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

Architects and Partners: The Dual Roles of Non-coding RNAs in Gene Fusion Events

Extensive research into gene fusions in cancer and other diseases has led to the discovery of novel biomarkers and therapeutic targets. Concurrently, various bioinformatics tools have been developed for fusion detection in RNA sequencing data, which, in the age of increasing affordability of sequencing, have delivered a large-scale identification of transcriptomic abnormalities. Historically, the focus of fusion transcript research was predominantly on coding RNAs and their resultant proteins, often overlooking non-coding RNAs (ncRNAs). This chapter discusses how ncRNAs are integral players in the landscape of gene fusions, detailing their contributions to the formation of gene fusions and their presence in chimeric transcripts. We delve into both linear and the more recently identified circular fusion RNAs, providing a comprehensive overview of the computational methodologies used to detect ncRNA-involved gene fusions. Additionally, we examine the inherent biases and limitations of these bioinformatics approaches, offering insights into the challenges and future directions in this dynamic field.

Pathogenicity Prediction of Gene Fusion in Structural Variations: A Knowledge Graph-Infused Explainable Artificial Intelligence (XAI) Framework

When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI.

Exploring the Relationship between Fusion Genes and MicroRNAs in Cancer

Fusion genes are key cancer driver genes that can be used as potential drug targets in precision therapies, and they can also serve as accurate diagnostic and prognostic biomarkers. The fusion genes can cause microRNA (miRNA/miR) aberrations in many types of cancer. Nevertheless, whether fusion genes incite miRNA aberrations as one of their many critical oncogenic functionalities for driving carcinogenesis needs further investigation. Recent discoveries of miRNA genes that are present within the regions of genomic rearrangements that initiate fusion gene-based intronic miRNA dysregulation have brought the fusion genes into the limelight and revealed their unexplored potential in the field of cancer biology. Fusion gene-based 'promoter-switch' event aberrantly activate the miRNA-related upstream regulatory signals, while fusion-based coding region alterations disrupt the original miRNA coding loci. Fusion genes can potentially regulate the miRNA aberrations regardless of the protein-coding capability of the resultant fusion transcript. Studies on out-of-frame fusion and nonrecurrent fusion genes that cause miRNA dysregulation have attracted the attention of researchers on fusion genes from an oncological perspective and therefore could have potential implications in cancer therapies. This review will provide insights into the role of fusion genes and miRNAs, and their possible interrelationships in cancer.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer

BACKGROUND

A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for patients with advanced or refractory lung cancer.

METHODS

Drug sensitivity screening and multi-omics datasets were acquired from lung cancer PDCs (n = 102). Integrative analysis was performed to explore drug candidates according to genetic variants, gene expression, and clinical profiles.

RESULTS

PDCs had genomic characteristics resembled with those of solid lung cancer tissues. PDC molecular subtyping classified patients into four groups: (1) inflammatory, (2) epithelial-to-mesenchymal transition (EMT)-like, (3) stemness, and (4) epithelial growth factor receptor (EGFR)-dominant. EGFR mutations of the EMT-like subtype were associated with a reduced response to EGFR-tyrosine kinase inhibitor therapy. Moreover, although RB1/TP53 mutations were significantly enriched in small-cell lung cancer (SCLC) PDCs, they were also present in non-SCLC PDCs. In contrast to its effect in the cell lines, alpelisib (a PI3K-AKT inhibitor) significantly inhibited both RB1/TP53 expression and SCLC cell growth in our PDC model. Furthermore, cell cycle inhibitors could effectively target SCLC cells. Finally, the upregulation of transforming growth factor-β expression and the YAP/TAZ pathway was observed in osimertinib-resistant PDCs, predisposing them to the EMT-like subtype. Our platform selected XAV939 (a WNT-TNKS-β-catenin inhibitor) for the treatment of osimertinib-resistant PDCs. Using an in vitro model, we further demonstrated that acquisition of osimertinib resistance enhances invasive characteristics and EMT, upregulates the YAP/TAZ-AXL axis, and increases the sensitivity of cancer cells to XAV939.

CONCLUSIONS

Our PDC models recapitulated the molecular characteristics of lung cancer, and pharmacogenomics analysis provided plausible therapeutic candidates.

