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Higher frequency but random distribution of EGFR mutation subtypes in familial lung cancer patients.

ABSTRACT: Despite the advancement of epidermal growth factor receptor (EGFR) inhibitors in lung cancer therapy, it remains unclear whether EGFR mutation status in familial lung cancers is different from that of sporadic cases. In this multicenter retrospective study, we compared both the EGFR mutation frequency and patterns between familial and sporadic cases. The results explored that family history of lung cancer is an independent predictor for higher EGFR mutation rate in 1713 lung adenocarcinoma patients (Odd ratio 1.68, 95% CI 1.06-2.67, P = 0.028). However, the distribution of EGFR mutation subtypes was similar to that of sporadic cases. Part of our study involved 40 lung cancer families with at least 2 tumor tissues available within each single family (n = 88) and there was no familial aggregation pattern in EGFR mutation subtypes. There were two families harboring the YAP1 R331W germline risk allele and EGFR mutation statuses among YAP1 family members also varied. These phenomena may hint at the direction of future research into lung carcinogenesis and EGFR mutagenesis.

SUBMITTER: Hsu KH

PROVIDER: S-EPMC5288187 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Higher frequency but random distribution of EGFR mutation subtypes in familial lung cancer patients.

Hsu Kuo-Hsuan KH Tseng Jeng-Sen JS Wang Chih-Liang CL Yang Tsung-Ying TY Tseng Chien-Hua CH Chen Hsuan-Yu HY Chen Kun-Chieh KC Tsai Chi-Ren CR Yang Cheng-Ta CT Yu Sung-Liang SL Su Kang-Yi KY Yu Chong-Jen CJ Ho Chao-Chi CC Hsia Te-Chun TC Wu Ming-Fang MF Chiu Kuo-Liang KL Liu Chien-Ming CM Yang Pan-Chyr PC Chen Jeremy J W JJ Chang Gee-Chen GC

Oncotarget 20160801 33

Despite the advancement of epidermal growth factor receptor (EGFR) inhibitors in lung cancer therapy, it remains unclear whether EGFR mutation status in familial lung cancers is different from that of sporadic cases. In this multicenter retrospective study, we compared both the EGFR mutation frequency and patterns between familial and sporadic cases. The results explored that family history of lung cancer is an independent predictor for higher EGFR mutation rate in 1713 lung adenocarcinoma patie ...[more]

PMID: 27449093

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Project description:BackgroundIdentification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.MethodsWe retrospectively studied 18F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).ResultsTwo sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.ConclusionsIn our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Dataset Information

Higher frequency but random distribution of EGFR mutation subtypes in familial lung cancer patients.

Publications

Higher frequency but random distribution of EGFR mutation subtypes in familial lung cancer patients.

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