1
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Achom M, Sadagopan A, Bao C, McBride F, Xu Q, Konda P, Tourdot RW, Li J, Nakhoul M, Gallant DS, Ahmed UA, O’Toole J, Freeman D, Mary Lee GS, Hecht JL, Kauffman EC, Einstein DJ, Choueiri TK, Zhang CZ, Viswanathan SR. A genetic basis for cancer sex differences revealed in Xp11 translocation renal cell carcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552029. [PMID: 37577497 PMCID: PMC10418269 DOI: 10.1101/2023.08.04.552029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Xp11 translocation renal cell carcinoma (tRCC) is a female-predominant kidney cancer driven by translocations between the TFE3 gene on chromosome Xp11.2 and partner genes located on either chrX or on autosomes. The rearrangement processes that underlie TFE3 fusions, and whether they are linked to the female sex bias of this cancer, are largely unexplored. Moreover, whether oncogenic TFE3 fusions arise from both the active and inactive X chromosomes in females remains unknown. Here we address these questions by haplotype-specific analyses of whole-genome sequences of 29 tRCC samples from 15 patients and by re-analysis of 145 published tRCC whole-exome sequences. We show that TFE3 fusions universally arise as reciprocal translocations with minimal DNA loss or insertion at paired break ends. Strikingly, we observe a near exact 2:1 female:male ratio in TFE3 fusions arising via X:autosomal translocation (but not via X inversion), which accounts for the female predominance of tRCC. This 2:1 ratio is at least partially attributable to oncogenic fusions involving the inactive X chromosome and is accompanied by partial re-activation of silenced chrX genes on the rearranged chromosome. Our results highlight how somatic alterations involving the X chromosome place unique constraints on tumor initiation and exemplify how genetic rearrangements of the sex chromosomes can underlie cancer sex differences.
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Affiliation(s)
- Mingkee Achom
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Medicine, Harvard Medical School; Boston, MA, USA
| | - Ananthan Sadagopan
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Chunyang Bao
- Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital; Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard; Cambridge, MA, USA
| | - Fiona McBride
- Department of Biomedical Informatics, Blavatnik Institute, Harvard Medical School; Boston, MA, USA
| | - Qingru Xu
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Prathyusha Konda
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Medicine, Harvard Medical School; Boston, MA, USA
| | - Richard W. Tourdot
- Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Biomedical Informatics, Blavatnik Institute, Harvard Medical School; Boston, MA, USA
| | - Jiao Li
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Medicine, Harvard Medical School; Boston, MA, USA
| | - Maria Nakhoul
- Department of Informatics & Analytics, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Daniel S. Gallant
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Usman Ali Ahmed
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Jillian O’Toole
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Dory Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Gwo-Shu Mary Lee
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
| | - Jonathan L. Hecht
- Department of Pathology, Beth Israel Deaconess Medical Center; Boston, MA, USA
| | - Eric C Kauffman
- Department of Urology, Roswell Park Comprehensive Cancer Center; Buffalo, New York, USA
| | - David J Einstein
- Division of Medical Oncology, Beth Israel Deaconess Medical Center; Boston, MA, USA
| | - Toni K. Choueiri
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Medicine, Harvard Medical School; Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital; Boston, MA, USA
| | - Cheng-Zhong Zhang
- Department of Data Science, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital; Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard; Cambridge, MA, USA
| | - Srinivas R. Viswanathan
- Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA, USA
- Department of Medicine, Harvard Medical School; Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard; Cambridge, MA, USA
- Department of Medicine, Brigham and Women’s Hospital; Boston, MA, USA
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2
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Comitani F, Nash JO, Cohen-Gogo S, Chang AI, Wen TT, Maheshwari A, Goyal B, Tio ES, Tabatabaei K, Mayoh C, Zhao R, Ho B, Brunga L, Lawrence JEG, Balogh P, Flanagan AM, Teichmann S, Huang A, Ramaswamy V, Hitzler J, Wasserman JD, Gladdy RA, Dickson BC, Tabori U, Cowley MJ, Behjati S, Malkin D, Villani A, Irwin MS, Shlien A. Diagnostic classification of childhood cancer using multiscale transcriptomics. Nat Med 2023; 29:656-666. [PMID: 36932241 PMCID: PMC10033451 DOI: 10.1038/s41591-023-02221-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 01/13/2023] [Indexed: 03/19/2023]
Abstract
The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.
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Affiliation(s)
- Federico Comitani
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Joshua O Nash
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Laboratory of Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Sarah Cohen-Gogo
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Astra I Chang
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Timmy T Wen
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Anant Maheshwari
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Bipasha Goyal
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Earvin S Tio
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Kevin Tabatabaei
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Chelsea Mayoh
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia
- School of Clinical Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Regis Zhao
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ben Ho
- Laboratory of Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Ledia Brunga
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Petra Balogh
- Department of Cellular and Molecular Pathology, Royal National Orthopaedic Hospital, Brockley Hill, Stanmore, UK
| | - Adrienne M Flanagan
- Department of Cellular and Molecular Pathology, Royal National Orthopaedic Hospital, Brockley Hill, Stanmore, UK
- Research Department of Pathology, University College London Cancer Institute, London, UK
| | | | - Annie Huang
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Vijay Ramaswamy
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Johann Hitzler
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- Program in Developmental and Stem Cell Biology, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Jonathan D Wasserman
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Rebecca A Gladdy
- Department of Surgical Oncology, Princess Margaret Cancer Centre/Mount Sinai Hospital, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Brendan C Dickson
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Uri Tabori
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Mark J Cowley
- Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia
- School of Clinical Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Sam Behjati
- Wellcome Sanger Institute, Hinxton, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - David Malkin
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Anita Villani
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Meredith S Irwin
- Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Adam Shlien
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
- Laboratory of Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
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3
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Meta-Analysis of MS-Based Proteomics Studies Indicates Interferon Regulatory Factor 4 and Nucleobindin1 as Potential Prognostic and Drug Resistance Biomarkers in Diffuse Large B Cell Lymphoma. Cells 2023; 12:cells12010196. [PMID: 36611989 PMCID: PMC9818977 DOI: 10.3390/cells12010196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/20/2022] [Accepted: 12/26/2022] [Indexed: 01/06/2023] Open
Abstract
The prognosis of diffuse large B cell lymphoma (DLBCL) is inaccurately predicted using clinical features and immunohistochemistry (IHC) algorithms. Nomination of a panel of molecules as the target for therapy and predicting prognosis in DLBCL is challenging because of the divergences in the results of molecular studies. Mass spectrometry (MS)-based proteomics in the clinic represents an analytical tool with the potential to improve DLBCL diagnosis and prognosis. Previous proteomics studies using MS-based proteomics identified a wide range of proteins. To achieve a consensus, we reviewed MS-based proteomics studies and extracted the most consistently significantly dysregulated proteins. These proteins were then further explored by analyzing data from other omics fields. Among all significantly regulated proteins, interferon regulatory factor 4 (IRF4) was identified as a potential target by proteomics, genomics, and IHC. Moreover, annexinA5 (ANXA5) and nucleobindin1 (NUCB1) were two of the most up-regulated proteins identified in MS studies. Functional enrichment analysis identified the light zone reactions of the germinal center (LZ-GC) together with cytoskeleton locomotion functions as enriched based on consistent, significantly dysregulated proteins. In this study, we suggest IRF4 and NUCB1 proteins as potential biomarkers that deserve further investigation in the field of DLBCL sub-classification and prognosis.
