Epidermal Growth Factor Receptor Mutant Lung Adenocarcinoma Research Articles

A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.

Purpose: This study aims to develop a predictive model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma by integrating computed tomography (CT) imaging features with clinical characteristics. Methods: A retrospective analysis was conducted using electronic medical records from 194 patients diagnosed with lung adenocarcinoma between January 2016 and December 2020, with approval from the institutional review board. Features were selected using LASSO regression, and predictive models were built using logistic regression, support vector machine, and random forest methods. Individual models were created for clinical features, CT imaging features, and a combined model to predict EGFR mutations. Results: The training set revealed that alcohol consumption, intrapulmonary metastasis, and pleural effusion were statistically significant in distinguishing between wild-type and mutation groups (p < 0.05). In the testing set, hilar and mediastinal lymphadenopathy showed statistical significance (p < 0.05). The combined model outperformed the individual clinical and CT imaging feature models. In the testing set, the logistic regression model achieved the highest AUC of 0.827, with sensitivity, specificity, and accuracy of 0.714, 0.712, and 0.712, respectively. Nomogram analysis identified lobulation as an important feature, with a predicted probability of up to 0.9. The decision curve analysis showed that the CT imaging feature model provided a higher net benefit compared to both the clinical feature model and the combined model. Conclusion: In summary, while the combined model outperformed the individual feature models in the testing set, the CT imaging feature model demonstrated the greatest clinical net benefit. Lobulation was identified as an important predictor of EGFR mutations in lung adenocarcinoma.

Innate immune dynamics in the context of multisite EGFR mutations in lung adenocarcinoma.

Based on favorable outcomes and decreased propensity for lymph node and distant metastasis, multiple ground-glass nodules (GGNs) are now predominantly recognized as early-stage primary independent lung cancer. In this study, we discuss a case involving a patient with reoperative multifocal GGNs who was ultimately diagnosed with early multiple intrapulmonary metastases and multifocal primary lung cancers. This patient exhibited multisite epidermal growth factor receptor (EGFR) mutations, including the classical L858R, exon 19 deletion and the rare V834L variant. Despite a high tumor burden and the presence of various EGFR driver mutations, the patient experienced prolonged dormancy and exceptionally slow lesion growth, even without any systemic treatment. Our research indicates that the patient's immune response against the tumor remained robust throughout the disease course. Furthermore, we found that pathways associated with integrin-mediated cell extracellular matrix adhesion played a role in activating her innate immune responses and regulating tumor dormancy. Our findings suggest that the interplay between cancer cell mutations and the tumor microenvironment (TME) phenotype during tumor evolution contributed to this patient's prolonged survival. Integrating these aspects for lung tumor stratification is expected to improve predictions of growth potential and aid in clinical decision making.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

ObjectiveTo investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients.Materials and methodsA retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC).Results399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR−). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively).ConclusionsRadiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

Effects of concurrent TP53 mutations on the efficacy and prognosis of targeted therapy for advanced EGFR mutant lung adenocarcinoma

BackgroundHow concurrent TP53 mutations affect targeted therapy of advanced Epidermal Growth Factor Receptor (EGFR) mutant lung adenocarcinoma remains controversial, particularly the deep classification of TP53 mutations. MethodsThis study retrospectively analyzed the clinical data of advanced EGFR mutant lung adenocarcinoma patients treated with EGFR-tyrosine kinase inhibitors (TKIs) in the First Affiliated Hospital of Soochow University. The survival rates were compared using Log-rank tests. Potential prognostic factors were identified using multivariate Cox hazard regression models. ResultsTotal 156 advanced lung adenocarcinoma patients treated with EGFR-TKIs were included in this study. Multivariate analysis showed that male [hazard rate (HR): 1.537, 95% confidence interval (CI): 1.055–2.240, P = 0.025], brain metastasis (HR: 1.707, 95%CI: 1.086–2.682, P = 0.020) and concurrent TP53 mutations (HR: 1.569, 95%CI: 1.051–2.341, P = 0.028) were independent negative predictors of progression-free survival (PFS). EGFR L858R mutations (HR: 2.475, 95%CI: 1.443–4.248, p = 0.001), smoking history (HR: 2.530, 95%CI: 1.352–4.733, P = 0.004) and concurrent TP53 mutations (HR: 2.326, 95%CI: 1.283–4.218, P = 0.005) were associated with worse survival. Further analysis revealed that mutations in TP53 exons 4, 5 and 8 (P<0.05), missense mutations (P = 0.006) and nondisruptive mutations (P<0.001) were associated with shorter PFS, whereas mutations in TP53 exons 5 and 7 (P<0.05), missense mutations and non-missense mutations (P = 0.006; P = 0.007), disruptive mutations and nondisruptive mutations (P = 0.013; P = 0.013) were all associated with poorer survival times. In addition, the PFS and overall survival (OS) of nondisruptive mutations in exon 7 were worse than those in other exons (P = 0.041; P<0.001). ConclusionsConcurrent TP53 mutations conferred worse EGFR-TKIs efficacy and prognosis in advanced EGFR mutant lung adenocarcinoma and the effects of different TP53 mutation types were heterogeneous.

