Comprehensive profiling and quantitation of oncogenic mutations in non small-cell lung carcinoma using single molecule amplification and re-sequencing technology
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Shirong Zhang1,*, Bing Xia1,*, Hong Jiang1, Limin Wang1, Rujun Xu1, Yanbin Shi2, Jianguang Zhang2, Mengnan Xu2, David S. Cram2, Shenglin Ma1
1Department of Oncology, Hangzhou First People’s Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China
2Berry Genomics Corporation, Beijing 100015, China
*These authors have contributed equally to this work
David S. Cram, email: [email protected]
Shenglin Ma, email: [email protected]
Keywords: non small-cell lung carcinoma, oncogenic mutations, single molecule amplification and re-sequencing technology, allele-specific amplification refractory mutation system
Received: December 30, 2015 Accepted: June 17, 2016 Published: July 07, 2016
Activating and resistance mutations in the tyrosine kinase domain of several oncogenes are frequently associated with non-small cell lung carcinoma (NSCLC). In this study we assessed the frequency, type and abundance of EGFR, KRAS, BRAF, TP53 and ALK mutations in tumour specimens from 184 patients with early and late stage disease using single molecule amplification and re-sequencing technology (SMART). Based on modelling of EGFR mutations, the detection sensitivity of the SMART assay was at least 0.1%. Benchmarking EGFR mutation detection against the gold standard ARMS-PCR assay, SMART assay had a sensitivity and specificity of 98.7% and 99.0%. Amongst the 184 samples, EGFR mutations were the most prevalent (59.9%), followed by KRAS (16.9%), TP53 (12.7%), EML4-ALK fusions (6.3%) and BRAF (4.2%) mutations. The abundance and types of mutations in tumour specimens were extremely heterogeneous, involving either monoclonal (51.6%) or polyclonal (12.6%) mutation events. At the clinical level, although the spectrum of tumour mutation(s) was unique to each patient, the overall patterns in early or advanced stage disease were relatively similar. Based on these findings, we propose that personalized profiling and quantitation of clinically significant oncogenic mutations will allow better classification of patients according to tumour characteristics and provide clinicians with important ancillary information for treatment decision-making.
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