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Jacobsen SB, Tfelt-Hansen J, Smerup MH, Andersen JD, Morling N. Comparison of whole transcriptome sequencing of fresh, frozen, and formalin-fixed, paraffin-embedded cardiac tissue. PLoS One 2023; 18:e0283159. [PMID: 36989279 PMCID: PMC10058139 DOI: 10.1371/journal.pone.0283159] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 03/02/2023] [Indexed: 03/30/2023] Open
Abstract
The use of fresh tissue for molecular studies is preferred but often impossible. Instead, frozen or formalin-fixed, paraffin-embedded (FFPE) tissues are widely used and constitute valuable resources for retrospective studies. We assessed the utility of cardiac tissue stored in different ways for gene expression analyses by whole transcriptome sequencing of paired fresh, frozen, and FFPE tissues. RNA extracted from FFPE was highly degraded. Sequencing of RNA from FFPE tissues yielded higher proportions of intronic and intergenic reads compared to RNA from fresh and frozen tissues. The global gene expression profiles varied with the storage conditions, particularly mitochondrial and long non-coding RNAs. However, we observed high correlations among protein-coding transcripts (ρ > 0.94) with the various storage conditions. We did not observe any significant storage effect on the allele-specific gene expression. However, FFPE had statistically significantly (p < 0.05) more discordant variant calls compared to fresh and frozen tissue. In conclusion, we found that frozen and FFPE tissues can be used for reliable gene expression analyses, provided that proper quality control is performed and caution regarding the technical variability is withheld.
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Affiliation(s)
- Stine Bøttcher Jacobsen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Tfelt-Hansen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Morten Holdgaard Smerup
- Department of Cardiothoracic Surgery, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jeppe Dyrberg Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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5
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Köhler SA, Brandl L, Strissel PL, Gloßner L, Ekici AB, Angeloni M, Ferrazzi F, Bahlinger V, Hartmann A, Beckmann MW, Eckstein M, Strick R. Improved Bladder Tumor RNA Isolation from Archived Tissues Using Methylene Blue for Normalization, Multiplex RNA Hybridization, Sequencing and Subtyping. Int J Mol Sci 2022; 23:ijms231810267. [PMID: 36142180 PMCID: PMC9499321 DOI: 10.3390/ijms231810267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 12/02/2022] Open
Abstract
Methylene blue (MB) is a dye used for histology with clinical importance and intercalates into nucleic acids. After MB staining of formalin fixed paraffin embedded (FFPE) muscle invasive bladder cancer (MIBC) and normal urothelium, specific regions could be microdissected. It is not known if MB influences RNA used for gene expression studies. Therefore, we analyzed MIBC using five different RNA isolation methods comparing patient matched FFPE and fresh frozen (FF) tissues pre-stained with or without MB. We demonstrate a positive impact of MB on RNA integrity with FF tissues using real time PCR with no interference of its chemical properties. FFPE tissues showed no improvement of RNA integrity, which we propose is due to formalin induced nucleotide crosslinks. Using direct multiplex RNA hybridization the best genes for normalization of MIBC and control tissues were identified from 34 reference genes. In addition, 5SrRNA and 5.8SrRNA were distinctive reference genes detecting <200 bp fragments important for mRNA analyses. Using these normalized RNAs from MB stained MIBC and applying multiplex RNA hybridization and mRNA sequencing, a minimal gene expression panel precisely identified luminal and basal MIBC tumor subtypes, important for diagnosis, prognosis and chemotherapy response.
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Affiliation(s)
- Stefanie A. Köhler
- Laboratory for Molecular Medicine, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
| | - Lisa Brandl
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Pamela L. Strissel
- Laboratory for Molecular Medicine, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Laura Gloßner
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Arif B. Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Miriam Angeloni
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Fulvia Ferrazzi
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
- Department of Nephropathology, Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Veronika Bahlinger
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Arndt Hartmann
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Matthias W. Beckmann
- Laboratory for Molecular Medicine, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
| | - Markus Eckstein
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Krankenhausstrasse 8-10, 91054 Erlangen, Germany
| | - Reiner Strick
- Laboratory for Molecular Medicine, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), University Hospital Erlangen, Östliche Stadtmauerstrasse 30, 91054 Erlangen, Germany
- Correspondence: ; Tel.: +49-91318536671
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Grätz C, Bui MLU, Thaqi G, Kirchner B, Loewe RP, Pfaffl MW. Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery. Life (Basel) 2022; 12:life12030386. [PMID: 35330136 PMCID: PMC8953338 DOI: 10.3390/life12030386] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 11/16/2022] Open
Abstract
In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.
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Affiliation(s)
- Christian Grätz
- Department of Animal Physiology and Immunology, School of Life Sciences, Technical University of Munich, Weihenstephaner Berg 3, 85354 Freising, Germany; (C.G.); (M.L.U.B.); (G.T.); (B.K.)
- GeneSurge GmbH, Ottostr. 3, 80333 München, Germany;
| | - Maria L. U. Bui
- Department of Animal Physiology and Immunology, School of Life Sciences, Technical University of Munich, Weihenstephaner Berg 3, 85354 Freising, Germany; (C.G.); (M.L.U.B.); (G.T.); (B.K.)
- GeneSurge GmbH, Ottostr. 3, 80333 München, Germany;
| | - Granit Thaqi
- Department of Animal Physiology and Immunology, School of Life Sciences, Technical University of Munich, Weihenstephaner Berg 3, 85354 Freising, Germany; (C.G.); (M.L.U.B.); (G.T.); (B.K.)
| | - Benedikt Kirchner
- Department of Animal Physiology and Immunology, School of Life Sciences, Technical University of Munich, Weihenstephaner Berg 3, 85354 Freising, Germany; (C.G.); (M.L.U.B.); (G.T.); (B.K.)
- GeneSurge GmbH, Ottostr. 3, 80333 München, Germany;
| | | | - Michael W. Pfaffl
- Department of Animal Physiology and Immunology, School of Life Sciences, Technical University of Munich, Weihenstephaner Berg 3, 85354 Freising, Germany; (C.G.); (M.L.U.B.); (G.T.); (B.K.)
- Correspondence: or
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7
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Albitar M, Zhang H, Goy A, Xu-Monette ZY, Bhagat G, Visco C, Tzankov A, Fang X, Zhu F, Dybkaer K, Chiu A, Tam W, Zu Y, Hsi ED, Hagemeister FB, Huh J, Ponzoni M, Ferreri AJM, Møller MB, Parsons BM, van Krieken JH, Piris MA, Winter JN, Li Y, Xu B, Young KH. Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms. Blood Cancer J 2022; 12:25. [PMID: 35105854 PMCID: PMC8807629 DOI: 10.1038/s41408-022-00617-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 01/10/2022] [Accepted: 01/18/2022] [Indexed: 12/20/2022] Open
Abstract
Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials.
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Affiliation(s)
- Maher Albitar
- Genomic Testing Cooperative, LCA, Irvine, CA, 92618, USA.
| | - Hong Zhang
- Genomic Testing Cooperative, LCA, Irvine, CA, 92618, USA
| | - Andre Goy
- John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, NJ, 07601, USA
| | | | - Govind Bhagat
- Columbia University Medical Center, New York, NY, 10027, USA
| | | | - Alexandar Tzankov
- Institute of Pathology, University Hospital Basel, 4054, Basel, Switzerland
| | | | - Feng Zhu
- Duke University Medical Center, Durham, NC, 27710, USA
| | - Karen Dybkaer
- Aalborg University Hospital, Aalborg, 5000-5270, Denmark
| | | | - Wayne Tam
- Weill Medical College of Cornell University, New York, NY, 10065, USA
| | - Youli Zu
- The Methodist Hospital, Houston, TX, 77030, USA
| | - Eric D Hsi
- Wake Forest University Medical Center, Winston-Salem, NC, 77055, USA
| | | | - Jooryung Huh
- Asan Medical Center, Ulsan University College of Medicine, Seoul, 05505, Korea
| | | | | | | | | | - J Han van Krieken
- Radboud University Nijmegen Medical Centre, 6500, Nijmegen, Netherlands
| | - Miguel A Piris
- Hospital Universitario Marqués de Valdecilla, 39008, Santander, Spain
| | - Jane N Winter
- Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Yong Li
- Baylor College of Medicine, Houston, TX, 77030, USA
| | - Bing Xu
- The First Affiliated Hospital of Xiamen University, 361004, Xiamen, Fujian, China.
| | - Ken H Young
- Duke University Medical Center, Durham, NC, 27710, USA. .,Duke Cancer Institute, Durham, NC, 27710, USA.
