A novel bioinformatics pipeline for identification and characterization of fusion transcripts in breast cancer and normal cell lines

Breaking paradigms: Long non-coding RNAs forming gene fusions with potential implications in cancer

Long non-coding RNAs (lncRNAs) are non-coding RNAs longer than 200 nucleotides with dynamic regulatory functions. They interact with a wide range of molecules such as DNA, RNA, and proteins to modulate diverse cellular functions through several mechanisms and, if deregulated, they can lead to cancer development and progression. Recently, it has been described that lncRNAs are susceptible to form gene fusions with mRNAs or other lncRNAs, breaking the paradigm of gene fusions consisting mainly of protein-coding genes. However, their biological significance in the tumor phenotype is still uncertain. Therefore, their recent identification opens a new line of research to study their biological role in tumorigenesis, and their potential as biomarkers with clinical relevance or as therapeutic targets. The present study aimed to review the lncRNA fusions identified so far and to know which of them have been associated with a potential function. We address the current challenges to deepen their study as well as the reasons why they represent a future therapeutic window in cancer.

Fast and sensitive validation of fusion transcripts in whole-genome sequencing data

BACKGROUND

In cancer, genomic rearrangements can create fusion genes that either combine protein-coding sequences from two different partner genes or place one gene under the control of the promoter of another gene. These fusion genes can act as oncogenic drivers in tumor development and several fusions involving kinases have been successfully exploited as drug targets. Expressed fusions can be identified in RNA sequencing (RNA-Seq) data, but fusion prediction software often has a high fraction of false positive fusion transcript predictions. This is problematic for both research and clinical applications.

RESULTS

We describe a method for validation of fusion transcripts detected by RNA-Seq in matched whole-genome sequencing (WGS) data. Our pipeline uses discordant read pairs to identify supported fusion events and analyzes soft-clipped read alignments to determine genomic breakpoints. We have tested it on matched RNA-Seq and WGS data for both tumors and cancer cell lines and show that it can be used to validate both new predicted gene fusions and experimentally validated fusion events. It was considerably faster and more sensitive than using BreakDancer and Manta, software that is instead designed to detect many different types of structural variants on a genome-wide scale.

CONCLUSIONS

We have developed a fast and very sensitive pipeline for validation of gene fusions detected by RNA-Seq in matched WGS data. It can be used to identify high-quality gene fusions for further bioinformatic and experimental studies, including validation of genomic breakpoints and studies of the mechanisms that generate fusions. In a clinical setting, it could help find expressed gene fusions for personalized therapy.

Recent advances in cancer fusion transcript detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

BACKGROUND

The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples.

RESULTS

We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy.

CONCLUSIONS

A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4⁺ and CD8⁺ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

Gene fusion as an important mechanism to generate new genes in the genus Oryza

Background

Events of gene fusion have been reported in several organisms. However, the general role of gene fusion as part of new gene origination remains unknown.

Results

We conduct genome-wide interrogations of four Oryza genomes by designing and implementing novel pipelines to detect fusion genes. Based on the phylogeny of ten plant species, we detect 310 fusion genes across four Oryza species. The estimated rate of origination of fusion genes in the Oryza genus is as high as 63 fusion genes per species per million years, which is fixed at 16 fusion genes per species per million years and much higher than that in flies. By RNA sequencing analysis, we find more than 44% of the fusion genes are expressed and 90% of gene pairs show strong signals of purifying selection. Further analysis of CRISPR/Cas9 knockout lines indicates that newly formed fusion genes regulate phenotype traits including seed germination, shoot length and root length, suggesting the functional significance of these genes.

Conclusions

We detect new fusion genes that may drive phenotype evolution in Oryza. This study provides novel insights into the genome evolution of Oryza.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02696-w.

Oncogenic Neuregulin 1 gene (NRG1) fusions in cancer: A potential new therapeutic opportunities

It is widely established that chromosomal rearrangements induce oncogenesis in solid tumors. However, discovering chromosomal rearrangements that are targetable and actionable remains a difficulty. Targeting gene fusion or chromosomal rearrangement seems to be a powerful strategy to address malignancies characterized by gene rearrangement. Oncogenic NRG1 fusions are relatively rare drivers that infrequently occur across most tumor types. NRG1 fusions exhibit unique biological properties and are difficult to identify owing to their large intronic regions. NRG1 fusions can be detected using a variety of techniques, including fluorescence in situ hybridization, immunohistochemistry, or next-generation sequencing (NGS), with NGS-based RNA sequencing being the most sensitive. Previous studies have shown that NRG1 fusion protein induces tumorigenesis, and numerous therapies targeting the ErbB signaling pathway, such as ErbB kinase inhibitors and monoclonal antibodies, have initially demonstrated encouraging anticancer efficacy in malignant tumors carrying NRG1 fusions. In this review, we present the characteristics and prevalence of NRG1 fusions in solid tumors. Additionally, we discuss the laboratory approaches for diagnosing NRG1 gene fusions. More importantly, we outline promising strategies for treating malignancies with NRG1 fusion.

Genion, an accurate tool to detect gene fusion from long transcriptomics reads

BACKGROUND

The advent of next-generation sequencing technologies empowered a wide variety of transcriptomics studies. A widely studied topic is gene fusion which is observed in many cancer types and suspected of having oncogenic properties. Gene fusions are the result of structural genomic events that bring two genes closely located and result in a fused transcript. This is different from fusion transcripts created during or after the transcription process. These chimeric transcripts are also known as read-through and trans-splicing transcripts. Gene fusion discovery with short reads is a well-studied problem, and many methods have been developed. But the sensitivity of these methods is limited by the technology, especially the short read length. Advances in long-read sequencing technologies allow the generation of long transcriptomics reads at a low cost. Transcriptomic long-read sequencing presents unique opportunities to overcome the shortcomings of short-read technologies for gene fusion detection while introducing new challenges.

