scholarly journals Comprehensive genome and transcriptome analysis reveals genetic basis for gene fusions in cancer

2017 ◽  
Author(s):  
Nuno A. Fonseca ◽  
Yao He ◽  
Liliana Greger ◽  
Alvis Brazma ◽  
Zemin Zhang ◽  
...  
Keyword(s):  
Genetic Alterations ◽  
Specific Gene ◽  
Fusion Partner ◽  
Gene Fusions ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Reading Frame ◽  
Diagnostic Values ◽  
Genetic Mechanisms ◽  
Dna Alterations

Gene fusions are an important class of cancer-driving events with therapeutic and diagnostic values, yet their underlying genetic mechanisms have not been systematically characterized. Here by combining RNA and whole genome DNA sequencing data from 1188 donors across 27 cancer types we obtained a list of 3297 high-confidence tumour-specific gene fusions, 82% of which had structural variant (SV) support and 2372 of which were novel. Such a large collection of RNA and DNA alterations provides the first opportunity to systematically classify the gene fusions at a mechanistic level. While many could be explained by single SVs, numerous fusions involved series of structural rearrangements and thus are composite fusions. We discovered 75 fusions of a novel class of inter-chromosomal composite fusions, termed bridged fusions, in which a third genomic location bridged two different genes. In addition, we identified 522 fusions involving non-coding genes and 157 ORF-retaining fusions, in which the complete open reading frame of one gene was fused to the UTR region of another. Although only a small proportion (5%) of the discovered fusions were recurrent, we found a set of highly recurrent fusion partner genes, which exhibited strong 5’ or 3’ bias and were significantly enriched for cancer genes. Our findings broaden the view of the gene fusion landscape and reveal the general properties of genetic alterations underlying gene fusions for the first time.

scholarly journals Immunohistological Expression of SOX-10 in Triple-Negative Breast Cancer: A Descriptive Analysis of 113 Samples

2020 ◽  
Vol 21 (17) ◽  
pp. 6407 ◽  
Author(s):  
Katharina Kriegsmann ◽  
Christa Flechtenmacher ◽  
Jörg Heil ◽  
Jörg Kriegsmann ◽  
Gunhild Mechtersheimer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Descriptive Analysis ◽  
Biological Significance ◽  
Genetic Alterations ◽  
Prognostic Impact ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Disease Free

Background: SRY-related HMG-box 10 (SOX-10) is commonly expressed in triple negative breast cancer (TNBC). However, data on the biological significance of SOX-10 expression is limited. Therefore, we investigated immunhistological SOX-10 expression in TNBC and correlated the results with genetic alterations and clinical data. Methods: A tissue microarray including 113 TNBC cases was stained by SOX-10. Immunohistological data of AR, BCL2, CD117, p53 and Vimentin was available from a previous study. Semiconductor-based panel sequencing data including commonly altered breast cancer genes was also available from a previous investigation. SOX-10 expression was correlated with clinicopathological, immunohistochemical and genetic data. Results: SOX-10 was significantly associated with CD117 and Vimentin, but not with AR expression. An association of SOX-10 with BCL2, EGFR or p53 staining was not observed. SOX-10-positive tumors harbored more often TP53 mutations but less frequent mutations of PIK3CA or alterations of the PIK3K pathway. SOX-10 expression had no prognostic impact either on disease-free, distant disease-free, or overall survival. Conclusions: While there might be a value of SOX-10 as a differential diagnostic marker to identify metastases of TNBC, its biological role remains to be investigated.

