ChimeraScan: a tool for identifying chimeric transcription in sequencing data

SDC1 is a critical transmembrane proteoglycan in breast cancer

There has been an increasing incidence of breast cancer around the world in recent years. As a burgeoning model of diagnosis and treatment, precision medicine has become a new trend in breast cancer management. Dysregulated glycometabolism is well established as a tumor feature. SDC1 is a glycometabolism-related gene and participates in the progression, metastasis, resistance and recurrence of malignant tumors. SDC1 promotes the development of breast cancer by disturbing tumor stem cell phenotypes, the cell cycle and apoptosis, and then modulates macrophage migration, epithelial-mesenchymal transformation, angiogenesis and the tumor-bone microenvironment. We summarized the recent advances regarding the role of SDC1 in the mechanism driving the occurrence of breast cancer, and evaluated its potential therapeutic contributions.

Fusion Transcript Detection from Short-Read RNA-Seq

Fusion proteins have been shown to play an important role in many different cancers and other diseases. While the causal mutation can often be found in the genome as a structural variant (SV), differentiating between normal variation within individuals and somatic variants with functional consequences can be time-consuming as well as expensive since it requires a whole-genome sequencing (WGS) method. RNA Sequencing (RNA-Seq) provides a much cheaper and more straightforward approach to the detection of functional somatic events such as overexpression of proto-oncogenes as well as gene fusion. This chapter aims to discuss the utility of RNA-Seq for fusion detection as well as provide a detailed analysis pipeline for the detection of fusion transcripts.

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.

PUM1-TRAF3 fusion protein activates non-canonical NF-κB signaling via rescued NIK in biliary tract cancer

Discovery and verification of diagnostic or therapeutic biomarkers for biliary tract cancer (BTC) is challenging owing to the low prevalence of the disease. Here, we identified and investigated the clinical impact of a fusion gene, Pumilio1-tumor necrosis factor receptor-associated factor 3 (PUM1-TRAF3), caused by 1;14 chromosomal translocation in BTC. PUM1-TRAF3 was initially identified in the RNA-sequencing of five BTC surgical tissues and confirmed by fluorescence in situ hybridization. Expression of the fusion gene was validated in an expanded cohort (5/55, 9.1%). Establishment and molecular assessment of PUM1-TRAF3 expressing BTC cells revealed that PUM1-TRAF3 activates non-canonical NF-κB signaling via NF-κB-inducing kinase (NIK). Abnormal TRAF3 activity, driven by competitive binding of PUM1-TRAF3 and TRAF3 to NIK, led to NIK rescue followed by P52/RelB nuclear translocation, all of which were reverted by an NIK inhibitor. The elevated expression of NIK and activated NF-κB signaling was observed in the PUM1-TRAF3-expressing regions of patient tissues. Expression of the PUM1-TRAF3 fusion was significantly correlated with strong NIK expression, which is associated with a poorer prognosis for patients with BTC. Overall, our study identifies a new fusion gene, PUM1-TRAF3, that activates NIK and non-canonical NF-κB signaling, which may be beneficial for developing precise treatment strategies for BTC.

Transcriptomic analysis and fusion gene identifications of midbody remnants released from colorectal cancer cells reveals they are molecularly distinct from exosomes and microparticles

Previously, we reported that human primary (SW480) and metastatic (SW620) colorectal (CRC) cells release three classes of membrane-encapsulated extracellular vesicles (EVs); midbody remnants (MBRs), exosomes (Exos), and microparticles (MPs). We reported that MBRs were molecularly distinct at the protein level. To gain further biochemical insights into MBRs, Exos, and MPs and their emerging role in CRC, we performed, and report here, for the first time, a comprehensive transcriptome and long noncoding RNA sequencing analysis and fusion gene identification of these three EV classes using the next-generation RNA sequencing technique. Differential transcript expression analysis revealed that MBRs have a distinct transcriptomic profile compared to Exos and MPs with a high enrichment of mitochondrial transcripts lncRNA/pseudogene transcripts that are predicted to bind to ribonucleoprotein complexes, spliceosome, and RNA/stress granule proteins. A salient finding from this study is a high enrichment of several fusion genes in MBRs compared to Exos, MPs, and cell lysates from their parental cells such as MSH2 (gene encoded DNA mismatch repair protein MSH2). This suggests potential EV-liquid biopsy targets for cancer detection. Importantly, the expression of cancer progression-related transcripts found in EV classes derived from SW480 (EGFR) and SW620 (MET and MACCA1) cell lines reflects their parental cell types. Our study is the report of RNA and fusion gene compositions within MBRs (including Exos and MPs) that could have an impact on EV functionality in cancer progression and detection using EV-based RNA/ fusion gene candidates for cancer biomarkers.

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

Recurrent UBE3C-LRP5 translocations in head and neck cancer with therapeutic implications

