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Stillger MN, Kurowski K, Bronsert P, Brombacher E, Kreutz C, Werner M, Tang L, Timme-Bronsert S, Schilling O. Neoadjuvant chemo- or chemo-radiation-therapy of pancreatic ductal adenocarcinoma differentially shift ECM composition, complement activation, energy metabolism and ribosomal proteins of the residual tumor mass. Int J Cancer 2024; 154:2162-2175. [PMID: 38353498 DOI: 10.1002/ijc.34867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/08/2023] [Accepted: 12/20/2023] [Indexed: 04/14/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, often diagnosed at stages that dis-qualify for surgical resection. Neoadjuvant therapies offer potential tumor regression and improved resectability. Although features of the tumor biology (e.g., molecular markers) may guide adjuvant therapy, biological alterations after neoadjuvant therapy remain largely unexplored. We performed mass spectrometry to characterize the proteomes of 67 PDAC resection specimens of patients who received either neoadjuvant chemo (NCT) or chemo-radiation (NCRT) therapy. We employed data-independent acquisition (DIA), yielding a proteome coverage in excess of 3500 proteins. Moreover, we successfully integrated two publicly available proteome datasets of treatment-naïve PDAC to unravel proteome alterations in response to neoadjuvant therapy, highlighting the feasibility of this approach. We found highly distinguishable proteome profiles. Treatment-naïve PDAC was characterized by enrichment of immunoglobulins, complement and extracellular matrix (ECM) proteins. Post-NCT and post-NCRT PDAC presented high abundance of ribosomal and metabolic proteins as compared to treatment-naïve PDAC. Further analyses on patient survival and protein expression identified treatment-specific prognostic candidates. We present the first proteomic characterization of the residual PDAC mass after NCT and NCRT, and potential protein candidate markers associated with overall survival. We conclude that residual PDAC exhibits fundamentally different proteome profiles as compared to treatment-naïve PDAC, influenced by the type of neoadjuvant treatment. These findings may impact adjuvant or targeted therapy options.
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Affiliation(s)
- Maren N Stillger
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Konrad Kurowski
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Core Facility for Histopathology and Digital Pathology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Peter Bronsert
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Core Facility for Histopathology and Digital Pathology, Medical Center-University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Brombacher
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
| | - Martin Werner
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Core Facility for Histopathology and Digital Pathology, Medical Center-University of Freiburg, Freiburg, Germany
| | - Laura Tang
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sylvia Timme-Bronsert
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Oliver Schilling
- Faculty of Medicine, Institute for Surgical Pathology, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, Core Facility for Histopathology and Digital Pathology, Medical Center-University of Freiburg, Freiburg, Germany
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2
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Kim KH, Migliozzi S, Koo H, Hong JH, Park SM, Kim S, Kwon HJ, Ha S, Garofano L, Oh YT, D'Angelo F, Kim CI, Kim S, Lee JY, Kim J, Hong J, Jang EH, Mathon B, Di Stefano AL, Bielle F, Laurenge A, Nesvizhskii AI, Hur EM, Yin J, Shi B, Kim Y, Moon KS, Kwon JT, Lee SH, Lee SH, Gwak HS, Lasorella A, Yoo H, Sanson M, Sa JK, Park CK, Nam DH, Iavarone A, Park JB. Integrated proteogenomic characterization of glioblastoma evolution. Cancer Cell 2024; 42:358-377.e8. [PMID: 38215747 PMCID: PMC10939876 DOI: 10.1016/j.ccell.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 07/11/2023] [Accepted: 12/14/2023] [Indexed: 01/14/2024]
Abstract
The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.
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Affiliation(s)
- Kyung-Hee Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Proteomics Core Facility, Research Core Center, Research Institute, National Cancer Center, Goyang, Korea
| | - Simona Migliozzi
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Harim Koo
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jun-Hee Hong
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seung Min Park
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Sooheon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Hyung Joon Kwon
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seokjun Ha
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Luciano Garofano
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Young Taek Oh
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fulvio D'Angelo
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chan Il Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Seongsoo Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Ji Yoon Lee
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jiwon Kim
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Jisoo Hong
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea
| | - Eun-Hae Jang
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Korea
| | - Bertrand Mathon
- Service de Neurochirurgie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France
| | - Anna-Luisa Di Stefano
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France; Department of Neurology, Foch Hospital, Suresnes, France
| | - Franck Bielle
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France
| | - Alice Laurenge
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France
| | | | - Eun-Mi Hur
- Laboratory of Neuroscience, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Korea; BK21 Four Future Veterinary Medicine Leading Education & Research Center, College of Veterinary Medicine, Seoul National University, Seoul, Korea
| | - Jinlong Yin
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Henan-Macquarie University Joint Centre for Biomedical Innovation, School of Life Sciences, Henan University, Kaifeng, Henan, China
| | - Bingyang Shi
- Henan-Macquarie University Joint Centre for Biomedical Innovation, School of Life Sciences, Henan University, Kaifeng, Henan, China
| | - Youngwook Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Kyung-Sub Moon
- Department of Neurosurgery, Chonnam National University Hwasun Hospital and Medical School, Hwasun, Korea
| | - Jeong Taik Kwon
- Department of Neurosurgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Shin Heon Lee
- Department of Neurosurgery, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Seung Hoon Lee
- Department of Neurosurgery, Eulji University School of Medicine, Daejeon, Korea
| | - Ho Shin Gwak
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Anna Lasorella
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA; Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Heon Yoo
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
| | - Marc Sanson
- Institut de Neurologie, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Paris, France; Sorbonne Université, Inserm, CNRS, UMR S 1127, Paris Brain Institute (ICM), Equipe labellisée LNCC, Paris, France; Onconeurotek, AP-HP, Hôpital Pitié-Salpêtrière, F-75013 Paris, France.
| | - Jason K Sa
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea.
| | - Chul-Kee Park
- Deparment of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea.
| | - Do-Hyun Nam
- Department of Neurosurgery and Samsung Advanced Institute for Health Sciences and Technology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA; Department of Neurological Surgery and Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Jong Bae Park
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea; Department of Clinical Research, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
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3
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Petralia F, Ma W, Yaron TM, Caruso FP, Tignor N, Wang JM, Charytonowicz D, Johnson JL, Huntsman EM, Marino GB, Calinawan A, Evangelista JE, Selvan ME, Chowdhury S, Rykunov D, Krek A, Song X, Turhan B, Christianson KE, Lewis DA, Deng EZ, Clarke DJB, Whiteaker JR, Kennedy JJ, Zhao L, Segura RL, Batra H, Raso MG, Parra ER, Soundararajan R, Tang X, Li Y, Yi X, Satpathy S, Wang Y, Wiznerowicz M, González-Robles TJ, Iavarone A, Gosline SJC, Reva B, Robles AI, Nesvizhskii AI, Mani DR, Gillette MA, Klein RJ, Cieslik M, Zhang B, Paulovich AG, Sebra R, Gümüş ZH, Hostetter G, Fenyö D, Omenn GS, Cantley LC, Ma'ayan A, Lazar AJ, Ceccarelli M, Wang P. Pan-cancer proteogenomics characterization of tumor immunity. Cell 2024; 187:1255-1277.e27. [PMID: 38359819 PMCID: PMC10988632 DOI: 10.1016/j.cell.2024.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/29/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024]
Abstract
Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.
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Affiliation(s)
- Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
| | - Francesca Pia Caruso
- BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy; Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Nicole Tignor
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joshua M Wang
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Daniel Charytonowicz
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Myvizhi Esai Selvan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Xiaoyu Song
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Berk Turhan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karen E Christianson
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David A Lewis
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeffrey R Whiteaker
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jacob J Kennedy
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Lei Zhao
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Rossana Lazcano Segura
- Departments of Pathology & Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Harsh Batra
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maria Gabriela Raso
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Edwin Roger Parra
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rama Soundararajan
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ximing Tang
- Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Ying Wang
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Maciej Wiznerowicz
- Department of Medical Biotechnology, Poznan University of Medical Sciences, 61-701 Poznań, Poland; International Institute for Molecular Oncology, 60-203 Poznań, Poland; Department of Oncology, Heliodor Swiecicki Clinical Hospital, 60-203 Poznań, Poland
| | - Tania J González-Robles
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Antonio Iavarone
- Department of Neurological Surgery, Department of Biochemistry, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Alexey I Nesvizhskii
- Departments of Pathology and Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Marcin Cieslik
- Departments of Pathology and Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Amanda G Paulovich
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zeynep H Gümüş
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Galen Hostetter
- Pathology and Biorepository Core, Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - David Fenyö
- Institute for Systems Genetics, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & Environmental Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, the University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Michele Ceccarelli
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA; Department of Public Health Sciences, University of Miami, Miami, FL, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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4
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Zhang Y, Li H, Shen Y, Wang S, Tian L, Yin H, Shi J, Xing A, Zhang J, Ali U, Sami A, Chen X, Gao C, Zhao Y, Lyu Y, Wang X, Chen Y, Tian Z, Wu SB, Wu L. Readthrough events in plants reveal plasticity of stop codons. Cell Rep 2024; 43:113723. [PMID: 38300801 DOI: 10.1016/j.celrep.2024.113723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 10/02/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
Stop codon readthrough (SCR) has important biological implications but remains largely uncharacterized. Here, we identify 1,009 SCR events in plants using a proteogenomic strategy. Plant SCR candidates tend to have shorter transcript lengths and fewer exons and splice variants than non-SCR transcripts. Mass spectrometry evidence shows that stop codons involved in SCR events can be recoded as 20 standard amino acids, some of which are also supported by suppressor tRNA analysis. We also observe multiple functional signals in 34 maize extended proteins and characterize the structural and subcellular localization changes in the extended protein of basic transcription factor 3. Furthermore, the SCR events exhibit non-conserved signature, and the extensions likely undergo protein-coding selection. Overall, our study not only characterizes that SCR events are commonly present in plants but also identifies the recoding plasticity of stop codons, which provides important insights into the flexibility of genetic decoding.
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Affiliation(s)
- Yuqian Zhang
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China; School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
| | - Hehuan Li
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Yanting Shen
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Shunxi Wang
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Lei Tian
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Haoqiang Yin
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Jiawei Shi
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Anqi Xing
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634, USA
| | - Jinghua Zhang
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Usman Ali
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Abdul Sami
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Xueyan Chen
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Chenxuan Gao
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Yangtao Zhao
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Yajing Lyu
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Xiaoxu Wang
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Yanhui Chen
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; College of Advanced Agriculture Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Shu-Biao Wu
- School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
| | - Liuji Wu
- National Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, Henan, China; School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
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5
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Feng S, Calinawan A, Pugliese P, Wang P, Ceccarelli M, Petralia F, Gosline SJC. Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution. Cell Rep Methods 2024; 4:100708. [PMID: 38412834 PMCID: PMC10921018 DOI: 10.1016/j.crmeth.2024.100708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 10/23/2023] [Accepted: 01/18/2024] [Indexed: 02/29/2024]
Abstract
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
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Affiliation(s)
- Song Feng
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Anna Calinawan
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
| | | | - Pei Wang
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
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6
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Choi S, Paek E. pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads. Mol Cell Proteomics 2024; 23:100743. [PMID: 38403075 PMCID: PMC10979277 DOI: 10.1016/j.mcpro.2024.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024] Open
Abstract
Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I-associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I-associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.
