Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay
| Autor: | Dimitrios Dougenis, Daniel J. Boffa, Konstantinos N. Syrigos, Vassiliki Zolota, Taxiarchis Botsis, Mark Gustavson, Elizabeth J. Killiam, Scott N. Gettinger, Robert J. Homer, David L. Rimm, Lynn T. Tanoue, Frank C. Detterbeck, Valsamo Anagnostou, Gerold Bepler, Anastasios Dimou |
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| Rok vydání: | 2011 |
| Předmět: |
Adult
Male Cancer Research Pathology medicine.medical_specialty Lung Neoplasms Fluorescent Antibody Technique Adenocarcinoma of Lung Adenocarcinoma Logistic regression Molecular classification Carcinoma Non-Small-Cell Lung Cytology Humans Medicine Epidermal growth factor receptor Lung cancer Aged Aged 80 and over Lung biology business.industry Proteins Histology Middle Aged medicine.disease Logistic Models medicine.anatomical_structure Oncology Tissue Array Analysis Carcinoma Squamous Cell biology.protein Female Non small cell business |
| Zdroj: | Cancer. 118:1607-1618 |
| ISSN: | 0008-543X |
| DOI: | 10.1002/cncr.26450 |
| Popis: | 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. Cancer 2011;. © 2011 American Cancer Society. |
| Databáze: | OpenAIRE |
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