = 1, 2,…, J m
An alternative ensemble classification strategy, the random forest algorithm, demonstrated comparable performance to gradient tree boosting in our experiments. There is recent precedent in the machine learning literature for initializing gradient tree boosting models with rankings learned via random forests for achieving superior performance to either algorithm alone . Interestingly, those authors found that posing web-search rankings as a classification task rather than a regression task increased the performance of a gradient boosted regression tree model, confirming our hypothesis in constructing the current Pegasus classifier.
This section highlights the performance of Pegasus in detecting driver gene fusions. First, we examine the performance of the classifier on the training data and compare its effectiveness to a recently published tool, Oncofuse, on a separately curated validation dataset. Next, we run Pegasus on two experimental datasets and demonstrate its role in reducing the search space of potential oncogenic drivers by accurately ranking fusion transcripts from a vast set of putative candidates. The first is the publicly available RNA-Seq data of GBM from TCGA. The second is a non-public set of 23 RNA-Seq samples from a cohort of patients with ALCL, with 2 out of the 23 samples reporting the NPM1-ALK fusion.
We analyze these datasets with ChimeraScan or deFuse and apply Pegasus to the entire set of detected fusions. It is worth specifying that in the reported results about chimeric transcript annotations, if two or more fusions share the same junction breakpoint coordinates, they are counted as a single fusion. The rationale is that according to the Pegasus fusion domain analysis, if two genes fuse in different samples with the same breakpoint they also share exactly the same domain. Conversely, if two genes occur in different samples with different junction breakpoint coordinates, the domain analysis accordingly changes.
Classifier performance on training corpus and independent validation set
The corpus of labeled data used to train the classifier comes from two sources. Positive examples, meaning true oncogenic driver fusions, are drawn from ChimerDB2.0, which contains 501 curated driver fusions. 1500 negative examples are then drawn from an internal collection of reactive lymph node tissue in patients with no clinical history of malignancy. The negative examples contain passenger fusions as well as read-through transcripts. We also supplement the negative training data with 416 deliberately frame-shifted transcripts from ChimerDB2.0 such that the necessary driver domains are lost. In total there are 501 positive examples and 1916 negative examples in the training corpus. The rationale for augmenting the negative set with 416 frame-shifted fusions from ChimerDB2.0 is to include the scenario of chimeric transcripts containing an oncogene at the 3’ position that is frame-shifted. Since such events occur at low frequency in normal lymph node tissue, this design choice improves the performance of the classifier (for a detailed discussion please refer to Additional file 1). In summary, the 501 fusions from ChimerDB2.0 form the positive training set and provide mostly in-frame fusions involving oncogenes. The 1500 fusions from normal tissue contribute to the negative set and provide both in-frame and frame-shifted fusions. The 416 deliberately frame-shifted fusions from ChimerDB2.0 complete the negative set and provide frame-shifted gene fusions mostly with an oncogene at the 3’ position.
We observe that the computationally expensive step of computing the fusion transcript reading frame is justified in the eyes of the classifier, as it is the single most informative feature. Looking a little further down the list we learn other transcript features that are highly informative of driver events, such as having breakpoints in the CDS and conserving domains shared with or interacting with known oncogenes.
Pegasus driver fusion predictions in non-public GBM data
Pegasus predictions on 15 private GBM RNA-Seq data
5’ Gene partner
3’ Gene partner
Pegasus driver score
Pegasus driver fusion predictions in public TCGA GBM data
Pegasus predictions on GBM RNA-Seq data
5’ Gene partner
3’ Gene partner
Pegasus driver score
Functional validation of new recurrent driver fusion in anaplastic large cell lymphoma
Anaplastic large cell lymphoma (ALCL) is a form of peripheral T-cell lymphoma that is often associated with translocations of the ALK gene. In 23 non-public ALCL samples (~450 million properly mapped reads) we detect a total of 5201 candidate fusion transcripts by means of deFuse  and ChimeraScan . Beyond the two NPM1-ALK fusion transcripts (PDS = 0.98) that are already reported, Pegasus properly annotates and reveals 16 new biologically relevant fusions in these 23 samples. All 16 candidate driver fusions have been validated with RT-PCR, and 4 gene fusions have successfully undergone functional assays and in vivo validation. An example of Pegasus’ effectiveness in functional domain analysis lies in the oncogenic role of TRAF1-ALK , a novel fusion in ALCL that Pegasus reports as driver.
