In-depth Proteomic Analysis of Six Types of Exudative Pleural Effusions for Nonsmall Cell Lung Cancer Biomarker Discovery

Pleural effusion (PE), a tumor-proximal body fluid, may be a promising source for biomarker discovery in human cancers. Because a variety of pathological conditions can lead to PE, characterization of the relative PE proteomic profiles from different types of PEs would accelerate discovery of potential PE biomarkers specifically used to diagnose pulmonary disorders. Using quantitative proteomic approaches, we identified 772 nonredundant proteins from six types of exudative PEs, including three malignant PEs (MPE, from lung, breast, and gastric cancers), one lung cancer paramalignant PE, and two benign diseases (tuberculosis and pneumonia). Spectral counting was utilized to semiquantify PE protein levels. Principal component analysis, hierarchical clustering, and Gene Ontology of cellular process analyses revealed differential levels and functional profiling of proteins in each type of PE. We identified 30 candidate proteins with twofold higher levels (q<0.05) in lung cancer MPEs than in the two benign PEs. Three potential markers, MET, DPP4, and PTPRF, were further verified by ELISA using 345 PE samples. The protein levels of these potential biomarkers were significantly higher in lung cancer MPE than in benign diseases or lung cancer paramalignant PE. The area under the receiver-operator characteristic curve for three combined biomarkers in discriminating lung cancer MPE from benign diseases was 0.903. We also observed that the PE protein levels were more clearly discriminated in effusions in which the cytological examination was positive and that they would be useful in rescuing the false negative of cytological examination in diagnosis of nonsmall cell lung cancer-MPE. Western blotting analysis further demonstrated that MET overexpression in lung cancer cells would contribute to the elevation of soluble MET in MPE. Our results collectively demonstrate the utility of label-free quantitative proteomic approaches in establishing differential PE proteomes and provide a new database of proteins that can be used to facilitate identification of pulmonary disorder-related biomarkers.The lungs are covered by parietal and visceral pleural membranes, including a small amount of fluid (10–20 ml) in the pleural cavity that helps the lungs expand and contract smoothly. Pleural effusions (PE)¹, an accumulation of pleural fluid, contain proteins originating from the plasma filtrate and are released by inflammatory or epithelial cells. PE is triggered by a variety of etiologies, including malignancies and benign diseases such as pneumonia (PN), tuberculosis (TB), pulmonary embolism, heart failure, renal dysfunction, and autoimmune disease (1). Based on their biochemical characteristics, PEs are classified as transudative or exudative; determination of the PE type is a crucial step in the differential diagnosis and management of PEs. Transudative effusions, generally caused by systemic diseases, can be effectively distinguished from exudative PEs using the established modified Light''s criteria (2, 3). However, further discrimination among different exudate types such as malignant and nonmalignant effusions (e.g. paramalignancies or acute and chronic inflammatory diseases) is sometimes diagnostically challenging because of similar biochemical and/or cellular profiles. For example, neutrophil-rich fluid is generally observed in patients with bacterial PN whereas lymphocytic effusions are generally observed in cancer or chronic inflammatory diseases such as TB (4).PEs caused by cancer are generally divided into two categories, malignant (MPE) and paramalignant (PMPE). MPEs result when cancer cells metastasize to the pleural cavity (stage IV), wherein exfoliated malignant cells are observed in pleural fluid by cytological examination or detected in percutaneous pleural biopsy, thoracoscopy, thoracotomy, or at autopsy (5). PMPE occurs in cancer patients with no evidence of tumor invasion in the pleural space and may be caused by airway obstruction with lung collapse, lymphatic obstruction, or the systemic effects of cancer treatment (5). A high percentage of MPEs (>75%) arise from lung, breast, and ovarian cancer or lymphoma/leukemia. Lung cancer is a major etiology underlying MPE (6); however, only ∼40–87% patients with MPE can be accurately diagnosed upon initial examination (7). Inaccurate diagnosis of MPE and PMPE underestimates or overestimates the disease stage and leads to inappropriate therapy. Thus, it is important to identify a specific and powerful biomarker to distinguish MPE from benign diseases and PMPE.Notably, tumor-proximal body fluids are promising sources for biomarker discovery because they represent a reservoir of in vivo tumor-secreted proteins without a large dynamic range or complexity of plasma or serum (8). Tumor-proximal fluids include PEs, nipple aspirate, stool, saliva, lavage, and ascites fluid. Previously, we utilized the powerful analytical capability of high-abundance protein depletion followed by one-dimensional SDS-PAGE combined with nano-LC-MS/MS (GeLC-MS/MS) for biomarker discovery to generate a comprehensive MPE proteome data set from 13 pooled nonsmall cell lung cancer (NSCLC) patients (9). Because a variety of pathological conditions can lead to exudative effusions, generating different PE proteomic profiles would accelerate discovery of potential PE biomarkers that can be used to discriminate between malignant and nonmalignant pulmonary disorders. The aim of this study is to establish differential PE proteomes from six types of exudative PEs, including three MPEs (from NSCLC, breast, and gastric cancers), one PMPE from NSCLC, and two benign diseases (TB and PN), using a label-free semiquantitative proteomics approach. Our results were verified by clinical validation of three potential biomarkers using an enzyme-linked immunosorbent assay (ELISA; Fig. 1).Open in a separate windowFig. 1.Biomarker discovery strategy for identifying differentially expressed proteins from six pleural effusion (PE) types. The strategy comprised prefractionation by removal of high-abundance proteins, GeLC-MS/MS, comparative analysis of the six PE proteomes based on spectral counts, proteome clustering, functional classification of differentially expressed proteins, and selection and validation of biomarker candidates by ELISA.