SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data

Detecting transcriptomic structural variants in heterogeneous contexts via the Multiple Compatible Arrangements Problem

BACKGROUND

Transcriptomic structural variants (TSVs)-large-scale transcriptome sequence change due to structural variation - are common in cancer. TSV detection from high-throughput sequencing data is a computationally challenging problem. Among all the confounding factors, sample heterogeneity, where each sample contains multiple distinct alleles, poses a critical obstacle to accurate TSV prediction.

RESULTS

To improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangements Problem (MCAP), which seeks k genome arrangements that maximize the number of reads that are concordant with at least one arrangement. This models a heterogeneous or diploid sample. We prove that MCAP is NP-complete and provide a 1 4 -approximation algorithm for k = 1 and a 3 4 -approximation algorithm for the diploid case ( k = 2 ) assuming an oracle for k = 1 . Combining these, we obtain a 3 16 -approximation algorithm for MCAP when k = 2 (without an oracle). We also present an integer linear programming formulation for general k. We characterize the conflict structures in the graph that require k > 1 alleles to satisfy read concordancy and show that such structures are prevalent.

CONCLUSIONS

We show that the solution to MCAP accurately addresses sample heterogeneity during TSV detection. Our algorithms have improved performance on TCGA cancer samples and cancer cell line samples compared to a TSV calling tool, SQUID. The software is available at https://github.com/Kingsford-Group/diploidsquid.

Racial Differences in the Association Between Luminal Master Regulator Gene Expression Levels and Breast Cancer Survival

PURPOSE

Compared with their European American (EA) counterparts, African American (AA) women are more likely to die from breast cancer in the United States. This disparity is greatest in hormone receptor-positive subtypes. Here we uncover biological factors underlying this disparity by comparing functional expression and prognostic significance of master transcriptional regulators of luminal differentiation.

EXPERIMENTAL DESIGN

Data and biospecimens from 262 AA and 293 EA patients diagnosed with breast cancer from 2001 to 2010 at a major medical center were analyzed by IHC for functional biomarkers of luminal differentiation, including estrogen receptor (ESR1) and its pioneer factors, FOXA1 and GATA3. Integrated comparison of protein levels with network-level gene expression analysis uncovered predictive correlations with race and survival.

RESULTS

Univariate or multivariate HRs for overall survival, estimated from digital IHC scoring of nuclear antigen, show distinct differences in the magnitude and significance of these biomarkers to predict survival based on race: ESR1 [EA HR = 0.47; 95% confidence interval (CI), 0.31-0.72 and AA HR = 0.77; 95% CI, 0.48-1.18]; FOXA1 (EA HR = 0.38; 95% CI, 0.23-0.63 and AA HR = 0.53; 95% CI, 0.31-0.88), and GATA3 (EA HR = 0.36; 95% CI, 0.23-0.56; AA HR = 0.57; CI, 0.56-1.4). In addition, we identify genes in the downstream regulons of these biomarkers highly correlated with race and survival.

CONCLUSIONS

Even within clinically homogeneous tumor groups, regulatory networks that drive mammary luminal differentiation reveal race-specific differences in their association with clinical outcome. Understanding these biomarkers and their downstream regulons will elucidate the intrinsic mechanisms that drive racial disparities in breast cancer survival.

Evidence of constraint in the 3D genome for trans-splicing in human cells

Fusion transcripts are commonly found in eukaryotes, and many aberrant fusions are associated with severe diseases, including cancer. One class of fusion transcripts is generated by joining separate transcripts through trans-splicing. However, the mechanism of trans-splicing in mammals remains largely elusive. Here we showed evidence to support an intuitive hypothesis that attributes trans-sphcing to the spatial proximity between premature transcripts. A novel trans-splicing detection tool (TSD) was developed to reliably identify intra-chromosomal trans-splicing events (iTSEs) from RNA-seq data. TSD can maintain a remarkable balance between sensitivity and accuracy, thus distinguishing it from most state-of-the-art tools. The accuracy of TSD was experimentally demonstrated by excluding potential false discovery from mosaic genome or template switching during PCR. We showed that iTSEs identified by TSD were frequently found between genomic regulatory elements, which are known to be more prone to interact with each other. Moreover, iTSE sites may be more physically adjacent to each other than random control in the tested human lymphoblastoid cell line according to Hi-C data. Our results suggest that trans-splicing and 3D genome architecture may be coupled in mammals and that our pipeline, TSD, may facilitate investigations of trans-splicing on a systematic and accurate level previously thought impossible.

A genomic and epigenomic atlas of prostate cancer in Asian populations

Prostate cancer is the second most common cancer in men worldwide¹. Over the past decade, large-scale integrative genomics efforts have enhanced our understanding of this disease by characterizing its genetic and epigenetic landscape in thousands of patients^2,3. However, most tumours profiled in these studies were obtained from patients from Western populations. Here we produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1 and 18% each had deletions in ZNF292 and CHD1. Alterations of the genome and epigenome were correlated and were predictive of disease phenotype and progression. Coding and noncoding mutations, as well as epimutations, converged on pathways that are important for prostate cancer, providing insights into this devastating disease. These discoveries underscore the importance of including population context in constructing comprehensive genomic maps for disease.