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Affiliation(s)
- Seunghyuk Choi
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea; Institute for Artificial Intelligence Research, Hanyang University, Seoul, Republic of Korea.
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7
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Rao D, Füssy Z, Brisbin MM, McIlvin MR, Moran DM, Allen AE, Follows MJ, Saito MA. Flexible B 12 ecophysiology of Phaeocystis antarctica due to a fusion B 12-independent methionine synthase with widespread homologues. Proc Natl Acad Sci U S A 2024; 121:e2204075121. [PMID: 38306482 PMCID: PMC10861871 DOI: 10.1073/pnas.2204075121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 11/13/2023] [Indexed: 02/04/2024] Open
Abstract
Coastal Antarctic marine ecosystems are significant in carbon cycling because of their intense seasonal phytoplankton blooms. Southern Ocean algae are primarily limited by light and iron (Fe) and can be co-limited by cobalamin (vitamin B12). Micronutrient limitation controls productivity and shapes the composition of blooms which are typically dominated by either diatoms or the haptophyte Phaeocystis antarctica. However, the vitamin requirements and ecophysiology of the keystone species P. antarctica remain poorly characterized. Using cultures, physiological analysis, and comparative omics, we examined the response of P. antarctica to a matrix of Fe-B12 conditions. We show that P. antarctica is not auxotrophic for B12, as previously suggested, and identify mechanisms underlying its B12 response in cultures of predominantly solitary and colonial cells. A combination of proteomics and proteogenomics reveals a B12-independent methionine synthase fusion protein (MetE-fusion) that is expressed under vitamin limitation and interreplaced with the B12-dependent isoform under replete conditions. Database searches return homologues of the MetE-fusion protein in multiple Phaeocystis species and in a wide range of marine microbes, including other photosynthetic eukaryotes with polymorphic life cycles as well as bacterioplankton. Furthermore, we find MetE-fusion homologues expressed in metaproteomic and metatranscriptomic field samples in polar and more geographically widespread regions. As climate change impacts micronutrient availability in the coastal Southern Ocean, our finding that P. antarctica has a flexible B12 metabolism has implications for its relative fitness compared to B12-auxotrophic diatoms and for the detection of B12-stress in a more diverse set of marine microbes.
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Affiliation(s)
- Deepa Rao
- Earth Atmospheric Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA02139
- Marine Chemistry and Geochemistry Department, Woods Hole, MA02543
| | - Zoltán Füssy
- Microbial and Environmental Genomics Department, J.C. Venter Institute, La Jolla, CA92037
| | | | | | - Dawn M. Moran
- Marine Chemistry and Geochemistry Department, Woods Hole, MA02543
| | - Andrew E. Allen
- Microbial and Environmental Genomics Department, J.C. Venter Institute, La Jolla, CA92037
- Integrative Oceanography Division, Scripps Instition of Oceanography, University of California San Diego, La Jolla, CA92037
| | - Michael J. Follows
- Earth Atmospheric Planetary Sciences Department, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Mak A. Saito
- Marine Chemistry and Geochemistry Department, Woods Hole, MA02543
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8
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Cao X, Sun S, Xing J. A Massive Proteogenomic Screen Identifies Thousands of Novel Peptides From the Human "Dark" Proteome. Mol Cell Proteomics 2024; 23:100719. [PMID: 38242438 PMCID: PMC10867589 DOI: 10.1016/j.mcpro.2024.100719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 01/01/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024] Open
Abstract
Although the human gene annotation has been continuously improved over the past 2 decades, numerous studies demonstrated the existence of a "dark proteome", consisting of proteins that were critical for biological processes but not included in widely used gene catalogs. The Genotype-Tissue Expression project generated more than 15,000 RNA-seq datasets from multiple tissues, which modeled 30 million transcripts in the human genome. To provide a resource of high-confidence novel proteins from the dark proteome, we screened 50,000 mass spectrometry runs from over 900 projects to identify proteins translated from the Genotype-Tissue Expression transcript model with proteomic support. We also integrated 3.8 million common genetic variants from the gnomAD database to improve peptide identification. As a result, we identified 170,529 novel peptides with proteomic evidence, of which 6048 passed the strictest standard we defined and were supported by PepQuery. We provided a user-friendly website (https://ncorf.genes.fun/) for researchers to check the evidence of novel peptides from their studies. The findings will improve our understanding of coding genes and facilitate genomic data interpretation in biomedical research.
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Affiliation(s)
- Xiaolong Cao
- Department of Anesthesiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Siqi Sun
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Jinchuan Xing
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
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9
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Joshi SK, Piehowski P, Liu T, Gosline SJC, McDermott JE, Druker BJ, Traer E, Tyner JW, Agarwal A, Tognon CE, Rodland KD. Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine. Annu Rev Pharmacol Toxicol 2024; 64:455-479. [PMID: 37738504 PMCID: PMC10950354 DOI: 10.1146/annurev-pharmtox-022723-113921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.
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Affiliation(s)
- Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Paul Piehowski
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Tao Liu
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Sara J C Gosline
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jason E McDermott
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, Oregon, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Division of Hematology and Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Karin D Rodland
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA;
- Pacific Northwest National Laboratory, Richland, Washington, USA
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10
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Santos LGC, Parreira VDSC, da Silva EMG, Santos MDM, Fernandes ADF, Neves-Ferreira AGDC, Carvalho PC, Freitas FCDP, Passetti F. SpliceProt 2.0: A Sequence Repository of Human, Mouse, and Rat Proteoforms. Int J Mol Sci 2024; 25:1183. [PMID: 38256255 PMCID: PMC10816255 DOI: 10.3390/ijms25021183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/15/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
SpliceProt 2.0 is a public proteogenomics database that aims to list the sequence of known proteins and potential new proteoforms in human, mouse, and rat proteomes. This updated repository provides an even broader range of computationally translated proteins and serves, for example, to aid with proteomic validation of splice variants absent from the reference UniProtKB/SwissProt database. We demonstrate the value of SpliceProt 2.0 to predict orthologous proteins between humans and murines based on transcript reconstruction, sequence annotation and detection at the transcriptome and proteome levels. In this release, the annotation data used in the reconstruction of transcripts based on the methodology of ternary matrices were acquired from new databases such as Ensembl, UniProt, and APPRIS. Another innovation implemented in the pipeline is the exclusion of transcripts predicted to be susceptible to degradation through the NMD pathway. Taken together, our repository and its applications represent a valuable resource for the proteogenomics community.
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Affiliation(s)
- Letícia Graziela Costa Santos
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
| | - Vinícius da Silva Coutinho Parreira
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
| | - Esdras Matheus Gomes da Silva
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
- Laboratory of Toxinology, Oswaldo Cruz Institute, Fundação Oswaldo Cruz (FIOCRUZ), Av. Brazil 4036, Campus Maré, Rio de Janeiro 21040-361, RJ, Brazil
| | - Marlon Dias Mariano Santos
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
| | - Alexander da Franca Fernandes
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
| | - Ana Gisele da Costa Neves-Ferreira
- Laboratory of Toxinology, Oswaldo Cruz Institute, Fundação Oswaldo Cruz (FIOCRUZ), Av. Brazil 4036, Campus Maré, Rio de Janeiro 21040-361, RJ, Brazil
| | - Paulo Costa Carvalho
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
| | - Flávia Cristina de Paula Freitas
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
- Departamento de Genética e Evolução, Universidade Federal de São Carlos (UFSCar), Rodovia Washington Luis, Km 235, São Carlos 13565-905, SP, Brazil
| | - Fabio Passetti
- Instituto Carlos Chagas, Fundação Oswaldo Cruz (FIOCRUZ), Rua Professor Algacyr Munhoz Mader 3775, Cidade Industrial De Curitiba, Curitiba 81310-020, PR, Brazil
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11
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Liu Q, Zhang J, Guo C, Wang M, Wang C, Yan Y, Sun L, Wang D, Zhang L, Yu H, Hou L, Wu C, Zhu Y, Jiang G, Zhu H, Zhou Y, Fang S, Zhang T, Hu L, Li J, Liu Y, Zhang H, Zhang B, Ding L, Robles AI, Rodriguez H, Gao D, Ji H, Zhou H, Zhang P. Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies. Cell 2024; 187:184-203.e28. [PMID: 38181741 DOI: 10.1016/j.cell.2023.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 09/25/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024]
Abstract
We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.
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Affiliation(s)
- Qian Liu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China; Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jing Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Chenchen Guo
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengcheng Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yilv Yan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Di Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Lele Zhang
- Central Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Huansha Yu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Hongwen Zhu
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yanting Zhou
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Shanhua Fang
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Tengfei Zhang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Hu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junqiang Li
- D1 Medical Technology, Shanghai 201800, China
| | - Yansheng Liu
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Li Ding
- Department of Medicine, McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Daming Gao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
| | - Hongbin Ji
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; School of Life Science and Technology, Shanghai Tech University, Shanghai 200120, China.
| | - Hu Zhou
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
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12
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Solovyeva EM, Utzinger S, Vissières A, Mitchelmore J, Ahrné E, Hermes E, Poetsch T, Ronco M, Bidinosti M, Merkl C, Serluca FC, Fessenden J, Naumann U, Voshol H, Meyer AS, Hoersch S. Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model. Mol Cell Proteomics 2024; 23:100683. [PMID: 37993104 PMCID: PMC10770608 DOI: 10.1016/j.mcpro.2023.100683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/02/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.
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Affiliation(s)
- Elizaveta M Solovyeva
- Research Informatics, Biomedical Research at Novartis, Basel, Switzerland; V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, Russia.
| | - Stephan Utzinger
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | | | - Joanna Mitchelmore
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | - Erik Ahrné
- Discovery Sciences, Biomedical Research at Novartis, Basel, Switzerland
| | - Erwin Hermes
- Discovery Sciences, Biomedical Research at Novartis, Basel, Switzerland
| | - Tania Poetsch
- Discovery Sciences, Biomedical Research at Novartis, Basel, Switzerland
| | - Marie Ronco
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | - Michael Bidinosti
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | - Claudia Merkl
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | - Fabrizio C Serluca
- Research Informatics, Biomedical Research at Novartis, Cambridge, Massachusetts, USA
| | - James Fessenden
- Neurodegenerative Diseases, Biomedical Research at Novartis, Cambridge, Massachusetts, USA
| | - Ulrike Naumann
- Discovery Sciences, Biomedical Research at Novartis, Basel, Switzerland
| | - Hans Voshol
- Discovery Sciences, Biomedical Research at Novartis, Basel, Switzerland
| | - Angelika S Meyer
- Diseases of Aging and Regenerative Medicine, Biomedical Research at Novartis, Basel, Switzerland
| | - Sebastian Hoersch
- Research Informatics, Biomedical Research at Novartis, Basel, Switzerland.
