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

Clinical Proteomics

Edward N Brody³  David O Wilson⁸  Thomas Muley⁶  William L Bigbee⁸  Michael Meister⁶  Rachel M Ostroff³  Wilbur Franklin⁷  Joel L Weissfeld⁴  Michael R Mehan³  Jill M Siegfried⁵  York E Miller⁹  Stephen A Williams³  William N Rom²  Harvey I Pass¹ 

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

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

DOI  :  10.1186/1559-0275-11-32

来源: Humana Press Inc

【 摘 要 】

Abstract

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】

Unknown