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hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer. Int J Mol Sci 2022; 23:ijms232314512. [PMID: 36498841 PMCID: PMC9738810 DOI: 10.3390/ijms232314512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Horizontal gene transfer (HGT) is well described in prokaryotes: it plays a crucial role in evolution, and has functional consequences in insects and plants. However, less is known about HGT in humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known human sequencing projects. Few of the existing tools for investigating HGT are highly automated. Thanks to the adoption of Nextflow for life sciences workflows, and to the standards and best practices curated by communities such as nf-core, fully automated, portable, and scalable pipelines can now be developed. Here we present nf-core/hgtseq to facilitate the analysis of HGT from sequencing data in different organisms. We showcase its performance by analysing six exome datasets from five mammals. Hgtseq can be run seamlessly in any computing environment and accepts data generated by existing exome and whole-genome sequencing projects; this will enable researchers to expand their analyses into this area. Fundamental questions are still open about the mechanisms and the extent or role of horizontal gene transfer: by releasing hgtseq we provide a standardised tool which will enable a systematic investigation of this phenomenon, thus paving the way for a better understanding of HGT.
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Butzmann A, Sridhar K, Jangam D, Song H, Singh A, Kumar J, Chisholm KM, Pinsky B, Huang F, Ohgami RS. Mutations in JAK/STAT and NOTCH1 Genes Are Enriched in Post-Transplant Lymphoproliferative Disorders. Front Oncol 2022; 11:790481. [PMID: 35111674 PMCID: PMC8801788 DOI: 10.3389/fonc.2021.790481] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/13/2021] [Indexed: 12/22/2022] Open
Abstract
Post-transplant lymphoproliferative disorders (PTLD) are diseases occurring in immunocompromised patients after hematopoietic stem cell transplantation (HCT) or solid organ transplantation (SOT). Although PTLD occurs rarely, it may be associated with poor outcomes. In most cases, PTLD is driven by Epstein-Barr virus (EBV) infection. Few studies have investigated the mutational landscape and gene expression profile of PTLD. In our study, we performed targeted deep sequencing and RNA-sequencing (RNA-Seq) on 16 cases of florid follicular hyperplasia (FFH) type PTLD and 15 cases of other PTLD types that include: ten monomorphic (M-PTLD), three polymorphic (P-PTLD), and two classic Hodgkin lymphoma type PTLDs (CHL-PTLD). Our study identified recurrent mutations in JAK3 in five of 15 PTLD cases and one of 16 FFH-PTLD cases, as well as 16 other genes that were mutated in M-PTLD, P-PTLD, CHL-PTLD and FFH-PTLD. Digital image analysis demonstrated significant differences in single cell area, major axis, and diameter when comparing cases of M-PTLD and P-PTLD to FFH-PTLD. No morphometric relationship was identified with regards to a specific genetic mutation. Our findings suggest that immune regulatory pathways play an essential role in PTLD, with the JAK/STAT pathway affected in many PTLDs.
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Affiliation(s)
- Alexandra Butzmann
- Agilent Technologies, Santa Clara, CA, United States
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
- *Correspondence: Alexandra Butzmann,
| | - Kaushik Sridhar
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
| | - Diwash Jangam
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Hanbing Song
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
| | - Amol Singh
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Jyoti Kumar
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Karen M. Chisholm
- Department of Laboratories, Seattle Children’s Hospital, Seattle, WA, United States
| | - Benjamin Pinsky
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Franklin Huang
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
| | - Robert S. Ohgami
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
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3
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Kang K, Imamovic L, Misiakou MA, Bornakke Sørensen M, Heshiki Y, Ni Y, Zheng T, Li J, Ellabaan MMH, Colomer-Lluch M, Rode AA, Bytzer P, Panagiotou G, Sommer MO. Expansion and persistence of antibiotic-specific resistance genes following antibiotic treatment. Gut Microbes 2022; 13:1-19. [PMID: 33779498 PMCID: PMC8018486 DOI: 10.1080/19490976.2021.1900995] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Oral antibiotics are commonly prescribed to non-hospitalized adults. However, antibiotic-induced changes in the human gut microbiome are often investigated in cohorts with preexisting health conditions and/or concomitant medication, leaving the effects of antibiotics not completely understood. We used a combination of omic approaches to comprehensively assess the effects of antibiotics on the gut microbiota and particularly the gut resistome of a small cohort of healthy adults. We observed that 3 to 19 species per individual proliferated during antibiotic treatment and Gram-negative species expanded significantly in relative abundance. While the overall relative abundance of antibiotic resistance gene homologs did not significantly change, antibiotic-specific gene homologs with presumed resistance toward the administered antibiotics were common in proliferating species and significantly increased in relative abundance. Virome sequencing and plasmid analysis showed an expansion of antibiotic-specific resistance gene homologs even 3 months after antibiotic administration, while paired-end read analysis suggested their dissemination among different species. These results suggest that antibiotic treatment can lead to a persistent expansion of antibiotic resistance genes in the human gut microbiota and provide further data in support of good antibiotic stewardship.Abbreviation: ARG - Antibiotic resistance gene homolog; AsRG - Antibiotic-specific resistance gene homolog; AZY - Azithromycin; CFX - Cefuroxime; CIP - Ciprofloxacin; DOX - Doxycycline; FDR - False discovery rate; GRiD - Growth rate index value; HGT - Horizontal gene transfer; NMDS - Non-metric multidimensional scaling; qPCR - Quantitative polymerase chain reaction; RPM - Reads per million mapped reads; TA - Transcriptional activity; TE - Transposable element; TPM - Transcripts per million mapped reads.
