Epidermal Growth Factor Receptor Mutations In Patients Research Articles

Prognostic significance of integrating total metabolic tumor volume and EGFR mutation status in patients with lung adenocarcinoma.

The objective of this study was to investigate the prognostic significance of total metabolic tumor volume (TMTV) derived from baseline 18F-2-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), in conjunction with epidermal growth factor receptor (EGFR) mutation status, among patients with lung adenocarcinoma (LUAD). We performed a retrospective analysis on 141 patients with LUAD (74 males, 67 females, median age 67 (range 34-86)) who underwent 18F-FDG PET/CT and had their EGFR mutation status determined. Optimal cutoff points for TMTV were determined using time-dependent receiver operating characteristic curve analysis. The survival difference was compared using Cox regression analysis and Kaplan‒Meier curves. The EGFR mutant patients (n = 79, 56.0%) exhibited significantly higher 2-year progression-free survival (PFS) and overall survival (OS) rates compared to those with EGFR wild-type (n = 62, 44.0%), with rates of 74.2% vs 69.2% (P = 0.029) and 86.1% vs 67.7% (P = 0.009), respectively. The optimal cutoff values of TMTV were 36.42 cm3 for PFS and 37.51 cm3 for OS. Patients with high TMTV exhibited significantly inferior 2-year PFS and OS, with rates of 22.4% and 38.1%, respectively, compared to those with low TMTV, who had rates of 85.8% and 95.0% (both P < 0.001). In both the EGFR mutant and wild-type groups, patients exhibiting high TMTV demonstrated significantly inferior 2-year PFS and OS compared to those with low TMTV. In multivariate analysis, EGFR mutation status (hazard ratio, HR, 0.41, 95% confidence interval, CI [0.18-0.94], P = 0.034) and TMTV (HR 8.08, 95% CI [2.34-28.0], P < 0.001) were independent prognostic factors of OS, whereas TMTV was also an independent prognosticator of PFS (HR 2.59, 95% CI [1.30-5.13], P = 0.007). Our study demonstrates that the integration of TMTV on baseline 18F-FDG PET/CT with EGFR mutation status improves the accuracy of prognostic evaluation for patients with LUAD.

Mutation Analysis of Epidermal Growth Factor Receptor Gene in Non-Small Cell Lung Cancer for Selection of Patients Eligible for Tyrosine Kinase Inhibitor Therapy

Introduction: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor therapy is effective as a first-line treatment of advanced non-small-cell lung cancer (NSCLC). This research study investigated the distribution of EGFR mutations in patients diagnosed with NSCLC to assist in selecting patients who could benefit from tyrosine kinase inhibitor therapy. Materials and Methods: This cross-sectional study was conducted between July 2017 and November 2022. A real-time multiplex ​Polymerase Chain Reaction (PCR) assay supplied by Roche Diagnostics was used to examine DNA obtained from 682 tumor biopsies collected from NSCLC patients. DNA amplification was performed in a Cobas z 480 instrument for mutation analysis. The PCR assay was designed using specific primers and probes to detect 43 different mutations targeting exons 18-21. Results: Among the 682 samples, 466 (68.3%) were males, and 216 were females. The male-to-female ratio was 2.1. Twenty per cent of the male and 37% of the female samples were positive for EGFR mutations. The most common mutations were the in-frame deletion of exon 19, followed by L858R in exon 21, exon 20 insertion and S769I, exon 18 G719X. In addition, three mutations, namely del exon 19, T790M and exon 20 insertions were also detected in a patient, suggesting an actively progressive disease. Conclusions: This study showed that EGFR mutations are more common in Pakistani female patients than males. Secondly, in-frame deletion of exon 19 and exon 21 mutation L858R are prevalent in most of the NSCLC patients. The prevalence of common and rare EGFR mutations in Pakistani patients provides an opportunity for a subset of patients' chance of therapy.

Is oligoprogression a potentially curable disease in epidermal growth factor receptor mutant lung adenocarcinoma?

Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) have shown impressive results in EGFR mutant lung cancer (LC) patients in terms of disease control rate with a positive impact on overall survival. Nevertheless, after months of treatment with targeted therapy, progression inevitably occurs. Some patients develop oligoprogression and local treatment is required for optimal disease control while maintaining EGFR-TKIs. This work features a clinical case of a patient harboring an EGFR mutant LC undergoing oligoprogression to EGFR-TKIs, first into the brain and afterward to the primary tumor, requiring local ablative strategies, including primary tumor resection three years after the start of osimertinib. Currently, the patient is still alive and continues with a complete response upon EGFR-TKIs maintenance. Hence, oligoprogression, even in driven oncogenic tumors, represents a distinct biological entity and potential curative disease that deserves particular consideration in multidisciplinary tumor boards. In this case, tumor primary resection after three years of the initial diagnosis represents a paradigm shift in the treatment of EGFR mutant patients.

Comparison of Confirmed Cytology Smears and Cell Blocks for Epidermal Growth Factor Receptor Mutation Testing in Non-Small Cell Lung Cancer.

Various cytologic specimens have been used to diagnose epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC). However, insufficient samples and lengthy DNA extraction procedures have led to inconsistent diagnostic results. To reduce manipulation losses and improve DNA extraction quality, we provide an improved procedure for DNA extraction from smear samples containing rare tumor cells in NSCLC. The effectiveness of this new method for DNA extraction and diagnosis was validated in 8 patients with pleural effusion smears and formalin-fixed paraffin-embedded cell blocks, and another with 2 smears. Smear samples with <5% tumor cells were collected, and visible particles were selected for DNA extraction after centrifugation. Qiagen formalin-fixed paraffin-embedded DNA extraction kit (Qiagen) was used for DNA extraction and the procedure was modified. The EGFR mutation analysis in both types of material used the EGFR mutation analysis kit (Therascreen EGFR RGQ PCR) and real-time polymerase chain reaction (Rotor-Gene Q). The DNA extraction amount of the smear was 2.6 to 258.8 ng/μL, and that of the cell block was 1.4 to 139.9 ng/μL. The DNA quantity and purity of DNA extracts isolated from both sample sources were sufficient for subsequent EGFR mutation detection, where mutation rates were similar and diagnostic results were consistent when smears or cell blocks were used. This improved method demonstrates that cytology smears can be used as a test material for the detection of EGFR mutations in patients with NSCLC with sparse cells.

Predicting EGFR Mutation in Lung Adenocarcinoma: Development and Validation of the EGFR Mutation Predictive Score (EMPS) in Bali, Indonesia.

The examination of epidermal growth factor receptor (EGFR) mutations may not be routinely available to all patients due to the limited availability and the expensive price of the examination, especially in area with limited resources such as in Indonesia. Therefore, we aimed to build a nomogram to predict the EGFR mutation in patients with lung adenocarcinoma by incorporating significant clinical and radiological parameters. We conducted an age-matched case-control study using 160 treatment-naïve patients [80 patients with EGFR-mutated (EGFRmut) and 80 with EGFR-wild-type (EGFRwt)] with pathologically confirmed lung adenocarcinomas with tumor specimens available for genetic analysis taken from 2017 through 2021 in Bali, Indonesia. Radiomics features were extracted from contrast CT images. The cut-off of the tumor diameter was defined using Receiver Operating Characteristic Curve. A conditional logistic regression model was constructed to identify significant risk factors, and a nomogram was developed for predicting the risk of EGFR mutation. A cohort was done to validate the nomogram. Being female, never-smoker, having a smaller tumor diameter (<48.5mm), located in the upper lobe, have bubble-like lucency and air-bronchogram in the chest CT scan were identified as independent risk factors of EGFR mutation at the multivariate logistic regression model. The forming normogram model produced an area under the curve of 0.993 (95 % CI = 0.98-1.00) and 0.91 (95 % CI = 0.84-0.99) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability. At the cut-off point of the normogram score 246 shows a sensitivity of 97.5%, a specificity of 98.8%, a positive predictive value of 99.0%, and a negative predictive value of 96.8%. Our study indicated that the EGFR Mutation Normogram could provide a non-invasive way to predict the risk of EGFR mutation in patients with lung adenocarcinoma in clinical practice. This normogram need to be validated in other area in Indonesia.

Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study.

Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making. To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM. This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis. A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733vs. 0.679) cohort. Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.