GOLT1A-KISS1 fusion is associated with metastasis in adenoid cystic carcinomas

Genetic alterations can drive carcinogenesis. Numerous studies have shown that gene fusion is associated with cancer progression and could provide valuable biomarkers for clinical diagnosis or targets for cancer therapy. Adenoid cystic carcinoma (ACC) is a rare form of adenocarcinoma, characterized by frequent local recurrence and high rates of distant metastasis, ultimately resulting in low survival rates. Owing to the lack of effective therapeutic targets and limited biomarkers for diagnosis, a deeper understanding of the molecular basis of ACC is urgently needed. Here, we show that gene fusion is associated with ACC metastasis. We identified a metastasis suppressor KISS1 fused with a close-by gene, GOLT1A, in highly metastatic ACC cell lines and human specimens. Such fusion blocks KISS1 translation, but not transcription, by introducing 5' upstream open reading frames (uORFs) in the GOLT1A-KISS1 fusion transcript. Deletion of these uORFs rescued KISS1 expression and reduced invasion and migration of metastatic ACC cells. We also detected GOLT1A-KISS1 fusion transcripts in other types of highly metastatic cancer cell lines. Taken together, our results highlight the significance of this novel GOLT1A-KISS1 gene fusion in tumor metastasis and provide a valuable biomarker for clinical diagnosis and future therapeutic targeting of ACC.

Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools

Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed.

ScaR-a tool for sensitive detection of known fusion transcripts: establishing prevalence of fusions in testicular germ cell tumors

Bioinformatics tools for fusion transcript detection from RNA-sequencing data are in general developed for identification of novel fusions, which demands a high number of supporting reads and strict filters to avoid false discoveries. As our knowledge of bona fide fusion genes becomes more saturated, there is a need to establish their prevalence with high sensitivity. We present ScaR, a tool that uses a supervised scaffold realignment approach for sensitive fusion detection in RNA-seq data. ScaR detects a set of 130 synthetic fusion transcripts from simulated data at a higher sensitivity compared to established fusion finders. Applied to fusion transcripts potentially involved in testicular germ cell tumors (TGCTs), ScaR detects the fusions RCC1-ABHD12B and CLEC6A-CLEC4D in 9% and 28% of 150 TGCTs, respectively. The fusions were not detected in any of 198 normal testis tissues. Thus, we demonstrate high prevalence of RCC1-ABHD12B and CLEC6A-CLEC4D in TGCTs, and their cancer specific features. Further, we find that RCC1-ABHD12B and CLEC6A-CLEC4D are predominantly expressed in the seminoma and embryonal carcinoma histological subtypes of TGCTs, respectively. In conclusion, ScaR is useful for establishing the frequency of known and validated fusion transcripts in larger data sets and detecting clinically relevant fusion transcripts with high sensitivity.

ArtiFuse-computational validation of fusion gene detection tools without relying on simulated reads

MOTIVATION

Gene fusions are an important class of transcriptional variants that can influence cancer development and can be predicted from RNA sequencing (RNA-seq) data by multiple existing tools. However, the real-world performance of these tools is unclear due to the lack of known positive and negative events, especially with regard to fusion genes in individual samples. Often simulated reads are used, but these cannot account for all technical biases in RNA-seq data generated from real samples.

RESULTS

Here, we present ArtiFuse, a novel approach that simulates fusion genes by sequence modification to the genomic reference, and therefore, can be applied to any RNA-seq dataset without the need for any simulated reads. We demonstrate our approach on eight RNA-seq datasets for three fusion gene prediction tools: average recall values peak for all three tools between 0.4 and 0.56 for high-quality and high-coverage datasets. As ArtiFuse affords total control over involved genes and breakpoint position, we also assessed performance with regard to gene-related properties, showing a drop-in recall value for low-expressed genes in high-coverage samples and genes with co-expressed paralogues. Overall tool performance assessed from ArtiFusions is lower compared to previously reported estimates on simulated reads. Due to the use of real RNA-seq datasets, we believe that ArtiFuse provides a more realistic benchmark that can be used to develop more accurate fusion gene prediction tools for application in clinical settings.

AVAILABILITY AND IMPLEMENTATION

ArtiFuse is implemented in Python. The source code and documentation are available at https://github.com/TRON-Bioinformatics/ArtiFusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction, Characterization, and In Silico Validation of Chimeric RNAs

Many chimeric RNA prediction software packages are available to assist the scientific community in searching for cancer-specific chimeric RNAs. These packages predict a large number of false positive events, which significantly hampers experimental validation of predicted chimeric RNAs. Here, we describe the detailed steps for (1) prediction of chimeric RNAs using EricScript software, (2) characterization of chimeric RNAs to discard most probable false positive events, and (3) in silico validation of chimeric RNA to select the potential cancer-specific events.

Validation of Chimeric Fusion Peptides Using Proteomics Data

Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer.