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Affiliation(s)
- Kang Kang
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark,Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany,CONTACT Lejla Imamovic Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, DK-2800, Lyngby, Denmark
| | - Lejla Imamovic
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark,Gianni Panagiotou Leibniz Institute for Natural Product Research and Infection Biology - Hans Knoell Institute, Adolf-Reichwein-Straße 23, 07745 Jena, Germany
| | - Maria-Anna Misiakou
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark,Morten O.A. Sommer Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, DK-2800, Lyngby, Denmark
| | - Maria Bornakke Sørensen
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark
| | - Yoshitaro Heshiki
- Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany,Kadoorie Biological Sciences Building, School of Biological Sciences, the University of Hong Kong, Hong Kong, S. A. R. China
| | - Yueqiong Ni
- Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany
| | - Tingting Zheng
- Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany,Kadoorie Biological Sciences Building, School of Biological Sciences, the University of Hong Kong, Hong Kong, S. A. R. China
| | - Jun Li
- Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany,Department of Infectious Diseases and Public Health, Colleague of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, S.A.R.China,School of Data Science, City University of Hong Kong, Hong Kong, S. A. R. China
| | - Mostafa M. H. Ellabaan
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark
| | - Marta Colomer-Lluch
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark
| | - Anne A. Rode
- Department of Medicine, Zealand University Hospital - Køge, Køge, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Peter Bytzer
- Department of Medicine, Zealand University Hospital - Køge, Køge, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Gianni Panagiotou
- Leibniz Institute for Natural Product Research and Infection Biology, Systems Biology and Bioinformatics - Hans Knoell Institute, Jena, Germany,Kadoorie Biological Sciences Building, School of Biological Sciences, the University of Hong Kong, Hong Kong, S. A. R. China,Department of Pharmacology and Pharmacy, the University of Hong Kong, Hong Kong, S. A. R. China
| | - Morten O.A. Sommer
- Novo Nordisk Foundation Center for Biosustainability,Technical University of Denmark, Lyngby, Denmark
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Scott S, Hallwirth CV, Hartkopf F, Grigson S, Jain Y, Alexander IE, Bauer DC, O W Wilson L. Isling: a tool for detecting integration of wild-type viruses and clinical vectors. J Mol Biol 2021; 434:167408. [PMID: 34929203 DOI: 10.1016/j.jmb.2021.167408] [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: 09/30/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 10/19/2022]
Abstract
Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.
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Affiliation(s)
- Suzanne Scott
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Claus V Hallwirth
- Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Felix Hartkopf
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Susanna Grigson
- College of Science and Engineering, Flinders University, Adelaide, Australia
| | - Yatish Jain
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia
| | - Ian E Alexander
- Gene Therapy Research Unit, Children's Medical Research Institute, Westmead, Australia; The Sydney Children's Hospitals Network, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia; Discipline of Child and Adolescent Health,Faculty of Medicine and Health,The University of Sydney, Sydney, New South Wales, Australia
| | - Denis C Bauer
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Discipline of Child and Adolescent Health,Faculty of Medicine and Health,The University of Sydney, Sydney, New South Wales, Australia; Macquarie University, Department of Biomedical Sciences, Faculty of Medicine and Health Science, Macquarie Park, Australia.
| | - Laurence O W Wilson
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, North Ryde, Australia; Macquarie University, Applied BioSciences, Faculty of Science and Engineering, Macquarie Park, Australia.
