Dataset Information

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

ABSTRACT: Objective: Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive. Methods: We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequencing (WES). Results: In the entire cohort, many known driver mutations were found, including EGFR (21.7%), BRAF (14.5%), and KRAS (6%). However, only one out of the 31 patients had the same somatic missense or truncated events within SM-GGNs, indicating the independent origins for almost all of these SM-GGNs. Many germline mutations with a low frequency in the Chinese population, and genes harboring both germline and somatic variations, were discovered in these pre-stage GGNs. These GGNs also bore large segments of copy number gains and/or losses. The CNV segment number tended to be positively correlated with the germline mutations (r = 0.57). The CNV sizes were correlated with the somatic mutations (r = 0.55). A moderate correlation (r = 0.54) was also shown between the somatic and germline mutations. Conclusion: Our data suggests that the precancerous unstable CNVs with potentially predisposing genetic backgrounds may foster the onset of driver mutations and the development of independent SM-GGNs during the local stimulation of mutagens.

SUBMITTER: Ren Y

PROVIDER: S-EPMC6482264 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

Ren Yijiu Y Huang Shujun S Dai Chenyang C Xie Dong D Zheng Larry L Xie Huikang H Zheng Hui H She Yunlang Y Zhou Fangyu F Wang Yue Y Li Pengpeng P Fei Ke K Jiang Gening G Zhang Yang Y Su Bo B Sweet-Cordero E Alejandro EA Tran Nhan Le NL Yang Yanan Y Patel Jai N JN Rolfo Christian C Rocco Gaetano G Cardona Andrés Felipe AF Tuzi Alessandro A Suter Matteo B MB Yang Ping P Xu Wayne W Chen Chang C

Frontiers in oncology 20190418

<b>Objective:</b> Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive. <b>Methods:</b> We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequenci ...[more]

PMID: 31058088

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Project description:ObjectiveTo develop a radiomic model based on low-dose CT (LDCT) to distinguish invasive adenocarcinomas (IAs) from adenocarcinoma in situ/minimally invasive adenocarcinomas (AIS/MIAs) manifesting as pure ground-glass nodules (pGGNs) and compare its performance with conventional quantitative and semantic features of LDCT, radiomic model of standard-dose CT, and intraoperative frozen section (FS).MethodsA total of 147 consecutive pathologically confirmed pGGNs were divided into primary cohort (43 IAs and 60 AIS/MIAs) and validation cohort (19 IAs and 25 AIS/MIAs). Logistic regression models were built using conventional quantitative and semantic features, selected radiomic features of LDCT and standard-dose CT, and intraoperative FS diagnosis, respectively. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.ResultsThe AUCs of quantitative-semantic model, radiomic model of LDCT, radiomic model of standard-dose CT, and FS model were 0.879 (95% CI, 0.801-0.935), 0.929 (95% CI, 0.862-0.971), 0.941 (95% CI, 0.876-0.978), and 0.884 (95% CI, 0.805-0.938) in the primary cohort and 0.897 (95% CI, 0.768-0.968), 0.933 (95% CI, 0.815-0.986), 0.901 (95% CI, 0.773-0.970), and 0.828 (95% CI, 0.685-0.925) in the validation cohort. No significant difference of the AUCs was found among these models in both the primary and validation cohorts (all p > 0.05).ConclusionThe LDCT-based quantitative-semantic score and radiomic signature, with good predictive performance, can be pre-operative and non-invasive biomarkers for assessing the invasive risk of pGGNs in lung cancer screening.Advances in knowledgeThe LDCT-based quantitative-semantic score and radiomic signature, with the equivalent performance to the radiomic model of standard-dose CT, can be pre-operative predictors for assessing the invasiveness of pGGNs in lung cancer screening and reducing excess examination and treatment.

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: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.

Dataset Information

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

Publications

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules.

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