Afatinib Treatment Alone or with Bevacizumab in a Real-World Cohort of Non-Small Cell Lung Cancer Patients with Epidermal Growth Factor Receptor Mutation

Background: Treatment outcome between afatinib alone or with bevacizumab in non-small cell lung cancer (NSCLC) patient with epidermal growth factor receptor (EGFR) mutation remains insufficiently reported. Methods: A total of 405 advanced NSCLC patients with sensitizing-EGFR mutation receiving first-line single-agent afatinib or with bevacizumab were grouped and propensity score-matched. Progression-free survival (PFS), overall survival (OS) and secondary T790M mutation were analyzed. Results: In the original cohort, 367 (90.6%) patients received afatinib treatment alone and 38 (9.4%) patients received afatinib plus bevacizumab. Patients who received bevacizumab combination were significantly younger (54.6 ± 10.9 vs. 63.9 ± 11.5; p < 0.001) compared to the afatinib alone group. After propensity score matching, the afatinib alone and afatinib plus bevacizumab groups contained 118 and 34 patients, respectively. A non-significantly higher objective response was noted in the afatinib plus bevacizumab group (82.4% vs. 67.8%; p = 0.133). In the propensity score-matched cohort, a bevacizumab add-on offered no increased PFS (16.1 vs. 15.0 months; p = 0.500), risk reduction of progression (HR 0.85 [95% CI, 0.52–1.40]; p = 0.528), OS benefit (32.1 vs. 42.0 months; p = 0.700), nor risk reduction of death (HR 0.85 [95% CI, 0.42–1.74] p = 0.660) compared to the single-agent afatinib. The secondary T790M rate in afatinib plus bevacizumab and afatinib alone groups was similar (56.3% vs. 49.4%, p = 0.794). Multivariate analysis demonstrated that EGFR L858R (OR 0.51 [95% CI, 0.26–0.97]; p = 0.044), EGFR uncommon mutation (OR 0.14 [95% CI, 0.02–0.64]; p = 0.021), and PFS longer than 12 months (OR 2.71 [95% CI, 1.39–5.41]; p = 0.004) were independent predictors of secondary T790M positivity. Conclusion: Bevacizumab treatment showed moderate efficacy in real-world, afatinib-treated NSCLC patients with EGFR-sensitizing mutation.

Efficacy and safety of first-line osimertinib treatment and postprogression patterns of care in patients with epidermal growth factor receptor activating mutation-positive advanced non-small cell lung cancer (Reiwa study): study protocol of a multicentre, real-world observational study

IntroductionOsimertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), is widely used as the first-line treatment for EGFR mutation-positive non-small cell lung cancer (NSCLC). Nevertheless, most cases ultimately acquire resistance to osimertinib, and no effective treatment has been currently established for cases having progressive disease (PD) with osimertinib. In clinical practice, EGFR-TKI therapy could be continued beyond response evaluation criteria in solid tumours (RECIST)-defined PD cases when they are clinically stable. Currently, the progression pattern of osimertinib and criteria for identifying patients who might benefit from osimertinib beyond PD are unknown. In addition, the efficacy and safety of osimertinib as the first-line treatment in real-world clinical practice remain unclear in Japan. This multicentre study was designed to evaluate the real-world data on first-line osimertinib and its post-treatment.Methods and analysisThe study enrols patients with EGFR mutation-positive, advanced or recurrent NSCLC who received EGFR-TKI as the first-line therapy after 1 September 2018, from October 2019 to August 2020, and those started on osimertinib will be followed up until August 2022. We will evaluate the efficacy and safety of the first-line osimertinib treatment, adherence to it, progression patterns on RECIST PD and subsequent treatment.Ethics and disseminationAll participating patients will provide written informed consent before entering the study. The protocol, amendments and patients’ informed consent forms will be approved before study commencement by the institutional review board or independent ethics committee at each participation site (Lead Ethics Committee; Japan Red Cross Medical Center (26 April 2019, order number 976)). Patients will be anonymised before registration into the study and their anonymised data will be collected from the case report form. The results of this study will be presented at the national and international conferences and submitted for publication.Trial registration numberUMIN000038683.

