scholarly journals Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 2055 ◽  
Author(s):  
Mingzhe Xu ◽  
Zhongmeng Zhao ◽  
Xuanping Zhang ◽  
Aiqing Gao ◽  
Shuyan Wu ◽  
...  
Keyword(s):  
Gene Networks ◽  
Gene Fusion ◽  
Fusion Gene ◽  
Driver Gene ◽  
Clinical Practices ◽  
Driver Genes ◽  
Multiple Networks ◽  
Fusion Data ◽  
Evaluation Algorithm ◽  
Chromosomal Mutations

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals ZRSR2 overexpression is a frequent and early event in castration-resistant prostate cancer development

2021 ◽  
Author(s):  
Haiqing He ◽  
Jun Hao ◽  
Xin Dong ◽  
Yu Wang ◽  
Hui Xue ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle Progression ◽  
Locally Advanced ◽  
Patient Treatment ◽  
Early Event ◽  
Driver Gene ◽  
Castration Resistant Prostate Cancer ◽  
Driver Genes ◽  
Castration Resistant ◽  
Pdx Models

Abstract Background Androgen deprivation therapy (ADT) remains the leading systemic therapy for locally advanced and metastatic prostate cancers (PCa). While a majority of PCa patients initially respond to ADT, the durability of response is variable and most patients will eventually develop incurable castration-resistant prostate cancer (CRPC). Our research objective is to identify potential early driver genes responsible for CRPC development. Methods We have developed a unique panel of hormone-naïve PCa (HNPC) patient-derived xenograft (PDX) models at the Living Tumor Laboratory. The PDXs provide a unique platform for driver gene discovery as they allow for the analysis of differentially expressed genes via transcriptomic profiling at various time points after mouse host castration. In the present study, we focused on genes with expression changes shortly after castration but before CRPC has fully developed. These are likely to be potential early drivers of CRPC development. Such genes were further validated for their clinical relevance using data from PCa patient databases. ZRSR2 was identified as a top gene candidate and selected for further functional studies. Results ZRSR2 is significantly upregulated in our PDX models during the early phases of CRPC development after mouse host castration and remains consistently high in fully developed CRPC PDX models. Moreover, high ZRSR2 expression is also observed in clinical CRPC samples. Importantly, elevated ZRSR2 in PCa samples is correlated with poor patient treatment outcomes. ZRSR2 knockdown reduced PCa cell proliferation and delayed cell cycle progression at least partially through inhibition of the Cyclin D1 (CCND1) pathway. Conclusion Using our unique HNPC PDX models that develop into CRPC after host castration, we identified ZRSR2 as a potential early driver of CRPC development.

scholarly journals FOXO1 gene involvement in a non-rhabdomyosarcomatous neoplasm

2021 ◽  
Author(s):  
Simon Haefliger ◽  
Muriel Genevay ◽  
Michel Bihl ◽  
Romina Marone ◽  
Daniel Baumhoer ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Soft Tissue ◽  
Genetic Heterogeneity ◽  
Gene Fusion ◽  
Fusion Gene ◽  
Soft Tissue Tumors ◽  
Gene Fusions ◽  
Mesenchymal Tumors ◽  
Myoepithelial Differentiation ◽  
Foxo1 Gene

AbstractMyoepithelial neoplasms of soft tissue are rare tumors with clinical, morphological, immunohistochemical, and genetic heterogeneity. The morphological spectrum of these tumors is broad, and the diagnosis often requires immunostaining to confirm myoepithelial differentiation. Rarely, tumors show a morphology that is typical for myoepithelial neoplasms, while the immunophenotype fails to confirm myoepithelial differentiation. For such lesions, the term “myoepithelioma-like” tumor was introduced. Recently, two cases of myoepithelioma-like tumors of the hands and one case of the foot were described with previously never reported OGT-FOXO gene fusions. Here, we report a 50-year-old woman, with a myoepithelial-like tumor localized in the soft tissue of the forearm and carrying a OGT-FOXO1 fusion gene. Our findings extend the spectrum of mesenchymal tumors involving members of the FOXO family of transcription factors and point to the existence of a family of soft tissue tumors that carry the gene fusion of the OGT-FOXO family.

