Detecting and visualizing gene fusions

Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Long Non-Coding RNAs as Functional Codes for Oral Cancer: Translational Potential, Progress and Promises

Oral cancer is one of the leading malignant tumors worldwide. Despite the advent of multidisciplinary approaches, the overall prognosis of patients with oral cancer is poor, mainly due to late diagnosis. There is an urgent need to develop valid biomarkers for early detection and effective therapies. Long non-coding RNAs (lncRNAs) are recognized as key elements of gene regulation, with pivotal roles in various physiological and pathological processes, including cancer. Over the past few years, an exponentially growing number of lncRNAs have been identified and linked to tumorigenesis and prognosis outcomes in oral cancer, illustrating their emerging roles in oral cancer progression and the associated signaling pathways. Herein, we aim to summarize the most recent advances made concerning oral cancer-associated lncRNA, and their expression, involvement, and potential clinical impact, reported to date, with a specific focus on the lncRNA-mediated molecular regulation in oncogenic signaling cascades and oral malignant progression, while exploring their potential, and challenges, for clinical applications as biomarkers or therapeutic targets for oral cancer.

INTEGRATE-Vis: a tool for comprehensive gene fusion visualization

Despite the increasing quantity of tools for accurately predicting gene fusion candidates from sequencing data, we are still faced with the critical challenge of visualizing the corresponding gene fusion products to infer their biological consequence (i.e. novel protein and increased gene expression). This is currently accomplished by manually inspecting and inferring the biological consequence of top scoring gene fusion candidates. This labor-intensive process could be made easier by automating the annotation of gene fusion products and generating easily interpretable visualizations. We developed a gene fusion visualization tool, called INTEGRATE-Vis, that generates comprehensive, highly customizable, publication-quality graphics focused on annotating each gene fusion at the transcript- and protein-level and assessing expression within an individual sample or across a patient cohort. INTEGRATE-Vis is the first comprehensive gene fusion visualization tool to help a user infer the potential consequence of a gene fusion event. It has potential utility in both research and clinical settings. INTEGRATE-Vis is available at https://github.com/ChrisMaherLab/INTEGRATE-Vis.

Platelet-derived growth factor receptors (PDGFRs) fusion genes involvement in hematological malignancies

PURPOSE

To investigate oncogenic platelet-derived growth factor receptor(PDGFR) fusion genes involvement in hematological malignancies, the advances in the PDGFR fusion genes diagnosis and development of PDGFR fusions inhibitors.

METHODS

Literature search was done using terms "PDGFR and Fusion" or "PDGFR and Myeloid neoplasm" or 'PDGFR and Lymphoid neoplasm' or "PDGFR Fusion Diagnosis" or "PDGFR Fusion Targets" in databases including PubMed, ASCO.org, and Medscape.

RESULTS

Out of the 36 fusions detected, ETV6(TEL)-PDGFRB and FIP1L1-PDGFRA fusions were frequently detected, 33 are as a result of chromosomal translocation, FIP1L1-PDGFRA and EBF1-PDGFRB are the result of chromosomal deletion and CDK5RAP2- PDGFRΑ is the result of chromosomal insertion. Seven of the 34 rare fusions have detectable reciprocals.

CONCLUSION

RNA aptamers are promising therapeutic target of PDGFRs and diagnostic tools of PDGFRs fusion genes. Also, PDGFRs have variable prospective therapeutic strategies including small molecules, RNA aptamers, and interference therapeutics as well as development of adaptor protein Lnk mimetic drugs.