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8
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Andersson N, Haltia UM, Färkkilä A, Wong SC, Eloranta K, Wilson DB, Unkila-Kallio L, Pihlajoki M, Kyrönlahti A, Heikinheimo M. Analysis of Non-Relapsed and Relapsed Adult Type Granulosa Cell Tumors Suggests Stable Transcriptomes during Tumor Progression. Curr Issues Mol Biol 2022; 44:686-698. [PMID: 35723333 PMCID: PMC8928977 DOI: 10.3390/cimb44020048] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/17/2022] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Adult-type granulosa cell tumor (AGCT) is a rare ovarian malignancy characterized by slow growth and hormonal activity. The prognosis of AGCT is generally favorable, but one-third of patients with low-stage disease experience a late relapse, and over half of them die of AGCT. To identify markers that would distinguish patients at risk for relapse, we performed Lexogen QuantSeq 3′ mRNA sequencing on formalin-fixed paraffin-embedded, archival AGCT tissue samples tested positive for the pathognomonic Forkhead Box L2 (FOXL2) mutation. We compared the transcriptomic profiles of 14 non-relapsed archival primary AGCTs (follow-up time 17–26 years after diagnosis) with 13 relapsed primary AGCTs (follow-up time 1.7–18 years) and eight relapsed tumors (follow-up time 2.8–18.9 years). Non-relapsed and relapsed primary AGCTs had similar transcriptomic profiles. In relapsed tumors three genes were differentially expressed: plasmalemma vesicle associated protein (PLVAP) was upregulated (p = 0.01), whereas argininosuccinate synthase 1 (ASS1) (p = 0.01) and perilipin 4 (PLIN4) (p = 0.02) were downregulated. PLVAP upregulation was validated using tissue microarray RNA in situ hybridization. In our patient cohort with extremely long follow-up, we observed similar gene expression patterns in both primary AGCT groups, suggesting that relapse is not driven by transcriptomic changes. These results reinforce earlier findings that molecular markers do not predict AGCT behavior or risk of relapse.
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Affiliation(s)
- Noora Andersson
- HUSLAB, Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland;
- Pediatric Research Center, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Tukholmankatu 8, 00290 Helsinki, Finland; (K.E.); (A.K.); (M.H.)
| | - Ulla-Maija Haltia
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 2, 00290 Helsinki, Finland; (U.-M.H.); (A.F.); (L.U.-K.)
| | - Anniina Färkkilä
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 2, 00290 Helsinki, Finland; (U.-M.H.); (A.F.); (L.U.-K.)
- Research Program for Systems Oncology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 8, 00290 Helsinki, Finland
| | | | - Katja Eloranta
- Pediatric Research Center, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Tukholmankatu 8, 00290 Helsinki, Finland; (K.E.); (A.K.); (M.H.)
| | - David B. Wilson
- Department of Pediatrics, Washington University in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110, USA;
- Department of Developmental Biology, Washington University School of Medicine, 660 S. Euclid Avenue Campus Box 8103, St. Louis, MO 63110, USA
| | - Leila Unkila-Kallio
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 2, 00290 Helsinki, Finland; (U.-M.H.); (A.F.); (L.U.-K.)
| | - Marjut Pihlajoki
- Pediatric Research Center, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Tukholmankatu 8, 00290 Helsinki, Finland; (K.E.); (A.K.); (M.H.)
- Correspondence:
| | - Antti Kyrönlahti
- Pediatric Research Center, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Tukholmankatu 8, 00290 Helsinki, Finland; (K.E.); (A.K.); (M.H.)
| | - Markku Heikinheimo
- Pediatric Research Center, Children’s Hospital, University of Helsinki and Helsinki University Hospital, Tukholmankatu 8, 00290 Helsinki, Finland; (K.E.); (A.K.); (M.H.)
- Department of Pediatrics, Washington University in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110, USA;
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9
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Macagno N, Pissaloux D, de la Fouchardière A, Karanian M, Lantuejoul S, Galateau Salle F, Meurgey A, Chassagne-Clement C, Treilleux I, Renard C, Roussel J, Gervasoni J, Cockenpot V, Crozes C, Baltres A, Houlier A, Paindavoine S, Alberti L, Duc A, Loarer FL, Dufresne A, Brahmi M, Corradini N, Blay JY, Tirode F. Wholistic approach - transcriptomic analysis and beyond using archival material for molecular diagnosis. Genes Chromosomes Cancer 2022; 61:382-393. [PMID: 35080790 DOI: 10.1002/gcc.23026] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/29/2021] [Indexed: 11/07/2022] Open
Abstract
Many neoplasms remain unclassified after histopathological examination, which requires further molecular analysis. To this regard, mesenchymal neoplasms are particularly challenging due to the combination of their rarity and the large number of subtypes, and many entities still lack robust diagnostic hallmarks. RNA transcriptomic profiles have proven to be a reliable basis for the classification of previously unclassified tumors and notably for mesenchymal neoplasms. Using exome-based RNA capture sequencing on more than 5000 samples of archival material (FFPE), the combination of expression profiles analyzes (including several clustering methods), fusion genes, and small nucleotide variations has been developed at the Centre Léon Bérard (CLB) in Lyon for the molecular diagnosis of challenging neoplasms and the discovery of new entities. The molecular basis of the technique, the protocol, and the bioinformatics algorithms used are described herein, as well as its advantages and limitations.
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Affiliation(s)
- Nicolas Macagno
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,Aix-Marseille University, Marmara institute, INSERM, U1251, MMG, DOD-CET, Marseille, France.,NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,CARADERM, French network of rare skin cancers, France
| | - Daniel Pissaloux
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France
| | - Arnaud de la Fouchardière
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France
| | - Marie Karanian
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Department of Biopathology, UNICANCER, Bergonié Institute, Bordeaux, France
| | - Sylvie Lantuejoul
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Grenoble Alpes University, Grenoble, France.,MESOPATH, MESOBANK, French network of mesothelioma, France
| | - Françoise Galateau Salle
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,MESOPATH, MESOBANK, French network of mesothelioma, France
| | - Alexandra Meurgey
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France.,NETSARC+, French Sarcoma Group (GSF-GETO) network, France
| | | | | | - Caroline Renard
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Juliette Roussel
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Julie Gervasoni
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Vincent Cockenpot
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Carole Crozes
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Aline Baltres
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Aurélie Houlier
- Department of Biopathology, UNICANCER, Centre Léon Bérard, Lyon, France
| | | | - Laurent Alberti
- INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France
| | - Adeline Duc
- INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France
| | - Francois Le Loarer
- NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,Department of Biopathology, UNICANCER, Bergonié Institute, Bordeaux, France
| | - Armelle Dufresne
- NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Department of Oncology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Mehdi Brahmi
- NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Department of Oncology, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Nadège Corradini
- NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Institute of pediatric oncology, IHOPe, UNICANCER, Centre Léon Bérard, Lyon, France
| | - Jean-Yves Blay
- NETSARC+, French Sarcoma Group (GSF-GETO) network, France.,Department of Oncology, UNICANCER, Centre Léon Bérard, Lyon, France.,Univ Lyon, Université Claude Bernard Lyon I, Lyon, France.,Headquarters, UNICANCER, Paris, France
| | - Franck Tirode
- INSERM 1052, CNRS 5286, Cancer Research Center of Lyon (CRCL), Lyon, France.,Department of Biopathology, UNICANCER, Bergonié Institute, Bordeaux, France.,Univ Lyon, Université Claude Bernard Lyon I, Lyon, France
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10
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Sorokin M, Rabushko E, Efimov V, Poddubskaya E, Sekacheva M, Simonov A, Nikitin D, Drobyshev A, Suntsova M, Buzdin A. Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability. Front Mol Biosci 2021; 8:737821. [PMID: 34888350 PMCID: PMC8650122 DOI: 10.3389/fmolb.2021.737821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.
