|Development of a rapid and practical mutation screening assay for human lung adenocarcinoma|
Authors: Helen Choi, Johannes Kratz, Patrick Pham, Sharon Lee, Roshni Ray, Yong-Won Kwon, Jian-Hua Mao, Hio Chung Kang, David Jablons, Il-Jin Kim
Affiliations: Thoracic Oncology Laboratory, Department of Surgery, University of California San Francisco, San Francisco, CA, USA, Life Sciences Division, Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, USA, Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
Published online on: Wednesday, March 7, 2012
Mortality after initial diagnosis of lung cancer is higher than from any other cancer. Although mutations in several genes, such as EGFR and K-ras, have been associated with clinical outcome, technical complexity, cost and time have rendered routine screening prohibitive for most lung cancer patients prior to treatment. In this study, using both novel and established technologies, we developed a clinically practical assay to survey the status of three frequently mutated genes in lung cancer (EGFR, K-ras and TP53) and two genes (BRAF and β-catenin) with known hotspot mutations in many other cancers. A single 96-well plate was designed targeting a total of 14 fragments (16 exons) from EGFR, K-ras, TP53, BRAF and β-catenin. In 96 lung adenocarcinoma patients, the mutation frequencies of three major genes (EGFR, K-ras and TP53) were between 21-24%. Fifty-six out of 96 (58%) patients had a mutation in at least one of the five genes. K-ras mutations positively correlated with smoking pack-years (p=0.035). EGFR mutations were frequent in never-smokers (p=0.0007), Asians (p=0.0204) and non-stage I lung cancer (p=0.016). There was also a trend towards an association between the presence of any mutation and improved recurrence-free survival (p=0.070). We demonstrate that our novel multigene mutation assay technology can rapidly and cost-effectively screen for mutations in lung adenocarcinoma. This screening assay can be used in the clinical setting for the large-scale validation of prognosis and/or predicting therapeutic response so that the majority of lung cancer patients can benefit from leveraging up-to-date knowledge on how mutation profiles may influence treatment options.