Integrative analysis of the melanoma transcriptome

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

New insights into lipid and fatty acid metabolism from Raman spectroscopy

One of the challenges facing biology is to understand metabolic events at a single cellular level. While approaches to examine dynamics of protein distribution or report on spatiotemporal location of signalling molecules are well-established, tools for the dissection of metabolism in single living cells are less common. Advances in Raman spectroscopy, such as stimulated Raman scattering (SRS), are beginning to offer new insights into metabolic events in a range of experimental systems, including model organisms and clinical samples, and across a range of disciplines. Despite the power of Raman imaging, it remains a relatively under-used technique to approach biological problems, in part because of the specialised nature of the analysis. To raise the profile of this method, here we consider some key studies which illustrate how Raman spectroscopy has revealed new insights into fatty acid and lipid metabolism across a range of cellular systems. The powerful and non-invasive nature of this approach offers a new suite of tools for biomolecular scientists to address how metabolic events within cells informs on or underpins biological function. We illustrate potential biological applications, discuss some recent advances, and offer a direction of travel for metabolic research in this area.

Oxidative State in Cutaneous Melanoma Progression: A Question of Balance

Reactive oxygen species (ROS) are highly bioactive molecules involved not only in tissue physiology but also in the development of different human conditions, including premature aging, cardiovascular pathologies, neurological and neurodegenerative disorders, inflammatory diseases, and cancer. Among the different human tumors, cutaneous melanoma, the most aggressive and lethal form of skin cancer, is undoubtedly one of the most well-known "ROS-driven tumor", of which one of the main causes is represented by ultraviolet (UV) rays' exposure. Although the role of excessive ROS production in melanoma development in pro-tumorigenic cell fate is now well established, little is known about its contribution to the progression of the melanoma metastatic process. Increasing evidence suggests a dual role of ROS in melanoma progression: excessive ROS production may enhance cellular growth and promote therapeutic resistance, but at the same time, it can also have cytotoxic effects on cancer cells, inducing their apoptosis. In this context, the aim of the present work was to focus on the relationship between cell redox state and the signaling pathways directly involved in the metastatic processes. In addition, oxidative or antioxidant therapeutic strategies for metastatic melanoma were also reviewed and discussed.

From standard therapies to monoclonal antibodies and immune checkpoint inhibitors - an update for reconstructive surgeons on common oncological cases

Malignancies represent a persisting worldwide health burden. Tumor treatment is commonly based on surgical and/or non-surgical therapies. In the recent decade, novel non-surgical treatment strategies involving monoclonal antibodies (mAB) and immune checkpoint inhibitors (ICI) have been successfully incorporated into standard treatment algorithms. Such emerging therapy concepts have demonstrated improved complete remission rates and prolonged progression-free survival compared to conventional chemotherapies. However, the in-toto surgical tumor resection followed by reconstructive surgery oftentimes remains the only curative therapy. Breast cancer (BC), skin cancer (SC), head and neck cancer (HNC), and sarcoma amongst other cancer entities commonly require reconstructive surgery to restore form, aesthetics, and functionality. Understanding the basic principles, strengths, and limitations of mAB and ICI as (neo-) adjuvant therapies and treatment alternatives for resectable or unresectable tumors is paramount for optimized surgical therapy planning. Yet, there is a scarcity of studies that condense the current body of literature on mAB and ICI for BC, SC, HNC, and sarcoma. This knowledge gap may result in suboptimal treatment planning, ultimately impairing patient outcomes. Herein, we aim to summarize the current translational endeavors focusing on mAB and ICI. This line of research may serve as an evidence-based fundament to guide targeted therapy and optimize interdisciplinary anti-cancer strategies.

scNanoHi-C: a single-cell long-read concatemer sequencing method to reveal high-order chromatin structures within individual cells

The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within an individual cell. To address this, herein, we developed scNanoHi-C, which applies Nanopore long-read sequencing to explore genome-wide proximal high-order chromatin contacts within individual cells. We show that scNanoHi-C can reliably and effectively profile 3D chromatin structures and distinguish structure subtypes among individual cells. This method could also be used to detect genomic variations, including copy-number variations and structural variations, as well as to scaffold the de novo assembly of single-cell genomes. Notably, our results suggest that extensive high-order chromatin structures exist in active chromatin regions across the genome, and multiway interactions between enhancers and their target promoters were systematically identified within individual cells. Altogether, scNanoHi-C offers new opportunities to investigate high-order 3D genome structures at the single-cell level.

Prediction of Cervical Lymph Nodes Metastasis in Papillary Thyroid Carcinoma (PTC) Using Nodal Staging Score (NSS)

Background

Cervical lymph node metastasis is commonly seen in papillary thyroid carcinoma. Surgery is the preferred treatment for PTC with cervical lymph node metastasis. There is no alternate ultrasound, neck CT, and thyroglobulin (Tg) methods to assess the occult lymph node metastasis. For moderate-and high-risk PTC, the number of lymph nodes to be dissected should be increased to remove the occult lymph node metastasis.

