scholarly journals Found in Transcription: Gene fusions arise through defects in RNA processing in the absence of chromosomal rearrangements

2019 ◽  
Author(s):  
Yue Jiang ◽  
Michael J. Apostolides ◽  
Mia Husić ◽  
Robert Siddaway ◽  
Man Yu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Chromosomal Rearrangements ◽  
Diffuse Intrinsic Pontine Glioma ◽  
Normal Brain ◽  
Sequencing Analysis ◽  
Tissue Samples ◽  
Complete Representation ◽  
Normal Tissues ◽  
Gene Dysregulation ◽  
Definition Of

AbstractRecent advancements in high throughput sequencing analysis have enabled the characterization of cancer-driving fusions, improving our understanding of cancer development. Most fusion calling methods, however, examine either RNA or DNA information alone and are limited to a rigid definition of what constitutes a fusion. For this study we developed a pipeline that incorporates several fusion calling methods and considers both RNA and DNA to capture a more complete representation of the tumour fusion landscape. Interestingly, most of the fusions we identified were specific to RNA, with no evidence of corresponding genomic restructuring. Further, while the average total number of fusions in tumour and normal brain tissue samples is comparable, their overall fusion profiles vary significantly. Tumours have an over-representation of fusions occurring between coding genes, whereas fusions involving intergenic or non-coding regions comprised the vast majority of those in normals. Tumours were also more abundant in unique, sample-specific fusions compared to normals, though several fusions exhibited strong recurrence in the tumour type examined (diffuse intrinsic pontine glioma; DIPG) and were absent from both normal tissues and other cancers. Intriguingly, tumours also show broad up- or down-regulation of spliceosomal gene expression, which significantly correlates with fusion number (p=0.007). Our results show that RNA-specific fusions are abundant in both tumour and normal tissue and are associated with spliceosomal gene dysregulation. RNA-specific fusions should be considered as a potential mechanism that may contribute to cancer formation initiation and maintenance alongside more traditional structural events.

RNA sequencing analysis for profiling activation of cancer-associated molecular pathways.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13032-e13032 ◽  
Author(s):  
Anton Buzdin ◽  
Andrew Garazha ◽  
Maxim Sorokin ◽  
Alex Glusker ◽  
Alexey Aleshin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Original Data ◽  
Tissue Expression ◽  
Molecular Pathways ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Healthy Human ◽  
Tissue Samples ◽  
Normal Tissues

e13032 Background: Intracellular molecular pathways (IMPs) control all major events in the living cell. They are considered hotspots in contemporary oncology because knowledge of IMPs activation is essential for understanding mechanisms of molecular pathogenesis in oncology. Profiling IMPs requires RNA-seq data for tumors and for a collection of reference normal tissues. However, there is a shortage now in such profiles for normal tissues from healthy human donors, uniformly profiled in a single series of experiments. Access to the largest dataset of normal profiles GTEx is only partly available through the dbGaP. In TCGA database, norms are adjacent to surgically removed tumors and may be affected by tumor-linked growth factors, inflammation and altered vascularization. ENCODE datasets were for the autopsies of normal tissues, but they can’t form statistically significant reference groups. Methods: Tissue samples representing 20 organs were taken from post-mortal human healthy donors killed in road accidents no later than 36 hours after death, blood samples were taken from healthy volunteers. Gene expression was profiled in RNA-seq experiments using the same reagents, equipment and protocols. Bioinformatic algorithms for IMP analysis were developed and validated using experimental and public gene expression datasets. Results: From original sequencing data we constructed the biggest fully open reference expression database of normal human tissues including 465 profiles termed Oncobox Atlas of Normal Tissue Expression (ANTE, original data: GSE120795). We next developed a method termed Oncobox for interrogating activation of IMPs in human cancers. It includes modules of expression data harmonization and comparison and an algorithm for automatic annotation of molecular pathways. The Oncobox system enables accurate scoring of thousands molecular pathways using RNA-seq data. Oncobox pathway analysis is also applicable for quantitative proteomics and microRNA data in oncology. Conclusions: The Oncobox system can be used for a plethora of applications in cancer research including finding differentially regulated genes and IMPs, and for discovery of new pathway-related diagnostic and prognostic biomarkers.

scholarly journals How Do We Define the Genomic Landscape?

