Prognostic analysis of patients with mutant and wild-type EGFR gene lung adenocarcinoma

Inter-equipment validation of PET-based radiomics for predicting EGFR mutation statuses in patients with non-small cell lung cancer

AIM

To validate the inter-equipment generality of the radiomics based on PET images to predict the EGFR mutation status of patients with non-small cell lung cancer.

MATERIALS AND METHODS

Patients were retrospectively collected in the departments of nuclear medicine of Heyi branch (Siemens equipment) and East branch (General Electric (GE) equipment) of the first affiliated hospital of Zhengzhou university. 5 predicting logistic regression models were established. The 1st one was trained and tested by the GE dataset; The 2nd one was trained and tested by the Siemens dataset; The 3rd one was trained and tested by the mixed dataset consisting of GE and Siemens. The 4th one was trained by GE and tested by Siemens; The 5th one was trained by Siemens and tested by GE.

RESULTS

For the 1st ∼ 5th models, the mean values of AUCs for training/testing datasets were 0.78/0.73, 0.74/0.72, 0.75/0.70, 0.74/0.65 and 0.68/0.63, respectively.

CONCLUSION

The AUCs of the models trained and tested on the datasets from the same equipment were higher than those for different equipment. The inter-equipment generality of the radiomics was not good enough in clinical practice.

Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (EGFR) mutation and subtypes in metastatic non-small cell lung cancer

Background

The preoperative identification of epidermal growth factor receptor (EGFR) mutations and subtypes based on magnetic resonance imaging (MRI) of brain metastases (BM) is necessary to facilitate individualized therapy. This study aimed to develop a deep learning model to preoperatively detect EGFR mutations and identify the location of EGFR mutations in patients with non-small cell lung cancer (NSCLC) and BM.

Methods

We included 160 and 72 patients who underwent contrast-enhanced T1-weighted (T1w-CE) and T2-weighted (T2W) MRI at Liaoning Cancer Hospital and Institute (center 1) and Shengjing Hospital of China Medical University (center 2) to form a training cohort and an external validation cohort, respectively. A multiscale feature fusion network (MSF-Net) was developed by adaptively integrating features based on different stages of residual network (ResNet) 50 and by introducing channel and spatial attention modules. The external validation set from center 2 was used to assess the performance of MSF-Net and to compare it with that of handcrafted radiomics features. Receiver operating characteristic (ROC) curves, accuracy, precision, recall, and F1-score were used to evaluate the effectiveness of the models. Gradient-weighted class activation mapping (Grad-CAM) was used to demonstrate the attention of the MSF-Net model.

Results

The developed MSF-Net generated a better diagnostic performance than did the handcrafted radiomics in terms of the microaveraged area under the curve (AUC) (MSF-Net: 0.91; radiomics: 0.80) and macroaveraged AUC (MSF-Net: 0.90; radiomics: 0.81) for predicting EGFR mutations and subtypes.

Conclusions

This study provides an end-to-end and noninvasive imaging tool for the preoperative prediction of EGFR mutation status and subtypes based on BM, which may be helpful for facilitating individualized clinical treatment plans.

Clinicopathological and prognostic implications of EGFR mutations subtypes in Moroccan non-small cell lung cancer patients: A first report

BACKGROUND

Non-small cell lung cancer (NSCLC) remains a significant global health concern, with EGFR mutations playing a pivotal role in guiding treatment decisions. This prospective study investigated the prevalence and clinical implications of EGFR mutations in Moroccan NSCLC patients.

METHODS

A cohort of 302 NSCLC patients was analyzed for EGFR mutations using multiple techniques. Demographic, clinical, and pathological characteristics were assessed, and overall survival (OS) outcomes were compared among different EGFR mutation subtypes.

RESULTS

EGFR mutations were present in 23.5% of patients, with common mutations (81.69%) dominating. Common mutations showed strong associations with female gender and non-smoking status, while rare mutations were associated with a positive smoking history. Patients with EGFR mutations receiving tyrosine kinase inhibitors (TKIs) had significantly improved OS compared to wild-type EGFR patients. Notably, patients with common EGFR mutations had the highest OS, while those with rare mutations had a shorter survival period, albeit not statistically significant.

CONCLUSION

This study highlights the relevance of EGFR mutation status in NSCLC patients, particularly in therapeutic decision-making. The association between smoking history and rare mutations suggests the need for tailored approaches. The survival advantage for patients with common EGFR mutations underscores the significance of personalized treatment strategies.

Anlotinib plus docetaxel vs. docetaxel alone for advanced non-small-cell lung cancer patients who failed first-line treatment: A multicenter, randomized phase II trial

OBJECTIVES

Given the modest efficacy of docetaxel in advanced non-small cell lung cancer (NSCLC), this study assesses the therapeutic potential and safety profile of anlotinib in combination with docetaxel compared to docetaxel monotherapy as a second-line therapy for patients with advanced NSCLC.