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.

Higher Prevalence and Poorer Prognosis of EGFR Mutant Lung Adenocarcinoma in US Hispanics

Introduction: Activating mutations in Epidermal growth factor receptor (EGFR) occur in approximately 15% White, 40-50% of Asian and 15% of Black patients with lung adenocarcinoma. However, its prevalence in the nearly 60 million U.S. Hispanics/Latinos has not been well characterized. Herein we evaluate EGFR mutation frequency in U.S. Hispanic/Latino patients with lung adenocarcinoma at an academic institute serving a large multi-ethnic area. Methods: We queried our prospective database (2015-2019) for lung adenocarcinoma patients who underwent surgical resection and had routine mutational analysis by a targeted gene panel. We identified 768 patients and were able to stratify 668 patients by self-identified race/ethnicity. We compared demographics (chi-square) and survival (Kaplan-Meier). Results: From 2015-2019, 668 patients met inclusion criteria and were evaluated for incidence of common targetable EGFR mutations. EGFR mutations were present in 30% of all patients with Hispanics/Latino experiencing an incidence of 35%, significantly more than non-Hispanic White patients, p=0.019. Overall survival at 3 years was not significantly different amongst racial/ethnic groups. However, in patients with EGFR mutations, 3-year overall survival was significantly worse in Hispanic/Latino patients in comparison to non-Hispanic White patients (62% vs 96%, p=0.021). There was no difference in the pathologic stage or surgical procedure amongst racial/ethnic groups. Discussion: Approximately one-third of U.S. Hispanics with lung adenocarcinoma displayed EGFR mutations which were associated with decreased overall survival compared to White and Asian patients. Increasing mutational analysis and investigation of biological differences of this growing ethnic group is essential for optimal targeted treatment strategies as well as in the design of future clinical trials.

Major hurdles to the use of tyrosine kinase inhibitors in clinical prevention/interception studies: Do preclinical studies with EGFR inhibitors suggest approaches to overcome some of the limitations.

There are major hurdles to the use of tyrosine kinase inhibitors (TKIs) and any other agents with significant toxicities (which means practically the preponderance of potential effective agents) in the context of prevention/anti-progression (interception) studies. We will discuss epidermal growth factor receptor (EGFR) inhibitors as examples, both in a primary prevention setting, where agent(s) are administered to individuals with no cancer but who might be considered at higher risk due to a variety of factors, and in anti-progression/interception studies, where agent(s) are administered to persons with known preinvasive lesions (e.g., colon adenomas, lung nodules, ductal carcinoma in situ (DCIS), or pancreatic intraepithelial neoplasia (PanIN) lesions in the pancreas) in an attempt to reverse or inhibit progression of these lesions. Multiple potential hurdles will be examined, including: a) toxicity of agents, b) the likely range of subtypes of cancers affected by a given treatment (e.g., EGFR inhibitors against EGFR mutant lung adenocarcinomas), c) the availability of practical endpoints besides the blocking of cancer formation or pharmacokinetics related to the agents administered in a primary prevention study, and d) the interpretation of the regression or blockage of new preinvasive lesions in the anti-progression study. Such an anti-progression approach may help address some of the factors commented on regarding primary prevention (toxicity, potential target organ cancer subtypes) but still leaves major questions regarding interpretation of modulation of preinvasive endpoints when it may not be clear how frequently they progress to clinical cancer. Additionally, we address whether certain recent preclinical findings might be able to reduce the toxicities associated with these agents and perhaps even increase their potential efficacy. Antibodies and TKIs other than the EGFR inhibitors are not discussed because few if any had been tested as monotherapies in humans, making their efficacy harder to predict, and because a number have relatively rare but quite striking toxicities. Furthermore, most of the practical hurdles raised regarding the EGFR inhibitors are relevant to the other TKIs. Finally, we briefly discuss whether early detection employing blood or serum samples may allow identification of high-risk groups more amenable to agents with greater toxicity.