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. SCI-13-SCI-13
Author(s):  
Sandeep S. Dave
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Conflicts Of Interest ◽  
Genomic Analysis ◽  
Future Directions ◽  
Tissue Samples ◽  
Genomic Landscape ◽  
Fresh Frozen ◽  
Definition Of ◽  
Formalin Fixed

High throughput sequencing is a revolutionary technology for the definition of the genomic features of tumors. This talk will provide a review of the relevant methodologies for non-experts in the field. The presentation will include a discussion of how high throughput sequencing is performed, its relative strengths and weaknesses, and how it is applicable to formalin-fixed and fresh/frozen tissue samples. The talk will also describe future directions in the genomic analysis of tumors. Disclosures No relevant conflicts of interest to declare.

scholarly journals Data analysis of high-throughput sequencing and microarray to identify key signatures of microribonucleic acids in glioblastoma

2021 ◽  
Vol 8 (3) ◽  
pp. 21-33
Author(s):  
A. A. Pushkin ◽  
E. A. Dzenkova ◽  
N. N. Timoshkina ◽  
D. Yu. Gvaldin
Keyword(s):  
Mirna Expression ◽  
High Throughput Sequencing ◽  
Patient Survival ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Normal Brain ◽  
Sequencing Data ◽  
Tissue Samples

Purpose of the study. This research was devoted to study of mRNA and miRNA expression patterns in glioglastomas using The Cancer Genome Atlas (TCGA) data, to search for genetic determinants that determine the prognosis of patient survival and to create of interaction networks for glioblastomas.Materials and methods. Based on the data of the open TCGA database groups of glioblastomas and conventionally normal brain tissue samples were formed. Survival gene and miRNA expression data were extracted for each sample. After the data stratification by groups the differential expression analysis and search the genes affecting patient survival was carried out. The enrichment analysis by functional affiliation and an interactome analysis were performed.Results. A total of 156 glioblastoma samples with mRNA sequencing data, 571 samples with microarray microRNA analysis data, and 15 control samples were analyzed. Networks of mRNA-miRNA interactions were built and expression profiles of genes and miRNAs characteristic of glioblastomas were developed. We have determined the genes which aberrant level is associated with survival and shown the pairwise DEG and DE of microRNA correlations.Conclusion. The microRNA-mRNA regulatory pairs identified for glioblastomas can stimulate the development of new therapeutic approaches based on subtype-specific regulatory mechanisms of oncogenesis.

TAMI-79. THERAPEUTIC REVERSAL OF PRENATAL PONTINE ID1 SIGNALING IN DIPG

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi215-vi215
Author(s):  
Viveka Nand Yadav ◽  
Micah K Harris ◽  
Dana Messinger ◽  
Chase Thomas ◽  
Jessica R Cummings ◽  
...  
Keyword(s):  
Tumor Model ◽  
Pediatric Brain Tumor ◽  
Diffuse Intrinsic Pontine Glioma ◽  
Normal Brain ◽  
Sequencing Analysis ◽  
In Vivo Models ◽  
Chip Sequencing ◽  
Tumor Expression

Abstract Diffuse intrinsic pontine glioma (DIPG) is a highly aggressive pediatric brain tumor with rare survival beyond two years. This poor prognosis is largely due to the tumor's highly infiltrative and invasive nature. Nearly 80% of DMGs harbor K27M mutation in the genes encoding histone H3.1 (H3F3A) or H3.3 (HISTIH3B), often with concurrent ACVR1 mutation. Inhibitor of DNA-binding (ID) proteins are key transcriptional regulators of genes involved in lineage commitment and are associated with invasiveness and poor clinical outcomes in multiple human cancers. Introduction of H3K27M and ACVR1 mutations increase ID1 expression in cultured astrocytes, but this has not been confirmed in human tumors or targeted therapeutically. We developed an in-utero electroporation (IUE) murine H3K27M-driven tumor model, which demonstrates increased ID1 expression in H3K27M- and ACVR1-mutated tumor cells. Exome and transcriptome sequencing analysis of multi-focal DMG tumors (n=52) and normal brain tissue revealed that increased ID1 expression is associated with H3K27M/ACVR1-mutation and brainstem location, and correlates with poor survival in patients. ChIP-sequencing for H3K27ac and H3K27me3 in multiple DMG tumors (n=5) revealed that the ID1 gene is epigenetically active, which matches the epigenetic state of murine prenatal hindbrain cells. Higher ID1-expressing astrocyte-like DIPG cells share a similar transcriptional program with ID1+/SPARCL1+ positive oligo/astrocyte-precursor (OAPC) cells from the developing human brain and demonstrate upregulation of gene sets involved in regulation of cell migration. Both genetic and pharmacologic [cannabidiol (CBD)] suppression of ID1 result in decreased DIPG cell invasion/migration in vitro and invasion/tumor growth in multiple in vivo models. Mechanistically, CBD reduces proliferation through production of reactive oxygen species. Further, DIPG patients treated off-trial with CBD (n=15) displayed reduced ID1 tumor expression and improved overall survival. In summary, ID1 is upregulated in DIPG through K27M-mediated epigenetic reactivation of a developmental OAPC-like transcriptional state, and ID1-driven invasiveness of DIPG is therapeutically targetable with CBD.

