Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area

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 cancers. 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 diseased
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.