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Including total EGFR staining in scoring improves EGFR mutations detection by mutation-specific antibodies and EGFR TKIs response prediction.

ABSTRACT: Epidermal growth factor receptor (EGFR) is a novel target for therapy in subsets of non-small cell lung cancer, especially adenocarcinoma. Tumors with EGFR mutations showed good response to EGFR tyrosine kinase inhibitors (TKIs). We aimed to identify the discriminating capacity of immunohistochemical (IHC) scoring to detect L858R and E746-A750 deletion mutation in lung adenocarcinoma patients and predict EGFR TKIs response. Patients with surgically resected lung adenocarcinoma were enrolled. EGFR mutation status was genotyped by PCR and direct sequencing. Mutation-specific antibodies for L858R and E746-A750 deletion were used for IHC staining. Receiver operating characteristic (ROC) curves were used to determine the capacity of IHC, including intensity and/or quickscore (Q score), in differentiating L858R and E746-A750 deletion. We enrolled 143 patients during September 2000 to May 2009. Logistic-regression-model-based scoring containing both L858R Q score and total EGFR expression Q score was able to obtain a maximal area under the curve (AUC: 0.891) to differentiate the patients with L858R. Predictive model based on IHC Q score of E746-A750 deletion and IHC intensity of total EGFR expression reached an AUC of 0.969. The predictive model of L858R had a significantly higher AUC than L858R intensity only (p?=?0.036). Of the six patients harboring complex EGFR mutations with classical mutation patterns, five had positive IHC staining. For EGFR TKI treated cancer recurrence patients, those with positive mutation-specific antibody IHC staining had better EGFR TKI response (p?=?0.008) and longer progression-free survival (p?=?0.012) than those without. In conclusion, total EGFR expression should be included in the IHC interpretation of L858R. After adjusting for total EGFR expression, the scoring method decreased the false positive rate and increased diagnostic power. According to the scoring method, the IHC method is useful to predict the clinical outcome and refine personalized therapy.

SUBMITTER: Wu SG

PROVIDER: S-EPMC3153495 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Including total EGFR staining in scoring improves EGFR mutations detection by mutation-specific antibodies and EGFR TKIs response prediction.

Wu Shang-Gin SG Chang Yih-Leong YL Lin Jou-Wei JW Wu Chen-Tu CT Chen Hsuan-Yu HY Tsai Meng-Feng MF Lee Yung-Chie YC Yu Chong-Jen CJ Shih Jin-Yuan JY

PloS one 20110809 8

Epidermal growth factor receptor (EGFR) is a novel target for therapy in subsets of non-small cell lung cancer, especially adenocarcinoma. Tumors with EGFR mutations showed good response to EGFR tyrosine kinase inhibitors (TKIs). We aimed to identify the discriminating capacity of immunohistochemical (IHC) scoring to detect L858R and E746-A750 deletion mutation in lung adenocarcinoma patients and predict EGFR TKIs response. Patients with surgically resected lung adenocarcinoma were enrolled. EGF ...[more]

PMID: 21858063

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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

Including total EGFR staining in scoring improves EGFR mutations detection by mutation-specific antibodies and EGFR TKIs response prediction.

Publications

Including total EGFR staining in scoring improves EGFR mutations detection by mutation-specific antibodies and EGFR TKIs response prediction.

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