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Affiliation(s)
- Maksim Sorokin
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States
| | - Elizaveta Rabushko
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Elena Poddubskaya
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marina Sekacheva
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexander Simonov
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Oncobox Ltd., Moscow, Russia
| | - Daniil Nikitin
- Oncobox Ltd., Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Aleksey Drobyshev
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,OmicsWay Corp., Walnut, CA, United States.,World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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11
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Jang JS, Holicky E, Lau J, McDonough S, Mutawe M, Koster MJ, Warrington KJ, Cuninngham JM. Application of the 3' mRNA-Seq using unique molecular identifiers in highly degraded RNA derived from formalin-fixed, paraffin-embedded tissue. BMC Genomics 2021; 22:759. [PMID: 34689749 PMCID: PMC8543821 DOI: 10.1186/s12864-021-08068-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 10/10/2021] [Indexed: 11/11/2022] Open
Abstract
Background Archival formalin-fixed, paraffin-embedded (FFPE) tissue samples with clinical and histological data are a singularly valuable resource for developing new molecular biomarkers. However, transcriptome analysis remains challenging with standard mRNA-seq methods as FFPE derived-RNA samples are often highly modified and fragmented. The recently developed 3′ mRNA-seq method sequences the 3′ region of mRNA using unique molecular identifiers (UMI), thus generating gene expression data with minimal PCR bias. In this study, we evaluated the performance of 3′ mRNA-Seq using Lexogen QuantSeq 3′ mRNA-Seq Library Prep Kit FWD with UMI, comparing with TruSeq Stranded mRNA-Seq and RNA Exome Capture kit. The fresh-frozen (FF) and FFPE tissues yielded nucleotide sizes range from 13 to > 70% of DV200 values; input amounts ranged from 1 ng to 100 ng for validation. Results The total mapped reads of QuantSeq 3′ mRNA-Seq to the reference genome ranged from 99 to 74% across all samples. After PCR bias correction, 3 to 56% of total sequenced reads were retained. QuantSeq 3′ mRNA-Seq data showed highly reproducible data across replicates in Universal Human Reference RNA (UHR, R > 0.94) at input amounts from 1 ng to 100 ng, and FF and FFPE paired samples (R = 0.92) at 10 ng. Severely degraded FFPE RNA with ≤30% of DV200 value showed good concordance (R > 0.87) with 100 ng input. A moderate correlation was observed when directly comparing QuantSeq 3′ mRNA-Seq data with TruSeq Stranded mRNA-Seq (R = 0.78) and RNA Exome Capture data (R > 0.67). Conclusion In this study, QuantSeq 3′ mRNA-Seq with PCR bias correction using UMI is shown to be a suitable method for gene quantification in both FF and FFPE RNAs. 3′ mRNA-Seq with UMI may be applied to severely degraded RNA from FFPE tissues generating high-quality sequencing data. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-08068-1.
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Affiliation(s)
- Jin Sung Jang
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA. .,Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
| | - Eileen Holicky
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA
| | - Julie Lau
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA
| | - Samantha McDonough
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA
| | - Mark Mutawe
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA
| | - Matthew J Koster
- Department of Internal Medicine, Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Kenneth J Warrington
- Department of Internal Medicine, Division of Rheumatology, Mayo Clinic, Rochester, MN, USA
| | - Julie M Cuninngham
- Genome Analysis Core, Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Stabile Research Building, 200 First Street SW, Rochester, MN, 55905, USA. .,Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
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12
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Basavaraja R, Drum JN, Sapuleni J, Bibi L, Friedlander G, Kumar S, Sartori R, Meidan R. Downregulated luteolytic pathways in the transcriptome of early pregnancy bovine corpus luteum are mimicked by interferon-tau in vitro. BMC Genomics 2021; 22:452. [PMID: 34134617 PMCID: PMC8207607 DOI: 10.1186/s12864-021-07747-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/21/2021] [Indexed: 12/15/2022] Open
Abstract
Background Maintenance of the corpus luteum (CL) beyond the time of luteolysis is essential for establishing pregnancy. Identifying the distinct features of early pregnancy CL remains unresolved, hence we analyzed here the transcriptome of CL on day 18 pregnant (P) and non-pregnant (NP) cows using RNA-Seq. CL of P cows expressed ISGs, verifying exposure to the pregnancy recognition signal, interferon-tau (IFNT), whereas the CL of NP cows had elevated luteal progesterone levels, implying that luteolysis had not yet commenced. Results The DEGs, IPA, and metascape canonical pathways, along with GSEA analysis, differed markedly in the CL of P cows from those of NP cows, at the same day of the cycle. Both metascape and IPA identified similar significantly enriched pathways such as interferon alpha/beta, sonic hedgehog pathway, TNFA, EDN1, TGFB1, and PDGF. However, type-1 interferon and sonic hedgehog pathways were positively enriched whereas most of the enriched pathways were downregulated in the P compared to NP samples. Thirty-four % of these pathways are known to be elevated by PGF2A during luteolysis. Notably, selective DEGs in luteinized granulosa cells were modulated by IFNT in vitro in a similar manner to their regulation in the CL of P cows. Conclusion This study unraveled the unique transcriptomic signature of the IFNT-exposed, early pregnancy CL, highlighting the abundance of downregulated pathways known to be otherwise induced during luteolysis. These and IFNT-regulated in vitro pregnancy-specific DEGs suggest that IFNT contributes to the characteristics and maintenance of early pregnancy CL. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07747-3.
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Affiliation(s)
- Raghavendra Basavaraja
- Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 7610001, Rehovot, Israel
| | - Jessica N Drum
- Department of Animal Science, University of São Paulo, Piracicaba, Brazil
| | - Jackson Sapuleni
- Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 7610001, Rehovot, Israel
| | - Lonice Bibi
- Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 7610001, Rehovot, Israel
| | - Gilgi Friedlander
- The Mantoux Bioinformatics institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Sai Kumar
- Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 7610001, Rehovot, Israel
| | - Roberto Sartori
- Department of Animal Science, University of São Paulo, Piracicaba, Brazil
| | - Rina Meidan
- Department of Animal Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 7610001, Rehovot, Israel.
