Aptamer-based Proteomics Platform Research Articles

Cerebrospinal Fluid Protein Changes in Preeclampsia.

The molecular mechanisms underlying seizure susceptibility in preeclampsia are unknown. We hypothesized that altered expression of distinct proteins in the cerebrospinal fluid (CSF) may reflect pathophysiological changes in the central nervous system that contribute to the neurological manifestations of severe preeclampsia. We obtained CSF samples from 13 patients with preeclampsia and 14 control patients during spinal anesthesia before delivery and analyzed them by SOMAscan, an aptamer-based proteomics platform for alterations in 1310 protein levels. Ingenuity Pathway Analysis was conducted to highlight relationships between preeclampsia-specific proteins found to be significantly altered. For 2 of the target proteins, we validated the difference in CSF concentrations by ELISA. SOMAscan revealed 82 proteins, whose expression levels were significantly different (P<0.05) in CSF from patients with preeclampsia versus controls. Principal component analysis achieved perfect separation of the preeclampsia and control groups in 2 dimensions. The differentially expressed proteins converge around 4 signaling molecules: TGF-β (transforming growth factor-β), VEGFA (vascular endothelial growth factor A), angiotensinogen, and IL-6 (interleukin-6). Within the TGF-β pathway, upregulation of activin A (301.6±47.4 versus 151.6±20.5 pg/mL; P=0.0074) and follistatin-related gene (5129±347 versus 3016±188 pg/mL; P<0.0001) in preeclampsia was confirmed by ELISA. In summary, signaling pathways important for vascular remodeling, inflammation, and neuronal growth, signaling, and electrophysiology were well represented among the proteins found to be altered in CSF in patients with preeclampsia.

Genetic Architecture of the Cardiovascular Risk Proteome.

We recently identified 156 proteins in human plasma that were each associated with the net Framingham Cardiovascular Disease Risk Score using an aptamer-based proteomic platform in Framingham Heart Study Offspring participants. Here we hypothesized that performing genome-wide association studies and exome array analyses on the levels of each of these 156 proteins might identify genetic determinants of risk-associated circulating factors and provide insights into early cardiovascular pathophysiology. We studied the association of genetic variants with the plasma levels of each of the 156 Framingham Cardiovascular Disease Risk Score-associated proteins using linear mixed-effects models in 2 population-based cohorts. We performed discovery analyses on plasma samples from 759 participants of the Framingham Heart Study Offspring cohort, an observational study of the offspring of the original Framingham Heart Study and their spouses, and validated these findings in plasma samples from 1421 participants of the MDCS (Malmö Diet and Cancer Study). To evaluate the utility of this strategy in identifying new biological pathways relevant to cardiovascular disease pathophysiology, we performed studies in a cell-model system to experimentally validate the functional significance of an especially novel genetic association with circulating apolipoprotein E levels. We identified 120 locus-protein associations in genome-wide analyses and 41 associations in exome array analyses, the majority of which have not been described previously. These loci explained up to 66% of interindividual plasma protein-level variation and, on average, accounted for 3 times the amount of variation explained by common clinical factors, such as age, sex, and diabetes mellitus status. We described overlap among many of these loci and cardiovascular disease genetic risk variants. Finally, we experimentally validated a novel association between circulating apolipoprotein E levels and the transcription factor phosphatase 1G. Knockdown of phosphatase 1G in a human liver cell model resulted in decreased apolipoprotein E transcription and apolipoprotein E protein levels in cultured supernatants. We identified dozens of novel genetic determinants of proteins associated with the Framingham Cardiovascular Disease Risk Score and experimentally validated a new role for phosphatase 1G in lipoprotein biology. Further, genome-wide and exome array data for each protein have been made publicly available as a resource for cardiovascular disease research.

Aptamer-Based Proteomic Profiling Reveals Novel Candidate Biomarkers and Pathways in Cardiovascular Disease.