A heuristic platform for clinical interpretation of cancer genome sequencing data.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10502-10502
Author(s):  
Eliezer Mendel Van Allen ◽  
Nikhil Wagle ◽  
Gregory Kryukov ◽  
Alexis Ramos ◽  
Gad Getz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Genome Sequencing ◽  
Heuristic Algorithm ◽  
Genetic Alterations ◽  
Cancer Genome ◽  
Clinical Samples ◽  
Whole Genome ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Clinical Interpretation

10502 Background: The ability to identify and effectively sort the full spectrum of biologically and therapeutically relevant genetic alterations identified by massively parallel sequencing may improve cancer care. A major challenge involves rapid and rational categorization of data-intensive output, including somatic mutations, insertions/deletions, copy number alterations, and rearrangements into ranked categories for clinician review. Methods: A database of clinically actionable alterations was created, consisting of over 100 annotated genes known to undergo somatic genomic alterations in cancer that may impact clinical decision-making. A heuristic algorithm was developed, which selectively identifies somatic alterations based on the clinically actionable alterations database. Remaining variants are sorted based on additional heuristics, including high priority alterations based on presence in the Cancer Gene Census, biologically significant cancer genes based on presence in COSMIC or MSigDB, and low priority alterations in the same gene family as biologically significant cancer genes. The heuristic algorithm was applied to whole exome sequencing data of clinical samples and whole genome sequencing data from a cohort of prostate cancer samples processed using established Broad Institute pipelines. Results: Application of the heuristic algorithm to the prostate cancer whole genome rearrangement data identified 172 (out of 5978) rearrangements involving actionable genes (averaging 2-3 events per tumor). Furthermore, two clinical samples processed prospectively were analyzed, yielding three potentially actionable alterations for clinical review. Conclusions: The heuristic model for clinical interpretation of next generation sequencing data may facilitate rapid analysis of tumor genomic information for clinician review by identifying and prioritizing alterations that can directly impact care. Our platform can also be applied to research data to prospectively explore clinically relevant findings from existing cohorts. Future analytical approaches using heuristic or probabilistic algorithms should underpin a robust prospective assessment of clinical cancer genome data.

MLL-SEPTIN gene fusions in hematological malignancies

2011 ◽  
Vol 392 (8-9) ◽  
pp. 713-724 ◽  
Author(s):  
Nuno Cerveira ◽  
Susana Bizarro ◽  
Manuel R. Teixeira
Keyword(s):  
Hematological Malignancies ◽  
De Novo ◽  
Chromosomal Translocations ◽  
Histone Acetyltransferases ◽  
Fusion Partner ◽  
Gene Fusions ◽  
Reading Frame ◽  
Partner Gene ◽  
Entire Open Reading Frame ◽  
Myeloid Neoplasia

AbstractThe mixed lineage leukemia (MLL) locus is involved in more than 60 different rearrangements with a remarkably diverse group of fusion partners in approximately 10% of human leukemias.MLLrearrangements include chromosomal translocations, gene internal duplications, chromosome 11q deletions or inversions andMLLgene insertions into other chromosomes, or vice versa. MLL fusion partners can be classified into four distinct categories: nuclear proteins, cytoplasmatic proteins, histone acetyltransferases and septins. Five different septin genes (SEPT2,SEPT5,SEPT6,SEPT9, andSEPT11) have been identified asMLLfusion partners, giving rise to chimeric fusion proteins in which the N terminus of MLL is fused, in frame, to almost the entire open reading frame of the septin partner gene. The rearranged alleles result from heterogeneous breaks in distinct introns of bothMLLand its septin fusion partner, originating distinct gene fusion variants.MLL-SEPTIN rearrangements have been repeatedly identified inde novoand therapy related myeloid neoplasia in both children and adults, and some clinicopathogenetic associations are being uncovered. The fundamental roles of septins in cytokinesis, membrane remodeling and compartmentalization can provide some clues on how abnormalities in the septin cytoskeleton and MLL deregulation could be involved in the pathogenesis of hematological malignancies.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

Developmental gene expression in Leishmania donovani: differential cloning and analysis of an amastigote-stage-specific gene

