Prognostic value of epidermal growth factor receptor mutations and histologic subtypes with lung adenocarcinoma

Segmentectomy versus lobectomy in the United States: Outcomes after resection for first primary lung cancer and treatment patterns for second primary lung cancers

OBJECTIVE

The study objective was to identify whether the results of JCOG0802 could be generalized to US clinical settings.

METHODS

Patients diagnosed with clinical stage IA (≤2 cm) non-small cell lung cancer who underwent segmentectomy versus lobectomy (2004-2017) in the National Cancer Database were identified. Overall survival of patients in the National Cancer Database was assessed using propensity score-matched analysis. A separate analysis of the Surveillance Epidemiology End Results database was conducted to evaluate treatment patterns of second primary lung cancers among patients who underwent segmentectomy versus lobectomy for a first primary lung cancer.

RESULTS

Of the 23,286 patients in the National Cancer Database meeting inclusion criteria, 1397 (6.0%) underwent segmentectomy and 21,889 (94.0%) underwent lobectomy. In a propensity score-matched analysis of all patients in the study cohort, there were no significant differences in overall survival between patients undergoing segmentectomy versus lobectomy (5-year overall survival: 79.9% [95% CI, 76.7%-82.0%] vs 81.8% [95% CI, 78.7%-84.4%], log-rank: P = .72). In subgroup analyses by tumor grade and histologic subtype, segmentectomy was associated with similar overall survival compared with lobectomy in all subgroups evaluated. In a propensity score-matched analysis of patients in the Surveillance Epidemiology End Results database, there were no significant differences in treatment patterns of second primary lung cancers between patients who underwent segmentectomy and patients who underwent lobectomy for their first primary lung cancer.

CONCLUSIONS

In this national analysis of US patients diagnosed with stage IA (≤2 cm) non-small cell lung cancer, there were no significant differences in overall survival between segmentectomy and lobectomy in the overall cohort or in subgroup analyses by tumor grade or histologic subtype.

miR-17-5p/HOXA7 Is a Potential Driver for Brain Metastasis of Lung Adenocarcinoma Related to Ferroptosis Revealed by Bioinformatic Analysis

Objectives

Present study aims to identify the essential mRNAs responsible for the development of brain neurovascular-related metastases (BNM) among lung adenocarcinoma (LUAD) patients. Further, we attempted to predict brain metastases more accurately and prevent their development in LUAD patients.

Methods

Transcriptome data analysis was used to identify differentially expressed mRNAs (DEMs) associated with brain metastasis, and thereby the ferroptosis index (FPI) is calculated using a computational model. Meanwhile, the DEmRNAs linked with FPI, and brain metastasis were derived by the intersection of these two groups of DEMs. We also constructed a ceRNA network containing these DEmRNAs, identifying the HCP5 /hsa-miR-17-5p/HOXA7 axis for analysis. Further, a clinical cohort was employed to validate the regulatory roles of molecules involved in the ceRNA regulatory axis.

Results

Here we report the development of a ceRNA network based on BNM-associated DEMs and FPI-associated DEmRNAs which includes three core miRNAs (hsa-miR-338-3p, hsa-miR-429, and hsa-miR-17-5p), three mRNAs (HOXA7, TBX5, and TCF21), and five lncRNAs (HCP5, LINC00460, TP53TG1). Using gene set enrichment analysis (GSEA) and survival analysis, the potential axis of HCP5 /hsa-miR-17-5p/HOXA7 was further investigated. It is found that HOXA7 and ferroptosis index are positively correlated while inhibiting tumor brain metastasis. It may be that HCP5 binds competitively with miR-17-5p and upregulates HOXA7 to increase iron death limiting brain cancer metastases.

Conclusions

The expression of both HOXA7 and HCP5 is positively correlated with FPI, indicating a possible link between ferroptosis and BNM. According to the results of our study, the ferroptosis-related ceRNA HCP5 /hsa-miR-17-5p/HOXA7 axis may contribute to the development of BNM in LUAD patients.

