TICdb: a collection of gene-mapped translocation breakpoints in cancer

Novo, Francisco J.; Mendíbil, Iñigo Ortiz de; Vizmanos, José Luis

doi:10.1186/1471-2164-8-33

Cited by 78 publications

(71 citation statements)

References 11 publications

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“…Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer provides extensive documentation of clinical cases, making possible the estimation of clinical frequencies of translocations. TICdb [5], on the other hand, provides manually curated mapped translocation breakpoints, allowing analysis of the sequences flanking those breakpoints. Several such analyses have been performed over the last years, highlighting the association of various sequence motifs with the presence of double-strand breaks (DSB) in some types of translocations.…”

Section: Introductionmentioning

confidence: 99%

Genomic Hallmarks of Genes Involved in Chromosomal Translocations in Hematological Cancer

Shugay¹,

Mendíbil²,

Vizmanos³

et al. 2012

PLoS Comput Biol

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Reciprocal chromosomal translocations (RCTs) leading to the formation of fusion genes are important drivers of hematological cancers. Although the general requirements for breakage and fusion are fairly well understood, quantitative support for a general mechanism of RCT formation is still lacking. The aim of this paper is to analyze available high-throughput datasets with computational and robust statistical methods, in order to identify genomic hallmarks of translocation partner genes (TPGs). Our results show that fusion genes are generally overexpressed due to increased promoter activity of 5′ TPGs and to more stable 3′-UTR regions of 3′ TPGs. Furthermore, expression profiling of 5′ TPGs and of interaction partners of 3′ TPGs indicates that these features can help to explain tissue specificity of hematological translocations. Analysis of protein domains retained in fusion proteins shows that the co-occurrence of specific domain combinations is non-random and that distinct functional classes of fusion proteins tend to be associated with different components of the gene fusion network. This indicates that the configuration of fusion proteins plays an important role in determining which 5′ and 3′ TPGs will combine in specific fusion genes. It is generally accepted that chromosomal proximity in the nucleus can explain the specific pairing of 5′ and 3′ TPGS and the recurrence of hematological translocations. Using recently available data for chromosomal contact probabilities (Hi-C) we show that TPGs are preferentially located in early replicated regions and occupy distinct clusters in the nucleus. However, our data suggest that, in general, nuclear position of TPGs in hematological cancers explains neither TPG pairing nor clinical frequency. Taken together, our results support a model in which genomic features related to regulation of expression and replication timing determine the set of candidate genes more likely to be translocated in hematological tissues, with functional constraints being responsible for specific gene combinations.

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Section: Introductionmentioning

confidence: 99%

Genomic Hallmarks of Genes Involved in Chromosomal Translocations in Hematological Cancer

Shugay¹,

Mendíbil²,

Vizmanos³

et al. 2012

PLoS Comput Biol

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“…ChimerKB represents a knowledgebase of fusion genes that were compiled from well-known public resources such as GenBank, Mitelman (1), OMIM (15), COSMIC (2), TICdb (4), dbCRID (16) fusion databases and PubMed articles. All entries were manually curated for disease, sequences, breakpoints and experimental evidences.…”

Section: System Design and Methodsmentioning

confidence: 99%

“…A number of databases have been developed to catalogue fusion genes of clinical value. Initial efforts include the Mitelman database (1), COSMIC (2), ChimerDB 1.0 (3) and TICdb (4). …”

Section: Introductionmentioning

confidence: 99%

OUP accepted manuscript

Lee

et al. 2016

Nucleic Acids Research

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Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.

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“…In short, we compiled a database of 548 reported fusion genes. This contained all records in the major public fusion gene databases [4], [17], [18] in addition to fusion genes reported from deep sequencing studies and those found in a thorough literature search. We then designed oligo probes targeting every exon in all of the fusion gene partners, in addition to chimeric exon-exon probes that target every possible exon-exon combination in fusion gene pairs.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Fusion Gene Status in Sarcomas Using a Custom Made Fusion Gene Microarray

et al. 2013

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Sarcomas are relatively rare malignancies and include a large number of histological subgroups. Based on morphology alone, the differential diagnoses of sarcoma subtypes can be challenging, but the identification of specific fusion genes aids correct diagnostication. The presence of individual fusion products are routinely investigated in Pathology labs. However, the methods used are time-consuming and based on prior knowledge about the expected fusion gene and often the most likely break-point. In this study, 16 sarcoma samples, representing seven different sarcoma subtypes with known fusion gene status from a diagnostic setting, were investigated using a fusion gene microarray. The microarray was designed to detect all possible exon-exon breakpoints between all known fusion genes in a single analysis. An automated scoring of the microarray data from the 38 known sarcoma-related fusion genes identified the correct fusion gene among the top-three hits in 11 of the samples. The analytical sensitivity may be further optimised, but we conclude that a sarcoma-fusion gene microarray is suitable as a time-saving screening tool to identify the majority of the correct fusion genes.

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TICdb: a collection of gene-mapped translocation breakpoints in cancer

Abstract: Background: Despite the importance of chromosomal translocations in the initiation and/or progression of cancer, a comprehensive catalog of translocation breakpoints in which these are precisely located on the reference sequence of the human genome is not available at present.

Cited by 78 publications

References 11 publications

Genomic Hallmarks of Genes Involved in Chromosomal Translocations in Hematological Cancer

Genomic Hallmarks of Genes Involved in Chromosomal Translocations in Hematological Cancer

OUP accepted manuscript

Assessment of Fusion Gene Status in Sarcomas Using a Custom Made Fusion Gene Microarray

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