Epidermal Growth Factor Receptor Genotype Research Articles

Prognostic Factors in Japanese EGFR Mutation-Positive Non-Small-Cell Lung Cancer: A Real-World Single-Center Retrospective Cohort Study.

The prognosis of patients with epidermal growth factor receptor (EGFR) mutation-positive lung cancer has improved significantly since the advent of EGFR tyrosine kinase inhibitors (EGFR-TKIs). We aimed to investigate the relationship between patient characteristics, EGFR genotype, therapeutic agents, and the prognosis of the patients with EGFR mutation-positive lung cancer. This retrospective cohort study analyzed 198 Japanese patients with unresectable EGFR mutation-positive lung cancer who were treated with EGFR-TKIs at Toho University Sakura Medical Center from April 2006 to December 2021. Factors associated with overall survival (OS) were analyzed using Cox proportional hazards analysis. Patients who received osimertinib had a significantly longer OS than did those not receiving it (median OS, 36.2 versus 20.7 months; p < 0.001).There were significant differences in OS between patients with EGFR mutation who received osimertinib as first-line treatment, T790M-positive patients who received osimertinib as second- or later-line treatment, and those who did not receive it (median OS, 28.2 versus 40.2 versus 20.7 months; p = 0.003). However, in T790M-negative patients, no significant difference in OS was noted between those who did and did not receive osimertinib as post-treatment (median OS, 28.0 versus 40.0 months; p = 0.619). Multivariate Cox proportional hazards analysis showed that osimertinib treatment was associated with longer OS (hazard ratio, 0.480; 95% confidence interval, 0.326-0.707; p < 0.001). The patients who were T790M-positive in the first-line treatment with first or second-generation EGFR-TKIs and were given osimertinib as the second or later line treatment had a better prognosis than the patients who were T790M-negative in the first-line treatment with first or second-generation EGFR-TKIs and could not receive osimertinib.

Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.

Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations. We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS). CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively. CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data

The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis

BackgroundThe precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks.MethodsTo comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights.ResultsThe experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization.ConclusionWith the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.

Arsenic Inhibits Proliferation and Induces Autophagy of Tumor Cells in Pleural Effusion of Patients with Non-Small Cell Lung Cancer Expressing EGFR with or without Mutations via PI3K/AKT/mTOR Pathway.

To clarify whether arsenic could exert inhibitory effects on tumor cells in pleural effusions of patients with non-small cell lung cancer (NSCLC), 36 NSCLC pleural effusion samples were collected from Changzheng Hospital and Ruijin Hospital, from 2019 to 2022. The genotype of epidermal growth factor receptor (EGFR) was identified. Tumor cells were isolated and treated with arsenic trioxide (ATO) or/and gefitinib. Additionally, six patients were intrapleurally administrated with ATO. Results showed that 25 samples bore EGFR wild type (WT) and 11 harbored EGFR mutations, including 6 with L858R, 3 with ΔE746-A750, and 2 with T790M. ATO diminished the number of tumor cells from patients with WT and mutant EGFR, down-regulated the expression or phosphorylation of EGFR, pmTOR, PI3K, PTEN, and p4E-BP1, and up-regulated the expression of LC3. Immunofluorescent experiments showed that ATO enhanced LC3 and P62. By contrast, gefitinib was only effective in those harboring EGFR sensitizing mutations. Notably, in patients with intrapleural ATO injection, the pleural effusion underwent a bloody to pale yellow color change, the volume of the pleural effusion was reduced, and the number of the tumor cells was significantly reduced. In conclusion, arsenic is effective against NSCLC with various EGFR genotypes in vitro and in vivo, and potentially circumvents gefitinib resistance.