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13
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Turnier JL, Pachman LM, Lowe L, Tsoi LC, Elhaj S, Menon R, Amoruso MC, Morgan GA, Gudjonsson JE, Berthier CC, Kahlenberg JM. Comparison of Lesional Juvenile Myositis and Lupus Skin Reveals Overlapping Yet Unique Disease Pathophysiology. Arthritis Rheumatol 2021; 73:1062-1072. [PMID: 33305541 DOI: 10.1002/art.41615] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/03/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Skin inflammation heralds systemic disease in juvenile myositis, yet we lack an understanding of pathogenic mechanisms driving skin inflammation in this disease. We undertook this study to define cutaneous gene expression signatures in juvenile myositis and identify key genes and pathways that differentiate skin disease in juvenile myositis from childhood-onset systemic lupus erythematosus (SLE). METHODS We used formalin-fixed paraffin-embedded skin biopsy samples from 15 patients with juvenile myositis (9 lesional, 6 nonlesional), 5 patients with childhood-onset SLE, and 8 controls to perform transcriptomic analysis and identify significantly differentially expressed genes (DEGs; q ≤ 5%) between patient groups. We used Ingenuity Pathway Analysis (IPA) to highlight enriched biologic pathways and validated DEGs by immunohistochemistry and quantitative real-time polymerase chain reaction. RESULTS Comparison of lesional juvenile myositis to control samples revealed 221 DEGs, with the majority of up-regulated genes representing interferon (IFN)-stimulated genes. CXCL10, CXCL9, and IFI44L represented the top 3 DEGs (fold change 23.2, 13.3, and 13.0, respectively; q < 0.0001). IPA revealed IFN signaling as the top canonical pathway. When compared to childhood-onset SLE, lesional juvenile myositis skin shared a similar gene expression pattern, with only 28 unique DEGs, including FBLN2, CHKA, and SLURP1. Notably, patients with juvenile myositis who were positive for nuclear matrix protein 2 (NXP-2) autoantibodies exhibited the strongest IFN signature and also demonstrated the most extensive Mx-1 immunostaining, both in keratinocytes and perivascular regions. CONCLUSION Lesional juvenile myositis skin demonstrates a striking IFN signature similar to that previously reported in juvenile myositis muscle and peripheral blood. Further investigation into the association of a higher IFN score with NXP-2 autoantibodies may provide insight into disease endotypes and pathogenesis.
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Affiliation(s)
| | - Lauren M Pachman
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | | | | | | | | | - Maria C Amoruso
- Northwestern University Feinberg School of Medicine, Chicago, Illinois
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14
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Kuksin M, Morel D, Aglave M, Danlos FX, Marabelle A, Zinovyev A, Gautheret D, Verlingue L. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur J Cancer 2021; 149:193-210. [PMID: 33866228 DOI: 10.1016/j.ejca.2021.03.005] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 02/08/2023]
Abstract
The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.
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Affiliation(s)
- Maria Kuksin
- ENS de Lyon, 15 Parvis René Descartes, 69007, Lyon, France; Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | - Daphné Morel
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; Département de Radiothérapie, Gustave Roussy Cancer Campus, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM UMR1030, Molecular Radiotherapy and Therapeutic Innovations, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | - Marine Aglave
- INSERM US23, CNRS UMS 3655, Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France
| | | | - Aurélien Marabelle
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM U1015, Gustave Roussy, Université Paris Saclay, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005, Paris, France; INSERM, U900, F-75005, Paris, France; MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006, Paris, France; Laboratory of Advanced Methods for High-dimensional Data Analysis, Lobachevsky University, 603000, Nizhny Novgorod, Russia
| | - Daniel Gautheret
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France; IHU PRISM, Gustave Roussy Cancer Campus, Gustave Roussy, 114 Rue Edouard Vaillant, 94800, Villejuif, France; Université Paris-Saclay, France
| | - Loïc Verlingue
- Département d'Innovations Thérapeutiques et Essais Précoces (DITEP), Gustave Roussy Cancer Campus, 114 rue Edouard Vaillant, 94800, Villejuif, France; INSERM UMR1030, Molecular Radiotherapy and Therapeutic Innovations, Gustave Roussy, 114 rue Edouard Vaillant, 94800, Villejuif, France; Institut Curie, PSL Research University, F-75005, Paris, France; Université Paris-Saclay, France.
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15
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Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:1-53. [PMID: 34340765 DOI: 10.1016/bs.apcsb.2021.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.
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16
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Cheng J, Guo Y, Guan G, Huang H, Jiang F, He J, Wu J, Guo Z, Liu X, Ao L. Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA. J Cell Mol Med 2021; 25:3622-3633. [PMID: 33719152 PMCID: PMC8034468 DOI: 10.1111/jcmm.16467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 02/19/2021] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA‐seq) is very challenging for non‐research‐oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow‐up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non‐research‐oriented samples which were formalin‐fixed paraffin‐embedded (FFPE) colorectal carcinoma samples (CRC) using RNA‐seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO‐based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post‐surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non‐research‐oriented clinical samples, compared to that of two published concise REO‐based signatures. In conclusion, REO‐based signatures with relatively more gene pairs could be robustly applied to non‐research‐oriented CRC samples.
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Affiliation(s)
- Jun Cheng
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan, China.,Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Yating Guo
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Guoxian Guan
- Department of Colorectal Surgery, The Affiliated Union Hospital of Fujian Medical University, Fuzhou, China
| | - Haiyan Huang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Fengle Jiang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Jun He
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Junling Wu
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Xing Liu
- Department of Colorectal Surgery, The Affiliated Union Hospital of Fujian Medical University, Fuzhou, China
| | - Lu Ao
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
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17
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Gerber N, Brunner MAT, Jagannathan V, Leeb T, Gerhards NM, Welle MM, Dettwiler M. Transcriptional Differences between Canine Cutaneous Epitheliotropic Lymphoma and Immune-Mediated Dermatoses. Genes (Basel) 2021; 12:160. [PMID: 33504055 PMCID: PMC7912288 DOI: 10.3390/genes12020160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/14/2021] [Accepted: 01/19/2021] [Indexed: 11/16/2022] Open
Abstract
Canine cutaneous epitheliotropic T-cell lymphoma (CETL) and immune-mediated T-cell predominant dermatoses (IMD) share several clinical and histopathological features, but differ substantially in prognosis. The discrimination of ambiguous cases may be challenging, as diagnostic tests are limited and may prove equivocal. This study aimed to investigate transcriptional differences between CETL and IMD, as a basis for further research on discriminating diagnostic biomarkers. We performed 100bp single-end sequencing on RNA extracted from formalin-fixed and paraffin-embedded skin biopsies from dogs with CETL and IMD, respectively. DESeq2 was used for principal component analysis (PCA) and differential gene expression analysis. Genes with significantly different expression were analyzed for enriched pathways using two different tools. The expression of selected genes and their proteins was validated by RT-qPCR and immunohistochemistry. PCA demonstrated the distinct gene expression profiles of CETL and IMD. In total, 503 genes were upregulated, while 4986 were downregulated in CETL compared to IMD. RT-qPCR confirmed the sequencing results for 5/6 selected genes tested, while the protein expression detected by immunohistochemistry was not entirely consistent. Our study revealed transcriptional differences between canine CETL and IMD, with similarities to human cutaneous lymphoma. Differentially expressed genes are potential discriminatory markers, but require further validation on larger sample collections.
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Affiliation(s)
- Nadja Gerber
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3001 Bern, Switzerland; (N.G.); (M.A.T.B.); (N.M.G.); (M.M.W.)
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
- Grosstierpraxis Weibel + Werner, Oberdorfstrasse 15, 3438 Lauperswil, Switzerland
| | - Magdalena A. T. Brunner
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3001 Bern, Switzerland; (N.G.); (M.A.T.B.); (N.M.G.); (M.M.W.)
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
| | - Vidhya Jagannathan
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109A, 3001 Bern, Switzerland
| | - Tosso Leeb
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109A, 3001 Bern, Switzerland
| | - Nora M. Gerhards
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3001 Bern, Switzerland; (N.G.); (M.A.T.B.); (N.M.G.); (M.M.W.)
- Wageningen Bioveterinary Research, Wageningen University & Research, Houtribweg 39, 8221 RA Lelystad, The Netherlands
| | - Monika M. Welle
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3001 Bern, Switzerland; (N.G.); (M.A.T.B.); (N.M.G.); (M.M.W.)
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
| | - Martina Dettwiler
- Institute of Animal Pathology, Vetsuisse Faculty, University of Bern, Länggassstrasse 122, 3001 Bern, Switzerland; (N.G.); (M.A.T.B.); (N.M.G.); (M.M.W.)
