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Álvez MB, Edfors F, von Feilitzen K, Zwahlen M, Mardinoglu A, Edqvist PH, Sjöblom T, Lundin E, Rameika N, Enblad G, Lindman H, Höglund M, Hesselager G, Stålberg K, Enblad M, Simonson OE, Häggman M, Axelsson T, Åberg M, Nordlund J, Zhong W, Karlsson M, Gyllensten U, Ponten F, Fagerberg L, Uhlén M
Nat Commun 14 (1) 4308 [2023-07-18; online 2023-07-18]
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.
Affinity Proteomics Uppsala [Collaborative]
Bioinformatics Support for Computational Resources [Service]
NGI Proteomics [Collaborative]
NGI Uppsala (SNP&SEQ Technology Platform) [Collaborative]
National Genomics Infrastructure [Collaborative]
PubMed 37463882
DOI 10.1038/s41467-023-39765-y
Crossref 10.1038/s41467-023-39765-y
pmc: PMC10354027
pii: 10.1038/s41467-023-39765-y