Impact of epidermal growth factor receptor gene RS1468727 polymorphism on survival of the patients with oral squamous cell carcinoma

Introduction/Objective. Genetic aberrations and environmental factors are known to play an important role in oral squamous cell carcinoma (OSCC). The aim of the study was to clarify the association of epidermal growth factor receptor (EGFR) gene polymorphism rs1468727 with overall survival (OS) in patients with OSCC. Methods. The study comprised a total of 61 patients diagnosed with OSCC. The follow-up period for each patient was three years from the date of surgery and during that period their genotypes for rs1468727 polymorphism of the EGFR gene were identified using real-time polymerase chain reaction. Binary logistic regression was used to investigate the influence of various variables on survival. Additionally, the ?2 test of independence and Man?Whitney U test were done to examine the interplay between two categorical variables and two independent samples. Results. Two variables demonstrated a statistically significant influence on OS: the TNM Classification of Malignant Tumors (TNM) stage and EGFR genotype. At the end of the follow-up period, 39 patients survived, with a noteworthy observation that more than half of the survivors had the EGFR rs1468727 CC genotype. The distribution of CC and CT genotypes was equal (?2 = 0.397, df = 2, p = 0.820) among patients who deceased, indicating that no statistically significant correlations were found between OS and demographic or tumor-related characteristics. Conclusion. EGFR rs1468727 homozygote (genotype CC) and TNM stage showed statistically significant influence on OS in the follow-up period. This study highlights the potential significance of homozygote EGFR rs1468727 CC in assessing the prognosis and treatment outcomes of patients undergoing surgery for OSCC.

The effect of EGFR-TKIs on survival in advanced non-small-cell lung cancer with EGFR mutations: A real-world study.

Few large-scale studies have been published using real-world data related to overall survival (OS) improvements in advanced epidermal growth factor receptor (EGFR)-mutant lung cancer patients; therefore, little is known regarding the characteristics of patients who could benefit most from EGFR-tyrosine kinase inhibitors (TKIs). Our study aimed to assess whether EGFR-TKI treatment confers survival benefits among advanced non-small-cell lung cancer (NSCLC) patients harboring EGFR mutations in the Chinese population. A total of 6451 advanced NSCLC patients were diagnosed between January 1, 2013 and June 30, 2019 in Zhejiang Cancer Hospital. Ultimately, 2864 patients with a confirmed EGFR mutation genotype were enrolled in our study. OS was measured from the time of diagnosis of advanced NSCLC until death or last follow-up. Median follow-up for OS of advanced EGFR-mutant NSCLC patients was 28.33 months in our study. Patients who received EGFR-TKIs demonstrated better survival compared to those without EGFR-TKI treatment (mOS: 29.77 vs. 22.97 months, p < 0.0001). A total of 451 patients switched to third-generation EGFR-TKI treatment and obtained a significantly better survival than those who adopted first-line third-generation EGFR-TKIs or those who did not receive third-generation EGFR-TKIs after disease progression with first- or second-generation EGFR-TKI treatment (mOS: 38.0 vs. 32.5 vs. 28.3 months, p < 0.0001). As for EGFR genotypes, patients with exon 19 deletion showed better OS, followed by those with L858R mutation (32.4 vs. 24.83 months, p=0.0013). NGS versus PCR testing showed no statistical differences with respect to survival outcomes (mOS: 27.5 vs. 27.47 months, p=0.6745). Advanced EGFR-mutant patients treated with EGFR-TKIs obtained absolute superior survival in the Chinese population.

Impact of LncRNA GAS5 Genetic Variants and the Epidermal Growth Factor Receptor Phenotypes on the Clinicopathological Characteristics of Lung Adenocarcinoma Patients.

The aim of the current study was to evaluate the combined effect of the single nucleotide polymorphism (SNP) in long non-coding RNA growth arrest-specific 5 (GAS5) and the phenotypes of epidermal growth factor receptor (EGFR) on the clinicopathological characteristics of lung adenocarcinoma. The present study examined the relationship between the GAS5 single-nucleotide polymorphisms (SNPs; rs145204276 Ins/Del, rs55829688 T/C) and the clinicopathological factors in 539 lung adenocarcinoma patients with or without EGFR mutations. We found that the genotype distributions of the two GAS5 SNPs between different EGFR genotypes were similar after adjusting for age, gender and smoking history. The GAS5 SNP rs145204276 Ins/Del + Del/Del illustrated a higher distribution with an advanced tumor stage (p = 0.030), larger tumor T status (p = 0.019), positive lymph node status (p = 0.014) and distal metastases (p = 0.011) in the EGFR wild type group. In the subgroup analysis of the EGFR wild type population, the presence of GAS5 SNP rs145204276 Ins/Del + Del/Del was correlated to an advanced tumor stage (p = 0.014) and distal metastases (p = 0.020) in non-smokers. In conclusion, these data indicate that the GAS5 SNP rs145204276 variant may help predict tumor stage, lymph node metastasis and distal metastases in patients with EGFR wild type lung adenocarcinoma.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study

Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment. We included 18 232 patients with lung cancer with CT imaging and EGFR gene sequencing from nine cohorts in China and the USA, including a prospective cohort in an Asian population (n=891) and The Cancer Imaging Archive cohort in a White population. These cohorts were divided into thick CT group and thin CT group. The FAIS was built for predicting EGFR genotype and progression-free survival of patients receiving EGFR-TKIs, and it was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further built two tumour-based deep learning models as comparison with the FAIS, and we explored the value of combining FAIS and clinical factors (the FAIS-C model). Additionally, we included 891 patients with 56-panel next-generation sequencing and 87 patients with RNA sequencing data to explore the biological mechanisms of FAIS. FAIS achieved AUCs ranging from 0·748 to 0·813 in the six retrospective and prospective testing cohorts, outperforming the commonly used tumour-based deep learning model. Genotype predicted by the FAIS-C model was significantly associated with prognosis to EGFR-TKIs treatment (log-rank p<0·05), an important complement to gene sequencing. Moreover, we found 29 prognostic deep learning features in FAIS that were able to identify patients with an EGFR mutation at high risk of TKI resistance. These features showed strong associations with multiple genotypes (p<0·05, t test or Wilcoxon test) and gene pathways linked to drug resistance and cancer progression mechanisms. FAIS provides a non-invasive method to detect EGFR genotype and identify patients with an EGFR mutation at high risk of TKI resistance. The superior performance of FAIS over tumour-based deep learning methods suggests that genotype and prognostic information could be obtained from the whole lung instead of only tumour tissues. National Natural Science Foundation of China.

The Relationship between Metabolic Syndrome and Plasma Metals Modified by EGFR and TNF-α Gene Polymorphisms

With the escalating global prevalence of metabolic syndrome (MetS), it is crucial to detect the high-risk population early and to prevent chronic diseases. Exposure to various metals has been indicated to promote MetS, but the findings were controversial, and the effect of genetic modification was not considered. Epidermal growth factor receptor (EGFR) was proposed to be involved in the pathway of metabolic disorders, and tumor necrotic factor-α (TNF-α) was regarded as an early inflammatory biomarker for MetS. This research aimed to analyze the impact of EGFR and TNF-α gene polymorphisms on the prevalence of MetS under environmental or occupational exposure to metals. We gathered data from 376 metal industrial workers and 639 non-metal workers, including physical parameters, biochemical data, and plasma concentrations of six metals. According to the genomic database of Taiwan Biobank, 23 single nucleotide polymorphisms (SNPs) on EGFR gene and 6 SNPs on TNF-α gene were incorporated in our research. We applied multivariable logistic regression to analyze the probability of MetS with various SNPs and metals. Our study revealed some susceptible and protective EGFR and TNF-α genotypes under excessive exposure to cobalt, zinc, selenium, and lead. Thus, we remind the high-risk population of taking measures to prevent MetS.

Computed Tomography Morphological Classification of Lung Adenocarcinoma and Its Correlation with Epidermal Growth Factor Receptor Mutation Status: A Report of 1075 Cases.

