PO-400 Arriba – fast and accurate gene fusion detection from RNA-seq data

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.

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.

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of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years.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.

Practical prediction model of the clinical response to programmed death-ligand 1 inhibitors in advanced gastric cancer

The identification of predictive biomarkers or models is necessary for the selection of patients who might benefit the most from immunotherapy. Seven histological features (signet ring cell [SRC], fibrous stroma, myxoid stroma, tumor-infiltrating lymphocytes [TILs], necrosis, tertiary lymphoid follicles, and ulceration) detected in surgically resected tissues (N = 44) were used to train a model. The presence of SRC became an optimal decision parameter for pathology alone (AUC = 0.78). Analysis of differentially expressed genes (DEGs) for the prediction of genomic markers showed that C-X-C motif chemokine ligand 11 (CXCL11) was high in responders (P < 0.001). Immunohistochemistry (IHC) was performed to verify its potential as a biomarker. IHC revealed that the expression of CXCL11 was associated with responsiveness (P = 0.003). The response prediction model was trained by integrating the results of the analysis of pathological factors and RNA sequencing (RNA-seq). When trained with the C5.0 decision tree model, the categorical level of the expression of CXCL11, a single variable, was shown to be the best model (AUC = 0.812). The AUC of the model trained with the random forest was 0.944. Survival analysis revealed that the C5.0-trained model (log-rank P = 0.01 for progression-free survival [PFS]; log-rank P = 0.012 for overall survival [OS]) and the random forest-trained model (log-rank P < 0.001 for PFS; log-rank P = 0.001 for OS) predicted prognosis more accurately than the PD-L1 test (log-rank P = 0.031 for PFS; log-rank P = 0.107 for OS).

A prediction model that identifies patients with gastric cancer who are likely to respond well to immunotherapy has been developed by researchers in South Korea. Hansoo Park at the Gwangju Institute of Science and Technology and co-workers identified several biomarkers in gastric cancer tissues that were associated with how well patients may respond to immunotherapy treatment. They found that patients with malignant cells known as signet ring cells were least likely to respond well to immune checkpoint inhibitor drugs. Conversely, high expression levels of a gene called CXCL11 was associated with a strong positive response to the drugs. The researchers used these and other biomarker data to build a model for selecting appropriate candidates for immunotherapy. Further research will refine this initial biomarker list for gastric cancer and help improve the model.

A MXI1-NUTM1 fusion protein with MYC-like activity suggests a novel oncogenic mechanism in a subset of NUTM1-rearranged tumors

Most NUTM1-rearranged neoplasms (NRNs) have fusions between NUTM1 and BRD (bromodomain-containing) family members and are termed NUT carcinomas (NCs) because they show some squamous differentiation. However, some NRNs are associated with fusions between NUTM1 and members of the MAD (MAX dimerization) gene family of MYC antagonists. Here we describe a small round cell malignancy from the gastro-esophageal junction with a previously unreported fusion between NUTM1 and the MAD family member MXI1. In contrast to NCs, the MXI1-NUTM1 tumor did not show squamous differentiation and did not express MYC, TP63 or SOX2, genes known to be targets of BRD-NUTM1 proteins and critical for NC oncogenesis. Transcriptome analysis showed paradoxical enrichment of MYC target genes in the MXI1-NUTM1 tumor despite the lack of MYC expression. When expressed in vitro MXI1-NUTM1 partially phenocopied MYC, enhancing cell proliferation and cooperating with oncogenic HRAS to produce anchorage-independent cell growth. These data provide evidence that MAD family members, which are normally repressors of MYC activity, can be converted into MYC-like mimics by fusion to NUTM1. The pathological features and novel oncogenic mechanism of the MXI1-NUTM1 tumor show that identification of NUTM1 fusion partners can be important for accurate diagnostic classification of some NRN subtypes, and potentially may guide therapeutic options.

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 .

Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs

BACKGROUND

In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods.

RESULTS

We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes).

CONCLUSION

Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.

Next-Generation Sequencing Approaches for the Identification of Pathognomonic Fusion Transcripts in Sarcomas: The Experience of the Italian ACC Sarcoma Working Group

This work describes the set-up of a shared platform among the laboratories of the Alleanza Contro il Cancro (ACC) Italian Research Network for the identification of fusion transcripts in sarcomas by using Next Generation Sequencing (NGS). Different NGS approaches, including anchored multiplex PCR and hybrid capture-based panels, were employed to profile a large set of sarcomas of different histotypes. The analysis confirmed the reliability of NGS RNA-based approaches in detecting sarcoma-specific rearrangements. Overall, the anchored multiplex PCR assay proved to be a fast and easy-to-analyze approach for routine diagnostics laboratories.

Structural variant identification and characterization

Structural variant (SV) differences between human genomes can cause germline and mosaic disease as well as inter-individual variation. De-regulation of accurate DNA repair and genomic surveillance mechanisms results in a large number of SVs in cancer. Analysis of the DNA sequences at SV breakpoints can help identify pathways of mutagenesis and regions of the genome that are more susceptible to rearrangement. Large-scale SV analyses have been enabled by high-throughput genome-level sequencing on humans in the past decade. These studies have shed light on the mechanisms and prevalence of complex genomic rearrangements. Recent advancements in both sequencing and other mapping technologies as well as calling algorithms for detection of genomic rearrangements have helped propel SV detection into population-scale studies, and have begun to elucidate previously inaccessible regions of the genome. Here, we discuss the genomic organization of simple and complex SVs, the molecular mechanisms of their formation, and various ways to detect them. We also introduce methods for characterizing SVs and their consequences on human genomes.

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.