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.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Epigenetic re-wiring of breast cancer by pharmacological targeting of C-terminal binding protein

The C-terminal binding protein (CtBP) is an NADH-dependent dimeric family of nuclear proteins that scaffold interactions between transcriptional regulators and chromatin-modifying complexes. Its association with poor survival in several cancers implicates CtBP as a promising target for pharmacological intervention. We employed computer-assisted drug design to search for CtBP inhibitors, using quantitative structure-activity relationship (QSAR) modeling and docking. Functional screening of these drugs identified 4 compounds with low toxicity and high water solubility. Micro molar concentrations of these CtBP inhibitors produces significant de-repression of epigenetically silenced pro-epithelial genes, preferentially in the triple-negative breast cancer cell line MDA-MB-231. This epigenetic reprogramming occurs through eviction of CtBP from gene promoters; disrupted recruitment of chromatin-modifying protein complexes containing LSD1, and HDAC1; and re-wiring of activating histone marks at targeted genes. In functional assays, CtBP inhibition disrupts CtBP dimerization, decreases cell migration, abolishes cellular invasion, and improves DNA repair. Combinatorial use of CtBP inhibitors with the LSD1 inhibitor pargyline has synergistic influence. Finally, integrated correlation of gene expression in breast cancer patients with nuclear levels of CtBP1 and LSD1, reveals new potential therapeutic vulnerabilities. These findings implicate a broad role for this class of compounds in strategies for epigenetically targeted therapeutic intervention.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

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.

Angiosarcoma patients treated with immune checkpoint inhibitors: a case series of seven patients from a single institution

BACKGROUND

Angiosarcoma is an uncommon endothelial malignancy and a highly aggressive soft tissue sarcoma. Due to its infiltrative nature, successful management of localized angiosarcoma is often challenging. Systemic chemotherapy is used in the metastatic setting and occasionally in patients with high-risk localized disease in neoadjuvant or adjuvant settings. However, responses tend to be short-lived and most patients succumb to metastatic disease. Novel therapies are needed for patients with angiosarcomas.

METHODS

We performed a retrospective analysis of patients with locally advanced or metastatic angiosarcoma, who were treated with checkpoint inhibitors at our institution. We collected their clinical information and outcome measurements. In one patient with achieved complete response, we analyzed circulating and infiltrating T cells within peripheral blood and tumor tissue.

RESULTS

We have treated seven angiosarcoma (AS) patients with checkpoint inhibitors either in the context of clinical trials or off label [Pembrolizumab + Axitinib (NCT02636725; n = 1), AGEN1884, a CTLA-4 inhibitor (NCT02694822; n = 2), Pembrolizumab (n = 4)]. Five patients had cutaneous angiosarcoma, one primary breast angiosarcoma and one radiation-associated breast angiosarcoma. At 12 weeks, 5/7 patients (71%) had partial response of their lesions either on imaging and/or clinical exam and two (29%) had progressive disease. 6/7 patients are alive to date and, thus far, 3/7 patients (43%) have progressed (median 3.4 months)- one achieved partial response after pembrolizumab was switched to ongoing Nivolumab/Ipilimumab, one died of progressive disease at 31 weeks (primary breast angiosarcoma) and one was placed on pazopanib. One patient had a complete response (CR) following extended treatment with monotherapy AGEN1884. No patient experienced any ≥ grade 2 toxicities.

CONCLUSIONS

This case series underscores the value of targeted immunotherapy in treating angiosarcoma. It also identifies genetic heterogeneity of cutaneous angiosarcomas and discusses specific genetic findings that may explain reported benefits from immunotherapy.

Establishment of patient-derived xenograft model in ovarian cancer and its influence factors analysis

AIM

Patient-derived xenograft (PDX) model has been applied to the study of breast cancer, lung cancer, colon cancer and other cancers. However, its feasibility in ovarian cancer has not been understood. This study aimed to establish ovarian cancer PDX model and reveal its influence factors.

METHODS

In this study, 27 patients in Obstetrics and Gynecology Hospital affiliated to Fudan University from May 2015 to May 2016 were employed to explore the method of PDX model in ovarian cancer and verify its feasibility.

RESULTS

Finally, five cases of PDX models were successfully established, and the tumor formation rate (TFR) was 18.52%. In addition, immunohistochemistry and transcriptome sequencing analysis showed that tumor of PDX model have similar gene expression, gene splicing, gene fusion and single nucleotide polymorphisms with primary tumor (R² = 0.741). Furthermore, it was revealed that compared to epithelial ovarian cancer, the TFR of PDX models with nonepithelial ovarian cancer was higher, while other factors such as the initiation site of tumor, the degree of tumor malignancy, the stage of tumor, the type of tumor and the species of experimental animals were not associated with the TFR.

CONCLUSION

Ovarian cancer PDX model, as a new scientific research model, can better keep the biological characteristics of primary tumor, which has great research value in ovarian cancer.

FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells

Fusion proteoforms are translation products derived from gene fusion. Although very rare, the fusion proteoforms play important roles in biomedical science. For example, fusion proteoforms influence the development of tumors by serving as cancer markers or cell cycle regulators. Although numerous studies have reported bioinformatics tools that can predict fusion transcripts, few proteogenomic tools are available that can predict and identify proteoforms. In this study, we develop a versatile proteogenomic tool "FusionPro," which facilitates the identification of fusion transcripts and their potential translatable peptides. FusionPro provides an independent gene fusion prediction module and can build sequence databases for annotated fusion proteoforms. FusionPro shows greater sensitivity than the available fusion finders when analyzing simulated or real RNA sequencing data sets. We use FusionPro to identify 18 fusion junction peptides and three potential fusion-derived peptides by MS/MS-based analysis of leukemia cell lines (Jurkat and K562) and ovarian cancer tissues from the Clinical Proteomic Tumor Analysis Consortium. Among the identified fusion proteins, we molecularly validate two fusion junction isoforms and a translation product of FAM133B:CDK6. Moreover, sequence analysis suggests that the fusion protein participates in the cell cycle progression. In addition, our prediction results indicate that fusion transcripts often have multiple fusion junctions and that these fusion junctions tend to be distributed in a nonrandom pattern at both the chromosome and gene levels. Thus, FusionPro allows users to detect various types of fusion translation products using a transcriptome-informed approach and to gain a comprehensive understanding of the formation and biological roles of fusion proteoforms.