Predicting EGFR mutational status from pathology images using a real-world dataset

Treatment of non-small cell lung cancer is increasingly biomarker driven with multiple genomic alterations, including those in the epidermal growth factor receptor (EGFR) gene, that benefit from targeted therapies. We developed a set of algorithms to assess EGFR status and morphology using a real-world advanced lung adenocarcinoma cohort of 2099 patients with hematoxylin and eosin (H&E) images exhibiting high morphological diversity and low tumor content relative to public datasets. The best performing EGFR algorithm was attention-based and achieved an area under the curve (AUC) of 0.870, a negative predictive value (NPV) of 0.954 and a positive predictive value (PPV) of 0.410 in a validation cohort reflecting the 15% prevalence of EGFR mutations in lung adenocarcinoma. The attention model outperformed a heuristic-based model focused exclusively on tumor regions, and we show that although the attention model also extracts signal primarily from tumor morphology, it extracts additional signal from non-tumor tissue regions. Further analysis of high-attention regions by pathologists showed associations of predicted EGFR negativity with solid growth patterns and higher peritumoral immune presence. This algorithm highlights the potential of deep learning tools to provide instantaneous rule-out screening for biomarker alterations and may help prioritize the use of scarce tissue for biomarker testing.

A nomogram based on 18F-fluorodeoxyglucose PET/CT and clinical features to predict epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.

Identifying epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LADC) is vital for treatment decision-making. This study aimed to establish a convenient and noninvasive nomogram prediction model based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging and clinical features to predict EGFR mutation status in patients with LADC. A total of 274 patients (male 130, female 144, median age 65 years) were enrolled in this retrospective study. Imaging data from 18F-FDG PET/CT and clinical information were analyzed, with the Mann-Whitney U test, Student's t-test, and chi-square test used to compare categorical or continuous covariates as appropriate. Logistic regression analyses were performed to identify independent variables associated with EGFR mutation status, from which the nomogram prediction model was constructed. Leave-one-out cross-validation was performed, and the discrimination ability and calibration of the nomogram were assessed by calculating the area under the curve of the receiver operating characteristic curve and the calibration curve. The clinical net benefit of the nomogram was evaluated. Of the 274 patients, 143 (52.2%) had EGFR mutations. Female sex [odds ratios (OR): 2.64, 95% confidence interval (CI): 1.29-5.45, P=0.008], non-smoking status (OR: 2.78, 95% CI: 1.30-5.88, P=0.008), mean standardized uptake value ≤9.23 (OR: 2.44, 95% CI: 1.35-4.55, P=0.004), metabolic tumor volume ≤17.72 cm3 (OR: 5.00, 95% CI: 2.38-12.50, P<0.001) and the presence of pleural retraction (OR: 1.88, 95% CI: 1.05-3.40, P=0.034) were independent predictors for EGFR mutations in LADCs. The nomogram based on these risk factors showed good predictive efficacy, with an area under the curve of 0.805 (95% CI: 0.753-0.857), a sensitivity of 90.2%, a specificity of 59.5% and an accuracy of 73.0%. The nomogram prediction model incorporating sex, smoking status, mean standardized uptake value, metabolic tumor volume, and the presence of pleural retraction could effectively discriminate EGFR-mutant from wild-type LADCs.