NIMG-52. RADIOGENOMICS SIGNATURES IN KEY DRIVER GENES IN GLIOBLASTOMA EVALUATED WITH AND WITHOUT THE PRESENCE OF CO-OCCURRING MUTATIONS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi141-vi141
Author(s):  
Anahita Fathi Kazerooni ◽  
Hamed Akbari ◽  
Spyridon Bakas ◽  
Erik Toorens ◽  
Chiharu Sako ◽  
...  
Keyword(s):  
Gabor Wavelet ◽  
Egfr Mutations ◽  
Driver Gene ◽  
Significant Heterogeneity ◽  
Driver Genes ◽  
Targeted Next Generation Sequencing ◽  
Promising Tool ◽  
Key Driver ◽  
In Vivo Characterization

Abstract PURPOSE Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations. METHODS We identified a retrospective cohort of 359 IDH-wildtype glioblastoma patients, with available pre-operative mpMRI (T1, T1Gd, T2, T2-FLAIR) scans and targeted next generation sequencing (NGS) data. Radiomic features, including morphologic, histogram, texture, and Gabor wavelet descriptors, were extracted from the mpMRI. Multivariate predictive models were trained using cross-validated SVM with LASSO feature selection to predict mutation status in key driver genes, EGFR, PTEN, TP53, and NF1. ML models and spatial population atlases of genetic mutations were generated for stratification of the tumors (1) with co-occurring mutations versus wildtypes, (2) with exclusive mutations in each driver gene versus the tumors without any mutations in the pathways associated with these genes. RESULTS ML models yielded AUCs of 0.75 (95%CI:0.62-0.88) / 0.87 (95%CI:0.70-1) for co-occurring / exclusive EGFR mutations, 0.69 (95%CI:0.58-0.80) / 0.80 (95%CI:0.61-0.99) for co-occurring / exclusive PTEN mutations, and 0.77 (95%CI:0.65-0.88) / 0.86 (95%CI:0.69-1) for co-occurring / exclusive TP53 cases. Spatial atlases revealed a predisposition of left temporal lobe for NF1 and right frontotemporal region for TP53 in mutually exclusive tumors, which was not observed in the co-occurring mutation atlases. CONCLUSION Our results suggest the presence of distinct radiogenomic signatures of several glioblastoma mutations, which become even more pronounced when respective mutations do not co-occur with other mutations. These in vivo signatures can contribute to pre-operative stratification of patients for molecular targeted therapies, and potentially longitudinal monitoring of mutational changes during treatment.

scholarly journals Ancient Origin of the Tryptophan Operon and the Dynamics of Evolutionary Change

2003 ◽  
Vol 67 (3) ◽  
pp. 303-342 ◽  
Author(s):  
Gary Xie ◽  
Nemat O. Keyhani ◽  
Carol A. Bonner ◽  
Roy A. Jensen
Keyword(s):  
Gene Fusion ◽  
Fusion Gene ◽  
Dynamic State ◽  
Specific Gene ◽  
Flux Analysis ◽  
Ancestral Gene ◽  
History Of ◽  
Tryptophan Operon ◽  
Operon Organization ◽  
Genome Representation

SUMMARY The seven conserved enzymatic domains required for tryptophan (Trp) biosynthesis are encoded in seven genetic regions that are organized differently (whole-pathway operons, multiple partial-pathway operons, and dispersed genes) in prokaryotes. A comparative bioinformatics evaluation of the conservation and organization of the genes of Trp biosynthesis in prokaryotic operons should serve as an excellent model for assessing the feasibility of predicting the evolutionary histories of genes and operons associated with other biochemical pathways. These comparisons should provide a better understanding of possible explanations for differences in operon organization in different organisms at a genomics level. These analyses may also permit identification of some of the prevailing forces that dictated specific gene rearrangements during the course of evolution. Operons concerned with Trp biosynthesis in prokaryotes have been in a dynamic state of flux. Analysis of closely related organisms among the Bacteria at various phylogenetic nodes reveals many examples of operon scission, gene dispersal, gene fusion, gene scrambling, and gene loss from which the direction of evolutionary events can be deduced. Two milestone evolutionary events have been mapped to the 16S rRNA tree of Bacteria, one splitting the operon in two, and the other rejoining it by gene fusion. The Archaea, though less resolved due to a lesser genome representation, appear to exhibit more gene scrambling than the Bacteria. The trp operon appears to have been an ancient innovation; it was already present in the common ancestor of Bacteria and Archaea. Although the operon has been subjected, even in recent times, to dynamic changes in gene rearrangement, the ancestral gene order can be deduced with confidence. The evolutionary history of the genes of the pathway is discernible in rough outline as a vertical line of descent, with events of lateral gene transfer or paralogy enriching the analysis as interesting features that can be distinguished. As additional genomes are thoroughly analyzed, an increasingly refined resolution of the sequential evolutionary steps is clearly possible. These comparisons suggest that present-day trp operons that possess finely tuned regulatory features are under strong positive selection and are able to resist the disruptive evolutionary events that may be experienced by simpler, poorly regulated operons.

