Investigating the Use of Novel Blood Processing Methods to Boost the Identification of Biomarkers for Non-Small Cell Lung Cancer: A Proof of Concept.

Abstract

Diagnosis of non-small cell lung cancer (NSCLC) currently relies on imaging; however, these methods are not effective for detecting early stage disease. Investigating blood-based protein biomarkers aims to simplify the diagnostic process and identify disease-associated changes before they can be seen by using imaging techniques. In this study, plasma and frozen whole blood cell pellets from NSCLC patients and healthy controls were processed using both classical and novel techniques to produce a unique set of four sample types from a single blood draw. These samples were analyzed using 12 immunoassays and liquid chromatography-mass spectrometry to collectively screen 3974 proteins. Analysis of all fractions produced a set of 522 differentially expressed proteins, with conventional blood analysis (proteomic analysis of plasma) accounting for only 7 of the total. Boosted regression tree analysis of the differentially expressed proteins produced a panel of 13 proteins that were able to discriminate between controls and NSCLC patients, with an area under the ROC curve (AUC) of 0.864 for the set. Our rapid and reproducible (<10% CV for technical replicates) blood preparation and analysis methods enabled the production of high-quality data from only 30 μL of complex samples that typically require significant fractionation prior to proteomic analysis. With our methods, almost 4000 proteins were identified from a single fraction over a 62.5 min gradient by LC-MS/MS.