In silico validation of RNA-Seq results can identify gene fusions with oncogenic potential in glioblastoma

RNA-Sequencing (RNA-Seq) can identify gene fusions in tumors, but not all these fusions have functional consequences. Using multiple data bases, we have performed an in silico analysis of fusions detected by RNA-Seq in tumor samples from 139 newly diagnosed glioblastoma patients to identify in-frame fusions with predictable oncogenic potential. Among 61 samples with fusions, there were 103 different fusions, involving 167 different genes, including 20 known oncogenes or tumor suppressor genes (TSGs), 16 associated with cancer but not oncogenes or TSGs, and 32 not associated with cancer but previously shown to be involved in fusions in gliomas. After selecting in-frame fusions able to produce a protein product and running Oncofuse, we identified 30 fusions with predictable oncogenic potential and classified them into four non-overlapping categories: six previously described in cancer; six involving an oncogene or TSG; four predicted by Oncofuse to have oncogenic potential; and 14 other in-frame fusions. Only 24 patients harbored one or more of these 30 fusions, and only two fusions were present in more than one patient: FGFR3::TACC3 and EGFR::SEPTIN14. This in silico study provides a good starting point for the identification of gene fusions with functional consequences in the pathogenesis or treatment of glioblastoma.

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.

Detection of gene mutations and gene-gene fusions in circulating cell-free DNA of glioblastoma patients: an avenue for clinically relevant diagnostic analysis

Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell‐free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole‐genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene–gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated (22.6 ± 5 ng·mL⁻¹), as compared to healthy controls (1.4 ± 0.4 ng·mL⁻¹) of the same average age. We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database (TP53, 18.75%; EGFR, 37.5%; NF1, 12.5%; LRP1B, 25%; IRS4, 25%). Using our gene–gene fusion database, ChiTaRS 5.0, we identified gene–gene fusions in cfDNA and tumour DNA, such as KDR–PDGFRA and NCDN–PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM (44% of all samples). Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR–ABL1 (in 8% of patients), COL1A1–PDGFB (8%), NIN–PDGFRB (8%), and FGFR1–BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L–ROS1 and GOPC–ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene–gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real‐time information on the evolving molecular landscape of the tumour.

MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates

MOTIVATION

Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function.

RESULTS

MetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers.

AVAILABILITY

MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FUNGI: Fusion Gene Integration Toolset

Motivation

Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules.

Results

We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance.

Availabilityand implementation

FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

FYN-TRAF3IP2 induces NF-κB signaling-driven peripheral T cell lymphoma

Angioimmunoblastic T cell lymphoma (AITL) and peripheral T cell lymphoma not-otherwise-specified (PTCL, NOS) have poor prognosis and lack driver actionable targets for directed therapies in most cases. Here we identify FYN-TRAF3IP2 as a recurrent oncogenic gene fusion in AITL and PTCL, NOS tumors. Mechanistically, we show that FYN-TRAF3IP2 leads to aberrant NF-κB signaling downstream of T cell receptor activation. Consistent with a driver oncogenic role, FYN-TRAF3IP2 expression in hematopoietic progenitors induces NF-κB-driven T cell transformation in mice and cooperates with loss of the Tet2 tumor suppressor in PTCL development. Moreover, abrogation of NF-κB signaling in FYN-TRAF3IP2-induced tumors with IκB kinase inhibitors delivers strong anti-lymphoma effects in vitro and in vivo. These results demonstrate an oncogenic and pharmacologically targetable role for FYN-TRAF3IP2 in PTCLs and call for the clinical testing of anti-NF-κB targeted therapies in these diseases.

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions

BACKGROUND

Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy.

RESULTS

We developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions.

CONCLUSIONS

annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.

DEEPrior: a deep learning tool for the prioritization of gene fusions

SUMMARY

In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user.