Since the first application of whole transcriptome sequencing to gene fusion discovery in 2009 , many new aberrant events have been reported, opening an exciting frontier for molecular understanding of cancer biology and targeted therapies. The unprecedented sensitivity of NGS technology, however, often yields numbers of fusion candidates too large to be experimentally validated. The frontier is made ever more exciting (and challenging) by large consortia such as TCGA and ICGC (International Cancer Genome Consortium) who are making available large sets of RNA-Seq samples spanning the spectrum of human malignancies. The analysis of this data has been revealing the limits of the theory that associates driver events with recurrent events. In fact, out of 161 RNA-seq GBM samples, the most frequent fusion (EGFR-SEPT14) occurs in only 6 samples, and the highly expressed FGFR3-TACC3 fusion is recurrent in only the 3% of GBM cases. Thus, this data suggest that in order to select relevant driver fusion candidates for biological validation, a functional analysis of the putative gene fusion candidate is necessary.
Here we present Pegasus, an accurate prediction tool for the discovery of new driver gene fusions in cancer studies. The proposed methodology is based on a computational model of the features that make chimeric transcript a driver oncogenic event. The framework provides a common interface for several fusion detection tools and it predicts driver events by properly analyzing the detected gene fusion candidates according to the assembled fused sequence.
The application of ensemble learning techniques reveals the most informative features in discriminating oncogenic gene fusions. The data confirm our intuition that an accurate analysis of fusion transcript sequence is necessary. The reading frame in particular is a dominant features in the discrimination of passenger and driver fusions. Similarly, the molecular characterization of the main reported oncogenic domains accurately increase the sensitivity and the PDS computation.
The problem of computationally assessing the biological and clinical relevance of a gene fusion is still very much an open question. However, some driver prediction tools have been recently proposed. To better determine Pegasus performance and accuracy, we compare our predicted results with Oncofuse. The data confirm that an approximate analysis of the fusion transcript sequence negatively impacts the performance of the algorithm. Using a set of known driver fusions as positive examples and a set of passenger fusions from normal tissue as negative examples, we observe the superior performance of Pegasus in ROC space where its AUC is 0.97.
Moreover, we demonstrate the practical role of the Pegasus framework in computing PDS scores that allow for triaging lists of gene fusion candidates for experimental validation in two actual case studies. In the first, we apply Pegasus to public GBM TCGA data and almost 50% of the detected driver fusions turn out to have been reported as a biologically relevant in independent studies ,. In the second, we compute Pegasus scores for internal ALCL data and we successfully detect novel oncogenic and targetable driver fusions that have undergone complete functional and experimental validation. In this work, we extensively report the driver fusion TRAF1-ALK that has been correctly detected and highly ranked by Pegasus.
Finally, the accuracy of Pegasus in detecting driver fusions in both the curated validation dataset and the real biological cases demonstrates the efficacy of the framework in supporting biological analysis and cancer research. We believe that the Pegasus prediction score, as well as the accurate annotations provided via our feature engineering, will be of great use to other investigators searching for biologically relevant gene fusions in NGS data.
Project name: Pegasus
Project home page: http://sourceforge.net/p/pegasus-fus.
Operating system: UNIX
Programming language: Java, Perl, Python, BASH.
This project is supported by U54 CA121852 (R.R.), R01 CA179044-01A1 (R.R.), R01 CA164152-01, Stewart Trust Foundation and The Italian Association for Cancer Research (AIRC) Special Program in Clinical Molecular Oncology (5x1000 No. 10007, Milan, Italy), Regione Piemonte (ONCOPROT, CIPE 25/2005), ImmOnc (Innovative approaches to boost the immune responses, Programma Operativo Regionale, Piattaforme Innovative BIO F.E.S.R. 2007/13, Asse 1 ‘Ricerca e innovazione’ della LR 34/2004), and the Oncology Program of Compagnia di San Paolo (Turin, Italy) (G.I.).
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