1
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Miliotis C, Ma Y, Katopodi XL, Karagkouni D, Kanata E, Mattioli K, Kalavros N, Pita-Juárez YH, Batalini F, Ramnarine VR, Nanda S, Slack FJ, Vlachos IS. Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci. Nat Commun 2024; 15:4319. [PMID: 38773080 PMCID: PMC11109163 DOI: 10.1038/s41467-024-48436-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 05/01/2024] [Indexed: 05/23/2024] Open
Abstract
The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3' untranslated region (3'-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3'-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3'-UTR eQTLs in immune-related genes. Our approach identifies numerous 3'-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.
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Affiliation(s)
- Christos Miliotis
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Harvard Program in Virology, Harvard University Graduate School of Arts and Sciences, Boston, MA, USA
| | - Yuling Ma
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xanthi-Lida Katopodi
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Dimitra Karagkouni
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cancer Center & Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Eleni Kanata
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kaia Mattioli
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolas Kalavros
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yered H Pita-Juárez
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Felipe Batalini
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Oncology, Department of Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Varune R Ramnarine
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shivani Nanda
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cancer Center & Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Frank J Slack
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Cancer Center & Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Ioannis S Vlachos
- Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cancer Center & Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Spatial Technologies Unit, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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2
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Luna Santamaría M, Andersson D, Parris TZ, Helou K, Österlund T, Ståhlberg A. Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis. Commun Biol 2024; 7:249. [PMID: 38429519 PMCID: PMC10907754 DOI: 10.1038/s42003-024-05955-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.
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Affiliation(s)
- Manuel Luna Santamaría
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Andersson
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Toshima Z Parris
- Sahlgrenska Center for Cancer Research, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Khalil Helou
- Sahlgrenska Center for Cancer Research, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Tobias Österlund
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden
| | - Anders Ståhlberg
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden.
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3
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Thomson AJ, Rehn JA, Heatley SL, Eadie LN, Page EC, Schutz C, McClure BJ, Sutton R, Dalla-Pozza L, Moore AS, Greenwood M, Kotecha RS, Fong CY, Yong ASM, Yeung DT, Breen J, White DL. Reproducible Bioinformatics Analysis Workflows for Detecting IGH Gene Fusions in B-Cell Acute Lymphoblastic Leukaemia Patients. Cancers (Basel) 2023; 15:4731. [PMID: 37835427 PMCID: PMC10571859 DOI: 10.3390/cancers15194731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by diverse genomic alterations, the most frequent being gene fusions detected via transcriptomic analysis (mRNA-seq). Due to its hypervariable nature, gene fusions involving the Immunoglobulin Heavy Chain (IGH) locus can be difficult to detect with standard gene fusion calling algorithms and significant computational resources and analysis times are required. We aimed to optimize a gene fusion calling workflow to achieve best-case sensitivity for IGH gene fusion detection. Using Nextflow, we developed a simplified workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion. We analysed samples from 35 patients harbouring IGH fusions (IGH::CRLF2 n = 17, IGH::DUX4 n = 15, IGH::EPOR n = 3) and assessed the detection rates for each caller, before optimizing the parameters to enhance sensitivity for IGH fusions. Initial results showed that FusionCatcher and Arriba outperformed STAR-Fusion (85-89% vs. 29% of IGH fusions reported). We found that extensive filtering in STAR-Fusion hindered IGH reporting. By adjusting specific filtering steps (e.g., read support, fusion fragments per million total reads), we achieved a 94% reporting rate for IGH fusions with STAR-Fusion. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL subtypes.
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Affiliation(s)
- Ashlee J. Thomson
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Jacqueline A. Rehn
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Susan L. Heatley
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
- Australian and New Zealand Children’s Oncology Group (ANZCHOG), Clayton, VIC 3168, Australia
| | - Laura N. Eadie
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Elyse C. Page
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Caitlin Schutz
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Barbara J. McClure
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
| | - Rosemary Sutton
- Molecular Diagnostics, Children’s Cancer Institute, Kensington, NSW 2750, Australia;
| | - Luciano Dalla-Pozza
- The Cancer Centre for Children, The Children’s Hospital at Westmead, Westmead, NSW 2145, Australia;
| | - Andrew S. Moore
- Oncology Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD 4101, Australia;
- Child Health Research Centre, The University of Queensland, Brisbane, QLD 4000, Australia
| | - Matthew Greenwood
- Department of Haematology and Transfusion Services, Royal North Shore Hospital, Sydney, NSW 2065, Australia;
- Faculty of Health and Medicine, University of Sydney, Sydney, NSW 2006, Australia
| | - Rishi S. Kotecha
- Department of Clinical Haematology, Oncology, Blood and Marrow Transplantation, Perth Children’s Hospital, Perth, WA 6009, Australia;
- Leukaemia Translational Research Laboratory, Telethon Kids Cancer Centre, Telethon Kids Institute, University of Western Australia, Perth, WA 6009, Australia
- Curtin Medical School, Curtin University, Perth, WA 6845, Australia
| | - Chun Y. Fong
- Department of Clinical Haematology, Austin Health, Heidelberg, VIC 3083, Australia;
| | - Agnes S. M. Yong
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia
- Division of Pathology & Laboratory, University of Western Australia Medical School, Perth, WA 6009, Australia
- Department of Haematology, Royal Perth Hospital, Perth, WA 6000, Australia
| | - David T. Yeung
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
- Haematology Department, Royal Adelaide Hospital and SA Pathology, Adelaide, SA 5000, Australia
| | - James Breen
- Black Ochre Data Labs, Indigenous Genomics, Telethon Kids Institute, Adelaide, SA 5000, Australia
- James Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia
| | - Deborah L. White
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia; (J.A.R.); (S.L.H.); (L.N.E.); (E.C.P.); (B.J.M.); (A.S.M.Y.); (D.T.Y.); (D.L.W.)
