A large-scale and robust dynamic MRM study of colorectal cancer biomarkers

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM), which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC.

We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population.