Dataset Information

EGFR Polymorphism and Survival of NSCLC Patients Treated with TKIs: A Systematic Review and Meta-Analysis.

ABSTRACT: Tyrosine kinase inhibitor- (TKI-) based therapy revolutionized the overall survival and the quality of life in non-small-cell lung cancer (NSCLC) patients that have epidermal growth factor receptor (EGFR) mutations. However, EGFR is a highly polymorphic and mutation-prone gene, with over 1200 single nucleotide polymorphisms (SNPs). Since the role of EFGR polymorphism on the treatment outcome is still a matter of debate, this research analyzed the available literature data, according to the PRISMA guidelines for meta-analyses. Research includes PubMed, Scopus, ISI Web of Science, and 14 of genome-wide association studies (GWAS) electronic databases in order to provide quantitative assessment of the association between ten investigated EGFR SNPs and the survival of NSCLC patients. The pooled HR and their 95% CI for OS and PFS for different EGFR polymorphisms using a random or fixed effect model based on the calculated heterogeneity between the studies was applied. The longest and the shortest median OSs were reported for the homozygous wild genotype and a variant allele carriers for rs712829 (-216G>T), respectively. Quantitative synthesis in our study shows that out of ten investigated EGFR SNPs (rs11543848, rs11568315, rs11977388, rs2075102, rs2227983, rs2293347, rs4947492, rs712829, rs712830, and rs7809028), only four, namely, rs712829 (-216G>T), rs11568315 (CA repeat), rs2293347 (D994D), and rs4947492, have been reported to affect the outcome of TKI-based NSCLC treatment. Of these, only -216G>T and variable CA repeat polymorphisms have been confirmed by meta-analysis of available data to significantly affect OS and PFS in gefitinib- or erlotinib-treated NSCLC patients.

SUBMITTER: Jurisic V

PROVIDER: S-EPMC7104312 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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EGFR Polymorphism and Survival of NSCLC Patients Treated with TKIs: A Systematic Review and Meta-Analysis.

Jurisic Vladimir V Vukovic Vladimir V Obradovic Jasmina J Gulyaeva Lyudmila F LF Kushlinskii Nikolay E NE Djordjević Nataša N

Journal of oncology 20200318

Tyrosine kinase inhibitor- (TKI-) based therapy revolutionized the overall survival and the quality of life in non-small-cell lung cancer (NSCLC) patients that have epidermal growth factor receptor (EGFR) mutations. However, EGFR is a highly polymorphic and mutation-prone gene, with over 1200 single nucleotide polymorphisms (SNPs). Since the role of EFGR polymorphism on the treatment outcome is still a matter of debate, this research analyzed the available literature data, a ...[more]

PMID: 32256580

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Project description:BackgroundsEpidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non-small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.MethodsA sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.ResultsThree hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.ConclusionsThe PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients' survival outcome of targeted therapy in clinical practice.

Dataset Information

EGFR Polymorphism and Survival of NSCLC Patients Treated with TKIs: A Systematic Review and Meta-Analysis.

Publications

<i>EGFR</i> Polymorphism and Survival of NSCLC Patients Treated with TKIs: A Systematic Review and Meta-Analysis.

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