Effect of EGFR-TKIs Combined with Cerebral Radiotherapy on Prognosis of Patients with EGFR-Mutant Lung Adenocarcinoma with Brain Metastasis

<h3>Purpose/Objective(s)</h3> Purpose: Epidermal growth factor receptor (EGFR)-mutant lung cancers have a high risk of developing brain metastases (BM). Craniocerebral radiotherapy has been considered as cornerstone for treatment of BM while BRAIN clinical trial showed that EGFR-TKI alone has effect on craniocerebral metastases. However, whether EGFR-TKI combined with radiotherapy can further increase the efficacy and improve the prognosis of patients still need to be further discussed. This retrospective study aimed to evaluate the difference in efficacy between targeted therapy alone and targeted combined radiotherapy in EGFR mutant lung adenocarcinoma patients with BM. <h3>Materials/Methods</h3> Methods and materials: 324 patients with advanced non-small cell lung cancer with EGFR mutations were enrolled in this retrospective cohort study. Propensity score matching (PSM) was conducted by using a nearest-neighbor algorithm (1:1) to adjust for demographical and clinical covariates. The patients were divided into two groups: EGFR-TKIs alone and EGFR-TKIs combined with cerebral radiotherapy. Intracranial progression-free survival (iPFS) and overall survival (OS) were calculated. Kaplan–Meier was used to compare iPFS and OS between the two groups. The brain radiotherapy contained WBRT, local radiotherapy, and WBRT+Boost. <h3>Results</h3> The median age at diagnosis was 57 (range, 23–81) years. A majority of the patients were women (57, 55.8%) and non-smokers (77, 75.5%). According to the matching results of PSM, we finally matched 51 pairs of patients. The median iPFS for EGFR-TKIs alone (n=37), EGFR-TKIs + cerebral radiotherapy (n=24) was 8.9 and 14.7 months, respectively (P=0.0334). And the median OS for EGFR-TKIs alone (n=52), EGFR-TKIs + cerebral radiotherapy (n=52) was 32.1 and 45.3 months, respectively (P=0.0106). <h3>Conclusion</h3> The present study showed that in EGFR-mutant lung adenocarcinoma patients with BM, targeted therapy combined with cerebral radiotherapy is optimal treatment mode for this subgroup of patients.

Landscape of EGFR mutations in lung adenocarcinoma: a single institute experience with comparison of PANAMutyper testing and targeted next-generation sequencing.

BackgroundActivating mutations in the tyrosine kinase domain of epidermal growth factor receptor (EGFR) are predictive biomarkers for response to EGFR–tyrosine kinase inhibitor (TKI) therapy in lung adenocarcinoma (LUAD). Here, we characterized the clinicopathologic features associated with EGFR mutations via peptide nucleic acid clamping-assisted fluorescence melting curve analysis (PANAMutyper) and evaluated the feasibility of targeted deep sequencing for detecting the mutations.MethodsWe examined EGFR mutations in exons 18 through 21 for 2,088 LUADs from July 2017 to April 2020 using PANAMutyper. Of these, we performed targeted deep sequencing in 73 patients and evaluated EGFR-mutation status and TKI clinical response.ResultsEGFR mutation was identified in 55.7% of LUADs by PANAMutyper, with mutation rates higher in females (69.3%) and never smokers (67.1%) and highest in the age range of 50 to 59 years (64.9%). For the 73 patients evaluated using both methods, next-generation sequencing (NGS) identified EGFR mutation–positive results in 14 of 61 patients (23.0%) who were EGFR-negative according to PANAMutyper testing. Of the 10 patients reportedly harboring a sensitizing mutation according to NGS, seven received TKI treatment, with all showing partial response or stable disease. In the 12 PANAMutyper-positive cases, NGS identified two additional mutations in exon 18, whereas a discordant negative result was observed in two cases.ConclusionsAlthough PANAMutyper identified high frequencies of EGFR mutations, targeted deep sequencing revealed additional uncommon EGFR mutations. These findings suggested that appropriate use of NGS may benefit LUAD patients with otherwise negative screening test results.

1002P Compared with the difference in efficacy between EGFR-TKIs alone and EGFR-TKIs combined with craniocerebral radiotherapy in EGFR mutant lung adenocarcinoma patients with brain metastasis. A propensity-score matched analysis

Epidermal growth factor receptor (EGFR)-mutant lung cancers have a high risk of developing brain metastases (BM). Craniocerebral radiotherapy has been considered as cornerstone for treatment of BM while BRAIN clinical trial showed that EGFR-TKI alone has effect on craniocerebral metastases. However, whether EGFR-TKIs combined with craniocerebral radiotherapy can further increase the efficacy and improve the prognosis of patients still need to be further discussed. This retrospective study aimed to evaluate the difference in efficacy between targeted therapy alone and targeted combined radiotherapy in EGFR mutant lung adenocarcinoma patients with BM.