scholarly journals Analysis of NTRK mutation and clinicopathologic factors in lung cancer patients in northeast China

2020 ◽  
Vol 35 (3) ◽  
pp. 36-40
Author(s):  
Hui Li ◽  
Shi Yan ◽  
Ying Liu ◽  
Lixia Ma ◽  
Xianhong Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Whole Blood ◽  
Copy Number ◽  
Northeast China ◽  
Fusion Gene ◽  
Gene Mutations ◽  
Driver Gene ◽  
Blood Samples ◽  
Clinicopathologic Factors ◽  
Whole Blood Samples

Objective: NTRK mutations and clinicopathological factors in patients with lung cancer in northeast China were analyzed by next-generation sequencing (NGS), and references were provided for patients with NTRK mutations undergoing targeted therapy in northeast China. Methods: A total of 224 specimens in 173 patients with lung cancer were collected. This included 51 patients with matched tissue and whole blood samples,133 tissue samples, 84 whole blood samples, and 7 pleural effusion samples. NGS (520 genes) was used to detected NTRK mutations and clinicopathologic factors. Results: NTRK mutation was detected in eight patients (8/173, 4.6%), including four NTRK missense mutations (4/173, 2.3%), two NTRK fusion gene mutations (2/173, 1.2%), and two NTRK copy number deletions (2/173, 1.2%). Among the eight patients with NTRK mutations, four were associated with lung cancer driver gene mutations (3/4 EGFR, 1/4ALK); NTRK in two patients was inconsistent in tissue and paired whole blood testing; NTRK missense mutation was detected in one patient, and NTRK copy number deletion was detected in the other; and NTRK wild type was detected in two patients. There was no correlation between NTRK mutation and clinicopathologic factors (including gender, age, pathological type, smoking status, metastasis site). Conclusion: NTRK mutation was only 4.6%, effective fusion gene mutation was 1.2%, and common driver gene mutation in lung cancer was evident in 50% of patients. The results of NTRK were inconsistent with matched tissues and whole blood. Therefore, patients with NTRK mutation should use a variety of specimen types and large target area sequencing (panel) analysis method to provide individualized treatment.

scholarly journals PATH-50. ROUTINE MOLECULAR DIAGNOSIS OF GLIOMA PATIENTS USING A GLIOMA-SPECIFIC NGS PANEL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi154-vi155
Author(s):  
Koji Yoshimoto ◽  
Nayuta Higa ◽  
Hajime Yonezawa ◽  
Hiroyuki Uchida ◽  
Toshiaki Akahane ◽  
...  
Keyword(s):  
Molecular Diagnosis ◽  
Gene Mutations ◽  
Chromosome 1 ◽  
Clinical Samples ◽  
Driver Gene ◽  
Driver Genes ◽  
Chromosome 1P ◽  
Ffpe Tumor ◽  
Grade Ii ◽  
Key Driver

