Detection of EGFR Mutation Status in Lung Adenocarcinoma Specimens with Different Proportions of Tumor Cells Using Two Methods of Differential Sensitivity

Significant advances in targeted therapy have been made in recent years for patients with lung adenocarcinoma. These targeted therapies have made molecular testing of paramount importance to drive therapeutic decisions. Material for testing is often limited, particularly in cytology specimens and small core biopsies. A reliable screening tool is invaluable in triaging limited tissue and selection for epidermal growth factor receptor ( EGFR) mutation testing. We hypothesized that the immunohistochemistry (IHC) profile of lung adenocarcinoma predicts EGFR mutation status. In this retrospective study, we evaluated the thyroid transcription factor-1 (TTF-1)/napsin A IHC profile and EGFR mutation status in 339 lung adenocarcinomas at our academic institution. In our cohort, we found that 92.3% of cases were positive for TTF-1 and/or napsin A by IHC with an EGFR positivity rate of 17.3%. Importantly, 7.7% of the cases were dual TTF-1/napsin A negative, and none of these cases contained EGFR mutations. This finding supports the use of TTF-1 and napsin A IHC to identify cases where EGFR mutation status will be negative, thus preserving limited tissue for other ancillary testing.

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Unique MicroRNA and mRNA Interactions in EGFR-Mutated Lung Adenocarcinoma

Journal of Clinical Medicine ◽

10.3390/jcm7110419 ◽

2018 ◽

Vol 7 (11) ◽

pp. 419 ◽

Cited By ~ 3

Author(s):

Sophia Subat ◽

Kentaro Inamura ◽

Hironori Ninomiya ◽

Hiroko Nagano ◽

Sakae Okumura ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Mrna Expression ◽

Regulatory Network ◽

Egfr Mutation ◽

Expression Profiles ◽

Integrative Analysis ◽

Future Research ◽

Mutation Status ◽

Egfr Mutated ◽

Insight Into

The EGFR gene was one of the first molecules to be selected for targeted gene therapy. EGFR-mutated lung adenocarcinoma, which is responsive to EGFR inhibitors, is characterized by a distinct oncogenic pathway in which unique microRNA (miRNA)–mRNA interactions have been observed. However, little information is available about the miRNA–mRNA regulatory network involved. Both miRNA and mRNA expression profiles were investigated using microarrays in 155 surgically resected specimens of lung adenocarcinoma with a known EGFR mutation status (52 mutated and 103 wild-type cases). An integrative analysis of the data was performed to identify the unique miRNA–mRNA regulatory network in EGFR-mutated lung adenocarcinoma. Expression profiling of miRNAs and mRNAs yielded characteristic miRNA/mRNA signatures (19 miRNAs/431 mRNAs) in EGFR-mutated lung adenocarcinoma. Five of the 19 miRNAs were previously listed as EGFR-mutation-specific miRNAs (i.e., miR-532-3p, miR-500a-3p, miR-224-5p, miR-502-3p, and miR-532-5p). An integrative analysis of miRNA and mRNA expression revealed a refined list of putative miRNA–mRNA interactions, of which 63 were potentially involved in EGFR-mutated tumors. Network structural analysis provided a comprehensive view of the complex miRNA–mRNA interactions in EGFR-mutated lung adenocarcinoma, including DUSP4 and MUC4 axes. Overall, this observational study provides insight into the unique miRNA–mRNA regulatory network present in EGFR-mutated tumors. Our findings, if validated, would inform future research examining the interplay of miRNAs and mRNAs in EGFR-mutated lung adenocarcinoma.

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Correlations Between the EGFR Mutation Status and Clinicopathological Features of Clinical Stage I Lung Adenocarcinoma

Medicine ◽

10.1097/md.0000000000001784 ◽

2015 ◽

Vol 94 (42) ◽

pp. e1784 ◽

Cited By ~ 8

Author(s):

Tetsuya Isaka ◽

Tomoyuki Yokose ◽

Hiroyuki Ito ◽

Masashi Nagata ◽

Hideyuki Furumoto ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Egfr Mutation ◽

Clinical Stage ◽

Clinicopathological Features ◽

Stage I ◽

Mutation Status

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EGFR gene copy number alterations are not a useful screening tool for predicting EGFR mutation status in lung adenocarcinoma

