Fine-Needle Aspiration Biopsies for Gene Expression Ratio-Based Diagnostic and Prognostic Tests in Malignant Pleural Mesothelioma
Authors:
- 15 January 2011
- journal article
- research article
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 17 (2) , 310-316
Abstract
Purpose: Malignant pleural mesothelioma (MPM) is an aggressive disease associated with median survival between 9 and 12 months. The correct diagnosis of MPM is sometimes challenging and usually requires solid tissue biopsies rather than fine-needle aspiration biopsies (FNA). We postulated that the accuracy of FNA-based diagnosis might be improved by the addition of molecular tests using a gene expression ratio-based algorithm and that prognostic tests could be similarly performed. Experimental Design: Two MPM and 2 lung cancer cell lines were used to establish the minimal quantity of RNA required to perform the gene ratio test. On the basis of these results, 276 ex vivo FNA biopsies from 63 MPM patients and 250 ex vivo FNA samples from 92 lung cancer patients were analyzed using previously described diagnostic and prognostic tests based on gene expression ratios. Results: We found that the sensitivity of the diagnostic test for MPM was 100% [95% confidence interval (CI): 95%–100%] and the specificity in primary lung adenocarcinoma was 90% (95% CI: 81%–95%). The FNA-based prognostic classification was concordant among 76% (95% CI: 65%–87%) of patients with the risk assignment in a subset of the matched surgical specimens previously analyzed by the prognostic test. Conclusions: Sufficient RNA can be extracted from most FNA biopsies to perform gene expression molecular tests. In particular, we show that the gene expression ratio algorithms performed well when applied to diagnosis and prognosis in MPM. This study provides support for the development of additional RNA molecular tests that may enhance the utility of FNA in the management of other solid cancers. Clin Cancer Res; 17(2); 310–16. ©2010 AACR.Keywords
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