Abstract AIM The 2016 WHO classification requires molecular diagnosis in routine glioma diagnostics. However, analysis of key driver gene mutations and chromosome 1p/19q co-deletions cannot be performed in a single platform. In this study, we evaluated the feasibility of a glioma-specific NGS panel for molecular diagnosis of glioma patients. MATERIALS AND METHODS We developed a glioma-specific NGS panel consisting of 48 genes, including glioma-relevant key driver genes and 21 genes mapped to chromosome 1 and 19. DNA was extracted from formaldehyde fixed-paraffin embedded (FFPE) tumor tissues histologically identified by a pathologist, and from patient-derived blood as a control. In this system, we implemented a molecular barcodes method to enhance confidence in clinical samples and analyzed 80 glioma patients (Grade II: 17 cases, Grade III: 16 cases, Grade IV: 47 cases). RESULTS From these 80 cases, IDH1 and H3F3A mutations were detected in 23 cases (29%) and 2 cases (5%), respectively. The 1p/19q co-deletion was detected in 15 cases (19%), with all cases also containing IDH1 mutations. In Grade IV cases, EGFR, PDGFR, and FGFR mutations were detected in 6% (amp 19%), 9%, and 4% (amp 17%) of cases, respectively. PTEN, TP53, NF1, RB1, and CDKN2A mutations were detected in 37% (del 72%), 45% (del 13%), 21% (del 23%), 15% (del 60%), and 2% (del 53%) of cases, respectively. CONCLUSION Diagnosis of glioma patients with this glioma-specific NGS panel is feasible.

scholarly journals Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A- and TET2-driver gene mutations

2020 ◽  
Author(s):  
Birgit Assmus ◽  
Sebastian Cremer ◽  
Klara Kirschbaum ◽  
David Culmann ◽  
Katharina Kiefer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Somatic Mutations ◽  
Prognostic Significance ◽  
Gene Mutations ◽  
Amplicon Sequencing ◽  
Driver Mutations ◽  
Driver Gene ◽  
Roc Curve Analysis ◽  
Driver Genes ◽  
Clone Size

Abstract Aims Somatic mutations of the epigenetic regulators DNMT3A and TET2 causing clonal expansion of haematopoietic cells (clonal haematopoiesis; CH) were shown to be associated with poor prognosis in chronic ischaemic heart failure (CHF). The aim of our analysis was to define a threshold of variant allele frequency (VAF) for the prognostic significance of CH in CHF. Methods and results We analysed bone marrow and peripheral blood-derived cells from 419 patients with CHF by error-corrected amplicon sequencing. Cut-off VAFs were optimized by maximizing sensitivity plus specificity from a time-dependent receiver operating characteristic (ROC) curve analysis from censored data. 56.2% of patients were carriers of a DNMT3A- (N = 173) or a TET2- (N = 113) mutation with a VAF &gt;0.5%, with 59 patients harbouring mutations in both genes. Survival ROC analyses revealed an optimized cut-off value of 0.73% for TET2- and 1.15% for DNMT3A-CH-driver mutations. Five-year-mortality was 18% in patients without any detected DNMT3A- or TET2 mutation (VAF &lt; 0.5%), 29% with only one DNMT3A- or TET2-CH-driver mutations above the respective cut-off level and 42% in patients harbouring both DNMT3A- and TET2-CH-driver mutations above the respective cut-off levels. In carriers of a DNMT3A mutation with VAF ≥ 1.15%, 5-year mortality was 31%, compared with 18% mortality in those with VAF &lt; 1.15% (P = 0.048). Likewise, in patients with TET2 mutations, 5-year mortality was 32% with VAF ≥ 0.73%, compared with 19% mortality with VAF &lt; 0.73% (P = 0.029). Conclusion The present study defines novel threshold levels for clone size caused by acquired somatic mutations in the CH-driver genes DNMT3A and TET2 that are associated with worse outcome in patients with CHF.

scholarly journals DriverDBv3: a multi-omics database for cancer driver gene research

2019 ◽  
Author(s):  
Shu-Hsuan Liu ◽  
Pei-Chun Shen ◽  
Chen-Yang Chen ◽  
An-Ni Hsu ◽  
Yi-Chun Cho ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Synergistic Effects ◽  
Driver Gene ◽  
Driver Genes ◽  
Specific Patient ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Significant Survival ◽  
Gene Database

Abstract An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

scholarly journals Interpreting pathways to discover cancer driver genes with Moonlight

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonio Colaprico ◽  
Catharina Olsen ◽  
Matthew H. Bailey ◽  
Gabriel J. Odom ◽  
Thilde Terkelsen ◽  
...  
Keyword(s):  
Tumor Suppressors ◽  
Molecular Mechanisms ◽  
Dual Role ◽  
Tissue Type ◽  
Driver Gene ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Therapeutic Decisions ◽  
Cancer Driver Genes

AbstractCancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

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