Dataset Information

Assessment of EGFR Mutation Status in Matched Plasma and Tumor Tissue of NSCLC Patients from a Phase I Study of Rociletinib (CO-1686).

ABSTRACT: PURPOSE:The evaluation of plasma testing for the EGFR resistance mutation T790M in NSCLC patients has not been broadly explored. We investigated the detection of EGFR activating and T790M mutations in matched tumor tissue and plasma, mostly from patients with acquired resistance to first-generation EGFR inhibitors. EXPERIMENTAL DESIGN:Samples were obtained from two studies, an observational study and a phase I trial of rociletinib, a mutant-selective inhibitor of EGFR that targets both activating mutations and T790M. Plasma testing was performed with the cobas EGFR plasma test and BEAMing. RESULTS:The positive percent agreement (PPA) between cobas plasma and tumor results was 73% (55/75) for activating mutations and 64% (21/33) for T790M. The PPA between BEAMing plasma and tumor results was 82% (49/60) for activating mutations and 73% (33/45) for T790M. Presence of extrathoracic (M1b) versus intrathoracic (M1a/M0) disease was found to be strongly associated with ability to identify EGFR mutations in plasma (P < 0.001). Rociletinib objective response rates (ORR) were 52% [95% confidence interval (CI), 31 - 74%] for cobas tumor T790M-positive and 44% (95% CI, 25 - 63%) for BEAMing plasma T790M-positive patients. A drop in plasma-mutant EGFR levels to ?10 molecules/mL was seen by day 21 of treatment in 7 of 8 patients with documented partial response. CONCLUSIONS:These findings suggest the cobas and BEAMing plasma tests can be useful tools for noninvasive assessment and monitoring of the T790M resistance mutation in NSCLC, and could complement tumor testing by identifying T790M mutations missed because of tumor heterogeneity or biopsy inadequacy. Clin Cancer Res; 22(10); 2386-95. ©2016 AACR.

SUBMITTER: Karlovich C

PROVIDER: S-EPMC6886231 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Assessment of EGFR Mutation Status in Matched Plasma and Tumor Tissue of NSCLC Patients from a Phase I Study of Rociletinib (CO-1686).

Karlovich Chris C Goldman Jonathan W JW Sun Jong-Mu JM Mann Elaina E Sequist Lecia V LV Konopa Krzysztof K Wen Wei W Angenendt Philipp P Horn Leora L Spigel David D Soria Jean-Charles JC Solomon Benjamin B Camidge D Ross DR Gadgeel Shirish S Paweletz Cloud C Wu Lin L Chien Sean S O'Donnell Patrick P Matheny Shannon S Despain Darrin D Rolfe Lindsey L Raponi Mitch M Allen Andrew R AR Park Keunchil K Wakelee Heather H

Clinical cancer research : an official journal of the American Association for Cancer Research 20160108 10

<h4>Purpose</h4>The evaluation of plasma testing for the EGFR resistance mutation T790M in NSCLC patients has not been broadly explored. We investigated the detection of EGFR activating and T790M mutations in matched tumor tissue and plasma, mostly from patients with acquired resistance to first-generation EGFR inhibitors.<h4>Experimental design</h4>Samples were obtained from two studies, an observational study and a phase I trial of rociletinib, a mutant-selective inhibitor of EGFR that targets ...[more]