Hepatic steatosis and respiratory diseases: a new panorama

Non-alcoholic fatty liver disease is defined as hepatic fat accumulation in more than 5% of hepatocytes, without other liver steatosis causes. It comprises a broad spectrum that can range from benign steatosis and progress to non-alcoholic steatohepatitis, fibrosis, and ultimately hepatocellular carcinoma. Non-alcoholic fatty liver is considered a multisystemic disease since it is related to multiple disorders, such as type 2 diabetes mellitus, polycystic ovary syndrome, chronic kidney disease, psoriasis, osteoporosis, hypothyroidism, cardiovascular diseases, and obstructive sleep apnea syndrome; it is becoming increasingly clear that it is also a risk factor for developing certain respiratory diseases. This article aims to understand the liver and chronic obstructive pulmonary disease mechanisms, obstructive sleep apnea syndrome, asthma, and lung cancer. Given that non-alcoholic fatty liver disease has a considerable impact on the patient's well-being and life quality, as well as on the costs they generate for the country's health services, it is essential to continue research, especially in areas such as the respiratory tract, as there is much misinformation about it.

Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients

Lung cancer ranks first in the incidence and mortality of cancer in the world, of which more than 80% are non-small cell lung cancer (NSCLC). The majority of NSCLC patients are in stage IIIB~IV when they are admitted to hospital and have no opportunity for surgery. Compared with traditional chemotherapy, specific targeted therapy has a higher selectivity and fewer adverse reactions, providing a new treatment direction for advanced NSCLC patients. Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are the widely used targeted therapy for NSCLC patients. Their efficacy and prognosis are closely related to the mutation status of the EGFR gene. Clinically, detecting EGFR gene mutation is often limited by difficulty obtaining tissue specimens, limited detecting technology, and economic conditions, so it is of great clinical significance to find indicators to predict EGFR gene mutation status. Clinicopathological characteristics, tumor markers, liquid biopsy, and other predictors are less invasive, economical, and easier to obtain. They can be monitored in real-time, which is supposed to predict EGFR mutation status and provide guidance for the accurate, individualized diagnosis and therapy of NSCLC patients. This article reviewed the correlation between the clinical indicators and EGFR gene mutation status in NSCLC patients.

Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study

Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.

Predictive value of EGFR mutation in non-small-cell lung cancer patients treated with platinum doublet postoperative chemotherapy

The mutation status of tumor tissue DNA (n = 389) of resected stage II‐III non‐squamous non–small‐cell lung cancer (Ns‐NSCLC) was analyzed using targeted deep sequencing as an exploratory biomarker study (JIPANG‐TR) for the JIPANG study, a randomized phase III study of pemetrexed/cisplatin (Pem/Cis) vs vinorelbine/cisplatin (Vnr/Cis). The TP53 mutation, common EGFR mutations (exon 19 deletion and L858R), and KRAS mutations were frequently detected. The frequency of the EGFR mutation was significant among female patients. Patients with an EGFR mutation‐positive status had a significantly shorter recurrence‐free survival (RFS) time (24 mo vs not reached) (HR, 1.64; 95% CI, 1.22‐2.21; P = .0011 for EGFR mutation status). Multivariable analysis identified both the pathological stage and EGFR mutation status as independent prognostic factors for RFS (HR, 1.78; 95% CI, 1.30‐2.44; P = .0003 for disease stage; and HR, 1.57; 95% CI, 1.15‐2.16; P = .0050 for EGFR mutation status). This study demonstrated that the EGFR mutation has either a poor prognostic or predictive impact on a poor response to postoperative chemotherapy with platinum doublet chemotherapy for stage II‐III Ns‐NSCLC patients. This result supports a role for mandatory molecular diagnosis of early‐stage Ns‐NSCLC for precision oncology and signifies the importance of adjuvant for the 3rd generation tyrosine kinase inhibitor rather than platinum‐based chemotherapy. This study is registered with the UMIN Clinical Trial Registry (UMIN 000012237).

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

Background

Many delayed diagnoses of lung adenocarcinoma (LADC) are identified due to poor understanding of protean imaging findings. Moreover, clarifying the relationship between computed tomography (CT) morphological classification and epidermal growth factor receptor (EGFR) mutations of LADC might inform therapeutic decision-making while obtaining pathological specimens is difficult. Here, we retrospectively analyzed CT manifestations of LADC and investigated the morphological classification of tumors in relation to EGFR mutation status.