Molecular Profiling Reclassifies Adult Astroblastoma into Known and Clinically Distinct Tumor Entities with Frequent Mitogen-Activated Protein Kinase Pathway Alterations

BACKGROUND

Astroblastoma (ABM) is a rare glial brain tumor. Recurrent meningioma 1 (MN1) alterations have been recently identified in most pediatric cases. Adolescent and adult cases, however, remain molecularly poorly defined.

MATERIALS AND METHODS

We performed clinical and molecular characterization of a retrospective cohort of 14 adult and 1 adolescent ABM.

RESULTS

Strikingly, we found that MN1 fusions are a rare event in this age group (1/15). Using methylation profiling and targeted sequencing, most cases were reclassified as either pleomorphic xanthoastrocytomas (PXA)-like or high-grade glioma (HGG)-like. PXA-like ABM show BRAF mutation (6/7 with V600E mutation and 1/7 with G466E mutation) and CD34 expression. Conversely, HGG-like ABM harbored specific alterations of diffuse midline glioma (2/5) or glioblastoma (GBM; 3/5). These latter patients showed an unfavorable clinical course with significantly shorter overall survival (p = .021). Mitogen-activated protein kinase pathway alterations (including FGFR fusion, BRAF and NF1 mutations) were present in 10 of 15 patients and overrepresented in the HGG-like group (3/5) compared with previously reported prevalence of these alterations in GBM and diffuse midline glioma.

CONCLUSION

We suggest that gliomas with astroblastic features include a variety of molecularly sharply defined entities. Adult ABM harboring molecular features of PXA and HGG should be reclassified. Central nervous system high-grade neuroepithelial tumors with MN1 alterations and histology of ABM appear to be uncommon in adults. Astroblastic morphology in adults should thus prompt thorough molecular investigation aiming at a clear histomolecular diagnosis and identifying actionable drug targets, especially in the mitogen-activated protein kinase pathway.

IMPLICATIONS FOR PRACTICE

Astroblastoma (ABM) remains a poorly defined and controversial entity. Although meningioma 1 alterations seem to define a large subset of pediatric cases, adult cases remain molecularly poorly defined. This comprehensive molecular characterization of 1 adolescent and 14 adult ABM revealed that adult ABM histology comprises several molecularly defined entities, which explains clinical diversity and identifies actionable targets. Namely, pleomorphic xanthoastrocytoma-like ABM cases show a favorable prognosis whereas high-grade glioma (glioblastoma and diffuse midline gliome)-like ABM show significantly worse clinical courses. These results call for in-depth molecular analysis of adult gliomas with astroblastic features for diagnostic and therapeutic purposes.

FusionScan: accurate prediction of fusion genes from RNA-Seq data

Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines

BACKGROUND

Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison.

RESULTS

We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA).

CONCLUSIONS

The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

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

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

Hybrid sequencing-based personal full-length transcriptomic analysis implicates proteostatic stress in metastatic ovarian cancer

Comprehensive molecular characterization of myriad somatic alterations and aberrant gene expressions at personal level is key to precision cancer therapy, yet limited by current short-read sequencing technology, individualized catalog of complete genomic and transcriptomic features is thus far elusive. Here, we integrated second- and third-generation sequencing platforms to generate a multidimensional dataset on a patient affected by metastatic epithelial ovarian cancer. Whole-genome and hybrid transcriptome dissection captured global genetic and transcriptional variants at previously unparalleled resolution. Particularly, single-molecule mRNA sequencing identified a vast array of unannotated transcripts, novel long noncoding RNAs and gene chimeras, permitting accurate determination of transcription start, splice, polyadenylation and fusion sites. Phylogenetic and enrichment inference of isoform-level measurements implicated early functional divergence and cytosolic proteostatic stress in shaping ovarian tumorigenesis. A complementary imaging-based high-throughput drug screen was performed and subsequently validated, which consistently pinpointed proteasome inhibitors as an effective therapeutic regime by inducing protein aggregates in ovarian cancer cells. Therefore, our study suggests that clinical application of the emerging long-read full-length analysis for improving molecular diagnostics is feasible and informative. An in-depth understanding of the tumor transcriptome complexity allowed by leveraging the hybrid sequencing approach lays the basis to reveal novel and valid therapeutic vulnerabilities in advanced ovarian malignancies.

NCLcomparator: systematically post-screening non-co-linear transcripts (circular, trans-spliced, or fusion RNAs) identified from various detectors

BACKGROUND

Non-co-linear (NCL) transcripts consist of exonic sequences that are topologically inconsistent with the reference genome in an intragenic fashion (circular or intragenic trans-spliced RNAs) or in an intergenic fashion (fusion or intergenic trans-spliced RNAs). On the basis of RNA-seq data, numerous NCL event detectors have been developed and detected thousands of NCL events in diverse species. However, there are great discrepancies in the identification results among detectors, indicating a considerable proportion of false positives in the detected NCL events. Although several helpful guidelines for evaluating the performance of NCL event detectors have been provided, a systematic guideline for measurement of NCL events identified by existing tools has not been available.