Single-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length selected for their ability to bind proteins with high specificity and affinity. Emerging DNA aptamer-based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have hindered proteomic analyses of large cohorts. To identify early biomarkers of myocardial injury, we applied an aptamer-based proteomic platform that measures 1129 proteins to a clinically relevant perturbational model of planned myocardial infarction (PMI), patients undergoing septal ablation for hypertrophic cardiomyopathy. Blood samples were obtained before and at 10 and 60 minutes after PMI, and protein changes were assessed by repeated-measures analysis of variance. The generalizability of our PMI findings was evaluated in a spontaneous myocardial infarction cohort (Wilcoxon rank-sum). We then tested the platform's ability to detect associations between proteins and Framingham Risk Score components in the Framingham Heart Study, performing regression analyses for each protein versus each clinical trait. We found 217 proteins that significantly changed in the peripheral vein blood after PMI in a derivation cohort (n=15; P<5.70E-5). Seventy-nine of these proteins were validated in an independent PMI cohort (n=15; P<2.30E-4); >85% were directionally consistent and reached nominal significance. We detected many protein changes that are novel in the context of myocardial injury, including Dickkopf-related protein 4, a WNT pathway inhibitor (peak increase 124%, P=1.29E-15) and cripto, a growth factor important in cardiac development (peak increase 64%, P=1.74E-4). Among the 40 validated proteins that increased within 1 hour after PMI, 23 were also elevated in patients with spontaneous myocardial infarction (n=46; P<0.05). Framingham Heart Study analyses revealed 156 significant protein associations with the Framingham Risk Score (n=899), including aminoacylase 1 (β=0.3386, P=2.54E-22) and trigger factor 2 (β=0.2846, P=5.71E-17). Furthermore, we developed a novel workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate aptamer specificity. Our results highlight an emerging proteomics tool capable of profiling >1000 low-abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies and large human cohorts, as well.

Abstract 2812: Detection of mesothelioma in asbestos exposed individuals with aptamer proteomic technology

Abstract Malignant pleural mesothelioma is an aggressive, asbestos-related pulmonary cancer which is increasing in incidence. This disease causes an estimated 15,000 to 20,000 deaths per year worldwide. Between 1940 and 1979, approximately 27.5 million people were occupationally exposed to asbestos in the United States. The incidence of pleural mesothelioma in the US is 3,000 new cases/year and will not peak for another 20 years. Mesothelioma has a latency period of 20-40 years from asbestos exposure, but once diagnosed this aggressive disease is often fatal within 14 months. Because diagnosis is difficult, most patients present at a clinically advanced stage where possibility of cure is minimal. Therefore, we have conducted a broad search for new serum biomarkers with our aptamer-based proteomic platform and defined a classifier for the detection of mesothelioma in asbestos exposed individuals. Secreted proteins and those released during apoptosis from tumor cells and surrounding tissues contain important biologic information that may enable early diagnosis and prognostic and therapeutic decisions in oncology. However, there is great difficulty in finding and quantifying such signals for large numbers of low abundance proteins. We therefore created a highly multiplexed proteomic assay that currently measures ∼850 proteins simultaneously from 15ul blood, with throughput of 300 samples/day. The average dynamic range of each protein in the assay is &amp;gt;3 logs – with nearly seven logs of dynamic range achieved through multiple dilutions – and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is &amp;lt;5%. This assay performance arises from the selection of high affinity aptamers that bind selectively to their target proteins with slow off-rates. The objective of this study was to discover proteins which are involved in malignant mesothelioma and to develop algorithms and classifiers for detection of the disease. To this end, blood samples from three study centers were analyzed with the aptamer proteomics platform in a prospectively designed case:control study. We compared 170 serum samples from 90 patients diagnosed with malignant mesothelioma to 80 asbestos exposed controls. These samples were divided into 75% for training and 25% set aside as a blinded test set for classifier development and verification. Nineteen significant biomarkers were discovered by applying a backwards selection strategy. Classifiers were built with subsets of these biomarkers resulting in an AUC of 0.95 or better with an overall accuracy of 93%. Applying a 13-plex Random Forest classifier to the blinded test set resulted in a specificity of 100% and sensitivity of 80% for distinction of asbestos exposed controls from mesothelioma, including detection of 15/19 Stage I/II cases. Refinement and confirmation of classifier performance will be established through ongoing validation studies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2812. doi:10.1158/1538-7445.AM2011-2812