- Dermfocus, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland; (V.J.); (T.L.)
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Abstract
Although the great majority of cancers share a defined group of hallmarks that is responsible for the uncontrolled growth of particular cell types, it is today clear that under the name of cancer we refer to hundreds of different diseases. Furthermore, each of these diseases has an intrinsic variability due to the genetic background in which it develops. The ability to correctly identify these diseases is urgently needed, because each of them may require a specific therapeutic treatment for successful cure. Cancer biomarkers can be extremely valuable tools for efficient diagnosis and prognosis of cancers. In order to succeed in distinguishing between cancer types and progression-associated genetic backgrounds, cancer biomarkers need to have a strong specificity for a particular disease condition. With the development of novel sequencing technologies, it became clear that the set of genes transcribed from human cells is not limited to genes that code for proteins. On the contrary, our cells contain thousands of RNA without any protein-coding potential. The observation that these transcripts have a much higher cell/tissue specificity of expression in comparison to protein-coding genes makes them a potentially very valuable source of novel cancer biomarkers.
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Affiliation(s)
- Ingram Iaccarino
- Hematopathology Section and Lymph Node Registry, University of Kiel, Kiel, Germany.
| | - Wolfram Klapper
- Hematopathology Section and Lymph Node Registry, University of Kiel, Kiel, Germany
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19
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Szymiczek A, Lone A, Akbari MR. Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review. Clin Genet 2020; 99:613-637. [PMID: 33340095 DOI: 10.1111/cge.13900] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022]
Abstract
Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.
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Affiliation(s)
- Agata Szymiczek
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Amna Lone
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Mohammad R Akbari
- Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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20
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Beg S, Bareja R, Ohara K, Eng KW, Wilkes DC, Pisapia DJ, Zoughbi WA, Kudman S, Zhang W, Rao R, Manohar J, Kane T, Sigouros M, Xiang JZ, Khani F, Robinson BD, Faltas BM, Sternberg CN, Sboner A, Beltran H, Elemento O, Mosquera JM. Integration of whole-exome and anchored PCR-based next generation sequencing significantly increases detection of actionable alterations in precision oncology. Transl Oncol 2020; 14:100944. [PMID: 33190043 PMCID: PMC7674614 DOI: 10.1016/j.tranon.2020.100944] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 10/17/2020] [Accepted: 10/22/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Frequency of clinically relevant mutations in solid tumors by targeted and whole-exome sequencing is ∼30%. Transcriptome analysis complements detection of actionable gene fusions in advanced cancer patients. Goal of this study was to determine the added value of anchored multiplex PCR (AMP)-based next-generation sequencing (NGS) assay to identify further potential drug targets, when coupled with whole-exome sequencing (WES). METHODS Selected series of fifty-six samples from 55 patients enrolled in our precision medicine study were interrogated by WES and AMP-based NGS. RNA-seq was performed in 19 cases. Clinically relevant and actionable alterations detected by three methods were integrated and analyzed. RESULTS AMP-based NGS detected 48 fusions in 31 samples (55.4%); 31.25% (15/48) were classified as targetable based on published literature. WES revealed 29 samples (51.8%) harbored targetable alterations. TMB-high and MSI-high status were observed in 12.7% and 1.8% of cases. RNA-seq from 19 samples identified 8 targetable fusions (42.1%), also captured by AMP-based NGS. When number of actionable fusions detected by AMP-based NGS were added to WES targetable alterations, 66.1% of samples had potential drug targets. When both WES and RNA-seq were analyzed, 57.8% of samples had targetable alterations. CONCLUSIONS This study highlights importance of an integrative genomic approach for precision oncology, including use of different NGS platforms with complementary features. Integrating RNA data (whole transcriptome or AMP-based NGS) significantly enhances detection of potential targets in cancer patients. In absence of fresh frozen tissue, AMP-based NGS is a robust method to detect actionable fusions using low-input RNA from archival tissue.
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Affiliation(s)
- Shaham Beg
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Rohan Bareja
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - Kentaro Ohara
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Kenneth Wha Eng
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - David C Wilkes
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - David J Pisapia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Wael Al Zoughbi
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Sarah Kudman
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Wei Zhang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Rema Rao
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Jyothi Manohar
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Troy Kane
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Michael Sigouros
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Jenny Zhaoying Xiang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Francesca Khani
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Brian D Robinson
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States
| | - Bishoy M Faltas
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Cora N Sternberg
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Andrea Sboner
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - Himisha Beltran
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, United States
| | - Juan Miguel Mosquera
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine and NewYork Presbyterian, New York, NY, United States.
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21
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Curty G, Beckerle GA, Iñiguez LP, Furler RL, de Carvalho PS, Marston JL, Champiat S, Heymann JJ, Ormsby CE, Reyes-Terán G, Soares MA, Nixon DF, Bendall ML, Leal FE, de Mulder Rougvie M. Human Endogenous Retrovirus Expression Is Upregulated in the Breast Cancer Microenvironment of HIV Infected Women: A Pilot Study. Front Oncol 2020; 10:553983. [PMID: 33194615 PMCID: PMC7649802 DOI: 10.3389/fonc.2020.553983] [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: 05/29/2020] [Accepted: 09/17/2020] [Indexed: 12/21/2022] Open
Abstract
In people living with HIV (PLWH), chronic inflammation can lead to cancer initiation and progression, besides driving a dysregulated and diminished immune responsiveness. HIV infection also leads to increased transcription of Human Endogenous Retroviruses (HERVs), which could increase an inflammatory environment and create a tumor growth suppressive environment with high expression of pro-inflammatory cytokines. In order to determine the impact of HIV infection to HERV expression on the breast cancer microenvironment, we sequenced total RNA from formalin-fixed paraffin-embedded (FFPE) breast cancer samples of women HIV-negative and HIV-positive for transcriptome and retrotranscriptome analyses. We performed RNA extraction from FFPE samples, library preparation and total RNA sequencing (RNA-seq). The RNA-seq analysis shows 185 differentially expressed genes: 181 host genes (178 upregulated and three downregulated) and four upregulated HERV transcripts in HIV-positive samples. We also explored the impact of HERV expression in its neighboring breast cancer development genes (BRCA1, CCND1, NBS1/NBN, RAD50, KRAS, PI3K/PIK3CA) and in long non-coding RNA expression (AC060780.1, also known as RP11-242D8.1). We found a significant positive association of HERV expression with RAD50 and with AC060780.1, which suggest a possible role of HERV in regulating breast cancer genes from PLWH with breast cancer. In addition, we found immune system, extracellular matrix organization and metabolic signaling genes upregulated in HIV-positive breast cancer. In conclusion, our findings provide evidence of transcriptional and retrotranscriptional changes in breast cancer from PLWH compared to non-HIV breast cancer, including dysregulation of HERVs, suggesting an indirect effect of the virus on the breast cancer microenvironment.
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Affiliation(s)
- Gislaine Curty
- Oncovirology Program, Instituto Nacional de Câncer, Rio de Janeiro, Brazil
| | - Greta A Beckerle
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Luis P Iñiguez
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Robert L Furler
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | | | - Jez L Marston
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Stephane Champiat
- Drug Development Department (DITEP), Gustave Roussy, Paris-Saclay University, Villejuif, France
| | - Jonas J Heymann
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Christopher E Ormsby
- Center for Research in Infectious Diseases (CIENI), National Institute of Respiratory Diseases (INER), Mexico City, Mexico
| | - Gustavo Reyes-Terán
- Center for Research in Infectious Diseases (CIENI), National Institute of Respiratory Diseases (INER), Mexico City, Mexico
| | - Marcelo A Soares
- Oncovirology Program, Instituto Nacional de Câncer, Rio de Janeiro, Brazil
| | - Douglas F Nixon
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Matthew L Bendall
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Fabio E Leal
- Oncovirology Program, Instituto Nacional de Câncer, Rio de Janeiro, Brazil
| | - Miguel de Mulder Rougvie
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, United States
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Sorokin M, Ignatev K, Poddubskaya E, Vladimirova U, Gaifullin N, Lantsov D, Garazha A, Allina D, Suntsova M, Barbara V, Buzdin A. RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens. Biomedicines 2020; 8:E114. [PMID: 32397474 PMCID: PMC7277916 DOI: 10.3390/biomedicines8050114] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/02/2020] [Accepted: 05/07/2020] [Indexed: 12/11/2022] Open
Abstract
RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.