RESULTS

We develop a software, NCLcomparator, for systematically post-screening the intragenic or intergenic NCL events identified by various NCL detectors. NCLcomparator first examine whether the input NCL events are potentially false positives derived from ambiguous alignments (i.e., the NCL events have an alternative co-linear explanation or multiple matches against the reference genome). To evaluate the reliability of the identified NCL events, we define the NCL score (NCLscore) based on the variation in the number of supporting NCL junction reads identified by the tools examined. Of the input NCL events, we show that the ambiguous alignment-derived events have relatively lower NCLscore values than the other events, indicating that an NCL event with a higher NCLscore has a higher level of reliability. To help selecting highly expressed NCL events, NCLcomparator also provides a series of useful measurements such as the expression levels of the detected NCL events and their corresponding host genes and the junction usage of the co-linear splice junctions at both NCL donor and acceptor sites.

CONCLUSION

NCLcomparator provides useful guidelines, with the input of identified NCL events from various detectors and the corresponding paired-end RNA-seq data only, to help users selecting potentially high-confidence NCL events for further functional investigation. The software thus helps to facilitate future studies into NCL events, shedding light on the fundamental biology of this important but understudied class of transcripts. NCLcomparator is freely accessible at https://github.com/TreesLab/NCLcomparator .

A recurrent novel MGA-NUTM1 fusion identifies a new subtype of high-grade spindle cell sarcoma

NUTM1-rearranged tumors are defined by the presence of a gene fusion between NUTM1 and various gene partners and typically follow a clinically aggressive disease course with poor outcomes despite conventional multimodality therapy. NUTM1-rearranged tumors display histologic features of a poorly differentiated carcinoma with areas of focal squamous differentiation and typically express the BRD4-NUTM1 fusion gene defining a distinct clinicopathologic entity-NUT carcinoma (NC). NCs with mesenchymal differentiation have rarely been described in the literature. In this report, we describe the characterization of two cases of high-grade spindle cell sarcoma harboring a novel MGA-NUTM1 fusion. Whole-genome sequencing identified the presence of complex rearrangements resulting in a MGA-NUTM1 fusion gene in the absence of other significant somatic mutations. Genetic rearrangement was confirmed by fluorescence in situ hybridization, and expression of the fusion gene product was confirmed by transcriptomic analysis. The fusion protein was predicted to retain nearly the entire protein sequence of both MGA (exons 1-22) and NUTM1 (exons 3-8). Histopathologically, both cases were high-grade spindle cell sarcomas without specific differentiation markers. In contrast to typical cases of NC, these cases were successfully treated with aggressive local control measures (surgery and radiation) and both patients remain alive without disease. These cases describe a new subtype of NUTM1-rearranged tumors warranting expansion of diagnostic testing to evaluate for the presence of MGA-NUTM1 or alternative NUTM1 gene fusions in the diagnostic workup of high-grade spindle cell sarcomas or small round blue cell tumors of ambiguous lineage.

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.

The Landscape and Implications of Chimeric RNAs in Cervical Cancer

BACKGROUND

Gene fusions and fusion products have been proven to be ideal biomarkers and drug targets for cancer. Even though a comprehensive study of cervical cancer has been conducted as part of the Cancer Genome Atlas (TCGA) project, few recurrent gene fusions have been found, and none above 3% of frequency.

METHODS

We believe that chimeric fusion RNAs generated by intergenic splicing represent a new repertoire of biomarkers and/or therapeutic targets. However, they would be missed when only genome sequences and fusions at DNA level are considered. We performed extensive data mining for chimeric RNAs using both our and TCGA cervical cancer RNA-Seq datasets. Multiple criteria were applied. We analyzed the landscape of chimeric RNAs at various levels, and from different angles.

FINDINGS

The chimeric RNA landscape changed as different filters were applied. 15 highly frequent (>10%) chimeric RNAs were identified. LHX6-NDUFA8 was detected exclusively in cervical cancer tissues and Pap smears, but not in normal controls. Mechanistically, it is not due to interstitial deletion, but a product of cis-splicing between adjacent genes. Silencing of another recurrent chimera, SLC2A11-MIF, resulted in cell cycle arrest and reduced cellular proliferation. This effect is unique to the chimera, and not shared by the two parental genes.

INTERPRETATION

Highly frequent chimeric RNAs are present in cervical cancers. They can be formed by intergenic splicing. Some have clear implications as potential biomarkers, or for shedding new light on the biology of the disease. FUND: Stand Up To Cancer and the National Science Foundation of China.

