1
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Suzuki N, Idogawa M, Emori M, Murase K, Arihara Y, Nakamura H, Usami M, Kubo T, Kinoshita I, Sugita S, Tokino T, Hasegawa T, Sakurai A, Takada K. LMNA::NTRK1 Fusion-positive Leiomyosarcoma: Discrepancy between DNA-based Comprehensive Genomic Profiling and RNA Sequencing. Intern Med 2024; 63:2215-2219. [PMID: 38104989 PMCID: PMC11358736 DOI: 10.2169/internalmedicine.2879-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023] Open
Abstract
A 26-year-old man presented with a tumor in the left soleus muscle. The tumor was diagnosed as a locally advanced leiomyosarcoma. The patient was treated with irradiation followed by wide resection. One year after surgery, the patient presented with multiple lung metastases. Despite aggressive sequential chemotherapy, systemic metastatic tumors continued to develop. To explore therapeutic options for the patient, we performed DNA-based CGP with FoundationOne® CDx (F1). F1 identified an out-of-strand rearrangement of the NOS1AP::NTRK1 gene, which has not been previously reported. In contrast, RNA sequencing revealed an in-frame LMNA::NTRK1 gene, which is an oncogenic fusion gene.
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Affiliation(s)
- Norito Suzuki
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Masashi Idogawa
- Department of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Japan
| | - Makoto Emori
- Department of Orthopaedic Surgery, Sapporo Medical University School of Medicine, Japan
| | - Kazuyuki Murase
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Yohei Arihara
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Hajime Nakamura
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Makoto Usami
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Tomohiro Kubo
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
| | - Ichiro Kinoshita
- Division of Clinical Cancer Genomics, Hokkaido University Hospital, Japan
| | - Shintaro Sugita
- Department of Surgical Pathology, Sapporo Medical University School of Medicine, Japan
| | - Takashi Tokino
- Department of Medical Genome Sciences, Research Institute for Frontier Medicine, Sapporo Medical University School of Medicine, Japan
| | - Tadashi Hasegawa
- Department of Surgical Pathology, Sapporo Medical University School of Medicine, Japan
| | - Akihiro Sakurai
- Department of Medical Genetics and Genomics, Sapporo Medical University School of Medicine, Japan
| | - Kohichi Takada
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Japan
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2
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Motta M, Barresi S, Pizzi S, Bifano D, Lopez Marti J, Garrido-Pontnou M, Flex E, Bruselles A, Giovannoni I, Rotundo G, Fragale A, Tirelli V, Vallese S, Ciolfi A, Bisogno G, Alaggio R, Tartaglia M. RAF1 gene fusions are recurrent driver events in infantile fibrosarcoma-like mesenchymal tumors. J Pathol 2024; 263:166-177. [PMID: 38629245 DOI: 10.1002/path.6272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/21/2023] [Accepted: 02/13/2024] [Indexed: 05/12/2024]
Abstract
Infantile fibrosarcomas (IFS) and congenital mesoblastic nephroma (CMN) are rare myofibroblastic tumors of infancy and early childhood commonly harboring the ETV6::NTRK3 gene fusion. IFS/CMN are considered as tumors with an 'intermediate prognosis' as they are locally aggressive, but rarely metastasize, and generally have a favorable outcome. A fraction of IFS/CMN-related neoplasms are negative for the ETV6::NTRK3 gene rearrangement and are characterized by other chimeric proteins promoting MAPK signaling upregulation. In a large proportion of these tumors, which are classified as IFS-like mesenchymal neoplasms, the contributing molecular events remain to be identified. Here, we report three distinct rearrangements involving RAF1 among eight ETV6::NTRK3 gene fusion-negative tumors with an original histological diagnosis of IFS/CMN. The three fusion proteins retain the entire catalytic domain of the kinase. Two chimeric products, GOLGA4::RAF1 and LRRFIP2::RAF1, had previously been reported as driver events in different cancers, whereas the third, CLIP1::RAF1, represents a novel fusion protein. We demonstrate that CLIP1::RAF1 acts as a bona fide oncoprotein promoting cell proliferation and migration through constitutive upregulation of MAPK signaling. We show that the CLIP1::RAF1 hyperactive behavior does not require RAS activation and is mediated by constitutive 14-3-3 protein-independent dimerization of the chimeric protein. As previously reported for the ETV6::NTRK3 fusion protein, CLIP1::RAF1 similarly upregulates PI3K-AKT signaling. Our findings document that RAF1 gene rearrangements represent a recurrent event in ETV6::NTRK3-negative IFS/CMN and provide a rationale for the use of inhibitors directed to suppress MAPK and PI3K-AKT signaling in these cancers. © 2024 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Marialetizia Motta
