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

Autor: Anagnostou VK; Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. valsamo.anagnostou@yale.edu, Dimou AT, Botsis T, Killiam EJ, Gustavson MD, Homer RJ, Boffa D, Zolota V, Dougenis D, Tanoue L, Gettinger SN, Detterbeck FC, Syrigos KN, Bepler G, Rimm DL
Jazyk: angličtina
Zdroj: Cancer [Cancer] 2012 Mar 15; Vol. 118 (6), pp. 1607-18. Date of Electronic Publication: 2011 Aug 25.
DOI: 10.1002/cncr.26450
Abstrakt: Background: 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).
Methods: 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.
Results: 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%.
Conclusions: 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.
(Copyright © 2011 American Cancer Society.)
Databáze: MEDLINE
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