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images

Objective. The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. Methods. Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. Results. The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94–0.98), 0.80 (95% CI: 0.72–0.88), and 0.73 (95% CI: 0.63–0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). Conclusion. In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.

Chẩn đoán đột biến gen EGFR trong ung thư phổi không tế bào nhỏ với các mẫu bệnh phẩm dịch khoang cơ thể

TÓM TẮT Đặt vấn đề: Dựa vào tính chất các mẫu dịch khoang cơ thể có hiện diện các mảnh DNA lơ lửng giúp thực hiện chẩn đoán đột biến EGFR. Từ nguyên lý này, chúng tôi thực hiện nghiên cứu với các mục tiêu sau: Khảo sát tỉ lệ dương tính đột biến EGFR trong các mẫu dịch khoang cơ thể; và hảo sát tỉ lệ chẩn đoán đột biến gen EGFR trong mẫu bệnh phẩm mô học đúc khối parafin với mẫu dịch khoang cơ thể trên cùng một bệnh nhân. Phương pháp nghiên cứu: Hồi cứu, thống kê mô tả cắt ngang. Các trường hợp ung thư phổi không tế bào nhỏ được chẩn đoán đột biến EGFR bằng mẫu bệnh phẩm đúc khối paraffine với Test EGFR Version 1 và mẫu bệnh phẩm dịch các khoang cơ thể (Dịch màng phổi, dịch màng tim, dịch màng bụng, dịch não tủy) với Test EGFR Version 2. Kết quả: Có 117 ca bệnh trong nghiên cứu: Kết quả chẩn đoán đột biến gen EGFR trên mẫu mô học đúc khối paraffine: (+) 49 ca # 41,88%, tương đương với các thống kê ở trong nước và thế giới (Châu Á). Đa số vẫn là hai loại đột biến nhạy thuốc TKIs Exon 19 Deletion và Exon 21 L858R (53% và 23%). Kết quả chẩn đoán đột biến EGFR trên các mẫu dịch khoang cơ thể: Đa số mẫu dịch khoang cơ thể thực hiện chẩn đoán đột biến EGFR là dịch màng phổi (91 ca # 77,77%). Tỉ lệ phát hiện đột biến trong mẫu dịch màng phổi và dịch não tủy cao nhất (29,67% & 83,33%). So sánh tỉ lệ phát hiện đột biến EGFR trên mẫu dịch khoang cơ thể (35 /117 ca # 29,91%) với tỉ lệ phát hiện trên mẫu mô học thấp hơn có ý nghĩa thống kê (29,91% ↔ 41,88% với P = 0,0125). So sánh với các nghiên cứu khác trên thế giới cho thấy đa số các nghiên cứu cho kết quả cao hơn so với nghiên cứu tại bệnh viện Phạm Ngọc Thạch. Kết luận: Khảo sát chẩn đoán đột biến EGFR trong dịch các khoang cơ thể, đặc biệt trong các mẫu dịch có quá ít tế bào ác tính, kết quả dương tính 29,91%. Tỉ lệ cao nhất trong dịch màng phổi và dịch não tủy. Tuy nhiên, khả năng phát hiện đột biến EGFR trong các dịch khoang cơ thể thấp hơn so với trên các bệnh phẩm mô học (29.91% < 41,88%). Và đô tương đồng giữa hai loại bệnh phẩm này là 71,42%. Cần nâng cao kỹ thuật thực hiện chẩn đoán đột biến EGFR trong mẫu dịch khoang cơ thể với các phương pháp có độ nhạy cao hơn: ddPCR, NGS… ABSTRACT DIAGNOSTIC EGFR GENE MUTATIONS IN NON SMALL CELL LUNG CANCER WITH SPECIMENS OF BODY CAVITY FLUIDS Introduction: Based on the nature of the body cavity fluid samples, there is the presence of suspended DNA fragments that help to make an EGFR mutation diagnosis. From this principle, we have conducted this research with the following objectives: Investigate the positive rate of EGFR mutations in body cavity fluid samples, and explore the diagnosis rate of EGFR gene mutations in paraffin block histology samples with body cavity fluid samples in the same patients. Methods: In a retrospective study, cases of NSCLC were diagnosed with EGFR mutations by paraffin block histological specimens with Test EGFR Version 1 and body cavity fluid samples (pleural fluid, pericardial fluid, peritoneal fluid, cerebrospinal fluid) with Test EGFR Version 2. Results: There are 117 cases in the research: Results of EGFR mutation diagnosis on paraffin block histology: (+) 49 cases # 41.88%, equivalent to statistics in Vietnam and the World (Asia). The majority are still two types of drug - sensitive mutants TKIs: Exon 19 Deletion and Exon 21 L858R (53% and 23%). Results of diagnosis of EGFR mutation in samples of body cavity fluids: Most samples of body cavity performing diagnosis of EGFR mutation were pleural fluid (91 cases # 77.77%). The highest rate of detection of mutations in pleural and cerebrospinal fluid samples (29.67% & 83.33%). Comparing the rate of detection of EGFR mutation in body fluid samples (35/117 cases # 29.91%) with the statistically lower rate of detection in histological samples (29.91%-41, 88% with P = 0.0125). Compared with other studies in the world, most studies have higher results than those at Pham Ngoc Thach Hospital. Conclusion: Survey on the diagnosis of EGFR mutations in body cavity fluid samples, especially in fluid samples with too few malignant cells, showed positive results of 29.91%. The highest percentage is in pleural fluid and cerebrospinal fluid. However, the ability to detect EGFR mutations in body cavity fluid samples was lower than in histological specimens (29.91% < 41.88%). And the similarity between these two samples is 71.42%. Therefore, it is necessary to improve the technique of performing EGFR mutation diagnosis in body cavity fluid samples with more sensitive methods: ddPCR, NGS... Keywords: Non small cell lung cancer (NSCLC), Formalin - Fixed Paraffin - Embedded Tissue (FFPET), Body cavity fluids, Cell Free DNA, Cellular DNA.