Head and neck cancer is a major cause of morbidity and mortality worldwide. The identification of genetic alterations in head and neck cancer may improve diagnosis and treatment outcomes. In this study, we report the identification and functional characterization of UBE3C-LRP5 translocation in head and neck cancer. Our whole transcriptome sequencing and RT-PCR analysis of 151 head and neck cancer tumor samples identified the LRP5-UBE3C and UBE3C-LRP5 fusion transcripts in 5.3% of patients of Indian origin (n = 151), and UBE3C-LRP5 fusion transcripts in 1.2% of TCGA-HNSC patients (n = 502). Further, whole genome sequencing identified the breakpoint of UBE3C-LRP5 translocation. We demonstrate that UBE3C-LRP5 fusion is activating in vitro and in vivo, and promotes the proliferation, migration, and invasion of head and neck cancer cells. In contrast, depletion of UBE3C-LRP5 fusion suppresses the clonogenic, migratory, and invasive potential of the cells. The UBE3C-LRP5 fusion activates the Wnt/β-catenin signaling by promoting nuclear accumulation of β-catenin, leading to upregulation of Wnt/β-catenin target genes, MYC, CCND1, TCF4, and LEF1. Consistently, treatment with the FDA-approved drug, pyrvinium pamoate, significantly reduced the transforming ability of cells expressing the fusion protein and improved survival in mice bearing tumors of fusion-overexpressing cells. Interestingly, fusion-expressing cells upon knockdown of CTNNB1, or LEF1 show reduced proliferation, clonogenic abilities, and reduced sensitivity to pyrvinium pamoate. Overall, our study suggests that the UBE3C-LRP5 fusion is a promising therapeutic target for head and neck cancer and that pyrvinium pamoate may be a potential drug candidate for treating head and neck cancer harboring this translocation.

Cell-type-specific tumour sensitivity identified with a bromodomain targeting PROTAC in adenoid cystic carcinoma

Salivary gland adenoid cystic carcinoma (ACC) is a rare malignancy with limited treatment options. The development of novel therapies is hindered by a lack of preclinical models. We have generated ACC patient-derived xenograft (PDX) lines that retain the physical and genetic properties of the original tumours, including the presence of the common MYB::NFIB or MYBL1::NFIB translocations. We have developed the conditions for the generation of both 2D and 3D tumour organoid patient-derived ACC models that retain MYB expression and can be used for drug studies. Using these models, we show in vitro and in vivo sensitivity of ACC cells to the bromodomain degrader, dBET6. Molecular studies show a decrease in BRD4 and MYB protein levels and target gene expression with treatment. The most prominent effect of dBET6 on tumours in vivo was a change in the relative composition of ACC cell types expressing either myoepithelial or ductal markers. We show that dBET6 inhibits the progenitor function of ACC cells, particularly in the myoepithelial marker-expressing population, revealing a cell-type-specific sensitivity. These studies uncover a novel mechanistic effect of bromodomain inhibitors on tumours and highlight the need to impact both cell-type populations for more effective treatments in ACC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A Protocol for the Detection of Fusion Transcripts Using RNA-Sequencing Data

Fusion transcripts are formed when two genes or their mRNAs fuse to produce a novel gene or chimeric transcript. Fusion genes are well-known cancer biomarkers used for cancer diagnosis and as therapeutic targets. Gene fusions are also found in normal physiology and lead to the evolution of novel genes that contribute to better survival and adaptation for an organism. Various in vitro approaches, such as FISH, PCR, RT-PCR, and chromosome banding techniques, have been used to detect gene fusion. However, all these approaches have low resolution and throughput. Due to the development of high-throughput next-generation sequencing technologies, the detection of fusion transcript becomes feasible using whole genome sequencing, RNA-Seq data, and bioinformatics tools. This chapter will overview the general computational protocol for fusion transcript detection from RNA-sequencing datasets.

New insight into circRNAs: characterization, strategies, and biomedical applications

Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine.

Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data

We present novoRNABreak, a unified framework for cancer specific novel splice junction and fusion transcript detection in RNA-seq data obtained from human cancer samples. novoRNABreak is based on a local assembly model, which offers a tradeoff between the alignment-based and de novo whole transcriptome assembly (WTA) methods. This approach is accurate and sensitive in assembling novel junctions that are difficult to directly align or have multiple alignments. Additionally, it is more efficient due to the strategy that focuses on junctions rather than full length transcripts. The performance of novoRNABreak is demonstrated by a comprehensive set of experiments using synthetic data generated based on genome reference, as well as real RNA-seq data from breast cancer and prostate cancer samples. The results show that our tool has a better performance by fully utilizing unmapped reads and precisely identifying the junctions where short reads or small exons have multiple alignments. novoRNABreak is a fully-fledged program available on GitHub (https://github.com/KChen-lab/novoRNABreak).

Genomic and transcriptomic profiling reveals key molecules in metastatic potentials and organ-tropisms of hepatocellular carcinoma

Metastasis is a landmark event for rapid postsurgical relapse and death of HCC patients. Although distinct genomic and transcriptomic profiling of HCC metastasis had been reported previously, the causal relationships of somatic mutants, mRNA levels and metastatic potentials were difficult to be established in clinic. Therefore, 11 human HCC cell lines and 7 monoclonal derivatives with definite metastatic potentials and tropisms were subjected to whole exome sequencing (WES) and whole transcriptome sequencing (WTS). TP53, MYO5A, ROS1 and ARID2 were the prominent mutants of metastatic drivers in HCC cells. During HCC clonal evaluation, TP53, MYO5A and ROS1 mutations occurred in the early stage, EXT2 and NIN in the late stage. NF1 mutant was unique in lung tropistic cell lines, RNF126 mutant in lymphatic tropistic ones. PER1, LMO2, GAS7, NR4A3 expression levels were positively associated with relapse-free survival (RFS) of HCC patients. The integrative analysis revealed 58 genes exhibited both somatic mutation and dysregulated mRNA levels in high metastatic cells. Altogether, metastatic drivers could accumulate gradually at different stages during HCC progression, some drivers might modulate HCC metastatic potentials and the others regulate metastatic tropisms.