Detection of rare cancers with aptamer proteomic technology

Abstract The need for sensitive, early detection of aggressive, rare malignancies such as pancreatic cancer and mesothelioma is high. Just as importantly, the stringent specificity required of diagnostic tests for these low prevalence diseases creates unique challenges. A diagnostic test which identified these rare diseases early in a significant number of patients without creating a large number of false positive results would be clinically important and would deliver health-economic benefits. However, there is great difficulty in precisely quantifying such signals for large numbers of low abundance proteins. Our group therefore created a highly multiplexed proteomic assay which is continuously expanding in breadth. It currently measures 825 proteins simultaneously from ~15ul blood, with throughput of 300 samples/day. The average dynamic range of each protein in the assay is &amp;gt;3 logs — with nearly seven logs of dynamic range achieved through multiple dilutions — and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is &amp;lt;5%. This assay performance arises from the selection of high affinity aptamers that bind selectively to their target proteins with slow offrates. We have applied this proteomics technology to the discovery of biomarkers and diagnostic algorithms for two rare malignancies, pancreatic cancer and mesothelioma. Pancreatic cancer is the fourth leading cause of cancer-related death in the USA. While the 5-year survival is only 5%, this has shown to be increased by early surgical intervention. Plasma samples were analyzed in a prospectively designed case:control study from 143 cases of pancreatic cancer and 116 controls of a similar age and gender distribution. 25% of each group was retained as a blinded verification set. In the training set, 47 markers were significantly different at a false-discovery-rate corrected value of p&amp;lt;0.001. An 11 marker panel had a Receiver Operating Characteristic (ROC) curve area under the curve (AUC) of 0.91. A specificity-driven decision threshold relevant to early detection in asymptomatic patients of 97.5% had a sensitivity of ~60% in the training set. This test performance was effectively maintained in the blinded verification set, with a specificity of 96.5% and a sensitivity of 65%. Other decision thresholds relevant to symptomatic patients enable a sensitivity-driven approach of 90% sensitivity and 75% specificity. The results of this test using the high specificity decision threshold will deliver a positive predictive value of greater than 10% in a population with a disease prevalence of 0.4% or more. Additionally, when the test is used in symptomatic subjects as a differential diagnostic, non-invasive, rapid and sensitive detection of pancreatic cancer enables swift clinical decisions for treatment of this aggressive disease. The second rare cancer analyzed in this clinical series was malignant pleural mesothelioma, which is an aggressive, asbestos-related pulmonary cancer. This disease causes an estimated 15,000 to 20,000 deaths per year worldwide. Between 1940 and 1979, approximately 27.5 million people were occupationally exposed to asbestos in the United States. The incidence of pleural mesothelioma in the US is 3,000 new cases/year and will not peak for another 20 years. Mesothelioma has a latency period of 20-40 years from asbestos exposure, but once diagnosed this aggressive disease is often fatal within 14 months. Because diagnosis is difficult, most patients present at a clinically advanced stage where possibility of cure is minimal. Therefore, we have conducted a broad search for new serum biomarkers with our aptamer-based proteomic platform and defined a classifier for the detection of mesothelioma in asbestos exposed individuals. Serum samples were analyzed with the aptamer proteomics platform in a prospectively designed case:control study of 357 serum samples obtained from patients diagnosed with mesothelioma or lung cancer compared to asbestos exposed controls, high risk smokers and benign lung disease. These samples were divided into a training and test set for classifier development and verification. The objective of the study was to discover proteins which are involved in mesothelioma and to develop algorithms and classifiers for the disease. The initial results are promising. Nineteen significant biomarkers were discovered. Classifiers were built with subsets of these biomarkers resulting in an AUC of 0.95 or better with an overall accuracy of 93%. Applying a 13-plex Random Forest classifier to the blinded test set resulted in a specificity of 100% and sensitivity of 80% for distinction of asbestos exposed controls from mesothelioma. Refinement and confirmation of classifier performance will be established through ongoing validation studies.