Predicting EGFR Mutation Status in Non–Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis

Recent advancements in artificial intelligence (AI) render a substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction in non-small cell lung cancer (NSCLC). We aimed to evaluate the performance and quality of AI algorithms that use radiomics features in predicting EGFR mutation status in patient with NSCLC. We searched PubMed (Medline), EMBASE, Web of Science, and IEEExplore for studies published up to February 28, 2022. Studies utilizing an AI algorithm (either conventional machine learning [cML] and deep learning [DL]) for predicting EGFR mutations in patients with NSLCL were included. We extracted binary diagnostic accuracy data and constructed a bivariate random-effects model to obtain pooled sensitivity, specificity, and 95% confidence interval. This study is registered with PROSPERO, CRD42021278738. Our search identified 460 studies, of which 42 were included. Thirty-five studies were included in the meta-analysis. The AI algorithms exhibited an overall area under the curve (AUC) value of 0.789 and pooled sensitivity and specificity levels of 72.2% and 73.3%, respectively. The DL algorithms outperformed cML in terms of AUC (0.822 vs. 0.775) and sensitivity (80.1% vs. 71.1%), but had lower specificity (70.0% vs. 73.8%, p-value<0.001) compared to cML. Subgroup analysis revealed that the use of positron-emission tomography/computed tomography, additional clinical information, deep feature extraction, and manual segmentation can improve diagnostic performance. DL algorithms can serve as a novel method for increasing predictive accuracy and thus have considerable potential for use in predicting EGFR mutation status in patient with NSCLC. We also suggest that guidelines on using AI algorithms in medical image analysis should be developed with a focus on oncologic radiomics.

Identification of EGFR mutation status in male patients with non-small-cell lung cancer: role of 18F-FDG PET/CT and serum tumor markers CYFRA21-1 and SCC-Ag

BackgroundThe high incidence of epidermal growth factor receptor (EGFR) mutations is usually found in female patients with lung adenocarcinoma who have never-smoked. However, reports concerning male patients are scarce. Thus, this study aimed to explore a novel approach based on 18F-fluoro-2-deoxy-2-deoxyglucose (18F-FDG) PET/CT and serum tumor markers (STMs) to determine EGFR mutation status in male patients with non-small-cell lung cancer (NSCLC).MethodsA total of 121 male patients with NSCLC were analyzed between October 2019 and March 2022. All patients underwent 18F-FDG PET/CT scan before treatment and monitored 8 STMs (cytokeratin 19 fragment [CYFRA21-1], squamous cell carcinoma-related antigen [SCC-Ag], carcinoembryonic antigen [CEA], neuron-specific enolase [NSE], carbohydrate antigen [CA] 50, CA125, CA72-4, and ferritin). A comparison was done between EGFR mutant and wild-type patients in terms of the maximum standardized uptake value of primary tumors (pSUVmax) and 8 STMs. We performed receiver operating characteristic (ROC) curve and multiple logistic regression analyses to determine predictors for EGFR mutation status.ResultsEGFR mutations were detected in 39 patients (32.2%). Compared with patients with EGFR wild-type, EGFR-mutant patients had lower concentrations of serum CYRFA21-1 (2.65 vs. 4.01, P = 0.002) and SCC-Ag (0.67 vs. 1.05, P = 0.006). No significant differences of CEA, NSE, CA 50, CA125, CA72-4 and ferritin were found between the two groups. The presence of EGFR mutations was significantly associated with low pSUVmax (< 8.75), low serum SCC-Ag (< 0.79 ng/mL) and CYFRA21-1 (< 2.91 ng/mL) concentrations. The area under ROC curve values were 0.679, 0.655, 0.685 and 0.754, respectively, for low CYFRA21-1, SCC-Ag, pSUVmax and the combination of these three factors.ConclusionsWe demonstrated that low concentrations of CYFRA21-1 and SCC-Ag, as well as low pSUVmax, were associated with EGFR mutations, and that the combination of these factors resulted in a higher differentiation of EGFR mutation status in male patients with NSCLC.

Factors Affecting 2-Year Survival in Patients With Non–Small Cell Lung Cancer Brain Metastases: Evidence from Indonesia’s National Cancer Center, Jakarta, Indonesia

