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

Valsamo K. Anagnostou, Anastasios T. Dimou, Taxiarchis Botsis, Elizabeth J. Killiam, Mark D. Gustavson, Robert J. Homer, Daniel Boffa, Vassiliki Zolota, Dimitrios Dougenis, Lynn Tanoue, Scott N. Gettinger, Frank C. Detterbeck, Konstantinos N. Syrigos, Gerold Bepler, David L. Rimm

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1607-1618
Number of pages12
JournalCancer
Volume118
Issue number6
DOIs
StatePublished - Mar 15 2012
Externally publishedYes

Keywords

  • histology prediction
  • lung adenocarcinoma
  • quantitative analysis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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