RESULTS

We present Genion, a sensitive and fast gene fusion detection method that can also detect read-through events. We compare Genion against a recently introduced long-read gene fusion discovery method, LongGF, both on simulated and real datasets. On simulated data, Genion accurately identifies the gene fusions and its clustering accuracy for detecting fusion reads is better than LongGF. Furthermore, our results on the breast cancer cell line MCF-7 show that Genion correctly identifies all the experimentally validated gene fusions.

CONCLUSIONS

Genion is an accurate gene fusion caller. Genion is implemented in C++ and is available at https://github.com/vpc-ccg/genion .

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

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

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.

PVT1: A long non-coding RNA recurrently involved in neoplasia-associated fusion transcripts

NGS technologies and bioinformatics tools allow the rapid identification of chimeric transcripts in cancer. More than 40,000 fusions are so far reported in the literature; however, for most of them, the role in oncogenesis is still not fully understood. This is the case for fusions involving the long non-coding RNA (lncRNA) Plasmacytoma variant translocation 1 (PVT1) (8q24.21). This lncRNA displays oncogenic functions in several cancer types interacting with microRNAs and proteins, but the role of PVT1 fusion transcripts is more obscure. These chimeras have been identified in both hematological malignancies and solid tumors, mainly arising from rearrangements and/or amplification of the 8q24 chromosomal region. In this review, we detail the full spectrum of PVT1 fusions in cancer, summarizing current knowledge about their genesis, function, and role as biomarkers.

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 .

Tet2-mediated epigenetic drive for astrocyte differentiation from embryonic neural stem cells

DNA methylation and demethylation at CpG di-nucleotide sites plays important roles in cell fate specification of neural stem cells (NSCs). We have previously reported that DNA methyltransferases, Dnmt1and Dnmt3a, serve to suppress precocious astrocyte differentiation from NSCs via methylation of astroglial lineage genes. However, whether active DNA demethylase also participates in astrogliogenesis remains undetermined. In this study, we discovered that a Ten-eleven translocation (Tet) protein, Tet2, which was critically involved in active DNA demethylation through oxidation of 5-Methylcytosine (5mC), drove astrocyte differentiation from NSCs by demethylation of astroglial lineage genes including Gfap. Moreover, we found that an NSC-specific bHLH transcription factor Olig2 was an upstream inhibitor for Tet2 expression through direct association with the Tet2 promoter, and indirectly inhibited astrocyte differentiation. Our research not only revealed a brand-new function of Tet2 to promote NSC differentiation into astrocytes, but also a novel mechanism for Olig2 to inhibit astrocyte formation.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

High expression of the vacuole membrane protein 1 (VMP1) is a potential marker of poor prognosis in HER2 positive breast cancer

Background

Fusion genes result from genomic structural changes, which can lead to alterations in gene expression that supports tumor development. The aim of the study was to use fusion genes as a tool to identify new breast cancer (BC) genes with a role in BC progression.

Methods

Fusion genes from breast tumors and BC cell lines were collected from publications. RNA-Seq data from tumors and cell lines were retrieved from databanks and analyzed for fusions with SOAPfuse or the analysis was purchased. Fusion genes identified in both tumors (n = 1724) and cell lines (n = 45) were confirmed by qRT-PCR and sequencing. Their individual genes were ranked by selection criteria that included correlation of their mRNA level with copy number. The expression of the top ranked gene was measured by qRT-PCR in normal tissue and in breast tumors from an exploratory cohort (n = 141) and a validation cohort (n = 277). Expression levels were correlated with clinical and pathological factors as well as the patients’ survival. The results were followed up in BC cohorts from TCGA (n = 818) and METABRIC (n = 2509).

Results

Vacuole membrane protein 1 (VMP1) was the most promising candidate based on specific selection criteria. Its expression was higher in breast tumor tissue than normal tissue (p = 1x10^-4), and its expression was significantly higher in HER2 positive than HER2 negative breast tumors in all four cohorts analyzed. High expression of VMP1 associated with breast cancer specific survival (BCSS) in cohort 1 (hazard ratio (HR) = 2.31, CI 1.27–4.18) and METABRIC (HR = 1.26, CI 1.02–1.57), and also after adjusting for HER2 expression in cohort 1 (HR = 2.03, CI 1.10–3.72). BCSS was not significant in cohort 2 or TCGA cohort, which may be due to differences in treatment regimens.

Conclusions

The results suggest that high VMP1 expression is a potential marker of poor prognosis in HER2 positive BC. Further studies are needed to elucidate how VMP1 could affect pathways supportive of tumorigenesis.

Novel TG-FGFR1 and TRIM33-NTRK1 transcript fusions in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is most common among all thyroid cancers. Multiple genomic alterations occur in PTC, and gene rearrangements are one of them. Here we screened 14 tumors for novel fusion transcripts by RNA‐Seq. Two samples harboring RET/PTC1 and RET/PTC3 rearrangements were positive controls whereas the remaining ones were negative regarding the common PTC alterations. We used Sanger sequencing to validate potential fusions. We detected 2 novel potentially oncogenic transcript fusions: TG‐FGFR1 and TRIM33‐NTRK1. We detected 4 novel fusion transcripts of unknown significance accompanying the TRIM33‐NTRK1 fusion: ZSWIM5‐TP53BP2, TAF4B‐WDR1, ABI2‐MTA3, and ARID1B‐PSMA1. Apart from confirming the presence of RET/PTC1 and RET/PTC3 in positive control samples, we also detected known oncogenic fusion transcripts in remaining samples: TFG‐NTRK1, ETV6‐NTRK3, MKRN1‐BRAF, EML4‐ALK, and novel isoform of CCDC6‐RET.

Identification of differentially expressed genes and typical fusion genes associated with three subtypes of breast cancer

BACKGROUND

This study aimed to identify the differentially expressed genes (DEGs) and the typical fusion genes in different types of breast cancers using RNA-seq.