Background: Brain metastases are a common complication in a wide range of cancers but are ubiquitous among patients with lung cancer. Limited data are available on the survival of patients with lung cancer and brain metastases in Indonesia. In this study, we aimed to identify the factors that might contribute to and predict survival in patients with non–small cell lung cancer (NSCLC) that resulted in brain metastases. Methods: This retrospective study on patients with NSCLC and brain metastases was conducted using data available from the medical records of the Dharmais National Cancer Hospital, Jakarta, Indonesia. The study outcome was survival time, which was associated with sex, age, smoking status, body mass index, number of brain metastases, tumor location, systemic therapy, and other therapies. Descriptive statistics, median survival, Kaplan-Meier graphs, and Cox regression were analyzed using SPSS version 27. Results: We included 111 patients with NSCLC and brain metastases in this study. The median patient age was 58 years. Long survivorship was observed among women (median: 95.4 weeks; P &lt; .0003), patients with epidermal growth factor receptor (EGFR) mutations (median: 41.8 weeks; P &lt; .0492), those who received chemotherapy (median: 58 weeks; P &lt; .000), and those who received a combination of surgery and whole brain radiotherapy (WBRT; median: 64.7 weeks; P = .0174). Multivariate analysis showed consistent results for the following factors: sex, EGFR mutations, systemic therapy, and surgery plus WBRT. Conclusions: Female sex and EGFR mutations in patients with NSCLC and brain metastases are associated with a high survival rate. Patients who have NSCLC with brain metastases will benefit from treatment with EGFR tyrosine kinase inhibitors, chemotherapy, and surgery plus WBRT.

Circulating EGFR Mutations in Patients with Lung Adenocarcinoma by Circulating Tumor Cell Isolation Systems: A Concordance Study.

Background: We developed a hybrid platform using a negative combined with a positive selection strategy to capture circulating tumor cells (CTCs) and detect epidermal growth factor receptor (EGFR) mutations in patients with metastatic lung adenocarcinoma. Methods: Blood samples were collected from patients with pathology-proven treatment-naïve stage IV lung adenocarcinoma. Genomic DNA was extracted from CTCs collected for EGFR mutational tests. The second set of CTC-EGFR mutational tests were performed after three months of anti-cancer therapy. Results: A total of 80 samples collected from 28 patients enrolled between July 2016 and August 2018. Seventeen patients had EGFR mutations, including Exon 19 deletion (n = 11), L858R (n = 5), and de-novo T790 and L858R (n = 1). Concordance between tissue and CTCs before treatment was 88.2% in EGFR- mutant patients and 90.9% in non-mutant patients. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of EGFR mutation tests for CTCs were 89.3%, 88.2%, 90.9%, 93.8%, and 83.3%, respectively. Conclusions: CTCs captured by a hybrid platform using a negative and positive selection strategy may serve as a suitable and reliable source of lung cancer tumor DNA for detecting EGFR mutations, including T790M.

Structure-based classification of EGFR mutations in operable pre-invasive and invasive non-small cell lung cancer: a cross-sectional study.

BackgroundIt has been reported that the structure-based approach for defining functional groups of epidermal growth factor receptor (EGFR) mutations predicts the efficacy of EGFR inhibitors better than the traditional exon-based approach in the advanced stage. However, less is known about this structure-based classification of EGFR mutations in operable early-stage lung adenocarcinoma.MethodsNon-small cell lung cancer (NSCLC) patients with pathological stage I–III or adenocarcinoma in situ (AIS) who had EGFR mutations identified in next-generation sequencing (NGS) testing were recruited. Both exon-based and structure-based groupings of EGFR mutations were compared between the AIS and stage I–III patients using Fisher’s exact test.ResultsIn total 1,012 patients including 66 AIS and 946 stage I–III patients were analyzed in the study. A total of 1185 EGFR mutations were identified in the 1,012 NSCLC patients, of whom 84.39% harbored a single EGFR mutation and 15.61% harbored complex EGFR mutations. As expected, L858R was more common than 19del in our population (39.33% vs. 35.67%). Interestingly, concurrent L858R and 19del mutations were identified in 9 patients (0.89%), and all these patients were diagnosed with multiple primary lung cancer. A higher percentage of atypical EGFR mutations was identified in the AIS cohort than in the stage I–III NSCLC cohort (33.33% vs. 21.66%, P=0.03). According to the structure-based classification of EGFR mutations, 86.07%, 7.11%, 5.04%, and 1.78% of the EGFR mutations were classified as classical-like, P-loop and α C-helix compressing (PACC), exon 20 insertions (Ex20ins), and T790M-like mutations, respectively. The composition of EGFR mutations was different between patients <65 and ≥65 years (P=0.0267) but similar between patients with AIS and stage I–III NSCLC (P=0.1436). However, a higher percentage of Ex20ins occurred in younger (<65 years) patients, nonsmoking patients, and patients with AIS (6.7% vs. 2.5%, P=0.003; 5.8% vs. 0.8%, P=0.0107; and 10.6% vs. 4.7%, P=0.0423, respectively).ConclusionsThis large cross-sectional study delineated the structure-based classification of EGFR mutations in patients with operable NSCLC. While the traditional exon-based EGFR grouping showed difference between AIS and stage I–III NSCLC cohort, no difference was identified in the structural approach. Which approach had better prediction of targeted therapy efficacy in adjuvant settings warrants further investigation.