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Stephens Z, O’Brien D, Dehankar M, Roberts LR, Iyer RK, Kocher JP. Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data. PLoS One 2021; 16:e0250915. [PMID: 34550971 PMCID: PMC8457494 DOI: 10.1371/journal.pone.0250915] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/08/2021] [Indexed: 01/14/2023] Open
Abstract
The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene's read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with long read validation. We demonstrate this concordance across 6 TCGA Hepatocellular carcinoma (HCC) tumor samples, identifying integrations of hepatitis B virus that are also supported by long reads. Additionally, we applied Exogene to targeted capture data from 426 previously studied HCC samples, achieving 98.9% concordance with existing methods and identifying 238 high-confidence integrations that were not previously reported. Exogene is applicable to multiple types of paired-end sequence data, including genome, exome, RNA-Seq and targeted capture.
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Affiliation(s)
- Zachary Stephens
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States of America
| | - Daniel O’Brien
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Mrunal Dehankar
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Lewis R. Roberts
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Ravishankar K. Iyer
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States of America
| | - Jean-Pierre Kocher
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
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6
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Causes and Consequences of HPV Integration in Head and Neck Squamous Cell Carcinomas: State of the Art. Cancers (Basel) 2021; 13:cancers13164089. [PMID: 34439243 PMCID: PMC8394665 DOI: 10.3390/cancers13164089] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 12/29/2022] Open
Abstract
A constantly increasing incidence in high-risk Human Papillomaviruses (HPV)s driven head and neck squamous cell carcinomas (HNSCC)s, especially of oropharyngeal origin, is being observed. During persistent infections, viral DNA integration into the host genome may occur. Studies are examining if the physical status of the virus (episomal vs. integration) affects carcinogenesis and eventually has further-reaching consequences on disease progression and outcome. Here, we review the literature of the most recent five years focusing on the impact of HPV integration in HNSCCs, covering aspects of detection techniques used (from PCR up to NGS approaches), integration loci identified, and associations with genomic and clinical data. The consequences of HPV integration in the human genome, including the methylation status and deregulation of genes involved in cell signaling pathways, immune evasion, and response to therapy, are also summarized.
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Yi S, Zhang X, Yang L, Huang J, Liu Y, Wang C, Schaid DJ, Chen J. 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies. Genome Biol 2021; 22:208. [PMID: 34256818 PMCID: PMC8276451 DOI: 10.1186/s13059-021-02418-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/24/2021] [Indexed: 11/10/2022] Open
Abstract
One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.
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Affiliation(s)
- Sangyoon Yi
- Department of Statistics, Texas A&M University, College Station, TX, 77843, USA
| | - Xianyang Zhang
- Department of Statistics, Texas A&M University, College Station, TX, 77843, USA.
| | - Lu Yang
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Yuanhang Liu
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel J Schaid
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jun Chen
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
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8
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Pischedda E, Crava C, Carlassara M, Zucca S, Gasmi L, Bonizzoni M. ViR: a tool to solve intrasample variability in the prediction of viral integration sites using whole genome sequencing data. BMC Bioinformatics 2021; 22:45. [PMID: 33541262 PMCID: PMC7863434 DOI: 10.1186/s12859-021-03980-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/27/2021] [Indexed: 12/16/2022] Open
Abstract
Background Several bioinformatics pipelines have been developed to detect sequences from viruses that integrate into the human genome because of the health relevance of these integrations, such as in the persistence of viral infection and/or in generating genotoxic effects, often progressing into cancer. Recent genomics and metagenomics analyses have shown that viruses also integrate into the genome of non-model organisms (i.e., arthropods, fish, plants, vertebrates). However, rarely studies of endogenous viral elements (EVEs) in non-model organisms have gone beyond their characterization from reference genome assemblies. In non-model organisms, we lack a thorough understanding of the widespread occurrence of EVEs and their biological relevance, apart from sporadic cases which nevertheless point to significant roles of EVEs in immunity and regulation of expression. The concomitance of repetitive DNA, duplications and/or assembly fragmentations in a genome sequence and intrasample variability in whole-genome sequencing (WGS) data could determine misalignments when mapping data to a genome assembly. This phenomenon hinders our ability to properly identify integration sites. Results To fill this gap, we developed ViR, a pipeline which solves the dispersion of reads due to intrasample variability in sequencing data from both single and pooled DNA samples thus ameliorating the detection of integration sites. We tested ViR to work with both in silico and real sequencing data from a non-model organism, the arboviral vector Aedes albopictus. Potential viral integrations predicted by ViR were molecularly validated supporting the accuracy of ViR results. Conclusion ViR will open new venues to explore the biology of EVEs, especially in non-model organisms. Importantly, while we generated ViR with the identification of EVEs in mind, its application can be extended to detect any lateral transfer event providing an ad-hoc sequence to interrogate.
