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Molecular Profiling of Thymoma and Thymic Carcinoma: Genetic Differences and Potential Novel Therapeutic Targets.

ABSTRACT: Thymoma and thymic carcinoma are thymic epithelial tumors (TETs). We performed a molecular profiling to investigate the pathogenesis of TETs and identify novel targets for therapy. We analyzed 37 thymomas (18 type A, 19 type B3) and 35 thymic carcinomas. The sequencing of 50 genes detected nonsynonymous mutations in 16 carcinomas affecting ALK, ATM, CDKN2A, ERBB4, FGFR3, KIT, NRAS and TP53. Only two B3 thymomas had a mutation in noncoding regions of the SMARCB1 and STK11 gene respectively. Three type A thymomas harbored a nonsynonymous HRAS mutation. Fluorescence in situ hybridization detected in 38 % of carcinomas a CDKN2A, in 32 % a TP53 and in 8 % an ATM gene deletion, whereas only one B3 thymoma exhibited a CDKNA deletion, and none of the type A thymomas showed a gene loss. Sequencing of the total miRNA pool of 5 type A thymomas and 5 thymic carcinomas identified the C19MC miRNA cluster as highly expressed in type A thymomas, but completely silenced in thymic carcinomas. Furthermore, the miRNA cluster C14MC was downregulated in thymic carcinomas. Among non-clustered miRNAs, the upregulation of miR-21, miR-9-3 and miR-375 and the downregulation of miR-34b, miR-34c, miR-130a and miR-195 in thymic carcinomas were most significant. The expression of ALK, HER2, HER3, MET, phospho-mTOR, p16INK4A, PDGFRA, PDGFRB, PD-L1, PTEN and ROS1 was investigated by immunohistochemistry. PDGFRA was increased in thymic carcinomas and PD-L1 in B3 thymomas and thymic carcinomas. In summary, our results reveal genetic differences between thymomas and thymic carcinomas and suggest potential novel targets for therapy.

SUBMITTER: Enkner F

PROVIDER: S-EPMC5487866 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Molecular Profiling of Thymoma and Thymic Carcinoma: Genetic Differences and Potential Novel Therapeutic Targets.

Enkner Franz F Pichlhöfer Bettina B Zaharie Alexandru Teodor AT Krunic Milica M Holper Tina Maria TM Janik Stefan S Moser Bernhard B Schlangen Karin K Neudert Barbara B Walter Karin K Migschitz Brigitte B Müllauer Leonhard L

Pathology oncology research : POR 20161114 3

Thymoma and thymic carcinoma are thymic epithelial tumors (TETs). We performed a molecular profiling to investigate the pathogenesis of TETs and identify novel targets for therapy. We analyzed 37 thymomas (18 type A, 19 type B3) and 35 thymic carcinomas. The sequencing of 50 genes detected nonsynonymous mutations in 16 carcinomas affecting ALK, ATM, CDKN2A, ERBB4, FGFR3, KIT, NRAS and TP53. Only two B3 thymomas had a mutation in noncoding regions of the SMARCB1 and STK11 gene respectively. Three ...[more]

PMID: 27844328

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Project description:BackgroundFibrolamellar carcinoma (FLC) is a rare primary liver cancer of young adults. A functional chimeric transcript resulting from the in-frame fusion of the DNAJ homolog, subfamily B, member 1 (DNAJB1), and the catalytic subunit of protein kinase A (PRKACA) genes on chromosome 19 is believed to be unique in FLC, with a possible role in pathogenesis, yet with no established therapeutic value. The objective of the current study was to understand the molecular landscape of FLC and to identify potential novel therapeutic targets.MethodsArchival fresh, formalin-fixed, paraffin-embedded samples from patients with FLC who prospectively consented to an institutional review board-approved protocol were analyzed using Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), a next-generation sequencing assay encompassing up to 468 key cancer genes. Custom targeted RNA-Seq was performed in selected patients. Demographics, treatment, and outcome data were collected prospectively. Survival outcomes were estimated and correlated with mutation and/or copy number alterations.ResultsA total of 33 tumor samples from 31 patients with FLC were analyzed. The median age of the patients at the time of diagnosis was 18 years and approximately 53% were women. The DNAJB1-PRKACA fusion transcript was detected in 100% of patients. In 10 of 31 patients in which MSK-IMPACT did not detect the fusion, its presence was confirmed by targeted RNA-Seq. TERT promoter mutation was the second most common, and was detected in 7 patients. The median follow up was 30 months (range, 6-153 months). The 3-year overall survival rate was 84% (95% CI, 61%-93%).ConclusionsThe DNAJB1-PRKACA fusion transcript is nonspecific and nonsensitive to FLC. Its potential therapeutic value currently is under evaluation. Opportunities currently are under development for therapy that may be driven or related to the DNAJB1-PRKACA fusion transcript or any therapeutic target identified from next-generation sequencing in patients with FLC.

Project description:BackgroundThe purpose of our study was to differentiate between thymoma and thymic carcinoma using a radiomics analysis based on the computed tomography (CT) image features.MethodsThe CT images of 61 patients with thymic epithelial tumors (TETs) who underwent contrast-enhanced CT with slice thickness <1 mm were analyzed. Pathological examination of the surgical specimens revealed thymoma in 45 and thymic carcinoma in 16. Tumor volume and the ratio of major axis to minor axis were calculated using a computer-aided diagnostic software. Sixty-one different radiomics features in the segmented CT images were extracted, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select the optimal radiomics features for predicting thymic carcinoma. The association between the quantitative values and a diagnosis of thymic carcinoma were analyzed with logistic regression models. Parameters identified as significant in univariate analysis were included in multiple analyses. Receiver-operating characteristic (ROC) curves were assessed to evaluate the diagnostic performance.ResultsThymic carcinoma was significantly predominant in men (P=0.001). Optimal radiomics features for predicting thymic carcinoma were as follows: gray-level co-occurrence matrix (GLCM)-homogeneity, GLCM-energy, compactness, large zone high gray-level emphasis (LZHGE), solidity, size of minor axis, and kurtosis. Multiple logistic regression analysis of these features revealed solidity and GLCM-energy as independent indicators associated with thymic carcinoma [odds ratio, 14.7 and 14.3; 95% confidence interval (CI): 1.6-139.0 and 3.0-68.7; and P=0.045 and 0.002, respectively]. Area under the curve (AUC) for diagnosing thymic carcinoma was 0.882 (sensitivity, 81.2%; specificity, 91.1%). Multivariate analysis adjusted for sex similarly revealed two features (solidity and GLCM-energy) as independent indicators associated with thymic carcinoma (odds ratio, 14.6 and 23.9; 95% CI: 2.4-89.2 and 1.9-302.8; P=0.004 and 0.014, respectively). Adjusted AUC for diagnosing thymic carcinoma was 0.921 (95% CI: 0.82-0.97): sensitivity, 62.5% and specificity, 100%.ConclusionsTwo texture features (GLCM-energy and solidity) were significant predictors of thymic carcinoma.

Dataset Information

Molecular Profiling of Thymoma and Thymic Carcinoma: Genetic Differences and Potential Novel Therapeutic Targets.

Publications

Molecular Profiling of Thymoma and Thymic Carcinoma: Genetic Differences and Potential Novel Therapeutic Targets.

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