MATERIALS AND METHODS

In this phase II study, patients with advanced NSCLC experiencing failure with first-line platinum-based regimens were randomized in a 1:1 ratio to receive either anlotinib plus docetaxel or docetaxel alone. Primary endpoint was progression-free survival (PFS), with overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety as secondary endpoints.

RESULTS

A total of 83 patients were randomized. The combination of anlotinib and docetaxel significantly extended median PFS to 4.4 months compared to 1.6 months for docetaxel alone (hazard ratio [HR] = 0.38, 95 % confidence interval [CI]: 0.23-0.63, P = 0.0002), and also demonstrated superior ORR (32.5 % vs. 9.3 %, P = 0.0089) and DCR (87.5 % vs. 53.5 %, P = 0.0007). Median OS was observed at 12.0 months in the combination group vs. 10.9 months in the monotherapy group (HR = 0.82, 95 % CI: 0.47-1.43, P = 0.4803). For patients previously treated with immunotherapy, the median PFS was notably longer at 7.8 vs. 1.7 months (HR = 0.22, 95 % CI: 0.09-0.51, P = 0.0290). The incidence of grade ≥ 3 treatment-related adverse events, predominantly leukopenia (15.0 % vs. 7.0 %) and neutropenia (10.0 % vs. 5.0 %), was manageable across both groups.

CONCLUSION

Anlotinib plus docetaxel offers a viable therapeutic alternative for patients with advanced NSCLC who failed first-line platinum-based treatments.

Combined radiomics of primary tumour and bone metastasis improve the prediction of EGFR mutation status and response to EGFR-TKI therapy for NSCLC

PURPOSE

To develop radiomics models of primary tumour and spinal metastases to predict epidermal growth factor receptor (EGFR) mutations and therapeutic response to EGFR-tyrosine kinase inhibitor (TKI) in patients with metastatic non-small-cell lung cancer (NSCLC).

METHODS

We enrolled 203 patients with spinal metastases between December 2017 and September 2021, classified as patients with the EGFR mutation or EGFR wild-type. All patients underwent thoracic CT and spinal MRI scans before any treatment. Radiomics analysis was performed to extract features from primary tumour and metastases images and identify predictive features with the least absolute shrinkage and selection operator. Radiomics signatures (RS) were constructed based on primary tumour (RS-Pri), metastases (RS-Met), and in combination (RS-Com) to predict EGFR mutation status and response to EGFR-TKI. Receiver operating characteristic (ROC) curve analysis with 10-fold cross-validation was applied to assess the performance of the models.

RESULTS

To predict the EGFR mutation status, the RS based on the combination of primary tumour and metastases improved the prediction AUCs compared to those based on the primary tumour or metastasis alone in the training (RS-Com-EGFR: 0.927) and validation (RS-Com-EGFR: 0.812) cohorts. To predict response to EGFR-TKI, the developed RS based on combined primary tumour and metastasis generated the highest AUCs in the training (RS-Com-TKI: 0.880) and validation (RS-Com-TKI: 0.798) cohort.

CONCLUSIONS

Primary NSCLC and spinal metastases can provide complementary information to predict the EGFR mutation status and response to EGFR-TKI. The developed models that integrate primary lesions and metastases may be potential imaging markers to guide individual treatment decisions.

Biological evaluation, docking studies, and in silico ADME prediction of some pyrimidine and pyridine derivatives as potential EGFRWT and EGFRT790M inhibitors

Herein, a set of pyridine and pyrimidine derivatives were assessed for their impact on the cell cycle and apoptosis. Human breast cancer (MCF7), hepatocellular carcinoma (HEPG2), larynx cancer (HEP2), lung cancer (H460), colon cancers (HCT116 and Caco2), and hypopharyngeal cancer (FADU), and normal Vero cell lines were used. Compounds 8 and 14 displayed outstanding effects on the investigated cell lines and were further tested for their antioxidant activity in MCF7, H460, FADU, HEP2, HEPG2, HCT116, Caco2, and Vero cells by measuring superoxide dismutase (SOD), malondialdehyde content (MDA), reduced glutathione (GSH), and nitric oxide (NO) content. Besides, Annexin V-FITC apoptosis detection and cell cycle DNA index using the HEPG-2 cell line were established on both compounds as well. Furthermore, compounds 8 and 14 were assessed for their EGFR kinase (Wild and T790M) inhibitory activities, revealing eligible potential. Additionally, molecular docking, ADME, and SAR studies were carried out for the investigated candidates.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases

Background

This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma.

Methods

A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis.

Results

Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets.

Conclusions

Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09985-4.

MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma.

MATERIALS AND METHODS

A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors.

RESULTS

A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram.