Methods

We included 1075 LADC patients undergoing chest CT and EGFR genotype examinations from January 2013 to January 2019. CT morphological characteristics of tumors were carefully evaluated and their correlation with EGFR mutation status was analyzed using the chi-squared test.

Results

Tumors were divided into eight types: I (peripheral solid nodule/mass; 526/1075, 48.93%), II (central solid nodule/mass; 220/1075, 20.47%), III (subsolid nodule/mass; 92/1075, 8.56%), IV (focal consolidation; 32/1075, 2.98%), V (cystic airspace; 14/1075, 1.30%), VI (multiple lesions with similar appearances to I–V; 85/1075, 7.91%), VII (diffuse consolidation; 53/1075, 4.93%), VIII (occult lesion usually obscured by nonobstructive atelectasis; 53/1075, 4.93%). Type III and IV tumors were more frequent in patients with EGFR mutation, whereas type II and VII tumors were more common in patients without EGFR mutation (all P < 0.05). However, we did not identify any significant associations between other tumor types and EGFR mutation status (all P > 0.05). Among patients with type VI tumors, EGFR mutation status was closely related to tumor density (all P < 0.05). Furthermore, type VII tumors were associated with 19 deletion mutation positive and non-L858R mutation positive (all P < 0.05).

Conclusion

LADC can be categorized into eight types based on CT imaging. Improving our understanding of the morphological classification and correlation with EGFR mutation status may contribute to the accurate diagnosis of LADC, while suggesting the presence of underlying EGFR genetic mutations.

Driver and novel genes correlated with metastasis of non-small cell lung cancer: A comprehensive analysis

Although mutations of genes are crucial events in tumorigenesis and development, the association between gene mutations and lung cancer metastasis is still largely unknown. The goal of this study is to identify driver and novel genes associated with non-small cell lung cancer (NSCLC) metastasis. Candidate genes were identified using a novel comprehensive analysis, which was based on bioinformatics technology and meta-analysis. Firstly, EGFR, KRAS, ALK, TP53, BRAF and PIK3CA were identified as candidate driver genes. Further meta-analysis identified that EGFR (Pooled OR 1.33, 95% CI 1.19, 1.50; P < .001) and ALK (Pooled OR 1.52, 95% CI 1.22, 1.89; P < .001) mutations were associated with distant metastasis of NSCLC. Besides, ALK (Pooled OR 2.40, 95% CI 1.71, 3.38; P < .001) mutation was associated with lymph node metastasis of NSCLC. In addition, thirteen novel gene mutations were identified to be correlated with NSCLC metastasis, including SMARCA1, GGCX, KIF24, LRRK1, LILRA4, OR2T10, EDNRB, NR1H4, ARID4A, PRKCI, PABPC5, ACAN and TLN1. Furthermore, elevated mRNA expression level of SMARCA1 and EDNRB was associated with poor overall survival in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. Additionally, pathway and protein-protein interactions network analyses found the two genes were correlated with epithelial-mesenchymal transition process. In conclusion, mutations of EGFR and ALK were significantly correlated with NSCLC metastasis. In addition, thirteen novel genes were identified to be associated with NSCLC metastasis, especially SMARCA1 in LUAD and EDNRB in LUSC.

Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images

BACKGROUND

Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (KRAS)] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations.

METHODS

We propose a multi-channel and multi-task deep learning (MMDL) model for the simultaneous prediction of EGFR and KRAS mutation statuses based on CT images. First, we decomposed each 3D lung nodule into 9 views. Then, we used the pre-trained inception-attention-resnet model for each view to learn the features of the nodules. By combining 9 inception-attention-resnet models to predict the types of gene mutations in lung nodules, the models were adaptively weighted, and the proposed MMDL model could be trained end-to-end. The MMDL model utilized multiple channels to characterize the nodule more comprehensively and integrate patient personal information into our learning process.

RESULTS

We trained the proposed MMDL model using a dataset of 363 patients collected by our partner hospital and conducted a multi-center validation on 162 patients in The Cancer Imaging Archive (TCIA) public dataset. The accuracies for the prediction of EGFR and KRAS mutations were, respectively, 79.43% and 72.25% in the training dataset and 75.06% and 69.64% in the validation dataset.

