201. Tissue banking of diagnostic lung cancer biopsies for extraction of high quality RNA.
- Author
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Lawson MH, Rassl DM, Cummings NM, Russell R, Morjaria JB, Brenton JD, Murphy G, and Rintoul RC
- Subjects
- Adenocarcinoma genetics, Adult, Aged, Aged, 80 and over, Biomarkers, Tumor genetics, Biomarkers, Tumor metabolism, Biopsy, Carcinoma, Non-Small-Cell Lung genetics, Carcinoma, Squamous Cell genetics, Female, Gene Expression Profiling, Humans, Immunoenzyme Techniques, Lung Neoplasms genetics, Male, Middle Aged, Oligonucleotide Array Sequence Analysis, RNA, Neoplasm isolation & purification, RNA, Neoplasm metabolism, Adenocarcinoma diagnosis, Carcinoma, Non-Small-Cell Lung diagnosis, Carcinoma, Squamous Cell diagnosis, Lung Neoplasms diagnosis, RNA, Neoplasm genetics, Tissue Banks
- Abstract
Introduction: There is a clear need to develop a practical approach to obtain high quality RNA for gene expression analysis from lung cancer patients. Current approaches are restricted to using material from surgical resection specimens. We systematically investigated whether high quality RNA could be obtained from routine lung cancer diagnostic biopsies to determine the optimum method., Methods: Extra biopsies were taken at diagnosis from patients later confirmed to have lung cancer. Comparisons were made between RNA extracted from samples snap frozen in liquid nitrogen and those treated with an RNA preservative before freezing. Further comparisons were made between biopsies taken by different methods., Results: Acceptable RNA for gene expression analysis was extracted from 72% of lung cancer biopsies. Use of an RNA preservative for storage allowed the extraction of higher quality, more intact RNA from biopsies gathered by both endobronchial forceps and transbronchial needle aspiration. High quality RNA could also be extracted from computed tomography-guided needle core biopsies., Conclusion: Banking lung cancer biopsy specimens by storage in an RNA preservative solution will allow use of a broader spectrum of lung cancers for gene expression analysis. We describe a model that makes personalized medicine for lung cancer patients a more practical proposition.
- Published
- 2010
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