TY - JOUR
T1 - Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay
AU - Anagnostou, Valsamo K.
AU - Dimou, Anastasios T.
AU - Botsis, Taxiarchis
AU - Killiam, Elizabeth J.
AU - Gustavson, Mark D.
AU - Homer, Robert J.
AU - Boffa, Daniel
AU - Zolota, Vassiliki
AU - Dougenis, Dimitrios
AU - Tanoue, Lynn
AU - Gettinger, Scott N.
AU - Detterbeck, Frank C.
AU - Syrigos, Konstantinos N.
AU - Bepler, Gerold
AU - Rimm, David L.
PY - 2012/3/15
Y1 - 2012/3/15
N2 - 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.
AB - 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.
KW - histology prediction
KW - lung adenocarcinoma
KW - quantitative analysis
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U2 - 10.1002/cncr.26450
DO - 10.1002/cncr.26450
M3 - Article
C2 - 22009766
AN - SCOPUS:84857998574
SN - 0008-543X
VL - 118
SP - 1607
EP - 1618
JO - Cancer
JF - Cancer
IS - 6
ER -