- Molecular Genetics and Functional Genomics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Sabina Barresi
- Pathology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Simone Pizzi
- Molecular Genetics and Functional Genomics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Delfina Bifano
- Pathology Unit, Santobono-Pausilipon Children's Hospital, Naples, Italy
| | - Jennifer Lopez Marti
- Department of Pathology, Hospital Nacional de Pediatria Juan P. Garrahan, Buenos Aires, Argentina
| | | | - Elisabetta Flex
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Alessandro Bruselles
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | | | - Giovannina Rotundo
- Molecular Genetics and Functional Genomics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Alessandra Fragale
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | | | - Silvia Vallese
- Pathology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Andrea Ciolfi
- Molecular Genetics and Functional Genomics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Gianni Bisogno
- Pediatric Hematology-Oncology Division, University Hospital, Padova, Italy
| | - Rita Alaggio
- Pathology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
- Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University, Latina, Italy
| | - Marco Tartaglia
- Molecular Genetics and Functional Genomics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
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3
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Singh S, Shi X, Haddox S, Elfman J, Ahmad SB, Lynch S, Manley T, Piczak C, Phung C, Sun Y, Sharma A, Li H. RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data. Brief Bioinform 2024; 25:bbae251. [PMID: 38796690 PMCID: PMC11128028 DOI: 10.1093/bib/bbae251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 03/30/2024] [Accepted: 05/09/2024] [Indexed: 05/28/2024] Open
Abstract
Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Xinrui Shi
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Samuel Haddox
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Justin Elfman
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Syed Basil Ahmad
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Sarah Lynch
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Tommy Manley
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Claire Piczak
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Christopher Phung
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Yunan Sun
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Aadi Sharma
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States
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4
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Mistretta B, Rankothgedera S, Castillo M, Rao M, Holloway K, Bhardwaj A, El Noafal M, Albarracin C, El-Zein R, Rezaei H, Su X, Akbani R, Shao XM, Czerniecki BJ, Karchin R, Bedrosian I, Gunaratne PH. Chimeric RNAs reveal putative neoantigen peptides for developing tumor vaccines for breast cancer. Front Immunol 2023; 14:1188831. [PMID: 37744342 PMCID: PMC10512078 DOI: 10.3389/fimmu.2023.1188831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 07/27/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction We present here a strategy to identify immunogenic neoantigen candidates from unique amino acid sequences at the junctions of fusion proteins which can serve as targets in the development of tumor vaccines for the treatment of breastcancer. Method We mined the sequence reads of breast tumor tissue that are usually discarded as discordant paired-end reads and discovered cancer specific fusion transcripts using tissue from cancer free controls as reference. Binding affinity predictions of novel peptide sequences crossing the fusion junction were analyzed by the MHC Class I binding predictor, MHCnuggets. CD8+ T cell responses against the 15 peptides were assessed through in vitro Enzyme Linked Immunospot (ELISpot). Results We uncovered 20 novel fusion transcripts from 75 breast tumors of 3 subtypes: TNBC, HER2+, and HR+. Of these, the NSFP1-LRRC37A2 fusion transcript was selected for further study. The 3833 bp chimeric RNA predicted by the consensus fusion junction sequence is consistent with a read-through transcription of the 5'-gene NSFP1-Pseudo gene NSFP1 (NSFtruncation at exon 12/13) followed by trans-splicing to connect withLRRC37A2 located immediately 3' through exon 1/2. A total of 15 different 8-mer neoantigen peptides discovered from the NSFP1 and LRRC37A2 truncations were predicted to bind to a total of 35 unique MHC class I alleles with a binding affinity of IC50<500nM.); 1 of which elicited a robust immune response. Conclusion Our data provides a framework to identify immunogenic neoantigen candidates from fusion transcripts and suggests a potential vaccine strategy to target the immunogenic neopeptides in patients with tumors carrying the NSFP1-LRRC37A2 fusion.
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Affiliation(s)
- Brandon Mistretta
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Sakuni Rankothgedera
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Micah Castillo
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Mitchell Rao
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Kimberly Holloway
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Anjana Bhardwaj
- Department of Breast Surgical Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, United States
| | - Maha El Noafal
- Department of Medicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Constance Albarracin
- Department of Pathology, The UT MD Anderson Cancer Center, Houston, TX, United States
| | - Randa El-Zein
- Department of Medicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Hengameh Rezaei
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
| | - Xiaoping Su
- Department of Bioinformatics & Computational Biology, University of Texas, MD Anderson Cancer Center, Houston, TX, United States
| | - Rehan Akbani
- Department of Bioinformatics & Computational Biology, University of Texas, MD Anderson Cancer Center, Houston, TX, United States
| | - Xiaoshan M. Shao
- Biomedical Engineering Department, Institute for Computational Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Brian J. Czerniecki
- Department of Molecular & Cellular Biology, Baylor College of Medicine, Houston, TX, United States
| | - Rachel Karchin
- Biomedical Engineering Department, Institute for Computational Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Isabelle Bedrosian
- Department of Breast Surgical Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX, United States
| | - Preethi H. Gunaratne
- Department of Biology & Biochemistry, University of Houston, Houston, TX, United States
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States
- Department of Breast Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
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5
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Li X, Shao M. On de novo Bridging Paired-end RNA-seq Data. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2023; 2023:41. [PMID: 38045531 PMCID: PMC10692976 DOI: 10.1145/3584371.3612987] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The high-throughput short-reads RNA-seq protocols often produce paired-end reads, with the middle portion of the fragments being unsequenced. We explore if the full-length fragments can be computationally reconstructed from the sequenced two ends in the absence of the reference genome-a problem here we refer to as de novo bridging. Solving this problem provides longer, more informative RNA-seq reads, and benefits downstream RNA-seq analysis such as transcript assembly, expression quantification, and splicing differential analysis. However, de novo bridging is a challenging and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data provides sufficient information for accurate bridging, let alone efficient algorithms that determine the true bridges. Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying compacted de Bruijn graph (cdBG) used in the latter task often contains millions of vertices and edges. We designed a new truncated Dijkstra's algorithm for this problem, and proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Dijkstra's algorithm from scratch for all vertices for further speeding up. These innovative techniques result in scalable algorithms that can bridge all paired-end reads in a cdBG with millions of vertices. Our experiments showed that paired-end RNA-seq reads can be accurately bridged to a large extent. The resulting tool is freely available at https://github.com/Shao-Group/rnabridge-denovo.
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Affiliation(s)
- Xiang Li
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Mingfu Shao
- Department of Computer Science and Engineering, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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6
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Singh S, Shi X, Ahmad SB, Manley T, Piczak C, Phung C, Sun Y, Lynch S, Sharma A, Li H. RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526869. [PMID: 36778443 PMCID: PMC9915620 DOI: 10.1101/2023.02.02.526869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.
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7
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Butler E, Xu L, Rakheja D, Schwettmann B, Toubbeh S, Guo L, Kim J, Skapek SX, Zheng Y. Exon skipping in genes encoding lineage-defining myogenic transcription factors in rhabdomyosarcoma. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006190. [PMID: 35933111 PMCID: PMC9528969 DOI: 10.1101/mcs.a006190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 07/25/2022] [Indexed: 11/24/2022] Open
Abstract
Rhabdomyosarcoma (RMS) is a childhood sarcoma composed of myoblast-like cells, which suggests a defect in terminal skeletal muscle differentiation. To explore potential defects in the differentiation program, we searched for mRNA splicing variants in genes encoding transcription factors driving skeletal muscle lineage commitment and differentiation. We studied two RMS cases and identified altered splicing resulting in "skipping" the second of three exons in MYOD1. RNA-Seq data from 42 tumors and additional RMS cell lines revealed exon 2 skipping in both MYOD1 and MYF5 but not in MYF6 or MYOG. Complementary molecular analysis of MYOD1 mRNA found evidence for exon skipping in 5 additional RMS cases. Functional studies showed that so-called MYODΔEx2 protein failed to robustly induce muscle-specific genes, and its ectopic expression conferred a selective advantage in cultured fibroblasts and an RMS xenograft. In summary, we present previously unrecognized exon skipping within MYOD1 and MYF5 in RMS, and we propose that alternative splicing can represent a mechanism to alter the function of these two transcription factors in RMS.
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Affiliation(s)
- Erin Butler
- University of Texas Southwestern Medical Center;
| | - Lin Xu
- University of Texas Southwestern Medical Center
| | | | | | | | - Lei Guo
- University of Texas Southwestern Medical Center
| | - Jiwoon Kim
- University of Texas Southwestern Medical Center
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8
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Shukla N, Levine MF, Gundem G, Domenico D, Spitzer B, Bouvier N, Arango-Ossa JE, Glodzik D, Medina-Martínez JS, Bhanot U, Gutiérrez-Abril J, Zhou Y, Fiala E, Stockfisch E, Li S, Rodriguez-Sanchez MI, O'Donohue T, Cobbs C, Roehrl MHA, Benhamida J, Iglesias Cardenas F, Ortiz M, Kinnaman M, Roberts S, Ladanyi M, Modak S, Farouk-Sait S, Slotkin E, Karajannis MA, Dela Cruz F, Glade Bender J, Zehir A, Viale A, Walsh MF, Kung AL, Papaemmanuil E. Feasibility of whole genome and transcriptome profiling in pediatric and young adult cancers. Nat Commun 2022; 13:2485. [PMID: 35585047 PMCID: PMC9117241 DOI: 10.1038/s41467-022-30233-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 04/21/2022] [Indexed: 02/07/2023] Open
Abstract
The utility of cancer whole genome and transcriptome sequencing (cWGTS) in oncology is increasingly recognized. However, implementation of cWGTS is challenged by the need to deliver results within clinically relevant timeframes, concerns about assay sensitivity, reporting and prioritization of findings. In a prospective research study we develop a workflow that reports comprehensive cWGTS results in 9 days. Comparison of cWGTS to diagnostic panel assays demonstrates the potential of cWGTS to capture all clinically reported mutations with comparable sensitivity in a single workflow. Benchmarking identifies a minimum of 80× as optimal depth for clinical WGS sequencing. Integration of germline, somatic DNA and RNA-seq data enable data-driven variant prioritization and reporting, with oncogenic findings reported in 54% more patients than standard of care. These results establish key technical considerations for the implementation of cWGTS as an integrated test in clinical oncology. Cancer whole-genome and transcriptome sequencing (cWGTS) has been challenging to implement in clinical settings. Here, the authors develop a workflow to deliver robust cWGTS analyses and reports within clinically-relevant timeframes for paediatric, adolescent and young adult solid tumour patients.
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Affiliation(s)
- N Shukla
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M F Levine
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - G Gundem
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - D Domenico
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - B Spitzer
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - N Bouvier
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J E Arango-Ossa
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - D Glodzik
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J S Medina-Martínez
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - U Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J Gutiérrez-Abril
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Y Zhou
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - E Fiala
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - E Stockfisch
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - S Li
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - T O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - C Cobbs
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M H A Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J Benhamida
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - F Iglesias Cardenas
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M Kinnaman
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - S Roberts
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M Ladanyi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - S Modak
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - S Farouk-Sait
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - E Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M A Karajannis
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - F Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - J Glade Bender
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - A Zehir
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - A Viale
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M F Walsh
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - A L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - E Papaemmanuil
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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9
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Rajappa P, Eng KW, Bareja R, Bander ED, Yuan M, Dua A, Maachani UB, Snuderl M, Pan H, Zhang T, Tosi U, Ivasyk I, Souweidane MM, Elemento O, Sboner A, Greenfield JP, Pisapia DJ. Utility of Multimodality Molecular Profiling for Pediatric Patients with Central Nervous System Tumors. Neurooncol Adv 2022; 4:vdac031. [PMID: 35475276 PMCID: PMC9034114 DOI: 10.1093/noajnl/vdac031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
As our molecular understanding of pediatric central nervous system (CNS) tumors evolves, so too do diagnostic criteria, prognostic biomarkers, and clinical management decision-making algorithms. Here, we explore the clinical utility of wide-breadth assays including whole-exome sequencing (WES), RNA sequencing (RNAseq), and methylation array profiling as an addition to more conventional diagnostic tools for pediatric CNS tumors.
Methods
This study comprises an observational, prospective cohort followed at a single academic medical center over three years. Paired tumor and normal control specimens from 53 enrolled pediatric patients with CNS tumors underwent WES. A subset of cases also underwent RNAseq (n=28) and/or methylation array analysis (n=27).
Results
RNAseq identified driver and/or targetable fusions in 7/28 cases, including potentially targetable NTRK fusions, and uncovered possible rationalized treatment options based on outlier gene expression in 23/28 cases. Methylation profiling added diagnostic confidence (8/27 cases) or diagnostic subclassification endorsed by the WHO (10/27 cases). WES detected clinically pertinent Tier 1 or Tier 2 variants in 36/53 patients. Of these, 16/17 SNVs/indels and 10/19 copy number alterations would have been detected by current in-house conventional tests including targeted sequencing panels.
Conclusions
Over a heterogeneous set of pediatric tumors, RNAseq and methylation profiling frequently yielded clinically relevant information orthogonal to conventional methods while WES demonstrated clinically-relevant added-value primarily via copy number assessment. Longitudinal cohorts comparing targeted molecular pathology workup versus broader genomic approaches including therapeutic selection based on RNA-expression data will be necessary to further evaluate the clinical benefits of these modalities in practice.
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Affiliation(s)
- Prajwal Rajappa
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Kenneth W Eng
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Rohan Bareja
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Evan D Bander
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | - Melissa Yuan
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | - Alisha Dua
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | | | - Matija Snuderl
- Department of Pathology, New York University Grossman School of Medicine, New York, NY
| | - Heng Pan
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Tuo Zhang
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Umberto Tosi
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | - Iryna Ivasyk
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | - Mark M Souweidane
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
| | - Olivier Elemento
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Andreas Sboner
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Jeffrey P Greenfield
- Department of Neurological Surgery, Weill Cornell Medicine, New York, NY
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
| | - David J Pisapia
- Englander Institute of Precision Medicine, Weill Cornell Medicine, New York, NY
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY
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10
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Deng W, Murugan S, Lindberg J, Chellappa V, Shen X, Pawitan Y, Vu TN. Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients. Front Genet 2022; 13:820493. [PMID: 35251131 PMCID: PMC8888970 DOI: 10.3389/fgene.2022.820493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 01/31/2022] [Indexed: 12/13/2022] Open
Abstract
Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15–30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.
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Affiliation(s)
- Wenjiang Deng
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sarath Murugan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Johan Lindberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Venkatesh Chellappa
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xia Shen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Biostatistics Group, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- *Correspondence: Yudi Pawitan, ; Trung Nghia Vu,
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- *Correspondence: Yudi Pawitan, ; Trung Nghia Vu,
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11
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Detroja R, Gorohovski A, Giwa O, Baum G, Frenkel-Morgenstern M. ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data. NAR Genom Bioinform 2021; 3:lqab112. [PMID: 34859212 PMCID: PMC8633610 DOI: 10.1093/nargab/lqab112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/21/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.
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Affiliation(s)
- Rajesh Detroja
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Alessandro Gorohovski
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Olawumi Giwa
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Gideon Baum
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Milana Frenkel-Morgenstern
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
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12
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Balan J, Jenkinson G, Nair A, Saha N, Koganti T, Voss J, Zysk C, Barr Fritcher EG, Ross CA, Giannini C, Raghunathan A, Kipp BR, Jenkins R, Ida C, Halling KC, Blackburn PR, Dasari S, Oliver GR, Klee EW. SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA. Front Genet 2021; 12:739054. [PMID: 34745213 PMCID: PMC8569241 DOI: 10.3389/fgene.2021.739054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/24/2021] [Indexed: 11/13/2022] Open
Abstract
Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified.
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Affiliation(s)
| | - Garrett Jenkinson
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Asha Nair
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Neiladri Saha
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Tejaswi Koganti
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Jesse Voss
- Division of Laboratory Genetics and Genomics, Mayo Clinic, Rochester, MN, United States
| | - Christopher Zysk
- Applied Genomics Division, Perkin Elmer, Waltham, MA, United States
| | | | - Christian A Ross
- Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Caterina Giannini
- Division of Anatomic Pathology, Mayo Clinic, Rochester, MN, United States
| | - Aditya Raghunathan
- Division of Anatomic Pathology, Mayo Clinic, Rochester, MN, United States
| | - Benjamin R Kipp
- Division of Anatomic Pathology, Mayo Clinic, Rochester, MN, United States
| | - Robert Jenkins
- Division of Laboratory Genetics and Genomics, Mayo Clinic, Rochester, MN, United States
| | - Cris Ida
- Division of Laboratory Genetics and Genomics, Mayo Clinic, Rochester, MN, United States
| | - Kevin C Halling
- Division of Laboratory Genetics and Genomics, Mayo Clinic, Rochester, MN, United States
| | - Patrick R Blackburn
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Surendra Dasari
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Gavin R Oliver
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Eric W Klee
- Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
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13
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Singh S, Li H. Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing. RNA Biol 2021; 18:254-267. [PMID: 34142643 DOI: 10.1080/15476286.2021.1940047] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.
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Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA
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14
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Liu Q, Hu Y, Stucky A, Fang L, Zhong JF, Wang K. LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing. BMC Genomics 2020; 21:793. [PMID: 33372596 PMCID: PMC7771079 DOI: 10.1186/s12864-020-07207-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 10/29/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate < 150 bp reads. However, there is a general lack of software tools for gene fusion detection from long-read RNA-seq data, which takes into account the high basecalling error rates and the presence of alignment errors. RESULTS In this study, we developed a fast computational tool, LongGF, to efficiently detect candidate gene fusions from long-read RNA-seq data, including cDNA sequencing data and direct mRNA sequencing data. We evaluated LongGF on tens of simulated long-read RNA-seq datasets, and demonstrated its superior performance in gene fusion detection. We also tested LongGF on a Nanopore direct mRNA sequencing dataset and a PacBio sequencing dataset generated on a mixture of 10 cancer cell lines, and found that LongGF achieved better performance to detect known gene fusions over existing computational tools. Furthermore, we tested LongGF on a Nanopore cDNA sequencing dataset on acute myeloid leukemia, and pinpointed the exact location of a translocation (previously known in cytogenetic resolution) in base resolution, which was further validated by Sanger sequencing. CONCLUSIONS In summary, LongGF will greatly facilitate the discovery of candidate gene fusion events from long-read RNA-Seq data, especially in cancer samples. LongGF is implemented in C++ and is available at https://github.com/WGLab/LongGF .
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Affiliation(s)
- Qian Liu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Yu Hu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Andres Stucky
- Department of Otolaryngology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Jiang F Zhong
- Department of Otolaryngology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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15
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Panagopoulos I, Gorunova L, Andersen K, Lund-Iversen M, Lobmaier I, Micci F, Heim S. NDRG1-PLAG1 and TRPS1-PLAG1 Fusion Genes in Chondroid Syringoma. Cancer Genomics Proteomics 2020; 17:237-248. [PMID: 32345665 DOI: 10.21873/cgp.20184] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 02/04/2020] [Accepted: 02/06/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND/AIM Chondroid syringoma is a rare benign tumor emanating from sweat glands. Although rearrangements of the pleomorphic adenoma gene 1 (PLAG1) have been reported in such tumors, information on PLAG1 fusion genes is very limited. MATERIALS AND METHODS Cytogenetic, fluorescence in situ hybridization, RNA sequencing, array comparative genomic hybridization, reverse transcription polymerase chain reaction, and Sanger sequencing analyses were performed on two chondroid syringoma cases. RESULTS Both tumors had structural rearrangements of chromosome 8. An NDRG1-PLAG1 transcript was found in the first tumor in which exon 3 of PLAG1 was fused with exon 1 of NDRG1. A TRPS1-PLAG1 chimeric transcript was detected in the second chondroid syringoma in which exon 2 or exon 3 of PLAG1 was fused with exon 1 of TRPS1. CONCLUSION The NDRG1-PLAG1 and TRPS1-PLAG1 resemble other PLAG1 fusion genes inasmuch as the expression of PLAG1 comes under the control of the NDRG1 or TRPS1 promoter.