AVAILABILITY AND IMPLEMENTATION

Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human transcription factor and protein kinase gene fusions in human cancer

Oncogenic gene fusions are estimated to account for up-to 20% of cancer morbidity. Recently sequence-level studies have established oncofusions throughout all tissue types. However, the functional implications of the identified oncofusions have often not been investigated. In this study, identified oncofusions from a fusion detection approach (DEEPEST) were analyzed in detail. Of the 28,863 oncofusions, we found almost 30% are expected to produce functional proteins with features from both parent genes. Kinases and transcription factors were the main gene families of the protein producing fusions. Considering their role as initiators, actors, and termination points of cellular signaling pathways, we focused our in-depth analyses on them. Domain architecture of the fusions and their wild-type interactors suggests that abnormal molecular context of protein domains caused by fusion events may unlock the oncogenic potential of the wild type counterparts of the fusion proteins. To understand overall oncofusion effects, we performed differential expression analysis using TCGA cancer project samples. Results indicated oncofusion-specific alterations in gene expression levels, and lower expression levels of components of key cellular pathways, in particular signal transduction and transcription regulation. The sum of results suggests that kinase and transcription factor oncofusions deregulate cellular signaling, possibly via acquiring novel functions.

FGviewer: an online visualization tool for functional features of human fusion genes

Among the diverse location of the breakpoints (BPs) of structural variants (SVs), the breakpoints of fusion genes (FGs) are located in the gene bodies. This broken gene context provided the aberrant functional clues to study disease genesis. Many tumorigenic fusion genes have retained or lost functional or regulatory domains and these features impacted tumorigenesis. Full annotation of fusion genes aided by the visualization tool based on two gene bodies will be helpful to study the functional aspect of fusion genes. To date, a specialized tool with effective visualization of the functional features of fusion genes is not available. In this study, we built FGviewer, a tool for visualizing functional features of human fusion genes, which is available at https://ccsmweb.uth.edu/FGviewer. FGviewer gets the input of fusion gene symbols, breakpoint information, or structural variants from whole-genome sequence (WGS) data. For any combination of gene pairs/breakpoints to be involved in fusion genes, the users can search the functional/regulatory aspect of the fusion gene in the three bio-molecular levels (DNA-, RNA-, and protein-levels) and one clinical level (pathogenic-level). FGviewer will be a unique online tool in disease research communities.

Genomics of Peripheral T-Cell Lymphoma and Its Implications for Personalized Medicine

Peripheral T-cell lymphoma (PTCL) is a rare, heterogenous group of mature T-cell neoplasms that comprise 10–15% of non-Hodgkin lymphoma cases in the United States. All subtypes of PTCL, except for ALK⁺ anaplastic T-cell lymphoma, are associated with poor prognosis, with median overall survival (OS) rates of 1–3 years. The diagnosis of PTCL is mainly based on clinical presentation, morphologic features, and immunophenotypes. Recent advances in genome sequencing and gene expression profiling have given new insights into the pathogenesis and molecular biology of PTCL. An enhanced understanding of its genomic landscape holds the promise of refining the diagnosis, prognosis, and management of PTCL. In this review, we examine recently discovered genetic abnormalities identified by molecular profiling in 3 of the most common types of PTCL: RHOA^G17V and epigenetic regulator mutations in angioimmunoblastic T-cell lymphoma, ALK expression and JAK/STAT3 pathway mutations in anaplastic T-cell lymphoma, and T-follicular helper phenotype and GATA3/TBX21 expression in PTCL-not otherwise specified. We also discuss the implications of these abnormalities for clinical practice, new/potential targeted therapies, and the role of personalized medicine in the management of PTCL.

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia

Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML.

Why Are Some Driver Mutations Rare?

Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.

Newly identified LMO3-BORCS5 fusion oncogene in Ewing sarcoma at relapse is a driver of tumor progression

Recently, we detected a new fusion transcript LMO3-BORCS5 in a patient with Ewing sarcoma within a cohort of relapsed pediatric cancers. LMO3-BORCS5 was as highly expressed as the characteristic fusion oncogene EWS/FLI1. However, the expression level of LMO3-BORCS5 at diagnosis was very low. Sanger sequencing depicted two LMO3-BORCS5 variants leading to loss of the functional domain LIM2 in LMO3 gene, and disruption of BORCS5. In vitro studies showed that LMO3-BORCS5 (i) increases proliferation, (ii) decreases expression of apoptosis-related genes and treatment sensitivity, and (iii) downregulates genes involved in differentiation and upregulates proliferative and extracellular matrix-related pathways. Remarkably, in vivo LMO3-BORCS5 demonstrated its high oncogenic potential by inducing tumors in mouse fibroblastic NIH-3T3 cell line. Moreover, BORCS5 probably acts, in vivo, as a tumor-suppressor gene. In conclusion, functional studies of fusion oncogenes at relapse are of great importance to define mechanisms involved in tumor progression and resistance to conventional treatments.

