Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay

Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
Cancer (Impact Factor: 4.89). 03/2012; 118(6):1607-18. DOI: 10.1002/cncr.26450
Source: PubMed


The importance of definitive histological subclassification has increased as drug trials have shown benefit associated with histology in nonsmall-cell lung cancer (NSCLC). The acuity of this problem is further exacerbated by the use of minimally invasive cytology samples. Here we describe the development and validation of a 4-protein classifier that differentiates primary lung adenocarcinomas (AC) from squamous cell carcinomas (SCC).
Quantitative immunofluorescence (AQUA) was employed to measure proteins differentially expressed between AC and SCC followed by logistic regression analysis. An objective 4-protein classifier was generated to define likelihood of AC in a training set of 343 patients followed by validation in 2 independent cohorts (n = 197 and n = 235). The assay was then tested on 11 cytology specimens.
Statistical modeling selected thyroid transcription factor 1 (TTF1), CK5, CK13, and epidermal growth factor receptor (EGFR) to generate a weighted classifier and to identify the optimal cutpoint for differentiating AC from SCC. Using the pathologist's final diagnosis as the criterion standard, the molecular test showed a sensitivity of 96% and specificity of 93%. Blinded analysis of the validation sets yielded sensitivity and specificity of 96% and 97%, respectively. Our assay classified the cytology specimens with a specificity of 100% and sensitivity of 87.5%.
Molecular classification of NSCLC using an objective quantitative test can be highly accurate and could be translated into a diagnostic platform for broad clinical application.

Download full-text


Available from: Robert J Homer, Jan 04, 2015
  • Source
    • "Detection of lung cancer in its early stage is the key in curing patient and automated diagnosis would play crucial roles in this matter (Ganesan et al. 2010a, 2010b). So far many scientists tried to propose new methods to classify the types of lung cancer in early stages (Edwards et al. 2000, Petersen and Petersen 2001, Beadsmoore and Screaton 2003, Boffa 2011, Anagnostou et al. 2012, Gilad et al. 2012, West et al. 2012). In some studies, bioinformatics or data mining models have been used. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC). Electronic supplementary material The online version of this article (doi:10.1186/2193-1801-2-238) contains supplementary material, which is available to authorized users.
    Full-text · Article · Dec 2013 · SpringerPlus
  • Source
    • "Lung cancer development is a multistep process characterized by genetic alterations and subsequently abnormal expression of cellular proteins [7,8]. During the process, cellular proteins, such as those associated with intracellular organelles or located on cell surfaces and secreted into the extracellular environment, play important biological roles in the regulation of cell growth and differentiation. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Lung cancer is the number one cause of cancer-related deaths in the United States and worldwide. The complex protein changes and/or signature of protein expression in lung cancer, particularly in non-small cell lung cancer (NSCLC) has not been well defined. Although several studies have investigated the protein profile in lung cancers, the knowledge is far from complete. Among early studies, mucin5B (MUC5B) has been suggested to play an important role in the tumor progression. MUC5B is the major gel-forming mucin in the airway. In this study, we investigated the overall protein profile and MUC5B expression in lung adenocarcinomas, the most common type of NSCLCs. Lung adenocarcinoma tissue in formalin-fixed paraffin-embedded (FFPE) blocks was collected and microdissected. Peptides from 8 tumors and 8 tumor-matched normal lung tissue were extracted and labeled with 8-channel iTRAQ reagents. The labeled peptides were identified and quantified by LC-MS/MS using an LTQ Orbitrap Velos mass spectrometer. MUC5B expression identified by iTRAQ labeling was further validated using immunohistochemistry (IHC) on tumor tissue microarray (TMA). A total of 1288 peptides from 210 proteins were identified and quantified in tumor tissues. Twenty-two proteins showed a greater than 1.5-fold differences between tumor and tumor-matched normal lung tissues. Fifteen proteins, including MUC5B, showed significant changes in tumor tissues. The aberrant expression of MUC5B was further identified in 71.1% of lung adenocarcinomas in the TMA.Discussions: A subset of tumor-associated proteins was differentially expressed in lung adenocarcinomas. The differential expression of MUC5B in lung adenocarcinomas suggests its role as a potential biomarker in the detection of adenocarcinomas.
    Full-text · Article · Nov 2013 · Clinical Proteomics
  • Source
    • "Recent applications of such TMA-based technologies have validated prognostic gene signatures for several tumor types, including prostate and lung cancer (Anagnostou et al., 2011; Dimou et al., 2011; Ding et al., 2011; Zender and Lowe, 2008). Furthermore, the development of sophisticated computerbased methodologies for TMA analysis (Beck et al., 2011) may facilitate better standardization of the results and also quantitatively assess clinically significant morphometric parameters (e.g., epithelialestromal ratio, multiple nuclear pleomorphisms, etc.) that would escape conventional pathological examination. "
    [Show abstract] [Hide abstract]
    ABSTRACT: It is a time of great promise and expectation for the applications of knowledge about mechanisms of cancer toward more effective and enduring therapies for human disease. Conceptualizations such as the hallmarks of cancer are providing an organizing principle with which to distill and rationalize the abject complexities of cancer phenotypes and genotypes across the spectrum of the human disease. A countervailing reality, however, involves the variable and often transitory responses to most mechanism-based targeted therapies, returning full circle to the complexity, arguing that the unique biology and genetics of a patient's tumor will in the future necessarily need to be incorporated into the decisions about optimal treatment strategies, the frontier of personalized cancer medicine. This perspective highlights considerations, metrics, and methods that may prove instrumental in charting the landscape of evaluating individual tumors so to better inform diagnosis, prognosis, and therapy. Integral to the consideration is remarkable heterogeneity and variability, evidently embedded in cancer cells, but likely also in the cell types composing the supportive and interactive stroma of the tumor microenvironment (e.g., leukocytes and fibroblasts), whose diversity in form, regulation, function, and abundance may prove to rival that of the cancer cells themselves. By comprehensively interrogating both parenchyma and stroma of patients' cancers with a suite of parametric tools, the promise of mechanism-based therapy may truly be realized.
    Full-text · Article · Feb 2012 · Molecular oncology
Show more