Identifying fusion transcripts using next generation sequencing

Fusion transcripts (i.e., chimeric RNAs) resulting from gene fusions have been used successfully for cancer diagnosis, prognosis, and therapeutic applications. In addition, many fusion transcripts are found in normal human cell lines and tissues, with some data supporting their role in normal physiology. Besides chromosomal rearrangement, intergenic splicing can generate them. Global identification of fusion transcripts becomes possible with the help of next generation sequencing technology like RNA-Seq. In the past decade, major advancements have been made for chimeric RNA discovery due to the development of advanced sequencing platform and software packages. However, current software tools behave differently in terms of specificity, sensitivity, time, and computational memory usage. Recent benchmarking studies showed that none of the tools are inclusive. The development of high performance (accurate and fast), and user-friendly fusion detection tool/pipeline is still an open quest. In this article, we review the existing software packages for fusion detection. We explain the methods of the tools, and discuss various factors that affect fusion detection. We summarize conclusions drawn from several comparative studies, and then discuss some of the pitfalls of these studies. We also describe the limitations of current tools, and suggest directions for future development. WIREs RNA 2016, 7:811-823. doi: 10.1002/wrna.1382 For further resources related to this article, please visit the WIREs website.

Discovering and understanding oncogenic gene fusions through data intensive computational approaches

Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.

Advanced Applications of RNA Sequencing and Challenges

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

Computational characterisation of cancer molecular profiles derived using next generation sequencing

Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.

ERG oncoprotein inhibits ANXA2 expression and function in prostate cancer

Overexpression of ERG in the prostate epithelium, due to chromosomal translocations, contributes to prostate tumorigenesis. Here, genomic analysis of ERG siRNA-treated prostate cells harboring the endogenous TMPRSS2-ERG fusion revealed an inverse relationship between ERG and Annexin A2 (ANXA2) expression at both the RNA and protein level. ANXA2, a Ca(2+)-dependent and phospholipid-binding protein, is involved in various cellular functions, including maintenance of epithelial cell polarity. Mechanistic studies defined the prostate-specific transcription start site of ANXA2 and showed that the recruitment of ERG to the ANXA2 promoter is required for transcriptional repression by ERG. Knockdown of ERG enhanced the apical localization of ANXA2, the bundling of actin filaments at cell-cell junctions and formation of a polarized epithelial phenotype. ERG overexpression disrupted ANXA2-mediated cell polarity and promoted epithelial-mesenchymal transition (EMT) by inhibiting CDC42 and RHOA, and by activating cofilin. Immunohistochemistry demonstrated a reciprocal relationship of ANXA2 and ERG expression in a large fraction of primary prostate cancer clinical specimens. ANXA2 was absent or markedly reduced in ERG(+) tumors, which were mostly well differentiated. ERG(-) tumors, meanwhile, expressed moderate to high levels of ANXA2, and were either poorly differentiated or displayed subsets of poorly differentiated cells. Taken together, the transcriptional repression of ANXA2 by ERG in prostate epithelial cells plays a critical role in abrogating differentiation, promoting EMT, and in the reciprocal correlation of ERG and ANXA2 expression observed in human prostate cancer.

IMPLICATIONS

ANXA2 is a new component of the ERG network with potential to enhance biologic stratification and therapeutic targeting of ERG-stratified prostate cancers.

Sequencing the AML genome, transcriptome, and epigenome

Leukemia is a disease that develops as a result of changes in the genomes of hematopoietic cells, a fact first appreciated by microscopic examination of the bone marrow cell chromosomes of affected patients. These studies revealed that specific subtypes of leukemia diagnoses correlated with specific chromosomal abnormalities, such as the t(15;17) of acute promyelocytic leukemia and the t(9;22) of chronic myeloid leukemia. Over time, our genomic characterization of hematologic malignancies has moved beyond the resolution of the microscope to that of individual nucleotides in the analysis of whole-genome sequencing (WGS) data using state-of-the-art massively parallel sequencing (MPS) instruments and algorithmic analyses of the resulting data. In addition to studying the genomic sequence alterations that occur in patients' genomes, these same instruments can decode the methylation landscape of the leukemia genome and the resulting RNA expression landscape of the leukemia transcriptome. Broad correlative analyses can then integrate these 3 data types to better inform researchers and clinicians about the biology of individual acute myeloid leukemia (AML) cases, facilitating improvements in care and prognosis.