Mutational landscape of the transcriptome offers putative targets for immunotherapy of myeloproliferative neoplasms

Ph-negative myeloproliferative neoplasms (MPNs) are hematological cancers that can be subdivided into entities with distinct clinical features. Somatic mutations in JAK2, CALR, and MPL have been described as drivers of the disease, together with a variable landscape of nondriver mutations. Despite detailed knowledge of disease mechanisms, targeted therapies effective enough to eliminate MPN cells are still missing. In this study of 113 MPN patients, we aimed to comprehensively characterize the mutational landscape of the granulocyte transcriptome using RNA sequencing data and subsequently examine the applicability of immunotherapeutic strategies for MPN patients. Following implementation of customized workflows and data filtering, we identified a total of 13 (12/13 novel) gene fusions, 231 nonsynonymous single nucleotide variants, and 21 insertions and deletions in 106 of 113 patients. We found a high frequency of SF3B1-mutated primary myelofibrosis patients (14%) with distinct 3' splicing patterns, many of these with a protein-altering potential. Finally, from all mutations detected, we generated a virtual peptide library and used NetMHC to predict 149 unique neoantigens in 62% of MPN patients. Peptides from CALR and MPL mutations provide a rich source of neoantigens as a result of their unique ability to bind many common MHC class I molecules. Finally, we propose that mutations derived from splicing defects present in SF3B1-mutated patients may offer an unexplored neoantigen repertoire in MPNs. We validated 35 predicted peptides to be strong MHC class I binders through direct binding of predicted peptides to MHC proteins in vitro. Our results may serve as a resource for personalized vaccine or adoptive cell-based therapy development.

Integrative molecular profiling identifies a novel cluster of estrogen receptor-positive breast cancer in very young women

Very young breast cancer patients are more common in Asian countries than Western countries and are thought to have worse prognosis than older patients. The aim of the current study was to identify molecular characteristics of young patients with estrogen receptor (ER)-positive breast cancer by analyzing mutations and copy number variants (CNV), and by applying expression profiling. The whole exome and transcriptome of 47 Korean young breast cancer (KYBR) patients (age <35) were analyzed. Genomic profiles were constructed using mutations, CNV and differential gene expression from sequencing data. Pathway analyses were also performed using gene sets to identify biological processes. Our data were compared with young ER+ breast cancer patients in The Cancer Genome Atlas (TCGA) dataset. TP53, PIK3CA and GATA3 were highly recurrent somatic mutation genes. APOBEC-associated mutation signature was more frequent in KYBR compared with young TCGA patients. Integrative profiling was used to classify our patients into 3 subgroups based on molecular characteristics. Group A showed luminal A-like subtype and IGF1R signal dysregulation. Luminal B patients were classified into groups B and C, which showed chromosomal instability and enrichment for APOBEC3A/B deletions, respectively. Group B was characterized by 11q13 (CCND1) amplification and activation of the ubiquitin-mediated proteolysis pathway. Group C showed 17q12 (ERBB2) amplification and lower ER and progesterone receptor expression. Group C was also distinguished by immune activation and lower epithelial-mesenchyme transition (EMT) degree compared with group B. This study showed that integrative genomic profiling could classify very young patients with breast cancer into molecular subgroups that are potentially linked to different clinical characteristics.

