Dataset Information

Genomic alterations of ground-glass nodular lung adenocarcinoma.

ABSTRACT: In-depth molecular pathogenesis of ground-glass nodular lung adenocarcinoma has not been well understood. The objectives of this study were to identify genomic alterations in ground-glass nodular lung adenocarcinomas and to investigate whether viral transcripts were detected in these tumors. Nine patients with pure (n = 4) and part-solid (n = 5) ground-glass nodular adenocarcinomas were included. Six were females with a median age of 58 years. We performed targeted exon sequencing and RNA sequencing. EGFR (n = 10), IDH2 (n = 2), TP53 (n = 1), PTEN (n = 1), EPHB4 (n = 1), and BRAF (n = 1) were identified as driver mutations by targeted exon sequencing. Vasculogenesis-associated genes including NOTCH4 and TGFBR3 expression were significantly downregulated in adenocarcinoma tissue versus normal tissue (adjusted P values < 0.001 for both NOTCH4 and TGFBR3). In addition, five novel fusion gene loci were identified in four lung adenocarcinomas. However, no significant virus-associated transcripts were detected in tumors. In conclusions, EGFR, IDH2, TP53, PTEN, EPHB4, and BRAF were identified as putative driver mutations of ground-glass nodular adenocarcinomas. Five novel fusion genes were also identified in four tumors. Viruses do not appear to be involved in the tumorigenesis of ground-glass nodular lung adenocarcinoma.

SUBMITTER: Lee H

PROVIDER: S-EPMC5955945 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Genomic alterations of ground-glass nodular lung adenocarcinoma.

Lee Hyun H Joung Je-Gun JG Shin Hyun-Tae HT Kim Duk-Hwan DH Kim Yujin Y Kim Hojoong H Kwon O Jung OJ Shim Young Mog YM Lee Ho Yun HY Lee Kyung Soo KS Choi Yoon-La YL Park Woong-Yang WY Hayes D Neil DN Um Sang-Won SW

Scientific reports 20180516 1

In-depth molecular pathogenesis of ground-glass nodular lung adenocarcinoma has not been well understood. The objectives of this study were to identify genomic alterations in ground-glass nodular lung adenocarcinomas and to investigate whether viral transcripts were detected in these tumors. Nine patients with pure (n = 4) and part-solid (n = 5) ground-glass nodular adenocarcinomas were included. Six were females with a median age of 58 years. We performed targeted exon sequencing and RNA sequen ...[more]

PMID: 29769567

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Project description:Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra- and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or peri-nodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Conclusions: The gross radiomic signature of intra- and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.

Project description:BackgroundThe concept of multi-step progression from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (ADC) has been proposed, and ground-glass nodules (GGNs) may play a critical role during the early lung tumorigenesis. We present the first comprehensive description of the genomic architecture of GGNs to unravel the genetic basis of GGN.MethodsWe investigated 30 GGN-like lungs ADC by performing >1,000× whole-exome sequencing (WES) and characterized the genomic variations and evaluate the relationship between the clinicopathologic and molecular characteristics in this disease.ResultsDespite the low somatic mutation burden, GGNs exhibited high intratumor heterogeneity (ITH) characterized by the proportion of subclonal mutations. Different mutagenesis shaped the genomes of GGN during cancer evolution and were mostly featured by molecular clock-like signatures that occur in clonal mutations and defective DNA mismatch signatures that occur in subclonal mutations. Moreover, 10.7-67.1% clonal mutations occurred after whole-genome doubling (WGD), indicating that WGD could be a frequent truncal event in GGNs. Samples with WGD showed higher genomic instability but lower ITH. These GGNs were characterized by recurrent focal copy-number changes that are highly associated with tumorigenesis, with only two genes (EGFR and RBM10) that were recurrently mutated. Additionally, GGNs with different pathological subtypes or computed tomography (CT) features exhibited distinct genetic characteristics. Lepidic predominant or pure GGNs in CT images carried a lower mutation burden and had a relatively stable genome than nonlepidic or mixed GGNs. GGNs with RBM10 mutations tended to accompany a pathologically lepidic pattern, indicating RBM10 may drive the distinct subtype of lung cancer with better prognosis.ConclusionsThese findings facilitated interpreting the genomic characteristics of GGNs, provided insight into the early stages of lung cancer evolution, and possessed potential clinical significance.

Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.ResultsFive radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.ConclusionsThe proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.Key points• CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Project description:ObjectivesTo explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground-glass opacity nodules (GGO) with high- and low Ki-67 expression levels.Materials and methodsFrom January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models.ResultsSeven hundred and sixty-nine patients with 769 GGOs were included in this study. Two hundred and forty-five GGOs were confirmed to be of high Ki-67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high- and low Ki-67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki-67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical-radiographic model (AUC = 0.675). Moreover, radiomic model combining both intra- and peri-nodular features showed better diagnostic efficacy than using intra-nodular features alone (AUC = 0.731 and 0.720, respectively).ConclusionsThe established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki-67 LI, which was higher than the clinical-radiographic model. Peri-nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki-67 expression level in lung adenocarcinoma GGOs.

Dataset Information

Genomic alterations of ground-glass nodular lung adenocarcinoma.

Publications

Genomic alterations of ground-glass nodular lung adenocarcinoma.

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