BackgroundMany delayed diagnoses of lung adenocarcinoma (LADC) are identified due to poor understanding of protean imaging findings. Moreover, clarifying the relationship between computed tomography (CT) morphological classification and epidermal growth factor receptor (EGFR) mutations of LADC might inform therapeutic decision-making while obtaining pathological specimens is difficult. Here, we retrospectively analyzed CT manifestations of LADC and investigated the morphological classification of tumors in relation to EGFR mutation status.MethodsWe included 1075 LADC patients undergoing chest CT and EGFR genotype examinations from January 2013 to January 2019. CT morphological characteristics of tumors were carefully evaluated and their correlation with EGFR mutation status was analyzed using the chi-squared test.ResultsTumors were divided into eight types: I (peripheral solid nodule/mass; 526/1075, 48.93%), II (central solid nodule/mass; 220/1075, 20.47%), III (subsolid nodule/mass; 92/1075, 8.56%), IV (focal consolidation; 32/1075, 2.98%), V (cystic airspace; 14/1075, 1.30%), VI (multiple lesions with similar appearances to I–V; 85/1075, 7.91%), VII (diffuse consolidation; 53/1075, 4.93%), VIII (occult lesion usually obscured by nonobstructive atelectasis; 53/1075, 4.93%). Type III and IV tumors were more frequent in patients with EGFR mutation, whereas type II and VII tumors were more common in patients without EGFR mutation (all P < 0.05). However, we did not identify any significant associations between other tumor types and EGFR mutation status (all P > 0.05). Among patients with type VI tumors, EGFR mutation status was closely related to tumor density (all P < 0.05). Furthermore, type VII tumors were associated with 19 deletion mutation positive and non-L858R mutation positive (all P < 0.05).ConclusionLADC can be categorized into eight types based on CT imaging. Improving our understanding of the morphological classification and correlation with EGFR mutation status may contribute to the accurate diagnosis of LADC, while suggesting the presence of underlying EGFR genetic mutations.

Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.

BackgroundThe epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.MethodsA total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.ResultsAmong the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUCtest =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively.ConclusionsThe radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.

Different efficacy of osimertinib in pre-TKI treated NSCLC patients with brain metastases harboring EGFR exon 19del and L858R mutant.

e21667 Background: Previous studies suggested that the efficacy of osimertinib was better in patients with epidermal growth factor receptor ( EGFR) exon 19 deletion (ex19del) than exon 21 Leu858Arg (L858R). However, the differential clinical outcomes of osimertinib in previous tyrosine kinase inhibitors (TKIs) treated non-small-cell lung cancer (NSCLC) patients with brain metastases (BrMs) according to the two different common EGFR genotypes remains undetermined. Methods: We retrospectively collected EGFR mutant (ex19del and L858R) NSCLC patients with BrMs and received osimertinib treatment between Jan 2016 and Feb 2019 in our hospital. Other eligible standards including Eastern Cooperative Oncology Group (ECOG) performance status (PS) 0-2, osimertinib 80mg monotherapy and neurologic symptoms existing was permitted to enroll in our study. Results: 78 patients with EGFR mutant NSCLC and BrMs were included in the study. EGFR mutation types were ex19del in 55% of patients (43/78) and L858R in 45% of patients (35/78). All of patients received at least one prior EGFR-TKI therapy, among which 46% (36/78) had gefitinib, 49% (38/78) had erlotinib, 19% (15/78) had icotinib. T790M status at osimertinib initiation was positive in 81% of patients (63/78), negative in 4% (3/78), unknown in 15% (12/78). The systemic objective response rate (ORR) was 46%. There was no apparent difference in systemic ORR (49% vs 43%, P = 0.598) between ex19del group and L858R group. The median progression free survival (PFS) and overall survival (OS) were 7.0 (range 4.7-9.3) and 18.8 (range 14.0-23.5) months for all patients. The median PFS in the ex19del and L858R subgroups was 9.1 (range 6.0-12.1) months and 5.3 (range 3.4-7.2) months, respectively ( P = 0.031). The median OS in the ex19del and L858R subgroups was 26.0 (range 13.7-38.3) months and 13.9 (range 7.7-20.2) months, respectively ( P = 0.033). Multivariate analysis identified L858R and ECOG PS 2 as poor independent predictors to the PFS and OS. Conclusions: The efficacy of osimertinib was associated with EGFR genotypes in pre-TKI treated NSCLC patients with BrMs, and L858R maybe an independent bad factor for long-term outcomes of osimertinib.

EP1.01-68 Impact of EGFR Genotype on the Efficacy of Osimertinib in Patients with Non-Small Cell Lung Cancer: A Prospective Observational Study