Rare EGFR E709-T710delinsX: Molecular characteristics and superior response to afatinib treatment in NSCLC patients

ObjectivesCurrently, whether patients with rare epidermal growth factor receptor (EGFR) mutations could benefit from EGFR tyrosine kinase inhibitors (TKIs) demands further studies. Limited clinical data are available regarding the molecular characteristics and clinical response in non-small-cell lung cancer (NSCLC) patients harboring EGFR E709-T710delinsX mutations, a rare mutation type in exon 18 of EGFR. In this study, we aimed to explore the molecular distribution and clinical outcome of EGFR E709-T710delinsX mutated Chinese patients. MethodsNext-generation sequencing (NGS) tests were performed in 15,078 NSCLC patients. A multicenter retrospective cohort involving 17 advanced lung cancer patients with EGFR E709-T710delinsX was collected to evaluate clinical responses to diverse therapies. In silico protein structure modeling was conducted to predict drug response. Results5905 EGFR mutant patients (39.2%, 5905/15078) were identified, with 26 cases (0.44%, 26/5905) harbored EGFR E709-T710delinsD. Afatinib showed a better overall objective response rate (ORR) compared with the first-generation (1G) EGFR TKIs and chemotherapy with significant difference. Superior progression free survival (PFS) was also observed in patients treated with afatinib (afatinib 10.85 m vs 1G EGFR TKIs 4.0 m vs chemotherapy 2.8 m, p = 0.0007). In silico protein structure modeling predicts better binding of afatinib with EGFR E709-T710delinsD compared with other EGFR TKIs. ConclusionsThis is the largest studies for EGFR E709-T710delinsX, with 26 cases with EGFR E709-T710delinsX being identified (0.44% in EGFR mutant NSCLC, 0.17% in NSCLC patients). This study also firstly revealed that afatinib might exert superior antitumor activity to the 1G EGFR TKIs and chemotherapy in EGFR E709-T710delinsX mutant patients.

Frequency of Epidermal Growth Factor Receptor Mutations among Iraqi Non-small Cell Lung Cancer Patients

Background: Epidermal Growth Factor Receptor (EGFR) mutations are actually currently utilized as genetic biomarkers for focused lung carcinoma treatment. Unfortunately, there is minimal data on Iraq’s proportion of mutations in EGFR gene. The current study’s objective is to find the mutations prevalence of EGFR gene mutations and the dominant EGFR mutations in patients with lung carcinoma type non-small cell (NSCLC) from Iraq. Methods: On tissues taken from paraffin-embedded blocks of NSCLC patients, RT-PCR was performed to investigate the mutations in EGFR gene. Results: This study involved 234 paraffin tissue blocks of NSCLC patients in addition to17 paraffin tissue blocks with invalid results, although it was tested more than three times. This study was used Polymerase chain reaction (PCR) technique to determine mutations in EGFR gene. Forty-seven out of 234 patients had an EGFR mutation (20%). The most prevalent anomaly found in exon19 Del (70.3%) followed by L 858 R (15%). High mutations were found among two age groups (60–69) and 70–79 which suggest increased mutation among old ages when compared with young people. However, no significant difference between EGFR mutation status and sex or age status was found. Conclusion: The prevalence of EGFR mutation among Iraqi patients is 20% which is compatible with a percentage of cases documented in Asian populations and the most prevalent EGFR mutant in Iraq cases is the EX19del.

Epidemiology and clinical impact of EGFR mutation in patients with lung cancer after radical surgical treatment.