CONCLUSION

This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.

Aberrant Expression of β-Catenin Correlates with Infiltrating Immune Cells and Prognosis in NSCLC

Aims: β-catenin is a critical regulating factor of the Wnt pathway, which is closely linked to tumorigenesis, tumor growth, metastasis, and tumor immunity. Our study focused on exploring the relationship between β-catenin and clinicopathological features, prognosis, as well as infiltrating immune cells and immune scores, so as to illustrate its clinical significance in NSCLC. Materials and Methods: The β-catenin mRNA (CTNNB1) and protein expression data were downloaded from the UALCAN and the UCSC Xena website, respectively. All tumor-immune infiltrating cells' data were downloaded from the TIMER platform and immune scores were downloaded from ESTIMATE website. The expression of β-catenin protein in our cohort was measured by immunohistochemistry. Results: β-catenin mRNA level was higher in lung adenocarcinoma (LUAD) compared to normal tissues (p < 0.001) and was related to overall survival (OS) (p < 0.001) and post-progression survival (PPS) (both p = 0.049) in LUAD. Aberrant β-catenin protein expression was higher in male and lung squamous cell carcinoma (LUSC) patients (both p = 0.001). Also, it was considered to be a prognosis factor independently (p = 0.034). In addition, β-catenin protein was negatively correlated with CD8⁺T cells (r = -0.128, p = 0.008), neutrophils (r = -0.198, p < 0.001), immune score (r = -0.109, p = 0.024), stromal score (r = -0.097, p = 0.045), and ESTIMATE score (r = -0.113, p = 0.020). Conclusions: Aberrant β-catenin protein expression was evidently higher in NSCLC and might serve as a biomarker for poor prognosis. Most importantly, β-catenin protein might play an important part in tumor immunity and the tumor microenvironment by inhibiting the infiltration of CD8+ T cells and neutrophils.

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma

PURPOSE

This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.

METHODS

A total of 665 patients with lung adenocarcinoma (528/137) were recruited from two different institutions. In the training set, an 18-layer convolutional neural network (CNN) and fivefold cross-validation strategy were used to establish a CNN model. Subsequently, an independent external validation cohort from the other institution was used to evaluate the predictive efficacy of the CNN model. Grad-weighted class activation mapping (Grad-CAM) technology was used for the visual interpretation of the CNN model. In addition, this study also compared the prediction abilities of the radiomics and CNN models. Receiver operating characteristic (ROC) curves, accuracy and precision values, and recall and F1-score were used to evaluate the effectiveness of the CNN model and compare its performance with that of the radiomics model.

RESULTS

In the validation set, the micro- and macroaverage values of the area under the ROC curve of the CNN model to identify the three EGFR subtypes were 0.78 and 0.79, respectively. All evaluation indicators of the CNN model were better than those of the radiomics model.

CONCLUSIONS

Our study confirmed the potential of DL for predicting the EGFR mutation status in lung adenocarcinoma. The imaging phenotypes of the three mutation subtypes were found to be different, which can provide a basis for choosing more accurate and personalized treatment in patients with lung adenocarcinoma.

3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

BACKGROUND

To establish a radiomic approach to identify epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients based on CT images, and to distinguish exon-19 deletion and exon-21 L858R mutation.

METHODS

Two hundred sixty-three patients who underwent pre-surgical contrast-enhanced CT and molecular testing were included, and randomly divided into the training (80%) and test (20%) cohort. Tumor images were three-dimensionally segmented to extract 1,672 radiomic features. Clinical features (age, gender, and smoking history) were added to build classification models together with radiomic features. Subsequently, the top-10 most relevant features were used to establish classifiers. For the classifying tasks including EGFR mutation, exon-19 deletion, and exon-21 L858R mutation, four logistic regression models were established for each task.

RESULTS

The training and test cohort consisted of 210 and 53 patients, respectively. Among the established models, the highest accuracy and sensitivity among the four models were 75.5% (61.7-86.2%) and 92.9% (76.5-99.1%) to classify EGFR mutation, respectively. The highest specificity values were 86.7% (69.3-96.2%) and 70.4% (49.8-86.3%) to classify exon-19 deletion and exon-21 L858R mutation, respectively.

CONCLUSIONS

CT radiomics can sensitively identify the presence of EGFR mutation, and increase the certainty of distinguishing exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma patients. CT radiomics may become a helpful non-invasive biomarker to select EGFR mutation patients for invasive sampling.