Homologous recombination deficiency signatures in gastrointestinal and thoracic cancers correlate with platinum therapy duration

There is emerging evidence about the predictive role of homologous recombination deficiency (HRD), but this is less defined in gastrointestinal (GI) and thoracic malignancies. We reviewed whole genome (WGS) and transcriptomic (RNA-Seq) data from advanced GI and thoracic cancers in the Personalized OncoGenomics trial (NCT02155621) to evaluate HRD scores and single base substitution (SBS)3, which is associated with BRCA1/2 mutations and potentially predictive of defective HRD. HRD scores were calculated by sum of loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions scores. Regression analyses examined the association between HRD and time to progression on platinum (TTPp). We included 223 patients with GI (n = 154) or thoracic (n = 69) malignancies. TTPp was associated with SBS3 (p < 0.01) but not HRD score in patients with GI malignancies, whereas neither was associated with TTPp in thoracic malignancies. Tumors with gBRCA1/2 mutations and a somatic second alteration exhibited high SBS3 and HRD scores, but these signatures were also present in several tumors with germline but no somatic second alterations, suggesting silencing of the wild-type allele or BRCA1/2 haploinsufficiency. Biallelic inactivation of an HR gene, including loss of XRCC2 and BARD1, was identified in BRCA1/2 wild-type HRD tumors and these patients had prolonged response to platinum. Thoracic cases with high HRD score were associated with high RECQL5 expression (p ≤ 0.025), indicating another potential mechanism of HRD. SBS3 was more strongly associated with TTPp in patients with GI malignancies and may be complementary to using HRD and BRCA status in identifying patients who benefit from platinum therapy.

Synergetic effect of Azacitidine and Sorafenib in treatment of a case of myeloid neoplasm with sole chromosomal abnormality t(8;22)(p11.2;q11.2)/BCR-FGFR1 rearrangement

The sole t(8;22)(p11.2;q11.2)/BCR- FGFR1 chromosomal abnormality formerly known as aCML is an extremely rare disease entity with a history of rapid progression. Though patients resemble phenotypically chronic myeloid leukemia, the treatment of patients with sole BCR-FGFR1 rearrangement are still challenging for clinicians due to rapid progressive nature and unavailability of uniform treatment guidelines. In present case study, we describe a case of myeloid neoplasm with sole chromosomal abnormality of t(8;22)(p11.2;q11.2)/BCR-FGFR1 rearrangement which is successfully managed by Sorafenib with Azacitidine. Hence our case report suggests that combination of Sorafenib and Azacitidine treatment is effective in sole BCR-FGFR1 rearrangement, however this combination therapy should be studied in large clinical trials.

RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data

Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.

Integrated Genomic Analysis Identifies UBTF Tandem Duplications as a Recurrent Lesion in Pediatric Acute Myeloid Leukemia

The genetics of relapsed pediatric acute myeloid leukemia (AML) has yet to be comprehensively defined. Here, we present the spectrum of genomic alterations in 136 relapsed pediatric AMLs. We identified recurrent exon 13 tandem duplications (TD) in upstream binding transcription factor (UBTF) in 9% of relapsed AML cases. UBTF-TD AMLs commonly have normal karyotype or trisomy 8 with cooccurring WT1 mutations or FLT3-ITD but not other known oncogenic fusions. These UBTF-TD events are stable during disease progression and are present in the founding clone. In addition, we observed that UBTF-TD AMLs account for approximately 4% of all de novo pediatric AMLs, are less common in adults, and are associated with poor outcomes and MRD positivity. Expression of UBTF-TD in primary hematopoietic cells is sufficient to enhance serial clonogenic activity and to drive a similar transcriptional program to UBTF-TD AMLs. Collectively, these clinical, genomic, and functional data establish UBTF-TD as a new recurrent mutation in AML.

SIGNIFICANCE

We defined the spectrum of mutations in relapsed pediatric AML and identified UBTF-TDs as a new recurrent genetic alteration. These duplications are more common in children and define a group of AMLs with intermediate-risk cytogenetic abnormalities, FLT3-ITD and WT1 alterations, and are associated with poor outcomes. See related commentary by Hasserjian and Nardi, p. 173. This article is highlighted in the In This Issue feature, p. 171.

Fusion Genes in Prostate Cancer: A Comparison in Men of African and European Descent

Simple Summary

Men of African origin have a 2–3 times greater chance of developing prostate cancer than those of European origin, and of patients that are diagnosed with the disease, men of African descent are 2 times more likely to die compared to white men. Men of African origin are still greatly underrepresented in genetic studies and clinical trials. This, unfortunately, means that new discoveries in cancer treatment are missing key information on the group with a greater chance of mortality. A fusion gene is a hybrid gene formed from two previously independent genes. Fusion genes have been found to be common in all main types of human cancer. The objective of this study was to increase our knowledge of fusion genes in prostate cancer using computational approaches and to compare fusion genes between men of African and European origin. This identified novel gene fusions unique to men of African origin and suggested that this group has a greater number of fusion genes.

Abstract

Prostate cancer is one of the most prevalent cancers worldwide, particularly affecting men living a western lifestyle and of African descent, suggesting risk factors that are genetic, environmental, and socioeconomic in nature. In the USA, African American (AA) men are disproportionately affected, on average suffering from a higher grade of the disease and at a younger age compared to men of European descent (EA). Fusion genes are chimeric products formed by the merging of two separate genes occurring as a result of chromosomal structural changes, for example, inversion or trans/cis-splicing of neighboring genes. They are known drivers of cancer and have been identified in 20% of cancers. Improvements in genomics technologies such as RNA-sequencing coupled with better algorithms for prediction of fusion genes has added to our knowledge of specific gene fusions in cancers. At present AA are underrepresented in genomic studies of prostate cancer. The primary goal of this study was to examine molecular differences in predicted fusion genes in a cohort of AA and EA men in the context of prostate cancer using computational approaches. RNA was purified from prostate tissue specimens obtained at surgery from subjects enrolled in the study. Fusion gene predictions were performed using four different fusion gene detection programs. This identified novel putative gene fusions unique to AA and suggested that the fusion gene burden was higher in AA compared to EA men.

Whole-genome and transcriptome analysis of advanced adrenocortical cancer highlights multiple alterations affecting epigenome and DNA repair pathways

Adrenocortical cancer (ACC) is a rare cancer of the adrenal gland. Several driver mutations have been identified in both primary and metastatic ACCs, but the therapeutic options are still limited. We performed whole-genome and transcriptome sequencing on seven patients with metastatic ACC. Integrative analysis of mutations, RNA expression changes, mutation signature, and homologous recombination deficiency (HRD) analysis was performed. Mutations affecting CTNNB1 and TP53 and frequent loss of heterozygosity (LOH) events were observed in our cohort. Alterations affecting genes involved in cell cycle (RB1, CDKN2A, CDKN2B), DNA repair pathways (MUTYH, BRCA2, ATM, RAD52, MLH1, MSH6), and telomere maintenance (TERF2 and TERT) consisting of somatic and germline mutations, structural variants, and expression outliers were also observed. HRDetect, which aggregates six HRD-associated mutation signatures, identified a subset of cases as HRD. Genomic alterations affecting genes involved in epigenetic regulation were also identified, including structural variants (SWI/SNF genes and histone methyltransferases), and copy gains and concurrent high expression of KDM5A, which may contribute to epigenomic deregulation. Findings from this study highlight HRD and epigenomic pathways as potential therapeutic targets and suggest a subgroup of patients may benefit from a diverse array of molecularly targeted therapies in ACC, a rare disease in urgent need of therapeutic strategies.

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.

Characterization of a Read-through Fusion Transcript, BCL2L2-PABPN1, Involved in Porcine Adipogenesis

cis-Splicing of adjacent genes (cis-SAGe) has been involved in multiple physiological and pathological processes in humans. However, to the best of our knowledge, there is no report of cis-SAGe in adipogenic regulation. In this study, a cis-SAGe product, BCL2L2–PABPN1 (BP), was characterized in fat tissue of pigs with RT-PCR and RACE method. BP is an in-frame fusion product composed of 333 aa and all the functional domains of both parents. BP is highly conserved among species and rich in splicing variants. BP was found to promote proliferation and inhibit differentiation of primary porcine preadipocytes. A total of 3074/44 differentially expressed mRNAs (DEmRs)/known miRNAs (DEmiRs) were identified in porcine preadipocytes overexpressing BP through RNA-Seq analysis. Both DEmRs and target genes of DEmiRs were involved in various fat-related pathways with MAPK and PI3K-Akt being the top enriched. PPP2CB, EGFR, Wnt5A and EHHADH were hub genes among the fat-related pathways identified. Moreover, ssc-miR-339-3p was found to be critical for BP regulating adipogenesis through integrated analysis of mRNA and miRNA data. The results highlight the role of cis-SAGe in adipogenesis and contribute to further revealing the mechanisms underlying fat deposition, which will be conductive to human obesity control.

Clinical response to nivolumab in an INI1-deficient pediatric chordoma correlates with immunogenic recognition of brachyury

Poorly differentiated chordoma (PDC) is a recently recognized subtype of chordoma characterized by expression of the embryonic transcription factor, brachyury, and loss of INI1. PDC primarily affects children and is associated with a poor prognosis and limited treatment options. Here we describe the molecular and immune tumour microenvironment profiles of two paediatric PDCs produced using whole-genome, transcriptome and whole-genome bisulfite sequencing (WGBS) and multiplex immunohistochemistry. Our analyses revealed the presence of tumour-associated immune cells, including CD8+ T cells, and expression of the immune checkpoint protein, PD-L1, in both patient samples. Molecular profiling provided the rationale for immune checkpoint inhibitor (ICI) therapy, which resulted in a clinical and radiographic response. A dominant T cell receptor (TCR) clone specific for a brachyury peptide-MHC complex was identified from bulk RNA sequencing, suggesting that targeting of the brachyury tumour antigen by tumour-associated T cells may underlie this clinical response to ICI. Correlative analysis with rhabdoid tumours, another INI1-deficient paediatric malignancy, suggests that a subset of tumours may share common immune phenotypes, indicating the potential for a therapeutically targetable subgroup of challenging paediatric cancers.

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

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

Transcriptomics paving the way for improved diagnostics and precision medicine of acute leukemia

Transcriptional profiling of acute leukemia, specifically by RNA-sequencing or whole transcriptome sequencing (WTS), has provided fundamental insights into its underlying disease biology and allows unbiased detection of oncogenic gene fusions, as well as of gene expression signatures that can be used for improved disease classification. While used as a research tool for many years, RNA-sequencing is becoming increasingly used in clinical diagnostics. Here, we highlight key transcriptomic studies of acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) that have improved our biological understanding of these heterogeneous malignant disorders and have paved the way for translation into clinical diagnostics. Recent single-cell transcriptomic studies of ALL and AML, which provide new insights into the cellular ecosystem of acute leukemia and point to future clinical utility, are also reviewed. Finally, we discuss current challenges that need to be overcome for a more wide-spread adoption of RNA-sequencing in clinical diagnostics and how this technology significantly can aid the identification of genetic alterations in current guidelines and of newly emerging disease entities, some of which are critical to identify because of the availability of targeted therapies, thereby paving the way for improved precision medicine of acute leukemia.

Diverse Oncogenic Fusions and Distinct Gene Expression Patterns Define the Genomic Landscape of Pediatric Papillary Thyroid Carcinoma

Pediatric papillary thyroid carcinoma (PPTC) is clinically distinct from adult-onset disease. Although there are higher rates of metastasis and recurrence in PPTC, prognosis remains highly favorable. Molecular characterization of PPTC has been lacking. Historically, only 40% to 50% of childhood papillary thyroid carcinoma (PTC) were known to be driven by genomic variants common to adult PTC; oncogenic drivers in the remainder were unknown. This contrasts with approximately 90% of adult PTC driven by a discrete number of variants. In this study, 52 PPTCs underwent candidate gene testing, followed in a subset by whole-exome and transcriptome sequencing. Within these samples, candidate gene testing identified variants in 31 (60%) tumors, while exome and transcriptome sequencing identified oncogenic variants in 19 of 21 (90%) remaining tumors. The latter were enriched for oncogenic fusions, with 11 nonrecurrent fusion transcripts, including two previously undescribed fusions, STRN-RET and TG-PBF. Most fusions were associated with 3' receptor tyrosine kinase (RTK) moieties: RET, MET, ALK, and NTRK3. For advanced (distally metastatic) tumors, a driver variant was described in 91%. Gene expression analysis defined three clusters that demonstrated distinct expression of genes involved in thyroid differentiation and MAPK signaling. Among RET-CCDC6-driven tumors, gene expression in pediatric tumors was distinguishable from that in adults. Collectively, these results show that the genomic landscape of pediatric PTC is different from adult PTC. Moreover, they identify genomic drivers in 98% of PPTCs, predominantly oncogenic fusion transcripts involving RTKs, with a pronounced impact on gene expression. Notably, most advanced tumors were driven by a variant for which targeted systemic therapy exists. SIGNIFICANCE: This study highlights important distinctions between the genomes and transcriptomes of pediatric and adult papillary thyroid carcinoma, with implications for understanding the biology, diagnosis, and treatment of advanced disease in children.

Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.

Lineage-defined leiomyosarcoma subtypes emerge years before diagnosis and determine patient survival

Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyze 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlight the very early origins of LMS. We uncover three specific subtypes of LMS that likely develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynecological origin harbor genomic dystrophin deletions and/or loss of dystrophin expression, acquire the highest burden of genomic mutation, and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP trappers and other DNA damage response inhibitors, suggesting a promising therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis.

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

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

Transcriptome Analysis Reveals MFGE8-HAPLN3 Fusion as a Novel Biomarker in Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is a highly aggressive cancer with poor prognosis. The lack of effective targeted therapies for TNBC remains a profound clinical challenge. Fusion transcripts play critical roles in carcinogenesis and serve as valuable diagnostic and therapeutic targets in cancer. The present study aimed to identify novel fusion transcripts in TNBC.

Methods

We analyzed the RNA sequencing data of 360 TNBC samples to identify and filter fusion candidates through SOAPfuse and ChimeraScan analysis. The characteristics, including recurrence, fusion type, chromosomal localization, TNBC subgroup distribution, and clinicopathological correlations, were analyzed in all candidates. Furthermore, we selected the promising fusion transcript and predicted its fusion type and protein coding capacity.

Results

Using the RNA sequencing data, we identified 189 fusion transcripts in TNBC, among which 22 were recurrent fusions. Compared to para-tumor tissues, TNBC tumor tissues accumulated more fusion events, especially in high-grade tumors. Interestingly, these events were enriched at specific chromosomal loci, and the distribution pattern varied in different TNBC subtypes. The vast majority of fusion partners were discovered on chromosomes 1p, 11q, 19p, and 19q. Besides, fusion events mainly clustered on chromosome 11 in the immunomodulatory subtype and chromosome 19 in the luminal androgen receptor subtype of TNBC. Considering the tumor specificity and frameshift mutation, we selected MFGE8-HAPLN3 as a novel biomarker and further validated it in TNBC samples using PCR and Sanger sequencing. Further, we successfully identified three types of MFGE8-HAPLN3 (E6-E2, E5-E3, and E6-E3) and predicted the ORF of E6-E2, which could encode a protein of 712 amino acids, suggesting its critical role in TNBC.

Conclusions

Improved bioinformatic stratification and comprehensive analysis identified the fusion transcript MFGE8-HAPLN3 as a novel biomarker with promising clinical application in the future.

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10-35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

Radiation-related genomic profile of papillary thyroid carcinoma after the Chernobyl accident

The 1986 Chernobyl nuclear power plant accident increased papillary thyroid carcinoma (PTC) incidence in surrounding regions, particularly for radioactive iodine (131I)-exposed children. We analyzed genomic, transcriptomic, and epigenomic characteristics of 440 PTCs from Ukraine (from 359 individuals with estimated childhood 131I exposure and 81 unexposed children born after 1986). PTCs displayed radiation dose-dependent enrichment of fusion drivers, nearly all in the mitogen-activated protein kinase pathway, and increases in small deletions and simple/balanced structural variants that were clonal and bore hallmarks of nonhomologous end-joining repair. Radiation-related genomic alterations were more pronounced for individuals who were younger at exposure. Transcriptomic and epigenomic features were strongly associated with driver events but not radiation dose. Our results point to DNA double-strand breaks as early carcinogenic events that subsequently enable PTC growth after environmental radiation exposure.

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.

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.