METHODS

GSE52643 was downloaded from Gene Expression Omnibus, which included 1 normal sample (MCF10A) and 7 breast cancer samples (BT-474, BT-20, MCF7, MDA-MB-231, MDA-MB-468, T47D, and ZR-75-1). The transcript abundance and the DEGs screening were performed by Cufflinks. The functional and pathway enrichment was analyzed by Gostats. SnowShoes-FTD was applied to identify the fusion genes.

RESULTS

We screened 430, 445, 397, 417, 369, 557, and 375 DEGs in BT-474, BT-20, MCF7, DA-MB-231, MDA-MB-468, T47D, and ZR-75-1, respectively, compared with MCF10A. DEGs in each comparison group (such as CD40 and CDH1) were significantly enriched in the functions of cell adhesion and extracellular matrix organization and pathways of CAMs and ECM receptor interaction. UCP2 was a common DEG in the 7 comparison groups. SFRP1 and MMP7 were significantly enriched in wnt/-catenin signaling pathway in MDA-MB-231. FAS was significantly enriched in autoimmune thyroid disease pathway in BT-474. Besides, we screened 96 fusion genes, such as ESR1-C6orf97 in ZR-75-1, COBRA1-C9orf167 in BT-20, and VAPB-IKZF3 and ACACA-STAC2 in BT-474.

CONCLUSIONS

The DEGs such as SFRP1, MMP7, CDH1, FAS, and UCP2 might be the potential biomarkers in breast cancer. Furthermore, some pivotal fusion genes like ESR1-C6orf97 with COBRA1-C9orf167 and VAPB-IKZF3 with ACACA-STAC2 were found in Luminal A and Luminal B breast cancer, respectively.

A fast detection of fusion genes from paired-end RNA-seq data

Background

Fusion genes are known to be drivers of many common cancers, so they are potential markers for diagnosis, prognosis or therapy response. The advent of paired-end RNA sequencing enhances our ability to discover fusion genes. While there are available methods, routine analyses of large number of samples are still limited due to high computational demands.

Results

We develop FuSeq, a fast and accurate method to discover fusion genes based on quasi-mapping to quickly map the reads, extract initial candidates from split reads and fusion equivalence classes of mapped reads, and finally apply multiple filters and statistical tests to get the final candidates. We apply FuSeq to four validated datasets: breast cancer, melanoma and glioma datasets, and one spike-in dataset. The results reveal high sensitivity and specificity in all datasets, and compare well against other methods such as FusionMap, TRUP, TopHat-Fusion, SOAPfuse and JAFFA. In terms of computational time, FuSeq is two-fold faster than FusionMap and orders of magnitude faster than the other methods.

Conclusions

With this advantage of less computational demands, FuSeq makes it practical to investigate fusion genes in large numbers of samples. FuSeq is implemented in C++ and R, and available at https://github.com/nghiavtr/FuSeqfor non-commercial uses.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5156-1) contains supplementary material, which is available to authorized users.

Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line

The SK-BR-3 cell line is one of the most important models for HER2+ breast cancers, which affect one in five breast cancer patients. SK-BR-3 is known to be highly rearranged, although much of the variation is in complex and repetitive regions that may be underreported. Addressing this, we sequenced SK-BR-3 using long-read single molecule sequencing from Pacific Biosciences and develop one of the most detailed maps of structural variations (SVs) in a cancer genome available, with nearly 20,000 variants present, most of which were missed by short-read sequencing. Surrounding the important ERBB2 oncogene (also known as HER2), we discover a complex sequence of nested duplications and translocations, suggesting a punctuated progression. Full-length transcriptome sequencing further revealed several novel gene fusions within the nested genomic variants. Combining long-read genome and transcriptome sequencing enables an in-depth analysis of how SVs disrupt the genome and sheds new light on the complex mechanisms involved in cancer genome evolution.

The convergent roles of the nuclear factor I transcription factors in development and cancer

The nuclear factor I (NFI) transcription factors play important roles during normal development and have been associated with developmental abnormalities in humans. All four family members, NFIA, NFIB, NFIC and NFIX, have a homologous DNA binding domain and function by regulating cell proliferation and differentiation via the transcriptional control of their target genes. More recently, NFI genes have also been implicated in cancer based on genomic analyses and studies of animal models in a variety of tumours across multiple organ systems. However, the association between their functions in development and in cancer is not well described. In this review, we summarise the evidence suggesting a converging role for the NFI genes in development and cancer. Our review includes all cancer types in which the NFI genes are implicated, focusing predominantly on studies demonstrating their oncogenic or tumour-suppressive potential. We conclude by presenting the challenges impeding our understanding of NFI function in cancer biology, and demonstrate how a developmental perspective may contribute towards overcoming such hurdles.

De novo assembly of Phlomis purpurea after challenging with Phytophthora cinnamomi

BACKGROUND

Phlomis plants are a source of biological active substances with potential applications in the control of phytopathogens. Phlomis purpurea (Lamiaceae) is autochthonous of southern Iberian Peninsula and Morocco and was found to be resistant to Phytophthora cinnamomi. Phlomis purpurea has revealed antagonistic effect in the rhizosphere of Quercus suber and Q. ilex against P. cinnamomi. Phlomis purpurea roots produce bioactive compounds exhibiting antitumor and anti-Phytophthora activities with potential to protect susceptible plants. Although these important capacities of P. purpurea have been demonstrated, there is no transcriptomic or genomic information available in public databases that could bring insights on the genes underlying this anti-oomycete activity.