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13
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Song YC, Das D, Zhang Y, Chen MX, Fernie AR, Zhu FY, Han J. Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants. Trends Biotechnol 2023; 41:1532-1548. [PMID: 37365082 DOI: 10.1016/j.tibtech.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023]
Abstract
Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.
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Affiliation(s)
- Yu-Chen Song
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Debatosh Das
- College of Agriculture, Food and Natural Resources (CAFNR), Division of Plant Sciences and Technology, 52 Agricultural Building, University of Missouri-Columbia, MO 65201, USA
| | - Youjun Zhang
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Mo-Xian Chen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria.
| | - Fu-Yuan Zhu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Jiangang Han
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
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14
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Fuchs S, Engelmann S. Small proteins in bacteria - Big challenges in prediction and identification. Proteomics 2023; 23:e2200421. [PMID: 37609810 DOI: 10.1002/pmic.202200421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/03/2023] [Accepted: 08/10/2023] [Indexed: 08/24/2023]
Abstract
Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.
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Affiliation(s)
- Stephan Fuchs
- Genome Competence Center (MF1), Department MFI, Robert-Koch-Institut, Berlin, Germany
| | - Susanne Engelmann
- Institute for Microbiology, Technische Universität Braunschweig, Braunschweig, Germany
- Microbial Proteomics, Helmholtzzentrum für Infektionsforschung GmbH, Braunschweig, Germany
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15
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Ghoshdastider U, Sendoel A. Exploring the pan-cancer landscape of posttranscriptional regulation. Cell Rep 2023; 42:113172. [PMID: 37742190 DOI: 10.1016/j.celrep.2023.113172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/28/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
Understanding the mechanisms underlying cancer gene expression is critical for precision oncology. Posttranscriptional regulation is a key determinant of protein abundance and cancer cell behavior. However, to what extent posttranscriptional regulatory mechanisms impact protein levels and cancer progression is an ongoing question. Here, we exploit cancer proteogenomics data to systematically compare mRNA-protein correlations across 14 different human cancer types. We identify two clusters of genes with particularly low mRNA-protein correlations across all cancer types, shed light on the role of posttranscriptional regulation of cancer driver genes and drug targets, and unveil a cohort of 55 mutations that alter systems-wide posttranscriptional regulation. Surprisingly, we find that decreased levels of posttranscriptional control in patients correlate with shorter overall survival across multiple cancer types, prompting further mechanistic studies into how posttranscriptional regulation affects patient outcomes. Our findings underscore the importance of a comprehensive understanding of the posttranscriptional regulatory landscape for predicting cancer progression.
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Affiliation(s)
- Umesh Ghoshdastider
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, 8952 Schlieren-Zurich, Switzerland
| | - Ataman Sendoel
- Institute for Regenerative Medicine (IREM), University of Zurich, Wagistrasse 12, 8952 Schlieren-Zurich, Switzerland.
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16
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Wang XY, Xu YM, Lau ATY. Proteogenomics in Cancer: Then and Now. J Proteome Res 2023; 22:3103-3122. [PMID: 37725793 DOI: 10.1021/acs.jproteome.3c00196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.
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Affiliation(s)
- Xiu-Yun Wang
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Yan-Ming Xu
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Andy T Y Lau
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
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17
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Skiadopoulou D, Vašíček J, Kuznetsova K, Bouyssié D, Käll L, Vaudel M. Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides. J Proteome Res 2023; 22:3190-3199. [PMID: 37656829 PMCID: PMC10563157 DOI: 10.1021/acs.jproteome.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Indexed: 09/03/2023]
Abstract
Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.
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Affiliation(s)
- Dafni Skiadopoulou
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - Jakub Vašíček
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - Ksenia Kuznetsova
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
| | - David Bouyssié
- Institut
de Pharmacologie et de Biologie Structurale (IPBS), Université
de Toulouse, CNRS, Université Toulouse III—Paul Sabatier
(UT3), 31000 Toulouse, France
| | - Lukas Käll
- Science
for Life Laboratory, School of Engineering Sciences in Chemistry,
Biotechnology and Health, KTH Royal Institute
of Technology, SE-100 44 Stockholm, Sweden
| | - Marc Vaudel
- Mohn
Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
- Computational
Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway
- Department
of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, N-0213 Oslo, Norway
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18
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Liao Y, Savage SR, Dou Y, Shi Z, Yi X, Jiang W, Lei JT, Zhang B. A proteogenomics data-driven knowledge base of human cancer. Cell Syst 2023; 14:777-787.e5. [PMID: 37619559 PMCID: PMC10530292 DOI: 10.1016/j.cels.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/11/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023]
Abstract
By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
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19
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Dou Y, Katsnelson L, Gritsenko MA, Hu Y, Reva B, Hong R, Wang YT, Kolodziejczak I, Lu RJH, Tsai CF, Bu W, Liu W, Guo X, An E, Arend RC, Bavarva J, Chen L, Chu RK, Czekański A, Davoli T, Demicco EG, DeLair D, Devereaux K, Dhanasekaran SM, Dottino P, Dover B, Fillmore TL, Foxall M, Hermann CE, Hiltke T, Hostetter G, Jędryka M, Jewell SD, Johnson I, Kahn AG, Ku AT, Kumar-Sinha C, Kurzawa P, Lazar AJ, Lazcano R, Lei JT, Li Y, Liao Y, Lih TSM, Lin TT, Martignetti JA, Masand RP, Matkowski R, McKerrow W, Mesri M, Monroe ME, Moon J, Moore RJ, Nestor MD, Newton C, Omelchenko T, Omenn GS, Payne SH, Petyuk VA, Robles AI, Rodriguez H, Ruggles KV, Rykunov D, Savage SR, Schepmoes AA, Shi T, Shi Z, Tan J, Taylor M, Thiagarajan M, Wang JM, Weitz KK, Wen B, Williams CM, Wu Y, Wyczalkowski MA, Yi X, Zhang X, Zhao R, Mutch D, Chinnaiyan AM, Smith RD, Nesvizhskii AI, Wang P, Wiznerowicz M, Ding L, Mani DR, Zhang H, Anderson ML, Rodland KD, Zhang B, Liu T, Fenyö D. Proteogenomic insights suggest druggable pathways in endometrial carcinoma. Cancer Cell 2023; 41:1586-1605.e15. [PMID: 37567170 PMCID: PMC10631452 DOI: 10.1016/j.ccell.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 03/25/2023] [Accepted: 07/18/2023] [Indexed: 08/13/2023]
Abstract
We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.
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Affiliation(s)
- Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lizabeth Katsnelson
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Yingwei Hu
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Boris Reva
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Iga Kolodziejczak
- International Institute for Molecular Oncology, 20-203 Poznań, Poland; Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Rita Jui-Hsien Lu
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Wen Bu
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaofang Guo
- Division of Gynecologic Oncology, University of South Florida Morsani College of Medicine and Tampa General Hospital Cancer Institute, Tampa, FL 33606, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Rebecca C Arend
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Alabama at Birmingham, Birmingham, AL 35249, USA
| | - Jasmin Bavarva
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Lijun Chen
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Rosalie K Chu
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Andrzej Czekański
- Wroclaw Medical University and Lower Silesian Oncology, Pulmonology and Hematology Center (DCOPIH), Wrocław, Poland
| | - Teresa Davoli
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Elizabeth G Demicco
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1X5, Canada
| | - Deborah DeLair
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Kelly Devereaux
- Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter Dottino
- Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bailee Dover
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Alabama at Birmingham, Birmingham, AL 35249, USA
| | - Thomas L Fillmore
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - McKenzie Foxall
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University of Alabama at Birmingham, Birmingham, AL 35249, USA
| | - Catherine E Hermann
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | | | - Marcin Jędryka
- Wroclaw Medical University and Lower Silesian Oncology, Pulmonology and Hematology Center (DCOPIH), Wrocław, Poland
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Isabelle Johnson
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Andrea G Kahn
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35249, USA
| | - Amy T Ku
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Chandan Kumar-Sinha
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paweł Kurzawa
- Heliodor Swiecicki Clinical Hospital in Poznan ul. Przybyszewskiego 49, 60-355 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Alexander J Lazar
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rossana Lazcano
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yi Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tung-Shing M Lih
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Tai-Tu Lin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - John A Martignetti
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ramya P Masand
- Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rafał Matkowski
- Wroclaw Medical University and Lower Silesian Oncology, Pulmonology and Hematology Center (DCOPIH), Wrocław, Poland
| | - Wilson McKerrow
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jamie Moon
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Michael D Nestor
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Chelsea Newton
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Athena A Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tujin Shi
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jimin Tan
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Mason Taylor
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - C M Williams
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yige Wu
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xu Zhang
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Rui Zhao
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - David Mutch
- Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Heliodor Swiecicki Clinical Hospital in Poznan ul. Przybyszewskiego 49, 60-355 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Li Ding
- Department of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Hui Zhang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Matthew L Anderson
- Division of Gynecologic Oncology, University of South Florida Morsani College of Medicine and Tampa General Hospital Cancer Institute, Tampa, FL 33606, USA.
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA.
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20
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Li Y, Porta-Pardo E, Tokheim C, Bailey MH, Yaron TM, Stathias V, Geffen Y, Imbach KJ, Cao S, Anand S, Akiyama Y, Liu W, Wyczalkowski MA, Song Y, Storrs EP, Wendl MC, Zhang W, Sibai M, Ruiz-Serra V, Liang WW, Terekhanova NV, Rodrigues FM, Clauser KR, Heiman DI, Zhang Q, Aguet F, Calinawan AP, Dhanasekaran SM, Birger C, Satpathy S, Zhou DC, Wang LB, Baral J, Johnson JL, Huntsman EM, Pugliese P, Colaprico A, Iavarone A, Chheda MG, Ricketts CJ, Fenyö D, Payne SH, Rodriguez H, Robles AI, Gillette MA, Kumar-Sinha C, Lazar AJ, Cantley LC, Getz G, Ding L. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell 2023; 186:3921-3944.e25. [PMID: 37582357 DOI: 10.1016/j.cell.2023.07.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/30/2022] [Accepted: 07/10/2023] [Indexed: 08/17/2023]
Abstract
Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Collin Tokheim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Matthew H Bailey
- Department of Biology and Simmons Center for Cancer Research, Brigham Young University, Provo, UT 84602, USA
| | - Tomer M Yaron
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Vasileios Stathias
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Yifat Geffen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Shankara Anand
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yo Akiyama
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yizhe Song
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Erik P Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wubing Zhang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Mustafa Sibai
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Victoria Ruiz-Serra
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain; Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David I Heiman
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Qing Zhang
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Francois Aguet
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Saravana M Dhanasekaran
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chet Birger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jessika Baral
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Emily M Huntsman
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Pietro Pugliese
- Department of Science and Technology, University of Sannio, 82100 Benevento, Italy
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Department of Neurological Surgery, Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Chandan Kumar-Sinha
- Michigan Center for Translational Pathology, Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Lazar
- Departments of Pathology & Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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21
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Gamallat Y, Bismar TA. Editorial: The Application of Proteogenomics to Urine Analysis for the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study. Cancers (Basel) 2023; 15:4143. [PMID: 37627171 PMCID: PMC10452380 DOI: 10.3390/cancers15164143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
In this editorial context, we aim to leverage the potential of proteogenomics, which integrates genomic and proteomic data, to discover novel biomarkers that can aid in the diagnosis and management of prostate cancer. We highlight the importance of proteogenomics for understanding the functional consequences of somatic mutations in cancer and demonstrating how proteogenomic analysis can provide insights into the effects of genetic alterations on the proteomic landscape and identify potential therapeutic targets. This article also emphasizes the potential of urine analysis for the detection of prostate cancer. Overall, our editorial paper provides general insights on the application of proteogenomics to urine analysis for the identification of novel biomarkers of prostate cancer.
