Dataset Information

Druggable oncogene fusions in invasive mucinous lung adenocarcinoma.

ABSTRACT:

Purpose

To identify druggable oncogenic fusions in invasive mucinous adenocarcinoma (IMA) of the lung, a malignant type of lung adenocarcinoma in which KRAS mutations frequently occur.

Experimental design

From an IMA cohort of 90 cases, consisting of 56 cases (62%) with KRAS mutations and 34 cases without (38%), we conducted whole-transcriptome sequencing of 32 IMAs, including 27 cases without KRAS mutations. We used the sequencing data to identify gene fusions, and then performed functional analyses of the fusion gene products.

Results

We identified oncogenic fusions that occurred mutually exclusively with KRAS mutations: CD74-NRG1, SLC3A2-NRG1, EZR-ERBB4, TRIM24-BRAF, and KIAA1468-RET. NRG1 fusions were present in 17.6% (6/34) of KRAS-negative IMAs. The CD74-NRG1 fusion activated HER2:HER3 signaling, whereas the EZR-ERBB4 and TRIM24-BRAF fusions constitutively activated the ERBB4 and BRAF kinases, respectively. Signaling pathway activation and fusion-induced anchorage-independent growth/tumorigenicity of NIH3T3 cells expressing these fusions were suppressed by tyrosine kinase inhibitors approved for clinical use.

Conclusions

Oncogenic fusions act as driver mutations in IMAs without KRAS mutations, and thus represent promising therapeutic targets for the treatment of such IMAs.

SUBMITTER: Nakaoku T

PROVIDER: S-EPMC6329293 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Druggable oncogene fusions in invasive mucinous lung adenocarcinoma.

Nakaoku Takashi T Tsuta Koji K Ichikawa Hitoshi H Shiraishi Kouya K Sakamoto Hiromi H Enari Masato M Furuta Koh K Shimada Yoko Y Ogiwara Hideaki H Watanabe Shun-ichi S Nokihara Hiroshi H Yasuda Kazuki K Hiramoto Masaki M Nammo Takao T Ishigame Teruhide T Schetter Aaron J AJ Okayama Hirokazu H Harris Curtis C CC Kim Young Hak YH Mishima Michiaki M Yokota Jun J Yoshida Teruhiko T Kohno Takashi T

Clinical cancer research : an official journal of the American Association for Cancer Research 20140411 12

<h4>Purpose</h4>To identify druggable oncogenic fusions in invasive mucinous adenocarcinoma (IMA) of the lung, a malignant type of lung adenocarcinoma in which KRAS mutations frequently occur.<h4>Experimental design</h4>From an IMA cohort of 90 cases, consisting of 56 cases (62%) with KRAS mutations and 34 cases without (38%), we conducted whole-transcriptome sequencing of 32 IMAs, including 27 cases without KRAS mutations. We used the sequencing data to identify gene fusions, and then performed ...[more]

PMID: 24727320

Similar Datasets

Project description:Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Results: Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models (P = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. Conclusion: The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.

Dataset Information

Druggable oncogene fusions in invasive mucinous lung adenocarcinoma.

Purpose

Experimental design

Results

Conclusions

Publications

Druggable oncogene fusions in invasive mucinous lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets