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

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. DESCRIPTION: We have created a database that describes the genomic location of 1,225 translocation breakpoints in human tumors, corresponding to 247 different genes, using information from publicly available sources. Junction sequences from reciprocal translocations were obtained from 655 different references (either from the literature or from nucleotide databases), and were mapped onto the reference sequence of the human genome using BLAST. All translocation breakpoints were thus referred to precise nucleotide positions (949 breakpoints) or gene fragments (introns or exons, 276 breakpoints) within specific Ensembl transcripts. CONCLUSION: TICdb is a comprehensive collection of finely mapped translocation breakpoints, freely available at . It should facilitate the analysis of sequences encompassing translocation breakpoints and the identification of factors driving translocation events in human tumors

Signatures of Selection in Fusion Transcripts Resulting From Chromosomal Translocations in Human Cancer

BACKGROUND: The recurrence and non-random distribution of translocation breakpoints in human tumors are usually attributed to local sequence features present in the vicinity of the breakpoints. However, it has also been suggested that functional constraints might contribute to delimit the position of translocation breakpoints within the genes involved, but a quantitative analysis of such contribution has been lacking. METHODOLOGY: We have analyzed two well-known signatures of functional selection, such as reading-frame compatibility and non-random combinations of protein domains, on an extensive dataset of fusion proteins resulting from chromosomal translocations in cancer. CONCLUSIONS: Our data provide strong experimental support for the concept that the position of translocation breakpoints in the genome of cancer cells is determined, to a large extent, by the need to combine certain protein domains and to keep an intact reading frame in fusion transcripts. Additionally, the information that we have assembled affords a global view of the oncogenic mechanisms and domain architectures that are used by fusion proteins. This can be used to assess the functional impact of novel chromosomal translocations and to predict the position of breakpoints in the genes involved

Estrategias para la detección y caracterización funcional de nuevas translocaciones cromosómicas en cáncer

Cancer is a heterogeneous group of diseases whose initiation and progression is determined by the appearance of genetic and epigenetic alterations. Chromosomal instability in particular is one of the signatures of haematological malignancies. Therefore, genomic rearrangements like translocations, that cause the appearance of chimeric transcripts or deregulated expression of genes, are common mechanisms in tumor cells. Molecular characterization of fusion sequences has shown that, at least for a few genes, the breakpoints tend to cluster in specific regions. As result, the distribution of the breakpoints follows a non-random pattern, with few sites where the breakpoints are more frequent than would be expected by chance. Although several studies have shown the role of nucleotide motifs and local characteristics of the sequences as the cause of such non-random distribution, the relevance of the functional factors in defining the position of the breakpoints involved in chromosomal translocations has not yet been tested experimentally. In order to explain the non-random distribution of breakpoints in chromosome translocations we have studied functional factors such as protein domains as well as the need to maintain an intact reading frame in the fusion product. Until recently, fusion genes have been associated almost exclusively with hematological malignancies and mesenchymal tumors. Recent studies have shown, however, that fusions are also responsible for the pathogenesis of certain epithelial cancers, such as prostate cancer and NSCLC. For this reason we hypothesize that there must exist other rearrangements causing fusion genes in other solid tumors, but its description has not been possible until now because of methodological problems. Therefore, we have searched for novel fusion genes search using a new methodology based on exon microarrays

Estrategias para la detección y caracterización funcional de nuevas translocaciones cromosómicas en cáncer

Cancer is a heterogeneous group of diseases whose initiation and progression is determined by the appearance of genetic and epigenetic alterations. Chromosomal instability in particular is one of the signatures of haematological malignancies. Therefore, genomic rearrangements like translocations, that cause the appearance of chimeric transcripts or deregulated expression of genes, are common mechanisms in tumor cells. Molecular characterization of fusion sequences has shown that, at least for a few genes, the breakpoints tend to cluster in specific regions. As result, the distribution of the breakpoints follows a non-random pattern, with few sites where the breakpoints are more frequent than would be expected by chance. Although several studies have shown the role of nucleotide motifs and local characteristics of the sequences as the cause of such non-random distribution, the relevance of the functional factors in defining the position of the breakpoints involved in chromosomal translocations has not yet been tested experimentally. In order to explain the non-random distribution of breakpoints in chromosome translocations we have studied functional factors such as protein domains as well as the need to maintain an intact reading frame in the fusion product. Until recently, fusion genes have been associated almost exclusively with hematological malignancies and mesenchymal tumors. Recent studies have shown, however, that fusions are also responsible for the pathogenesis of certain epithelial cancers, such as prostate cancer and NSCLC. For this reason we hypothesize that there must exist other rearrangements causing fusion genes in other solid tumors, but its description has not been possible until now because of methodological problems. Therefore, we have searched for novel fusion genes search using a new methodology based on exon microarrays

Genomic Hallmarks of Genes Involved in Chromosomal Translocations in Hematological Cancer

<div><p>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.</p> </div

Unsupervised clustering of translocation fusion proteins based on their domain profiles yields 6 translocation classes (C0 to C5).

<p><b>A</b>: Relative probabilities of proteins encoded by 5′ or 3′ TPGs to have at least one domain of D/P/H/K/O functional class for each of the six translocation classes. <b>B</b>. Network of TPGs (arrows point from 5′ to 3′ TPG) showing how translocation classes are consistent with specific combinations of TPGs (MLL translocations include classes C3 and C4, ALK and PDGFRB translocations are almost exclusively class C1, etc). Edge thickness indicates number of different TPG variants comprising a given translocation.</p

Characteristics of 3′UTRs of 5′ and 3′ TPGs.

<p><b>A</b>: 3′UTR length. <b>B</b>: number of conserved elements (phastConsElements44way track, hg18 genome assembly from <a href="http://genome.ucsc.edu/" target="_blank">http://genome.ucsc.edu/</a>. <b>C:</b> number of microRNA target sites (PITA Top Targets from <a href="http://ophid.utoronto.ca/mirDIP" target="_blank">http://ophid.utoronto.ca/mirDIP</a>. ***, **: P<0.0001, 0.001, Wilcoxon signed rank test.</p