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Affiliation(s)
- Yaser Gamallat
- Department of Pathology and Laboratory Medicine, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada;
- Department of Oncology, Biochemistry and Molecular Biology, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Tarek A. Bismar
- Department of Pathology and Laboratory Medicine, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada;
- Department of Oncology, Biochemistry and Molecular Biology, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Departments of Pathology & Laboratory Medicine, Alberta Precision Laboratories, Rockyview General Hospital, Calgary, AB T2V 1P9, Canada
- Prostate Cancer Center, Rockyview General Hospital, Calgary, AB T2V 1P9, Canada
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22
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Li Y, Dou Y, Da Veiga Leprevost F, Geffen Y, Calinawan AP, Aguet F, Akiyama Y, Anand S, Birger C, Cao S, Chaudhary R, Chilappagari P, Cieslik M, Colaprico A, Zhou DC, Day C, Domagalski MJ, Esai Selvan M, Fenyö D, Foltz SM, Francis A, Gonzalez-Robles T, Gümüş ZH, Heiman D, Holck M, Hong R, Hu Y, Jaehnig EJ, Ji J, Jiang W, Katsnelson L, Ketchum KA, Klein RJ, Lei JT, Liang WW, Liao Y, Lindgren CM, Ma W, Ma L, MacCoss MJ, Martins Rodrigues F, McKerrow W, Nguyen N, Oldroyd R, Pilozzi A, Pugliese P, Reva B, Rudnick P, Ruggles KV, Rykunov D, Savage SR, Schnaubelt M, Schraink T, Shi Z, Singhal D, Song X, Storrs E, Terekhanova NV, Thangudu RR, Thiagarajan M, Wang LB, Wang JM, Wang Y, Wen B, Wu Y, Wyczalkowski MA, Xin Y, Yao L, Yi X, Zhang H, Zhang Q, Zuhl M, Getz G, Ding L, Nesvizhskii AI, Wang P, Robles AI, Zhang B, Payne SH. Proteogenomic data and resources for pan-cancer analysis. Cancer Cell 2023; 41:1397-1406. [PMID: 37582339 PMCID: PMC10506762 DOI: 10.1016/j.ccell.2023.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/15/2022] [Accepted: 06/27/2023] [Indexed: 08/17/2023]
Abstract
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Yifat Geffen
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Anna P Calinawan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - François Aguet
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Yo Akiyama
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Shankara Anand
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Chet Birger
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Antonio Colaprico
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Corbin Day
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | | | - Myvizhi Esai Selvan
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Steven M Foltz
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Tania Gonzalez-Robles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Zeynep H Gümüş
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Heiman
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | | | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiayi Ji
- Tisch Cancer Institute and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lizabeth Katsnelson
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | | | - Robert J Klein
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Caleb M Lindgren
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Weiping Ma
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lei Ma
- ICF, Rockville, MD 20850, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Wilson McKerrow
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | | | - Robert Oldroyd
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | | | - Pietro Pugliese
- Department of Sciences and Technologies, University of Sannio, Benevento 82100, Italy
| | - Boris Reva
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Paul Rudnick
- Spectragen Informatics, Bainbridge Island, WA 98110, USA
| | - Kelly V Ruggles
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Dmitry Rykunov
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Tobias Schraink
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Xiaoyu Song
- Tisch Cancer Institute and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Erik Storrs
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - Liang-Bo Wang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Ying Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yi Xin
- ICF, Rockville, MD 20850, USA
| | - Lijun Yao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Qing Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | | | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Cancer Center and Department of Pathology, Mass. General Hospital, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | - Pei Wang
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, UT 84602, USA.
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23
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Kwok N, Aretz Z, Takao S, Ser Z, Cifani P, Kentsis A. Integrative Proteogenomics Using ProteomeGenerator2. J Proteome Res 2023; 22:2750-2764. [PMID: 37418425 PMCID: PMC10783198 DOI: 10.1021/acs.jproteome.3c00005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and protein sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as noncanonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2.
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Affiliation(s)
- Nathaniel Kwok
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
- Doctor of Medicine Program, Weill Cornell Medicine, New York, NY
- Department of Graduate Medical Education, HCA TriStar-Centennial Medical Center, Nashville, TN
| | - Zita Aretz
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
- Physiology Biophysics and Systems Biology Program, Weill Cornell Graduate School, Cornell University, New York, NY
| | - Sumiko Takao
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center New York, NY
| | - Zheng Ser
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Paolo Cifani
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alex Kentsis
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center New York, NY
- Departments of Pediatrics, Pharmacology, and Physiology & Biophysics, Weill Cornell Medical College, Cornell University, New York, NY
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24
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Xu J, Kong L, Oliver BA, Li B, Creasey EA, Guzman G, Schenone M, Carey KL, Carr SA, Graham DB, Deguine J, Xavier RJ. Constitutively active autophagy in macrophages dampens inflammation through metabolic and post-transcriptional regulation of cytokine production. Cell Rep 2023; 42:112708. [PMID: 37392388 PMCID: PMC10503440 DOI: 10.1016/j.celrep.2023.112708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 06/13/2023] [Indexed: 07/03/2023] Open
Abstract
Autophagy is an essential cellular process that is deeply integrated with innate immune signaling; however, studies that examine the impact of autophagic modulation in the context of inflammatory conditions are lacking. Here, using mice with a constitutively active variant of the autophagy gene Beclin1, we show that increased autophagy dampens cytokine production during a model of macrophage activation syndrome and in adherent-invasive Escherichia coli (AIEC) infection. Moreover, loss of functional autophagy through conditional deletion of Beclin1 in myeloid cells significantly enhances innate immunity in these contexts. We further analyzed primary macrophages from these animals with a combination of transcriptomics and proteomics to identify mechanistic targets downstream of autophagy. Our study reveals glutamine/glutathione metabolism and the RNF128/TBK1 axis as independent regulators of inflammation. Altogether, our work highlights increased autophagic flux as a potential approach to reduce inflammation and defines independent mechanistic cascades involved in this control.
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Affiliation(s)
- Jinjin Xu
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lingjia Kong
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Blayne A Oliver
- Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bihua Li
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elizabeth A Creasey
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gaelen Guzman
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Monica Schenone
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jacques Deguine
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
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25
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Zargar SM, Hami A, Manzoor M, Mir RA, Mahajan R, Bhat KA, Gani U, Sofi NR, Sofi PA, Masi A. Buckwheat OMICS: present status and future prospects. Crit Rev Biotechnol 2023:1-18. [PMID: 37482536 DOI: 10.1080/07388551.2023.2229511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/31/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023]
Abstract
Buckwheat (Fagopyrum spp.) is an underutilized resilient crop of North Western Himalayas belonging to the family Polygonaceae and is a source of essential nutrients and therapeutics. Common Buckwheat and Tatary Buckwheat are the two main cultivated species used as food. It is the only grain crop possessing rutin, an important metabolite with high nutraceutical potential. Due to its inherent tolerance to various biotic and abiotic stresses and a short life cycle, Buckwheat has been proposed as a model crop plant. Nutritional security is one of the major concerns, breeding for a nutrient-dense crop such as Buckwheat will provide a sustainable solution. Efforts toward improving Buckwheat for nutrition and yield are limited due to the lack of available: genetic resources, genomics, transcriptomics and metabolomics. In order to harness the agricultural importance of Buckwheat, an integrated breeding and OMICS platforms needs to be established that can pave the way for a better understanding of crop biology and developing commercial varieties. This, coupled with the availability of the genome sequences of both Buckwheat species in the public domain, should facilitate the identification of alleles/QTLs and candidate genes. There is a need to further our understanding of the molecular basis of the genetic regulation that controls various economically important traits. The present review focuses on: the food and nutritional importance of Buckwheat, its various omics resources, utilization of omics approaches in understanding Buckwheat biology and, finally, how an integrated platform of breeding and omics will help in developing commercially high yielding nutrient rich cultivars in Buckwheat.
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Affiliation(s)
- Sajad Majeed Zargar
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Ammarah Hami
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Madhiya Manzoor
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Rakeeb Ahmad Mir
- Department of Biotechnology, School of Life Sciences, Central University of Kashmir, Ganderbal, India
| | - Reetika Mahajan
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Kaiser A Bhat
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Umar Gani
- Plant Sciences and Agrotechnology Division, CSIR-Indian Institute of Integrative Medicine, Jammu, India
| | - Najeebul Rehman Sofi
- MRCFC, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, India
| | - Parvaze A Sofi
- Division of Plant Breeding and Genetics, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir, Srinagar, India
| | - Antonio Masi
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
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26
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Chen Y, Cao X, Loh KH, Slavoff SA. Chemical labeling and proteomics for characterization of unannotated small and alternative open reading frame-encoded polypeptides. Biochem Soc Trans 2023; 51:1071-1082. [PMID: 37171061 PMCID: PMC10317152 DOI: 10.1042/bst20221074] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/27/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023]
Abstract
Thousands of unannotated small and alternative open reading frames (smORFs and alt-ORFs, respectively) have recently been revealed in mammalian genomes. While hundreds of mammalian smORF- and alt-ORF-encoded proteins (SEPs and alt-proteins, respectively) affect cell proliferation, the overwhelming majority of smORFs and alt-ORFs remain uncharacterized at the molecular level. Complicating the task of identifying the biological roles of smORFs and alt-ORFs, the SEPs and alt-proteins that they encode exhibit limited sequence homology to protein domains of known function. Experimental techniques for the functionalization of these gene classes are therefore required. Approaches combining chemical labeling and quantitative proteomics have greatly advanced our ability to identify and characterize functional SEPs and alt-proteins in high throughput. In this review, we briefly describe the principles of proteomic discovery of SEPs and alt-proteins, then summarize how these technologies interface with chemical labeling for identification of SEPs and alt-proteins with specific properties, as well as in defining the interactome of SEPs and alt-proteins.