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Affiliation(s)
- Maxim Sorokin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Kirill Ignatev
- Karelia Republic Oncological Hospital, 185000 Petrozavodsk, Russia;
| | - Elena Poddubskaya
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Vitamed Oncological Clinical Center, 121309 Moscow, Russia
| | - Uliana Vladimirova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
| | - Nurshat Gaifullin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
| | - Dmitriy Lantsov
- Kaluga Regional Oncological Hospital, 248007 Kaluga, Russia;
| | | | - Daria Allina
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Maria Suntsova
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
| | - Victoria Barbara
- Oncological Dispensary of the Republic of Karelia, 185002 Petrozavodsk, Russia;
| | - Anton Buzdin
- Institute of Personalized Medicine, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia; (M.S.); (E.P.); (D.A.); (M.S.)
- Omicsway Corp., Walnut, CA 91789, USA;
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia;
- Moscow Institute of Physics and Technology, 141701 Moscow, Russia
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23
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Arroyo M, Bautista R, Larrosa R, Cobo MÁ, Claros MG. Biomarker potential of repetitive-element transcriptome in lung cancer. PeerJ 2019; 7:e8277. [PMID: 31875158 PMCID: PMC6925957 DOI: 10.7717/peerj.8277] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 11/22/2019] [Indexed: 12/11/2022] Open
Abstract
Since repetitive elements (REs) account for nearly 53% of the human genome, profiling its transcription after an oncogenic change might help in the search for new biomarkers. Lung cancer was selected as target since it is the most frequent cause of cancer death. A bioinformatic workflow based on well-established bioinformatic tools (such as RepEnrich, RepBase, SAMTools, edgeR and DESeq2) has been developed to identify differentially expressed RNAs from REs. It was trained and tested with public RNA-seq data from matched sequencing of tumour and healthy lung tissues from the same patient to reveal differential expression within the RE transcriptome. Healthy lung tissues express a specific set of REs whose expression, after an oncogenic process, is strictly and specifically changed. Discrete sets of differentially expressed REs were found for lung adenocarcinoma, for small-cell lung cancer, and for both cancers. Differential expression affects more HERV-than LINE-derived REs and seems biased towards down-regulation in cancer cells. REs behaving consistently in all patients were tested in a different patient cohort to validate the proposed biomarkers. Down-regulation of AluYg6 and LTR18B was confirmed as potential lung cancer biomarkers, while up-regulation of HERVK11D-Int is specific for lung adenocarcinoma and up-regulation of UCON88 is specific for small cell lung cancer. Hence, the study of RE transcriptome might be considered another research target in cancer, making REs a promising source of lung cancer biomarkers.
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Affiliation(s)
- Macarena Arroyo
- U.G.C. Médico-Quirúrgica de Enfermedades Respiratorias, Hospital Regional Universitario de Málaga, Málaga, Spain.,Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain
| | - Rocío Bautista
- Andalusian Platform for Bioinformatics-SCBI, Universidad de Málaga, Málaga, Spain
| | - Rafael Larrosa
- Andalusian Platform for Bioinformatics-SCBI, Universidad de Málaga, Málaga, Spain.,Department of Computer Architecture, Universidad de Málaga, Málaga, Spain
| | - Manuel Ángel Cobo
- Area of Oncology and Rare Diseases (IBIMA), Hospital Regional Universitario de Málaga, Málaga, Spain
| | - M Gonzalo Claros
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain.,Andalusian Platform for Bioinformatics-SCBI, Universidad de Málaga, Málaga, Spain.,Area of Oncology and Rare Diseases (IBIMA), Hospital Regional Universitario de Málaga, Málaga, Spain
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24
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Marczyk M, Fu C, Lau R, Du L, Trevarton AJ, Sinn BV, Gould RE, Pusztai L, Hatzis C, Symmans WF. The impact of RNA extraction method on accurate RNA sequencing from formalin-fixed paraffin-embedded tissues. BMC Cancer 2019; 19:1189. [PMID: 31805884 PMCID: PMC6896723 DOI: 10.1186/s12885-019-6363-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 11/14/2019] [Indexed: 01/06/2023] Open
Abstract
Background Utilization of RNA sequencing methods to measure gene expression from archival formalin-fixed paraffin-embedded (FFPE) tumor samples in translational research and clinical trials requires reliable interpretation of the impact of pre-analytical variables on the data obtained, particularly the methods used to preserve samples and to purify RNA. Methods Matched tissue samples from 12 breast cancers were fresh frozen (FF) and preserved in RNAlater or fixed in formalin and processed as FFPE tissue. Total RNA was extracted and purified from FF samples using the Qiagen RNeasy kit, and in duplicate from FFPE tissue sections using three different kits (Norgen, Qiagen and Roche). All RNA samples underwent whole transcriptome RNA sequencing (wtRNAseq) and targeted RNA sequencing for 31 transcripts included in a signature of sensitivity to endocrine therapy. We assessed the effect of RNA extraction kit on the reliability of gene expression levels using linear mixed-effects model analysis, concordance correlation coefficient (CCC) and differential analysis. All protein-coding genes in the wtRNAseq and three gene expression signatures for breast cancer were assessed for concordance. Results Despite variable quality of the RNA extracted from FFPE samples by different kits, all had similar concordance of overall gene expression from wtRNAseq between matched FF and FFPE samples (median CCC 0.63–0.66) and between technical replicates (median expression difference 0.13–0.22). More than half of genes were differentially expressed between FF and FFPE, but with low fold change (median |LFC| 0.31–0.34). Two out of three breast cancer signatures studied were highly robust in all samples using any kit, whereas the third signature was similarly discordant irrespective of the kit used. The targeted RNAseq assay was concordant between FFPE and FF samples using any of the kits (CCC 0.91–0.96). Conclusions The selection of kit to purify RNA from FFPE did not influence the overall quality of results from wtRNAseq, thus variable reproducibility of gene signatures probably relates to the reliability of individual gene selected and possibly to the algorithm. Targeted RNAseq showed promising performance for clinical deployment of quantitative assays in breast cancer from FFPE samples, although numerical scores were not identical to those from wtRNAseq and would require calibration.
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Affiliation(s)
- Michal Marczyk
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.,Data Mining Division, Silesian University of Technology, Gliwice, Poland
| | - Chunxiao Fu
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rosanna Lau
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lili Du
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexander J Trevarton
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bruno V Sinn
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Rebekah E Gould
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Christos Hatzis
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - W Fraser Symmans
- Department of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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25
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Lin X, Qiu L, Song X, Hou J, Chen W, Zhao J. A comparative analysis of RNA sequencing methods with ribosome RNA depletion for degraded and low-input total RNA from formalin-fixed and paraffin-embedded samples. BMC Genomics 2019; 20:831. [PMID: 31703614 PMCID: PMC6842158 DOI: 10.1186/s12864-019-6166-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 10/02/2019] [Indexed: 02/06/2023] Open
Abstract
Background Formalin-fixed and paraffin-embedded (FFPE) blocks held in clinical laboratories are an invaluable resource for clinical research, especially in the era of personalized medicine. It is important to accurately quantitate gene expression with degraded and small amounts of total RNA from FFPE materials. Results High concordance in transcript quantifications were shown between FF and FFPE samples using the same kit. The gene expression using the TaKaRa kit showed a difference with other kits, which may be due to the different principle of rRNA depletion or the amount of input total RNA. For seriously degraded RNA from FFPE samples, libraries could be constructed with as low as 50 ng of total RNA, although there was residual rRNA in the libraries. Data analysis with HISAT demonstrated that the unique mapping ratio, percentage of exons in unique mapping reads and number of detected genes decreased along with the decreasing quality of input RNA. Conclusions The method of RNA library construction with rRNA depletion can be used for clinical FFPE samples. For degraded and low-input RNA samples, it is still possible to obtain repeatable RNA expression profiling but with a low unique mapping ratio and high residual rRNA.
