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The purpose of this study is to investigate the sensitivity,specificity and concordance rate of EGFR testing results in plasma in comparison of results in matched tumor tissues tested by amplification refractory mutation system(ARMS). Moreover, the investigators correlate our findings in plasma with survival of advanced patients.
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Non-small cell lung cancer (NSCLC), accounting for 80% of all lung cancers, is a leading cause of cancer deaths worldwide. Inhibition of epidermal growth factor receptor (EGFR) kinase activity by EGFR tyrosine kinase inhibitors, such as gefitinib and erlotinib, can result in improved response and prolonged progression-free survival (PFS) in selected non-small cell lung cancer (NSCLC) patients harboring sensitizing EGFR mutations, especially the exon 19del and exon 21(L858R) mutations. Unfortunately, about 50%-60% patients who accept EGFR tyrosine kinase inhibitors with sensitizing mutations will receive resistance mutations, the T790M resistance is a target of active pharmaceutical development. So EGFR mutation testing is a step in identifying the right patients for EGFR tyrosine kinase inhibitors treatment. Now tissue samples and tumor cytologic samples are accepted as appropriate sample types for EGFR mutation detection, but these samples are not always available on or after diagnosis, and, even when available, they may be of insufficient quality or quantity for mutation testing. Noninvasive techniques for tumor genotyping may be needed to fully realize, such as plasma sample. Cell-free DNA (cf-DNA) in plasma is a kind of fresh and realtime sample, and has been shown to be promising for the detection of sensitizing EGFR mutations even the resistance mutation(T790M). However, a challenge was also raised about how to detect the low abundance of mutant alleles in plasma. In our study Droplet Digital polymerase chain reaction and Realtime polymerase chain reaction will be used to assess the EGFR mutation in plasma DNA samples from patients with advanced NSCLC before and after EGFR tyrosine kinase inhibitors therapy.
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Ning Chang, Postgraduate
Data sourced from clinicaltrials.gov
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