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Suwakulsiri W, Xu R, Rai A, Chen M, Shafiq A, Greening DW, Simpson RJ. Transcriptomic analysis and fusion gene identifications of midbody remnants released from colorectal cancer cells reveals they are molecularly distinct from exosomes and microparticles. Proteomics 2024; 24:e2300058. [PMID: 38470197 DOI: 10.1002/pmic.202300058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 03/13/2024]
Abstract
Previously, we reported that human primary (SW480) and metastatic (SW620) colorectal (CRC) cells release three classes of membrane-encapsulated extracellular vesicles (EVs); midbody remnants (MBRs), exosomes (Exos), and microparticles (MPs). We reported that MBRs were molecularly distinct at the protein level. To gain further biochemical insights into MBRs, Exos, and MPs and their emerging role in CRC, we performed, and report here, for the first time, a comprehensive transcriptome and long noncoding RNA sequencing analysis and fusion gene identification of these three EV classes using the next-generation RNA sequencing technique. Differential transcript expression analysis revealed that MBRs have a distinct transcriptomic profile compared to Exos and MPs with a high enrichment of mitochondrial transcripts lncRNA/pseudogene transcripts that are predicted to bind to ribonucleoprotein complexes, spliceosome, and RNA/stress granule proteins. A salient finding from this study is a high enrichment of several fusion genes in MBRs compared to Exos, MPs, and cell lysates from their parental cells such as MSH2 (gene encoded DNA mismatch repair protein MSH2). This suggests potential EV-liquid biopsy targets for cancer detection. Importantly, the expression of cancer progression-related transcripts found in EV classes derived from SW480 (EGFR) and SW620 (MET and MACCA1) cell lines reflects their parental cell types. Our study is the report of RNA and fusion gene compositions within MBRs (including Exos and MPs) that could have an impact on EV functionality in cancer progression and detection using EV-based RNA/ fusion gene candidates for cancer biomarkers.
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Affiliation(s)
- Wittaya Suwakulsiri
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science (LIMS), School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Victoria, Australia
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Darlington, New South Wales, Australia
| | - Rong Xu
- Nanobiotechnology Laboratory, Australia Centre for Blood Diseases, Centre Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Alin Rai
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Maoshan Chen
- Laboratory of Radiation Biology, Department of Blood Transfusion, Laboratory Medicine Centre, The Second Affiliated Hospital, Army Medical University, Chongqing, China
| | - Adnan Shafiq
- Department of Cell & Developmental Biology, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
| | - David W Greening
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Richard J Simpson
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science (LIMS), School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Victoria, Australia
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Tripathi S, Shirnekhi HK, Gorman SD, Chandra B, Baggett DW, Park CG, Somjee R, Lang B, Hosseini SMH, Pioso BJ, Li Y, Iacobucci I, Gao Q, Edmonson MN, Rice SV, Zhou X, Bollinger J, Mitrea DM, White MR, McGrail DJ, Jarosz DF, Yi SS, Babu MM, Mullighan CG, Zhang J, Sahni N, Kriwacki RW. Defining the condensate landscape of fusion oncoproteins. Nat Commun 2023; 14:6008. [PMID: 37770423 PMCID: PMC10539325 DOI: 10.1038/s41467-023-41655-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 09/13/2023] [Indexed: 09/30/2023] Open
Abstract
Fusion oncoproteins (FOs) arise from chromosomal translocations in ~17% of cancers and are often oncogenic drivers. Although some FOs can promote oncogenesis by undergoing liquid-liquid phase separation (LLPS) to form aberrant biomolecular condensates, the generality of this phenomenon is unknown. We explored this question by testing 166 FOs in HeLa cells and found that 58% formed condensates. The condensate-forming FOs displayed physicochemical features distinct from those of condensate-negative FOs and segregated into distinct feature-based groups that aligned with their sub-cellular localization and biological function. Using Machine Learning, we developed a predictor of FO condensation behavior, and discovered that 67% of ~3000 additional FOs likely form condensates, with 35% of those predicted to function by altering gene expression. 47% of the predicted condensate-negative FOs were associated with cell signaling functions, suggesting a functional dichotomy between condensate-positive and -negative FOs. Our Datasets and reagents are rich resources to interrogate FO condensation in the future.
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Affiliation(s)
- Swarnendu Tripathi
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Hazheen K Shirnekhi
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Scott D Gorman
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Arrakis Therapeutics, 830 Winter St, Waltham, MA, 02451, USA
| | - Bappaditya Chandra
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - David W Baggett
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cheon-Gil Park
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ramiz Somjee
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Rhodes College, Memphis, TN, USA
- Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO, 63110, USA
| | - Benjamin Lang
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Center of Excellence for Data-Driven Discovery, Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Seyed Mohammad Hadi Hosseini
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Center of Excellence for Data-Driven Discovery, Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Brittany J Pioso
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yongsheng Li
- Livestrong Cancer Institutes, Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Ilaria Iacobucci
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Qingsong Gao
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Michael N Edmonson
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Stephen V Rice
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - John Bollinger
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Diana M Mitrea
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Dewpoint Therapeutics, 451 D Street, Suite 104, Boston, MA, 02210, USA
| | - Michael R White
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- IDEXX Laboratories, Inc., One IDEXX Drive, Westbrook, ME, 04092, USA
| | - Daniel J McGrail
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - S Stephen Yi
- Livestrong Cancer Institutes, Department of Oncology, Dell Medical School, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Biomedical Engineering, and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
| | - M Madan Babu
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Center of Excellence for Data-Driven Discovery, Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Charles G Mullighan
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, USA
| | - Richard W Kriwacki
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Sciences Center, Memphis, TN, USA.
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Hafstað V, Häkkinen J, Persson H. Fast and sensitive validation of fusion transcripts in whole-genome sequencing data. BMC Bioinformatics 2023; 24:359. [PMID: 37741966 PMCID: PMC10518092 DOI: 10.1186/s12859-023-05489-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/20/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND In cancer, genomic rearrangements can create fusion genes that either combine protein-coding sequences from two different partner genes or place one gene under the control of the promoter of another gene. These fusion genes can act as oncogenic drivers in tumor development and several fusions involving kinases have been successfully exploited as drug targets. Expressed fusions can be identified in RNA sequencing (RNA-Seq) data, but fusion prediction software often has a high fraction of false positive fusion transcript predictions. This is problematic for both research and clinical applications. RESULTS We describe a method for validation of fusion transcripts detected by RNA-Seq in matched whole-genome sequencing (WGS) data. Our pipeline uses discordant read pairs to identify supported fusion events and analyzes soft-clipped read alignments to determine genomic breakpoints. We have tested it on matched RNA-Seq and WGS data for both tumors and cancer cell lines and show that it can be used to validate both new predicted gene fusions and experimentally validated fusion events. It was considerably faster and more sensitive than using BreakDancer and Manta, software that is instead designed to detect many different types of structural variants on a genome-wide scale. CONCLUSIONS We have developed a fast and very sensitive pipeline for validation of gene fusions detected by RNA-Seq in matched WGS data. It can be used to identify high-quality gene fusions for further bioinformatic and experimental studies, including validation of genomic breakpoints and studies of the mechanisms that generate fusions. In a clinical setting, it could help find expressed gene fusions for personalized therapy.
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Affiliation(s)
- Völundur Hafstað
- Faculty of Medicine, Department of Clinical Sciences Lund, Oncology, Lund University Cancer Centre, Lund, Sweden
| | - Jari Häkkinen
- Faculty of Medicine, Department of Clinical Sciences Lund, Oncology, Lund University Cancer Centre, Lund, Sweden
| | - Helena Persson
- Faculty of Medicine, Department of Clinical Sciences Lund, Oncology, Lund University Cancer Centre, Lund, Sweden.