1994 ◽  
Vol 14 (5) ◽  
pp. 2975-2984
Author(s):  
H Charest ◽  
G Matlashewski
Keyword(s):  
Life Cycle ◽  
Leishmania Donovani ◽  
Repetitive Sequences ◽  
Protozoan Parasite ◽  
Life Cycle Stage ◽  
Immune Serum ◽  
Sand Fly ◽  
Specific Gene ◽  
Reading Frame ◽  
Developmental Gene Expression

Leishmania protozoans are the causative agents of leishmaniasis, a major parasitic disease in humans. During their life cycle, Leishmania protozoans exist as flagellated promastigotes in the sand fly vector and as nonmotile amastigotes in the mammalian hosts. The promastigote-to-amastigote transformation occurs in the phagolysosomal compartment of the macrophage cell and is a critical step for the establishment of the infection. To study this cytodifferentiation process, we differentially screened an amastigote cDNA library with life cycle stage-specific cDNA probes and isolated seven cDNAs representing amastigote-specific transcripts. Five of these were closely related (A2 series) and recognized, by Northern (RNA) blot analyses, a 3.5-kb transcript in amastigotes and in amastigote-infected macrophages. Expression of the amastigote-specific A2 gene was induced in promastigotes when they were transferred from culture medium at 26 degrees C and pH 7.4 to medium at 37 degrees C and pH 4.5, conditions which mimic the macrophage phagolysosomal environment. A2 genes are clustered in tandem arrays, and a 6-kb fragment corresponding to a unit of the cluster was cloned and partially sequenced. An open reading frame found within the A2-transcribed region potentially encoded a 22-kDa protein containing repetitive sequences. The recombinant A2 protein produced in Escherichia coli cells was specifically recognized by immune serum from a patient with visceral leishmaniasis. The A2 protein repetitive element has strong homology with an S antigen of Plasmodium falciparum, the protozoan parasite responsible for malaria. Both the A2 protein of Leishmania donovani and the S antigen of P. falciparum are stage specific and developmentally expressed in mammalian hosts.

scholarly journals Identification of MEF2-regulated genes during muscle differentiation

2004 ◽  
Vol 20 (1) ◽  
pp. 143-151 ◽  
Author(s):  
James Paris ◽  
Carl Virtanen ◽  
Zhibin Lu ◽  
Mark Takahashi
Keyword(s):  
Gene Regulation ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Neuromuscular Junctions ◽  
Muscle Differentiation ◽  
Signaling Cascades ◽  
Specific Gene ◽  
Genetic Mechanisms ◽  
Selection Parameters ◽  
First Time

Although a great deal has been elucidated concerning the mechanisms regulating muscle differentiation, little is known about transcription factor-specific gene regulation. Our understanding of the genetic mechanisms regulating cell differentiation is quite limited. Much of what has been defined centers on regulatory signaling cascades and transcription factors. Surprisingly few studies have investigated the association of genes with specific transcription factors. To address these issues, we have utilized a method coupling chromatin immunoprecipitation and CpG microarrays to characterize the genes associated with MEF2 in differentiating C2C12 cells. Results demonstrated a defined binding pattern over the course of differentiation. Filtered data demonstrated 9 clones to be elevated at 0 h, 792 at 6 h, 163 by 1 day, and 316 at 3 days. Using unbiased selection parameters, we selected a subset of 291 prospective candidates. Clones were sequenced and filtered for removal of redundancy between clones and for the presence of repetitive elements. We were able to place 50 of these on the mouse genome, and 20 were found to be located near well-annotated genes. From this list, previously undefined associations with MEF2 were discovered. Many of these genes represent proteins involved in neurogenesis, neuromuscular junctions, signaling and metabolism. The remaining clones include many full-length cDNA and represent novel gene targets. The results of this study provides for the first time, a unique look at gene regulation at the level of transcription factor binding in differentiating muscle.

