Assessment of a high-throughput mass spectrometry method to accelerate biomarker discovery in clinical cancer cohorts using volumetric absorptive microsampling (VAMS) devices.

Abstract

Identification of biomarkers of early-stage disease typically requires analysis of very large cohorts which can only be reasonably achieved using high-throughput methods. We have developed and optimised novel methods for whole blood analysis using volumetric absorptive microsampling devices to produce over 3000 protein identifications by LCMS on a Q Exactive HF-X Orbitrap. These methods were tested using a set of whole blood samples from lung cancer patients and matching healthy controls finding 455 differentially expressed proteins, using a mid-throughput method enabling analysis of 18 samples per day. To increase throughput for larger clinical cohorts, a 60-sample per day method was tested on a Sciex ZenoTOF 7600. The high-throughput method produced 1.5-fold fewer protein identifications and a higher overall % CV compared to the mid-throughput method. Despite the lower numbers, it produced a set of 36 disease-relevant and discriminatory differentially expressed proteins that, using a machine learning model, could differentiate between the disease and control samples with an area under the ROC curve (AUC) of 88.9% using random forest algorithms. These data support the use of high-throughput mass spectrometry methods to screen large cohorts for diagnostic biomarkers that can then be followed up with more targeted analyses.