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RNA-Seq for the detection of gene fusions in solid tumors: development and validation of the JAX FusionSeq™ 2.0 assay. J Mol Med (Berl) 2022; 100:323-335. [PMID: 35013752 DOI: 10.1007/s00109-021-02149-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 10/19/2022]
Abstract
Whole transcriptome sequencing (RNA-Seq) has gained prominence for the detection of fusions in solid tumors. Here, we describe the development and validation of an in-house RNA-Seq-based test system (FusionSeq™ 2.0) for the detection of clinically actionable gene fusions, in formalin-fixed paraffin-embedded (FFPE) specimens, using seventy tumor samples with varying fusion status. Conditions were optimized for RNA input of 50 ng, shown to be adequate to call known fusions at as low as 20% neoplastic content. Evaluation of assay performance between FFPE and fresh-frozen (FF) tissues exhibited little to no difference in fusion calling capability. Performance analysis of the assay validation data determined 100% accuracy, sensitivity, specificity, and reproducibility. This clinically developed and validated RNA-Seq-based approach for fusion detection in FPPE samples was shown to be on par if not superior to off-the-shelf commercially offered assays. With gene fusions implicated in a variety of cancer types, offering high-quality, low-cost molecular testing services for FFPE specimens will serve to best benefit the patient and the advancement of precision medicine in molecular oncology. KEY MESSAGES: A custom RNA-Seq-based test system (FusionSeq™ 2.0) for the detection of clinically actionable gene fusions, Evaluation of assay performance between FFPE and fresh-frozen (FF) tissues exhibited little to no difference in fusion calling capability. The assay can be performed with low RNA input and neoplastic content. Performance characteristics of the assay validation data determined 100% accuracy, sensitivity, specificity, and reproducibility.
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Role and Involvement of TENM4 and miR-708 in Breast Cancer Development and Therapy. Cells 2022; 11:cells11010172. [PMID: 35011736 PMCID: PMC8750459 DOI: 10.3390/cells11010172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/31/2021] [Accepted: 01/02/2022] [Indexed: 12/20/2022] Open
Abstract
Teneurin 4 (TENM4) is a transmembrane protein that is codified by the ODZ4 gene and is involved in nervous system development, neurite outgrowth, and neuronal differentiation. In line with its involvement in the nervous system, TENM4 has also been implicated in several mental disorders such as bipolar disorder, schizophrenia, and autism. TENM4 mutations and rearrangements have recently been identified in a number of tumors. This, combined with impaired expression in tumors, suggests that it may potentially be involved in tumorigenesis. Most of the TENM4 mutations that are observed in tumors occur in breast cancer, in which TENM4 plays a role in cells’ migration and stemness. However, the functional role that TENM4 plays in breast cancer still needs to be better evaluated, and further studies are required to better understand the involvement of TENM4 in breast cancer progression. Herein, we review the currently available data for TENM4′s role in breast cancer and propose its use as both a novel target with which to ameliorate patient prognosis and as a potential biomarker. Moreover, we also report data on the tumorigenic role of miR-708 deregulation and the possible use of this miRNA as a novel therapeutic molecule, as miR-708 is spliced out from TENM4 mRNA.
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Liu Z, Chen X, Roberts R, Huang R, Mikailov M, Tong W. Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma. Front Pharmacol 2021; 12:608778. [PMID: 33967751 PMCID: PMC8105087 DOI: 10.3389/fphar.2021.608778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 03/23/2021] [Indexed: 11/13/2022] Open
Abstract
High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.
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Affiliation(s)
- Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
| | - Xi Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge, United Kingdom.,University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States
| | - Mike Mikailov
- Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States
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The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer. Cancers (Basel) 2021; 13:cancers13061343. [PMID: 33809651 PMCID: PMC8002233 DOI: 10.3390/cancers13061343] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 03/05/2021] [Accepted: 03/14/2021] [Indexed: 01/23/2023] Open
Abstract
Simple Summary Human cancer genomes show a variety of alterations, such as single base changes, deletions, insertions, copy number changes, and gene fusions. Analyzing fusion gene transcripts may yield a novel and effective approach for selecting cancer treatments. However, few comprehensive analyses of gene fusions in non-small cell lung cancer (NSCLC) patients have been performed. Here, we characterized the fusion gene landscape of NSCLC in a case study of Taiwanese lung cancer patients. We concluded that some fusion genes likely play driver roles in carcinogenesis, while others act as passengers. We demonstrated that by using RNA-sequencing to detect gene fusion events, putative therapeutic drug targets could be identified, potentially leading to more precise therapies for NSCLC. Abstract Background: Analyzing fusion gene transcripts may yield an effective approach for selecting cancer treatments. However, few comprehensive analyses of fusions in non-small cell lung cancer (NSCLC) patients have been performed. Methods: We enrolled 54 patients with NSCLC, and performed RNA-sequencing (RNA-Seq). STAR (Spliced Transcripts Alignment to a Reference)-Fusion was used to identify fusions. Results: Of the 218 fusions discovered, 24 had been reported and the rest were novel. Three fusions had the highest occurrence rates. After integrating our gene expression and fusion data, we found that samples harboring fusions containing ASXL1, CACNA1A, EEF1A1, and RET also exhibited increased expression of these genes. We then searched for mutations and fusions in cancer driver genes in each sample and found that nine patients carried both mutations and fusions in cancer driver genes. Furthermore, we found a trend for mutual exclusivity between gene fusions and mutations in the same gene, with the exception of DMD, and we found that EGFR mutations are associated with the number of fusion genes. Finally, we identified kinase gene fusions, and potentially druggable fusions, which may play roles in lung cancer therapy. Conclusion: The clinical use of RNA-Seq for detecting driver fusion genes may play an important role in the treatment of lung cancer.
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Teneurins: Role in Cancer and Potential Role as Diagnostic Biomarkers and Targets for Therapy. Int J Mol Sci 2021; 22:ijms22052321. [PMID: 33652578 PMCID: PMC7956758 DOI: 10.3390/ijms22052321] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 02/22/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Teneurins have been identified in vertebrates as four different genes (TENM1-4), coding for membrane proteins that are mainly involved in embryonic and neuronal development. Genetic studies have correlated them with various diseases, including developmental problems, neurological disorders and congenital general anosmia. There is some evidence to suggest their possible involvement in cancer initiation and progression, and drug resistance. Indeed, mutations, chromosomal alterations and the deregulation of teneurins expression have been associated with several tumor types and patient survival. However, the role of teneurins in cancer-related regulatory networks is not fully understood, as both a tumor-suppressor role and pro-tumoral functions have been proposed, depending on tumor histotype. Here, we summarize and discuss the literature data on teneurins expression and their potential role in different tumor types, while highlighting the possibility of using teneurins as novel molecular diagnostic and prognostic biomarkers and as targets for cancer treatments, such as immunotherapy, in some tumors.
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Van AAN, Kunkel MT, Baffi TR, Lordén G, Antal CE, Banerjee S, Newton AC. Protein kinase C fusion proteins are paradoxically loss of function in cancer. J Biol Chem 2021; 296:100445. [PMID: 33617877 PMCID: PMC8008189 DOI: 10.1016/j.jbc.2021.100445] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/11/2021] [Accepted: 02/18/2021] [Indexed: 12/02/2022] Open
Abstract
Within the AGC kinase superfamily, gene fusions resulting from chromosomal rearrangements have been most frequently described for protein kinase C (PKC), with gene fragments encoding either the C-terminal catalytic domain or the N-terminal regulatory moiety fused to other genes. Kinase fusions that eliminate regulatory domains are typically gain of function and often oncogenic. However, several quality control pathways prevent accumulation of aberrant PKC, suggesting that PKC fusions may paradoxically be loss of function. To explore this topic, we used biochemical, cellular, and genome editing approaches to investigate the function of fusions that retain the portion of the gene encoding either the catalytic domain or regulatory domain of PKC. Overexpression studies revealed that PKC catalytic domain fusions were constitutively active but vulnerable to degradation. Genome editing of endogenous genes to generate a cancer-associated PKC fusion resulted in cells with detectable levels of fusion transcript but no detectable protein. Hence, PKC catalytic domain fusions are paradoxically loss of function as a result of their instability, preventing appreciable accumulation of protein in cells. Overexpression of a PKC regulatory domain fusion suppressed both basal and agonist-induced endogenous PKC activity, acting in a dominant-negative manner by competing for diacylglycerol. For both catalytic and regulatory domain fusions, the PKC component of the fusion proteins mediated the effects of the full-length fusions on the parameters examined, suggesting that the partner protein is dispensable in these contexts. Taken together, our findings reveal that PKC gene fusions are distinct from oncogenic fusions and present a mechanism by which loss of PKC function occurs in cancer.