Objective

This study was designed to develop a nodal staging score model to predict the likelihood of lymph node metastasis in papillary thyroid carcinoma (PTC), and further guide the treatments. Material and Methods. Data were collected from the SEER database. Patients with PTC from 2000 to 2005 were selected. The beta-binomial model was adopted to establish a nodal staging score (NSS)-based model. The NSS-based model was built according to gender, age, extrathyroidal invasion, tumor multifocality, tumor size, and T stage of the patients. A total of 12,431 PTC patients were included in our study. Various types of lymph nodes were examined based on various categories (incidence, risk assessment) to evaluate the results.

Results

5,959 (47.9%) patients in the study were positive and 6,472 (52.1%) were confirmed negative for lymph node metastasis. The corrected incidence of lymph node metastasis was higher than that of direct calculation, regardless of the factors that affected lymph node metastasis. There were significant differences in the OS of PTC patients among the four groups and T stage (p is less than 0.05), indicating that cervical lymph node metastasis would have an impact on the prognosis of patients.

Conclusion

In conclusion, an NSS-based model base on a variety of clinicopathological factors can be used to predict lymph node metastasis. It is important to evaluate the risk of occult lymph node metastasis in the treatment of PTC.. Since, this statistical model can describe the risk of occult lymph node metastasis in patients; therefore, it can be used as basis for decision-making related to the number of lymph nodes that can be dissected in operations.

Potential Biomarker Identification by RNA-seq analysis in Antibiotic-related Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS): a Pilot Study

One of the most severe forms of cutaneous adverse drug reactions is 'drug reaction with eosinophilia and systemic symptoms' (DRESS), hence subsequent avoidance of the causal drug is imperative. However, attribution of drug culpability in DRESS is challenging and standard skin allergy tests are not recommended due to patient safety reasons. Whilst incidence of DRESS is relatively low, between 1:1000 to 1:10,000 drug exposures, antibiotics are a commoner cause of DRESS and absence of confirmatory diagnostic test can result in unnecessary avoidance of efficacious treatment. We therefore sought to identify potential biomarkers for development of a diagnostic test in antibiotic-associated DRESS. Peripheral blood mononuclear cells (PBMCs) from a 'discovery' cohort (n = 5) challenged to causative antibiotic or control were analysed for transcriptomic profile. A panel of genes was then tested in a validation cohort (n = 6) and compared to tolerant controls and other inflammatory conditions which can clinically mimic DRESS. A scoring system to identify presence of drug hypersensitivity was developed based on gene expression alterations of this panel. The DRESS transcriptomic panel identified antibiotic-DRESS cases in a validation cohort but was not altered in other inflammatory conditions. Machine learning or differential expression selection of a biomarker panel consisting of six genes (STAC, GPR183, CD40, CISH, CD4, and CCL8) showed high sensitivity and specificity (100% and 85.7-100% respectively) for identification of the culprit drug in these cohorts of antibiotic-associated DRESS. Further work is required to determine whether the same panel can be repeated for larger cohorts, different medications, and other T cell mediated drug hypersensitivity reactions.

Individualized Proteogenomics Reveals the Mutational Landscape of Melanoma Patients in Response to Immunotherapy

Simple Summary

Melanoma is the most aggressive form of skin cancer, with a rapidly increasing incidence rate. Due to ineffective treatment options in the late stage melanoma, patients have an overall poor prognosis. Over the last decades, the role of the immune system in the control of tumor progression has been established and immune checkpoint inhibitors (ICi) have shown remarkable clinical activity. While current trials suggest durable responses in patient under ICi therapy, there is increasing evidence pointing towards existence of innate and acquired resistance to ICi therapy; and it is now clear that personalized medicine will be critical for effective patient therapy. Proteogenomics is a powerful tool to study the mode of action of disease-associated mutations at the genome, transcriptome, proteome and PTM level. Here, we applied a proteogenomic workflow to study melanoma samples from human tumors. Such workflow may be applicable to other patient-derived samples and different cancer types.

Abstract

Immune checkpoint inhibitors are used to restore or augment antitumor immune responses and show great promise in the treatment of melanoma and other types of cancers. However, only a small percentage of patients are fully responsive to immune checkpoint inhibition, mostly due to tumor heterogeneity and primary resistance to therapy. Both of these features are largely driven by the accumulation of patient-specific mutations, pointing to the need for personalized approaches in diagnostics and immunotherapy. Proteogenomics integrates patient-specific genomic and proteomic data to study cancer development, tumor heterogeneity and resistance mechanisms. Using this approach, we characterized the mutational landscape of four clinical melanoma patients. This enabled the quantification of hundreds of sample-specific amino acid variants, among them many that were previously not reported in melanoma. Changes in abundance at the protein and phosphorylation site levels revealed patient-specific over-represented pathways, notably linked to melanoma development (MAPK1 activation) or immunotherapy (NLRP1 inflammasome). Personalized data integration resulted in the prediction of protein drug targets, such as the drugs vandetanib and bosutinib, which were experimentally validated and led to a reduction in the viability of tumor cells. Our study emphasizes the potential of proteogenomic approaches to study personalized mutational landscapes, signaling networks and therapy options.

Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding

Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell–cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell–cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.

SMOOTH-seq: single-cell genome sequencing of human cells on a third-generation sequencing platform

There is no effective way to detect structure variations (SVs) and extra-chromosomal circular DNAs (ecDNAs) at single-cell whole-genome level. Here, we develop a novel third-generation sequencing platform-based single-cell whole-genome sequencing (scWGS) method named SMOOTH-seq (single-molecule real-time sequencing of long fragments amplified through transposon insertion). We evaluate the method for detecting CNVs, SVs, and SNVs in human cancer cell lines and a colorectal cancer sample and show that SMOOTH-seq reliably and effectively detects SVs and ecDNAs in individual cells, but shows relatively limited accuracy in detection of CNVs and SNVs. SMOOTH-seq opens a new chapter in scWGS as it generates high fidelity reads of kilobases long.

Robustness of differential gene expression analysis of RNA-seq

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a ’population’ of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.

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.

C1orf61 promotes hepatocellular carcinoma metastasis and increases the therapeutic response to sorafenib

C1orf61 is a specific transcriptional activator that is highly up-regulated during weeks 4-9 of human embryogenesis, the period in which most organs develop. We have previously demonstrated that C1orf61 acts as a tumor activator in human hepatocellular carcinoma (HCC) tumorigenesis and metastasis. However, the underlying molecular mechanisms of tumor initiation and progression in HCC remain obscure. In this study, we demonstrated that the pattern of C1orf61 expression was closely correlated with metastasis in liver cancer cells. Gene expression profiling analysis indicated that C1orf61 regulated diverse genes related to cell growth, migration, invasion and epithelial-mesenchymal transition (EMT). Results showed that C1orf61 promotes hepatocellular carcinoma metastasis by inducing cellular EMT in vivo and in vitro. Moreover, C1orf61-induced cellular EMT and migration are involved in the activation of the STAT3 and Akt cascade pathways. In addition, C1orf61 expression improved the efficacy of the anticancer therapy sorafenib in HCC patients. For the first time, we report a regulatory pathway by which C1orf61 promoted cancer cell metastasis and regulated the therapeutic response to sorafenib. These findings increased our understanding of the molecular events that regulate metastasis and treatment in HCC.

Selecting Classification Methods for Small Samples of Next-Generation Sequencing Data

Next-generation sequencing has emerged as an essential technology for the quantitative analysis of gene expression. In medical research, RNA sequencing (RNA-seq) data are commonly used to identify which type of disease a patient has. Because of the discrete nature of RNA-seq data, the existing statistical methods that have been developed for microarray data cannot be directly applied to RNA-seq data. Existing statistical methods usually model RNA-seq data by a discrete distribution, such as the Poisson, the negative binomial, or the mixture distribution with a point mass at zero and a Poisson distribution to further allow for data with an excess of zeros. Consequently, analytic tools corresponding to the above three discrete distributions have been developed: Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). However, it is unclear what the real distributions would be for these classifications when applied to a new and real dataset. Considering that count datasets are frequently characterized by excess zeros and overdispersion, this paper extends the existing distribution to a mixture distribution with a point mass at zero and a negative binomial distribution and proposes a zero-inflated negative binomial logistic discriminant analysis (ZINBLDA) for classification. More importantly, we compare the above four classification methods from the perspective of model parameters, as an understanding of parameters is necessary for selecting the optimal method for RNA-seq data. Furthermore, we determine that the above four methods could transform into each other in some cases. Using simulation studies, we compare and evaluate the performance of these classification methods in a wide range of settings, and we also present a decision tree model created to help us select the optimal classifier for a new RNA-seq dataset. The results of the two real datasets coincide with the theory and simulation analysis results. The methods used in this work are implemented in the open-scource R scripts, with a source code freely available at https://github.com/FocusPaka/ZINBLDA.

Visualizing Subcellular Enrichment of Glycogen in Live Cancer Cells by Stimulated Raman Scattering

Glycogen, a branched glucose polymer, helps regulate glucose homeostasis through immediate storage and release of glucose. Reprogramming of glycogen metabolism has recently been suggested to play an emerging role in cancer progression and tumorigenesis. However, regulation of metabolic rewiring for glycogen synthesis and breakdown in cancer cells remains less understood. Despite the availability of various glycogen detection methods, selective visualization of glycogen in living cells with high spatial resolution has proven to be highly challenging. Here, we present an optical imaging strategy to visualize glycogen in live cancer cells with minimal perturbation by combining stimulated Raman scattering microscopy with metabolic incorporation of deuterium-labeled glucose. We revealed the subcellular enrichment of glycogen in live cancer cells and achieved specific glycogen mapping through distinct spectral identification. Using this method, different glycogen metabolic phenotypes were characterized in a series of patient-derived BRAF mutant melanoma cell lines. Our results indicate that cell lines manifesting high glycogen storage level showed increased tolerance to glucose deficiency among the studied melanoma phenotypes. This method opens up the possibility for noninvasive study of complex glycogen metabolism at subcellular resolution and may help reveal new features of glycogen regulation in cancer systems.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.