PMID: 26747242

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Project description:Rociletinib (CO-1686), a 2,4-diaminopyrimidine derivative, is a highly potent tyrosine kinase inhibitor (TKI) that acts on epidermal growth factor receptor (EGFR) with L858R/T790M mutations. We supposed radioiodinated CO-1686 would function as a useful tool for monitoring EGFR L858R/T790M mutations. To aid in patient selection before therapy with EGFR-TKIs, this study aimed to develop a 125I-labeled derivative of CO-1686, N-{3-[(2-{[4-(4-acetylpiperazin-1-yl)-2-methoxyphenyl]amino}-5-(trifluoromethyl)pyrimidine-4-yl] amino}-5-([125I]iodophenyl)acrylamide ([125I]ICO1686) and evaluate its selectivity toward EGFR L858R/T790M. Radiosynthesis was performed by iododestannylation of the corresponding tributylstannyl precursor with [125I]NaI and N-chlorosuccinimide. The selectivity of the tracer for detecting EGFR L858R/T790M was evaluated using three relevant non-small cell lung cancer (NSCLC) cell lines-H1975, H3255 and H441 overexpressing the dual mutation EGFR L858R/T790M, active mutant EGFR L858R and wild-type EGFR, respectively. The nonradioactive ICO1686 and the precursor compound were successfully synthesized. A novel radiolabeled probe, [125I]ICO1686, was prepared with high radiochemical yield (77%) and purity (>99%). ICO1686 exhibited high cytotoxicity toward H1975 (IC50 0.20 ± 0.05 ?M) and H3255 (IC50 0.50 ± 0.21 ?M), which is comparable to that of CO-1686. In contrast, the cytotoxicity of ICO1686 toward H441 was 10-fold lower than that toward H1975. In the cell uptake study, the radioactivity uptake of [125I]ICO1686 in H1975 was 101.52% dose/mg, whereas the uptakes in H3255 and H441 were 33.52 and 8.95% dose/mg, respectively. The uptake of [125I]ICO1686 in H1975 was greatly reduced to 45.61% dose/mg protein by treatment with excess CO-1686. In vivo biodistribution study of the radiotracer found that its accumulation in H1975 tumor (1.77 ± 0.43% ID/g) was comparable to that in H3255 tumor (1.63 ± 0.23% ID/g) and the accumulation in H1975 tumor was not reduced by pretreatment with an excess dose of CO-1686. Although this radiotracer exhibited highly specific in vitro uptake in target cancer cells, structural modification is required to improve in vivo biodistribution.

Project description:To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Project description:Purpose: This study aimed to compare the performance of radiomics and deep learning in predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and tried to explore a model with excellent prediction performance to accurately predict EGFR mutation status in patients with non-small cell lung cancer (NSCLC). Method: PET/CT images of 194 NSCLC patients from Xijing Hospital were collected and divided into a training set and a validation set according to the ratio of 7:3. Statistics were made on patients' clinical characteristics, and a large number of features were extracted based on their PET/CT images (4306 radiomics features and 2048 deep learning features per person) with the pyradiomics toolkit and 3D convolutional neural network. Then a radiomics model (RM), a deep learning model (DLM), and a hybrid model (HM) were established. The performance of the three models was compared by receiver operating characteristic (ROC) curves, sensitivity, specificity, accuracy, calibration curves, and decision curves. In addition, a nomogram based on a deep learning score (DS) and the most significant clinical characteristic was plotted. Result: In the training set composed of 138 patients (64 with EGFR mutation and 74 without EGFR mutation), the area under the ROC curve (AUC) of HM (0.91, 95% CI: 0.86-0.96) was higher than that of RM (0.82, 95% CI: 0.75-0.89) and DLM (0.90, 95% CI: 0.85-0.95). In the validation set composed of 57 patients (32 with EGFR mutation and 25 without EGFR mutation), the AUC of HM (0.85, 95% CI: 0.77-0.93) was also higher than that of RM (0.68, 95% CI: 0.52-0.84) and DLM (0.79, 95% CI: 0.67-0.91). In all, HM achieved better diagnostic performance in predicting EGFR mutation status in NSCLC patients than two other models. Conclusion: Our study showed that the deep learning model based on PET/CT images had better performance than radiomics model in diagnosing EGFR mutation status of NSCLC patients based on PET/CT images. Combined with the most statistically significant clinical characteristic (smoking) and deep learning features, our hybrid model had better performance in predicting EGFR mutation types of patients than two other models, which could enable NSCLC patients to choose more personalized treatment schemes.

Dataset Information

Assessment of EGFR Mutation Status in Matched Plasma and Tumor Tissue of NSCLC Patients from a Phase I Study of Rociletinib (CO-1686).

Publications

Assessment of EGFR Mutation Status in Matched Plasma and Tumor Tissue of NSCLC Patients from a Phase I Study of Rociletinib (CO-1686).

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