A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans

Gene fusions have a very important role in the study of cancer development. In this regard, predicting the probability of protein fusion transcripts of developing into a cancer is a very challenging and yet not fully explored research problem. To this date, all the available approaches in literature try to explain the oncogenic potential of gene fusions based on protein domain analysis, that is cancer-specific and not easy to adapt to newly developed information. In our work, we choose the raw protein sequences as the input baseline, and propose the use of deep learning, and more specifically Convolutional Neural Networks, to infer the oncogenity probability score of gene fusion transcripts and to group them into a number of categories (e.g., oncogenic/not oncogenic). This is an inherently flexible methodology that, unlike previous approaches, can be re-trained with very less efforts on newly available data (for example, from a different cancer). Based on experimental results on a large dataset of pre-annotated gene fusions, our method is able to predict the oncogenity potential of gene fusion transcripts with accuracy of about 72%, which increases to 86% if we consider the only instances that are classified with a high confidence level.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

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.

Molecular Signatures of Fusion Proteins in Cancer

Although gene fusions are recognized as driver mutations in a wide variety of cancers, the general molecular mechanisms underlying oncogenic fusion proteins are insufficiently understood. Here, we employ large-scale data integration and machine learning and (1) identify three functionally distinct subgroups of gene fusions and their molecular signatures; (2) characterize the cellular pathways rewired by fusion events across different cancers; and (3) analyze the relative importance of over 100 structural, functional, and regulatory features of ∼2200 gene fusions. We report subgroups of fusions that likely act as driver mutations and find that gene fusions disproportionately affect pathways regulating cellular shape and movement. Although fusion proteins are similar across different cancer types, they affect cancer type-specific pathways. Key indicators of fusion-forming proteins include high and nontissue specific expression, numerous splice sites, and higher centrality in protein-interaction networks. Together, these findings provide unifying and cancer type-specific trends across diverse oncogenic fusion proteins.

Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma

Immune checkpoint inhibitors have been successful across several tumor types; however, their efficacy has been uncommon and unpredictable in glioblastomas (GBM), where <10% of patients show long-term responses. To understand the molecular determinants of immunotherapeutic response in GBM, we longitudinally profiled 66 patients, including 17 long-term responders, during standard therapy and after treatment with PD-1 inhibitors (nivolumab or pembrolizumab). Genomic and transcriptomic analysis revealed a significant enrichment of PTEN mutations associated with immunosuppressive expression signatures in non-responders, and an enrichment of MAPK pathway alterations (PTPN11, BRAF) in responders. Responsive tumors were also associated with branched patterns of evolution from the elimination of neoepitopes as well as with differences in T cell clonal diversity and tumor microenvironment profiles. Our study shows that clinical response to anti-PD-1 immunotherapy in GBM is associated with specific molecular alterations, immune expression signatures, and immune infiltration that reflect the tumor's clonal evolution during treatment.

Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor

Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in ∼14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer

Numerous gene fusions have been uncovered across multiple cancer types. Although the ability to target several of these fusions has led to the development of some successful anti-cancer drugs, most of them are not druggable. Understanding the molecular pathways of a fusion is important in determining its function in oncogenesis and in developing therapeutic strategies for patients harboring the fusion. However, the molecular pathways have been elucidated for only a few fusions, in part because of the labor-intensive nature of the required functional assays. Therefore, we developed a domain-based network approach to infer the pathways of a fusion. Molecular interactions of a fusion are first predicted by using its protein domain composition, and its associated pathways are then inferred from these molecular interactions. We demonstrated the capabilities of this approach by primarily applying it to the well-studied BCR-ABL1 fusion. The approach was also applied to two undruggable fusions in sarcoma, EWS-FL1 and FUS-DDIT3. We successfully identified known genes and pathways associated with these fusions and satisfactorily validated these predictions using several benchmark sets. The predictions of EWS-FL1 and FUS-DDIT3 also correlate with results of high-throughput drug screening. To our best knowledge, this is the first approach for inferring pathways of fusions.

We present a computational framework, FusionPathway, to infer the oncogenesis pathways of a fusion and help develop therapeutic strategies in these pathways for patients harboring the fusion. In this work, we successfully validated the capabilities of this approach through its application to the well-studied BCR-ABL1 fusion and two undruggable fusions in sarcoma, EWS-FL1 and FUS-DDIT3. Especially, the predictions of EWS-FL1 and FUS-DDIT3 correlate well with results of high-throughput drug screening in sarcoma cells. Therefore, FusionPathway can be an effective method to infer pathways and potential therapeutic targets that are associated with those undruggable fusions. Our results of BCR-ABL1 also suggest that FusionPathway may be able to help elucidate pathway-dependent mechanisms of resistances to those kinase fusion-targeting therapies and develop strategies to overcome the resistances. In addition, the developed R package of FusionPathways (https://github.com/perwu/FusionPathway/) can help people easily apply our approach to study other important fusions in cancer.