Novel functional proteins coded by the human genome discovered in metastases of melanoma patients

In the advanced stages, malignant melanoma (MM) has a very poor prognosis. Due to tremendous efforts in cancer research over the last 10 years, and the introduction of novel therapies such as targeted therapies and immunomodulators, the rather dark horizon of the median survival has dramatically changed from under 1 year to several years. With the advent of proteomics, deep-mining studies can reach low-abundant expression levels. The complexity of the proteome, however, still surpasses the dynamic range capabilities of current analytical techniques. Consequently, many predicted protein products with potential biological functions have not yet been verified in experimental proteomic data. This category of 'missing proteins' (MP) is comprised of all proteins that have been predicted but are currently unverified. As part of the initiative launched in 2016 in the USA, the European Cancer Moonshot Center has performed numerous deep proteomics analyses on samples from MM patients. In this study, nine MPs were clearly identified by mass spectrometry in MM metastases. Some MPs significantly correlated with proteins that possess identical PFAM structural domains; and other MPs were significantly associated with cancer-related proteins. This is the first study to our knowledge, where unknown and novel proteins have been annotated in metastatic melanoma tumour tissue.

From Tank to Treatment: Modeling Melanoma in Zebrafish

Melanoma is the deadliest form of skin cancer and one of few cancers with a growing incidence. A thorough understanding of its pathogenesis is fundamental to developing new strategies to combat mortality and morbidity. Zebrafish—due in large part to their tractable genetics, conserved pathways, and optical properties—have emerged as an excellent system to model melanoma. Zebrafish have been used to study melanoma from a single tumor initiating cell, through metastasis, remission, and finally into relapse. In this review, we examine seminal zebrafish studies that have advanced our understanding of melanoma.

Dietary inclusion of plant ingredients induces epigenetic changes in the intestine of zebrafish

Epigenetic modifications, such as DNA methylation, can be regulated by nutrition and dietary factors. There has been a large increase in the use of sustainable plant-based protein sources in fish feed due to limitations of fishmeal resources, which are needed to sustain a rapidly growing aquaculture industry. With this major transition from marine ingredients to plant-based diets, fish are abruptly introduced to changes in dietary composition and exposed to a variety of phytochemicals, some of which known to cause epigenetic changes in mammals. However, the effect of plant ingredients on the epigenome of fish is barely understood. In the present study, the nutriepigenomic effects of the addition of pea, soy, and wheat gluten protein concentrate to aquafeeds were investigated using zebrafish as a model. A genome-wide analysis of DNA methylation patterns was performed by reduced representation bisulphite sequencing to examine global epigenetic alterations in the mid intestine after a 42-day feeding trial. We found that inclusion of 30% of wheat gluten, pea and soy protein concentrate in the diet induced epigenetic changes in the mid intestine of zebrafish. A large number of genes and intergenic regions were differentially methylated with plant-based diets. The genes concerned were related to immunity, NF-κB system, ubiquitin-proteasome pathway, MAPK pathway, and the antioxidant defence system. Epigenetic regulation of several biological processes, including neurogenesis, cell adhesion, response to stress and immunity was also observed. Ultimately, the observed epigenetic changes may enable zebrafish to rapidly regulate inflammation and maintain intestinal homoeostasis when fed plant protein-based diets.

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.

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/.

Active repurposing of drug candidates for melanoma based on GWAS, PheWAS and a wide range of omics data

Background

Drug repurposing is a swift, safe, and cheap drug discovery method. Melanoma disorders present low survival and high mortality rates and are challenging to diagnose and treat. Moreover, there is a high volume of worldwide investigations that are attempting to find melanoma-related genes of influence, which can be identified as responsive targets for reliable treatment.

Method

In this study, we used a wide range of data analyses to analyze over 1100 genes and proteins of influence with respect to cutaneous malignant melanoma. Our analysis included various investigational results from genome- and phenome-wide association studies (GWAS and PheWAS, respectively), biomedical, transcriptomic, and metabolomic datasets. We then researched the DrugBank for potential melanoma targets from the selected list. We excluded known melanoma targets to obtain a list of druggable proteins. We performed a precise analysis of the drugs’ pathogenesis and checked the expression profiles of the selected drugs having high associations with known anti-melanoma drugs.

Result

We found 35 drugs that interacted with 20 unique targets. These drugs appear to have high melanoma treatment potentials. We confirmed our results with previous studies and found supporting references for 30 of these drugs.

