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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    • "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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    • "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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