Abstract 3362: Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial

Abstract Introduction: Non-small cell lung cancer (NSCLC) response to gefitinib depends on epidermal growth factor receptor (EGFR) mutational status. Response differs according to tumor heterogeneity, notably through clonal selection according to genetic background. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model topredict the effect of EGFR mutations on tumor size in advanced LUAD patients using a mechanistic representation of tumor progression, including response to gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. Methods: 5-step in silico model development: (i) Model Building: Biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. (ii) Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. (iii) Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population.(iv) Validation: A VPOP with comparable baseline characteristics was tested against published patient data.(v) Benchmarking against a Bayesian reference model : (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Results: Our model computed in silico data comparable to the reference model without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. Therefore, the ISELA model has a better percentage of the literature data area contained in the simulated one while the Bayesian model presents a better percentage of the simulated data contained in the literature one. Conclusion: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results with published data, and compared it to an already published model with the same context of use. Both models successfully provide a reliable description of longitudinal tumor size when compared to each other or to observed data. Our model provides a benchmark for future in silico clinical trials. Citation Format: Michael Duruisseaux, Adèle L'Hostis, Emmanuel Peyronnet, Evgueni Jacob, Ben M.W. Illigens, Jim Bosley, Riad Kahoul, Jean-Louis Palgen, Claudio Monteiro. Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3362.

Multiscale in silico knowledge-based model predicts tumor growth in advanced EGFR+ NSCLC patients on gefitinib.

e20565 Background: Tumor heterogeneity and treatment resistance remains a continued threat to patients with advanced non-small cell lung cancer (NSCLC), despite the emergence of targeted medicines, e.g. against epidermal growth factor receptor (EGFR) alterations. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model to predict the effect of known oncogenic EGFR mutations (common EGFR mutations, EGFR exon 20 insertions). The model provides mechanistic representation of tumor progression, including response to gefitinib and captures tumor heterogeneity, patient age, gender, initial clinical disease stage, and smoking status. Methods: 5-step in silico model development: Model building: biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from &gt; 300 published papers and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR-downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were incorporated into a knowledge-based model containing 27-97 variables, 108-258 parameters and 13-83 ODEs reflecting intra-tumor clonal heterogeneity in a mechanistic manner. Calibration: published spheroid, xenograft and clinical data were used for stepwise calibration to find the correct parameter values. Relevant virtual populations (VPOPs) matching real patients baseline characteristics were generated for model benchmarking and validation. Benchmarking against a published data-based model: coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Validation: a VPOP with comparable baseline characteristics was simulated and results compared against a published patient dataset that wasn’t used in any step during calibration. Matching of prediction over clinical data was done using both coverage and bootstrapped log-rank metrics. Results: Our model provides comparable outputs to the data-based model without having access to the exact original data (our model: precision of 62%, overlap of 91% VS data-based model: precision of 72% and an overlap of 86%). Besides, as a validation criterion, our model also successfully reproduces the time to progression observed in an independent clinical trial (coverage &gt; 99%, negative log-rank tests &gt; 98%). Conclusions: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results both against an existing data-based model and a published clinical data. Our model highlights the potential of in silico modeling to better understand complex diseases progression and support efficient development of innovative therapies.

Bone metastasis reduces responsiveness to EGFR-TKIs in patients with EGFR-mutated advanced lung adenocarcinoma.

