MassIVE MSV000088180

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Biomarker candidates for tumours identified from deep-profiled plasma stem predominantly from the low abundant area

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Description

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 2732 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. [doi:10.25345/C5ZZ8W] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: plasma ; biomarker ; discovery ; depletion ; data independent acquisition

Contact

Principal Investigators:
(in alphabetical order) 		Lukas Reiter, Research and Development, Switzerland
Submitting User: roland_bruderer

Publications

Marco Tognetti, Kamil Sklodowski, Sebastian Müller, Dominique Kamber, Jan Muntel, Roland Bruderer, Lukas Reiter.
Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.
J Proteome Res. 2022 May 23. doi: 10.1021/acs.jproteome.2c00122. Epub ahead of print. PMID: 35605973.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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