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia;
- Australian and New Zealand Children’s Oncology Group (ANZCHOG), Clayton, VIC 3168, Australia
- Australian Genomics Health Alliance (AGHA), The Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
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4
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Tretter C, de Andrade Krätzig N, Pecoraro M, Lange S, Seifert P, von Frankenberg C, Untch J, Zuleger G, Wilhelm M, Zolg DP, Dreyer FS, Bräunlein E, Engleitner T, Uhrig S, Boxberg M, Steiger K, Slotta-Huspenina J, Ochsenreither S, von Bubnoff N, Bauer S, Boerries M, Jost PJ, Schenck K, Dresing I, Bassermann F, Friess H, Reim D, Grützmann K, Pfütze K, Klink B, Schröck E, Haller B, Kuster B, Mann M, Weichert W, Fröhling S, Rad R, Hiltensperger M, Krackhardt AM. Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification. Nat Commun 2023; 14:4632. [PMID: 37532709 PMCID: PMC10397250 DOI: 10.1038/s41467-023-39570-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/19/2023] [Indexed: 08/04/2023] Open
Abstract
Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.
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Affiliation(s)
- Celina Tretter
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Niklas de Andrade Krätzig
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Matteo Pecoraro
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Sebastian Lange
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Philipp Seifert
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Clara von Frankenberg
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Johannes Untch
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Gabriela Zuleger
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Mathias Wilhelm
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Computational Mass Spectrometry, Freising, Germany
| | - Daniel P Zolg
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
| | - Florian S Dreyer
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Eva Bräunlein
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Thomas Engleitner
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Sebastian Uhrig
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Precision Oncology Program, NCT Heidelberg, Heidelberg, Germany
| | - Melanie Boxberg
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Katja Steiger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Julia Slotta-Huspenina
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Sebastian Ochsenreither
- German Cancer Consortium (DKTK), partner site Berlin and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Charité Comprehensive Cancer Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Hematology, Oncology and Tumor Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nikolas von Bubnoff
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Sebastian Bauer
- German Cancer Consortium (DKTK), partner site Essen and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medical Oncology and Sarcoma Center, West German Cancer Center, University Hospital Essen, Essen, Germany
| | - Melanie Boerries
- German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Medical Bioinformatics and Systems Medicine (IBSM), Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp J Jost
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Clinical Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
- University Comprehensive Cancer Center Graz, Medical University of Graz, Graz, Austria
| | - Kristina Schenck
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Iska Dresing
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Florian Bassermann
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
| | - Helmut Friess
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Daniel Reim
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Department of Surgery, Munich, Germany
| | - Konrad Grützmann
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Core Unit Molecular Tumor Diagnostics (CMTD), NCT Dresden, Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Katrin Pfütze
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Barbara Klink
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
| | - Evelin Schröck
- German Cancer Consortium (DKTK), partner site Dresden and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Clinical Genetics, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- ERN GENTURIS, Hereditary Cancer Syndrome Center Dresden, Dresden, Germany
- National Center for Tumor Diseases Dresden (NCT/UCC), Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Bernhard Haller
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of AI and Informatics in Medicine, Munich, Germany
| | - Bernhard Kuster
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Life Sciences, Chair of Proteomics and Bioanalytics, Freising, Germany
- Technical University of Munich, TUM School of Life Sciences, Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Freising, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Plank Institute of Biochemistry, Munich, Germany
| | - Wilko Weichert
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, Institute of Pathology, Munich, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), partner site Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roland Rad
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IInd Medical Department, Munich, Germany
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany
- Technical University of Munich, TUM School of Medicine, Institute of Molecular Oncology and Functional Genomics, Munich, Germany
| | - Michael Hiltensperger
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany
| | - Angela M Krackhardt
- German Cancer Consortium (DKTK), partner site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Technical University of Munich, TUM School of Medicine, Klinikum rechts der Isar, IIIrd Medical Department, Munich, Germany.
- Technical University of Munich, TUM School of Medicine, Center for Translational Cancer Research (TranslaTUM), Munich, Germany.
- Malteser Krankenhaus St. Franziskus-Hospital, Flensburg, Germany.
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5
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Tan X, Xu L, Jian X, Ouyang J, Hu B, Yang X, Wang T, Xie L. PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions. Cells 2023; 12:cells12050782. [PMID: 36899918 PMCID: PMC10000440 DOI: 10.3390/cells12050782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.
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Affiliation(s)
- Xiaoxiu Tan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Linfeng Xu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Xingxing Jian
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Jian Ouyang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Bo Hu
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Xinrong Yang
- Liver Cancer Institute, Fudan University, Shanghai 200032, China
| | - Tao Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Correspondence: (T.W.); (L.X.)
| | - Lu Xie
- Shanghai-MOST Key Laboratory of Health and Disease Genomics & Institute of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
- Correspondence: (T.W.); (L.X.)
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6
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Rehn J, Mayoh C, Heatley SL, McClure BJ, Eadie LN, Schutz C, Yeung DT, Cowley MJ, Breen J, White DL. RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL). PLoS Genet 2022; 18:e1010300. [PMID: 36251721 PMCID: PMC9612819 DOI: 10.1371/journal.pgen.1010300] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/27/2022] [Accepted: 09/22/2022] [Indexed: 12/05/2022] Open
Abstract
RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.