RESULTS

Using Illumina technology we obtained a de novo assembly of P. purpurea transcriptome and differential transcript abundance to identify putative defence related genes in challenged versus non-challenged plants. A total of 1,272,600,000 reads from 18 cDNA libraries were merged and assembled into 215,739 transcript contigs. BLASTX alignment to Nr NCBI database identified 124,386 unique annotated transcripts (57.7%) with significant hits. Functional annotation identified 83,550 out of 124,386 unique transcripts, which were mapped to 141 pathways. 39% of unigenes were assigned GO terms. Their functions cover biological processes, cellular component and molecular functions. Genes associated with response to stimuli, cellular and primary metabolic processes, catalytic and transporter functions were among those identified. Differential transcript abundance analysis using DESeq revealed significant differences among libraries depending on post-challenge times. Comparative cyto-histological studies of P. purpurea roots challenged with P. cinnamomi zoospores and controls revealed specific morphological features (exodermal strips and epi-cuticular layer), that may provide a constitutive efficient barrier against pathogen penetration. Genes involved in cutin biosynthesis and in exodermal Casparian strips formation were up-regulated.

CONCLUSIONS

The de novo assembly of transcriptome using short reads for a non-model plant, P. purpurea, revealed many unique transcripts useful for further gene expression, biological function, genomics and functional genomics studies. The data presented suggest a combination of a constitutive resistance and an increased transcriptional response from P. purpurea when challenged with the pathogen. This knowledge opens new perspectives for the understanding of defence responses underlying pathogenic oomycete/plant interaction upon challenge with P. cinnamomi.

De novo assembly and characterization of breast cancer transcriptomes identifies large numbers of novel fusion-gene transcripts of potential functional significance

Background

Gene-fusion or chimeric transcripts have been implicated in the onset and progression of a variety of cancers. Massively parallel RNA sequencing (RNA-Seq) of the cellular transcriptome is a promising approach for the identification of chimeric transcripts of potential functional significance. We report here the development and use of an integrated computational pipeline for the de novo assembly and characterization of chimeric transcripts in 55 primary breast cancer and normal tissue samples.

Methods

An integrated computational pipeline was employed to screen the transcriptome of breast cancer and control tissues for high-quality RNA-sequencing reads. Reads were de novo assembled into contigs followed by reference genome mapping. Chimeric transcripts were detected, filtered and characterized using our R-SAP algorithm. The relative abundance of reads was used to estimate levels of gene expression.

Results

De novo assembly allowed for the accurate detection of 1959 chimeric transcripts to nucleotide level resolution and facilitated detailed molecular characterization and quantitative analysis. A number of the chimeric transcripts are of potential functional significance including 79 novel fusion-protein transcripts and many chimeric transcripts with alterations in their un-translated leader regions. A number of chimeric transcripts in the cancer samples mapped to genomic regions devoid of any known genes. Several ‘pro-neoplastic’ fusions comprised of genes previously implicated in cancer are expressed at low levels in normal tissues but at high levels in cancer tissues.

Conclusions

Collectively, our results underscore the utility of deep sequencing technologies and improved bioinformatics workflows to uncover novel and potentially significant chimeric transcripts in cancer and normal somatic tissues.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0289-7) contains supplementary material, which is available to authorized users.

Recurrent rearrangements of the Myb/SANT-like DNA-binding domain containing 3 gene (MSANTD3) in salivary gland acinic cell carcinoma

Pathogenic gene fusions have been identified in several histologic types of salivary gland neoplasia, but not previously in acinic cell carcinoma (AcCC). To discover novel gene fusions, we performed whole-transcriptome sequencing surveys of three AcCC archival cases. In one specimen we identified a novel HTN3-MSANTD3 gene fusion, and in another a novel PRB3-ZNF217 gene fusion. The structure of both fusions was consistent with the promoter of the 5’ partner (HTN3 or PRB3), both highly expressed salivary gland genes, driving overexpression of full-length MSANTD3 or ZNF217. By fluorescence in situ hybridization of an expanded AcCC case series, we observed MSANTD3 rearrangements altogether in 3 of 20 evaluable cases (15%), but found no additional ZNF217 rearrangements. MSANTD3 encodes a previously uncharacterized Myb/SANT domain-containing protein. Immunohistochemical staining demonstrated diffuse nuclear MSANTD3 expression in 8 of 27 AcCC cases (30%), including the three cases with MSANTD3 rearrangement. MSANTD3 displayed heterogeneous expression in normal salivary ductal epithelium, as well as among other histologic types of salivary gland cancer though without evidence of translocation. In a broader survey, MSANTD3 showed variable expression across a wide range of normal and neoplastic human tissue specimens. In preliminary functional studies, engineered MSANTD3 overexpression in rodent salivary gland epithelial cells did not enhance cell proliferation, but led to significant upregulation of gene sets involved in protein synthesis. Our findings newly identify MSANTD3 rearrangement as a recurrent event in salivary gland AcCC, providing new insight into disease pathogenesis, and identifying a putative novel human oncogene.

Gene expression patterns regulating embryogenesis based on the integrated de novo transcriptome assembly of the Japanese flounder

The Japanese flounder (Paralichthys olivaceus) is one of the most important commercial and biological marine fishes. However, the molecular biology involved during embryogenesis and early development of the Japanese flounder remains largely unknown due to a lack of genomic resources. A comprehensive and integrated transcriptome is necessary to study the molecular mechanisms of early development and to allow for the detailed characterization of gene expression patterns during embryogenesis; this approach is critical to understanding the processes that occur prior to mesectoderm formation during early embryonic development. In this study, more than 117.8 million 100bp PE reads were generated from pooled RNA extracted from unfertilized eggs to 41dph (days post-hatching) embryos and were sequenced using Illumina pair-end sequencing technology. In total, 121,513 transcripts (≥200bp) were obtained using de novo assembly. A sequence similarity search indicated that 52,338 transcripts show significant similarity to 22,462 known proteins from the NCBI non-redundant database and the Swiss-Prot protein database and were annotated using Blast2GO. GO terms were assigned to 44,627 transcripts with 12,006 functional terms, and 10,024 transcripts were assigned to 133 KEGG pathways. Furthermore, gene expression differences between the unfertilized egg and the gastrula embryo were analysed using Illumina RNA-Seq with single-read sequencing technology, and 24,837 differentially and specifically expressed transcripts were identified and included 5,286 annotated transcripts and 19,569 non-annotated transcripts. All of the expressed transcripts in the unfertilized egg and gastrula embryo were further classified as maternal, zygotic, or maternal-zygotic transcripts, which may help us to understand the roles of these transcripts during the embryonic development of the Japanese flounder. Thus, the results will contribute to an improved understanding of the gene expression patterns and signalling pathways that control the molecular mechanisms of early embryonic development.