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Affiliation(s)
- Elisa Pischedda
- Department of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy
| | - Cristina Crava
- Department of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.,ERI BIOTECMED, Universitat de Valencia, 46010, Valencia, Spain
| | - Martina Carlassara
- Department of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy
| | | | - Leila Gasmi
- Department of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy
| | - Mariangela Bonizzoni
- Department of Biology and Biotechnology, University of Pavia, 27100, Pavia, Italy.
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Butzmann A, Sridhar K, Jangam D, Kumar J, Sahoo MK, Shahmarvand N, Warnke R, Rangasamy E, Pinsky BA, Ohgami RS. A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis. Int J Mol Med 2020; 46:1466-1476. [PMID: 32945366 PMCID: PMC7447311 DOI: 10.3892/ijmm.2020.4686] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 04/22/2020] [Indexed: 11/29/2022] Open
Abstract
Angioimmunoblastic T-cell lymphoma (AITL) is a uniquely aggressive mature T-cell neoplasm. In recent years, recurrent genetic mutations in ras homolog family member A (RHOA), tet methylcytosine dioxygenase 2 (TET2), DNA methyltransferase 3 alpha (DNMT3A) and isocitrate dehydrogenase [NADP(+)] 2 (IDH2) have been identified as associated with AITL. However, a deep molecular study assessing both DNA mutations and RNA expression profile combined with digital image analysis is lacking. The present study aimed to evaluate the significance of molecular and morphologic features by high resolution digital image analysis in several cases of AITL. To do so, a total of 18 separate tissues from 10 patients with AITL were collected and analyzed. The results identified recurrent mutations in RHOA, TET2, DNMT3A, and IDH2, and demonstrated increased DNA mutations in coding, promoter and CCCTC binding factor (CTCF) binding sites in RHOA mutated AITLs vs. RHOA non-mutated cases, as well as increased overall survival in RHOA mutated patients. In addition, single cell computational digital image analysis morphologically characterized RHOA mutated AITL cells as distinct from cells from RHOA mutation negative patients. Computational analysis of single cell morphological parameters revealed that RHOA mutated cells have decreased eccentricity (more circular) compared with RHOA non-mutated AITL cells. In conclusion, the results from the present study expand our understanding of AITL and demonstrate that there are specific cell biological and morphological manifestations of RHOA mutations in cases of AITL.
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Affiliation(s)
| | - Kaushik Sridhar
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Diwash Jangam
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Jyoti Kumar
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | | | - Nahid Shahmarvand
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Roger Warnke
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
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Indolent In Situ B-Cell Neoplasms With MYC Rearrangements Show Somatic Mutations in MYC and TNFRSF14 by Next-generation Sequencing. Am J Surg Pathol 2020; 43:1720-1725. [PMID: 31368914 DOI: 10.1097/pas.0000000000001338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Systemic high-grade B-cell lymphomas (HGBCLs) with MYC gene rearrangements are clinically aggressive. In situ lesions with indolent behavior have not been described to date. We have identified 2 cases of in situ B-cell neoplasms with MYC rearrangements (IS-BCN, MYC) occurring, and focally confined to ≤4 lymphoid follicles in otherwise healthy individuals and without clinical progression despite minimal intervention (surgical only). Morphologically similar to systemic HGBCLs, the low power view of these lesions showed a starry sky pattern with numerous mitotic figures. High power imaging demonstrated these cells to be medium-large in size with irregular nuclear contours, immature chromatin, and prominent nucleoli. Immunophenotypically these cells were light chain restricted, positive for CD20, CD10, c-Myc, and dim or negative for BCL2 with a Ki67 proliferative index of >95%. By fluorescence in situ hybridization studies, we detected MYC translocations in these cells but no rearrangements in BCL2 or BCL6. Microdissection of neoplastic cells in these patients followed by targeted next-generation sequencing identified a mutation in MYC, D2N, and an indel in TNFRSF14. Mutations in ID3 or TCF3 were not identified. Although rare, these lesions should be separated from HGBCLs involving follicles but with systemic spread which has been previously described. Unlike systemic lymphomas with MYC gene rearrangements, these in situ B-cell neoplasms with MYC rearrangements did not require systemic therapy and no progression has been seen in either patient beyond 1 year (29 and 16 mo). Our work offers pathologic and biologic insight into the early process of B-cell neoplasia.
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