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Affiliation(s)
- An-Angela N Van
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA; Biomedical Sciences Graduate Program, University of California at San Diego, La Jolla, California, USA
| | - Maya T Kunkel
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA
| | - Timothy R Baffi
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA; Biomedical Sciences Graduate Program, University of California at San Diego, La Jolla, California, USA
| | - Gema Lordén
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA
| | - Corina E Antal
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA; Biomedical Sciences Graduate Program, University of California at San Diego, La Jolla, California, USA
| | - Sourav Banerjee
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA
| | - Alexandra C Newton
- Department of Pharmacology, University of California at San Diego, La Jolla, California, USA.
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10
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Gaonkar KS, Marini F, Rathi KS, Jain P, Zhu Y, Chimicles NA, Brown MA, Naqvi AS, Zhang B, Storm PB, Maris JM, Raman P, Resnick AC, Strauch K, Taroni JN, Rokita JL. annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions. BMC Bioinformatics 2020; 21:577. [PMID: 33317447 PMCID: PMC7737294 DOI: 10.1186/s12859-020-03922-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 12/03/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy. RESULTS We developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions. CONCLUSIONS annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.
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Affiliation(s)
- Krutika S Gaonkar
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Thrombosis and Hemostasis, Mainz, Germany
| | - Komal S Rathi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Payal Jain
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yuankun Zhu
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nicholas A Chimicles
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Miguel A Brown
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ammar S Naqvi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Bo Zhang
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Phillip B Storm
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John M Maris
- Division of Oncology, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pichai Raman
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jaclyn N Taroni
- Alex's Lemonade Stand Foundation Childhood Cancer Data Lab, Philadelphia, PA, USA
| | - Jo Lynne Rokita
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Bioinformatics and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
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11
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Lovino M, Ciaburri MS, Urgese G, Di Cataldo S, Ficarra E. DEEPrior: a deep learning tool for the prioritization of gene fusions. Bioinformatics 2020; 36:3248-3250. [PMID: 32016382 PMCID: PMC7214024 DOI: 10.1093/bioinformatics/btaa069] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
SUMMARY In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. AVAILABILITY AND IMPLEMENTATION Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Gianvito Urgese
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino 10129, Italy
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12
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Kim P, Yiya K, Zhou X. FGviewer: an online visualization tool for functional features of human fusion genes. Nucleic Acids Res 2020; 48:W313-W320. [PMID: 32421816 PMCID: PMC7319540 DOI: 10.1093/nar/gkaa364] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/17/2020] [Accepted: 04/27/2020] [Indexed: 11/27/2022] Open
Abstract
Among the diverse location of the breakpoints (BPs) of structural variants (SVs), the breakpoints of fusion genes (FGs) are located in the gene bodies. This broken gene context provided the aberrant functional clues to study disease genesis. Many tumorigenic fusion genes have retained or lost functional or regulatory domains and these features impacted tumorigenesis. Full annotation of fusion genes aided by the visualization tool based on two gene bodies will be helpful to study the functional aspect of fusion genes. To date, a specialized tool with effective visualization of the functional features of fusion genes is not available. In this study, we built FGviewer, a tool for visualizing functional features of human fusion genes, which is available at https://ccsmweb.uth.edu/FGviewer. FGviewer gets the input of fusion gene symbols, breakpoint information, or structural variants from whole-genome sequence (WGS) data. For any combination of gene pairs/breakpoints to be involved in fusion genes, the users can search the functional/regulatory aspect of the fusion gene in the three bio-molecular levels (DNA-, RNA-, and protein-levels) and one clinical level (pathogenic-level). FGviewer will be a unique online tool in disease research communities.
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Affiliation(s)
- Pora Kim
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ke Yiya
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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13
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Cai Z, Xue H, Xu Y, Köhler J, Cheng X, Dai Y, Zheng J, Wang H. Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs. Gigascience 2020; 9:5848590. [PMID: 32470133 PMCID: PMC7259471 DOI: 10.1093/gigascience/giaa054] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 03/01/2020] [Accepted: 04/29/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods. RESULTS We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes). CONCLUSION Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.
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Affiliation(s)
- Zhaoqing Cai
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Hongzhang Xue
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yue Xu
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Jens Köhler
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Xiaojie Cheng
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Yao Dai
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Jie Zheng
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Haiyun Wang
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
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14
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Jang YE, Jang I, Kim S, Cho S, Kim D, Kim K, Kim J, Hwang J, Kim S, Kim J, Kang J, Lee B, Lee S. ChimerDB 4.0: an updated and expanded database of fusion genes. Nucleic Acids Res 2020; 48:D817-D824. [PMID: 31680157 PMCID: PMC7145594 DOI: 10.1093/nar/gkz1013] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 12/14/2022] Open
Abstract
Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http://www.kobic.re.kr/chimerdb/.
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Affiliation(s)
- Ye Eun Jang
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Insu Jang
- Korean Bioinformation Center, Korean Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Sunkyu Kim
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Subin Cho
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Daehan Kim
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Keonwoo Kim
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Jaewon Kim
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jimin Hwang
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Sangok Kim
- Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jaesang Kim
- Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Byungwook Lee
- Korean Bioinformation Center, Korean Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Sanghyuk Lee
- Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Republic of Korea.,Department of Life Science, Ewha Womans University, Seoul 03760, Republic of Korea
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15
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Singh S, Qin F, Kumar S, Elfman J, Lin E, Pham LP, Yang A, Li H. The landscape of chimeric RNAs in non-diseased tissues and cells. Nucleic Acids Res 2020; 48:1764-1778. [PMID: 31965184 PMCID: PMC7038929 DOI: 10.1093/nar/gkz1223] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 12/13/2019] [Accepted: 01/20/2020] [Indexed: 12/17/2022] Open
Abstract
Chimeric RNAs and their encoded proteins have been traditionally viewed as unique features of neoplasia, and have been used as biomarkers and therapeutic targets for multiple cancers. Recent studies have demonstrated that chimeric RNAs also exist in non-cancerous cells and tissues, although large-scale, genome-wide studies of chimeric RNAs in non-diseased tissues have been scarce. Here, we explored the landscape of chimeric RNAs in 9495 non-diseased human tissue samples of 53 different tissues from the GTEx project. Further, we established means for classifying chimeric RNAs, and observed enrichment for particular classifications as more stringent filters are applied. We experimentally validated a subset of chimeric RNAs from each classification and demonstrated functional relevance of two chimeric RNAs in non-cancerous cells. Importantly, our list of chimeric RNAs in non-diseased tissues overlaps with some entries in several cancer fusion databases, raising concerns for some annotations. The data from this study provides a large repository of chimeric RNAs present in non-diseased tissues, which can be used as a control dataset to facilitate the identification of true cancer-specific chimeras.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Fujun Qin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Shailesh Kumar
- National Institute of Plant Genome Research (NIPGR), New Delhi 110067, India
| | - Justin Elfman
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Lin
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Lam-Phong Pham
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Amy Yang
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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16
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Vellichirammal NN, Albahrani A, Banwait JK, Mishra NK, Li Y, Roychoudhury S, Kling MJ, Mirza S, Bhakat KK, Band V, Joshi SS, Guda C. Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 19:1379-1398. [PMID: 32160708 PMCID: PMC7044684 DOI: 10.1016/j.omtn.2020.01.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/03/2020] [Accepted: 01/14/2020] [Indexed: 01/26/2023]
Abstract
Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.
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Affiliation(s)
| | - Abrar Albahrani
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Jasjit K Banwait
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA; Bioinformatics and Systems Biology Core. University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Nitish K Mishra
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - You Li
- HitGen, South Keyuan Road 88, Chengdu, China
| | - Shrabasti Roychoudhury
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Mathew J Kling
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Sameer Mirza
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Kishor K Bhakat
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Vimla Band
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Shantaram S Joshi
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA; Bioinformatics and Systems Biology Core. University of Nebraska Medical Center, Omaha, NE 68198, USA.
