Tony K F Yung1, K C Allen Chan, Tony S K Mok, Joanna Tong, Ka-Fai To, Y M Dennis Lo. 1. State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, People's Republic of China.
Abstract
PURPOSE: We aim to develop a digital PCR-based method for the quantitative detection of the two common epidermal growth factor receptor (EGFR) mutations (in-frame deletion at exon 19 and L858R at exon 21) in the plasma and tumor tissues of patients suffering from non-small cell lung cancers. These two mutations account for >85% of clinically important EGFR mutations associated with responsiveness to tyrosine kinase inhibitors. EXPERIMENTAL DESIGN: DNA samples were analyzed using a microfluidics system that simultaneously performed 9,180 PCRs at nanoliter scale. A single-mutant DNA molecule in a clinical specimen could be detected and the quantities of mutant and wild-type sequences were precisely determined. RESULTS: Exon 19 deletion and L858R mutation were detectable in 6 (17%) and 9 (26%) of 35 pretreatment plasma samples, respectively. When compared with the sequencing results of the tumor samples, the sensitivity and specificity of plasma EGFR mutation analysis were 92% and 100%, respectively. The plasma concentration of the mutant sequences correlated well with the clinical response. Decreased concentration was observed in all patients with partial or complete clinical remission, whereas persistence of mutation was observed in a patient with cancer progression. In one patient, tyrosine kinase inhibitor was stopped after an initial response and the tumor-associated EGFR mutation reemerged 4 weeks after stopping treatment. CONCLUSION: The sensitive detection and accurate quantification of low abundance EGFR mutations in tumor tissues and plasma by microfluidics digital PCR would be useful for predicting treatment response, monitoring disease progression and early detection of treatment failure associated with acquired drug resistance.
PURPOSE: We aim to develop a digital PCR-based method for the quantitative detection of the two common epidermal growth factor receptor (EGFR) mutations (in-frame deletion at exon 19 and L858R at exon 21) in the plasma and tumor tissues of patients suffering from non-small cell lung cancers. These two mutations account for >85% of clinically important EGFR mutations associated with responsiveness to tyrosine kinase inhibitors. EXPERIMENTAL DESIGN: DNA samples were analyzed using a microfluidics system that simultaneously performed 9,180 PCRs at nanoliter scale. A single-mutant DNA molecule in a clinical specimen could be detected and the quantities of mutant and wild-type sequences were precisely determined. RESULTS: Exon 19 deletion and L858R mutation were detectable in 6 (17%) and 9 (26%) of 35 pretreatment plasma samples, respectively. When compared with the sequencing results of the tumor samples, the sensitivity and specificity of plasma EGFR mutation analysis were 92% and 100%, respectively. The plasma concentration of the mutant sequences correlated well with the clinical response. Decreased concentration was observed in all patients with partial or complete clinical remission, whereas persistence of mutation was observed in a patient with cancer progression. In one patient, tyrosine kinase inhibitor was stopped after an initial response and the tumor-associated EGFR mutation reemerged 4 weeks after stopping treatment. CONCLUSION: The sensitive detection and accurate quantification of low abundance EGFR mutations in tumor tissues and plasma by microfluidics digital PCR would be useful for predicting treatment response, monitoring disease progression and early detection of treatment failure associated with acquired drug resistance.
Authors: David J McBride; Arto K Orpana; Christos Sotiriou; Heikki Joensuu; Philip J Stephens; Laura J Mudie; Eija Hämäläinen; Lucy A Stebbings; Leif C Andersson; Adrienne M Flanagan; Virginie Durbecq; Michail Ignatiadis; Olli Kallioniemi; Caroline A Heckman; Kari Alitalo; Henrik Edgren; P Andrew Futreal; Michael R Stratton; Peter J Campbell Journal: Genes Chromosomes Cancer Date: 2010-11 Impact factor: 5.006
Authors: Peiyong Jiang; Carol W M Chan; K C Allen Chan; Suk Hang Cheng; John Wong; Vincent Wai-Sun Wong; Grace L H Wong; Stephen L Chan; Tony S K Mok; Henry L Y Chan; Paul B S Lai; Rossa W K Chiu; Y M Dennis Lo Journal: Proc Natl Acad Sci U S A Date: 2015-02-02 Impact factor: 11.205
Authors: Maurice Chan; Mei Wen Chan; Ting Wei Loh; Hai Yang Law; Chui Sheun Yoon; Sint Sint Than; Jia Mei Chua; Chow Yin Wong; Wei Sean Yong; Yoon Sim Yap; Gay Hui Ho; Peter Ang; Ann Siew Gek Lee Journal: J Mol Diagn Date: 2011-05 Impact factor: 5.568