Dataset Information

Impact of Somatic Mutations in Non-Small-Cell Lung Cancer: A Retrospective Study of a Chinese Cohort.

ABSTRACT: Background:Somatic mutations are important biomarkers for selecting an optimal targeted therapy and predicting outcomes for non-small-cell lung cancer (NSCLC) patients that are often detected from tissue samples. However, tissue samples are not always readily available from these patients. The exploration of using circulating tumor DNA (ctDNA) to identify somatic mutations offers an alternative source that should be explored. Methods:In this retrospective study, we included 280 patients diagnosed with adenocarcinoma between 2017 and 2018 in a hospital in eastern China. Tissue or ctDNA was collected, and a wide spectrum of somatic mutations was analyzed by targeted next-generation sequencing platforms. Associations among the mutation status, biomarkers, screening methods, disease stages, and interaction with treatment with overall survival (OS) were investigated. Results:We found that the EGFR L858R mutation was the most frequently identified mutation in adenocarcinoma in this population by both methods, followed by KRAS (p=3.7e-09), PIK3CA (p=5e-04), and HER2 mutations (p=6.3e-03). We observed that EGFR mutations were significantly mutually exclusive with KRAS, HER2, and MET. FGFR1 mutations were significantly more abundantly detected in the ctDNA group. We found an interaction effect between EGFR mutation and target therapies. The ability of the targeted therapy to improve OS in patients with a single EGFR mutation (HR=0.069, p=0.07) approached significance, but this was not the case for the patients with more than one EGFR mutation or without an EGFR mutation (HR=0.813, p=0.725). Furthermore, the effect of chemotherapy was more predominant in the EGFR group in comparison to the control group. Conclusion:These findings provide useful information on the distribution of somatic mutations via different screening methods and how this related to the optimal treatment selection in Chinese patients with NSCLC.

SUBMITTER: Shen HB

PROVIDER: S-EPMC7443461 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Impact of Somatic Mutations in Non-Small-Cell Lung Cancer: A Retrospective Study of a Chinese Cohort.

Shen Hai-Bo HB Li Jie J Yao Yuan-Shan YS Yang Zhen-Hua ZH Zhou Yin-Jie YJ Chen Wei W Hu Tian-Jun TJ

Cancer management and research 20200819

<h4>Background</h4>Somatic mutations are important biomarkers for selecting an optimal targeted therapy and predicting outcomes for non-small-cell lung cancer (NSCLC) patients that are often detected from tissue samples. However, tissue samples are not always readily available from these patients. The exploration of using circulating tumor DNA (ctDNA) to identify somatic mutations offers an alternative source that should be explored.<h4>Methods</h4>In this retrospective study, we included 280 pa ...[more]

PMID: 32884354

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Project description:PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR+] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC = 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDRWilcoxon, FDRNoether) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDRWilcoxon = 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR+ vs. EGFR-negative, AUC = 0.67, FDRNoether = 0.0032), as well as differentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDRNoether = 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC = 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

Dataset Information

Impact of Somatic Mutations in Non-Small-Cell Lung Cancer: A Retrospective Study of a Chinese Cohort.

Publications

Impact of Somatic Mutations in Non-Small-Cell Lung Cancer: A Retrospective Study of a Chinese Cohort.

Similar Datasets

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