e21085 Background: The response of epidermal growth factor receptor (EGFR) - tyrosine kinase inhibitors (TKIs) to bone metastasis site in patients with EGFR mutated lung adenocarcinoma remains to be determined. Methods: This is a retrospective analysis of the efficacy of EGFR-TKIs for lung adenocarcinoma patients with EGFR mutations, comparing patients with bone metastases (BM) and those with no bone metastases (NBM). BM patients need extra imaging to assess the efficacy of bone metastasis sites . Overall response rate (ORR), disease control rate (DCR), median progression-free survival (PFS) and progression patterns were evaluated. Results: Of the 502 patients, 175 were enrolled (BM 96, NBM 79) from January 2012 to December 2017. 125 patients had disease progression on January 31, 2018. The clinical characteristics of the two groups were not significantly difference. The median PFS was 11.0 months in NBM group and 7.0 months in BM group ( P= 0.013) . The median PFS in patients with multiple BM (≥ 4) was significantly lower than that in patients with oligometastases (1-3) and NBM (7.0 months vs 10.0 months vs 11.0 months, P = 0.003). Multivariate analysis confirmed that bone metastasis was an independently negative predictor of PFS for EGFR-TKIs ( P = 0.017). The ORR and DCR of NBM group were higher than those of BM group (70.9% vs 56.3%, P = 0.046 and 96.2% vs 85.4%, P= 0.017). Bone was one of the frequently progressive sites of EGFR-TKIs therapy, accounting for 52 of 125 (41.6%). The incidence of bone progression in NBM patients was significantly lower than that in BM patients(11.8% vs 62.2%, P &lt; 0.001). Conclusions: Bone metastasis, especially multiple bone metastasis, reduces the response to EGFR-TKIs for lung adenocarcinoma with EGFR mutations. Bone metastasis monitoring should be a routine clinical practice for BM patients.

Role of GPER1 in the Mechanism of EGFR-TKIs Resistance in Lung Adenocarcinoma.

Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) have a good clinical efficacy in lung adenocarcinoma harboring activating-mutation EGFR. Such EGFR mutations are more frequently observed in women and non-smokers. EGFR mutations are frequently reported to correlate with estrogen receptor (ER) α and/or β-expressions in lung adenocarcinoma. However, the role of GPER1, a novel G-protein-coupled estrogen receptor, in the estrogen signaling pathway and the association between its expression and EGFR mutation in lung adenocarcinoma are less well understood. Here, we aimed to examine ERα, Erβ, and GPER1 expressions, and to analyze their roles in the mechanism of EGFR-TKIs resistance in lung adenocarcinoma. We report an enhanced cytoplasmic expression of GPER1 in tissue samples. The nuclear GPER1 positively correlated with ER expression while the nuclear and also cytoplasmic expressing GPER1 negatively correlated with ER expression. Further, TKI resistance results in higher cytoplasmic GPER1 expression and decreased ER and nuclear GPER1 expression with evidence for GPER1 translocation to cell surface during the resistance. GPER1 itself is capable of regulating ER expression with concomitant regulation of MAPK signaling, and co-inhibition of GPER1 and ERs attenuates ERK1/2 and Akt phosphorylation. The results were also verified in vivo in mice where GPER1 silencing slowed tumor progression which was further potentiated by gefitinib.

Clinical Characteristics and Prognosis of 76 Lung Adenocarcinoma Patients Harboring EGFR Mutations with Pleural Effusion at Initial Diagnosis: A Single-center Retrospective Study

背景与目的恶性胸腔积液是肺腺癌患者常见临床表现之一，肺腺癌初诊时合并胸腔积液提示预后不佳。表皮生长因子受体(epidermal growth factor receptor, EGFR)突变主要发生在肺腺癌患者中，不同亚型预后不同。初诊时不同亚型EGFR突变阳性合并胸腔积液肺腺癌患者的临床特征及预后因素目前尚不明确，本研究拟探讨此类患者的临床特征及预后影响因素，旨在为此类患者提供诊治参考。方法回顾性分析2012年1月-2021年6月期间，北京大学第三医院肿瘤化疗与放射病科收治的初诊时EGFR突变阳性且合并胸腔积液肺腺癌患者的临床特征、治疗方法、治疗疗效和一线治疗中位无进展生存期(progression-free survival, PFS)，采用Pearson卡方检验或Fisher精确概率法进行组间比较，采用Kaplan-Meier法进行生存分析，采用Cox比例风险回归模型进行多因素分析。结果共筛选出符合入组条件的患者76例，EGFR经典突变19del、21L858R和非经典突变的发生率分别为46.0%、38.2%和15.8%，3种突变亚型在性别、年龄、发病时有无呼吸困难、是否合并其他远处转移、胸腔积液部位、胸腔积液量、有无合并其他部位积液、肿瘤原发灶-淋巴结-转移(tumor-node-metastasis, TNM)分期、有无合并其他基因突变、胸腔积液的治疗方法等方面无显著差异(P > 0.05)。在EGFR经典突变19del、21L858R和非经典突变患者中，一线应用化疗的占比分别为17.1%、20.7%和58.3%(P=0.001)；一线疾病控制率分别为94.3%、75.9%和50.0%(P=0.003)；胸腔积液控制率分别为94.3%、79.3%和66.7%(P=0.04)；PFS分别为287 d、327 d和55 d(P=0.001)。单因素分析显示EGFR突变亚型、胸腔积液控制情况、一线治疗药物、一线治疗疗效与PFS显著相关(P < 0.05)，Cox多因素分析显示仅EGFR突变类型、一线治疗疗效是PFS的独立预后因素(P < 0.05)。结论初诊EGFR突变阳性且合并胸腔积液的肺腺癌患者中，EGFR经典突变(19del和21L858R)患者的PFS显著优于非经典突变者，提升一线治疗疗效是改善此类患者预后的关键。

