BackgroundThe interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine.Methodology/Principal FindingsWe present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states.Conclusions/SignificanceWe describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Author contributions In an academic-industry partnership, SomaLogic, Inc. and the academic collaborators worked together on study design, interpretation of the data and preparation of the manuscript. S.A.W., P.G. and N.W. were responsible for designing, writing and final editing of the manuscript and responses to reviewer comments. In addition to all authors being generally involved in the program, specific contributions were as follows: M.K. and M.J.S. were accountable for the data from the Whitehall II study and advised on the study design for the CV and diabetes models. C.L. and N.W. were accountable for the data from the Fenland study and advising on diabetes risk and behavioral models. C.B. and M.A.S. were accountable for the data from the Heritage Family study. C.J. was accountable for the data from the HUNT3 study. R.O. was accountable for the data from the Covance study.

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Pseudomonas aeruginosa produces two secreted phospholipase C (PLC) enzymes. The expression of both PLCs is regulated by Pi. One of the PLCs is hemolytic, and one is nonhemolytic. Low-stringency hybridization studies suggested that the genes encoding these two PLCs shared DNA homology. This information was used to clone pkcN, the gene encoding the 77-kilodalton nonhemolytic PLC, PLC-N. A fragment of plcN was used to mutate the chromosomal copy of plcN by the generation of a gene interruption mutation. This mutant produces 55% less total PLC activity than the wild type, confirming the successful cloning of plcN. plcN was sequenced and encodes a protein which is 40% identical to the hemolytic PLC (PLC-H). The majority of the homology lies within the NH2 two-thirds of the proteins, while the remaining third of the amino acid sequence of the two proteins shows very little homology. Both PLCs hydrolyze phosphatidylcholine; however, each enzyme has a distinct substrate specificity. PLC-H hydrolyzes sphingomyelin in addition to phosphatidylcholine, whereas PLC-N is active on phosphatidylserine as well as phosphatidylcholine. These studies suggest structure-function relationships between PLC activity and hemolysis.

BackgroundLung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease.Methodology/Principal FindingsWe conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC.Conclusions/SignificanceThis study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer.

BackgroundMalignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection.Methodology/Principal FindingsWe conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.95±0.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy.SignificanceThe SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease.

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Multiplexed photoaptamer-based arrays that allow for the simultaneous measurement of multiple proteins of interest in serum samples are described. Since photoaptamers covalently bind to their target analytes before fluorescent signal detection, the arrays can be vigorously washed to remove background proteins, providing the potential for superior signal-to-noise ratios and lower limits of quantification in biological matrices. Data are presented here for a 17-plex photoaptamer array exhibiting limits of detection below 10 fM for several analytes including interleukin-16, vascular endothelial growth factor, and endostatin and able to measure proteins in 10% serum samples. The assays are simple, scalable, and reproducible. Affinity of the capture reagent is shown to be directly correlated to the limit of detection for the analyte on the array.