Next generation pan-cancer blood proteome profiling using proximity extension assay

TLDR

Blood proteome profiling in cancer patients can improve understanding of disease etiology, enabling earlier diagnosis, risk stratification, and better monitoring of subtypes. The study aims to use next‑generation protein profiling to explore blood proteome signatures across major cancer types. Plasma levels of 1,463 proteins were measured in minute blood samples from over 1,400 cancer patients at diagnosis, and the data are made available through an open‑access Disease Blood Atlas, where machine‑learning classification models identify cancer‑specific protein signatures. Machine‑learning models identified cancer‑specific protein signatures, highlighting the potential of next‑generation plasma profiling for precision oncology.

Abstract

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.