Genomic characterization of small cell carcinomas of the uterine cervix

Small cell carcinoma (SCC) of the uterine cervix is a rare and aggressive form of neuroendocrine carcinoma, which resembles small cell lung cancer (SCLC) in its histology and poor survival rate. Here, we sought to define the genetic underpinning of SCCs of the uterine cervix and compare their mutational profiles with those of human papillomavirus (HPV)‐positive head and neck squamous cell carcinomas, HPV‐positive cervical carcinomas, and SCLCs using publicly available data. Using a combination of whole‐exome and targeted massively parallel sequencing, we found that the nine uterine cervix SCCs, which were HPV18‐positive (n = 8) or HPV16‐positive (n = 1), harbored a low mutation burden, few copy number alterations, and other than TP53 in two cases no recurrently mutated genes. The majority of mutations were likely passenger missense mutations, and only few affected previously described cancer‐related genes. Using RNA‐sequencing, we identified putative viral integration sites on 18q12.3 and on 8p22 in two SCCs of the uterine cervix. The overall nonsilent mutation rate of uterine cervix SCCs was significantly lower than that of SCLCs, HPV‐driven cervical adeno‐ and squamous cell carcinomas, or HPV‐positive head and neck squamous cell carcinomas. Unlike SCLCs, which are reported to harbor almost universal TP53 and RB1 mutations and a dominant tobacco smoke‐related signature 4, uterine cervix SCCs rarely harbored mutations affecting these genes (2/9, 22% TP53; 0% RB1) and displayed a dominant aging (67%) or APOBEC mutational signature (17%), akin to HPV‐driven cancers, including cervical adeno‐ and squamous cell carcinomas and head and neck squamous cell carcinomas. Taken together, in contrast to SCLCs, which are characterized by highly recurrent TP53 and RB1 alterations, uterine cervix SCCs were positive for HPV leading to inactivation of the suppressors p53 and RB, suggesting that these SCCs are convergent phenotypes.

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.

The acquisition of molecular drivers in pediatric therapy-related myeloid neoplasms

Pediatric therapy-related myeloid neoplasms (tMN) occur in children after exposure to cytotoxic therapy and have a dismal prognosis. The somatic and germline genomic alterations that drive these myeloid neoplasms in children and how they arise have yet to be comprehensively described. We use whole exome, whole genome, and/or RNA sequencing to characterize the genomic profile of 84 pediatric tMN cases (tMDS: n = 28, tAML: n = 56). Our data show that Ras/MAPK pathway mutations, alterations in RUNX1 or TP53, and KMT2A rearrangements are frequent somatic drivers, and we identify cases with aberrant MECOM expression secondary to enhancer hijacking. Unlike adults with tMN, we find no evidence of pre-existing minor tMN clones (including those with TP53 mutations), but rather the majority of cases are unrelated clones arising as a consequence of cytotoxic therapy. These studies also uncover rare cases of lineage switch disease rather than true secondary neoplasms.

Bioinformatics workflows for clinical applications in precision oncology

High-throughput molecular profiling of tumors is a fundamental aspect of precision oncology, enabling the identification of genomic alterations that can be targeted therapeutically. In this context, a patient is matched to a specific drug or therapy based on the tumor's underlying genetic driver events rather than the histologic classification. This approach requires extensive bioinformatics methodology and workflows, including raw sequencing data processing and quality control, variant calling and annotation, integration of different molecular data types, visualization and finally reporting the data to physicians, cancer researchers and pharmacologists in a format that is readily interpretable for clinical decision making. This review comprises a broad overview of these bioinformatics aspects and discusses the multiple analytical, technical and interpretational challenges that remain to efficiently translate molecular findings into personalized treatment recommendations.

RAP: A Web Tool for RNA-Seq Data Analysis

Since 1950 main studies of RNA regarded its role in the protein synthesis. Later insights showed that only a small portion of RNA codes for proteins where the rest could have different functional roles. With the advent of Next Generation Sequencing (NGS) and in particular with RNA-seq technology the cost of sequencing production dropped down. Among the NGS application areas, the transcriptome analysis, that is, the analysis of transcripts in a cell, their quantification for a specific developmental stage or treatment condition, became more and more adopted in the laboratories. As a consequence in the last decade new insights were gained in the understanding of both transcriptome complexity and involvement of RNA molecules in cellular processes. For what concerns computational advances, bioinformatics research developed new methods for analyzing RNA-seq data. The comparison among transcriptome profiles from several samples is often a difficult task for nonexpert programmers. Here, in this chapter, we introduce RAP (RNA-Seq Analysis Pipeline), a completely automated web tool for transcriptome analysis. It is a user-friendly web tool implementing a detailed transcriptome workflow to detect differential expressed genes and transcript, identify spliced junctions and constitutive or alternative polyadenylation sites and predict gene fusion events. Through the web interface the researchers can get all this information without any knowledge of the underlying High Performance Computing infrastructure.

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.

LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing

BACKGROUND

Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate < 150 bp reads. However, there is a general lack of software tools for gene fusion detection from long-read RNA-seq data, which takes into account the high basecalling error rates and the presence of alignment errors.

RESULTS

In this study, we developed a fast computational tool, LongGF, to efficiently detect candidate gene fusions from long-read RNA-seq data, including cDNA sequencing data and direct mRNA sequencing data. We evaluated LongGF on tens of simulated long-read RNA-seq datasets, and demonstrated its superior performance in gene fusion detection. We also tested LongGF on a Nanopore direct mRNA sequencing dataset and a PacBio sequencing dataset generated on a mixture of 10 cancer cell lines, and found that LongGF achieved better performance to detect known gene fusions over existing computational tools. Furthermore, we tested LongGF on a Nanopore cDNA sequencing dataset on acute myeloid leukemia, and pinpointed the exact location of a translocation (previously known in cytogenetic resolution) in base resolution, which was further validated by Sanger sequencing.

