Validation of a blood protein signature for non-small cell lung cancer

Clinical Proteomics

Rachel M Ostroff²  Edward N Brody²  York E Miller⁷  Wilbur Franklin⁴  Michael Meister⁹  Thomas Muley⁹  William N Rom⁵  Harvey I Pass¹  David O Wilson³  Joel L Weissfeld⁶  William L Bigbee³  Jill M Siegfried⁸  Stephen A Williams²  Michael R Mehan² 

[1] Langone Medical Center and Cancer Center, New York University School of Medicine, New York, NY, USA;SomaLogic, Inc., Boulder, CO, USA;University of Pittsburgh Cancer Institute, School of Medicine, Pittsburgh, PA, USA;University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, CO, USA;New York University School of Medicine, New York, NY, USA;Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA;Denver Veterans Affairs Medical Center, Denver, CO, USA;University of Minnesota, Minneapolis, MN, USA;Thoraxklinik at University Hospital Heidelberg and Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany

关键词: Sample bias;    Preanalytic variability;    Diagnosis;    Squamous cell carcinoma;    Proteomic;    SOMAmer;    Biomarker;    Lung cancer;   

Others  :  1026143
DOI  :  10.1186/1559-0275-11-32

 received in 2014-02-24, accepted in 2014-07-14,  发布年份 2014

【 摘 要 】

Background

CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.

Results

Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.

Conclusions

Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.

【 授权许可】

   
2014 Mehan et al.; licensee BioMed Central Ltd.

【 图 表 】

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