Somatic genome editing with the RCAS-TVA-CRISPR-Cas9 system for precision tumor modeling

To accurately recapitulate the heterogeneity of human diseases, animal models require to recreate multiple complex genetic alterations. Here, we combine the RCAS-TVA system with the CRISPR-Cas9 genome editing tools for precise modeling of human tumors. We show that somatic deletion in neural stem cells of a variety of known tumor suppressor genes (Trp53, Cdkn2a, and Pten) leads to high-grade glioma formation. Moreover, by simultaneous delivery of pairs of guide RNAs we generate different gene fusions with oncogenic potential, either by chromosomal deletion (Bcan-Ntrk1) or by chromosomal translocation (Myb-Qk). Lastly, using homology-directed-repair, we also produce tumors carrying the homologous mutation to human BRAF V600E, frequently identified in a variety of tumors, including different types of gliomas. In summary, we have developed an extremely versatile mouse model for in vivo somatic genome editing, that will elicit the generation of more accurate cancer models particularly appropriate for pre-clinical testing.

The Pathological Spectrum of Systemic Anaplastic Large Cell Lymphoma (ALCL)

Anaplastic large cell lymphoma (ALCL) represents a group of malignant T-cell lymphoproliferations that share morphological and immunophenotypical features, namely strong CD30 expression and variable loss of T-cell markers, but differ in clinical presentation and prognosis. The recognition of anaplastic lymphoma kinase (ALK) fusion proteins as a result of chromosomal translocations or inversions was the starting point for the distinction of different subgroups of ALCL. According to their distinct clinical settings and molecular findings, the 2016 revised World Health Organization (WHO) classification recognizes four different entities: systemic ALK-positive ALCL (ALK+ ALCL), systemic ALK-negative ALCL (ALK− ALCL), primary cutaneous ALCL (pC-ALCL), and breast implant-associated ALCL (BI-ALCL), the latter included as a provisional entity. ALK is rearranged in approximately 80% of systemic ALCL cases with one of its partner genes, most commonly NPM1, and is associated with favorable prognosis, whereas systemic ALK− ALCL shows heterogeneous clinical, phenotypical, and genetic features, underlining the different oncogenesis between these two entities. Recognition of the pathological spectrum of ALCL is crucial to understand its pathogenesis and its boundaries with other entities. In this review, we will focus on the morphological, immunophenotypical, and molecular features of systemic ALK+ and ALK− ALCL. In addition, BI-ALCL will be discussed.

Gene Fusion in Malignant Glioma: An Emerging Target for Next-Generation Personalized Treatment

Malignant gliomas are heterogeneous diseases in genetic basis. The development of sequencing techniques has identified many gene rearrangements encoding novel oncogenic fusions in malignant glioma to date. Understanding the gene fusions and how they regulate cellular processes in different subtypes of glioma will shed light on genomic diagnostic approaches for personalized treatment. By now, studies of gene fusions in glioma remain limited, and no medication has been approved for treating the malignancy harboring gene fusions. This review will discuss the current characterization of gene fusions occurring in both adult and pediatric malignant gliomas, their roles in oncogenesis, and the potential clinical implication as therapeutic targets.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

confFuse: High-Confidence Fusion Gene Detection across Tumor Entities

Background: Fusion genes play an important role in the tumorigenesis of many cancers. Next-generation sequencing (NGS) technologies have been successfully applied in fusion gene detection for the last several years, and a number of NGS-based tools have been developed for identifying fusion genes during this period. Most fusion gene detection tools based on RNA-seq data report a large number of candidates (mostly false positives), making it hard to prioritize candidates for experimental validation and further analysis. Selection of reliable fusion genes for downstream analysis becomes very important in cancer research. We therefore developed confFuse, a scoring algorithm to reliably select high-confidence fusion genes which are likely to be biologically relevant.

