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

SomaLogic, Boulder, Colorado, United States of America.
PLoS ONE (Impact Factor: 3.23). 12/2010; 5(12):e15003. DOI: 10.1371/journal.pone.0015003
Source: PubMed


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.
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.
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.

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Available from: Jeffrey J Walker
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    • "Extraction diagnostic biomarkers for cancer, their validation and testing for clinical use is considered now as a data analysis challenge [34] [74]. The classical methods of supervised classification are widely used to meet this challenge: linear and quadratic discriminant analysis [6] [8] [48] [82], decision trees [1] [9] [33] [50] [60] [62] [66] [75] [80] [88], logistic regression [4] [8] [41] [42], k nearest neighbors (KNN) approach [33] [66] [82] [83] and naïve Bayes model for probability density function estimation [8] [64]. "
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    ABSTRACT: The canine lymphoma blood test detects the levels of two biomarkers, the acute phase proteins (C-Reactive Protein and Haptoglobin). This test can be used for diagnostics, for screening, and for remission monitoring as well. We analyze clinical data, test various machine learning methods and select the best approach to these problems. Three families of methods, decision trees, kNN (including advanced and adaptive kNN) and probability density evaluation with radial basis functions, are used for classification and risk estimation. Several pre-processing approaches were implemented and compared. The best of them are used to create the diagnostic system. For the differential diagnosis the best solution gives the sensitivity and specificity of 83.5% and 77%, respectively (using three input features, CRP, Haptoglobin and standard clinical symptom). For the screening task, the decision tree method provides the best result, with sensitivity and specificity of 81.4% and >99%>99%, respectively (using the same input features). If the clinical symptoms (Lymphadenopathy) are considered as unknown then a decision tree with CRP and Hapt only provides sensitivity 69% and specificity 83.5%. The lymphoma risk evaluation problem is formulated and solved. The best models are selected as the system for computational lymphoma diagnosis and evaluation of the risk of lymphoma as well. These methods are implemented into a special web-accessed software and are applied to the problem of monitoring dogs with lymphoma after treatment. It detects recurrence of lymphoma up to two months prior to the appearance of clinical signs. The risk map visualisation provides a friendly tool for exploratory data analysis.
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    • "We generated proteomic data with SOMAscan V2, which measures 1033 analytes, an increase of 232 proteins over V1 used in the original lung cancer studies [20]. From this new analysis, we identified 15 candidate biomarkers (Additional file 1) and built a 7-protein Random Forest (RF) model with an AUC of 0.85 for all training samples (Figure 2 and Table 2). "
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    • "Multiplex the tumor-associated antigens have been extensively explored by ELISA [32], [33], [34], [35], [36] or microarray [37]. This study is the first report by combining Luminex platform and HaloTag technology to detect humoral immune response in lung cancer patients. "
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