8552 Background: The epidemiology of epidermal growth factor receptor (EGFR) mutation in lung adenocarcinoma and its clinical impact are well-known. However, most studies have focused on advanced-stage inoperable cancer. Data on the frequency of EGFR mutation in surgically resected lung cancer are limited. Recent studies have shown a promising effect of Osimertinib in adjuvant settings. Hence, the need to estimate the target population in the real-world data. This study aimed to assess the occurrence of EGFR mutation in patients after radical surgical treatment of lung adenocarcinoma and explore its prognostic impact compared to EGFR negative group. Methods: This single-center retrospective analysis included the group of 732 consecutive Caucasian patients with histopathologically confirmed lung adenocarcinoma, evaluated for EGFR mutations expression, who underwent anatomical resection between January 2016, and December 2020. EGFR status was assessed by cobas EGFR mutation test v2. The frequency of EGFR mutations, disease-free survival (DFS) and overall survival (OS) in EGFR positive and EGFR negative groups were analyzed. Results: EGFR mutations were found in 65 surgical patients (8.9%) and did not differ from patients with advanced stages of lung adenocarcinoma (7.9%). EGFR mutations occurred more frequently in females than males, 48 out of 344 (14%) and 17 out of 388 (4.4%), respectively. Deletions within exon 19 and the L858R mutation in exon 21 constituted 49.2% and 24.6% of all mutations, respectively, while others comprised 26.2%. One case of L858R mutation coincided with T790M in exon 20 mutation. Detailed results divided by stages are presented in the Table. The occurrence of EGFR mutation had no significant influence on DFS and OS in patients after radical resection. Conclusions: The frequency of EGFR mutation in postoperative lung cancer was comparable to the occurrence in the general lung cancer population. EGFR mutation did not affect DFS and OS in patients after radical resection. [Table: see text]

Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.

Identifying an epidermal growth factor receptor (EGFR) mutation is important because EGFR tyrosine kinase inhibitors are the first-line treatment of choice for patients with EGFR mutation-positive lung adenocarcinomas (LUAC). This study is aimed at developing and validating a radiomics-based machine learning (ML) approach to identify EGFR mutations in patients with LUAC. We retrospectively collected data from 201 patients with positive EGFR mutation LUAC (140 in the training cohort and 61 in the validation cohort). We extracted 1316 radiomics features from preprocessed CT images and selected 14 radiomics features and 1 clinical feature which were most relevant to mutations through filter method. Subsequently, we built models using 7 ML approaches and established the receiver operating characteristic (ROC) curve to assess the discriminating performance of these models. In terms of predicting EGFR mutation, the model derived from radiomics features and combined models (radiomics features and relevant clinical factors) had an AUC of 0.79 (95% confidence interval (CI): 0.77-0.82), 0.86 (0.87-0.88), respectively. Our study offers a radiomics-based ML model using filter methods to detect the EGFR mutation in patients with LUAC. This convenient and low-cost method may be of help to noninvasively identify patients before obtaining tumor sample for molecule testing.

Cost-Effectiveness Analysis of Afatinib versus Gefitinib in Non-small Cell Lung Cancer (NSCLC) with Epidermal Growth Factor Receptor (EGFR) Mutation in Indonesia: Observational studies with Retrospectives

Background: A cost effectiveness study is a method in pharmacoeconomic research that can be used as a reference for decision makers or clinicians in determining effective and efficient treatment for patients, one of which is a Non-Small Cell Lung Cancer patient who has an EGFR mutation. Research related to the cost effectiveness of the benefits afatinib and gefitinib is still very limited, especially in Indonesia. This study aims to determine the cost-effectiveness of afatinib versus gefitinib in NSCLC with EGFR mutation patients. Methods and Material: This study is an observational study with a retrospective approach that observes medical record records (to determine clinical outcomes) and financial records (to determine direct medical costs) in NSCLC patients with EGFR mutations at dr Sardjito Hospital Yogyakarta and Dr Kariadi Hospital Semarang, Java Island., Indonesia in the period January 2016 - June 2019. Result: Direct medical cost for NSCLC patients with EGFR mutations who received afatinib therapy was an average of Rp. 120,881,289, - ± 5,353,480.07 per patient, while those who received gefitinib therapy were an average of 90,750,186, - ± 3,369,967,722. Effectiveness data have been published in previous studies. The ICER PFS value shows that afatinib is cost-effectiveness compared to gefitinib with a value of Rp. 106, 345, 069.4/PFS (year). This ICER value is less than 2 times the value of Gross Domestic Product/GDP (Rp. 118, 200,000, -) which is around 1.8 times the value of GDP, whereas in OS afatinib is no better than gefitinib. Conclusion: Afatinib cost effectiveness based on real world retrospectively compared to gefitinib with ICER PFS value of Rp. 106, 345, 069.4/year.