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Affiliation(s)
- Jacqueline Rehn
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Chelsea Mayoh
- Children’s Cancer Institute, Kensington, New South Wales, Australia
- School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - Susan L Heatley
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
- Australian and New Zealand Children’s Oncology Group (ANZCHOG), Clayton, Victoria, Australia
| | - Barbara J McClure
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Laura N Eadie
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Caitlin Schutz
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - David T Yeung
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
- Department of Haematology, Royal Adelaide Hospital and SA Pathology, Adelaide, South Australia, Australia
| | - Mark J Cowley
- Children’s Cancer Institute, Kensington, New South Wales, Australia
- School of Clinical Medicine, UNSW Sydney, Sydney, New South Wales, Australia
| | - James Breen
- Black Ochre Data Labs, Telethon Kids Institute, Adelaide, South Australia, Australia
- Australian National University, Canberra, Australian Capital Territory, Australia
- * E-mail:
| | - Deborah L White
- Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
- Faculty of Health and Medical Science, University of Adelaide, Adelaide, South Australia, Australia
- Australian and New Zealand Children’s Oncology Group (ANZCHOG), Clayton, Victoria, Australia
- Australian Genomics Health Alliance (AGHA), Parkville, Victoria, Australia
- Faculty of Sciences, University of Adelaide, Adelaide, South Australia, Australia
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7
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FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines. Commun Biol 2022; 5:975. [PMID: 36114280 PMCID: PMC9481582 DOI: 10.1038/s42003-022-03397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 04/22/2022] [Indexed: 11/08/2022] Open
Abstract
The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline. FVC is a method for calling specific gene variants from whole genome data, for potential use in clinical diagnosis and human genetics research.
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8
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Crawford J, Christensen BC, Chikina M, Greene CS. Widespread redundancy in -omics profiles of cancer mutation states. Genome Biol 2022; 23:137. [PMID: 35761387 PMCID: PMC9238138 DOI: 10.1186/s13059-022-02705-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear. RESULTS We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type. CONCLUSIONS Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.
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Affiliation(s)
- Jake Crawford
- grid.25879.310000 0004 1936 8972Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Brock C. Christensen
- grid.254880.30000 0001 2179 2404Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH USA ,grid.254880.30000 0001 2179 2404Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH USA
| | - Maria Chikina
- grid.21925.3d0000 0004 1936 9000Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
| | - Casey S. Greene
- grid.430503.10000 0001 0703 675XDepartment of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO USA ,grid.430503.10000 0001 0703 675XCenter for Health AI, University of Colorado School of Medicine, Aurora, CO USA
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9
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Katzir R, Rudberg N, Yizhak K. Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample. Nat Commun 2022; 13:3092. [PMID: 35654823 PMCID: PMC9163107 DOI: 10.1038/s41467-022-30753-2] [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: 07/22/2021] [Accepted: 05/12/2022] [Indexed: 11/09/2022] Open
Abstract
Detection of somatic mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). We have previously developed a tool for detection of somatic mutations using tumor RNA and a matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA sequencing data without a matched-normal sample. This is accomplished via a machine-learning approach that classifies mutations as either somatic or germline based on various features. When applied to RNA-sequencing of >450 melanoma samples high precision and recall are achieved, and both mutational signatures and driver genes are correctly identified. Finally, we show that RNA-based TMB is significantly associated with patient survival, showing similar or higher significance level as compared to DNA-based TMB. Our pipeline can be utilized in many future applications, analyzing novel and existing datasets where only RNA is available.
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Affiliation(s)
- Rotem Katzir
- Center for Bioinformatics and Computational Biology, Department of Computer Science and the University of Maryland Institute of Advanced Computer Studies (UMIACS), University of Maryland, College Park, MD, 20742, USA.,Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, 31096, Israel
| | - Noam Rudberg
- Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, 31096, Israel
| | - Keren Yizhak
- Department of Cell Biology and Cancer Science, Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, 31096, Israel.
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10
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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11
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Comprehensive RNA dataset of tissue and plasma from patients with esophageal cancer or precursor lesions. Sci Data 2022; 9:86. [PMID: 35288573 PMCID: PMC8921197 DOI: 10.1038/s41597-022-01176-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 01/25/2022] [Indexed: 11/29/2022] Open
Abstract
AbstractIn the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy and disease esophageal tissue sample were included. In total, this comprehensive dataset consists of 102 sequenced libraries from 51 samples. Based on this data, 119 expression profiles are available for three biotypes, including miRNA (51), mRNA (51) and circRNA (17). This unique resource allows for discovery of novel biomarkers and disease mechanisms, comparison of tissue and liquid biopsy profiles, integration of coding and non-coding RNA patterns, and can serve as a validation dataset in other RNA landscaping studies. Moreover, structural RNA differences can be identified in this dataset, including protein coding mutations, fusion genes, and circular RNAs.
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12
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Ozcan Z, San Lucas FA, Wong JW, Chang K, Stopsack KH, Fowler J, Jakubek YA, Scheet P. Chromosomal imbalances detected via RNA-sequencing in 28 cancers. Bioinformatics 2022; 38:1483-1490. [PMID: 34999743 PMCID: PMC8896613 DOI: 10.1093/bioinformatics/btab861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/05/2021] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. RESULTS We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. AVAILABILITY AND IMPLEMENTATION The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zuhal Ozcan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Francis A San Lucas
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Justin W Wong
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kyle Chang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Konrad H Stopsack
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jerry Fowler
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yasminka A Jakubek
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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13
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Uribe-Salazar JM, Kaya G, Sekar A, Weyenberg K, Ingamells C, Dennis MY. Evaluation of CRISPR gene-editing tools in zebrafish. BMC Genomics 2022; 23:12. [PMID: 34986794 PMCID: PMC8734261 DOI: 10.1186/s12864-021-08238-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/04/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Zebrafish have practical features that make them a useful model for higher-throughput tests of gene function using CRISPR/Cas9 editing to create 'knockout' models. In particular, the use of G0 mosaic mutants has potential to increase throughput of functional studies significantly but may suffer from transient effects of introducing Cas9 via microinjection. Further, a large number of computational and empirical tools exist to design CRISPR assays but often produce varied predictions across methods leaving uncertainty in choosing an optimal approach for zebrafish studies. METHODS To systematically assess accuracy of tool predictions of on- and off-target gene editing, we subjected zebrafish embryos to CRISPR/Cas9 with 50 different guide RNAs (gRNAs) targeting 14 genes. We also investigate potential confounders of G0-based CRISPR screens by assaying control embryos for spurious mutations and altered gene expression. RESULTS We compared our experimental in vivo editing efficiencies in mosaic G0 embryos with those predicted by eight commonly used gRNA design tools and found large discrepancies between methods. Assessing off-target mutations (predicted in silico and in vitro) found that the majority of tested loci had low in vivo frequencies (< 1%). To characterize if commonly used 'mock' CRISPR controls (larvae injected with Cas9 enzyme or mRNA with no gRNA) exhibited spurious molecular features that might exacerbate studies of G0 mosaic CRISPR knockout fish, we generated an RNA-seq dataset of various control larvae at 5 days post fertilization. While we found no evidence of spontaneous somatic mutations of injected larvae, we did identify several hundred differentially-expressed genes with high variability between injection types. Network analyses of shared differentially-expressed genes in the 'mock' injected larvae implicated a number of key regulators of common metabolic pathways, and gene-ontology analysis revealed connections with response to wounding and cytoskeleton organization, highlighting a potentially lasting effect from the microinjection process that requires further investigation. CONCLUSION Overall, our results provide a valuable resource for the zebrafish community for the design and execution of CRISPR/Cas9 experiments.