A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

PurposeThis study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).MethodsThis study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis.ResultsThe radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736–0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer–Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort).ConclusionRadiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

Modelled Economic Analysis for Dacomitinib–A Cost Effectiveness Analysis in Treating Patients With EGFR-Mutation-Positive Non-Small Cell Lung Cancer in China

ObjectivesTo establish the cost-effectiveness of dacomitinib compared to gefitinib from the Chinese healthcare system perspective.PatientsAdvanced non-small cell lung cancer (NSCLC) harbouring epidermal growth factor receptor (EGFR) mutations.MethodsPartitioned survival analysis was undertaken to examine the cost-effectiveness of dacomitinib utilising individual patient data (IPD) from the pivotal randomised controlled trial (RCT) (ARCHER 1050). The three health states modelled were progression-free, post-progression, and death. Parametric survival distributions were fitted to IPD against the Kaplan-Meier survival curves corresponding to progression-free survival (PFS) and overall survival (OS) outcomes by randomised groups. Costs included drug acquisition and administration, outpatient management (outpatient consultation and examinations), and best supportive care costs. Utility weights were sourced from the pivotal trial and other published literature. The incremental cost-effectiveness ratio (ICER) was calculated with costs and quality-adjusted life years (QALYs) discounted at an annual rate of 5%. Both deterministic and probabilistic sensitivity analyses were undertaken.ResultsIn the base case, dacomitinib (CNY 265,512 and 1.95 QALY) was associated with higher costs and QALY gains compared to gefitinib (CNY 247,048 and 1.61 QALYs), resulting in an ICER of CNY 58,947/QALY. Using the empirical WTP/QALY threshold, dacomitinib is a cost-effective treatment strategy for patients with EGFR-mutation-positive advanced NSCLC. The probabilistic sensitivity analysis suggested that dacomitinib had a 97% probability of being cost-effective.ConclusionsDacomitinib is a cost-effective treatment strategy in treating patients with EGFR-mutation-positive NSCLC from the Chinese healthcare system perspective. The uncertainty around the cost-effectiveness of dacomitinib could be reduced if long-term survival data become available. Clinical Trial RegistrationNCT01024413