RT Journal Article
SR Electronic
T1 Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.05.463153
DO 10.1101/2021.10.05.463153
A1 Marco Tognetti
A1 Kamil Sklodowski
A1 Sebastian Müller
A1 Dominique Kamber
A1 Jan Muntel
A1 Roland Bruderer
A1 Lukas Reiter
YR 2021
UL http://biorxiv.org/content/early/2021/10/06/2021.10.05.463153.abstract
AB The plasma proteome has the potential to enable a holistic analysis of the health state of an individual. However, plasma biomarker discovery is difficult due to its high dynamic range and variability. Here, we present a novel automated analytical approach for deep plasma profiling and applied it to a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancer.Using a controlled quantitative experiment, we demonstrate a 257% increase in protein identification and a 263% increase in significantly differentially abundant proteins over neat plasma.In the cohort, we identified 2,732 proteins. Using machine learning, we discovered biomarker candidates such as STAT3 in colorectal cancer and developed models that classify the disease state. For pancreatic cancer, a separation by stage was achieved.Importantly, biomarker candidates came predominantly from the low abundance region, demonstrating the necessity to deeply profile because they would have been missed by shallow profiling.Competing Interest StatementThe authors R.B., M.T., K.S., D.K., J.M., S.M., and L.R. are full-time employees of Biognosys AG (Schlieren-Zurich, Switzerland). Spectronaut is a trademark of Biognosys AG.CVCoefficient of variationDDAData-dependent acquisitionDIAData-independent acquisitionFDAFood and drug administrationLCLiquid chromatographyMSMass spectrometryPTMPost-Translational Modification