Detection of rare cancers with aptamer proteomic technology
| Autor: | Randall E. Brand, Brad Black, Alex Stewart, Harvey I. Pass, Malti Nikrad, Herbert J. Zeh, Stephen Levin, Michael Harbut, James A. Moser, Steven Williams, Rachel Ostroff, Mike Mehan |
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| Rok vydání: | 2010 |
| Předmět: | |
| Zdroj: | Clinical Cancer Research. 16:A3-A3 |
| ISSN: | 1557-3265 1078-0432 |
| Popis: | 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 >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 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 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. |
| Databáze: | OpenAIRE |
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