Custom Gene Capture and Next-Generation Sequencing to Resolve Discordant ALK Status by FISH and IHC in Lung Adenocarcinoma

INTRODUCTION

We performed a genomic study in lung adenocarcinoma cases with discordant anaplastic lymphoma receptor tyrosine kinase gene (ALK) status by fluorescent in situ hybridization (FISH) and immunohistochemical (IHC) analysis.

METHODS

DNA from formalin-fixed paraffin-embedded tissues of 16 discordant (four FISH-positive/IHC-negative and 12 FISH-negative/IHC-positive) cases by Vysis ALK Break Apart FISH and ALK IHC testing (ALK1 clone) were subjected to whole gene capture and next-generation sequencing (NGS) of nine genes, including ALK, echinoderm microtubule associated protein like 4 gene (EML4), kinesin family member 5B gene (KIF5B), staphylococcal nuclease and tudor domain containing 1 gene (SND1), BRAF, ret proto-oncogene (RET), ezrin gene (EZR), ROS1, and telomerase reverse transcriptase (TERT). All discordant cases (except one FISH-negative/IHC-positive case without sufficient tissue) were analyzed by IHC with D5F3 antibody. In one case with fresh frozen tissue, whole transcriptome sequencing was also performed. Twenty-six concordant (16 FISH-positive/IHC-positive and 10 FISH-negative/IHC-negative) cases were included as controls.

RESULTS

In four ALK FISH-positive/IHC-negative cases, no EML4-ALK fusion gene was observed by NGS, but in one case using fresh frozen tissue, we identified EML4-baculoviral AIP repeat containing 6 gene (BIRC6) and AP2 associated kinase 1 gene (AAK1)-ALK fusion genes. Whole transcriptome sequencing revealed a highly expressed EML4-BIRC6 fusion transcript and a minimally expressed AAK1 transcript. Among the 12 FISH-negative/IHC-positive cases, no evidence of ALK gene rearrangement was detected by NGS. Eleven of 12 FISH-negative/IHC-positive cases detected by ALK1 clone were concordant by repeat ALK IHC with D5F3 antibody (i.e., FISH-negative/IHC-negative by D5F3 clone). Among the 16 ALK FISH-positive/IHC-positive positive controls, whole gene capture identified ALK gene fusion in 15 cases, including in one case with Huntington interacting protein 1 gene (HIP1)-ALK. No ALK fusion gene was observed in any of the 10 FISH-negative/IHC-negative cases. Other fusion genes involving ROS1, EZR, BRAF, and SND1 were also found.

CONCLUSIONS

ALK FISH results appeared to be false-positive in three of four FISH-positive/IHC-negative cases, whereas no false-negative ALK FISH case was identified among 12 ALK FISH-negative/IHC-positive cases by ALK1 clone, which was in keeping with the concordant FISH-negative/IHC-negative status by D5F3 clone. Our targeted whole gene capture approach using formalin-fixed paraffin embedded samples was effective for detecting rearrangements involving ALK and other actionable oncogenes.

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.

Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma

Gene fusions and fusion products were thought to be unique features of neoplasia. However, more and more studies have identified fusion RNAs in normal physiology. Through RNA sequencing of 27 human noncancer tissues, a large number of fusion RNAs were found. By analyzing fusion transcriptome, we observed close clusterings between samples of same or similar tissues, supporting the feasibility of using fusion RNA profiling to reveal connections between biological samples. To put the concept into use, we selected alveolar rhabdomyosarcoma (ARMS), a myogenic pediatric cancer whose exact cell of origin is not clear. PAX3-FOXO1 (paired box gene 3 fused with forkhead box O1) fusion RNA, which is considered a hallmark of ARMS, was recently found during normal muscle cell differentiation. We performed and analyzed RNA sequencing from various time points during myogenesis and uncovered many chimeric fusion RNAs. Interestingly, we found that the fusion RNA profile of RH30, an ARMS cell line, is most similar to the myogenesis time point when PAX3-FOXO1 is expressed. In contrast, full transcriptome clustering analysis failed to uncover this connection. Strikingly, all of the 18 chimeric RNAs in RH30 cells could be detected at the same myogenic time point(s). In addition, the seven chimeric RNAs that follow the exact transient expression pattern as PAX3-FOXO1 are specific to rhabdomyosarcoma cells. Further testing with clinical samples also confirmed their specificity to rhabdomyosarcoma. These results provide further support for the link between at least some ARMSs and the PAX3-FOXO1-expressing myogenic cells and demonstrate that fusion RNA profiling can be used to investigate the etiology of fusion-gene-associated cancers.

Impact of RNA degradation on fusion detection by RNA-seq

Background

RNA-seq is a well-established method for studying the transcriptome. Popular methods for library preparation in RNA-seq such as Illumina TruSeq® RNA v2 kit use a poly-A pulldown strategy. Such methods can cause loss of coverage at the 5′ end of genes, impacting the ability to detect fusions when used on degraded samples. The goal of this study was to quantify the effects RNA degradation has on fusion detection when using poly-A selected mRNA and to identify the variables involved in this process.

Results

Using both artificially and naturally degraded samples, we found that there is a reduced ability to detect fusions as the distance of the breakpoint from the 3′ end of the gene increases. The median transcript coverage decreases exponentially as a function of the distance from the 3′ end and there is a linear relationship between the coverage decay rate and the RNA integrity number (RIN). Based on these findings we developed plots that show the probability of detecting a gene fusion (“sensitivity”) as a function of the distance of the fusion breakpoint from the 3′ end.