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Affiliation(s)
- José M Uribe-Salazar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA
| | - Gulhan Kaya
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
| | - Aadithya Sekar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
| | - KaeChandra Weyenberg
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
| | - Cole Ingamells
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA
| | - Megan Y Dennis
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, School of Medicine, University of California, Davis, Davis, CA, USA.
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA.
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14
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Ring A, Campo D, Porras TB, Kaur P, Forte VA, Tripathy D, Lu J, Kang I, Press MF, Jeong YJ, Snow A, Zhu Y, Zada G, Wagle N, Lang JE. Circulating Tumor Cell Transcriptomics as Biopsy Surrogates in Metastatic Breast Cancer. Ann Surg Oncol 2022; 29:2882-2894. [PMID: 35000083 PMCID: PMC8989945 DOI: 10.1245/s10434-021-11135-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/11/2021] [Indexed: 01/28/2023]
Abstract
BACKGROUND Metastatic breast cancer (MBC) and the circulating tumor cells (CTCs) leading to macrometastases are inherently different than primary breast cancer. We evaluated whether whole transcriptome RNA-Seq of CTCs isolated via an epitope-independent approach may serve as a surrogate for biopsies of macrometastases. METHODS We performed RNA-Seq on fresh metastatic tumor biopsies, CTCs, and peripheral blood (PB) from 19 newly diagnosed MBC patients. CTCs were harvested using the ANGLE Parsortix microfluidics system to isolate cells based on size and deformability, independent of a priori knowledge of cell surface marker expression. RESULTS Gene expression separated CTCs, metastatic biopsies, and PB into distinct groups despite heterogeneity between patients and sample types. CTCs showed higher expression of immune oncology targets compared with corresponding metastases and PB. Predictive biomarker (n = 64) expression was highly concordant for CTCs and metastases. Repeat observation data post-treatment demonstrated changes in the activation of different biological pathways. Somatic single nucleotide variant analysis showed increasing mutational complexity over time. CONCLUSION We demonstrate that RNA-Seq of CTCs could serve as a surrogate biomarker for breast cancer macrometastasis and yield clinically relevant insights into disease biology and clinically actionable targets.
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Affiliation(s)
- Alexander Ring
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA ,Present Address: Department of Hematology and Medical Oncology, University Hospital Zurich, Zurich, Switzerland
| | - Daniel Campo
- Department of Biological Sciences, University of Southern California, Los Angeles, CA USA
| | - Tania B. Porras
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Pushpinder Kaur
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Victoria A. Forte
- Division of Medical Oncology, Department of Medicine, SUNY Downstate Medical Center, New York, NY USA
| | - Debu Tripathy
- Department of Breast Medical Oncology, UT MD Anderson Cancer Center, Houston, TX USA
| | - Janice Lu
- Division of Medical Oncology, Department of Medicine and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Irene Kang
- Department of Pathology and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Michael F. Press
- Department of Pathology and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Young Ju Jeong
- Department of Surgery, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Anson Snow
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Yue Zhu
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Gabriel Zada
- Department of Neurosurgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Naveed Wagle
- Division of Medical Oncology, Department of Medicine and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA
| | - Julie E. Lang
- Division of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer Center, University of Southern California, Los Angeles, CA USA ,Present Address: Division of Breast Services, Department of General Surgery, Cleveland Clinic Breast Cancer Program, Cleveland, Ohio USA
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15
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López-Ginés C, Muñoz-Hidalgo L, San-Miguel T, Megías J, Triviño JC, Calabuig S, Roldán P, Cerdá-Nicolás M, Monleón D. Whole-exome sequencing, EGFR amplification and infiltration patterns in human glioblastoma. Am J Cancer Res 2021; 11:5543-5558. [PMID: 34873478 PMCID: PMC8640814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignant primary brain tumor in adults. This cancer shows rapid, highly infiltrative growth, that invades individually or in small groups the surrounding tissue. The aggressive tumor biology of GBM has devastating consequences with a median survival of 15 months. GBM often has Epidermal Growth Factor Receptor (EGFR) abnormalities. Despite recent advances in the study of GBM tumor biology, it is unclear whether mutations in GBM are related to EGFR amplification and relevant phenotypes like tumor infiltration. This study aimed to perform whole-exome sequencing analysis in 30 human GBM samples for identifying mutational portraits associated with EGFR amplification and infiltrative patterns. Our results show that EGFR-amplified tumors have overall higher mutation rates than EGFR-no-amplified. Six genes out of 2029 candidate genes show mutations associated with EGFR amplification status. Mutations in these genes for GBM are novel, not previously reported in GBM, and with little presence in the TCGA database. GPR179, USP48, and BLK show mutation only in EGFR-amplified cases, and all the affected cases exhibit diffuse infiltrative patterns. On the other hand, mutations in ADGB, EHHADH, and PTPN13, were present only in the EGFR-no-amplified group with a more diverse infiltrative phenotype. Overall, our work identified different mutational portraits of GBM related to well-established features like EGFR amplification and tumor infiltration.