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Affiliation(s)
- Yanran Chen
- Department of Chemistry, Yale University, New Haven, CT, U.S.A
- Institute for Biomolecular Design and Discovery, Yale University, West Haven, CT, U.S.A
| | - Xiongwen Cao
- Department of Chemistry, Yale University, New Haven, CT, U.S.A
- Institute for Biomolecular Design and Discovery, Yale University, West Haven, CT, U.S.A
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, U.S.A
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Ken H. Loh
- Institute for Biomolecular Design and Discovery, Yale University, West Haven, CT, U.S.A
- Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, U.S.A
| | - Sarah A. Slavoff
- Department of Chemistry, Yale University, New Haven, CT, U.S.A
- Institute for Biomolecular Design and Discovery, Yale University, West Haven, CT, U.S.A
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, U.S.A
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27
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Hunt AL, Nutcharoen A, Randall J, Papazian A, Deeken J, Maxwell GL, Bateman NW, Petricoin EF, Benyounes A, Conrads TP, Cannon TL. Integration of Multi-omic Data in a Molecular Tumor Board Reveals EGFR-Associated ALK-Inhibitor Resistance in a Patient With Inflammatory Myofibroblastic Cancer. Oncologist 2023:7187076. [PMID: 37255276 PMCID: PMC10400139 DOI: 10.1093/oncolo/oyad129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/14/2023] [Indexed: 06/01/2023] Open
Abstract
Inflammatory myofibroblastic tumors (IMTs) are intermediate-grade mesenchymal neoplasms commonly characterized by chromosomal rearrangements causing constitutive activation of anaplastic lymphoma kinase (ALK) and/or ALK mutations causing reduced sensitivity to ALK tyrosine kinase inhibitors (TKI). We present a patient with an IMT who initially responded to first-line alectinib, but who later suffered disease relapse and presently survives with moderate residual disease after receiving second-line lorlatinib. Biopsy specimens were analyzed using next generation sequencing (DNA-seq and RNA-seq) and reverse phase protein microarray (RPPA) as part of an institutional Molecular Tumor Board (MTB) study. An EML4-ALK rearrangement and EGFR activation (pEGFRY1068) were present in both the primary and recurrent tumors, while a secondary ALK I1171N mutation was exclusive to the latter. EGFR signaling in the background of a secondary ALK mutation is correlated with reduced ALK TKI sensitivity in vitro, implicating an important mechanism of drug resistance development in this patient. The RPPA results also critically demonstrate that ALK signaling (ALKY1604) was not activated in the recurrent tumor, thereby indicating that standard-of-care use of third- or fourth-line ALK TKI would not likely be efficacious or durable. These results underscore the importance of real-time clinical integration of functional protein drug target activation data with NGS in the MTB setting for improving selection of patient-tailored therapy.
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Affiliation(s)
- Allison L Hunt
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Annandale, VA, USA
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
| | | | - Jamie Randall
- Inova Schar Cancer Institute, Inova Health System, Fairfax, VA, USA
| | - Alyssa Papazian
- Inova Schar Cancer Institute, Inova Health System, Fairfax, VA, USA
| | - John Deeken
- Inova Schar Cancer Institute, Inova Health System, Fairfax, VA, USA
| | - G Larry Maxwell
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Annandale, VA, USA
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University, Bethesda, MD, USA
| | - Nicholas W Bateman
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA
| | - Emanuel F Petricoin
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
- Theralink Technologies, Inc., Golden, CO, USA
| | - Amin Benyounes
- Inova Schar Cancer Institute, Inova Health System, Fairfax, VA, USA
| | - Thomas P Conrads
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Annandale, VA, USA
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University, Bethesda, MD, USA
| | - Timothy L Cannon
- Inova Schar Cancer Institute, Inova Health System, Fairfax, VA, USA
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28
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Zou Y, van Breukelen B, Pronker M, Reiding K, Heck AJR. Proteogenomic Features of the Highly Polymorphic Histidine-rich Glycoprotein (HRG) Arose Late in Evolution. Mol Cell Proteomics 2023:100585. [PMID: 37244517 PMCID: PMC10388577 DOI: 10.1016/j.mcpro.2023.100585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023] Open
Abstract
Histidine-rich glycoprotein (HRG) is a liver-produced protein circulating in human serum at high concentrations of around 125 μg/mL. HRG belongs to the family of type-3 cystatins and has been implicated in a plethora of biological processes, albeit that its precise function is still not well understood. Human HRG is a highly polymorphic protein, with at least 5 variants with minor allele frequencies (MAF) of more than 10%, variable in populations from different parts of the world. Considering these 5 mutations we can theoretically expect 35 = 243 possible possibly genetic HRG variants in the population. Here, we purified HRG from serum of 44 individual donors and investigated by proteomics the occurrence of different allotypes, each being either homozygote or heterozygote for each of the 5 mutation sites. We observed that some mutational combinations in HRG were highly favored, while others were apparently missing, although they ought to be present based on the independent assembly of these 5 mutation sites. To further explore this behavior, we extracted data from the 1000 genome project (n ∼ 2500 genomes) and assessed the frequency of different HRG mutants in this larger dataset, observing a prevailing agreement with our proteomics data. From all the proteogenomic data we conclude that the 5 different mutation sites in HRG are not occurring independently, but several mutations at different sites are fully mutually exclusive, whereas other are highly intwined. Specific mutations do also affect HRG glycosylation. As the levels of HRG have been suggested as a protein biomarker in a variety of biological processes (e.g., aging, COVID-19 severity, severity of bacterial infections), we here conclude that the highly polymorphic nature of the protein needs to be considered in such proteomics evaluations, as these mutations may affect HRG's abundance, structure, post-translational modifications, and function.
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Affiliation(s)
- Yang Zou
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Bas van Breukelen
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Matti Pronker
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Karli Reiding
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, the Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, the Netherlands.
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29
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Levitsky LI, Ivanov MV, Goncharov AO, Kliuchnikova AA, Bubis JA, Lobas AA, Solovyeva EM, Pyatnitskiy MA, Ovchinnikov RK, Kukharsky MS, Farafonova TE, Novikova SE, Zgoda VG, Tarasova IA, Gorshkov MV, Moshkovskii SA. Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing. J Proteome Res 2023. [PMID: 37158322 DOI: 10.1021/acs.jproteome.2c00740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.
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Affiliation(s)
- Lev I Levitsky
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Mark V Ivanov
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Anton O Goncharov
- Federal Research and Clinical Center of Physical-Chemical Medicine, 1a, Malaya Pirogovskaya, 119435 Moscow, Russia
- Pirogov Russian National Research Medical University, 1, Ostrovityanova, 117997 Moscow, Russia
| | - Anna A Kliuchnikova
- Federal Research and Clinical Center of Physical-Chemical Medicine, 1a, Malaya Pirogovskaya, 119435 Moscow, Russia
- Institute of Biomedical Chemistry, 10, Pogodinskaya, 119121 Moscow, Russia
| | - Julia A Bubis
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Anna A Lobas
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Elizaveta M Solovyeva
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | | | - Ruslan K Ovchinnikov
- Pirogov Russian National Research Medical University, 1, Ostrovityanova, 117997 Moscow, Russia
- Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 1, Severny Proezd, Chernogolovka 142432, Moscow Region, Russia
| | - Michail S Kukharsky
- Pirogov Russian National Research Medical University, 1, Ostrovityanova, 117997 Moscow, Russia
- Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 1, Severny Proezd, Chernogolovka 142432, Moscow Region, Russia
| | | | | | - Victor G Zgoda
- Institute of Biomedical Chemistry, 10, Pogodinskaya, 119121 Moscow, Russia
| | - Irina A Tarasova
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Mikhail V Gorshkov
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 38, bld. 2, Leninsky Prospect, 119334 Moscow, Russia
| | - Sergei A Moshkovskii
- Federal Research and Clinical Center of Physical-Chemical Medicine, 1a, Malaya Pirogovskaya, 119435 Moscow, Russia
- Pirogov Russian National Research Medical University, 1, Ostrovityanova, 117997 Moscow, Russia
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30
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Spät P, Krauspe V, Hess WR, Maček B, Nalpas N. Deep Proteogenomics of a Photosynthetic Cyanobacterium. J Proteome Res 2023. [PMID: 37146978 DOI: 10.1021/acs.jproteome.3c00065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Cyanobacteria, the evolutionary ancestors of plant chloroplasts, contribute substantially to the Earth's biogeochemical cycles and are of great interest for a sustainable economy. Knowledge of protein expression is the key to understanding cyanobacterial metabolism; however, proteome studies in cyanobacteria are limited and cover only a fraction of the theoretical proteome. Here, we performed a comprehensive proteogenomic analysis of the model cyanobacterium Synechocystis sp. PCC 6803 to characterize the expressed (phospho)proteome, re-annotate known and discover novel open reading frames (ORFs). By mapping extensive shotgun mass spectrometry proteomics data onto a six-frame translation of the Synechocystis genome, we refined the genomic annotation of 64 ORFs, including eight completely novel ORFs. Our study presents the largest reported (phospho)proteome dataset for a unicellular cyanobacterium, covering the expression of about 80% of the theoretical proteome under various cultivation conditions, such as nitrogen or carbon limitation. We report 568 phosphorylated S/T/Y sites that are present on numerous regulatory proteins, including the transcriptional regulators cyAbrB1 and cyAbrB2. We also catalogue the proteins that have never been detected under laboratory conditions and found that a large portion of them is plasmid-encoded. This dataset will serve as a resource, providing dedicated information on growth condition-dependent protein expression and phosphorylation.
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Affiliation(s)
- Philipp Spät
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
| | - Vanessa Krauspe
- Genetics & Experimental Bioinformatics, Institute of Biology III, University of Freiburg, Schänzlestraße 1, 79104 Freiburg im Breisgau, Germany
| | - Wolfgang R Hess
- Genetics & Experimental Bioinformatics, Institute of Biology III, University of Freiburg, Schänzlestraße 1, 79104 Freiburg im Breisgau, Germany
| | - Boris Maček
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
| | - Nicolas Nalpas
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany
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Monsivais D, Parks SE, Chandrashekar DS, Varambally S, Creighton CJ. Using cancer proteomics data to identify gene candidates for therapeutic targeting. Oncotarget 2023; 14:399-412. [PMID: 37141409 DOI: 10.18632/oncotarget.28420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023] Open
Abstract
Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.
