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Next generation pan-cancer blood proteome profiling using proximity extension assay.
Álvez, María Bueno; Edfors, Fredrik; von Feilitzen, Kalle; Zwahlen, Martin; Mardinoglu, Adil; Edqvist, Per-Henrik; Sjöblom, Tobias; Lundin, Emma; Rameika, Natallia; Enblad, Gunilla; Lindman, Henrik; Höglund, Martin; Hesselager, Göran; Stålberg, Karin; Enblad, Malin; Simonson, Oscar E; Häggman, Michael; Axelsson, Tomas; Åberg, Mikael; Nordlund, Jessica; Zhong, Wen; Karlsson, Max; Gyllensten, Ulf; Ponten, Fredrik; Fagerberg, Linn; Uhlén, Mathias.
Afiliação
  • Álvez MB; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Edfors F; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • von Feilitzen K; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Zwahlen M; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Mardinoglu A; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Edqvist PH; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK.
  • Sjöblom T; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Lundin E; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Rameika N; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Enblad G; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Lindman H; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Höglund M; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Hesselager G; Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Stålberg K; Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Enblad M; Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
  • Simonson OE; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
  • Häggman M; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
  • Axelsson T; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
  • Åberg M; Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Nordlund J; Department of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden.
  • Zhong W; Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Karlsson M; Science for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden.
  • Gyllensten U; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Ponten F; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Fagerberg L; Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
  • Uhlén M; Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Nat Commun ; 14(1): 4308, 2023 07 18.
Article em En | MEDLINE | ID: mdl-37463882
ABSTRACT
A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Hematológicas / Neoplasias Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Hematológicas / Neoplasias Idioma: En Ano de publicação: 2023 Tipo de documento: Article