Conclusions

This study developed a strategy to assess the impact that RNA degradation has on the ability to detect gene fusions by RNA-seq.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3161-9) contains supplementary material, which is available to authorized users.

Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer

Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than intermixed stromal cells. This is especially true in estrogen receptor (ER)-negative tumors. Overall, 59% of substitutions were expressed. Nonsense mutations showed lower expression levels than expected, with patterns characteristic of nonsense-mediated decay. 14% of 4,234 rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusions, and premature polyadenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data reveals the rules by which transcriptional machinery interprets somatic mutation.

•

Greater transcriptional activity in cancer than stromal cells, particularly when ER-ve

•

Intron mutations only infrequently affect splicing, even at essential splice sites

•

Distinctive RNA effects of sense-to-antisense and gene-to-intergenic rearrangements

•

Exhaustive pipeline for identifying aberrant transcripts from RNA-sequencing data

Novel fusion genes and chimeric transcripts in ependymal tumors

We have previously identified two ALK rearrangements in a subset of ependymal tumors using a combination of cytogenetic data and RNA sequencing. The aim of this study was to perform an unbiased search for fusion transcripts in our entire series of ependymal tumors. Fusion analysis was performed using the FusionCatcher algorithm on 12 RNA-sequenced ependymal tumors. Candidate transcripts were prioritized based on the software's filtering and manual visualization using the BLAST (Basic Local Alignment Search Tool) and BLAT (BLAST-like alignment tool) tools. Genomic and reverse transcriptase PCR with subsequent Sanger sequencing was used to validate the potential fusions. Fluorescent in situ hybridization (FISH) using locus-specific probes was also performed. A total of 841 candidate chimeric transcripts were identified in the 12 tumors, with an average of 49 unique candidate fusions per tumor. After algorithmic and manual filtering, the final list consisted of 24 potential fusion events. Raw RNA-seq read sequences and PCR validation supports two novel fusion genes: a reciprocal fusion gene involving UQCR10 and C1orf194 in an adult spinal ependymoma and a TSPAN4-CD151 fusion gene in a pediatric infratentorial anaplastic ependymoma. Our previously reported ALK rearrangements and the RELA and YAP1 fusions found in supratentorial ependymomas were until now the only known fusion genes present in ependymal tumors. The chimeric transcripts presented here are the first to be reported in infratentorial or spinal ependymomas. Further studies are required to characterize the genomic rearrangements causing these fusion genes, as well as the frequency and functional importance of the fusions. © 2016 Wiley Periodicals, Inc.

Integrated mate-pair and RNA sequencing identifies novel, targetable gene fusions in peripheral T-cell lymphoma

Peripheral T-cell lymphomas (PTCLs) represent a heterogeneous group of T-cell malignancies that generally demonstrate aggressive clinical behavior, often are refractory to standard therapy, and remain significantly understudied. The most common World Health Organization subtype is PTCL, not otherwise specified (NOS), essentially a "wastebasket" category because of inadequate understanding to assign cases to a more specific diagnostic entity. Identification of novel fusion genes has contributed significantly to improving the classification, biologic understanding, and therapeutic targeting of PTCLs. Here, we integrated mate-pair DNA and RNA next-generation sequencing to identify chromosomal rearrangements encoding expressed fusion transcripts in PTCL, NOS. Two of 11 cases had novel fusions involving VAV1, encoding a truncated form of the VAV1 guanine nucleotide exchange factor important in T-cell receptor signaling. Fluorescence in situ hybridization studies identified VAV1 rearrangements in 10 of 148 PTCLs (7%). These were observed exclusively in PTCL, NOS (11%) and anaplastic large cell lymphoma (11%). In vitro, ectopic expression of a VAV1 fusion promoted cell growth and migration in a RAC1-dependent manner. This growth was inhibited by azathioprine, a clinically available RAC1 inhibitor. We also identified novel kinase gene fusions, ITK-FER and IKZF2-ERBB4, as candidate therapeutic targets that show similarities to known recurrent oncogenic ITK-SYK fusions and ERBB4 transcript variants in PTCLs, respectively. Additional novel and potentially clinically relevant fusions also were discovered. Together, these findings identify VAV1 fusions as recurrent and targetable events in PTCLs and highlight the potential for clinical sequencing to guide individualized therapy approaches for this group of aggressive malignancies.

Recurrent hormone-binding domain truncated ESR1 amplifications in primary endometrial cancers suggest their implication in hormone independent growth

The estrogen receptor alpha (ERα) is highly expressed in both endometrial and breast cancers, and represents the most prevalent therapeutic target in breast cancer. However, anti-estrogen therapy has not been shown to be effective in endometrial cancer. Recently it has been shown that hormone-binding domain alterations of ERα in breast cancer contribute to acquired resistance to anti-estrogen therapy. In analyses of genomic data from The Cancer Genome Atlas (TCGA), we observe that endometrial carcinomas manifest recurrent ESR1 gene amplifications that truncate the hormone-binding domain encoding region of ESR1 and are associated with reduced mRNA expression of exons encoding the hormone-binding domain. These findings support a role for hormone-binding alterations of ERα in primary endometrial cancer, with potentially important therapeutic implications.

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.

Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data

RNA-Seq made possible the global identification of fusion transcripts, i.e. "chimeric RNAs". Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users, and developers to have an unbiased assessment of the performance of existing fusion detection tools. Toward this goal, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. We have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data.