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Affiliation(s)
| | | | | | - Javier Megías
- Departament of Pathology, University of ValenciaValencia, Spain
| | | | - Silvia Calabuig
- Departament of Pathology, University of ValenciaValencia, Spain
| | - Pedro Roldán
- Department of Neurosurgery, University Clinical Hospital ValenciaValencia, Spain
| | | | - Daniel Monleón
- Departament of Pathology, University of ValenciaValencia, Spain
- Health Research Institute INCLIVAValencia, Spain
- CIBERFES_ISCIIIValencia, Spain
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16
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Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics. Cancers (Basel) 2021; 13:cancers13225653. [PMID: 34830809 PMCID: PMC8616234 DOI: 10.3390/cancers13225653] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/04/2021] [Accepted: 11/09/2021] [Indexed: 12/11/2022] Open
Abstract
B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.
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17
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Spatial variation in gene expression of Tasmanian devil facial tumors despite minimal host transcriptomic response to infection. BMC Genomics 2021; 22:698. [PMID: 34579650 PMCID: PMC8477496 DOI: 10.1186/s12864-021-07994-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 09/08/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Transmissible cancers lie at the intersection of oncology and infectious disease, two traditionally divergent fields for which gene expression studies are particularly useful for identifying the molecular basis of phenotypic variation. In oncology, transcriptomics studies, which characterize the expression of thousands of genes, have identified processes leading to heterogeneity in cancer phenotypes and individual prognoses. More generally, transcriptomics studies of infectious diseases characterize interactions between host, pathogen, and environment to better predict population-level outcomes. Tasmanian devils have been impacted dramatically by a transmissible cancer (devil facial tumor disease; DFTD) that has led to widespread population declines. Despite initial predictions of extinction, populations have persisted at low levels, due in part to heterogeneity in host responses, particularly between sexes. However, the processes underlying this variation remain unknown. RESULTS We sequenced transcriptomes from healthy and DFTD-infected devils, as well as DFTD tumors, to characterize host responses to DFTD infection, identify differing host-tumor molecular interactions between sexes, and investigate the extent to which tumor gene expression varies among host populations. We found minimal variation in gene expression of devil lip tissues, either with respect to DFTD infection status or sex. However, 4088 genes were differentially expressed in tumors among our sampling localities. Pathways that were up- or downregulated in DFTD tumors relative to normal tissues exhibited the same patterns of expression with greater intensity in tumors from localities that experienced DFTD for longer. No mRNA sequence variants were associated with expression variation. CONCLUSIONS Expression variation among localities may reflect morphological differences in tumors that alter ratios of normal-to-tumor cells within biopsies. Phenotypic variation in tumors may arise from environmental variation or differences in host immune response that were undetectable in lip biopsies, potentially reflecting variation in host-tumor coevolutionary relationships among sites that differ in the time since DFTD arrival.
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18
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Lee J, Cho S, Hong SE, Kang D, Choi H, Lee JM, Yoon JH, Cho BS, Lee S, Kim HJ, Kim M, Kim Y. Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning. Front Oncol 2021; 11:717616. [PMID: 34497767 PMCID: PMC8419339 DOI: 10.3389/fonc.2021.717616] [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: 06/01/2021] [Accepted: 07/21/2021] [Indexed: 11/24/2022] Open
Abstract
BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.
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Affiliation(s)
- Jaewoong Lee
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | | | - Seong-Eui Hong
- Next Generation Sequencing (NGS) Division, Theragen Bio Co. Ltd., Seongnam-si, South Korea
| | - Dain Kang
- Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hayoung Choi
- Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jong-Mi Lee
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jae-Ho Yoon
- Department of Hematology, Catholic Hematology Hospital and Leukemia Research Institute, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Byung-Sik Cho
- Department of Hematology, Catholic Hematology Hospital and Leukemia Research Institute, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Seok Lee
- Department of Hematology, Catholic Hematology Hospital and Leukemia Research Institute, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hee-Je Kim
- Department of Hematology, Catholic Hematology Hospital and Leukemia Research Institute, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Myungshin Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yonggoo Kim
- Department of Laboratory Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.,Catholic Genetic Laboratory Center, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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19
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Identification of cancer-related mutations in human pluripotent stem cells using RNA-seq analysis. Nat Protoc 2021; 16:4522-4537. [PMID: 34363070 DOI: 10.1038/s41596-021-00591-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 06/16/2021] [Indexed: 01/10/2023]
Abstract
Human pluripotent stem cells (hPSCs) are known to acquire genetic aberrations during in vitro propagation. In addition to recurrent chromosomal aberrations, it has recently been shown that these cells also gain point mutations in cancer-related genes, predominantly in TP53. The need for routine quality control of hPSCs is critical for both basic research and clinical applications. Here we discuss the relevance of detecting mutations for various hPSCs applications, and present a detailed protocol to identify cancer-related point mutations using data from RNA sequencing, an assay commonly performed during the growth and differentiation of hPSCs. In this protocol, we describe how to process and align the sequencing data, analyze it and conservatively interpret the results in order to generate an accurate estimation of mutations in tumor-related genes. This pipeline is designed to work in high throughput and is available as a software container at https://github.com/elyadlezmi/RNA2CM . The protocol requires minimal command-line skills and can be carried out in 1-2 d.
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20
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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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21
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Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool. Sci Rep 2021; 11:10780. [PMID: 34031450 PMCID: PMC8144223 DOI: 10.1038/s41598-021-89927-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/27/2021] [Indexed: 12/05/2022] Open
Abstract
Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system’s predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.