SeqOthello: querying RNA-seq experiments at scale

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Identification of the UBA2-WTIP fusion gene in acute myeloid leukemia

Identifying and targeting oncogenic fusion genes have revolutionized the treatment of leukemia, such as PML-RARα fusion gene in acute promyelocytic leukemia. Here we identified an intrachromosomal fusion gene located on chromosome 19q.13 between UBA2 and WTIP gene in a case of acute myeloid leukemia. The UBA2-WTIP fusion gene contains the N-terminal E1_enzyme_family, VAE_Ubl domains of UBA2, and the C-terminal LIM domains of WTIP. The UBA2-WTIP fusion was detected by reverse transcriptase polymerase chain reaction and Sanger sequencing in 19 of 56 acute myeloid leukemia samples (33.9%). Ectopic expression of the UBA2-WTIP fusion in human acute myeloid leukemia KG-1a cells showed enhanced cell proliferation both in vitro and in vivo. The UBA2-WTIP fusion induced phosphorylation of STAT3, STAT5 and ERK1/2, and abrogates WTIP-mediated mammalian processing body formation. Finally, triptolide displayed selective cytotoxicity against KG-1a cells harboring the UBA2-WTIP fusion. Collectively, our findings suggest that the UBA2-WTIP fusion is an oncogenic fusion gene, as well as a promising therapeutic target for the treatment of acute myeloid leukemia.

STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts

Motivation

The biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer and recently discovered circular RNA (circRNA) are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, software available for circRNA detection and quantification is limited.

Results

Here, we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circRNA and high precision fusions in a rapid, efficient and scalable manner that is appropriate for high dimensional medical omics datasets.

Availability and implementation

STARChip is available at https://github.com/LosicLab/STARChip.

Supplementary information

Supplementary data are available at Bioinformatics online.

Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .

Characterization of fusion genes in common and rare epithelial ovarian cancer histologic subtypes

Gene fusions play a critical role in some cancers and can serve as important clinical targets. In epithelial ovarian cancer (EOC), the contribution of fusions, especially by histological type, is unclear. We therefore screened for recurrent fusions in a histologically diverse panel of 220 EOCs using RNA sequencing. The Pipeline for RNA-Sequencing Data Analysis (PRADA) was used to identify fusions and allow for comparison with The Cancer Genome Atlas (TCGA) tumors. Associations between fusions and clinical prognosis were evaluated using Cox proportional hazards regression models. Nine recurrent fusions, defined as occurring in two or more tumors, were observed. CRHR1-KANSL1 was the most frequently identified fusion, identified in 6 tumors (2.7% of all tumors). This fusion was not associated with survival; other recurrent fusions were too rare to warrant survival analyses. One recurrent in-frame fusion, UBAP1-TGM7, was unique to clear cell (CC) EOC tumors (in 10%, or 2 of 20 CC tumors). We found some evidence that CC tumors harbor more fusions on average than any other EOC histological type, including high-grade serous (HGS) tumors. CC tumors harbored a mean of 7.4 fusions (standard deviation [sd] = 7.4, N = 20), compared to HGS EOC tumors mean of 2.0 fusions (sd = 3.3, N = 141). Few fusion genes were detected in endometrioid tumors (mean = 0.24, sd = 0.74, N = 55) or mucinous tumors (mean = 0.25, sd = 0.5, N = 4) tumors. To conclude, we identify one fusion at 10% frequency in the CC EOC subtype, but find little evidence for common (> 5% frequency) recurrent fusion genes in EOC overall, or in HGS subtype-specific EOC tumors.

SQUID: transcriptomic structural variation detection from RNA-seq

Transcripts are frequently modified by structural variations, which lead to fused transcripts of either multiple genes, known as a fusion gene, or a gene and a previously non-transcribed sequence. Detecting these modifications, called transcriptomic structural variations (TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to predict both fusion-gene and non-fusion-gene TSVs accurately from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model and doubles the precision on simulation data compared to other approaches. Using SQUID, we identify novel non-fusion-gene TSVs on TCGA samples.

VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Background

RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology has led to the continuous development of new tools for every step of analysis from alignment to downstream pathway analysis. However, effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.

Results

Using the workflow management system Snakemake we have developed a user friendly, fast, efficient, and comprehensive pipeline for RNA-seq analysis. VIPER (Visualization Pipeline for RNA-seq analysis) is an analysis workflow that combines some of the most popular tools to take RNA-seq analysis from raw sequencing data, through alignment and quality control, into downstream differential expression and pathway analysis. VIPER has been created in a modular fashion to allow for the rapid incorporation of new tools to expand the capabilities. This capacity has already been exploited to include very recently developed tools that explore immune infiltrate and T-cell CDR (Complementarity-Determining Regions) reconstruction abilities. The pipeline has been conveniently packaged such that minimal computational skills are required to download and install the dozens of software packages that VIPER uses.

Conclusions

VIPER is a comprehensive solution that performs most standard RNA-seq analyses quickly and effectively with a built-in capacity for customization and expansion.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2139-9) contains supplementary material, which is available to authorized users.

Next-generation sequencing approach for the diagnosis of human diseases: open challenges and new opportunities

The rapid evolution and widespread use of next generation sequencing (NGS) in clinical laboratories has allowed an incredible progress in the genetic diagnostics of several inherited disorders. However, the new technologies have brought new challenges. In this review we consider the important issue of NGS data analysis, as well as the interpretation of unknown genetic variants and the management of the incidental findings. Moreover, we focus the attention on the new professional figure of bioinformatics and the new role of medical geneticists in clinical management of patients. Furthermore, we consider some of the main clinical applications of NGS, taking into consideration that there will be a growing progress in this field in the forthcoming future.