Results: confFuse takes multiple parameters into account in order to assign each fusion candidate a confidence score, of which score ≥8 indicates high-confidence fusion gene predictions. These parameters were manually curated based on our experience and on certain structural motifs of fusion genes. Compared with alternative tools, based on 96 published RNA-seq samples from different tumor entities, our method can significantly reduce the number of fusion candidates (301 high-confidence from 8,083 total predicted fusion genes) and keep high detection accuracy (recovery rate 85.7%). Validation of 18 novel, high-confidence fusions detected in three breast tumor samples resulted in a 100% validation rate.

Conclusions: confFuse is a novel downstream filtering method that allows selection of highly reliable fusion gene candidates for further downstream analysis and experimental validations. confFuse is available at https://github.com/Zhiqin-HUANG/confFuse.

Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer

Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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

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

Anaplastic large cell lymphoma: pathology, genetics, and clinical aspects

Anaplastic large cell lymphoma (ALCL) was first described in 1985 as a large-cell neoplasm with anaplastic morphology immunostained by the Ki-1 antibody, which recognizes CD30. In 1994, the nucleophosmin (NPM)-anaplastic lymphoma kinase (ALK) fusion receptor tyrosine kinase was identified in a subset of patients, leading to subdivision of this disease into ALK-positive and -negative ALCL in the present World Health Organization classification. Due to variations in morphology and immunophenotype, which may sometimes be atypical for lymphoma, many differential diagnoses should be considered, including solid cancers, lymphomas, and reactive processes. CD30 and ALK are key molecules involved in the pathogenesis, diagnosis, and treatment of ALCL. In addition, signal transducer and activator of transcription 3 (STAT3)-mediated mechanisms are relevant in both types of ALCL, and fusion/mutated receptor tyrosine kinases other than ALK have been reported in ALK-negative ALCL. ALK-positive ALCL has a better prognosis than ALK-negative ALCL or other peripheral T-cell lymphomas. Patients with ALK-positive ALCL are usually treated with anthracycline-based regimens, such as combination cyclophosphamide, doxorubicin, vincristine, and prednisolone (CHOP) or CHOEP (CHOP plus etoposide), which provide a favorable prognosis, except in patients with multiple International Prognostic Index factors. For targeted therapies, an anti-CD30 monoclonal antibody linked to a synthetic antimitotic agent (brentuximab vedotin) and ALK inhibitors (crizotinib, alectinib, and ceritinib) are being used in clinical settings.

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/.

Identifying fusion transcripts using next generation sequencing

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

Inconsistent results in the analysis of ALK rearrangements in non-small cell lung cancer

BACKGROUND

Identification of targetable EML4-ALK fusion proteins has revolutionized the treatment of a minor subgroup of non-small cell lung cancer (NSCLC) patients. Although fluorescence in situ hybridization (FISH) is regarded as the gold standard for detection of ALK rearrangements, ALK immunohistochemistry (IHC) is often used as screening tool in clinical practice. In order to unbiasedly analyze the diagnostic impact of such a screening strategy, we compared ALK IHC with ALK FISH in three large representative Swedish NSCLC cohorts incorporating clinical parameters and gene expression data.

METHODS

ALK rearrangements were detected using FISH on tissue microarrays (TMAs), including tissue from 851 NSCLC patients. In parallel, ALK protein expression was detected using IHC, applying the antibody clone D5F3 with two different protocols (the FDA approved Ventana CDx assay and our in house Dako IHC protocol). Gene expression microarray data (Affymetrix) was available for 194 patients.

RESULTS

ALK rearrangements were detected in 1.7 % in the complete cohort and 2.0 % in the non-squamous cell carcinoma subgroup. ALK protein expression was observed in 1.8 and 1.4 % when applying the Ventana assay or the in house Dako protocol, respectively. The specificity and accuracy of IHC was high (> 98 %), while the sensitivity was between 69 % (Ventana) and 62 % (in house Dako protocol). Furthermore, only 67 % of the ALK IHC positive cases were positive with both IHC assays. Gene expression analysis revealed that 6/194 (3 %) tumors showed high ALK gene expression (≥ 6 AU) and of them only three were positive by either FISH or IHC.