scholarly journals Genome-wide analysis reveals recurrent structural abnormalities of TP63 and other p53-related genes in peripheral T-cell lymphomas

Blood ◽  
2012 ◽  
Vol 120 (11) ◽  
pp. 2280-2289 ◽  
Author(s):  
George Vasmatzis ◽  
Sarah H. Johnson ◽  
Ryan A. Knudson ◽  
Rhett P. Ketterling ◽  
Esteban Braggio ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cell ◽  
Chromosomal Rearrangements ◽  
Survival Rates ◽  
Dominant Negative ◽  
Sequencing Analysis ◽  
Tissue Samples ◽  
T Cell Lymphomas ◽  
Genome Wide ◽  
Peripheral T Cell Lymphomas

Abstract Peripheral T-cell lymphomas (PTCLs) are aggressive malignancies of mature T lymphocytes with 5-year overall survival rates of only ∼ 35%. Improvement in outcomes has been stymied by poor understanding of the genetics and molecular pathogenesis of PTCL, with a resulting paucity of molecular targets for therapy. We developed bioinformatic tools to identify chromosomal rearrangements using genome-wide, next-generation sequencing analysis of mate-pair DNA libraries and applied these tools to 16 PTCL patient tissue samples and 6 PTCL cell lines. Thirteen recurrent abnormalities were identified, of which 5 involved p53-related genes (TP53, TP63, CDKN2A, WWOX, and ANKRD11). Among these abnormalities were novel TP63 rearrangements encoding fusion proteins homologous to ΔNp63, a dominant-negative p63 isoform that inhibits the p53 pathway. TP63 rearrangements were seen in 11 (5.8%) of 190 PTCLs and were associated with inferior overall survival; they also were detected in 2 (1.2%) of 164 diffuse large B-cell lymphomas. As TP53 mutations are rare in PTCL compared with other malignancies, our findings suggest that a constellation of alternate genetic abnormalities may contribute to disruption of p53-associated tumor suppressor function in PTCL.

scholarly journals SomVarIUS: somatic variant identification from unpaired tissue samples

2015 ◽  
Vol 32 (6) ◽  
pp. 808-813 ◽  
Author(s):  
Kyle S. Smith ◽  
Vinod K. Yadav ◽  
Shanshan Pei ◽  
Daniel A. Pollyea ◽  
Craig T. Jordan ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Variant Calling ◽  
Computational Method ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Somatic Variant ◽  
Tissue Samples ◽  
Normal Tissues ◽  
High Throughput Sequencing Data ◽  
Oncogenic Mutations

Abstract Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of  ∼150 ×  coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

A New Approach for Predicting the Value of Gene Expression: Two-way Collaborative Filtering

2019 ◽  
Vol 14 (6) ◽  
pp. 480-490 ◽  
Author(s):  
Tuncay Bayrak ◽  
Hasan Oğul
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Collaborative Filtering ◽  
High Throughput Sequencing ◽  
Mean Squared Error ◽  
Experimental Studies ◽  
Kernel Functions ◽  
Feature Representation ◽  
Sequencing Analysis ◽  
Computational Systems Biology

Background: Predicting the value of gene expression in a given condition is a challenging topic in computational systems biology. Only a limited number of studies in this area have provided solutions to predict the expression in a particular pattern, whether or not it can be done effectively. However, the value of expression for the measurement is usually needed for further meta-data analysis. Methods: Because the problem is considered as a regression task where a feature representation of the gene under consideration is fed into a trained model to predict a continuous variable that refers to its exact expression level, we introduced a novel feature representation scheme to support work on such a task based on two-way collaborative filtering. At this point, our main argument is that the expressions of other genes in the current condition are as important as the expression of the current gene in other conditions. For regression analysis, linear regression and a recently popularized method, called Relevance Vector Machine (RVM), are used. Pearson and Spearman correlation coefficients and Root Mean Squared Error are used for evaluation. The effects of regression model type, RVM kernel functions, and parameters have been analysed in our study in a gene expression profiling data comprising a set of prostate cancer samples. Results: According to the findings of this study, in addition to promising results from the experimental studies, integrating data from another disease type, such as colon cancer in our case, can significantly improve the prediction performance of the regression model. Conclusion: The results also showed that the performed new feature representation approach and RVM regression model are promising for many machine learning problems in microarray and high throughput sequencing analysis.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

Sign in / Sign up

Export Citation Format

Share Document