Recurrent chimeric fusion RNAs in non-cancer tissues and cells

Gene fusions and their products (RNA and protein) were once thought to be unique features to cancer. However, chimeric RNAs can also be found in normal cells. Here, we performed, curated and analyzed nearly 300 RNA-Seq libraries covering 30 different non-neoplastic human tissues and cells as well as 15 mouse tissues. A large number of fusion transcripts were found. Most fusions were detected only once, while 291 were seen in more than one sample. We focused on the recurrent fusions and performed RNA and protein level validations on a subset. We characterized these fusions based on various features of the fusions, and their parental genes. They tend to be expressed at higher levels relative to their parental genes than the non-recurrent ones. Over half of the recurrent fusions involve neighboring genes transcribing in the same direction. A few sequence motifs were found enriched close to the fusion junction sites. We performed functional analyses on a few widely expressed fusions, and found that silencing them resulted in dramatic reduction in normal cell growth and/or motility. Most chimeras use canonical splicing sites, thus are likely products of 'intergenic splicing'. We also explored the implications of these non-pathological fusions in cancer and in evolution.

Reproducible, Scalable Fusion Gene Detection from RNA-Seq

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

Transcriptome Sequencing for the Detection of Chimeric Transcripts

The occurrence of chimeric transcripts has been reported in many cancer cells and seen as potential biomarkers and therapeutic targets. Modern high-throughput sequencing technologies offer a way to investigate individual chimeric transcripts and the systematic information of associated gene expressions about underlying genome structural variations and genomic interactions. The detection methods of finding chimeric transcripts from massive amount of short read sequence data are discussed here. Both assembly-based and alignment-based methods are used for the investigation of chimeric transcripts.

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection.

Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.

Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping. To evaluate INTEGRATE, we compared it with eight additional gene fusion discovery tools using the well-characterized breast cell line HCC1395 and peripheral blood lymphocytes derived from the same patient (HCC1395BL). The predictions subsequently underwent a targeted validation leading to the discovery of 131 novel fusions in addition to the seven previously reported fusions. Overall, INTEGRATE only missed six out of the 138 validated fusions and had the highest accuracy of the nine tools evaluated. Additionally, we applied INTEGRATE to 62 breast cancer patients from The Cancer Genome Atlas (TCGA) and found multiple recurrent gene fusions including a subset involving estrogen receptor. Taken together, INTEGRATE is a highly sensitive and accurate tool that is freely available for academic use.

An integrated genomic analysis of Tudor domain-containing proteins identifies PHD finger protein 20-like 1 (PHF20L1) as a candidate oncogene in breast cancer

Tudor domain-containing proteins (TDRDs), which recognize and bind to methyl-lysine/arginine residues on histones and non-histone proteins, play critical roles in regulating chromatin architecture, transcription, genomic stability, and RNA metabolism. Dysregulation of several TDRDs have been observed in various types of cancer. However, neither the genomic landscape nor clinical significance of TDRDs in breast cancer has been explored comprehensively. Here, we performed an integrated genomic and transcriptomic analysis of 41 TDRD genes in breast cancer (TCGA and METABRIC datasets) and identified associations among recurrent copy number alterations, gene expressions, clinicopathological features, and survival of patients. Among seven TDRDs that had the highest frequency (>10%) of gene amplification, the plant homeodomain finger protein 20-like 1 (PHF20L1) was the most commonly amplified (17.62%) TDRD gene in TCGA breast cancers. Different subtypes of breast cancer had different patterns of copy number and expression for each TDRD. Notably, amplification and overexpression of PHF20L1 were more prevalent in aggressive basal-like and Luminal B subtypes and were significantly associated with shorter survival of breast cancer patients. Furthermore, knockdown of PHF20L1 inhibited cell proliferation in PHF20L1-amplified breast cancer cell lines. PHF20L1 protein contains N-terminal Tudor and C-terminal plant homeodomain domains. Detailed characterization of PHF20L1 in breast cancer revealed that the Tudor domain likely plays a critical role in promoting cancer. Mechanistically, PHF20L1 might participate in regulating DNA methylation by stabilizing DNA methyltransferase 1 (DNMT1) protein in breast cancer. Thus, our results demonstrated the oncogenic potential of PHF20L1 and its association with poor prognostic parameters in breast cancer.

Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment

Accurate assessment of genetic variation in human DNA sequencing studies remains a nontrivial challenge in clinical genomics and genome informatics. Ascribing functional roles and/or clinical significances to single nucleotide variants identified from a next-generation sequencing study is an important step in genome interpretation. Experimental characterization of all the observed functional variants is yet impractical; thus, the prediction of functional and/or regulatory impacts of the various mutations using in silico approaches is an important step toward the identification of functionally significant or clinically actionable variants. The relationships between genotypes and the expressed phenotypes are multilayered and biologically complex; such relationships present numerous challenges and at the same time offer various opportunities for the design of in silico variant assessment strategies. Over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants in the protein coding regions. In this review, we provide an overview of the bioinformatics resources for the prediction, annotation and visualization of coding single nucleotide variants. We discuss the currently available approaches and major challenges from the perspective of protein sequence, structure, function and interactions that require consideration when interpreting the impact of putatively functional variants. We also discuss the relevance of incorporating integrated workflows for predicting the biomedical impact of the functionally important variations encoded in a genome, exome or transcriptome. Finally, we propose a framework to classify variant assessment approaches and strategies for incorporation of variant assessment within electronic health records.

Transcriptome Profiling of Pediatric Core Binding Factor AML

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

The promise of omics-based approaches to cancer prevention

Cancer is a complex category of diseases caused in large part by genetic or genomic, transcriptomic, and epigenetic or epigenomic alterations in affected cells and the surrounding microenvironment. Carcinogenesis reflects the clonal expansion of cells that progressively acquire these genetic and epigenetic alterations-changes that, in turn, lead to modifications at the RNA level. Gradually advancing technology and most recently, the advent of next-generation sequencing (NGS), combined with bioinformatics analytic tools, have revolutionized our ability to interrogate cancer cells. The ultimate goal is to apply these high-throughput technologies to the various aspects of clinical cancer care: cancer-risk assessment, diagnosis, as well as target identification for treatment and prevention. In this article, we emphasize how the knowledge gained through large-scale omics-oriented approaches, with a focus on variations at the level of nucleic acids, can inform the field of chemoprevention.

