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Clinical chemistry

Quantifying EGFR alterations in the lung cancer genome with nanofluidic digital PCR arrays.


PMID 20207772

Abstract

The EGFR [epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)] gene is known to harbor genomic alterations in advanced lung cancer involving gene amplification and kinase mutations that predict the clinical response to EGFR-targeted inhibitors. Methods for detecting such molecular changes in lung cancer tumors are desirable. We used a nanofluidic digital PCR array platform and 16 cell lines and 20 samples of genomic DNA from resected tumors (stages I-III) to quantify the relative numbers of copies of the EGFR gene and to detect mutated EGFR alleles in lung cancer. We assessed the relative number of EGFR gene copies by calculating the ratio of the number of EGFR molecules (measured with a 6-carboxyfluorescein-labeled Scorpion assay) to the number of molecules of the single-copy gene RPP30 (ribonuclease P/MRP 30kDa subunit) (measured with a 6-carboxy-X-rhodamine-labeled TaqMan assay) in each panel. To assay for the EGFR L858R (exon 21) mutation and exon 19 in-frame deletions, we used the ARMS and Scorpion technologies in a DxS/Qiagen EGFR29 Mutation Test Kit for the digital PCR array. The digital array detected and quantified rare gefitinib/erlotinib-sensitizing EGFR mutations (0.02%-9.26% abundance) that were present in formalin-fixed, paraffin-embedded samples of early-stage resectable lung tumors without an associated increase in gene copy number. Our results also demonstrated the presence of intratumor molecular heterogeneity for the clinically relevant EGFR mutated alleles in these early-stage lung tumors. The digital PCR array platform allows characterization and quantification of oncogenes, such as EGFR, at the single-molecule level. Use of this nanofluidics platform may provide deeper insight into the specific roles of clinically relevant kinase mutations during different stages of lung tumor progression and may be useful in predicting the clinical response to EGFR-targeted inhibitors.

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