Pathology ◽

10.1097/pat.0000000000000027 ◽

2014 ◽

Vol 46 (1) ◽

pp. 32-36

Author(s):

Prudence A. Russell ◽

Y.U. Yong ◽

D.O. Hongdo ◽

Timothy D. Clay ◽

Melissa M. Moore ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Copy Number ◽

Screening Tool ◽

Egfr Mutation ◽

Gene Copy Number ◽

Gene Copy ◽

Copy Number Alterations ◽

Mutation Status ◽

Egfr Gene

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Brain metastases in lung adenocarcinoma: impact of EGFR mutation status on incidence and survival

Radiology and Oncology ◽

10.2478/raon-2014-0016 ◽

2014 ◽

Vol 48 (2) ◽

pp. 173-183 ◽

Cited By ~ 31

Author(s):

Karmen Stanic ◽

Matjaz Zwitter ◽

Nina Turnsek Hitij ◽

Izidor Kern ◽

Aleksander Sadikov ◽

...

Keyword(s):

Brain Metastases ◽

Lung Adenocarcinoma ◽

Cumulative Incidence ◽

Egfr Mutation ◽

Growth Factor Receptor ◽

Egfr Mutations ◽

Mutation Status ◽

Course Of Disease ◽

Epidermal Growth ◽

Egfr Mutant

AbstractBackground. The brain represents a frequent progression site in lung adenocarcinoma. This study was designed to analyse the association between the epidermal growth factor receptor (EGFR) mutation status and the frequency of brain metastases (BM) and survival in routine clinical practice.Patients and methods. We retrospectively analysed the medical records of 629 patients with adenocarcinoma in Slovenia who were tested for EGFR mutations in order to analyse the cumulative incidence of BM, the time from the diagnosis to the development of BM (TDBM), the time from BM to death (TTD) and the median survival.Results. Out of 629 patients, 168 (27%) had BM, 90 patients already at the time of diagnosis. Additional 78 patients developed BM after a median interval of 14.3 months; 25.8 months in EGFR positive and 11.8 months in EGFR negative patients, respectively (p = 0.002). EGFR mutations were present in 47 (28%) patients with BM. The curves for cumulative incidence of BM in EGFR positive and negative patients demonstrate a trend for a higher incidence of BM in EGFR mutant patients at diagnosis (19% vs. 13%, p = 0.078), but no difference later during the course of the disease. The patients with BM at diagnosis had a statistically longer TTD (7.3 months) than patients who developed BM later (3.1 months). The TTD in EGFR positive patients with BM at diagnosis was longer than in EGFR negative patients (12.6 vs. 6.8, p = 0.005), while there was no impact of EGFR status on the TTD of patients who developed BM later.Conclusions. Except for a non-significant increase of frequency of BM at diagnosis in EGFR positive patients, EGFR status had no influence upon the cumulative incidence of BM. EGFR positive patients had a longer time to CNS progression. While EGFR positive patients with BM at diagnosis had a longer survival, EGFR status had no influence on TTD in patients who developed BM later during the course of disease.

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Impact of EGFR mutation status in patients with completely resected lung adenocarcinoma treated with adjuvant platinum-based chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.15_suppl.7036 ◽

2011 ◽

Vol 29 (15_suppl) ◽

pp. 7036-7036

Author(s):

K. Yoh ◽

K. Goto ◽

S. Niho ◽

H. Ohmatsu ◽

K. Nagai ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Egfr Mutation ◽

Mutation Status ◽

Platinum Based Chemotherapy

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Nomogram Based on Preoperative CT Imaging Predicts The EGFR Mutation Status in Lung Adenocarcinoma

10.21203/rs.3.rs-80072/v1 ◽

2020 ◽

Author(s):

Guojin Zhang ◽

Jing zhang ◽

Yuntai Cao ◽

Zhiyong Zhao ◽

Shenglin Li ◽

...