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22
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Sánchez-Fernández P, Riobello C, Costales M, Vivanco B, Cabal VN, García-Marín R, Suárez-Fernández L, López F, Cabanillas R, Hermsen MA, Llorente JL. Next-generation sequencing for identification of actionable gene mutations in intestinal-type sinonasal adenocarcinoma. Sci Rep 2021; 11:2247. [PMID: 33500480 PMCID: PMC7838394 DOI: 10.1038/s41598-020-80242-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 11/26/2020] [Indexed: 01/30/2023] Open
Abstract
Intestinal-type sinonasal adenocarcinoma (ITAC) is a rare tumor carrying poor prognosis and needing new treatment options. The aim of this study was to identify actionable gene mutations that can guide new personalized target-specific therapies in ITAC patients. A series of 48 tumor and 27 corresponding germline DNA samples were analyzed by next generation sequencing using a panel of 120 genes. In total, 223 sequence variants were found in 70 genes. Matched tumor/germline comparison in 27 cases revealed that 57% were in fact germline variants. In 20 of these 27 cases, 58 somatic variants in 33 different genes were identified, the most frequent being PIK3CA (5 cases), APC and ATM (4 cases), and KRAS, NF1, LRP1B and BRCA1 (3 cases). Many of the somatic gene variants affected PI3K, MAPK/ERK, WNT and DNA repair signaling pathways, although not in a mutually exclusive manner. None of the alterations were related to histological ITAC subtype, tumor stage or survival. Our data showed that thorough interpretation of somatic mutations requires sequencing analysis of the corresponding germline DNA. Potentially actionable somatic mutations were found in 20 of 27 cases, 8 of which being biomarkers of FDA-approved targeted therapies. Our data implicate new possibilities for personalized treatment of ITAC patients.
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Affiliation(s)
- Paula Sánchez-Fernández
- grid.411052.30000 0001 2176 9028Department Otolaryngology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Cristina Riobello
- grid.10863.3c0000 0001 2164 6351Department Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Centro de Investigación Biomédica en Red (CIBER-ONC), Edf. FINBA, N-1 F49, C/ Avenida de Roma s/n, 33011 Oviedo, Spain
| | - María Costales
- grid.411052.30000 0001 2176 9028Department Otolaryngology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Blanca Vivanco
- grid.411052.30000 0001 2176 9028Department Pathology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Virginia N. Cabal
- grid.10863.3c0000 0001 2164 6351Department Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Centro de Investigación Biomédica en Red (CIBER-ONC), Edf. FINBA, N-1 F49, C/ Avenida de Roma s/n, 33011 Oviedo, Spain
| | - Rocío García-Marín
- grid.10863.3c0000 0001 2164 6351Department Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Centro de Investigación Biomédica en Red (CIBER-ONC), Edf. FINBA, N-1 F49, C/ Avenida de Roma s/n, 33011 Oviedo, Spain
| | - Laura Suárez-Fernández
- grid.10863.3c0000 0001 2164 6351Department Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Centro de Investigación Biomédica en Red (CIBER-ONC), Edf. FINBA, N-1 F49, C/ Avenida de Roma s/n, 33011 Oviedo, Spain
| | - Fernando López
- grid.411052.30000 0001 2176 9028Department Otolaryngology, Hospital Universitario Central de Asturias, Oviedo, Spain
| | | | - Mario A. Hermsen
- grid.10863.3c0000 0001 2164 6351Department Head and Neck Oncology, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Centro de Investigación Biomédica en Red (CIBER-ONC), Edf. FINBA, N-1 F49, C/ Avenida de Roma s/n, 33011 Oviedo, Spain
| | - José Luis Llorente
- grid.411052.30000 0001 2176 9028Department Otolaryngology, Hospital Universitario Central de Asturias, Oviedo, Spain
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23
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Quaglieri A, Flensburg C, Speed TP, Majewski IJ. Finding a suitable library size to call variants in RNA-Seq. BMC Bioinformatics 2020; 21:553. [PMID: 33261552 PMCID: PMC7708150 DOI: 10.1186/s12859-020-03860-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/03/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. Here we specifically address how overall library size influences the detection of somatic mutations in RNA-seq data in two acute myeloid leukaemia datasets. RESULTS : We simulated shallower sequencing depths by downsampling 45 acute myeloid leukaemia samples (100 bp PE) that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%, average loss of 7%). The sensitivity in recovering insertions and deletions varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort (50 bp PE) and assessed the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 6% average loss in sensitivity using 40M fragments. CONCLUSIONS Between 30M and 40M 100 bp PE reads are needed to recover 90-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.
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Affiliation(s)
- Anna Quaglieri
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia. .,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.
| | - Christoffer Flensburg
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.,Department of Mathematics and Statistics, The University of Melbourne, 813 Swanston Street, Melbourne, 3010, Australia
| | - Ian J Majewski
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia. .,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.
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24
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Hashimoto S, Noguchi E, Bando H, Miyadera H, Morii W, Nakamura T, Hara H. Neoantigen prediction in human breast cancer using RNA sequencing data. Cancer Sci 2020; 112:465-475. [PMID: 33155341 PMCID: PMC7780012 DOI: 10.1111/cas.14720] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/08/2020] [Accepted: 11/02/2020] [Indexed: 12/30/2022] Open
Abstract
Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA‐seq) data has been reported to be a low‐accuracy but cost‐effective tool, but the feasibility of RNA‐seq data for neoantigen prediction has not been fully examined. In the present study, we used whole‐exome sequencing (WES) and RNA‐seq data of tumor and matched normal samples from six breast cancer patients to evaluate the utility of RNA‐seq data instead of WES data in variant calling to detect neoantigen candidates. Somatic variants were called in three protocols using: (i) tumor and normal WES data (DNA method, Dm); (ii) tumor and normal RNA‐seq data (RNA method, Rm); and (iii) combination of tumor RNA‐seq and normal WES data (Combination method, Cm). We found that the Rm had both high false‐positive and high false‐negative rates because this method depended greatly on the expression status of normal transcripts. When we compared the results of Dm with those of Cm, only 14% of the neoantigen candidates detected in Dm were identified in Cm, but the majority of the missed candidates lacked coverage or variant allele reads in the tumor RNA. In contrast, about 70% of the neoepitope candidates with higher expression and rich mutant transcripts could be detected in Cm. Our results showed that Cm could be an efficient and a cost‐effective approach to predict highly expressed neoantigens in tumor samples.