CONCLUSIONS

In summary, LongGF will greatly facilitate the discovery of candidate gene fusion events from long-read RNA-Seq data, especially in cancer samples. LongGF is implemented in C++ and is available at https://github.com/WGLab/LongGF .

A fusion of CD63-BCAR4 identified in lung adenocarcinoma promotes tumorigenicity and metastasis

Background

Recently, fusion variants of the breast cancer anti-oestrogen-resistance 4 (BCAR4) gene were recurrently discovered in lung adenocarcinoma from the genome-wide studies. However, the functional characterisation of BCAR4 fusion has not been investigated.

Methods

Based on the analysis of RNA-sequencing data, we identified a fusion transcript of CD63–BCAR4 in a Korean patient with lung adenocarcinoma who did not harbour any known activating mutations in EGFR and KRAS genes. To investigate the oncogenic effect of CD63–BCAR4, in vitro and in vivo animal experiments were performed.

Results

In vitro experiments showed strongly enhanced cell migration and proliferation by the exogenous expression of CD63–BCAR4 protein in bronchial epithelial cells. Cell migration was notably reduced after knockdown of BCAR4 fusion by small-interfering RNA. The tumorigenic and metastatic capability of the CD63–BCAR4 fusion was confirmed by using the mouse xenograft model. Fusion-overexpressed cells result in metastasis to the liver and lung as well as the primary tumours after subcutaneous injection into mice. Cyclin D1, MMP1, Slug and mesenchymal markers were significantly increased after CD63–BCAR4 overexpression in the in vitro and in vivo experiments.

Conclusions

Taken together, our results suggest a newly identified fusion gene, CD63–BCAR4 as a potential novel oncogene in lung adenocarcinoma.

Genomic profile of MYCN non-amplified neuroblastoma and potential for immunotherapeutic strategies in neuroblastoma

Background

MYCN amplification is the most important genomic feature in neuroblastoma (NB). However, limited studies have been conducted on the MYCN non-amplified NB including low- and intermediate-risk NB. Here, the genomic characteristics of MYCN non-amplified NB were studied to allow for the identification of biomarkers for molecular stratification.

Methods

Fifty-eight whole exome sequencing (WES) and forty-eight whole transcriptome sequencing (WTS) samples of MYCN non-amplified NB were analysed. Forty-one patients harboured WES and WTS pairs.

Results

In the MYCN non-amplified NB WES data, maximum recurrent mutations were found in MUC4 (26%), followed by RBMXL3 (19%), ALB (17%), and MUC16 and SEPD8 (14% each). Two gene fusions, CCDC32-CBX3 (10%) and SAMD5-SASH1 (6%), were recurrent in WTS analysis, and these fusions were detected mostly in non-high-risk patients with ganglioneuroblastoma histology. Analysis of risk-group-specific biomarkers showed that several genes and gene sets were differentially expressed between the risk groups, and some immune-related pathways tended to be activated in the high-risk group. Mutational signatures 6 and 18, which represent DNA mismatch repair associated mutations, were commonly detected in 60% of the patients. In the tumour mutation burden (TMB) analysis, four patients showed high TMB (> 3 mutations/Mb), and had mutations in genes related to either MMR or homologous recombination. Excluding four outlier samples with TMB > 3 Mb, high-risk patients had significantly higher levels of TMB compared with the non-high-risk patients.

Conclusions

This study provides novel insights into the genomic background of MYCN non-amplified NB. Activation of immune-related pathways in the high-risk group and the results of TMB and mutational signature analyses collectively suggest the need for further investigation to discover potential immunotherapeutic strategies for NB.

Uncovering Clinically Relevant Gene Fusions with Integrated Genomic and Transcriptomic Profiling of Metastatic Cancers

PURPOSE

Gene fusions are important oncogenic drivers and many are actionable. Whole-genome and transcriptome (WGS and RNA-seq, respectively) sequencing can discover novel clinically relevant fusions.

EXPERIMENTAL DESIGN

Using WGS and RNA-seq, we reviewed the prevalence of fusions in a cohort of 570 patients with cancer, and compared prevalence to that predicted with commercially available panels. Fusions were annotated using a consensus variant calling pipeline (MAVIS) and required that a contig of the breakpoint could be constructed and supported from ≥2 structural variant detection approaches.

RESULTS

In 570 patients with advanced cancer, MAVIS identified 81 recurrent fusions by WGS and 111 by RNA-seq, of which 18 fusions by WGS and 19 by RNA-seq were noted in at least 3 separate patients. The most common fusions were EML4-ALK in thoracic malignancies (9/69, 13%), and CMTM8-CMTM7 in colorectal cancer (4/73, 5.5%). Combined genomic and transcriptomic analysis identified novel fusion partners for clinically relevant genes, such as NTRK2 (novel partners: SHC3, DAPK1), and NTRK3 (novel partners: POLG, PIBF1).

CONCLUSIONS

Utilizing WGS/RNA-seq facilitates identification of novel fusions in clinically relevant genes, and detected a greater proportion than commercially available panels are expected to find. A significant benefit of WGS and RNA-seq is the innate ability to retrospectively identify variants that becomes clinically relevant over time, without the need for additional testing, which is not possible with panel-based approaches.

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.