The Diagnostic Accuracy of Liquid Biopsy in EGFR-Mutated NSCLC: A Systematic Review and Meta-Analysis of 40 Studies

Epidermal growth factor receptor (EGFR) mutations are the most common carcinogenic driver mutations in non-small-cell lung cancer (NSCLC) patients, while invasive tissue biopsy has certain inherent defects. PubMed, Ovid Medline, Embase, and the Cochrane Library were systematically searched on January 4, 2020, using the keywords "liquid biopsy," "EGFR," and "NSCLC." The pooled sensitivity and specificity of EGFR mutations in paired tissue and blood were calculated. The accuracy was assessed by receiver operating characteristic curve. The meta-regression of the subgroup was performed to analyze the heterogeneity. Hazard ratio (HR) and 95% confidence interval (CI) were combined for evaluating the impact of EGFR mutation in tissue and liquid blood biopsy. A total of 40 studies with 5,995 patients were involved in the study. The pooled sensitivity was 68% (95% CI = 60-75%), and the specificity was 98% (95% CI = 95-99%). The diagnostic odds ratio was 88 (95% CI = 40-195), and the area under the curve was 0.91 (95% CI = 0.88-0.93). In the meta-regression, the sensitivity and specificity remain lower in the Asian studies than non-Asian studies (sensitivity: 66% vs. 73%, P = 0.04; specificity: 96% vs. 97%, P = 0.03, respectively). The EGFR mutation was associated with a better progression-free survival than wild type in both tissue (HR = 0.54, 95% CI = 0.34-0.85, P = 0.007) and blood (HR = 0.81, 95% CI = 0.71-0.92, P = 0.001) detection. Peripheral blood liquid biopsy had a better specificity for detecting EGFR mutation in NSCLC patients, while tissue biopsy still needs to be undertaken for negative blood biopsy patients due to its lower sensitivity.

Upregulated expression of pyruvate kinase M2 mRNA predicts poor prognosis in lung adenocarcinoma

Background

Pyruvate kinase M2 (PKM2) is critical regulator contributing to Warburg effect. However, the expression pattern and prognostic value of PKM2 remain unknown in lung adenocarcinoma (LUAD). The aim of this study is to clarify the prognostic value of PKM2 via intergrated bioinformatics analysis.

Methods

Firstly, mRNA expression levels of PKM2 in LUAD were systematically analyzed using the ONCOMINE and TCGA databases. Then, the association between PKM2 expression and clinical parameters was investigated by UALCAN. The Kaplan-Meier Plotter was used to assess the prognostic significance of PKM2. Finally, the relationship between PKM2 expression and its genetic and epigenetic changes was evaluated with MEXPRESS and MethHC database.

Results

Pooled analysis showed that PKM2 is frequently upregulated expression in LUAD. Subsequently, PKM2 expression was identified to be positively associated with tumor stage and lymph node metastasis and also strongly correlated with worse OS (P = 2.80e-14), PPS (P = 0.022), FP (P = 1.30e-6) and RFS (P = 3.41e-8). Importantly, our results demonstrated that over-expressed PKM2 is associated with PKM2 hypomethylation and copy number variations (CNVs).

Conclusion

This study confirms that over-expressed PKM2 in LUAD is associated with poor prognosis, suggesting that PKM2 might act as a promising prognostic biomarker and novel therapeutic target for LUAD.

Concurrent proton-pump inhibitors increase risk of death for lung cancer patients receiving 1st-line gefitinib treatment - a nationwide population-based study

Purpose

Concurrent proton pump inhibitor (PPI) use might reduce the plasma concentration of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Clinically, the adverse effect of PPIs on patients with non-small cell lung cancer (NSCLC) treated with first-line EGFR TKIs remains controversial. This study was conducted to evaluate whether the combined use of gefitinib with PPIs affected NSCLC outcomes.

Patients and methods

We performed a nationwide cohort study of patients newly diagnosed with NSCLC between 1997 and 2013 using the Taiwan Cancer Registry and Taiwan National Health Insurance databases. We identified patients who were treated with first-line EGFR TKIs and analyzed the association between use of PPIs and TKI treatment outcome. We defined the coverage ratio of PPIs as duration of PPI treatment in days divided by duration of TKIs in days. Patients who exhibited an overlap of >20% between PPI and TKI usage days were defined as having a high coverage ratio.

Results

A total of 1278 patients were treated with first-line gefitinib, 309 of which took PPIs at the same time and 145 had a high PPI coverage ratio. Patients had similar time to failure regardless of their PPI coverage ratio during gefitinib treatment. However, higher PPI coverage ratio significantly decreased overall survival (OS) compared with that of patients with a lower PPI coverage ratio or no PPI treatment in univariate analysis (median OS, 13.5, 16.7, and 21.8 months, respectively, p<0.01) and multivariate analyses (high coverage ratio HR: 1.67; low coverage ratio HR: 1.29). Exposure to PPIs during first line gefitinib treatment significantly decreased overall survival of patients with NSCLC.

Conclusion

Concurrent use of PPIs was associated with lower overall survival in patients with EGFR-mutant NSCLC under first-line gefitinib treatment.