CONCLUSION

The overall frequency of ALK rearrangements based on FISH was lower than previously reported. The sensitivity of both IHC assays was low, and the concordance between the FISH and the IHC assays poor, questioning current strategies to screen with IHC prior to FISH or completely replace FISH by IHC.

The genetics of nodal marginal zone lymphoma

Nodal marginal zone lymphoma (NMZL) is a rare, indolent B-cell tumor that is distinguished from splenic marginal zone lymphoma (SMZL) by the different pattern of dissemination. NMZL still lacks distinct markers and remains orphan of specific cancer gene lesions. By combining whole-exome sequencing, targeted sequencing of tumor-related genes, whole-transcriptome sequencing, and high-resolution single nucleotide polymorphism array analysis, we aimed at disclosing the pathways that are molecularly deregulated in NMZL and we compare the molecular profile of NMZL with that of SMZL. These analyses identified a distinctive pattern of nonsilent somatic lesions in NMZL. In 35 NMZL patients, 41 genes were found recurrently affected in ≥3 (9%) cases, including highly prevalent molecular lesions of MLL2 (also known as KMT2D; 34%), PTPRD (20%), NOTCH2 (20%), and KLF2 (17%). Mutations of PTPRD, a receptor-type protein tyrosine phosphatase regulating cell growth, were enriched in NMZL across mature B-cell tumors, functionally caused the loss of the phosphatase activity of PTPRD, and were associated with cell-cycle transcriptional program deregulation and increased proliferation index in NMZL. Although NMZL shared with SMZL a common mutation profile, NMZL harbored PTPRD lesions that were otherwise absent in SMZL. Collectively, these findings provide new insights into the genetics of NMZL, identify PTPRD lesions as a novel marker for this lymphoma across mature B-cell tumors, and support the distinction of NMZL as an independent clinicopathologic entity within the current lymphoma classification.

Clonal evolution of glioblastoma under therapy

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

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.

Emerging Role of Genomic Rearrangements in Breast Cancer: Applying Knowledge from Other Cancers

Significant advances in our knowledge of cancer genomes are rapidly changing the way we think about tumor biology and the heterogeneity of cancer. Recent successes in genomically-guided treatment approaches accompanied by more sophisticated sequencing techniques have paved the way for deeper investigation into the landscape of genomic rearrangements in cancer. While considerable research on solid tumors has focused on point mutations that directly alter the coding sequence of key genes, far less is known about the role of somatic rearrangements. With many recurring alterations observed across tumor types, there is an obvious need for functional characterization of these genomic biomarkers in order to understand their relevance to tumor biology, therapy, and prognosis. As personalized therapy approaches are turning toward genomic alterations for answers, these biomarkers will become increasingly relevant to the practice of precision medicine. This review discusses the emerging role of genomic rearrangements in breast cancer, with a particular focus on fusion genes. In addition, it raises several key questions on the therapeutic value of such rearrangements and provides a framework to evaluate their significance as predictive and prognostic biomarkers.

Reproducible, Scalable Fusion Gene Detection from RNA-Seq

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

Convergent mutations and kinase fusions lead to oncogenic STAT3 activation in anaplastic large cell lymphoma

A systematic characterization of the genetic alterations driving ALCLs has not been performed. By integrating massive sequencing strategies, we provide a comprehensive characterization of driver genetic alterations (somatic point mutations, copy number alterations, and gene fusions) in ALK(-) ALCLs. We identified activating mutations of JAK1 and/or STAT3 genes in ∼20% of 88 [corrected] ALK(-) ALCLs and demonstrated that 38% of systemic ALK(-) ALCLs displayed double lesions. Recurrent chimeras combining a transcription factor (NFkB2 or NCOR2) with a tyrosine kinase (ROS1 or TYK2) were also discovered in WT JAK1/STAT3 ALK(-) ALCL. All these aberrations lead to the constitutive activation of the JAK/STAT3 pathway, which was proved oncogenic. Consistently, JAK/STAT3 pathway inhibition impaired cell growth in vitro and in vivo.