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17
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z. Genomic basis for RNA alterations in cancer. Nature 2020; 578:129-136. [PMID: 32025019 PMCID: PMC7054216 DOI: 10.1038/s41586-020-1970-0] [Citation(s) in RCA: 241] [Impact Index Per Article: 60.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 12/11/2019] [Indexed: 01/27/2023]
Abstract
Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed 'bridged' fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.
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Affiliation(s)
| | - Claudia Calabrese
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,000000041936877Xgrid.5386.8Weill Cornell Medical College, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- 0000 0001 2180 6431grid.4280.eNational University of Singapore, Singapore, Singapore ,0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - André Kahles
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - Yuichi Shiraishi
- 0000 0001 2151 536Xgrid.26999.3dThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada ,0000 0001 2297 6811grid.266102.1University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
| | - Junjun Zhang
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- 0000 0001 2193 314Xgrid.8756.cUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- 0000000122483208grid.10698.36The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- 0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- 0000000121901201grid.83440.3bUniversity College London, London, UK
| | - Jan O. Korbel
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- 0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,0000 0004 1937 0626grid.4714.6Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA ,000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.1Ulm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore ,0000 0004 0385 0924grid.428397.3Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- 0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- 0000 0004 0626 690Xgrid.419890.dOntario Institute for Cancer Research, Toronto, Ontario, Canada ,0000 0001 2157 2938grid.17063.33University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- 0000 0001 2160 926Xgrid.39382.33Baylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA ,000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China ,China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- 0000 0001 2034 1839grid.21155.32BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Angela N. Brooks
- 0000 0001 0740 6917grid.205975.cUniversity of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.34Broad Institute, Cambridge, MA USA ,0000 0001 2106 9910grid.65499.37Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- 0000 0004 0620 715Xgrid.418377.eGenome Institute of Singapore, Singapore, Singapore ,0000 0004 0620 9745grid.410724.4National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- 0000 0001 2156 2780grid.5801.cETH Zurich, Zurich, Switzerland ,0000 0001 2171 9952grid.51462.34Memorial Sloan Kettering Cancer Center, New York, NY USA ,000000041936877Xgrid.5386.8Weill Cornell Medical College, New York, NY USA ,0000 0001 2223 3006grid.419765.8SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,0000 0004 0478 9977grid.412004.3University Hospital Zurich, Zurich, Switzerland
| | - Roland F. Schwarz
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,0000 0001 1014 0849grid.419491.0Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Consortium (DKTK), partner site Berlin, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- 0000 0000 9709 7726grid.225360.0European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,0000 0004 0495 846Xgrid.4709.aEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,0000 0004 0492 0584grid.7497.dGerman Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- 0000 0001 2256 9319grid.11135.37Peking University, Beijing, China
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18
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Balamurali D, Gorohovski A, Detroja R, Palande V, Raviv-Shay D, Frenkel-Morgenstern M. ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps. Nucleic Acids Res 2020; 48:D825-D834. [PMID: 31747015 PMCID: PMC7145514 DOI: 10.1093/nar/gkz1025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/18/2019] [Accepted: 10/26/2019] [Indexed: 12/11/2022] Open
Abstract
Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.
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Affiliation(s)
- Deepak Balamurali
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Alessandro Gorohovski
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Rajesh Detroja
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Vikrant Palande
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Dorith Raviv-Shay
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Milana Frenkel-Morgenstern
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
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19
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Barresi V, Cosentini I, Scuderi C, Napoli S, Di Bella V, Spampinato G, Condorelli DF. Fusion Transcripts of Adjacent Genes: New Insights into the World of Human Complex Transcripts in Cancer. Int J Mol Sci 2019; 20:ijms20215252. [PMID: 31652751 PMCID: PMC6862657 DOI: 10.3390/ijms20215252] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 12/12/2022] Open
Abstract
The awareness of genome complexity brought a radical approach to the study of transcriptome, opening eyes to single RNAs generated from two or more adjacent genes according to the present consensus. This kind of transcript was thought to originate only from chromosomal rearrangements, but the discovery of readthrough transcription opens the doors to a new world of fusion RNAs. In the last years many possible intergenic cis-splicing mechanisms have been proposed, unveiling the origins of transcripts that contain some exons of both the upstream and downstream genes. In some cases, alternative mechanisms, such as trans-splicing and transcriptional slippage, have been proposed. Five databases, containing validated and predicted Fusion Transcripts of Adjacent Genes (FuTAGs), are available for the scientific community. A comparative analysis revealed that two of them contain the majority of the results. A complete analysis of the more widely characterized FuTAGs is provided in this review, including their expression pattern in normal tissues and in cancer. Gene structure, intergenic splicing patterns and exon junction sequences have been determined and here reported for well-characterized FuTAGs. The available functional data and the possible roles in cancer progression are discussed.
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Affiliation(s)
- Vincenza Barresi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Ilaria Cosentini
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Chiara Scuderi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Salvatore Napoli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Virginia Di Bella
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Giorgia Spampinato
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
| | - Daniele Filippo Condorelli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, 95123 Catania, Italy.
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20
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Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep 2019; 23:227-238.e3. [PMID: 29617662 PMCID: PMC5916809 DOI: 10.1016/j.celrep.2018.03.050] [Citation(s) in RCA: 340] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 02/25/2018] [Accepted: 03/13/2018] [Indexed: 12/11/2022] Open
Abstract
Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy.
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21
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Wu H, Li X, Li H. Gene fusions and chimeric RNAs, and their implications in cancer. Genes Dis 2019; 6:385-390. [PMID: 31832518 PMCID: PMC6889028 DOI: 10.1016/j.gendis.2019.08.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 08/03/2019] [Accepted: 08/21/2019] [Indexed: 01/26/2023] Open
Abstract
Gene fusions are appreciated as ideal cancer biomarkers and therapeutic targets. Chimeric RNAs are traditionally thought to be products of gene fusions, and thus, also cancer-specific. Recent research has demonstrated that chimeric RNAs can be generated by intergenic splicing in the absence of gene fusion, and such chimeric RNAs are also found in normal physiology. These new findings challenge the traditional theory of chimeric RNAs exclusivity to cancer, and complicates use of chimeric RNAs in cancer detection. Here, we provide an overview of gene fusions and chimeric RNAs, and emphasize their differences. We note that gene fusions are able to generate chimeric RNAs in accordance with the central dogma of biology, and that chimeric RNAs may also be able to influence the generation of the gene fusions per the “horse before the cart” hypothesis. We further expand upon the “horse before the cart” hypothesis, summarizing current evidence in support of the theory and exploring its potential impact on the field.
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Affiliation(s)
- Hao Wu
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, China
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
| | - Xiaorong Li
- Department of Gastrointestinal Surgery, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, China
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
- Corresponding author. Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA. Fax: +1 434 2437244. http://lilab.medicine.virginia.edu
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22
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ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins. PLoS Comput Biol 2019; 15:e1007239. [PMID: 31437145 PMCID: PMC6705771 DOI: 10.1371/journal.pcbi.1007239] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/03/2019] [Indexed: 01/10/2023] Open
Abstract
Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein-protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/.
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23
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Hu X, Wang Q, Tang M, Barthel F, Amin S, Yoshihara K, Lang FM, Martinez-Ledesma E, Lee SH, Zheng S, Verhaak RGW. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res 2019; 46:D1144-D1149. [PMID: 29099951 PMCID: PMC5753333 DOI: 10.1093/nar/gkx1018] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/17/2017] [Indexed: 01/29/2023] Open
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.