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Affiliation(s)
- Diana Monsivais
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sydney E Parks
- Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA
- Cancer and Cell Biology Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Darshan S Chandrashekar
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Genomic Diagnostics and Bioinformatics, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Sooryanarayana Varambally
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA
- The Informatics Institute, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
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Ofek P, Yeini E, Arad G, Danilevsky A, Pozzi S, Luna CB, Dangoor SI, Grossman R, Ram Z, Shomron N, Brem H, Hyde TM, Geiger T, Satchi-Fainaro R. Deoxyhypusine hydroxylase: A novel therapeutic target differentially expressed in short-term vs long-term survivors of glioblastoma. Int J Cancer 2023. [PMID: 37141410 DOI: 10.1002/ijc.34545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/13/2023] [Accepted: 03/10/2023] [Indexed: 05/06/2023]
Abstract
Glioblastoma (GB) is the most aggressive neoplasm of the brain. Poor prognosis is mainly attributed to tumor heterogeneity, invasiveness and drug resistance. Only a small fraction of GB patients survives longer than 24 months from the time of diagnosis (ie, long-term survivors [LTS]). In our study, we aimed to identify molecular markers associated with favorable GB prognosis as a basis to develop therapeutic applications to improve patients' outcome. We have recently assembled a proteogenomic dataset of 87 GB clinical samples of varying survival rates. Following RNA-seq and mass spectrometry (MS)-based proteomics analysis, we identified several differentially expressed genes and proteins, including some known cancer-related pathways and some less established that showed higher expression in short-term (<6 months) survivors (STS) compared to LTS. One such target found was deoxyhypusine hydroxylase (DOHH), which is known to be involved in the biosynthesis of hypusine, an unusual amino acid essential for the function of the eukaryotic translation initiation factor 5A (eIF5A), which promotes tumor growth. We consequently validated DOHH overexpression in STS samples by quantitative polymerase chain reaction (qPCR) and immunohistochemistry. We further showed robust inhibition of proliferation, migration and invasion of GB cells following silencing of DOHH with short hairpin RNA (shRNA) or inhibition of its activity with small molecules, ciclopirox and deferiprone. Moreover, DOHH silencing led to significant inhibition of tumor progression and prolonged survival in GB mouse models. Searching for a potential mechanism by which DOHH promotes tumor aggressiveness, we found that it supports the transition of GB cells to a more invasive phenotype via epithelial-mesenchymal transition (EMT)-related pathways.
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Affiliation(s)
- Paula Ofek
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eilam Yeini
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gali Arad
- Department of Molecular Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Artem Danilevsky
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Sabina Pozzi
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Christian Burgos Luna
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sahar Israeli Dangoor
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rachel Grossman
- Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Zvi Ram
- Department of Neurosurgery, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Noam Shomron
- Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel
| | - Henry Brem
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA
- Department of Psychiatry & Behavioral Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tamar Geiger
- Department of Molecular Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ronit Satchi-Fainaro
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel
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Reilly L, Seddighi S, Singleton AB, Cookson MR, Ward ME, Qi YA. Variant biomarker discovery using mass spectrometry-based proteogenomics. Front Aging 2023; 4:1191993. [PMID: 37168844 PMCID: PMC10165118 DOI: 10.3389/fragi.2023.1191993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023]
Abstract
Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.
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Affiliation(s)
- Luke Reilly
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Sahba Seddighi
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Andrew B. Singleton
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Mark R. Cookson
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Michael E. Ward
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Yue A. Qi
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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Guilloy N, Brunet MA, Leblanc S, Jacques JF, Hardy MP, Ehx G, Lanoix J, Thibault P, Perreault C, Roucou X. OpenCustomDB: Integration of Unannotated Open Reading Frames and Genetic Variants to Generate More Comprehensive Customized Protein Databases. J Proteome Res 2023; 22:1492-1500. [PMID: 36961377 PMCID: PMC10167680 DOI: 10.1021/acs.jproteome.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
Proteomic diversity in biological samples can be characterized by mass spectrometry (MS)-based proteomics using customized protein databases generated from sets of transcripts previously detected by RNA-seq. This diversity has only been increased by the recent discovery that many translated alternative open reading frames rest unannotated at unsuspected locations of mRNAs and ncRNAs. These novel protein products, termed alternative proteins, have been left out of all previous custom database generation tools. Consequently, genetic variations that impact alternative open reading frames and variant peptides from their translated proteins are not detectable with current computational workflows. To fill this gap, we present OpenCustomDB, a bioinformatics tool that uses sample-specific RNaseq data to identify genomic variants in canonical and alternative open reading frames, allowing for more than one coding region per transcript. In a test reanalysis of a cohort of 16 patients with acute myeloid leukemia, 5666 peptides from alternative proteins were detected, including 201 variant peptides. We also observed that a significant fraction of peptide-spectrum matches previously assigned to peptides from canonical proteins got better scores when reassigned to peptides from alternative proteins. Custom protein libraries that include sample-specific sequence variations of all possible open reading frames are promising contributions to the development of proteomics and precision medicine. The raw and processed proteomics data presented in this study can be found in PRIDE repository with accession number PXD029240.
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Affiliation(s)
- Noé Guilloy
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Montreal, Québec H2X 3Y7, Canada
| | - Marie A Brunet
- Department of Pediatrics, Université de Sherbrooke, Sherbrooke, Québec J1E 4K8, Canada
| | - Sébastien Leblanc
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Montreal, Québec H2X 3Y7, Canada
| | - Jean-François Jacques
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Montreal, Québec H2X 3Y7, Canada
| | - Marie-Pierre Hardy
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Québec H3C 3J7, Canada
| | - Grégory Ehx
- Interdisciplinary Cluster for Applied Geno-Proteomics (GIGA-I3), University of Liège, Liège B-4000, Belgium
| | - Joël Lanoix
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Québec H3C 3J7, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Québec H3C 3J7, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Québec H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, Québec H3C 3J7, Canada
| | - Xavier Roucou
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec J1E 4K8, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Montreal, Québec H2X 3Y7, Canada
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Jager S, Cramer DAT, Heck AJR. Normal Alpha-1-Antitrypsin Variants Display in Serum Allele-Specific Protein Levels. J Proteome Res 2023; 22:1331-1338. [PMID: 36946534 PMCID: PMC10088046 DOI: 10.1021/acs.jproteome.2c00833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Alpha-1-antitrypsin (A1AT or SERPINA1) has been proposed as a putative biomarker distinguishing healthy from diseased donors throughout several proteomics studies. However, the SERPINA1 gene displays high variability of frequent occurring genotypes among the general population. These different genotypes may affect A1AT expression and serum protein concentrations, and this is often not known, ignored, and/or not reported in serum proteomics studies. Here, we address allele-specific protein serum levels of A1AT in donors carrying the normal M variants of A1AT by measuring the proteoform profiles of purified A1AT from 81 serum samples, originating from 52 donors. When focusing on heterozygous donors, our data clearly reveal a statistically relevant difference in allele-specific protein serum levels of A1AT. In donors with genotype PI*M1VM1A, the experimentally observed ratio was approximately 1:1 (M1V/M1A, 1.00:0.96 ± 0.07, n = 17). For individuals with genotype PI*M1VM2, this ratio was 1:1.28 (M1V/M2, 1.00:1.31, ±0.19, n = 7). For genotypes PI*M1VM3 and PI*M1AM3, a significant higher amount of M3 was observed compared to the M1-subtypes (M1V/M3, 1.00:1.84 ± 0.35, n = 8; M1A/M3, 1.00:1.61 ± 0.33, n = 5). We argue that these observations are important and should be considered when analyzing serum A1AT levels before proposing A1AT as a putative serum biomarker.
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Affiliation(s)
- Shelley Jager
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Dario A T Cramer
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, Utrecht 3584 CH, The Netherlands
- Netherlands Proteomics Center, Padualaan 8, Utrecht 3584 CH, The Netherlands
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Hadjeras L, Heiniger B, Maaß S, Scheuer R, Gelhausen R, Azarderakhsh S, Barth-Weber S, Backofen R, Becher D, Ahrens CH, Sharma CM, Evguenieva-Hackenberg E. Unraveling the small proteome of the plant symbiont Sinorhizobium meliloti by ribosome profiling and proteogenomics. Microlife 2023; 4:uqad012. [PMID: 37223733 PMCID: PMC10117765 DOI: 10.1093/femsml/uqad012] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 03/07/2023] [Indexed: 05/25/2023]
Abstract
The soil-dwelling plant symbiont Sinorhizobium meliloti is a major model organism of Alphaproteobacteria. Despite numerous detailed OMICS studies, information about small open reading frame (sORF)-encoded proteins (SEPs) is largely missing, because sORFs are poorly annotated and SEPs are hard to detect experimentally. However, given that SEPs can fulfill important functions, identification of translated sORFs is critical for analyzing their roles in bacterial physiology. Ribosome profiling (Ribo-seq) can detect translated sORFs with high sensitivity, but is not yet routinely applied to bacteria because it must be adapted for each species. Here, we established a Ribo-seq procedure for S. meliloti 2011 based on RNase I digestion and detected translation for 60% of the annotated coding sequences during growth in minimal medium. Using ORF prediction tools based on Ribo-seq data, subsequent filtering, and manual curation, the translation of 37 non-annotated sORFs with ≤ 70 amino acids was predicted with confidence. The Ribo-seq data were supplemented by mass spectrometry (MS) analyses from three sample preparation approaches and two integrated proteogenomic search database (iPtgxDB) types. Searches against standard and 20-fold smaller Ribo-seq data-informed custom iPtgxDBs confirmed 47 annotated SEPs and identified 11 additional novel SEPs. Epitope tagging and Western blot analysis confirmed the translation of 15 out of 20 SEPs selected from the translatome map. Overall, by combining MS and Ribo-seq approaches, the small proteome of S. meliloti was substantially expanded by 48 novel SEPs. Several of them are part of predicted operons and/or are conserved from Rhizobiaceae to Bacteria, suggesting important physiological functions.
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Affiliation(s)
- Lydia Hadjeras
- Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany
| | | | | | | | - Rick Gelhausen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany
| | - Saina Azarderakhsh
- Institute of Microbiology and Molecular Biology, University of Giessen, 35392 Giessen, Germany
| | - Susanne Barth-Weber
- Institute of Microbiology and Molecular Biology, University of Giessen, 35392 Giessen, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany
| | - Dörte Becher
- Institute of Microbiology, University of Greifswald, 17489 Greifswald, Germany
| | - Christian H Ahrens
- Corresponding author. Molecular Ecology, Agroscope and SIB Swiss Institute of Bioinformatics, 8046 Zurich, Switzerland. E-mail:
| | - Cynthia M Sharma
- Corresponding author. Institute of Molecular Infection Biology, University of Würzburg, 97080 Würzburg, Germany. E-mail:
| | - Elena Evguenieva-Hackenberg
- Corresponding author. Institute of Microbiology and Molecular Biology, University of Giessen, 35392 Giessen, Germany. E-mail:
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Wang P, Wu X, Shi Z, Tao S, Liu Z, Qi K, Xie Z, Qiao X, Gu C, Yin H, Cheng M, Gu X, Liu X, Tang C, Cao P, Xu S, Zhou B, Gu T, Bian Y, Wu J, Zhang S. A large-scale proteogenomic atlas of pear. Mol Plant 2023; 16:599-615. [PMID: 36733253 DOI: 10.1016/j.molp.2023.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/10/2023] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Pear is an important fruit tree that is widely distributed around the world. The first pear genome map was reported from our laboratory approximately 10 years ago. To further study global protein expression patterns in pear, we generated pear proteome data based on 24 major tissues. The tissue-resolved profiles provided evidence of the expression of 17 953 proteins. We identified 4294 new coding events and improved the pear genome annotation via the proteogenomic strategy based on 18 090 peptide spectra with peptide spectrum matches >1. Among the eight randomly selected new short coding open reading frames that were expressed in the style, four promoted and one inhibited the growth of pear pollen tubes. Based on gene coexpression module analysis, we explored the key genes associated with important agronomic traits, such as stone cell formation in fruits. The network regulating the synthesis of lignin, a major component of stone cells, was reconstructed, and receptor-like kinases were implicated as core factors in this regulatory network. Moreover, we constructed the online database PearEXP (http://www.peardb.org.cn) to enable access to the pear proteogenomic resources. This study provides a paradigm for in-depth proteogenomic studies of woody plants.
