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Background: Mutational analysis of the Epidermal Growth Factor Receptor (EGFR) and K-ras genes to select non-small cell lung cancer (NSCLC) patients for treatment with novel EGFR tyrosine kinase inhibitors is an appealing possibility currently under investigation. Although frozen tumor tissue would probably be the optimal source for analysis, the most common source of tumor material is fixed and paraffin embedded (FPE) archival specimens. Here, we evaluate how different procedures of tissue sample processing and preservation may affect the outcome of EGFR and K-ras mutation analysis. Furthermore, we compare the sensitivity of the analysis using genomic DNA (gDNA) versus RNA. Methods: We used PCR amplification and direct sequencing to analyze EGFR and K-ras genes in paired FPE and frozen tumor samples corresponding to 47 NSCLC patients. In frozen samples, the analysis was carried out using both gDNA and RNA extracted in parallel. Results: Whereas 100% of frozen samples were successfully amplified, the rate of successful PCR amplification in FPE samples was approximately 50%. We detected three previously described EGFR point mutations in 2 samples. In ten other samples, a K-ras mutation was observed. These mutations were detected in DNA extracted from frozen samples as well as in DNA obtained from FPE tissue. In addition, 10 nucleotide changes, were detected in FPE samples that were not detected in the frozen specimens. Upon re-analysis, these nucleotide changes could not be confirmed and were most likely the result of paraffin embedding and fixation procedures. All mutations found in gDNA were also detected in the corresponding RNA and, in two cases, the presence of the mutant allele was easier to identify by using RNA. Conclusions: Our results indicate that RNA extracted from frozen tissue is the preferred source for EGFR and K-ras mutation testing. When analyzing FPE samples, reducing the size of the amplified fragments would increase PCR success rate, and care should be taken to control for false-positive results.

Background: Fibrosis is associated with various cardiac pathologies and dysfunction. Current quantification methods are time-consuming and laborious. We describe a semi-automated quantification technique for myocardial fibrosis and validated this using traditional methods.