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Affiliation(s)
- Xin Hu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Program in Bioinformatics and Biostatistics, The University of Texas Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Qianghu Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ming Tang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Floris Barthel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Samirkumar Amin
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, 951-8510, Japan
| | - Frederick M Lang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Emmanuel Martinez-Ledesma
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soo Hyun Lee
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Siyuan Zheng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Roel G W Verhaak
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
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24
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Kim CY, Na K, Park S, Jeong SK, Cho JY, Shin H, Lee MJ, Han G, Paik YK. FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells. Mol Cell Proteomics 2019; 18:1651-1668. [PMID: 31208993 PMCID: PMC6683003 DOI: 10.1074/mcp.ra119.001456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/23/2019] [Indexed: 01/21/2023] Open
Abstract
Fusion proteoforms are translation products derived from gene fusion. Although very rare, the fusion proteoforms play important roles in biomedical science. For example, fusion proteoforms influence the development of tumors by serving as cancer markers or cell cycle regulators. Although numerous studies have reported bioinformatics tools that can predict fusion transcripts, few proteogenomic tools are available that can predict and identify proteoforms. In this study, we develop a versatile proteogenomic tool "FusionPro," which facilitates the identification of fusion transcripts and their potential translatable peptides. FusionPro provides an independent gene fusion prediction module and can build sequence databases for annotated fusion proteoforms. FusionPro shows greater sensitivity than the available fusion finders when analyzing simulated or real RNA sequencing data sets. We use FusionPro to identify 18 fusion junction peptides and three potential fusion-derived peptides by MS/MS-based analysis of leukemia cell lines (Jurkat and K562) and ovarian cancer tissues from the Clinical Proteomic Tumor Analysis Consortium. Among the identified fusion proteins, we molecularly validate two fusion junction isoforms and a translation product of FAM133B:CDK6. Moreover, sequence analysis suggests that the fusion protein participates in the cell cycle progression. In addition, our prediction results indicate that fusion transcripts often have multiple fusion junctions and that these fusion junctions tend to be distributed in a nonrandom pattern at both the chromosome and gene levels. Thus, FusionPro allows users to detect various types of fusion translation products using a transcriptome-informed approach and to gain a comprehensive understanding of the formation and biological roles of fusion proteoforms.
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Affiliation(s)
- Chae-Yeon Kim
- ‡Interdisciplinary Program of Integrated OMICS for Biomedical Science, The Graduate School, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Keun Na
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Saeram Park
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Seul-Ki Jeong
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Jin-Young Cho
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Heon Shin
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Min Jung Lee
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Gyoonhee Han
- ¶Department of Pharmacy, College of Pharmacy, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea
| | - Young-Ki Paik
- §Yonsei Proteome Research Center, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
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25
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Kim P, Park A, Han G, Sun H, Jia P, Zhao Z. TissGDB: tissue-specific gene database in cancer. Nucleic Acids Res 2019; 46:D1031-D1038. [PMID: 29036590 PMCID: PMC5753286 DOI: 10.1093/nar/gkx850] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2017] [Accepted: 09/15/2017] [Indexed: 12/11/2022] Open
Abstract
Tissue-specific gene expression is critical in understanding biological processes, physiological conditions, and disease. The identification and appropriate use of tissue-specific genes (TissGenes) will provide important insights into disease mechanisms and organ-specific therapeutic targets. To better understand the tissue-specific features for each cancer type and to advance the discovery of clinically relevant genes or mutations, we built TissGDB (Tissue specific Gene DataBase in cancer) available at http://zhaobioinfo.org/TissGDB. We collected and curated 2461 tissue specific genes (TissGenes) across 22 tissue types that matched the 28 cancer types of The Cancer Genome Atlas (TCGA) from three representative tissue-specific gene expression resources: The Human Protein Atlas (HPA), Tissue-specific Gene Expression and Regulation (TiGER), and Genotype-Tissue Expression (GTEx). For these 2461 TissGenes, we performed gene expression, somatic mutation, and prognostic marker-based analyses across 28 cancer types using TCGA data. Our analyses identified hundreds of TissGenes, including genes that universally kept or lost tissue-specific gene expression, with other features: cancer type-specific isoform expression, fusion with oncogenes or tumor suppressor genes, and markers for protective or risk prognosis. TissGDB provides seven categories of annotations: TissGeneSummary, TissGeneExp, TissGene-miRNA, TissGeneMut, TissGeneNet, TissGeneProg, TissGeneClin.
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Affiliation(s)
- Pora Kim
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Aekyung Park
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon 57922, Korea
| | - Guangchun Han
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Hua Sun
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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26
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Kim P, Jang YE, Lee S. FusionScan: accurate prediction of fusion genes from RNA-Seq data. Genomics Inform 2019; 17:e26. [PMID: 31610622 PMCID: PMC6808644 DOI: 10.5808/gi.2019.17.3.e26] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 03/21/2019] [Indexed: 01/10/2023] Open
Abstract
Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.
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Affiliation(s)
- Pora Kim
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea
| | - Ye Eun Jang
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea.,Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Korea
| | - Sanghyuk Lee
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea.,Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Korea.,Department of Life Science, Ewha Womans University, Seoul 03760, Korea
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27
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Xia LC, Bell JM, Wood-Bouwens C, Chen JJ, Zhang NR, Ji HP. Identification of large rearrangements in cancer genomes with barcode linked reads. Nucleic Acids Res 2019; 46:e19. [PMID: 29186506 PMCID: PMC5829571 DOI: 10.1093/nar/gkx1193] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/17/2017] [Indexed: 01/08/2023] Open
Abstract
Large genomic rearrangements involve inversions, deletions and other structural changes that span Megabase segments of the human genome. This category of genetic aberration is the cause of many hereditary genetic disorders and contributes to pathogenesis of diseases like cancer. We developed a new algorithm called ZoomX for analysing barcode-linked sequence reads—these sequences can be traced to individual high molecular weight DNA molecules (>50 kb). To generate barcode linked sequence reads, we employ a library preparation technology (10X Genomics) that uses droplets to partition and barcode DNA molecules. Using linked read data from whole genome sequencing, we identify large genomic rearrangements, typically greater than 200kb, even when they are only present in low allelic fractions. Our algorithm uses a Poisson scan statistic to identify genomic rearrangement junctions, determine counts of junction-spanning molecules and calculate a Fisher's exact test for determining statistical significance for somatic aberrations. Utilizing a well-characterized human genome, we benchmarked this approach to accurately identify large rearrangement. Subsequently, we demonstrated that our algorithm identifies somatic rearrangements when present in lower allelic fractions as occurs in tumors. We characterized a set of complex cancer rearrangements with multiple classes of structural aberrations and with possible roles in oncogenesis.
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Affiliation(s)
- Li C Xia
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John M Bell
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA
| | - Christina Wood-Bouwens
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jiamin J Chen
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nancy R Zhang
- Department of Statistics, the Wharton School, University of Pennsylvania, Philadelphia, PA 18014, USA
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.,Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA
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28
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Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers. Proc Natl Acad Sci U S A 2019; 116:15524-15533. [PMID: 31308241 DOI: 10.1073/pnas.1900391116] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling 10-fold fewer false-positive fusions in nontransformed human tissues. We leverage the increased precision of DEEPEST to discover fundamental cancer biology. Namely, 888 candidate oncogenes are identified based on overrepresentation in DEEPEST calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs, demonstrating a previously unappreciated prevalence and potential for function. DEEPEST also reveals a high enrichment for fusions involving oncogenes in cancers, including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. Specific protein domains are enriched in DEEPEST calls, indicating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.