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Affiliation(s)
- Peng Wang
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao Wu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Zebin Shi
- Institute of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Shutian Tao
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhe Liu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Kaijie Qi
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhihua Xie
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Xin Qiao
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Chao Gu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Hao Yin
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Mengyu Cheng
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiaoyu Gu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Xueying Liu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Chao Tang
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Peng Cao
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | | | | | - Tingting Gu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Yangyang Bian
- College of Life Sciences, Northwest University, Xi'an 710127, China
| | - Juyou Wu
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China.
| | - Shaoling Zhang
- Sanya Institute of Nanjing Agricultural University, National Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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39
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Yang Y, Wang C, Li Z, Lu Q, Li Y. Precise diagnosis and treatment of non-muscle invasive bladder cancer - A clinical perspective. Front Oncol 2023; 13:1042552. [PMID: 36798814 PMCID: PMC9927396 DOI: 10.3389/fonc.2023.1042552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/20/2023] [Indexed: 02/01/2023] Open
Abstract
According to the guidelines, transurethral resection of bladder tumor (TURBT) followed by intravesical therapy remains the standard strategy for the management of non-muscle invasive bladder cancer (NMIBC). However, even if patients receive standard strategy, the risk of postoperative recurrence and progression is high. From the clinical perspective, the standard strategy needs to be optimized and improved. Compared to conventional TURBT, the technique of en bloc resection of bladder tumor (ERBT) removes the tumor tissue in one piece, thus following the principles of cancer surgery. Meanwhile, the integrity and spatial orientation of tumor tissue is protected during the operation, which is helpful for pathologists to make accurate histopathological analysis. Then, urologists can make a postoperative individualized treatment plan based on the patient's clinical characteristics and histopathological results. To date, there is no strong evidence that NMIBC patients treated with ERBT achieve better oncological prognosis, which indicates that ERBT alone does not yet improve patient outcomes. With the development of enhanced imaging technology and proteogenomics technology, en bloc resection combined with these technologies will make it possible to achieve precise diagnosis and treatment of bladder cancer. In this review, the authors analyze the current existing shortcomings of en bloc resection and points out its future direction, in order to promote continuous optimization of the management strategy of bladder cancer.
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Affiliation(s)
| | | | | | - Qiang Lu
- *Correspondence: Qiang Lu, ; Yuanwei Li,
| | - Yuanwei Li
- *Correspondence: Qiang Lu, ; Yuanwei Li,
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40
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Lobas AA, Solovyeva EM, Levitsky LI, Goncharov AO, Lyssuk EY, Larin SS, Moshkovskii SA, Gorshkov MV. Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data. Int J Mol Sci 2023; 24. [PMID: 36768787 DOI: 10.3390/ijms24032466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/31/2023] Open
Abstract
Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.
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41
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García-Campa L, Valledor L, Pascual J. The Integration of Data from Different Long-Read Sequencing Platforms Enhances Proteoform Characterization in Arabidopsis. Plants (Basel) 2023; 12:511. [PMID: 36771596 PMCID: PMC9920879 DOI: 10.3390/plants12030511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 06/18/2023]
Abstract
The increasing availability of massive omics data requires improving the quality of reference databases and their annotations. The combination of full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics has been successfully used for increasing proteoform characterization, which is a main ongoing goal in biology. However, the potential of including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data has not been explored. In this paper, we analyzed the impact of combining Iso-Seq- and ONT-DRS-derived data on the identification of proteoforms in Arabidopsis MS proteomics data. To this end, we selected a proteomics dataset corresponding to senescent leaves and we performed protein searches using three different protein databases: AtRTD2 and AtRTD3, built from the homonymous transcriptomes, regarded as the most complete and up-to-date available for the species; and a custom hybrid database combining AtRTD3 with publicly available ONT-DRS transcriptomics data generated from Arabidopsis leaves. Our results show that the inclusion and combination of long-read sequencing data from Iso-Seq and ONT-DRS into a proteogenomic workflow enhances proteoform characterization and discovery in bottom-up proteomics studies. This represents a great opportunity to further investigate biological systems at an unprecedented scale, although it brings challenges to current protein searching algorithms.
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Affiliation(s)
- Lara García-Campa
- Plant Physiology, Department of Organisms and Systems Biology, University of Oviedo, 33003 Oviedo, Spain
- University Institute of Biotechnology of Asturias, University of Oviedo, 33003 Oviedo, Spain
| | - Luis Valledor
- Plant Physiology, Department of Organisms and Systems Biology, University of Oviedo, 33003 Oviedo, Spain
- University Institute of Biotechnology of Asturias, University of Oviedo, 33003 Oviedo, Spain
| | - Jesús Pascual
- Plant Physiology, Department of Organisms and Systems Biology, University of Oviedo, 33003 Oviedo, Spain
- University Institute of Biotechnology of Asturias, University of Oviedo, 33003 Oviedo, Spain
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42
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Haga Y, Minegishi Y, Ueda K. Frontiers in mass spectrometry-based clinical proteomics for cancer diagnosis and treatment. Cancer Sci 2023; 114:1783-1791. [PMID: 36661476 PMCID: PMC10154896 DOI: 10.1111/cas.15731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/10/2023] [Accepted: 01/15/2023] [Indexed: 01/21/2023] Open
Abstract
Numerous omics studies, primarily genomics analyses, have been conducted to fully understand the molecular biological characteristics of cancer. In recent years, the depth of proteomic analysis, which comprehensively analyzes proteins and molecules that function directly in vivo, has increased dramatically. Proteomics using mass spectrometry (MS) is a promising technology to directly examine proteoforms, including post-translational modifications and variants originating from genomic aberrations. Recent advances in MS-based proteomics have enabled direct, in depth, and quantitative analysis of the expression levels of various cancer-related proteins, as well as their cancer-specific proteoforms, and proteins that fluctuate with cancer initiation and progression in cell lines and tissue samples. Additionally, the integration of proteomic data with genomic, epigenomic, and transcriptomic data has formed the growing field of proteogenomics, which is already yielding new biological and diagnostic knowledge. Deep proteomic profiling provides clinically useful information in various aspects, including understanding the mechanisms of cancer development and progression and discovering targets for diagnosis and drug development. Furthermore, it is expected to make a significant contribution to the promotion of personalized medicine. In this review, recent advances and impacts in MS-based clinical proteomics are highlighted with a focus on oncology.
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Affiliation(s)
- Yoshimi Haga
- Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yuriko Minegishi
- Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Koji Ueda
- Cancer Proteomics Group, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
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43
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Babačić H, Galardi S, Umer HM, Hellström M, Uhrbom L, Maturi N, Cardinali D, Pellegatta S, Michienzi A, Trevisi G, Mangiola A, Lehtiö J, Ciafrè SA, Pernemalm M. Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures. Mol Oncol 2023; 17:238-260. [PMID: 36495079 PMCID: PMC9892829 DOI: 10.1002/1878-0261.13355] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) cancer stem cells (GSCs) contribute to GBM's origin, recurrence, and resistance to treatment. However, the understanding of how mRNA expression patterns of GBM subtypes are reflected at global proteome level in GSCs is limited. To characterize protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry. We quantified > 10 000 proteins in two independent GSC panels and propose a GSC-associated proteomic signature characterizing two distinct phenotypic conditions; one defined by proteins upregulated in proneural and classical GSCs (GPC-like), and another by proteins upregulated in mesenchymal GSCs (GM-like). The GM-like protein set in GBM tissue was associated with necrosis, recurrence, and worse overall survival. Through proteogenomics, we discovered 252 non-canonical peptides in the GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered non-protein-coding, including variants of the heterogeneous ribonucleoproteins implicated in RNA splicing. In summary, GSCs express two protein sets that have an inverse association with clinical outcomes in GBM. The discovery of non-canonical protein sequences questions existing gene models and pinpoints new protein targets for research in GBM.
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Affiliation(s)
- Haris Babačić
- Department of Oncology and PathologyKarolinska Institute, Science for Life LaboratoryStockholmSweden
| | - Silvia Galardi
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataItaly
| | - Husen M. Umer
- Department of Oncology and PathologyKarolinska Institute, Science for Life LaboratoryStockholmSweden
| | - Mats Hellström
- Department of Immunology, Genetics and PathologyUppsala UniversitySweden
| | - Lene Uhrbom
- Department of Immunology, Genetics and PathologyUppsala UniversitySweden
| | | | - Deborah Cardinali
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataItaly
| | - Serena Pellegatta
- Unit of Immunotherapy of Brain Tumors, Department of Molecular Neuro‐Oncology, Foundation IRCCSInstitute for Neurology Carlo BestaMilanItaly
| | | | - Gianluca Trevisi
- Neurosurgical UnitHospital Spirito Santo, Pescara, “G. D'Annunzio” UniversityChietiItaly
| | - Annunziato Mangiola
- Neurosurgical UnitHospital Spirito Santo, Pescara, “G. D'Annunzio” UniversityChietiItaly
| | - Janne Lehtiö
- Department of Oncology and PathologyKarolinska Institute, Science for Life LaboratoryStockholmSweden
| | - Silvia Anna Ciafrè
- Department of Biomedicine and PreventionUniversity of Rome Tor VergataItaly
| | - Maria Pernemalm
- Department of Oncology and PathologyKarolinska Institute, Science for Life LaboratoryStockholmSweden
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44
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Li Y, Lih TSM, Dhanasekaran SM, Mannan R, Chen L, Cieslik M, Wu Y, Lu RJH, Clark DJ, Kołodziejczak I, Hong R, Chen S, Zhao Y, Chugh S, Caravan W, Naser Al Deen N, Hosseini N, Newton CJ, Krug K, Xu Y, Cho KC, Hu Y, Zhang Y, Kumar-Sinha C, Ma W, Calinawan A, Wyczalkowski MA, Wendl MC, Wang Y, Guo S, Zhang C, Le A, Dagar A, Hopkins A, Cho H, Leprevost FDV, Jing X, Teo GC, Liu W, Reimers MA, Pachynski R, Lazar AJ, Chinnaiyan AM, Van Tine BA, Zhang B, Rodland KD, Getz G, Mani DR, Wang P, Chen F, Hostetter G, Thiagarajan M, Linehan WM, Fenyö D, Jewell SD, Omenn GS, Mehra R, Wiznerowicz M, Robles AI, Mesri M, Hiltke T, An E, Rodriguez H, Chan DW, Ricketts CJ, Nesvizhskii AI, Zhang H, Ding L. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell 2023; 41:139-163.e17. [PMID: 36563681 PMCID: PMC9839644 DOI: 10.1016/j.ccell.2022.12.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/18/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022]
Abstract
Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.