Transcriptome and Gene Fusion Analysis of Synchronous Lesions Reveals lncMRPS31P5 as a Novel Transcript Involved in Colorectal Cancer

Fusion genes and epigenetic regulators (i.e., miRNAs and long non-coding RNAs) constitute essential pieces of the puzzle of the tumor genomic landscape, in particular in mechanisms behind the adenoma-to-carcinoma progression of colorectal cancer (CRC). In this work, we aimed to identify molecular signatures of the different steps of sporadic CRC development in eleven patients, of which synchronous samples of adenomas, tumors, and normal tissues were analyzed by RNA-Seq. At a functional level, tumors and adenomas were all characterized by increased activity of the cell cycle, cell development, cell growth, and biological proliferation functions. In contrast, organic survival and apoptosis-related functions were inhibited both in tumors and adenomas at different levels. At a molecular level, we found that three individuals shared a tumor-specific fusion named MRPS31-SUGT1, generated through an intra-chromosomal translocation on chromosome 13, whose sequence resulted in being 100% identical to the long non-coding RNA (lncRNA) MRPS31P5. Our analyses suggest that MRPS31P5 could take part to a competitive endogenous (ce)RNA network by acting as a miRNA sponge or/and as an interactor of other mRNAs, and thus it may be an important gene expression regulatory factor and could be used as a potential biomarker for the detection of early CRC events.

Integrative genomic analysis of salivary duct carcinoma

Salivary duct carcinoma (SDC) is one of the most aggressive subtypes of salivary gland cancers. Conventional chemotherapy and/or radiation have shown only limited clinical efficacy in the treatment of recurrent or metastatic SDC. Currently, clinically approved targeted-therapeutics are not generally applicable except in very limited cases, and there exists a strong need for the development of treatment against this unique tumor type. To further interrogate genomic features of SDC, we have conducted multi-omic profiling of the SDC to describe the genomic alterations prevalent in this disease. Whole-genome sequencing, whole exome-sequencing and transcriptome sequencing were performed on a discovery cohort of 10 SDC samples. Targeted genomic profiling was performed in additional 32 SDC samples to support the findings obtained from the original discovery cohort. The cancer cohort was characterized by an average mutation burden of 85 somatic exonic mutations per tumor sample. The cohort harbored a mutational signature of BRCA and APOBEC/AID. Several genes, including TP53, RB1, SMAD4, HRAS, APC, PIK3CA and GNAQ were recurrently somatically altered in SDC. A novel fusion gene, generated by genomic rearrangement, MYB-NHSL1, was also noted. Our findings represent a significant layer in the systematic understanding of potentially clinically useful genomic and molecular targets for a subset of recurrent/metastatic SDC.

Recurrent Fusions Between YAP1 and KMT2A in Morphologically Distinct Neoplasms Within the Spectrum of Low-grade Fibromyxoid Sarcoma and Sclerosing Epithelioid Fibrosarcoma

Sclerosing epithelioid fibrosarcoma (SEF) is an aggressive soft tissue sarcoma. In the majority of cases, there is overexpression of MUC4, and most cases show EWSR1-CREB3L1 gene fusions. A subset of SEF displays composite histologic features of SEF and low-grade fibromyxoid sarcoma (LGFMS). These "hybrid" tumors are more likely to harbor the FUS-CREB3L2 fusion, which is also seen in most LGFMS. We, here, characterize a series of 8 soft tissue neoplasms with morphologic features highly overlapping with LGFMS and SEF but lacking MUC4 expression and EWSR1/FUS-CREB3L gene fusions. Seven tumors showed fusions of the YAP1 and KMT2A genes, and 1 had a fusion of PRRX1 and KMT2D; all but 1 case displayed reciprocal gene fusions. At gene expression profiling, YAP1 and KMT2A/PRRX1 and KMT2D tumors were distinct from LGFMS/SEF. The patients were 4 female individuals and 4 male individuals aged 11 to 91 years. Tumors with known locations were in the lower extremity (5), trunk (2), and upper extremity (1); 3 originated in acral locations. Tumor size ranged from 2.5 to 13 cm. Proportions of SEF-like and LGFMS-like areas varied considerably among tumors. All tumors that showed infiltrative growth and mitotic figures per 10 HPFs ranged from 0 to 18. Tumor necrosis was present in 1 case. Follow-up was available for 5 patients (11 to 321 mo), 2 of whom developed local recurrences, and 1 died of metastatic disease. The clinical behavior of these soft tissue sarcomas remains to be further delineated in larger series with extended follow-up; however, our limited clinical data indicate that they are potentially aggressive.

Dysregulation of cancer genes by recurrent intergenic fusions

Background

Gene fusions have been studied extensively, as frequent drivers of tumorigenesis as well as potential therapeutic targets. In many well-known cases, breakpoints occur at two intragenic positions, leading to in-frame gene-gene fusions that generate chimeric mRNAs. However, fusions often occur with intergenic breakpoints, and the role of such fusions has not been carefully examined.

Results

We analyze whole-genome sequencing data from 268 patients to catalog gene-intergenic and intergenic-intergenic fusions and characterize their impact. First, we discover that, in contrast to the common assumption, chimeric oncogenic transcripts—such as those involving ETV4, ERG, RSPO3, and PIK3CA—can be generated by gene-intergenic fusions through splicing of the intervening region. Second, we find that over-expression of an upstream or downstream gene by a fusion-mediated repositioning of a regulatory sequence is much more common than previously suspected, with enhancers sometimes located megabases away. We detect a number of recurrent fusions, such as those involving ANO3, RGS9, FUT5, CHI3L1, OR1D4, and LIPG in breast; IGF2 in colon; ETV1 in prostate; and IGF2BP3 and SIX2 in thyroid cancers.

Conclusion

Our findings elucidate the potential oncogenic function of intergenic fusions and highlight the wide-ranging consequences of structural rearrangements in cancer genomes.