Dysregulated circulating miR-4429 serves as a novel non-invasive biomarker and is correlated with EGFR mutation in patients with non-small cell lung cancer.

This study aimed to investigate the correlation between microRNA (miR)-4429 and epidermal growth factor receptor (EGFR), the expression, and clinical significance of miR-4429 in patients with non-small cell lung cancer (NSCLC), and the relationship between miR-4429 and EGFR mutation in NSCLC patients. Blood samples were collected from 122 NSCLC patients and 72 healthy volunteers. miR-4429 expression and EGFR mRNA expression were detected by real-time quantitative polymerase chain reaction. Correlation between miR-4429 and EGFR was evaluated by dual-luciferase reporter assay and the Pearson correlation analysis. The ability of serum miR4429 to discriminate between NSCLC patients and healthy controls, and to discriminate between EGFR wild-type (EGFR-W) and EGFR mutant-type (EGFR-M) patients was assessed using receiver operating characteristic analysis. The relationship between miR-4429 and NSCLC patients’ survival was identified by KaplanMeier survival curves and log-rank test. The prognostic value of miR-4429 in NSCLC patients was evaluated by Cox regression analysis. miR-4429 could directly bind to EGFR. Serum miR-4429, decreased in NSCLC patients, was negatively correlated with serum EGFR mRNA expression in NSCLC patients. In addition, miR-4429 had a high diagnostic value for screening NSCLC patients from healthy controls, and was independently correlated with survival prognosis of NSCLC patients. Moreover, miR4429 was decreased in EGFR-M patients, which had a certain screening ability for EGFRM patients. Our findings indicate that miR-4429 is negatively correlated with EGFR in NSCLC, and may function as a diagnostic and prognostic biomarker for NSCLC patients. Additionally, miR-4429 is associated with EGFR mutation in NSCLC patients.

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis

PurposeThis study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer. MethodsA systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation. Results34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 −2.57), air bronchogram OR 1.60 (95%CI 1.38 – 1.85), vascular convergence OR 1.39 (95%CI 1.12 – 1.74), pleural retraction OR 1.99 (95%CI 1.72 – 2.31), spiculation OR 1.42 (95%CI 1.19 – 1.70), cavitation OR 0.70 (95%CI 0.57 – 0.86), early disease stage OR 1.58 (95%CI 1.14 – 2.18), non-smoker status OR 2.79 (95%CI 2.34 – 3.31), female gender OR 2.33 (95%CI 1.97 – 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81. ConclusionsGGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.

The co-mutation of EGFR and tumor-related genes leads to a worse prognosis and a higher level of tumor mutational burden in Chinese non-small cell lung cancer patients.

BackgroundLung cancer is the leading cause of cancer mortality in China. The clinicopathologic features and genetic profile of Chinese lung cancer patients need to be investigated. This study evaluated the gene mutation profile, analyzed the frequency of concurrent genes in epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) patients, and determined its prognostic value.MethodsWe collected the clinical data from 151 initially diagnosed patients NSCLC. Tumor samples underwent targeted next-generation sequencing (NGS). Progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier method.ResultsAmong the 151 participants, the mutational profile revealed alterations in 29 genes, where TP53 (37.09%) and EGFR (30.46%) exhibited the highest mutation rates. Mutations in the EGFR gene were most prevalent (40%) in adenocarcinoma (LUAD) and were only present in 8.8% of participants with squamous cell carcinoma (LUSC). The most frequently mutated genes in LUAD patients were TP53 (47%), followed by KRAS (11.7%). In all 39 participants with EGFR mutations, TP53, KRAS, PIK3CA, APC, and FBXW7 were also mutated. Those with only EGFR mutation appeared to have a better prognosis; however, the difference was not statistically significant. Tumor mutational burden (TMB) was roughly significantly increased in patients who harbored EGFR and other mutant driver genes, compared with only EGFR mutant patients. The TMB value was significantly higher in those with P53 mutation than in P53 wild-type patients.ConclusionsWe described the genetic profiles of NSCLC and compared the difference in genetic profiles between LUAD and LUSC. The concomitant genetic alterations might be a poor prognostic factor for patients with EGFR mutation, and TMB might be prognostically related to the co-mutations of EGFR and other genes.

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.