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Affiliation(s)
- Ioannis Panagopoulos
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Ludmila Gorunova
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Kristin Andersen
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Marius Lund-Iversen
- Department of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Ingvild Lobmaier
- Department of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Francesca Micci
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Sverre Heim
- Section for Cancer Cytogenetics, Institute for Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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16
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Lomov N, Zerkalenkova E, Lebedeva S, Viushkov V, Rubtsov MA. Cytogenetic and molecular genetic methods for chromosomal translocations detection with reference to the KMT2A/MLL gene. Crit Rev Clin Lab Sci 2020; 58:180-206. [PMID: 33205680 DOI: 10.1080/10408363.2020.1844135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Acute leukemias (ALs) are often associated with chromosomal translocations, in particular, KMT2A/MLL gene rearrangements. Identification or confirmation of these translocations is carried out by a number of genetic and molecular methods, some of which are routinely used in clinical practice, while others are primarily used for research purposes. In the clinic, these methods serve to clarify diagnoses and monitor the course of disease and therapy. On the other hand, the identification of new translocations and the confirmation of known translocations are of key importance in the study of disease mechanisms and further molecular classification. There are multiple methods for the detection of rearrangements that differ in their principle of operation, the type of problem being solved, and the cost-result ratio. This review is intended to help researchers and clinicians studying AL and related chromosomal translocations to navigate this variety of methods. All methods considered in the review are grouped by their principle of action and include karyotyping, fluorescence in situ hybridization (FISH) with probes for whole chromosomes or individual loci, PCR and reverse transcription-based methods, and high-throughput sequencing. Another characteristic of the described methods is the type of problem being solved. This can be the discovery of new rearrangements, the determination of unknown partner genes participating in the rearrangement, or the confirmation of the proposed rearrangement between the two genes. We consider the specifics of the application, the basic principle of each method, and its pros and cons. To illustrate the application, examples of studying the rearrangements of the KMT2A/MLL gene, one of the genes that are often rearranged in AL, are mentioned.
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Affiliation(s)
- Nikolai Lomov
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Elena Zerkalenkova
- Laboratory of Cytogenetics and Molecular Genetics Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Svetlana Lebedeva
- Laboratory of Cytogenetics and Molecular Genetics Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Vladimir Viushkov
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Mikhail A Rubtsov
- Department of Molecular Biology, Faculty of Biology, M.V. Lomonosov Moscow State University, Moscow, Russia.,Department of Biochemistry, Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
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17
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Walter W, Pfarr N, Meggendorfer M, Jost P, Haferlach T, Weichert W. Next-generation diagnostics for precision oncology: Preanalytical considerations, technical challenges, and available technologies. Semin Cancer Biol 2020; 84:3-15. [PMID: 33171257 DOI: 10.1016/j.semcancer.2020.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/23/2020] [Accepted: 10/24/2020] [Indexed: 12/13/2022]
Abstract
Molecular diagnostics as the centrepiece of precision oncology has gone through revolutionary developments over the last decade, becoming tremendously broad, deep and precise with still ongoing advancements. In the majority of scenarios, treatment selection for cancer patients without any type of molecular characterization is no longer conceivable. Considering the impact of sample quality on the reliability of molecular analyses and the importance of the results for the fate of an individual patient, it is surprising how sparsely preanalytical and analytical requirements are addressed scientifically. Standardization and rigorous quality assessment continue to play only a marginal role in the field. Within this review, we will systematically discuss influencing preanalytic parameters and technology setups affecting molecular test results. We will shed light on the specifics of different analytes, technical modalities, and analysis pipelines. The review will have a certain focus on broad molecular genetic tumour testing with next generation sequencing but will go beyond that including other molecular diagnostic modalities and will give a glimpse into the future of molecular testing.