Lung cancer risk stratification using methylation profile in the oral epithelium

2018 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Hiroaki Harada ◽  
Kazuaki Miyamaoto ◽  
Masami Kimura ◽  
Tetsuro Ishigami ◽  
Kiyomi Taniyama ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Lung Cancer Risk ◽  
Cancer Risk Assessment ◽  
Oral Epithelium ◽  
Specific Gene ◽  
Aberrant Methylation ◽  
Cancer Genes

Background Assuming that the entire airway is affected by the same inhaled carcinogen, similar molecular alterations may occur in the lung and oral cavity. Thus, we hypothesized that DNA methylation profiles in the oral epithelium may be a promising biomarker for lung cancer risk stratification. Methods A methylation-specific polymerase chain reaction was performed on oral epithelium from 16 patients with lung cancer and 32 controls without lung cancer. Genes showing aberrant methylation profiles in the oral epithelium were compared between patients and controls. Results The analysis revealed that HOXD11 and PCDHGB6 were methylated more frequently in patients than in controls ( p = 0.0055 and p = 0.0247, respectively). Combined analyses indicated that 8 of 16 (50%) patients and 3 of 32 (9.4%) controls showed DNA methylation in both genes ( p = 0.0016). Among the population limited to current and former smokers, 6 of 11 (54.5%) patients showed methylation in both genes, compared to 1 of 17 (5.9%) controls ( p = 0.0037). In a subgroup analysis limited to the population above 50-years old, 8 of 16 (50%) patients and 2 of 16 (12.5%) controls showed methylation in both genes ( p = 0.0221). Conclusions The results of this study indicate that specific gene methylation in the oral epithelium might be a promising biomarker for lung cancer risk assessment, especially among smokers. Risk stratification through the analysis of DNA methylation profiles in the oral epithelium may be a useful and less invasive first-step approach in an efficient two-step lung cancer screening strategy.

scholarly journals Genome Assemblies of the Warthog and Kenyan Domestic Pig Provide Insights into Suidae Evolution and Candidate Genes for African Swine Fever Tolerance

2021 ◽  
Author(s):  
Wen Feng ◽  
Lei Zhou ◽  
Pengju Zhao ◽  
Heng Du ◽  
Chenguang Diao ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Resistance ◽  
African Swine Fever ◽  
Gene Families ◽  
Specific Gene ◽  
Chromosome 2 ◽  
Sequencing Data ◽  
Domestic Pig ◽  
Phacochoerus Africanus ◽  
Contraction And Expansion

As warthog (Phacochoerus africanus) has innate immunity against African swine fever (ASF), it is critical to understanding the evolutionary novelty of warthog to explain its specific ASF resistance. Here, we present two completed new genomes of one warthog and one Kenyan domestic pig, as the fundamental genomic references to decode the genetic mechanism on ASF tolerance. Our results indicated, multiple genomic variations, including gene losses, independent contraction and expansion of specific gene families, likely moulded warthog's genome to adapt the environment. Importantly, the analysis of presence and absence of genomic sequences revealed that, the warthog genome had a DNA sequence absence of the lactate dehydrogenase B (LDHB) gene on chromosome 2 compared to the reference genome. The overexpression and siRNA of LDHB indicated that its inhibition on the replication of ASFV. The Combining with large scale sequencing data of 123 pigs from all over world, contraction and expansion of TRIM genes families revealed that TRIM family genes in the warthog genome were potentially responsible for its tolerance to ASF. Our results will help further improve the understanding of genetic resistance ASF in pigs.

scholarly journals Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer

2021 ◽  
Author(s):  
Shaolong Cao ◽  
Jennifer Wang ◽  
Shuangxi Ji ◽  
Peng Yang ◽  
Matthew Montierth ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Mrna Expression ◽  
Genetic Alterations ◽  
Individual Variability ◽  
Cancer Type ◽  
Sequencing Data ◽  
Increased Risk ◽  
Mrna Content ◽  
Risk Of Disease ◽  
Pan Cancer

Abstract Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.

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