CONCLUSIONS

The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting EGFR and KRAS mutations in NSCLC.

Molecular gene mutation profiles, TMB and the impact of prognosis in Caucasians and east Asian patients with lung adenocarcinoma

Background

The difference in molecular gene mutation profile, tumor mutational burden (TMB) and their prognostic effects in lung adenocarcinoma between different ethnic groups are still unknown. A retrospective analysis was used to investigate the differences in lung adenocarcinoma driver gene mutations, TMB, and their impact on prognosis across different ethnic groups.

Methods

The incidence of epidermal growth factor receptor (EGFR) mutations and follow-up data of 647 Chinese lung adenocarcinoma patients were compared with the data from 522 Caucasian patients in The Cancer Genome Atlas (TCGA) database. Moreover, a comprehensive analysis was performed to compare the differences in gene mutation frequency, signaling pathway variation, and TMB using the whole-exome sequencing (WES) data of Chinese patients with that of Caucasian patients.

Results

A comparison of tumor signaling pathways and gene mutation profiles between Caucasians and Chinese revealed ethnic variations in the incidence of mutations in TGF-β and RTK-RAS signaling pathways, with P values of 0.012 and 0.016, respectively. In the Caucasian population, the mutations in 5 signaling pathways and 18 genes were all significantly correlated with TMB, whereas in the Chinese population, only mutations in the Notch pathway and 6 genes were found to be associated with TMB-high. EGFR mutations showed a better prognosis in Chinese patients with lung adenocarcinoma, while the opposite was found in Caucasians patients.

Conclusions

Variations in the incidence of mutations in signaling pathways involved in lung adenocarcinoma and the correlation of the signaling pathways with TMB may exist across different ethnic groups.

MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data

Background

Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data.

Results

In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively.

Conclusions

The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.

Clinical characteristics and survival in non-small cell lung cancer patients by smoking history: a population-based cohort study

Introduction: Approximately, 10-15% of lung cancer patients have never smoked. Previous epidemiological studies on non-tobacco associated lung cancer have been hampered by selected data from a small number of hospitals or limited numbers of patients. By use of data from large population-based registers with national coverage, this study aims to compare characteristics and survival of patients with non-small cell lung cancer (NSCLC) with different smoking histories.Methods: Swedish national population-based registers were used to retrieve data on patients diagnosed with primary NSCLC between 2002 and 2016. The Kaplan-Meier method and Cox proportional hazard models were used to estimate overall survival and lung cancer-specific survival by smoking history.Results: In total, 41,262 patients with NSCLC were included. Of those, 4624 (11%) had never smoked. Never-smokers were more often women and older compared to ever smokers (current and former). Adenocarcinoma was proportionally more common in never-smokers (77%) compared to current (52%) and former smokers (57%). Stage IV disease was more common in never-smokers (57%) than in current (48%) and former smokers (48%). Epidermal growth factor receptor mutation was observed more in never-smokers (37%) compared to current (5%) and former smokers (9%). Both lung cancer-specific and overall survival were higher for never-smokers compared to current smokers.Conclusions: The observed differences in characteristics between never-smokers and smokers, and the higher survival in never-smokers compared to smokers from this large population-based study provide further evidence that lung cancer in never-smokers is clinically different to tobacco-associated lung cancer. The findings from this study emphasise the need for an improved understanding of genetics, pathogenesis, mechanisms and progression of non-tobacco associated lung cancer that may help prevent lung cancer or identify individually targeted treatments.

Kinesin Family Member 18A (KIF18A) Contributes to the Proliferation, Migration, and Invasion of Lung Adenocarcinoma Cells In Vitro and In Vivo

Objective

To determine the expression levels of KIF18A in lung adenocarcinoma and its relationship with the clinicopathologic features of patients undergoing radical colectomy and explore the potential role in the progression of lung adenocarcinoma.