A T790M of the epidermal growth factor receptor (EGFR) is the most frequently encountered mutation occurring acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC). The aim of this study was to assess the differential clinical outcomes of osimertinib therapy in NSCLC patients with T790M according to the type of active EGFR mutation, i.e. exon 19 deletion or L858R point mutation. We conducted a prospective observational cohort study to evaluate the efficacy and safety of osimertinib in patients with major EGFR mutation and T790M-positive advanced NSCLC who had disease progression after first-line EGFR-TKI therapy. The efficacy of osimertinib was evaluated according to the type of EGFR mutation. A total of 51 patients were included in this study. The exon 19 deletion was found in 33 (65%) patients, and the L858R point mutation in 18 patients (35%). An objective response was obtained in 29 patients, indicating an objective response rate of 58.8%. The response rate was 69.7% in patients with exon 19 deletion and 38.9% in patients with L858R point mutation, indicating a statistically significant difference (P =0.033). The median progression-free survival (PFS) and overall survival (OS) of the entire patient population were 7.8 and 15.5 months, respectively. Median PFS in the exon 19 deletion and L858R point mutation groups was 8.0 months and 5.2 months, respectively, indicating a statistically significant difference (P =0.045). Median OS in the exon 19 deletion and L858R point mutation groups was 19.8 months and 12.9 months, respectively, indicating a statistically significant difference (P =0.0015). Multivariate analysis identified exon 19 deletion as a favorable independent predictor of PFS and OS. Investigators should consider the proportions of sensitive EGFR mutation types as a stratification factor in designing or reviewing clinical studies involving osimertinib.

Evaluation of PCR-HRM, RFLP, and direct sequencing as simple and cost-effective methods to detect common EGFR mutations in plasma cell-free DNA of non-small cell lung cancer patients.

Lung cancer patients with mutations in epidermal growth factor receptor (EGFR) gene are treated with tyrosine kinase inhibitor (TKI). We aimed to evaluate polymerase chain reaction (PCR)-high-resolution melting (HRM), restriction fragment length polymorphism (RFLP), and direct sequencing (DS) to detect EGFR mutations in cell-free DNA (cfDNA) before and after TKI treatment in real-world settings of a developing country. Paired cytology and plasma samples were collected from 116 treatment-naïve lung cancer patients. DNA from both plasma and cytology specimens was isolated and analyzed using PCR-HRM (to detect exon 19 insertion/deletion), RFLP (to genotypes L858R and L861Q), and DS (to detect uncommon mutations G719A, G719C, or G719S [G719Xaa] in exon 18 and T790M and insertion mutations in exon 20). EGFR genotypes were obtained in all 116 (100%) cfDNA and 110/116 (94.82%) of cytological specimens of treatment-naïve patient (baseline samples). EGFR-activating mutations were detected in 46/110 (40.6%) plasma samples, and 69/110 (63.2%) mutations were found in routine cytology samples. Using cytological EGFR genotypes as reference, we found that sensitivity and specificity of baseline plasma EGFR testing varied from 9.1% to 39.39% and 83.12% to 96.55%, respectively. In particular, the sensitivity and specificity of this assay in detecting baseline T790M mutations in exon 20 were 30% and 89.58%, respectively. Three months after TKI treatment, plasma T790M and insertion exon 20 mutations appeared in 5.4% and 2.7% patients, respectively. Despite low sensitivity, combined DS, RFLP, and PCR-HRM was able to detect EGFR mutations in plasma cfDNA with high specificity. Moreover, TKI resistance exon 20 insertions mutation was detected as early as 3 months post TKI treatment.

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung adenocarcinoma using non-invasive computed tomography (CT).We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images, EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was proposed to predict the EGFR mutation status by CT scanning.By training in 14 926 CT images, the deep learning model achieved encouraging predictive performance in both the primary cohort (n=603; AUC 0.85, 95% CI 0.83–0.88) and the independent validation cohort (n=241; AUC 0.81, 95% CI 0.79–0.83), which showed significant improvement over previous studies using hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.

Treatment outcome comparisons between exons 19 and 21 EGFR mutations for non-small-cell lung cancer patients with malignant pleural effusion after first-line and second-line tyrosine kinase inhibitors.