Genetic landscape of hepatitis B virus-associated diffuse large B-cell lymphoma

Hepatitis B virus (HBV) infection is endemic in some parts of Asia, Africa, and South America and remains to be a significant public health problem in these areas. It is known as a leading risk factor for the development of hepatocellular carcinoma, but epidemiological studies have also shown that the infection may increase the incidence of several types of B-cell lymphoma. Here, by characterizing altogether 275 Chinese diffuse large B-cell lymphoma (DLBCL) patients, we showed that patients with concomitant HBV infection (surface antigen positive [HBsAg⁺]) are characterized by a younger age, a more advanced disease stage at diagnosis, and reduced overall survival. Furthermore, by whole-genome/exome sequencing of 96 tumors and the respective peripheral blood samples and targeted sequencing of 179 tumors from these patients, we observed an enhanced rate of mutagenesis and a distinct set of mutation targets in HBsAg⁺ DLBCL genomes, which could be partially explained by the activities of APOBEC and activation-induced cytidine deaminase. By transcriptome analysis, we further showed that the HBV-associated gene expression signature is contributed by the enrichment of genes regulated by BCL6, FOXO1, and ZFP36L1. Finally, by analysis of immunoglobulin heavy chain gene sequences, we showed that an antigen-independent mechanism, rather than a chronic antigenic simulation model, is favored in HBV-related lymphomagenesis. Taken together, we present the first comprehensive genomic and transcriptomic study that suggests a link between HBV infection and B-cell malignancy. The genetic alterations identified in this study may also provide opportunities for development of novel therapeutic strategies.

Identification of novel enriched recurrent chimeric COL7A1-UCN2 in human laryngeal cancer samples using deep sequencing

BACKGROUND

As hybrid RNAs, transcription-induced chimeras (TICs) may have tumor-promoting properties, and some specific chimeras have become important diagnostic markers and therapeutic targets for cancer.

METHODS

We examined 23 paired laryngeal cancer (LC) tissues and adjacent normal mucous membrane tissue samples (ANMMTs). Three of these pairs were used for comparative transcriptomic analysis using high-throughput sequencing. Furthermore, we used real-time polymerase chain reaction (RT-PCR) for further validation in 20 samples. The Kaplan-Meier method and Cox regression model were used for the survival analysis.

RESULTS

We identified 87 tumor-related TICs and found that COL7A1-UCN2 had the highest frequency in LC tissues (13/23; 56.5%), whereas none of the ANMMTs were positive (0/23; p < 0.0001). COL7A1-UCN2, generated via alternative splicing in LC tissue cancer cells, had disrupted coding regions, but it down-regulated the mRNA expression of COL7A1 and UCN2. Both COL7A1 and UCN2 were down-expressed in LC tissues as compared to their paired ANMMTs. The COL7A1:β-actin ratio in COL7A1-UCN2-positive LC samples was significantly lower than that in COL7A1-UCN2-negative samples (p = 0.019). Likewise, the UCN2:β-actin ratio was also decreased (p = 0.21). Furthermore, COL7A1-UCN2 positivity was significantly associated with the overall survival of LC patients (p = 0.032; HR, 13.2 [95%CI, 1.2-149.5]).

CONCLUSION

LC cells were enriched in the recurrent chimera COL7A1-UCN2, which potentially affected cancer stem cell transition, promoted epithelial-mesenchymal transition in LC, and resulted in poorer prognoses.

FuSpot: a web-based tool for visual evaluation of fusion candidates

BACKGROUND

Gene fusions often occur in cancer cells and in some cases are the main driver of oncogenesis. Correct identification of oncogenic gene fusions thus has implications for targeted cancer therapy. Recognition of this potential has led to the development of a myriad of sequencing-based fusion detection tools. However, given the same input, many of these detectors will find different fusion points or claim different sets of supporting data. Furthermore, the rate at which these tools falsely detect fusion events in data varies greatly. This discrepancy between tools underscores the fact that computation algorithms still cannot perfectly evaluate evidence; especially when provided with small amounts of supporting data as is typical in fusion detection. We assert that when evidence is provided in an easily digestible form, humans are more proficient in identifying true positives from false positives.

RESULTS

We have developed a web tool that, given the genomic coordinates of a candidate fusion breakpoint, will extract fusion and non-fusion reads adjacent to the fusion point from partner transcripts, and color code reads by transcript origin and read orientation for ease of intuitive inspection by the user. Fusion partner transcript read alignments are performed using a novel variant of the Smith-Waterman algorithm.

CONCLUSIONS

Combined with dynamic filtering parameters, the visualization provided by our tool introduces a powerful new investigative step that allows researchers to comprehensively evaluate fusion evidence. Additionally, this allows quick identification of false positives that may deceive most fusion detectors, thus eliminating unnecessary gene fusion validation. We apply our visualization tool to publicly available datasets and provide examples of true as well as false positives reported by open source fusion detection tools.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Informatics for cancer immunotherapy

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.

