Identification of candidate genes for lung cancer somatic mutation test kits

Abstract

Over the past three decades, mortality from lung cancer has sharply and continuously increased in China, ascending to the first cause of death among all types of cancer. The ability to identify the actual sequence of gene mutations may help doctors determine which mutations lead to precancerous lesions and which produce invasive carcinomas, especially using next-generation sequencing (NGS) technology. In this study, we analyzed the latest lung cancer data in the COSMIC database, in order to find genomic “hotspots” that are frequently mutated in human lung cancer genomes. The results revealed that the most frequently mutated lung cancer genes are EGFR, KRAS and TP53. In recent years, EGFR and KRAS lung cancer test kits have been utilized for detecting lung cancer patients, but they presented many disadvantages, as they proved to be of low sensitivity, labor-intensive and time-consuming. In this study, we constructed a more complete catalogue of lung cancer mutation events including 145 mutated genes. With the genes of this list it may be feasible to develop a NGS kit for lung cancer mutation detection.

V843I, a Lung Cancer Predisposing EGFR Mutation, Is Responsible for Resistance to EGFR Tyrosine Kinase Inhibitors

“…In the most recent report of lung cancer data in the COSMIC database, V843I accounted for only 0.05% of all registered EGFR mutations, compared with the higher frequency of T790M, which accounted for approximately 3% of all mutations. 22 The present study, for the first time, corroborates the hypothesized dual role of the V843I mutation in tumorigenesis and TKI drug resistance.…”

“…In addition, the cell line with the double mutation was resistant to afatinib and dacomitinib, second-generation EGFR TKIs that are effective for lung cancers with the T790M mutation. 22 These results are notable considering the previous report that, whereas approximately 14% of lung cancers harbored secondary mutations in combination with sensitizing mutations in EGFR, most such secondary mutations did not affect the clinical response to TKIs. 31 To test how the structural features of the mutated EGFR might cause resistance to TKIs, we performed protein modeling of EGFR with mutations (L858R, T790M, L858R + V843I, and L858R + T790M substitutions).…”

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

“…Deletion and substitution mutation data were extracted for both EGFR and TP53 because these two genetic events were reported in the literature to be the most predominant lung cancer somatic mutations [ 14 , 35 ]. Moreover, we discovered that 99.67% of KRAS mutations in the IGDB.NSCLC database are substitution while deletion mutation data are a negligible 0.00131%.…”

“…Some examples of the oncogenes are epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), MYC, and BCL-2, and common examples of tumor suppressor genes are tumor suppressor p53 (TP53) and retinoblastoma (RB) [ 9 – 13 ]. As recent as 2013, Chen et al [ 14 ] carried out a study to identify genes that carry somatic mutations of various types in lung cancer and reported 145 genes with high mutation frequencies. The study established that the three most frequently mutated genes in lung cancer are EGFR, KRAS, and TP53 with mutation frequencies of 10957, 3106, and 2034, respectively.…”

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation

“…This implies that the promises of the targeted therapies to promptly arrest mutations in the lung may be elusive in the absence of relevant methods for screening and early detection of lung cancer mutations. Researchers have suggested that frequently mutated biomarker genes can be leveraged by designing kits for screening and early detection of lung cancer [ 16 ]. In line with this suggestion, a lung cancer prediction method was developed in [ 17 ].…”