Dataset Information

TumorFusions: an integrative resource for cancer-associated transcript fusions.

ABSTRACT: Gene fusion represents a class of molecular aberrations in cancer and has been exploited for therapeutic purposes. In this paper we describe TumorFusions, a data portal that catalogues 20 731 gene fusions detected in 9966 well characterized cancer samples and 648 normal specimens from The Cancer Genome Atlas (TCGA). The portal spans 33 cancer types in TCGA. Fusion transcripts were identified via a uniform pipeline, including filtering against a list of 3838 transcript fusions detected in a panel of 648 non-neoplastic samples. Fusions were mapped to somatic DNA rearrangements identified using whole genome sequencing data from 561 cancer samples as a means of validation. We observed that 65% of transcript fusions were associated with a chromosomal alteration, which is annotated in the portal. Other features of the portal include links to SNP array-based copy number levels and mutational patterns, exon and transcript level expressions of the partner genes, and a network-based centrality score for prioritizing functional fusions. Our portal aims to be a broadly applicable and user friendly resource for cancer gene annotation and is publicly available at http://www.tumorfusions.org.

SUBMITTER: Hu X

PROVIDER: S-EPMC5753333 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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TumorFusions: an integrative resource for cancer-associated transcript fusions.

Hu Xin X Wang Qianghu Q Tang Ming M Barthel Floris F Amin Samirkumar S Yoshihara Kosuke K Lang Frederick M FM Martinez-Ledesma Emmanuel E Lee Soo Hyun SH Zheng Siyuan S Verhaak Roel G W RGW

Nucleic acids research 20180101 D1

Gene fusion represents a class of molecular aberrations in cancer and has been exploited for therapeutic purposes. In this paper we describe TumorFusions, a data portal that catalogues 20 731 gene fusions detected in 9966 well characterized cancer samples and 648 normal specimens from The Cancer Genome Atlas (TCGA). The portal spans 33 cancer types in TCGA. Fusion transcripts were identified via a uniform pipeline, including filtering against a list of 3838 transcript fusions detected in a panel ...[more]

PMID: 29099951

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Project description:Transcript fusions as a result of chromosomal rearrangements have been a focus of attention in cancer as they provide attractive therapeutic targets. To identify novel fusion transcripts with the potential to be exploited therapeutically, we analyzed RNA sequencing, DNA copy number and gene mutation data from 4366 primary tumor samples. To avoid false positives, we implemented stringent quality criteria that included filtering of fusions detected in RNAseq data from 364 normal tissue samples. Our analysis identified 7887 high confidence fusion transcripts across 13 tumor types. Our fusion prediction was validated by evidence of a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole-genome sequencing data were available. Cancers with higher levels of genomic instability showed a corresponding increase in fusion transcript frequency, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations, suggesting an important role for tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4366 samples (7.4%). Potentially druggable kinase fusions involving ALK, ROS, RET, NTRK and FGFR gene families were detected in bladder carcinoma (3.3%), glioblastoma (4.4%), head and neck cancer (1.0%), low-grade glioma (1.5%), lung adenocarcinoma (1.6%), lung squamous cell carcinoma (2.3%) and thyroid carcinoma (8.7%), suggesting a potential for application of kinase inhibitors across tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes were detected in 111 samples (2.5%) and may additionally be considered as therapeutic targets. In summary, we described the landscape of transcript fusions detected across a large number of tumor samples and revealed fusion events with clinical relevance that have not been previously recognized. Our results support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated.

Project description:Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

Dataset Information

TumorFusions: an integrative resource for cancer-associated transcript fusions.

Publications

TumorFusions: an integrative resource for cancer-associated transcript fusions.

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