Comparison of two multiplexed technologies for profiling >1,000 serum proteins that may associate with tumor burden

Abstract

Background: In this pilot study, we perform a preliminary comparison of two targeted multiplex proteomics technologies for discerning serum protein concentration changes that may correlate to tumor burden in ovarian cancer (OC) patients. Methods: Using the proximity extension assay (PEA) and Quantibody® Kiloplex Array (QKA), we measured >1,000 proteins in the pre-surgical and post-surgical serum from nine OC patients (N=18 samples). We expect that proteins that have decreased significantly in the post-surgical serum concentration may correlate to tumor burden in each patient. Duplicate sera from two healthy individuals were used as controls (N=4 samples). We employed in-house ELISAs to measure five proteins with large serum concentration changes in pre- and post-surgical sera, from four of the original nine patients and the two original controls. Results: Both platforms showed a weak correlation with clinical cancer antigen 125 (CA125) data. The two multiplexed platforms showed a significant correlation with each other for >400 overlapping proteins. PEA uncovered 15 proteins, while QKA revealed 11 proteins, with more than a two-fold post-surgical decrease in at least six of the nine patients. Validation using single enzyme-linked immunosorbent assays (ELISAs) showed at least a two-fold post-surgical decrease in serum concentration of the same patients, as indicated by the two multiplex assays. Conclusion: Both methods identified proteins that had significantly decreased in post-surgical serum concentration, as well as recognizing proteins that had been implicated in OC patients. Our findings from a limited sample size suggest that novel targeted proteomics platforms are promising tools for identifying candidate serological tumor-related proteins. However further studies are essential for the improvement of accuracy and avoidance of false results.

Validation of new, circulating biomarkers for glioma

“…The list of the 3,000 proteins that are included in the OLINK panel can be found on the OLINK Website (info@olink.com). As per our previous validation [7], PEA is reproducible, with coe cients of variation (CVs) of < 20% for > 99% of the proteins. Our own efforts to discover novel glioma biomarkers have been published elsewhere [8].…”

“…We analyzed a cohort of the following plasma samples, provided by the Northwestern University Brain Tumor Biobank that were collected at diagnosis but before therapy, from patients with gliomas (N = 30), and meningiomas (as benign controls) (N = 20). Analysis of these samples was performed at OLINK Proteomics facilities using the PEA technology, as described in detail elsewhere [5][6][7]. The list of the 3,000 proteins that are included in the OLINK panel can be found on the OLINK Website (info@olink.com).…”

Discovery of novel glioma serum biomarkers by proximity extension assay

“…We used the Olink Proximity Extension Assay (PEA) to explore panel of 3000 proteins (Olink Proteomics, Waltham, MA, USA) to analyze a cohort of the following plasmas, provided by the Northwestern University Brain Tumor Biobank: 20 glioblastomas, 5 anaplastic astrocytomas, 5 anaplastic oligodendrogliomas and 20 meningiomas (as controls). All analyses were performed at Olink Proteomics facilities using PEA described in detail elsewhere [7,8]. The list of 3000 proteins can be found on the Olink Website (info@olink.com).…”

“…The list of 3000 proteins can be found on the Olink Website (info@olink.com). We previously validated PEA and found it to be reproducible, with CVs of < 20% for > 99% of the proteins [8]. After analysis, Olink provided an excel file consisting of 50 patients (plasma samples) with 3000 quantified proteins per sample (approximately 150,000 data points).…”

Discovery and preliminary validation of a new panel of personalized ovarian cancer biomarkers for individualized detection of recurrence

“…The assay is described in detail in our previous publication. 16 Because we are identifying personalized biomarkers in a surveillance cohort, we are interested in proteins that show significant within-individual increase upon recurrence, in each recurrent patient. Therefore, we first calculated the % rate of change in protein levels between consecutive timepoints to longitudinally assess the protein kinetics within each patient.…”