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Affiliation(s)
- Sachie Hashimoto
- Department of Breast and Endocrine Surgery, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan
| | - Emiko Noguchi
- Department of Medical Genetics, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Hiroko Bando
- Department of Breast and Endocrine Surgery, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Hiroko Miyadera
- Department of Medical Genetics, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Wataru Morii
- Department of Medical Genetics, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Takako Nakamura
- Department of Medical Genetics, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Hisato Hara
- Department of Breast and Endocrine Surgery, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
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25
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Gopanenko AV, Kosobokova EN, Kosorukov VS. Main Strategies for the Identification of Neoantigens. Cancers (Basel) 2020; 12:E2879. [PMID: 33036391 PMCID: PMC7600129 DOI: 10.3390/cancers12102879] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/01/2020] [Accepted: 10/05/2020] [Indexed: 12/24/2022] Open
Abstract
Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.
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Affiliation(s)
| | | | - Vyacheslav S. Kosorukov
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, 115478 Moscow, Russia; (A.V.G.); (E.N.K.)
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26
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Zhang Q, Luo M, Liu CJ, Guo AY. CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles. Brief Bioinform 2020; 22:5854406. [PMID: 32510568 DOI: 10.1093/bib/bbaa093] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/26/2020] [Accepted: 04/28/2020] [Indexed: 01/28/2023] Open
Abstract
Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.
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27
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Adashek JJ, Kato S, Parulkar R, Szeto CW, Sanborn JZ, Vaske CJ, Benz SC, Reddy SK, Kurzrock R. Transcriptomic silencing as a potential mechanism of treatment resistance. JCI Insight 2020; 5:134824. [PMID: 32493840 DOI: 10.1172/jci.insight.134824] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/29/2020] [Indexed: 12/14/2022] Open
Abstract
Next-generation sequencing (NGS) has not revealed all the mechanisms underlying resistance to genomically matched drugs. Here, we performed in 1417 tumors whole-exome tumor (somatic)/normal (germline) NGS and whole-transcriptome sequencing, the latter focusing on a clinically oriented 50-gene panel in order to examine transcriptomic silencing of putative driver alterations. In this large-scale study, approximately 13% of the somatic single nucleotide variants (SNVs) were unexpectedly not expressed as RNA; 23% of patients had ≥1 nonexpressed SNV. SNV-bearing genes consistently transcribed were TP53, PIK3CA, and KRAS; those with lower transcription rates were ALK, CSF1R, ERBB4, FLT3, GNAS, HNF1A, KDR, PDGFRA, RET, and SMO. We also determined the frequency of tumor mutations being germline, rather than somatic, in these and an additional 462 tumors with tumor/normal exomes; 33.8% of germline SNVs within the gene panel were rare (not found after filtering through variant information domains) and at risk of being falsely reported as somatic. Both the frequency of silenced variant transcription and the risk of falsely identifying germline mutations as somatic/tumor related are important phenomena. Therefore, transcriptomics is a critical adjunct to genomics when interrogating patient tumors for actionable alterations, because, without expression of the target aberrations, there will likely be therapeutic resistance.
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Affiliation(s)
- Jacob J Adashek
- Department of Internal Medicine, University of South Florida, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Shumei Kato
- Center for Personalized Cancer Therapy and Division of Hematology and Oncology, Department of Medicine, University of California, San Diego, Moores Cancer Center, La Jolla, California, USA
| | | | | | | | | | | | | | - Razelle Kurzrock
- Center for Personalized Cancer Therapy and Division of Hematology and Oncology, Department of Medicine, University of California, San Diego, Moores Cancer Center, La Jolla, California, USA
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28
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Gu M, Zwiebel M, Ong SH, Boughton N, Nomdedeu J, Basheer F, Nannya Y, Quiros PM, Ogawa S, Cazzola M, Rad R, Butler AP, Vijayabaskar MS, Vassiliou GS. RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing. Haematologica 2020; 105:e290-e293. [PMID: 31649132 PMCID: PMC7271607 DOI: 10.3324/haematol.2019.230821] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Muxin Gu
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Maximillian Zwiebel
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- German Consortium for Translational Cancer Research (DKTK), Partnering Site, Munich, Germany
| | - Swee Hoe Ong
- Cancer Ageing and Somatic Mutation, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Nick Boughton
- Core Software Services, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Josep Nomdedeu
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Hospital de la Santa Creu I Sant Pau, Barcelona, Spain
| | - Faisal Basheer
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Department of Haematology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Yasuhito Nannya
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Pedro M Quiros
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mario Cazzola
- Fondazione IRCCS Policlinico San Matteo and University of Pavia, Pavia, Italy
| | - Roland Rad
- German Consortium for Translational Cancer Research (DKTK), Partnering Site, Munich, Germany
| | - Adam P Butler
- Cancer Ageing and Somatic Mutation, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - M S Vijayabaskar
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - George S Vassiliou
- Haematological Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Wellcome Trust-MRC Stem Cell Institute, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Department of Haematology, Cambridge University Hospitals NHS Trust, Cambridge, UK
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29
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Molecular-Clinical Correlation in Pediatric Medulloblastoma: A Cohort Series Study of 52 Cases in Taiwan. Cancers (Basel) 2020; 12:cancers12030653. [PMID: 32168907 PMCID: PMC7139704 DOI: 10.3390/cancers12030653] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/09/2020] [Accepted: 03/09/2020] [Indexed: 12/27/2022] Open
Abstract
In 2016, a project was initiated in Taiwan to adopt molecular diagnosis of childhood medulloblastoma (MB). In this study, we aimed to identify a molecular-clinical correlation and somatic mutation for exploring risk-adapted treatment, drug targets, and potential genetic predisposition. In total, 52 frozen tumor tissues of childhood MBs were collected. RNA sequencing (RNA-Seq) and DNA methylation array data were generated. Molecular subgrouping and clinical correlation analysis were performed. An adjusted Heidelberg risk stratification scheme was defined for updated clinical risk stratification. We selected 51 genes for somatic variant calling using RNA-Seq data. Relevant clinical findings were defined. Potential drug targets and genetic predispositions were explored. Four core molecular subgroups (WNT, SHH, Group 3, and Group 4) were identified. Genetic backgrounds of metastasis at diagnosis and extent of tumor resection were observed. The adjusted Heidelberg scheme showed its applicability. Potential drug targets were detected in the pathways of DNA damage response. Among the 10 patients with SHH MBs analyzed using whole exome sequencing studies, five patients exhibited potential genetic predispositions and four patients had relevant germline mutations. The findings of this study provide valuable information for updated risk adapted treatment and personalized care of childhood MBs in our cohort series and in Taiwan.