Genomic analysis of atypical fibroxanthoma

Atypical fibroxanthoma (AFX), is a rare type of skin cancer affecting older individuals with sun damaged skin. Since there is limited genomic information about AFX, our study seeks to improve the understanding of AFX through whole-exome and RNA sequencing of 8 matched tumor-normal samples. AFX is a highly mutated malignancy with recurrent mutations in a number of genes, including COL11A1, ERBB4, CSMD3, and FAT1. The majority of mutations identified were UV signature (C>T in dipyrimidines). We observed deletion of chromosomal segments on chr9p and chr13q, including tumor suppressor genes such as KANK1 and CDKN2A, but no gene fusions were found. Gene expression profiling revealed several biological pathways that are upregulated in AFX, including tumor associated macrophage response, GPCR signaling, and epithelial to mesenchymal transition (EMT). To further investigate the presence of EMT in AFX, we conducted a gene expression meta-analysis that incorporated RNA-seq data from dermal fibroblasts and keratinocytes. Ours is the first study to employ high throughput sequencing for molecular profiling of AFX. These data provide valuable insights to inform models of carcinogenesis and additional research towards tumor-directed therapy.

Comparative transcriptome analysis of soybean response to bean pyralid larvae

BACKGROUND

Soybean is one of most important oilseed crop worldwide, however, its production is often limited by many insect pests. Bean pyralid is one of the major soybean leaf-feeding insects in China. To explore the defense mechanisms of soybean resistance to bean pyralid, the comparative transcriptome sequencing was completed between the leaves infested with bean pyralid larvae and no worm of soybean (Gantai-2-2 and Wan82-178) on the Illumina HiSeq™ 2000 platform.

RESULTS

In total, we identified 1744 differentially expressed genes (DEGs) in the leaves of Gantai-2-2 (1064) and Wan82-178 (680) fed by bean pyralid for 48 h, compared to 0 h. Interestingly, 315 DEGs were shared by Gantai-2-2 and Wan82-178, while 749 and 365 DEGs specifically identified in Gantai-2-2 and Wan82-178, respectively. When comparing Gantai-2-2 with Wan82-178, 605 DEGs were identified at 0 h feeding, and 468 DEGs were identified at 48 h feeding. Gene Ontology (GO) annotation analysis revealed that the DEGs were mainly involved in the metabolic process, single-organism process, cellular process, responses to stimulus, catalytic activities and binding. Pathway analysis showed that most of the DEGs were associated with the plant-pathogen interaction, phenylpropanoid biosynthesis, phenylalanine metabolism, flavonoid biosynthesis, peroxisome, plant hormone signal transduction, terpenoid backbone biosynthesis, and so on. Finally, we used qRT-PCR to validate the expression patterns of several genes and the results showed an excellent agreement with deep sequencing.

CONCLUSIONS

According to the comparative transcriptome analysis results and related literature reports, we concluded that the response to bean pyralid feeding might be related to the disturbed functions and metabolism pathways of some key DEGs, such as DEGs involved in the ROS removal system, plant hormone metabolism, intracellular signal transduction pathways, secondary metabolism, transcription factors, biotic and abiotic stresses. We speculated that these genes may have played an important role in synthesizing substances to resist insect attacks in soybean. Our results provide a valuable resource of soybean defense genes that will benefit other studies in this field.

ChimeRScope: a novel alignment-free algorithm for fusion transcript prediction using paired-end RNA-Seq data

The RNA-Seq technology has revolutionized transcriptome characterization not only by accurately quantifying gene expression, but also by the identification of novel transcripts like chimeric fusion transcripts. The ‘fusion’ or ‘chimeric’ transcripts have improved the diagnosis and prognosis of several tumors, and have led to the development of novel therapeutic regimen. The fusion transcript detection is currently accomplished by several software packages, primarily relying on sequence alignment algorithms. The alignment of sequencing reads from fusion transcript loci in cancer genomes can be highly challenging due to the incorrect mapping induced by genomic alterations, thereby limiting the performance of alignment-based fusion transcript detection methods. Here, we developed a novel alignment-free method, ChimeRScope that accurately predicts fusion transcripts based on the gene fingerprint (as k-mers) profiles of the RNA-Seq paired-end reads. Results on published datasets and in-house cancer cell line datasets followed by experimental validations demonstrate that ChimeRScope consistently outperforms other popular methods irrespective of the read lengths and sequencing depth. More importantly, results on our in-house datasets show that ChimeRScope is a better tool that is capable of identifying novel fusion transcripts with potential oncogenic functions. ChimeRScope is accessible as a standalone software at (https://github.com/ChimeRScope/ChimeRScope/wiki) or via the Galaxy web-interface at (https://galaxy.unmc.edu/).

Statistical algorithms improve accuracy of gene fusion detection

Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.

confFuse: High-Confidence Fusion Gene Detection across Tumor Entities

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

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

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

chimeraviz: a tool for visualizing chimeric RNA

SUMMARY

Advances in high-throughput RNA sequencing have enabled more efficient detection of fusion transcripts, but the technology and associated software used for fusion detection from sequencing data often yield a high false discovery rate. Good prioritization of the results is important, and this can be helped by a visualization framework that automatically integrates RNA data with known genomic features. Here we present chimeraviz , a Bioconductor package that automates the creation of chimeric RNA visualizations. The package supports input from nine different fusion-finder tools: deFuse, EricScript, InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION.

AVAILABILITY AND IMPLEMENTATION

chimeraviz is an R package available via Bioconductor ( https://bioconductor.org/packages/release/bioc/html/chimeraviz.html ) under Artistic-2.0. Source code and support is available at GitHub ( https://github.com/stianlagstad/chimeraviz ).

CONTACT

rolf.i.skotheim@rr-research.no.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.