Background: Seer Proteograph^TM now unlocks the ability to proteomically profile complex liquid biopsy types with deep sampling, scalability, and automation. While this platform is now being employed for discovery of disease-associated biomarkers in human specimens, the genetic malleability and low biological variance of murine models provides a unique opportunity to investigate the relationship between disease biology and biomarker dynamics. We employ the platform here to systematically probe the effect of cancer stage, driver mutations, and tissue of origin on plasma proteome composition in murine cancer. The primary objective of this study was to test the feasibility of the Proteograph^TM platform in the detection and quantification of murine plasma proteins.

Methods: Plasma from multiple autochthonous genetic models of Kras-mutant cancers was processed using Proteograph^TM Rise. Matched tissue from select arms was cryomilled and prepared using PreOmics iST. Peptides were analyzed with 60 min total acquisition length liquid chromatography-mass spectrometry (LC-MS) on a Bruker timsTOF Pro with dia-PASEF. Leftover tissue peptides were pooled and fractionated for analysis by DDA-PASEF to generate a spectral library. Raw data were searched using DIA-NN on Seer PAS.

Results: An average of 4440 protein groups and 25481 peptides were observed per sample with 6682 proteins and 51710 peptides identified across the entire study. Low technical assay and within-arm biological variability were observed with median coefficient of variation from protein intensities 40.8%. Statistical analysis revealed numerous differentially expressed proteins with striking differences across several models. Functional enrichment profiling revealed novel insights into the specific ontology associated with these differences.

Conclusion: Deep sampling into the plasma proteomic dynamic range with low within-arm variability and resulting biological insights of this data demonstrate the power and feasibility of Proteograph^TM in a murine model and how this unique application can be leveraged to (a) systematically probe multiple determinant factors affecting plasma proteome composition and (b) potentially unravel the mechanisms linking these proteomic changes with the underlying disease biology.