Single-cell transcriptome analysis revealed a suppressive tumor immune microenvironment in EGFR mutant lung adenocarcinoma

BackgroundsImmunotherapy is less effective in patients with epidermal growth factor receptor (EGFR) mutant non-small-cell lung cancer (NSCLC). Lower programmed cell death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB) are...

Effect of WW Domain-Containing Oxidoreductase Gene Polymorphism on Clinicopathological Characteristics of Patients with EGFR Mutant Lung Adenocarcinoma in Taiwan.

Lung adenocarcinoma is the most common histological type of non-small cell lung cancer, which accounts for the majority of lung cancers. Previous studies have showed that dysregulation of WW domain-containing oxidoreductase (WWOX) participates in the generation of several cancer types, including lung cancer. However, whether these WWOX polymorphisms are related to the clinical risk of epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma is worthy of investigation. The present study examined the relationship between the WWOX single-nucleotide polymorphisms (SNPs; rs11545028, rs12918952, rs3764340, rs73569323, and rs383362) and the clinicopathological factors in lung adenocarcinoma patients with or without EGFR mutations. We found that there was no significant difference in the genotype distribution of WWOX polymorphism between EGFR wild-type and EGFR mutant in patients with lung adenocarcinoma. Our results demonstrated that the presence of at least one G genotype (CG and GG) allele on WWOX rs3764340 was associated with a significantly higher risk of nearby lymph node involvement in those patients harboring EGFR mutations (odds ratio (OR) = 3.881, p = 0.010) compared with the CC genotype. Furthermore, in the subgroup of lung adenocarcinoma patients with the EGFR-L858R mutation, both WWOX rs3764340 C/G (OR = 5.209, p = 0.023) and rs73569323 C/T polymorphisms (OR = 3.886, p = 0.039) exhibited significant associations with the size of primary tumors and the invasion of adjacent tissues. In conclusion, these data indicate that WWOX SNPs may help predict tumor growth and invasion in patients with EGFR mutant lung adenocarcinoma, especially those with the EGFR-L858R mutant in Taiwan.

Prediction model based on 18F-FDG PET/CT radiomic features and clinical factors of EGFR mutations in lung adenocarcinoma.

The aim of this study was to build a prediction model for epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. A retrospective analysis was performed on 88 patients with lung adenocarcinoma. All patients underwent an 18F-FDG PET/CT scan and genetic testing of EGFR before the treatment. In the training set, the radiomic features and clinical factors were screened out, and model-1 based on CT radiomic features, model-2 based on PET radiomic features, model-3 based on clinical factors, and model-4 based on radiomic features combined with clinical factors were established, respectively. The performance of the prediction model was assessed by area under the receiver operating characteristic (ROC) curve (AUC). The DeLong test was used to compare the performance of the models to screen out the optimal model, and then built the nomogram of the optimal model. The effect and clinical utility of the nomogram was verified in the validation cohort. In our analysis, model-4 was superior to the other prediction models in identifying EGFR mutations. The AUC was 0.864 (95% CI: 0.777-0.950), with a sensitivity of 0.714 and a specificity of 0.784. The nomogram of model-4 was established. In the validation cohort, the concordance index (C-index) value of the calibration curve of the nomogram model was 0.778 (95%CI: 0.585-0.970), and the nomogram had a good clinical utility. We demonstrated that the model based on 18F-FDG PET/CT radiomic features combined with clinical factors could predict EGFR mutations in lung adenocarcinoma, which was expected to be an important supplement to molecular diagnosis.