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Affiliation(s)
- Xiaojing Lin
- Department of Thoracic Surgery, Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lihong Qiu
- Department of Thoracic Surgery, Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xue Song
- Department of Thoracic Surgery, Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Junyan Hou
- Department of Thoracic Surgery, Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weizhi Chen
- Department of Thoracic Surgery, Cancer Hospital Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jun Zhao
- Genecast Precision Medicine Technology Institute, Room 903-908, Health work, Huayuan North Road 35, Haidian District, Beijing, 100191, China.
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26
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Roth SH, Levanon EY, Eisenberg E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity. Nat Methods 2019; 16:1131-1138. [PMID: 31636457 DOI: 10.1038/s41592-019-0610-9] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 08/20/2019] [Indexed: 12/19/2022]
Abstract
Adenosine-to-inosine (A-to-I) RNA editing by the adenosine deaminase that acts on RNA (ADAR) enzymes is a common RNA modification, preventing false activation of the innate immune system by endogenous double-stranded RNAs. Methods for quantification of ADAR activity are sought after, due to an increasing interest in the role of ADARs in cancer and autoimmune disorders, as well as attempts to harness the ADAR enzymes for RNA engineering. Here, we present the Alu editing index (AEI), a robust and simple-to-use computational tool devised for this purpose. We describe its properties and demonstrate its superiority to current quantification methods of ADAR activity. The AEI is used to map global editing across a large dataset of healthy human samples and identify putative regulators of ADAR, as well as previously unknown factors affecting the observed Alu editing levels. These should be taken into account in future comparative studies of ADAR activity. The AEI tool is available at https://github.com/a2iEditing/RNAEditingIndexer.
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Affiliation(s)
- Shalom Hillel Roth
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Erez Y Levanon
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Eli Eisenberg
- Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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27
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Buzdin A, Sorokin M, Garazha A, Glusker A, Aleshin A, Poddubskaya E, Sekacheva M, Kim E, Gaifullin N, Giese A, Seryakov A, Rumiantsev P, Moshkovskii S, Moiseev A. RNA sequencing for research and diagnostics in clinical oncology. Semin Cancer Biol 2019; 60:311-323. [PMID: 31412295 DOI: 10.1016/j.semcancer.2019.07.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/16/2019] [Indexed: 12/26/2022]
Abstract
Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.
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Affiliation(s)
- Anton Buzdin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
| | - Maxim Sorokin
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | | | | | - Alex Aleshin
- Stanford University School of Medicine, Stanford, 94305, CA, USA
| | - Elena Poddubskaya
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Vitamed Oncological Clinics, Moscow, Russia
| | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ella Kim
- Johannes Gutenberg University Mainz, Mainz, Germany
| | - Nurshat Gaifullin
- Lomonosov Moscow State University, Faculty of Medicine, Moscow, Russia
| | | | | | | | - Sergey Moshkovskii
- Institute of Biomedical Chemistry, Moscow, 119121, Russia; Pirogov Russian National Research Medical University (RNRMU), Moscow, 117997, Russia
| | - Alexey Moiseev
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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28
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Watza D, Lusk CM, Dyson G, Purrington KS, Chen K, Wenzlaff AS, Ratliff V, Neslund-Dudas C, Bepler G, Schwartz AG. Prognostic modeling of the immune-centric transcriptome reveals interleukin signaling candidates contributing to differential patient outcomes. Carcinogenesis 2019; 39:1447-1454. [PMID: 30202894 DOI: 10.1093/carcin/bgy119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/04/2018] [Indexed: 12/28/2022] Open
Abstract
Immunotherapy is a promising advancement in the treatment of non-small-cell lung carcinoma (NSCLC), although much of how lung tumors interact with the immune system in the natural course of disease remains unknown. We investigated the impact of the expression of immune-centric genes and pathways in tumors on patient survival to reveal novel candidates for immunotherapeutic research. Tumor transcriptomes and detailed clinical characteristics were obtained from patients with NSCLC who were participants of either the Inflammation, Health and Lung Epidemiology (INHALE) (discovery, N = 280) or The Cancer Genome Atlas (TCGA) Lung (replication, N = 1026) studies. Expressions of 2253 genes derived from 48 major immune pathways were assessed for association with patient prognosis using a multivariable Cox model and pathway effects were assessed with an in-house implementation of the Gene Set Enrichment Analysis (GSEA) algorithm. Prognosis-guided gene and pathway analysis of immune-centric expression in tumors revealed significant survival enrichments across both cohorts. The 'Interleukin Signaling' pathway, containing 430 genes, was found to be statistically and significantly enriched with prognostic signal in both the INHALE (P = 0.008) and TCGA (P = 0.039) datasets. Subsequent leading-edge analysis identified a subset of genes (N = 23) shared between both cohorts, driving the pathway enrichment. Cumulative expression of this leading-edge gene signature was a strong predictor of patient survival [discovery: hazard ratio (HR) = 1.59, P = 3.0 × 10-8; replication: HR = 1.29, P = 7.4 × 10-7]. These data demonstrate the impact of immune-centric expression on patient outcomes in NSCLC. Furthermore, prognostic gene effects were localized to discrete immune pathways, of which Interleukin Signaling had the greatest impact on overall survival and the subset of genes driving these effects have promise for future therapeutic intervention.
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Affiliation(s)
- Donovan Watza
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Christine M Lusk
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Gregory Dyson
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Kristen S Purrington
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Kang Chen
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA.,Department of Biochemistry Microbiology and Immunology, Wayne State University, Detroit, MI, USA.,Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University.,Mucosal Immunology Studies Team, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Detroit, MI, USA
| | - Angela S Wenzlaff
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Valerie Ratliff
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Christine Neslund-Dudas
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA.,Henry Ford Cancer Institute, Henry Ford Health System, Detroit, MI, USA
| | - Gerold Bepler
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Ann G Schwartz
- Department of Oncology, Wayne State University, Detroit, MI, USA.,Barbara Ann Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
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29
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Xiong B, Yang Y, Fineis FR, Wang JP. DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis. Genome Biol 2019; 20:75. [PMID: 30992037 PMCID: PMC6466807 DOI: 10.1186/s13059-019-1682-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/27/2019] [Indexed: 01/09/2023] Open
Abstract
RNA degradation affects RNA-seq quality when profiling transcriptional activities in cells. Here, we show that transcript degradation is both gene- and sample-specific and is a common and significant factor that may bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for degradation bias. We propose a novel pipeline named DegNorm to adjust the read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling for the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of simulated and real RNA-seq data.
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Affiliation(s)
- Bin Xiong
- Department of Statistics, Northwestern University, Evanston, IL, 60208, USA
| | - Yiben Yang
- Department of Statistics, Northwestern University, Evanston, IL, 60208, USA
| | - Frank R Fineis
- Department of Statistics, Northwestern University, Evanston, IL, 60208, USA
| | - Ji-Ping Wang
- Department of Statistics, Northwestern University, Evanston, IL, 60208, USA.
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