Next generation pan-cancer blood proteome profiling using proximity extension assayDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden.
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Department of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Science for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
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Number of Authors: 262023 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 4308Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 14, no 1, article id 4308
National Category
Cancer and Oncology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:kth:diva-333884DOI: 10.1038/s41467-023-39765-yISI: 001037322100032PubMedID: 37463882Scopus ID: 2-s2.0-85165345608OAI: oai:DiVA.org:kth-333884DiVA, id: diva2:1787987
Note
QC 20230815
2023-08-152023-08-152023-12-07Bibliographically approved