In conclusion, this investigation can be applied to various diseases for the efficient and economical repurposing of various drug compounds. For further validation, the results may be applicable for in vivo tests and clinical trials.

Chromosomal Localization of Candidate Genes for Fiber Growth and Color in Alpaca (Vicugna pacos)

The alpaca (Vicugna pacos) is an economically important and cultural signature species in Peru. Thus, molecular genomic information about the genes underlying the traits of interest, such as fiber properties and color, is critical for improved breeding and management schemes. Current knowledge about the alpaca genome, particularly the chromosomal location of such genes of interest is limited and lags far behind other livestock species. The main objective of this work was to localize alpaca candidate genes for fiber growth and color using fluorescence in situ hybridization (FISH). We report the mapping of candidate genes for fiber growth COL1A1, CTNNB1, DAB2IP, KRT15, KRTAP13-1, and TNFSF12 to chromosomes 16, 17, 4, 16, 1, and 16, respectively. Likewise, we report the mapping of candidate genes for fiber color ALX3, NCOA6, SOX9, ZIC1, and ZIC5 to chromosomes 9, 19, 16, 1, and 14, respectively. In addition, since KRT15 clusters with five other keratin genes (KRT31, KRT13, KRT9, KRT14, and KRT16) in scaffold 450 (Vic.Pac 2.0.2), the entire gene cluster was assigned to chromosome 16. Similarly, mapping NCOA6 to chromosome 19, anchored scaffold 34 with 8 genes, viz., RALY, EIF2S2, XPOTP1, ASIP, AHCY, ITCH, PIGU, and GGT7 to chromosome 19. These results are concordant with known conserved synteny blocks between camelids and humans, cattle and pigs.

RNA-Seq analysis revealed genes associated with UV-induced cell necrosis through MAPK/TNF-α pathways in human dermal fibroblast cells as an inducer of premature photoaging

Exposing the skin to solar UV radiation induces cascades of signaling pathways and biological alterations such as redox imbalance, suppression of antioxidant genes and programmed cell death. Therefore, the aim of this study was to use RNA-Seq to unravel the effects of UV radiation on Normal Human Adult Fibroblast cells (NHDF). Cells were exposed to UV (20 mJ/cm2 for 3 mins) and incubated for 24 h. Total mRNA from the cells generated libraries of 72,080,648 and 40,750,939 raw reads from UV-treated and control cells respectively. Of the differentially expressed genes (DEGs) produced 2,007 were up-regulated and 2,791 were down-regulated (fold change ≥2, p < 0.05). The expression of 4 genes was validated with RT-qPCR. Chemokine signaling pathways in cancer were significantly activated and antioxidant genes were down-regulated. This study applied Next Generation Sequencing technology to reveal the genes and pathways involved in UV-induced human dermal fibroblast cells necrosis.

The Landscape of Circular RNA in Cancer

Circular RNAs (circRNAs) are an intriguing class of RNA due to their covalently closed structure, high stability, and implicated roles in gene regulation. Here, we used an exome capture RNA sequencing protocol to detect and characterize circRNAs across >2,000 cancer samples. When compared against Ribo-Zero and RNase R, capture sequencing significantly enhanced the enrichment of circRNAs and preserved accurate circular-to-linear ratios. Using capture sequencing, we built the most comprehensive catalog of circRNA species to date: MiOncoCirc, the first database to be composed primarily of circRNAs directly detected in tumor tissues. Using MiOncoCirc, we identified candidate circRNAs to serve as biomarkers for prostate cancer and were able to detect circRNAs in urine. We further detected a novel class of circular transcripts, termed read-through circRNAs, that involved exons originating from different genes. MiOncoCirc will serve as a valuable resource for the development of circRNAs as diagnostic or therapeutic targets across cancer types.

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.

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information

Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation.

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.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer

Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.

Single-Cell RNA-Sequencing in Glioma

PURPOSE OF REVIEW

In this review, we seek to summarize the literature concerning the use of single-cell RNA-sequencing for CNS gliomas.

RECENT FINDINGS

Single-cell analysis has revealed complex tumor heterogeneity, subpopulations of proliferating stem-like cells and expanded our view of tumor microenvironment influence in the disease process. Although bulk RNA-sequencing has guided our initial understanding of glioma genetics, this method does not accurately define the heterogeneous subpopulations found within these tumors. Single-cell techniques have appealing applications in cancer research, as diverse cell types and the tumor microenvironment have important implications in therapy. High cost and difficult protocols prevent widespread use of single-cell RNA-sequencing; however, continued innovation will improve accessibility and expand our of knowledge gliomas.

Intergenic disease-associated regions are abundant in novel transcripts

BACKGROUND

Genotyping of large populations through genome-wide association studies (GWAS) has successfully identified many genomic variants associated with traits or disease risk. Unexpectedly, a large proportion of GWAS single nucleotide polymorphisms (SNPs) and associated haplotype blocks are in intronic and intergenic regions, hindering their functional evaluation. While some of these risk-susceptibility regions encompass cis-regulatory sites, their transcriptional potential has never been systematically explored.