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30
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Coelho ACMF, Fonseca AL, Martins DL, Lins PBR, da Cunha LM, de Souza SJ. neoANT-HILL: an integrated tool for identification of potential neoantigens. BMC Med Genomics 2020; 13:30. [PMID: 32087727 PMCID: PMC7036241 DOI: 10.1186/s12920-020-0694-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 02/11/2020] [Indexed: 02/08/2023] Open
Abstract
Background Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.
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Affiliation(s)
- Ana Carolina M F Coelho
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
| | - André L Fonseca
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
| | - Danilo L Martins
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
| | - Paulo B R Lins
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil
| | - Lucas M da Cunha
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil.,PhD Program in Bioinformatics, UFRN, Natal, Brazil
| | - Sandro J de Souza
- Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil. .,Brain Institute, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil. .,Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.
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31
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Abstract
Limited clinical activity has been seen in osteosarcoma (OS) patients treated with immune checkpoint inhibitors (ICI). To gain insights into the immunogenic potential of these tumors, we conducted whole genome, RNA, and T-cell receptor sequencing, immunohistochemistry and reverse phase protein array profiling (RPPA) on OS specimens from 48 pediatric and adult patients with primary, relapsed, and metastatic OS. Median immune infiltrate level was lower than in other tumor types where ICI are effective, with concomitant low T-cell receptor clonalities. Neoantigen expression in OS was lacking and significantly associated with high levels of nonsense-mediated decay (NMD). Samples with low immune infiltrate had higher number of deleted genes while those with high immune infiltrate expressed higher levels of adaptive resistance pathways. PARP2 expression levels were significantly negatively associated with the immune infiltrate. Together, these data reveal multiple immunosuppressive features of OS and suggest immunotherapeutic opportunities in OS patients. The efficacy of immune checkpoint inhibitors (ICI) in osteosarcoma has been limited. Here, the authors investigate the immunogenomic landscape of osteosarcoma, and integrated analyses highlight features related to a suppressed immune microenvironment.
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32
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Bioinformatic methods for cancer neoantigen prediction. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 164:25-60. [PMID: 31383407 DOI: 10.1016/bs.pmbts.2019.06.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.
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33
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Muto Y, Moroishi T, Ichihara K, Nishiyama M, Shimizu H, Eguchi H, Moriya K, Koike K, Mimori K, Mori M, Katayama Y, Nakayama KI. Disruption of FBXL5-mediated cellular iron homeostasis promotes liver carcinogenesis. J Exp Med 2019; 216:950-965. [PMID: 30877170 PMCID: PMC6446870 DOI: 10.1084/jem.20180900] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 01/15/2019] [Accepted: 02/25/2019] [Indexed: 12/17/2022] Open
Abstract
Hepatocellular iron overload elicited by ablation of the iron-sensing ubiquitin ligase FBXL5 promotes liver carcinogenesis induced by exposure to a chemical carcinogen or hepatitis virus, suggesting that FBXL5 is a previously unrecognized oncosuppressor in liver carcinogenesis in mice. Hepatic iron overload is a risk factor for progression of hepatocellular carcinoma (HCC), although the molecular mechanisms underlying this association have remained unclear. We now show that the iron-sensing ubiquitin ligase FBXL5 is a previously unrecognized oncosuppressor in liver carcinogenesis in mice. Hepatocellular iron overload elicited by FBXL5 ablation gave rise to oxidative stress, tissue damage, inflammation, and compensatory proliferation of hepatocytes and to consequent promotion of liver carcinogenesis induced by exposure to a chemical carcinogen. The tumor-promoting outcome of FBXL5 deficiency in the liver was also found to be effective in a model of virus-induced HCC. FBXL5-deficient mice thus constitute the first genetically engineered mouse model of liver carcinogenesis promoted by iron overload. In addition, dysregulation of FBXL5-mediated cellular iron homeostasis was found to be associated with poor prognosis in human HCC, suggesting that FBXL5 plays a key role in defense against hepatocarcinogenesis.
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Affiliation(s)
- Yoshiharu Muto
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Toshiro Moroishi
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Kazuya Ichihara
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Masaaki Nishiyama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hideyuki Shimizu
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kyoji Moriya
- Department of Infection Control and Prevention, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University, Beppu Hospital, Beppu, Japan
| | - Masaki Mori
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Yuta Katayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
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34
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Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM, Ong HS. Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics. Front Genet 2019; 10:49. [PMID: 30809243 PMCID: PMC6379253 DOI: 10.3389/fgene.2019.00049] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 01/21/2019] [Indexed: 12/11/2022] Open
Abstract
There is a growing attention toward personalized medicine. This is led by a fundamental shift from the ‘one size fits all’ paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data “10 Vs” and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.
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Affiliation(s)
- Pawel Suwinski
- Malaysian Genomics Resource Centre Berhad, Kuala Lumpur, Malaysia
| | - ChuangKee Ong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.,Centre of Genomics Research, Precision Medicine and Genomics, AstraZeneca UK Limited, London, United Kingdom
| | - Maurice H T Ling
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia
| | - Yang Ming Poh
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia
| | - Asif M Khan
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia.,Graduate School of Medicine, Perdana University, Serdang, Malaysia
| | - Hui San Ong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Serdang, Malaysia
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