Dataset Information

Genetic Features of Lung Adenocarcinoma with Ground- Glass Opacity: What Causes the Invasiveness of Lung Adenocarcinoma?

ABSTRACT: Background:Lung adenocarcinoma (LUAD) with ground-glass opacity (GGO) can become aggravated, but the reasons for this aggravation are not fully understood. The goal of this study was to analyze the genetic features and causes of progression of GGO LUAD. Methods:LUAD tumor samples and normal tissues were analyzed using an Illumina HiSeq 4000 system. After the tumor mutational burden (TMB) was calculated, the identified mutations were classified as those found only in GGO LUAD, those present only in non- GGO LUAD, and those common to both tissue types. Ten high-frequency genes were selected from each domain, after which protein interaction network analysis was conducted. Results:Overall, 227 mutations in GGO LUAD, 212 in non-GGO LUAD, and 48 that were common to both tumor types were found. The TMB was 8.8 in GGO and 7.8 in non-GGO samples. In GGO LUAD, mutations of FCGBP and SFTPA1 were identified. FOXQ1, IRF5, and MAGEC1 mutations were common to both types, and CDC27 and NOTCH4 mutations were identified in the non-GGO LUAD. Protein interaction network analysis indicated that IRF5 (common to both tissue types) and CDC27 (found in the non-GGO LUAD) had significant biological functions related to the cell cycle and proliferation. Conclusion:In conclusion, GGO LUAD exhibited a higher TMB than non-GGO LUAD. No clinically meaningful mutations were found to be specific to GGO LUAD, but mutations involved in the epithelial-mesenchymal transition or cell cycle were found in both tumor types and in non-GGO tissue alone. These findings could explain the non-invasiveness of GGO-type LUAD.

SUBMITTER: Kim D

PROVIDER: S-EPMC7553832 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Genetic Features of Lung Adenocarcinoma with Ground- Glass Opacity: What Causes the Invasiveness of Lung Adenocarcinoma?

Kim Dohun D Lee Jong-Young JY Yoo Jin Young JY Cho Jun Yeun JY

The Korean journal of thoracic and cardiovascular surgery 20201001 5

<h4>Background</h4>Lung adenocarcinoma (LUAD) with ground-glass opacity (GGO) can become aggravated, but the reasons for this aggravation are not fully understood. The goal of this study was to analyze the genetic features and causes of progression of GGO LUAD.<h4>Methods</h4>LUAD tumor samples and normal tissues were analyzed using an Illumina HiSeq 4000 system. After the tumor mutational burden (TMB) was calculated, the identified mutations were classified as those found only in GGO LUAD, thos ...[more]

PMID: 33020345

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Project description:Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723-0.924), 0.833 (95% CI, 0.733-0.934), and 0.848 (95% CI, 0.750-0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.

Dataset Information

Genetic Features of Lung Adenocarcinoma with Ground- Glass Opacity: What Causes the Invasiveness of Lung Adenocarcinoma?

Publications

Genetic Features of Lung Adenocarcinoma with Ground- Glass Opacity: What Causes the Invasiveness of Lung Adenocarcinoma?

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