RESULTS

To detect rare tissue-specific expression, we employed the transcript-enrichment method CaptureSeq on 21 human tissues to identify 1775 multi-exonic transcripts from 561 intronic and intergenic haploblocks associated with 392 traits and diseases, covering 73.9 Mb (2.2%) of the human genome. We show that a large proportion (85%) of disease-associated haploblocks express novel multi-exonic non-coding transcripts that are tissue-specific and enriched for GWAS SNPs as well as epigenetic markers of active transcription and enhancer activity. Similarly, we captured transcriptomes from 13 melanomas, targeting nine melanoma-associated haploblocks, and characterized 31 novel melanoma-specific transcripts that include fusion proteins, novel exons and non-coding RNAs, one-third of which showed allelically imbalanced expression.

CONCLUSIONS

This resource of previously unreported transcripts in disease-associated regions ( http://gwas-captureseq.dingerlab.org ) should provide an important starting point for the translational community in search of novel biomarkers, disease mechanisms, and drug targets.

Novel prognostic marker PRMT1 regulates cell growth via downregulation of CDKN1A in HCC

Hepatocellular carcinoma (HCC) is a major type of liver cancer caused by the hepatitis B and C viruses, alcohol and exposure to aflatoxin. For HCC treatment, anticancer drugs have been widely used, but drug resistance in advanced HCC is an important problem, resulting in a continuous need for novel therapeutic targets. Therefore, in this study, we established a screening pipeline based on RNA-seq to screen novel therapeutic/prognostic targets in HCC and identified PRMT1 (Protein Arginine Methyltransferase 1). In the prognostic analysis, the overexpression of PRMT1 was clearly associated with poor prognosis in a number of HCC patient cohorts. Moreover, after PRMT1 knockdown, HCC cell lines exhibited cell growth and spheroid formation suppression, an increase in Sub-G1 cells by FACS analysis, and enrichment of the cell cycle pathway via functional enrichment analysis. With these results, we demonstrated that PRMT1 could be a novel prognostic marker and therapeutic target for HCC therapy.

Ensemble Modeling Approach Targeting Heterogeneous RNA-Seq data: Application to Melanoma Pseudogenes

We studied the transcriptome landscape of skin cutaneous melanoma (SKCM) using 103 primary tumor samples from TCGA, and measured the expression levels of both protein coding genes and non-coding RNAs (ncRNAs). In particular, we emphasized pseudogenes potentially relevant to this cancer. While cataloguing the profiles based on the known biotypes, all the employed RNA-Seq methods generated just a small consensus of significant biotypes. We thus designed an approach to reconcile the profiles from all methods following a simple strategy: we selected genes that were confirmed as differentially expressed by the ensemble predictions obtained in a regression model. The main advantages of this approach are: 1) Selection of a high-confidence gene set identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-driven outcomes to predict an averaged profile; 3) Method-specific assessment of prediction power and significance. Furthermore, the approach can be generalized to any biological system for which noisy RNA-Seq profiles are computed. As our analyses concerned bio-annotations of both high-quality protein coding genes and ncRNAs, we considered the associations between pseudogenes and parental genes (targets). Among the candidate targets that were validated, we identified PINK1, which is studied in patients with Parkinson and cancer (especially melanoma).

Advances in chromosomal translocations and fusion genes in sarcomas and potential therapeutic applications

Chromosomal translocations and fusion genes are very common in human cancer especially in subtypes of sarcomas, such as rhabdomyosarcoma, Ewing's sarcoma, synovial sarcoma and liposarcoma. The discovery of novel chromosomal translocations and fusion genes in different tumors are due to the advancement of next-generation sequencing (NGS) technologies such as whole genome sequencing. Recently, many novel chromosomal translocations and gene fusions have been identified in different types of sarcoma through NGS approaches. In addition to previously known sarcoma fusion genes, these novel specific fusion genes and associated molecular events represent important targets for novel therapeutic approaches in the treatment of sarcomas. This review focuses on recent advances in chromosomal translocations and fusion genes in sarcomas and their potential therapeutic applications in the treatment of sarcomas.

Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma

We postulated that multicentric glioblastoma (GBM) represents more invasiveness form than solitary GBM and has their own genomic characteristics. From May 2004 to June 2010 we retrospectively identified 51 treatment-naïve GBM patients with available clinical information from the Samsung Medical Center data registry. Multicentricity of the tumor was defined as the presence of multiple foci on the T1 contrast enhancement of MR images or having high signal for multiple lesions without contiguity of each other on the FLAIR image. Kaplan-Meier survival analysis demonstrated that multicentric GBM had worse prognosis than solitary GBM (median, 16.03 vs. 20.57 months, p < 0.05). Copy number variation (CNV) analysis revealed there was an increase in 11 regions, and a decrease in 17 regions, in the multicentric GBM. Gene expression profiling identified 738 genes to be increased and 623 genes to be decreased in the multicentric radiophenotype (p < 0.001). Integration of the CNV and expression datasets identified twelve representative genes: CPM, LANCL2, LAMP1, GAS6, DCUN1D2, CDK4, AGAP2, TSPAN33, PDLIM1, CLDN12, and GTPBP10 having high correlation across CNV, gene expression and patient outcome. Network and enrichment analyses showed that the multicentric tumor had elevated fibrotic signaling pathways compared with a more proliferative and mitogenic signal in the solitary tumors. Noninvasive radiological imaging together with integrative radiogenomic analysis can provide an important tool in helping to advance personalized therapy for the more clinically aggressive subset of GBM.

Metastatic melanoma moves on: translational science in the era of personalized medicine

Progress in understanding and treating metastatic melanoma is the result of decades of basic and translational research as well as the development of better in vitro tools for modeling the disease. Here, we review the latest therapeutic options for metastatic melanoma and the known genetic and non-genetic mechanisms of resistance to these therapies, as well as the in vitro toolbox that has provided the greatest insights into melanoma progression. These include next-generation sequencing technologies and more complex 2D and 3D cell culture models to functionally test the data generated by genomics approaches. The combination of hypothesis generating and hypothesis testing paradigms reviewed here will be the foundation for the next phase of metastatic melanoma therapies in the coming years.

RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers

Identification of appropriate reference genes (RGs) is critical to accurate data interpretation in quantitative real-time PCR (qPCR) experiments. In this study, we have utilised next generation RNA sequencing (RNA-seq) to analyse the transcriptome of a panel of non-melanoma skin cancer lesions, identifying genes that are consistently expressed across all samples. Genes encoding ribosomal proteins were amongst the most stable in this dataset. Validation of this RNA-seq data was examined using qPCR to confirm the suitability of a set of highly stable genes for use as qPCR RGs. These genes will provide a valuable resource for the normalisation of qPCR data for the analysis of non-melanoma skin cancer.

Transcriptome Analysis of Canine Cutaneous Melanoma and Melanocytoma Reveals a Modulation of Genes Regulating Extracellular Matrix Metabolism and Cell Cycle

Interactions between tumor cells and tumor microenvironment are considered critical in carcinogenesis, tumor invasion and metastasis. To examine transcriptome changes and to explore the relationship with tumor microenvironment in canine cutaneous melanocytoma and melanoma, we extracted RNA from formalin-fixed, paraffin-embedded (FFPE) specimens and analyzed them by means of RNA-seq for transcriptional analysis. Melanocytoma and melanoma samples were compared to detect differential gene expressions and significant enriched pathways were explored to reveal functional relations between differentially expressed genes. The study demonstrated a differential expression of 60 genes in melanomas compared to melanocytomas. The differentially expressed genes cluster in the extracellular matrix-receptor interaction, protein digestion and absorption, focal adhesion and PI3K-Akt (phosphoinositide 3-kinase/protein kinase B) signaling pathways. Genes encoding for several collagen proteins were more commonly differentially expressed. Results of the RNA-seq were validated by qRT-PCR and protein expression of some target molecules was investigated by means of immunohistochemistry. We hypothesize that the developing melanoma actively promotes collagen metabolism and extracellular matrix remodeling as well as enhancing cell proliferation and survival contributing to disease progression and metastasis. In this study, we also detected unidentified genes in human melanoma expression studies and uncover new candidate drug targets for further testing in canine melanoma.

The novel fusion transcript NR5A2-KLHL29FT is generated by an insertion at the KLHL29 locus

BACKGROUND

Novel fusion transcripts (FTs) caused by chromosomal rearrangement are common factors in the development of cancers. In the current study, the authors used massively parallel RNA sequencing to identify new FTs in colon cancers.

METHODS

RNA sequencing (RNA-Seq) and TopHat-Fusion were used to identify new FTs in colon cancers. The authors then investigated whether the novel FT nuclear receptor subfamily 5, group A, member 2 (NR5A2)-Kelch-like family member 29 FT (KLHL29FT) was transcribed from a genomic chromosomal rearrangement. Next, the expression of NR5A2-KLHL29FT was measured by quantitative real-time polymerase chain reaction in colon cancers and matched corresponding normal epithelia.

RESULTS

The authors identified the FT NR5A2-KLHL29FT in normal and cancerous epithelia. While investigating this transcript, it was unexpectedly found that it was due to an uncharacterized polymorphic germline insertion of the NR5A2 sequence from chromosome 1 into the KLHL29 locus at chromosome 2, rather than a chromosomal rearrangement. This germline insertion, which occurred at a population frequency of 0.40, appeared to bear no relationship to cancer development. Moreover, expression of NR5A2-KLHL29FT was validated in RNA specimens from samples with insertions of NR5A2 at the KLHL29 gene locus, but not from samples without this insertion. It is interesting to note that NR5A2-KLH29FT expression levels were significantly lower in colon cancers than in matched normal colonic epithelia (P =.029), suggesting the potential participation of NR5A2-KLHL29FT in the origin or progression of this tumor type.