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Hsiao SJ, Zehir A, Sireci AN, Aisner DL. Detection of Tumor NTRK Gene Fusions to Identify Patients Who May Benefit from Tyrosine Kinase (TRK) Inhibitor Therapy. J Mol Diagn 2019; 21:553-571. [PMID: 31075511 PMCID: PMC7456740 DOI: 10.1016/j.jmoldx.2019.03.008] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/17/2019] [Accepted: 03/01/2019] [Indexed: 01/04/2023] Open
Abstract
Chromosomal rearrangements involving the NTRK1, NTRK2, and NTRK3 genes (NTRK genes), which encode the high-affinity nerve growth factor receptor (TRKA), brain-derived neurotrophic factor/neurotrophin-3 (BDNF/NT-3) growth factor receptor (TRKB), and neurotrophin-3 (NT-3) growth factor receptor (TRKC) tyrosine kinases (TRK proteins), act as oncogenic drivers in a broad range of pediatric and adult tumor types. NTRK gene fusions have been shown to be actionable genomic events that are predictive of response to TRK kinase inhibitors, making their routine detection an evolving clinical priority. In certain exceedingly rare tumor types, NTRK gene fusions may be seen in the overwhelming majority of cases, whereas in a range of common cancers, reported incidences are in the range of 0.1% to 2%. Herein, we review the structure of the three NTRK genes and the nature and incidence of NTRK gene fusions in different solid tumor types, and we summarize the clinical data showing the importance of identifying tumors harboring such genomic events. We also outline the laboratory techniques that can be used to diagnose NTRK gene fusions in clinical samples. Finally, we propose a diagnostic algorithm for solid tumors to facilitate the identification of patients with TRK fusion cancer. This algorithm accounts for the widely varying frequencies by tumor histology and the underlying prevalence of TRK expression in the absence of NTRK gene fusions and is based on a combination of fluorescence in situ hybridization, next-generation sequencing, and immunohistochemistry assays.
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Affiliation(s)
- Susan J Hsiao
- Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York
| | - Ahmet Zehir
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anthony N Sireci
- Department of Medical Affairs, Loxo Oncology, Inc., Stamford, Connecticut
| | - Dara L Aisner
- Department of Pathology, University of Colorado, Aurora, Colorado.
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30
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Kumar R, Nagpal G, Kumar V, Usmani SS, Agrawal P, Raghava GPS. HumCFS: a database of fragile sites in human chromosomes. BMC Genomics 2019; 19:985. [PMID: 30999860 PMCID: PMC7402404 DOI: 10.1186/s12864-018-5330-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 11/29/2018] [Indexed: 11/25/2022] Open
Abstract
Background Fragile sites are the chromosomal regions that are susceptible to breakage, and their frequency varies among the human population. Based on the frequency of fragile site induction, they are categorized as common and rare fragile sites. Common fragile sites are sensitive to replication stress and often rearranged in cancer. Rare fragile sites are the archetypal trinucleotide repeats. Fragile sites are known to be involved in chromosomal rearrangements in tumors. Human miRNA genes are also present at fragile sites. A better understanding of genes and miRNAs lying in the fragile site regions and their association with disease progression is required. Result HumCFS is a manually curated database of human chromosomal fragile sites. HumCFS provides useful information on fragile sites such as coordinates on the chromosome, cytoband, their chemical inducers and frequency of fragile site (rare or common), genes and miRNAs lying in fragile sites. Protein coding genes in the fragile sites were identified by mapping the coordinates of fragile sites with human genome Ensembl (GRCh38/hg38). Genes present in fragile sites were further mapped to DisGenNET database, to understand their possible link with human diseases. Human miRNAs from miRBase was also mapped on fragile site coordinates. In brief, HumCFS provides useful information about 125 human chromosomal fragile sites and their association with 4921 human protein-coding genes and 917 human miRNA’s. Conclusion User-friendly web-interface of HumCFS and hyper-linking with other resources will help researchers to search for genes, miRNAs efficiently and to intersect the relationship among them. For easy data retrieval and analysis, we have integrated standard web-based tools, such as JBrowse, BLAST etc. Also, the user can download the data in various file formats such as text files, gff3 files and Bed-format files which can be used on UCSC browser. Database URL:http://webs.iiitd.edu.in/raghava/humcfs/ Electronic supplementary material The online version of this article (10.1186/s12864-018-5330-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rajesh Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Gandharva Nagpal
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Vinod Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Salman Sadullah Usmani
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Piyush Agrawal
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 110020, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, 160036, India
| | - Gajendra P S Raghava
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, 110020, India.
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31
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Beekhof R, van Alphen C, Henneman AA, Knol JC, Pham TV, Rolfs F, Labots M, Henneberry E, Le Large TY, de Haas RR, Piersma SR, Vurchio V, Bertotti A, Trusolino L, Verheul HM, Jimenez CR. INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases. Mol Syst Biol 2019; 15:e8250. [PMID: 30979792 PMCID: PMC6461034 DOI: 10.15252/msb.20188250] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/15/2019] [Accepted: 03/20/2019] [Indexed: 12/19/2022] Open
Abstract
Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.
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Affiliation(s)
- Robin Beekhof
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Carolien van Alphen
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alex A Henneman
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jaco C Knol
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Thang V Pham
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frank Rolfs
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mariette Labots
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Evan Henneberry
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tessa Ys Le Large
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Richard R de Haas
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sander R Piersma
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Valentina Vurchio
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Andrea Bertotti
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Livio Trusolino
- Department of Oncology, Candiolo Cancer Institute IRCCS, University of Torino, Torino, Italy
| | - Henk Mw Verheul
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Connie R Jimenez
- Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- OncoProteomics Laboratory, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Zhu C, Wu L, Lv Y, Guan J, Bai X, Lin J, Liu T, Yang X, Robson SC, Sang X, Xue C, Zhao H. The fusion landscape of hepatocellular carcinoma. Mol Oncol 2019; 13:1214-1225. [PMID: 30903738 PMCID: PMC6487730 DOI: 10.1002/1878-0261.12479] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 02/26/2019] [Accepted: 02/27/2019] [Indexed: 12/30/2022] Open
Abstract
Most cases of hepatocellular carcinoma (HCC) are already advanced at the time of diagnosis, which limits treatment options. Challenges in early‐stage diagnosis may be due to the genetic complexity of HCC. Gene fusion plays a critical function in tumorigenesis and cancer progression in multiple cancers, yet the identities of fusion genes as potential diagnostic markers in HCC have not been investigated. Here, we employed STAR‐Fusion and identified 43 recurrent fusion events in our own and four public RNA‐seq datasets. We identified 2354 different gene fusions in two hepatitis B virus (HBV)‐HCC patients. Validation analysis against the four RNA‐seq datasets revealed that only 1.8% (43/2354) were recurrent fusions. Comparison with the four fusion databases demonstrated that 19 recurrent fusions were not previously annotated to diseases and three were annotated as disease‐related fusion events. Finally, we validated six of the novel fusion events, including RP11‐476K15.1‐CTD‐2015H3.2, by RT‐PCR and Sanger sequencing of 14 pairs of HBV‐related HCC samples. In summary, our study provides new insights into gene fusions in HCC and may contribute to the development of anti‐HCC therapy.
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Affiliation(s)
- Chengpei Zhu
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liangcai Wu
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanling Lv
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,My Health Gene Technology Co., Ltd., Service Centre of Tianjin Chentang Science and Technology Commercial District, China
| | - Jinxia Guan
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,My Health Gene Technology Co., Ltd., Service Centre of Tianjin Chentang Science and Technology Commercial District, China
| | - Xue Bai
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianzhen Lin
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tingting Liu
- My Health Gene Technology Co., Ltd., Service Centre of Tianjin Chentang Science and Technology Commercial District, China
| | - Xiaobo Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Simon C Robson
- Liver Center and The Transplant Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xinting Sang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenghai Xue
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,My Health Gene Technology Co., Ltd., Service Centre of Tianjin Chentang Science and Technology Commercial District, China.,Joint Laboratory of Large-scale Medical Data Pattern Mining and Application, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Haitao Zhao
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Kim P, Zhou X. FusionGDB: fusion gene annotation DataBase. Nucleic Acids Res 2019; 47:D994-D1004. [PMID: 30407583 PMCID: PMC6323909 DOI: 10.1093/nar/gky1067] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 10/05/2018] [Accepted: 11/01/2018] [Indexed: 12/26/2022] Open
Abstract
Gene fusion is one of the hallmarks of cancer genome via chromosomal rearrangement initiated by DNA double-strand breakage. To date, many fusion genes (FGs) have been established as important biomarkers and therapeutic targets in multiple cancer types. To better understand the function of FGs in cancer types and to promote the discovery of clinically relevant FGs, we built FusionGDB (Fusion Gene annotation DataBase) available at https://ccsm.uth.edu/FusionGDB. We collected 48 117 FGs across pan-cancer from three representative fusion gene resources: the improved database of chimeric transcripts and RNA-seq data (ChiTaRS 3.1), an integrative resource for cancer-associated transcript fusions (TumorFusions), and The Cancer Genome Atlas (TCGA) fusions by Gao et al. For these ∼48K FGs, we performed functional annotations including gene assessment across pan-cancer fusion genes, open reading frame (ORF) assignment, and retention search of 39 protein features based on gene structures of multiple isoforms with different breakpoints. We also provided the fusion transcript and amino acid sequences according to multiple breakpoints and transcript isoforms. Our analyses identified 331, 303 and 667 in-frame FGs with retaining kinase, DNA-binding, and epigenetic factor domains, respectively, as well as 976 FGs lost protein-protein interaction. FusionGDB provides six categories of annotations: FusionGeneSummary, FusionProtFeature, FusionGeneSequence, FusionGenePPI, RelatedDrug and RelatedDisease.