Discovery, Annotation, and Functional Analysis of Long Noncoding RNAs Controlling Cell-Cycle Gene Expression and Proliferation in Breast Cancer Cells

We describe a computational approach that integrates GRO-seq and RNA-seq data to annotate long noncoding RNAs (lncRNAs), with increased sensitivity for low-abundance lncRNAs. We used this approach to characterize the lncRNA transcriptome in MCF-7 human breast cancer cells, including >700 previously unannotated lncRNAs. We then used information about the (1) transcription of lncRNA genes from GRO-seq, (2) steady-state levels of lncRNA transcripts in cell lines and patient samples from RNA-seq, and (3) histone modifications and factor binding at lncRNA gene promoters from ChIP-seq to explore lncRNA gene structure and regulation, as well as lncRNA transcript stability, regulation, and function. Functional analysis of selected lncRNAs with altered expression in breast cancers revealed roles in cell proliferation, regulation of an E2F-dependent cell-cycle gene expression program, and estrogen-dependent mitogenic growth. Collectively, our studies demonstrate the use of an integrated genomic and molecular approach to identify and characterize growth-regulating lncRNAs in cancers.

Common Oncogene Mutations and Novel SND1-BRAF Transcript Fusion in Lung Adenocarcinoma from Never Smokers

Lung adenocarcinomas from never smokers account for approximately 15 to 20% of all lung cancers and these tumors often carry genetic alterations that are responsive to targeted therapy. Here we examined mutation status in 10 oncogenes among 89 lung adenocarcinomas from never smokers. We also screened for oncogene fusion transcripts in 20 of the 89 tumors by RNA-Seq. In total, 62 tumors had mutations in at least one of the 10 oncogenes, including EGFR (49 cases, 55%), K-ras (5 cases, 6%), BRAF (4 cases, 5%), PIK3CA (3 cases, 3%), and ERBB2 (4 cases, 5%). In addition to ALK fusions identified by IHC/FISH in four cases, two previously known fusions involving EZR- ROS1 and KIF5B-RET were identified by RNA-Seq as well as a third novel fusion transcript that was formed between exons 1–9 of SND1 and exons 2 to 3′ end of BRAF. This in-frame fusion was observed in 3/89 tested tumors and 2/64 additional never smoker lung adenocarcinoma samples. Ectopic expression of SND1-BRAF in H1299 cells increased phosphorylation levels of MEK/ERK, cell proliferation, and spheroid formation compared to parental mock-transfected control. Jointly, our results suggest a potential role of the novel BRAF fusion in lung cancer development and therapy.

Detecting and targetting oncogenic fusion proteins in the genomic era

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

Folate receptor-α (FOLR1) expression and function in triple negative tumors

Folate receptor alpha (FOLR1) has been identified as a potential prognostic and therapeutic target in a number of cancers. A correlation has been shown between intense overexpression of FOLR1 in breast tumors and poor prognosis, yet there is limited examination of the distribution of FOLR1 across clinically relevant breast cancer subtypes. To explore this further, we used RNA-seq data from multiple patient cohorts to analyze the distribution of FOLR1 mRNA across breast cancer subtypes comprised of estrogen receptor positive (ER+), human epidermal growth factor receptor positive (HER2+), and triple negative (TNBC) tumors. FOLR1 expression varied within breast tumor subtypes; triple negative/basal tumors were significantly associated with increased expression of FOLR1 mRNA, compared to ER+ and HER2+ tumors. However, subsets of high level FOLR1 expressing tumors were observed in all clinical subtypes. These observations were supported by immunohistochemical analysis of tissue microarrays, with the largest number of 3+ positive tumors and highest H-scores of any subtype represented by triple negatives, and lowest by ER+ tumors. FOLR1 expression did not correlate to common clinicopathological parameters such as tumor stage and nodal status. To delineate the importance of FOLR1 overexpression in triple negative cancers, RNA-interference was used to deplete FOLR1 in overexpressing triple negative cell breast lines. Loss of FOLR1 resulted in growth inhibition, whereas FOLR1 overexpression promoted folate uptake and growth advantage in low folate conditions. Taken together, our data suggests patients with triple negative cancers expressing high FOLR1 expression represent an important population of patients that may benefit from targeted anti-FOLR1 therapy. This may prove particularly helpful for a large number of patients who would typically be classified as triple negative and who to this point have been left without any targeted treatment options.

Chimeric RNAs as potential biomarkers for tumor diagnosis

Cancers claim millions of lives each year. Early detection that can enable a higher chance of cure is of paramount importance to cancer patients. However, diagnostic tools for many forms of tumors have been lacking. Over the last few years, studies of chimeric RNAs as biomarkers have emerged. Numerous reports using bioinformatics and screening methodologies have described more than 30,000 expressed sequence tags (EST) or cDNA sequences as putative chimeric RNAs. While cancer cells have been well known to contain fusion genes derived from chromosomal translocations, rearrangements or deletions, recent studies suggest that trans-splicing in cells may be another source of chimeric RNA production. Unlike cis-splicing, trans-splicing takes place between two pre-mRNA molecules, which are in most cases derived from two different genes, generating a chimeric non-co-linear RNA. It is possible that trans-splicing occurs in normal cells at high frequencies but the resulting chimeric RNAs exist only at low levels. However the levels of certain RNA chimeras may be elevated in cancers, leading to the formation of fusion genes. In light of the fact that chimeric RNAs have been shown to be overrepresented in various tumors, studies of the mechanisms that produce chimeric RNAs and identification of signature RNA chimeras as biomarkers present an opportunity for the development of diagnoses for early tumor detection.

The landscape and therapeutic relevance of cancer-associated transcript fusions

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