CONCLUSIONS

NR5A2-KLHL29FT was generated from a polymorphism insertion of the NR5A2 sequence into the KLHL29 locus. NR5A2-KLHL29FT may influence the origin or progression of colon cancer. Moreover, researchers should be aware that similar FTs may occur due to transchromosomal insertions that are not correctly annotated in genome databases, especially with current assembly algorithms. Cancer 2017;123:1507-1515. © 2017 American Cancer Society.

The novel fusion transcript NR5A2-KLHL29FT is generated by an insertion at the KLHL29 locus

BACKGROUND

Novel fusion transcripts (FTs) caused by chromosomal rearrangement are common factors in the development of cancers. In the current study, the authors used massively parallel RNA sequencing to identify new FTs in colon cancers.

METHODS

RNA sequencing (RNA-Seq) and TopHat-Fusion were used to identify new FTs in colon cancers. The authors then investigated whether the novel FT nuclear receptor subfamily 5, group A, member 2 (NR5A2)-Kelch-like family member 29 FT (KLHL29FT) was transcribed from a genomic chromosomal rearrangement. Next, the expression of NR5A2-KLHL29FT was measured by quantitative real-time polymerase chain reaction in colon cancers and matched corresponding normal epithelia.

RESULTS

The authors identified the FT NR5A2-KLHL29FT in normal and cancerous epithelia. While investigating this transcript, it was unexpectedly found that it was due to an uncharacterized polymorphic germline insertion of the NR5A2 sequence from chromosome 1 into the KLHL29 locus at chromosome 2, rather than a chromosomal rearrangement. This germline insertion, which occurred at a population frequency of 0.40, appeared to bear no relationship to cancer development. Moreover, expression of NR5A2-KLHL29FT was validated in RNA specimens from samples with insertions of NR5A2 at the KLHL29 gene locus, but not from samples without this insertion. It is interesting to note that NR5A2-KLH29FT expression levels were significantly lower in colon cancers than in matched normal colonic epithelia (P =.029), suggesting the potential participation of NR5A2-KLHL29FT in the origin or progression of this tumor type.

CONCLUSIONS

NR5A2-KLHL29FT was generated from a polymorphism insertion of the NR5A2 sequence into the KLHL29 locus. NR5A2-KLHL29FT may influence the origin or progression of colon cancer. Moreover, researchers should be aware that similar FTs may occur due to transchromosomal insertions that are not correctly annotated in genome databases, especially with current assembly algorithms. Cancer 2017;123:1507-1515. © 2017 American Cancer Society.

ChimPipe: accurate detection of fusion genes and transcription-induced chimeras from RNA-seq data

Background

Chimeric transcripts are commonly defined as transcripts linking two or more different genes in the genome, and can be explained by various biological mechanisms such as genomic rearrangement, read-through or trans-splicing, but also by technical or biological artefacts. Several studies have shown their importance in cancer, cell pluripotency and motility. Many programs have recently been developed to identify chimeras from Illumina RNA-seq data (mostly fusion genes in cancer). However outputs of different programs on the same dataset can be widely inconsistent, and tend to include many false positives. Other issues relate to simulated datasets restricted to fusion genes, real datasets with limited numbers of validated cases, result inconsistencies between simulated and real datasets, and gene rather than junction level assessment.

Results

Here we present ChimPipe, a modular and easy-to-use method to reliably identify fusion genes and transcription-induced chimeras from paired-end Illumina RNA-seq data. We have also produced realistic simulated datasets for three different read lengths, and enhanced two gold-standard cancer datasets by associating exact junction points to validated gene fusions. Benchmarking ChimPipe together with four other state-of-the-art tools on this data showed ChimPipe to be the top program at identifying exact junction coordinates for both kinds of datasets, and the one showing the best trade-off between sensitivity and precision. Applied to 106 ENCODE human RNA-seq datasets, ChimPipe identified 137 high confidence chimeras connecting the protein coding sequence of their parent genes. In subsequent experiments, three out of four predicted chimeras, two of which recurrently expressed in a large majority of the samples, could be validated. Cloning and sequencing of the three cases revealed several new chimeric transcript structures, 3 of which with the potential to encode a chimeric protein for which we hypothesized a new role. Applying ChimPipe to human and mouse ENCODE RNA-seq data led to the identification of 131 recurrent chimeras common to both species, and therefore potentially conserved.

Conclusions

ChimPipe combines discordant paired-end reads and split-reads to detect any kind of chimeras, including those originating from polymerase read-through, and shows an excellent trade-off between sensitivity and precision. The chimeras found by ChimPipe can be validated in-vitro with high accuracy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3404-9) contains supplementary material, which is available to authorized users.

Bioinformatics and Drug Discovery

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data

Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.