Dataset Information

Dynamic cfDNA Analysis by NGS in EGFR T790M-Positive Advanced NSCLC Patients Failed to the First-Generation EGFR-TKIs.

ABSTRACT:

Purpose

Circulating cell-free DNA (cfDNA) level has been demonstrated to be associated with efficacy in first generation EGFR TKIs in non-small cell lung cancer (NSCLC). However, the role of dynamic cfDNA analysis using next-generation sequencing (NGS) in patients with subsequent third-generation EGFR TKIs remains unclear.

Methods

From 2016 to 2019, 81 NSCLC patients with EGFR T790M mutation either in tissue or plasma who received third-generation EGFR TKIs treatment were enrolled. CfDNA were sequenced by NGS with a 425-gene panel. The association of clinical characteristics, pretreatment, dynamic cfDNA and T790M level with outcomes in patients treated with the third-generation TKIs were analyzed.

Results

In univariate analysis, the median PFS of patients with undetectable cfDNA level during treatment was significantly longer than those with detectable cfDNA (16.97 vs. 6.10 months; HR 0.2109; P < 0.0001). The median PFS of patients with undetectable T790M level during treatment was significantly longer than those with detectable T790M (14.1 vs. 4.4 months; HR 0.2192; P < 0.001). Cox hazard proportion model showed that cfDNA clearance was an independent predictor for longer PFS (HR 0.3085; P < 0.001) and longer OS (HR 0.499; P = 0.034). The most common resistant mutations of the third-generation TKIs were EGFR C797S (24%). CDK6 CNV, GRIN2A, BRCA2, EGFR D761N, EGFR Q791H, EGFR V843I, and ERBB4 mutation genes may possibly be new resistant mechanisms.

Conclusions

Patients with undetectable cfDNA during the third-generation EGFR TKI treatment have superior clinical outcomes, and dynamic cfDNA analysis by NGS is valuable to explore potential resistant mechanisms.

SUBMITTER: Ma L

PROVIDER: S-EPMC8030263 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo explore the potential of CT radiomics in detecting acquired T790M mutation and predicting prognosis in patients with advanced lung adenocarcinoma with progression after first- or second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy.Materials and methodsContrast-enhanced thoracic CT was collected from 250 lung adenocarcinoma patients (with acquired T790M mutation, n = 146, without mutation, n = 104) after progression on first- or second-generation TKIs. Radiomic features were extracted from each volume of interest. The maximum relevance minimum redundancy and the least absolute shrinkage and selection operator (LASSO) regression method were used to select the optimized features in detecting acquired T790M mutation. Univariate Cox regression and LASSO Cox regression were used to establish the radiomics model to predict the progression-free survival of osimertinib treatment. Finally, nomograms (which) combined clinical factors with radscore to predict the acquired T790M mutation and prognosis were built separately. In addition, the two nomograms were validated by the concordance index (C-index), decision curve analysis (DCA), and calibration curve analysis where appropriate.ResultsClinical factors including the progression-free survival of first-line EGFR TKIs, EGFR mutation, and N stage and 12 radiomic features were useful in predicting the acquired T790M mutation. The area under the receiver operating characteristic curves (AUC) of clinical, radiomics, and nomogram models were 0.70, 0.74, and 0.78 in the training set and 0.71, 0.71, and 0.76 in the validation set, respectively. The DCA and calibration curve analysis demonstrated a good performance of the nomogram model. Clinical factors including age and first-generation EGFR TKIs and 12 radiomic features were useful in patients' outcome prediction. The C-index of the combined nomogram was 0.686 in the training set and 0.630 in the validation set, respectively. Calibration curves demonstrated a relatively poor performance of the nomogram model.ConclusionNomogram combined clinical factors with radiomic features might be helpful to detect whether patients developed acquired T790M mutation or not after progression on first- or second-generation EGFR TKIs. Nomogram prognostic model combined clinical factors with radiomic features might have a limited value in predicting the survival of patients harboring acquired T790M mutation treated with osimertinib.