Keyword(s):

Risk Factors ◽

Lung Adenocarcinoma ◽

Egfr Mutation ◽

Multivariate Logistic Regression Analysis ◽

Clinical Risk Factors ◽

Smoking History ◽

Mutation Status ◽

Clinical Risk ◽

Development And Validation ◽

Ct Features

Abstract Background: Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma.Methods: This study retrospectively collected medical records of 403 patients with histologically confirmed lung adenocarcinoma from January 2016 and June 2020. The patients were divided into development and validation cohorts. The preoperative information on all patients was obtained, including clinical characteristics and computed tomography (CT) features. Multivariate logistic regression analysis was used to develop the predictive model. We combined CT features and clinical risk factors and used them to build a prediction nomogram. The performance of the nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. The nomogram was further validated in an independent external cohort.Results: The predictive factors incorporated in the personalized prediction nomogram included smoking history (OR, 0.2; 95% CI: 0.1, 0.4; P < 0.001), bubble-like lucency (OR, 2.2; 95% CI: 1.3, 3.8; P = 0.003), pleural attachment (OR, 0.4; 95% CI: 0.2, 0.7, P = 0.001) and thickened adjacent bronchovascular bundles (OR, 3.1; 95% CI: 1.8, 5.3; P < 0.001). Based on these parameters, the prediction model has good discrimination and calibration ability. The area under the curve in the development and validation cohorts were 0.784 (95% CI: 0.733, 0.835) and 0.740 (95% CI: 0.643, 0.838), respectively. Decision curve analysis showed that the model was clinically useful.Conclusions: This study presented a nomogram that contained CT features and clinical risk factors, which could conveniently and non-invasively predict EGFR mutation status in patients with lung adenocarcinoma before surgery.

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Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.709137 ◽

2021 ◽

Vol 11 ◽

Author(s):

Guotao Yin ◽

Ziyang Wang ◽

Yingchao Song ◽

Xiaofeng Li ◽

Yiwen Chen ◽

...

Keyword(s):

Deep Learning ◽

Lung Adenocarcinoma ◽

Fdg Pet ◽

Egfr Mutation ◽

Learning Models ◽

Data Set ◽

Mutation Status ◽

Pet Ct ◽

Testing Data ◽

Fdg Pet Ct

ObjectiveThe purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).MethodsThree hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET.ResultsThe AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SECT (p<0.05).ConclusionThe stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.

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A Predictive Model of EGFR Mutation Status in Patients with Lung Adenocarcinoma Based on PET/CT Metabolic Indexes and Clinicopathological Variables

10.21203/rs.3.rs-115858/v1 ◽

2020 ◽

Author(s):

Yanlong Yang ◽

Shuchen Shi ◽

Qianzhun Huang ◽

Wenzhao Zhong ◽

Juntao Lin ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Growth Pattern ◽

Egfr Mutation ◽

Clinicopathological Characteristics ◽

Metabolic Parameters ◽

Smoking History ◽

Egfr Mutations ◽

Wild Type ◽

Mutation Status ◽

Pet Ct

Abstract PurposeThe purpose of this study was to create a mathematical model based on the metabolic parameters of PET/CT with clinicopathological characteristics to predict the EGFR mutation status of patients with lung adenocarcinoma.MethodsThis study retrospectively enrolled patients with lung adenocarcinoma who underwent surgical treatment at two centres in China between January 2012 and December 2015. PET/CT metabolic parameters and Classical EGFR mutation status detection by molecular pathology were performed before and after surgery, and we analysed the associations of EGFR mutation status with patient sex, age, smoking history, maximum primary lesion diameter, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment (CYFRA21-1), TNM stage and histopathological subtype of lung adenocarcinoma.ResultsA total of 310 patients were included, comprising 161 with EGFR mutations (51.9%) and 149 with wild-type EGFR (48.1%). EGFR mutations were more common in females, non-smokers, and those with stage IV disease, a low SUVmax, and ≤35 mm nodules, whereas wild-type EGFR was more common in males, smokers, and those with a solid growth pattern. Multivariate analysis suggested that liver SUVratio, smoking history, tumour size, TNM stage, and solid growth pattern can predict EGFR mutation status, and these factors were used to construct a mathematical model.ConclusionThe prediction model constructed in this study based on clinicopathological characteristics and PET/CT parameters might offer a basis by which to predict Classical EGFR status and provide a certain reference value for guiding the use of EGFR-tyrosine kinase inhibitor (EGFR-TKI) treatment in patients with lung adenocarcinoma.

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