Mike Mehan

Unlocking Biomarker Discovery: Large Scale Application of Aptamer Proteomic Technology for Early Detection of Lung Cancer

Background Lung 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 Findings We 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/Significance This 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.

Life's simple measures: unlocking the proteome.

Using modified nucleotides and selecting for slow off-rates in the SELEX procedure, we have evolved a special class of aptamers, called SOMAmers (slow off-rate modified aptamers), which bind tightly and specifically to proteins in body fluids. We use these in a novel assay that yields 1:1 complexes of the SOMAmers with their cognate proteins in body fluids. Measuring the SOMAmer concentrations of the resultant complexes reflects the concentration of the proteins in the fluids. This is simply done by hybridization to complementary sequences on solid supports, but it can also be done by any other DNA quantification technology (including NexGen sequencing). We use measurements of over 1000 proteins in under 100 μL of serum or plasma to answer important medical questions, two of which are reviewed here. A number of bioinformatics methods have guided our discoveries, including principal component analysis. We use various methods to evaluate sample handling procedures in our clinical samples and can identify many parameters that corrupt proteomics analysis.

Detection of rare cancers with aptamer proteomic technology

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.

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

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 >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 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