Methods

Immunohistochemical assays were performed to explore the expression levels of KIF18A in 82 samples of lung adenocarcinoma and corresponding normal tissues. According to the levels of KIF18A expression in lung adenocarcinoma tissue samples, patients were classified into the KIF18A high expression group and low expression group. Clinical data related to the perioperative clinical features (age, gender, smoking, tumor size, differentiation, clinical stage, and lymph node metastasis), the potential correlation between KIF18A expression levels, and clinical features were analyzed, and the effects of KIF18A on lung adenocarcinoma cell proliferation, migration, and invasion were measured by colony formation assay, MTT assay, wound healing assay, and transwell assays. The possible effects of KIF18A on tumor growth and metastasis were measured in mice through tumor growth and tumor metastasis assays in vivo.

Results

KIF18A in lung adenocarcinoma tissues. Further, KIF18A was significantly associated to clinical characteristic features including the tumor size (P = 0.033) and clinical stage (P = 0.041) of patients with lung adenocarcinoma. Our data also investigated that KIF18A depletion dramatically impairs the proliferation, migration, and invasion capacity of lung adenocarcinoma cells in vitro and inhibits tumor growth and metastasis in mice.

Conclusions

Our study reveals the involvement of KIF18A in the progression and metastasis of lung adenocarcinoma and provides a novel therapeutic target for the treatment of lung adenocarcinoma.

Obesity and nonalcoholic fatty liver disease associated with adenocarcinoma in patients with lung cancer

Lung cancer has become the leading cause of cancer-related deaths around the world. In addition to genetic risk factors and smoking, the metabolic risk factors remain to be elusive.To evaluate the associations between obesity, nonalcoholic fatty liver disease (NAFLD) and pulmonary adenocarcinoma in patients with lung cancer.Consecutive operation-proven lung cancer patients with assessment of metabolic disorders and liver ultrasound in 2009 and 2013 were retrospectively enrolled. T-test and multivariate logistic regression were applied to evaluate the contribution of individual factors to lung adenocarcinoma, as well as the synergistic effects between these factors.Among 3664 lung cancer patients with ultrasound examination, 2844 cases were enrolled for further analysis. Of them, 1053 (37.0%) were females, 1242 (43.7%) were cigarette smokers, 1658 (58.3%) were diagnosed as lung adenocarcinoma, 744 (26.2%) had obesity, and 614 (21.6%) had NAFLD. Proportion of female gender, nonsmoker, obesity, NAFLD, and serum lipid levels in patients with adenocarcinoma were significantly higher than those in other subtypes of lung cancer, and in 2013 than in 2009 (all P < .01). NAFLD and obesity were shown as independent factors and positively associated with pulmonary adenocarcinoma, along with female gender and nonsmoking, higher serum levels of cholesterol. NAFLD and other contributing factors exhibited no synergistic effects on adenocarcinoma.Obesity and NAFLD might increase the risk for pulmonary adenocarcinoma, especially in nonsmoking females, and underscore the need for further study into carcinogenic mechanisms and preventive interventions.

Tumor-based gene expression biomarkers to predict survival following curative intent resection for stage I lung adenocarcinoma

Background

Prognostic biomarkers are needed in clinical setting to predict outcome after resection for early-stage lung adenocarcinoma. The goal of this study is to validate tumor-based single-gene expression biomarkers with demonstrated prognostic value in order to move them along the clinical translation pipeline.

Methods

Prognostic genes were selected from the literature and the best candidates measured by quantitative real-time polymerase chain reaction (qPCR) in tumors of 233 patients with stage I adenocarcinoma. Significant prognostic genes were then validated in an independent set of 210 patients matching the first set in terms of histology, stage, and clinical data.

Results

Eleven genes with demonstrated prognostic value were selected from the literature. Complementary analyses in public databases and our own microarray dataset led to the investigation of six genes associated with good (BTG2, SELENBP1 and NFIB) or poor outcome (RRM1, EZH2 and FOXM1). In the first set of patients, EZH2 and RRM1 were significantly associated with better survival on top of age, sex and pathological stage (EZH2 p = 3.2e-02, RRM1 p = 5.9e-04). The prognostic values of EZH2 and RRM1 were not replicated in the second set of patients. A trend was observed for both genes in the joint analyses (n = 443) with higher expression associated with worse outcome.

Conclusion

Adenocarcinoma-specific mRNA expression levels of EZH2 and RRM1 are associated with poor post-surgical survival in the first set of patients, but not replicated in a clinically and pathologically matched independent validation set. This study highlights challenges associated with clinical translation of prognostic biomarkers.