Recent studies demonstrated a significantly increased frequency of epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC) patients with malignant pleural effusions (MPEs). The purpose of this study is to investigate the effect of first-line and second-line EGFR-tyrosine kinase inhibitors (TKIs) in the treatment of NSCLC with MPEs harboring exon 19 deletion and L858R mutation. From 2010 to 2015, 203 NSCLC patients with MPEs harboring EGFR mutation treated with EGFR-TKIs were reviewed. The efficacy were evaluated with Pearson chi-square or Fisher's exact tests, Log-rank test and Cox proportional hazards model. The objective response rate (ORR) and disease control rate (DCR) for patients treated with first-line and second-line EGFR-TKIs were 21.9%, 91.4% and 14.7%, 85.3%, respectively. The overall median PFS and OS of enrolled NSCLC patients with MPE were 9.3 months (95% CI, 8.4-10.2 months), 20.9 months (95% CI, 18.9-22.9 months) after first-line TKIs, and 7.6 months (95% CI, 6.6-8.6 months), 15.3 months (95% CI, 13.6-15.9 months) after second-line TKIs. The exon 19 deletion arm had a longer median PFS (9.4 vs 7.1 months, p=0.003) and OS (16.8 vs 13.8 months, p=0.003) compared with the L858R mutation arm after second-line TKIs. In a conclusion, EGFR genotype was an independent predictor of PFS and OS. No significant side effects differences between the two mutation groups was observed for first or second-line EGFR-TKIs. This study demonstrated that EGFR mutations are significant predictors for advanced NSCLC patients with MPE receiving second-line EGFR-TKIs treatment.

Efficacy of Second-line Tyrosine Kinase Inhibitors in the Treatment of Metastatic Advanced Non-small-cell Lung Cancer Harboring Exon 19 and 21 EGFR Mutations.

Background: Although superior clinical benefits of first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in the treatment of advanced non-small-cell lung cancer (NSCLC) had been reported with different sensitivity, the sensitivity of second-line TKIs in NSCLC patients with different EFGR mutations was unknown. The purpose of this study is to investigate the clinical outcome of second-line EGFR-TKIs in the treatment of NSCLC patients according to different EGFR genotypes.Methods: The treatment outcomes of 166 NSCLC patients with different EGFR mutations treated by second-line TKIs were retrospectively reviewed. The efficacy was evaluated with Pearson chi-square or Fisher's exact tests, Log-rank test and Cox proportional hazards model.Results: The disease control rate (DCR) and objective response rate (ORR) of enrolled NSCLC patients were 77.7% and 11.4%, respectively. The exon 19 deletion group had a significantly longer median progression-free survival (PFS) (6.7 vs. 4.5 months, P=0.002) and overall survival (OS) (13.7 vs. 11.7 months, P=0.02) compared with the exon 19 L858R mutation group for NSCLC patients, as well for patients with brain metastasis [PFS: (6.7 vs. 3.9 months, p<0.001), OS: (13.7 vs. 7.9 months, p=0.006)]. No significant difference on PFS and OS was observed between exon 19 deletion and L858R mutation group for patients with bone metastasis. EGFR genotype and ECOG PS were independent predictors of PFS. Never smoking, exon 19 deletion, EGOC PS (0-1) and no brain metastasis were correlated with longer OS. No significant difference on side effect between exon 19 and 21 mutation group was observed.Conclusions: NSCLC patients harboring exon 19 deletion achieved better PFS and OS than those with L858R mutation, indicating that EGFR mutation is a significant prognostic factor for advanced NSCLC patients with and without brain metastasis receiving second-line EGFR-TKIs treatment.

P3.02b-002 Treatment Outcome Comparison between Exon 19 and 21 EGFR Mutations after Second-Line TKIs in Advanced NSCLC Patients: Topic: EGFR Biomarkers