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Affiliation(s)
| | - Nicole Pfarr
- Institute of Pathology, Technical University Munich, Germany
| | | | - Philipp Jost
- Medical Department III for Hematology and Oncology, Klinikum rechts der Isar, Technical University Munich, Germany; German Cancer Consostium (DKTK), Partner Site Munich, Germany
| | | | - Wilko Weichert
- Institute of Pathology, Technical University Munich, Germany; German Cancer Consostium (DKTK), Partner Site Munich, Germany.
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18
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Tsimberidou AM, Fountzilas E, Bleris L, Kurzrock R. Transcriptomics and solid tumors: The next frontier in precision cancer medicine. Semin Cancer Biol 2020; 84:50-59. [PMID: 32950605 DOI: 10.1016/j.semcancer.2020.09.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 08/16/2020] [Accepted: 09/09/2020] [Indexed: 01/08/2023]
Abstract
Transcriptomics, which encompasses assessments of alternative splicing and alternative polyadenylation, identification of fusion transcripts, explorations of noncoding RNAs, transcript annotation, and discovery of novel transcripts, is a valuable tool for understanding cancer mechanisms and identifying biomarkers. Recent advances in high-throughput technologies have enabled large-scale gene expression profiling. Importantly, RNA expression profiling of tumor tissue has been successfully used to determine clinically actionable molecular alterations. The WINTHER precision medicine clinical trial was the first prospective trial in diverse solid malignancies that assessed both genomics and transcriptomics to match treatments to specific molecular alterations. The use of transcriptome analysis in WINTHER and other trials increased the number of targetable -omic changes compared to genomic profiling alone. Other applications of transcriptomics involve the evaluation of tumor and circulating noncoding RNAs as predictive and prognostic biomarkers, the improvement of risk stratification by the use of prognostic and predictive multigene assays, the identification of fusion transcripts that drive tumors, and an improved understanding of the impact of DNA changes as some genomic alterations are silenced at the RNA level. Finally, RNA sequencing and gene expression analysis have been incorporated into clinical trials to identify markers predicting response to immunotherapy. Many issues regarding the complexity of the analysis, its reproducibility and variability, and the interpretation of the results still need to be addressed. The integration of transcriptomics with genomics, proteomics, epigenetics, and tumor immune profiling will improve biomarker discovery and our understanding of disease mechanisms and, thereby, accelerate the implementation of precision oncology.
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Affiliation(s)
- Apostolia M Tsimberidou
- The University of Texas MD Anderson Cancer Center, Department of Investigational Cancer Therapeutics, Houston, TX, USA.
| | - Elena Fountzilas
- Department of Medical Oncology, Euromedica General Clinic, Thessaloniki, Greece
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, TX, USA
| | - Razelle Kurzrock
- Center for Personalized Cancer Therapy and Division of Hematology and Oncology, UC San Diego Moores Cancer Center, San Diego, CA, USA
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19
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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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Cmero M, Davidson NM, Oshlack A. Using equivalence class counts for fast and accurate testing of differential transcript usage. F1000Res 2019; 8:265. [PMID: 31143443 PMCID: PMC6524746 DOI: 10.12688/f1000research.18276.2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2019] [Indexed: 12/16/2022] Open
Abstract
Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.
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Affiliation(s)
- Marek Cmero
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
| | - Nadia M. Davidson
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
- School of BioScience, University of Melbourne, Parkville, Victoria, Australia
| | - Alicia Oshlack
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
- School of BioScience, University of Melbourne, Parkville, Victoria, Australia
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Cmero M, Davidson NM, Oshlack A. Fast and accurate differential transcript usage by testing equivalence class counts. F1000Res 2019; 8:265. [PMID: 31143443 PMCID: PMC6524746 DOI: 10.12688/f1000research.18276.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/20/2019] [Indexed: 10/12/2023] Open
Abstract
Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing differential expression testing directly on equivalence class read counts (ECs). Methods: Here we demonstrate that ECs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform many types of analysis.
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Affiliation(s)
- Marek Cmero
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
| | - Nadia M. Davidson
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
- School of BioScience, University of Melbourne, Parkville, Victoria, Australia
| | - Alicia Oshlack
- Murdoch Childrens Research Institute, Parkville, Victoria, 3052, Australia
- School of BioScience, University of Melbourne, Parkville, Victoria, Australia
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