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Affiliation(s)
- Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Tung-Shing M Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Rahul Mannan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jiu-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Iga Kołodziejczak
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Runyu Hong
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Siqi Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yanyan Zhao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Seema Chugh
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wagma Caravan
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Nataly Naser Al Deen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Noshad Hosseini
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Yuanwei Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
| | - Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Yuping Zhang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandan Kumar-Sinha
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anna Calinawan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Mathematics, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuefan Wang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Shenghao Guo
- Department of Biomedical Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA
| | - Cissy Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA
| | - Anne Le
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Aniket Dagar
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alex Hopkins
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hanbyul Cho
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Xiaojun Jing
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Melissa A Reimers
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Russell Pachynski
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Alexander J Lazar
- Departments of Pathology and Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brian A Van Tine
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Gad Getz
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | | | | | - W Marston Linehan
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | - Gilbert S Omenn
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rohit Mehra
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Heliodor Swiecicki Clinical Hospital in Poznań, ul. Przybyszewskiego 49, 60-355 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher J Ricketts
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21213, USA; Department of Chemical and Biomolecular Engineering, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA; Department of Genetics, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA.
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45
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Roberts BR, Laffoon SB, Roberts AM, Porter T, Fowler C, Masters CL, Dratz EA, Laws SM. Discovery of a Missense Mutation (Q222K) of the APOE Gene from the Australian Imaging, Biomarker and Lifestyle Study. J Alzheimers Dis Rep 2023; 7:165-172. [PMID: 36891255 PMCID: PMC9986708 DOI: 10.3233/adr-220075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/04/2023] [Indexed: 02/05/2023] Open
Abstract
After age, polymorphisms of the Apolipoprotein E (APOE) gene are the biggest risk factor for the development of Alzheimer's disease (AD). During our investigation to discovery biomarkers in plasma, using 2D gel electrophoresis, we found an individual with and unusual apoE isoelectric point compared to APOE ɛ2, ɛ3, and ɛ4 carriers. Whole exome sequencing of APOE from the donor confirmed a single nucleotide polymorphism (SNP) in exon 4, translating to a rare Q222K missense mutation. The apoE ɛ4 (Q222K) mutation did not form dimers or complexes observed for apoE ɛ2 & ɛ3 proteins.
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Affiliation(s)
- Blaine R Roberts
- Emory School of Medicine, Department of Biochemistry, Department of Neurology, Atlanta, GA, USA.,Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia.,Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, USA
| | - Scott B Laffoon
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia.,Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, USA
| | - Anne M Roberts
- Emory School of Medicine, Department of Biochemistry, Department of Neurology, Atlanta, GA, USA.,Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia.,Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Chris Fowler
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Edward A Dratz
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA, Australia.,Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
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46
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Kanno T, Konno R, Miyako K, Nakajima T, Yokoyama S, Sasamoto S, Asou HK, Ohzeki J, Kawashima Y, Hasegawa Y, Ohara O, Endo Y. Characterization of proteogenomic signatures of differentiation of CD4+ T cell subsets. DNA Res 2022; 30:6964968. [PMID: 36579714 PMCID: PMC9886070 DOI: 10.1093/dnares/dsac054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
Functionally distinct CD4+ helper T (Th) cell subsets, including Th1, Th2, Th17, and regulatory T cells (Treg), play a pivotal role in the regulation of acquired immunity. Although the key proteins involved in the regulation of Th cell differentiation have already been identified how the proteogenomic landscape changes during the Th cell activation remains unclear. To address this issue, we characterized proteogenomic signatures of differentiation to each Th cell subsets by RNA sequencing and liquid chromatography-assisted mass spectrometry, which enabled us to simultaneously quantify more than 10,000 protein-coding transcripts and 8,000 proteins in a single-shot. The results indicated that T cell receptor activation affected almost half of the transcript and protein levels in a low correlative and gene-specific manner, and specific cytokine treatments modified the transcript and protein profiles in a manner specific to each Th cell subsets: Th17 and Tregs particularly exhibited unique proteogenomic signatures compared to other Th cell subsets. Interestingly, the in-depth proteome data revealed that mRNA profiles alone were not enough to delineate functional changes during Th cell activation, suggesting that the proteogenomic dataset obtained in this study serves as a unique and indispensable data resource for understanding the comprehensive molecular mechanisms underlying effector Th cell differentiation.
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Affiliation(s)
| | | | - Keisuke Miyako
- Department of Applied Genomics Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Takahiro Nakajima
- Department of Frontier Research and Development, Laboratory of Medical Omics Research, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Satoru Yokoyama
- Department of Frontier Research and Development, Laboratory of Medical Omics Research, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Shigemi Sasamoto
- Department of Frontier Research and Development, Laboratory of Medical Omics Research, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Hikari K Asou
- Department of Frontier Research and Development, Laboratory of Medical Omics Research, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Junichiro Ohzeki
- Department of Frontier Research and Development, Laboratory of Medical Omics Research, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Yusuke Kawashima
- Department of Applied Genomics Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Yoshinori Hasegawa
- Department of Applied Genomics Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Osamu Ohara
- Department of Applied Genomics Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan
| | - Yusuke Endo
- To whom correspondence should be addressed. Tel. +81-438-52-3929, Fax: +81-438-52-3954. (Y.E.)
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47
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Vašíček J, Skiadopoulou D, Kuznetsova KG, Wen B, Johansson S, Njølstad PR, Bruckner S, Käll L, Vaudel M. Finding haplotypic signatures in proteins. Gigascience 2022; 12:giad093. [PMID: 37919975 PMCID: PMC10622322 DOI: 10.1093/gigascience/giad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/24/2023] [Accepted: 10/08/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown. FINDINGS Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches. CONCLUSIONS As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.
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Affiliation(s)
- Jakub Vašíček
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5008, Norway
| | - Dafni Skiadopoulou
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5008, Norway
| | - Ksenia G Kuznetsova
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5008, Norway
| | - Bo Wen
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Stefan Johansson
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen 5021, Norway
| | - Pål R Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen 5021, Norway
| | - Stefan Bruckner
- Chair of Visual Analytics, Institute for Visual and Analytic Computing, University of Rostock, Rostock 18051, Germany
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH–Royal Institute of Technology, Solna 17121, Sweden
| | - Marc Vaudel
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen 5021, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen 5008, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo 0473, Norway
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Srivastava M, Copin R, Choy A, Zhou A, Olsen O, Wolf S, Shah D, Rye-Weller A, Chen L, Chan N, Coppola A, Lanza K, Negron N, Ni M, Atwal GS, Kyratsous CA, Olson W, Salzler R. Proteogenomic identification of Hepatitis B virus (HBV) genotype-specific HLA-I restricted peptides from HBV-positive patient liver tissues. Front Immunol 2022; 13:1032716. [PMID: 36582233 PMCID: PMC9793402 DOI: 10.3389/fimmu.2022.1032716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
The presentation of virus-derived peptides by HLA class I molecules on the surface of an infected cell and the recognition of these HLA-peptide complexes by, and subsequent activation of, CD8+ cytotoxic T cells provides an important mechanism for immune protection against viruses. Recent advances in proteogenomics have allowed researchers to discover a growing number of unique HLA-restricted viral peptides, resulting in a rapidly expanding repertoire of targets for immunotherapeutics (i.e. bispecific antibodies, engineered T-cell receptors (TCRs), chimeric antigen receptor T-cells (CAR-Ts)) to infected tissues. However, genomic variability between viral strains, such as Hepatitis-B virus (HBV), in combination with differences in patient HLA alleles, make it difficult to develop therapeutics against these targets. To address this challenge, we developed a novel proteogenomics approach for generating patient-specific databases that enable the identification of viral peptides based on the viral transcriptomes sequenced from individual patient liver samples. We also utilized DNA sequencing of patient samples to identify HLA genotypes and assist in target selection. Liver samples from 48 HBV infected patients, primarily from Asia, were examined to reconstruct patient-specific HBV genomes, identify regions within the human chromosomes targeted by HBV integrations and obtain a comprehensive view of HBV peptide epitopes using our HLA class-I (HLA-I) immunopeptidomics discovery platform. Two previously reported HLA associated HBV-derived peptides, HLA-A02 binder FLLTRILTI (S194-202) from the large surface antigen and HLA-A11 binder STLPETTVVRR (C141-151) from the capsid protein were validated by our discovery platform, but both were detected at very low frequencies. In addition, we identified and validated, using heavy peptide analogues, novel strain-specific HBV-HLA associated peptides, such as GSLPQEHIVQK (P606-616) and variants. Overall, our novel approach can guide the development of bispecific antibody, TCR-T, or CAR-T based therapeutics for the treatment of HBV-related HCC and inform vaccine development.
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49
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Abstract
Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.
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Affiliation(s)
- Chad J Creighton
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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50
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Fierro-Monti I, Wright JC, Choudhary JS, Vizcaíno JA. Identifying individuals using proteomics: are we there yet? Front Mol Biosci 2022; 9:1062031. [PMID: 36523653 PMCID: PMC9744771 DOI: 10.3389/fmolb.2022.1062031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/16/2022] [Indexed: 08/31/2023] Open
Abstract
Multi-omics approaches including proteomics analyses are becoming an integral component of precision medicine. As clinical proteomics studies gain momentum and their sensitivity increases, research on identifying individuals based on their proteomics data is here examined for risks and ethics-related issues. A great deal of work has already been done on this topic for DNA/RNA sequencing data, but it has yet to be widely studied in other omics fields. The current state-of-the-art for the identification of individuals based solely on proteomics data is explained. Protein sequence variation analysis approaches are covered in more detail, including the available analysis workflows and their limitations. We also outline some previous forensic and omics proteomics studies that are relevant for the identification of individuals. Following that, we discuss the risks of patient reidentification using other proteomics data types such as protein expression abundance and post-translational modification (PTM) profiles. In light of the potential identification of individuals through proteomics data, possible legal and ethical implications are becoming increasingly important in the field.
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Affiliation(s)
- Ivo Fierro-Monti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | | | | | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
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