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Affiliation(s)
- Pora Kim
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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34
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Cheng YW, Chen YM, Zhao QQ, Zhao X, Wu YR, Chen DZ, Liao LD, Chen Y, Yang Q, Xu LY, Li EM, Xu JZ. Long Read Single-Molecule Real-Time Sequencing Elucidates Transcriptome-Wide Heterogeneity and Complexity in Esophageal Squamous Cells. Front Genet 2019; 10:915. [PMID: 31636653 PMCID: PMC6787290 DOI: 10.3389/fgene.2019.00915] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 08/29/2019] [Indexed: 02/05/2023] Open
Abstract
Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules. Here, we combined single-molecule real-time sequencing with RNA-seq to generate high-quality long reads and to survey the transcriptional program in esophageal squamous cells. Compared with the recent annotations of human transcriptome (Ensembl 38 release 91), single-molecule real-time data identified many unannotated transcripts, novel isoforms of known genes and an expanding repository of long intergenic noncoding RNAs (lincRNAs). By integrating with annotation of lincRNA catalog, 1,521 esophageal-cancer-specific lincRNAs were defined from single-molecule real-time reads. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these lincRNAs and their target genes are involved in a variety of cancer signaling pathways. Isoform usage analysis revealed the shifted alternative splicing patterns, which can be recaptured from clinical samples or supported by previous studies. Utilizing vigorous searching criteria, we also detected multiple transcript fusions, which are not documented in current gene fusion database or readily identified from RNA-seq reads. Two novel fusion transcripts were verified based on real-time PCR and Sanger sequencing. Overall, our long-read single-molecule sequencing largely expands current understanding of full-length transcriptome in esophageal cells and provides novel insights on the transcriptional diversity during oncogenic transformation.
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Affiliation(s)
- Yin-Wei Cheng
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Yun-Mei Chen
- Tianjin Novogene Bioinformatics Technology Co., Ltd, Tianjin, China
| | - Qian-Qian Zhao
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Xing Zhao
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Ya-Ru Wu
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Dan-Ze Chen
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
| | - Lian-Di Liao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- China Institute of Oncologic Pathology, Shantou University Medical College, Shantou, China
| | - Yang Chen
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Qian Yang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
| | - Li-Yan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- China Institute of Oncologic Pathology, Shantou University Medical College, Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Jian-Zhen Xu,
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Jian-Zhen Xu,
| | - Jian-Zhen Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, China
- Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical College (SUMC), Shantou, China
- *Correspondence: Li-Yan Xu, ; En-Min Li, ; Jian-Zhen Xu,
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Rebolledo-Jaramillo B, Ziegler A. Teneurins: An Integrative Molecular, Functional, and Biomedical Overview of Their Role in Cancer. Front Neurosci 2018; 12:937. [PMID: 30618566 PMCID: PMC6297388 DOI: 10.3389/fnins.2018.00937] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 11/28/2018] [Indexed: 12/16/2022] Open
Abstract
Teneurins are large transmembrane proteins originally identified in Drosophila. Their essential role in development of the central nervous system is conserved throughout species, and evidence supports their involvement in organogenesis of additional tissues. Homophilic and heterophilic interactions between Teneurin paralogues mediate cellular adhesion in crucial processes such as neuronal pathfinding and synaptic organization. At the molecular level, Teneurins are proteolytically processed into distinct subdomains that have been implicated in extracellular and intracellular signaling, and in transcriptional regulation. Phylogenetic studies have shown a high degree of intra- and interspecies conservation of Teneurin genes. Accordingly, the occurrence of genetic variants has been associated with functional and phenotypic alterations in experimental systems, and with some inherited or sporadic conditions. Recently, tumor-related variations in Teneurin gene expression have been associated with patient survival in different cancers. Although these findings were incidental and molecular mechanisms were not addressed, they suggested a potential utility of Teneurin transcript levels as biomarkers for disease prognosis. Mutations and chromosomal alterations affecting Teneurin genes have been found occasionally in tumors, but literature remains scarce. The analysis of open-access molecular and clinical datasets derived from large oncologic cohorts provides an invaluable resource for the identification of additional somatic mutations. However, Teneurin variants have not been classified in terms of pathogenic risk and their phenotypic impact remains unknown. On this basis, is it plausible to hypothesize that Teneurins play a role in carcinogenesis? Does current evidence support a tumor suppressive or rather oncogenic function for these proteins? Here, we comprehensively discuss available literature with integration of molecular evidence retrieved from open-access databases. We show that Teneurins undergo somatic changes comparable to those of well-established cancer genes, and discuss their involvement in cancer-related signaling pathways. Current data strongly suggest a functional contribution of Teneurins to human carcinogenesis.
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Affiliation(s)
| | - Annemarie Ziegler
- Center for Genetics and Genomics, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
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36
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Gioiosa S, Bolis M, Flati T, Massini A, Garattini E, Chillemi G, Fratelli M, Castrignanò T. Massive NGS data analysis reveals hundreds of potential novel gene fusions in human cell lines. Gigascience 2018; 7:5026623. [PMID: 29860514 PMCID: PMC6207142 DOI: 10.1093/gigascience/giy062] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 05/29/2018] [Indexed: 01/19/2023] Open
Abstract
Background Gene fusions derive from chromosomal rearrangements. The resulting chimeric transcripts are often endowed with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with different prognosis and, in some cases, they can provide specific drug targets. To date, many efforts have been carried out to study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive next-generation sequencing dataset for all existing human tumor cell lines has provided the opportunity to further investigate these data in order to identify novel and still uncharacterized gene fusion events. Results In our work, we have extensively reanalyzed 935 paired-end RNA-sequencing experiments downloaded from the Cancer Cell Line Encyclopedia repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has been performed by the execution of four gene fusion detection algorithms. The results have been further prioritized by running a Bayesian classifier that makes an in silico validation. The collection of fusion events supported by all of the predictive software results in a robust set of ∼1,700 in silico predicted novel candidates suitable for downstream analyses. Given the huge amount of data and information produced, computational results have been systematized in a database named LiGeA. The database can be browsed through a dynamic and interactive web portal, further integrated with validated data from other well-known repositories. Taking advantage of the intuitive query forms, the users can easily access, navigate, filter, and select the putative gene fusions for further validations and studies. They can also find suitable experimental models for a given fusion of interest. Conclusions We believe that the LiGeA resource can represent not only the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant cell lines but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer biomarkers and specific drug targets.
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Affiliation(s)
- Silvia Gioiosa
- SCAI-Super Computing Applications and Innovation Department, CINECA, Rome, Italy.,National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | - Marco Bolis
- Laboratory of Molecular Biology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri," Milano, Italy
| | - Tiziano Flati
- SCAI-Super Computing Applications and Innovation Department, CINECA, Rome, Italy.,National Council of Research, CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | | | - Enrico Garattini
- Laboratory of Molecular Biology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri," Milano, Italy
| | - Giovanni Chillemi
- SCAI-Super Computing Applications and Innovation Department, CINECA, Rome, Italy
| | - Maddalena Fratelli
- Laboratory of Molecular Biology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri," Milano, Italy
| | - Tiziana Castrignanò
- SCAI-Super Computing Applications and Innovation Department, CINECA, Rome, Italy
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Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data. Mol Genet Genomics 2018; 293:1217-1229. [PMID: 29882166 DOI: 10.1007/s00438-018-1454-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 05/31/2018] [Indexed: 10/14/2022]
Abstract
Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse .
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Wang C, Yin R, Dai J, Gu Y, Cui S, Ma H, Zhang Z, Huang J, Qin N, Jiang T, Geng L, Zhu M, Pu Z, Du F, Wang Y, Yang J, Chen L, Wang Q, Jiang Y, Dong L, Yao Y, Jin G, Hu Z, Jiang L, Xu L, Shen H. Whole-genome sequencing reveals genomic signatures associated with the inflammatory microenvironments in Chinese NSCLC patients. Nat Commun 2018; 9:2054. [PMID: 29799009 PMCID: PMC5967326 DOI: 10.1038/s41467-018-04492-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 05/01/2018] [Indexed: 01/03/2023] Open
Abstract
Chinese lung cancer patients have distinct epidemiologic and genomic features, highlighting the presence of specific etiologic mechanisms other than smoking. Here, we present a comprehensive genomic landscape of 149 non-small cell lung cancer (NSCLC) cases and identify 15 potential driver genes. We reveal that Chinese patients are specially characterized by not only highly clustered EGFR mutations but a mutational signature (MS3, 33.7%), that is associated with inflammatory tumor-infiltrating B lymphocytes (P = 0.001). The EGFR mutation rate is significantly increased with the proportion of the MS3 signature (P = 9.37 × 10−5). TCGA data confirm that the infiltrating B lymphocyte abundance is significantly higher in the EGFR-mutated patients (P = 0.007). Additionally, MS3-high patients carry a higher contribution of distant chromosomal rearrangements >1 Mb (P = 1.35 × 10−7), some of which result in fusions involving genes with important functions (i.e., ALK and RET). Thus, inflammatory infiltration may contribute to the accumulation of EGFR mutations, especially in never-smokers. The distinct genomic and epidemiological features of Chinese lung cancer patients suggest the presence of alternative causal mechanisms. Here, the authors present the genomic landscape of 149 Chinese NSCLC patients and reveal distinct mutational signatures associated with inflammatory microenvironments.
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Affiliation(s)
- Cheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China.,Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 211116, Nanjing, China
| | - Rong Yin
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, 210029, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Yayun Gu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Shaohua Cui
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Zhihong Zhang
- Department of Pathology, First Affiliated Hospital, Nanjing Medical University, 210029, Nanjing, China
| | - Jiaqi Huang
- Cellular Biomedicine Group, Inc., 200233, Shanghai, China
| | - Na Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Tao Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Liguo Geng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Zhening Pu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Fangzhi Du
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Jianshui Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Liang Chen
- Department of Thoracic Surgery, First Affiliated Hospital, Nanjing Medical University, 210029, Nanjing, China
| | - Qianghu Wang
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 211116, Nanjing, China
| | - Yue Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Lili Dong
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, China
| | - Yihong Yao
- Cellular Biomedicine Group, Inc., 200233, Shanghai, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China
| | - Liyan Jiang
- Department of Respiratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, China.
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, 210029, Nanjing, China.
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 211116, Nanjing, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 211116, Nanjing, China.
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Panigrahi P, Jere A, Anamika K. FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer. PLoS One 2018; 13:e0196588. [PMID: 29715310 PMCID: PMC5929557 DOI: 10.1371/journal.pone.0196588] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/16/2018] [Indexed: 12/15/2022] Open
Abstract
Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.
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Affiliation(s)
| | - Abhay Jere
- LABS, Persistent Systems, Pingala-Aryabhata, Erandwane, Pune, India
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40
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Hintzsche JD, Yoo M, Kim J, Amato CM, Robinson WA, Tan AC. IMPACT web portal: oncology database integrating molecular profiles with actionable therapeutics. BMC Med Genomics 2018; 11:26. [PMID: 29697364 PMCID: PMC5918430 DOI: 10.1186/s12920-018-0350-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background With the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs. Results IMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes. Conclusion IMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT.
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Affiliation(s)
- Jennifer D Hintzsche
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.,Division of Medical Oncology, Department of Medicine, Robinson Melanoma Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Minjae Yoo
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jihye Kim
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Carol M Amato
- Division of Medical Oncology, Department of Medicine, Robinson Melanoma Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - William A Robinson
- Division of Medical Oncology, Department of Medicine, Robinson Melanoma Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Aik Choon Tan
- Division of Medical Oncology, Department of Medicine, Translational Bioinformatics and Cancer Systems Biology Laboratory, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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41
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Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS, Liu Y, Akbani R, Feng B, Donehower LA, Miller C, Shen Y, Karimi M, Chen H, Kim P, Jia P, Shinbrot E, Zhang S, Liu J, Hu H, Bailey MH, Yau C, Wolf D, Zhao Z, Weinstein JN, Li L, Ding L, Mills GB, Laird PW, Wheeler DA, Shmulevich I, Monnat RJ, Xiao Y, Wang C. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep 2018; 23:239-254.e6. [PMID: 29617664 PMCID: PMC5961503 DOI: 10.1016/j.celrep.2018.03.076] [Citation(s) in RCA: 675] [Impact Index Per Article: 112.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 03/07/2018] [Accepted: 03/19/2018] [Indexed: 12/20/2022] Open
Abstract
DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.
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Affiliation(s)
| | - Linghua Wang
- Department of Genomic Medicine, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael T Zimmermann
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226-0509, USA; Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA
| | | | - Galen F Gao
- The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Andrew D Cherniack
- The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA
| | - Huihui Fan
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Hui Shen
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Gregory P Way
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Yuexin Liu
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bin Feng
- TESARO Inc., Waltham, MA 02451, USA
| | - Lawrence A Donehower
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chase Miller
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, 3128 TAMU, Texas A&M University, College Station, TX 77843, USA
| | - Mostafa Karimi
- Department of Electrical and Computer Engineering, 3128 TAMU, Texas A&M University, College Station, TX 77843, USA
| | - Haoran Chen
- Department of Electrical and Computer Engineering, 3128 TAMU, Texas A&M University, College Station, TX 77843, USA
| | - Pora Kim
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Eve Shinbrot
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shaojun Zhang
- Department of Genomic Medicine, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA
| | - Jianfang Liu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA 15963, USA
| | - Hai Hu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA 15963, USA
| | - Matthew H Bailey
- Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63110, USA
| | - Christina Yau
- University of California, San Francisco, San Francisco, CA 94115, USA; Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Denise Wolf
- University of California, San Francisco, San Francisco, CA 94115, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - John N Weinstein
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Li
- Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer, Houston, TX 77030, USA
| | - Li Ding
- Division of Oncology, Department of Medicine, Washington University, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University, St. Louis, MO 63110, USA; Department of Genetics, Washington University, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University, St. Louis, MO 63110, USA
| | - Gordon B Mills
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Peter W Laird
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Raymond J Monnat
- Departments of Pathology & Genome Sciences, University of Washington, Seattle, WA 98195-7705, USA.
| | | | - Chen Wang
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA; Department of Obstetrics and Gynecology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA.
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42
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Li Z, Qin F, Li H. Chimeric RNAs and their implications in cancer. Curr Opin Genet Dev 2017; 48:36-43. [PMID: 29100211 DOI: 10.1016/j.gde.2017.10.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 09/06/2017] [Accepted: 10/02/2017] [Indexed: 11/26/2022]
Abstract
Chimeric RNAs have been believed to be solely produced by gene fusions resulting from chromosomal rearrangement, thus unique features of cancer. Detected chimeric RNAs have also been viewed as surrogates for the presence of gene fusions. However, more and more research has demonstrated that chimeric RNAs in general are not a hallmark of cancer, but rather widely present in non-cancerous cells and tissues. At the same time, they may be produced by other mechanisms other than chromosomal rearrangement. The field of non-canonical chimeric RNAs is still in its infancy, with many challenges ahead, including the lack of a unified terminology. However, we believe that these non-canonical chimeric RNAs will have significant impacts in cancer detection and treatment.
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Affiliation(s)
- Zi Li
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA; Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Fujun Qin
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Hui Li
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA.
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