Although superior clinical benefits of first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in the treatment of advanced non-small-cell lung cancer (NSCLC) had been reported with different sensitivity. The sensitivity of second-line TKIs in NSCLC patients with different EFGR mutations was unknown. The purpose of this study is to investigate the clinical outcome of NSCLC patients with and without brain and/or bone metastases in different EGFR tumor genotypes receiving EGFR-TKIs as a second-line treatment. The treatment outcomes of 166 NSCLC patients harboring either the exon 19 deletion or the L858R point mutation of EGFR treated by second-line TKIs were retrospectively reviewed. The disease control rate (DCR) and objective response rate (ORR) of enrolled NSCLC patients were 77.7% and 11.4%, respectively. The exon 19 deletion group had a significantly longer median progression-free survival (PFS) (6.7 vs. 4.5 months, P=0.002) and overall survival (OS) (13.7 vs. 11.7 months, P=0.02) compared with the L858R mutation group for NSCLC patients, as well for patients with brain metastasis [PFS: (6.7 vs. 3.9 months, p<0.001), OS: (13.7 vs. 7.9 months, p=0.006)]. The median PFS and OS for NSCLC patients with bone metastasis were 4.8 months (95% Cl, 4.0-5.6 months) and 12.9 months (95% Cl, 8.7-17.1 months), respectively. No significant difference was observed for patients with bone metastasis for different mutations. EGFR genotype and ECOG PS were independent predictors of PFS for both NSCLC patients with and without brain metastasis. Never smoking (P=0.001), exon 19 deletion (P=0.03), EGOC PS (0-1) (P<0.001) and no brain metastasis (p=0.01) were correlated with longer OS for all NSCLC patients. For patients with brain metastasis, age at disease progression (p=0.009), genotype (p=0.02) and EGOC PS (p<0.001) were independent predictors of OS. For patients with bone metastasis, EGOC PS (p<0.001) was an independent predictor for both PFS and OS. Female (P=0.01), never smoking (P=0.005), number of bone metastases (P=0.03) and EGOC PS (0-1) (P=0.002) were related to a longer PFS. EGOC PS (0-1) (P<0.001) was associated with a longer OS. Rash, fatigue, and anorexia were the three most frequent side effects observed No significant side effects difference between exon 19 and 21 mutation groups were observed. NSCLC patients harboring exon 19 deletion achieved better PFS and OS than those with L858R mutation, indicating that EGFR mutations are significant prognostic factors for advanced NSCLC patients with and without brain metastasis receiving second-line EGFR-TKIs treatment.

P3.02b-089 Treatment of NSCLC Patients with Malignant Pleural Effusion Harboring Exon 19 and 21 EGFR Mutations after First-Line and Second-Line TKIs: Topic: EGFR Clinical

Recent studies demonstrated a significantly increased frequency of epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC) patients with malignant pleural effusions (MPEs). However, the sensitivity of tyrosine kinase inhibitors (TKIs) in NSCLC patients with MPE for different EFGR mutations was less reported. The purpose of this study is to investigate the effect of first-line and second-line EGFR-TKIs in the treatment of NSCLC with MPEs harboring exon 19 deletion and L858R mutation. From 2010 to 2015, 203 NSCLC patients with MPEs harboring EGFR mutation treated with EGFR-TKIs were reviewed. The efficacy were evaluated with Pearson chi-square or Fisher's exact tests, Log-rank test and Cox proportional hazards model. The objective response rate (ORR) and disease control rate (DCR) for patients treated with first-line and second-line EGFR-TKIs were 21.9%, 91.4% and 14.7%, 85.3%, respectively. The overall median PFS and OS of enrolled NSCLC patients with MPE were 9.3 months (95% Cl, 8.4-10.2 months), 20.9 months (95% Cl, 18.9-22.9 months) after first-line TKIs, and 7.6 months (95% Cl, 6.6-8.6 months), 15.3 months (95% Cl, 13.6-15.9 months) after second-line TKIs, respectively. The exon 19 deletion arm had a longer median PFS (9.4 vs. 7.1 months, p=0.003) and OS (16.8 vs. 13.8 months, p=0.003) compared with the L858R mutation arm after second-line TKIs. ECOG PS (PFS: P=0.004; OS: P=0.01) and TNM stage (PFS: P<0.001; OS: P=0.002) were independent predictors of PFS and OS for NSCLC patients with MPE treated by first-line TKIs. ECOG PS (0-1) (PFS: p=0.004; OS: p<0.001), TNM stage (IVa) (PFS: p=0.04; OS: p=0.007) and exon 19 deletions (PFS: p=0.02; OS: p=0.02) were related to a longer PFS and OS for patients treated with second-line TKIs. Gender was also an independent predictor of OS (p=0.04) for patients treated with second-line TKIs. There was no significant difference on side effects between NSCLC patients with exon 19 and 21 mutations in the first or second-line EGFR-TKIs. EGFR genotype was an independent predictor of PFS and OS. No significant side effects differences between the two mutation groups was observed for first or second-line EGFR-TKIs. This study demonstrated that EGFR mutations are significant predictors for advanced NSCLC patients with MPE receiving second-line EGFR-TKIs treatment.