Dataset Information

EZH2 and POU2F3 Can Aid in the Distinction of Thymic Carcinoma from Thymoma.

ABSTRACT: Thymic carcinoma is an aggressive malignancy that can be challenging to distinguish from thymoma using histomorphology. We assessed two emerging markers for these entities, EZH2 and POU2F3, and compared them with conventional immunostains. Whole slide sections of 37 thymic carcinomas, 23 type A thymomas, 13 type B3 thymomas, and 8 micronodular thymomas with lymphoid stroma (MNTLS) were immunostained for EZH2, POU2F3, CD117, CD5, TdT, BAP1, and MTAP. POU2F3 (≥10% hotspot staining), CD117, and CD5 showed 100% specificity for thymic carcinoma versus thymoma with 51%, 86%, and 35% sensitivity, respectively, for thymic carcinoma. All POU2F3 positive cases were also positive for CD117. All thymic carcinomas showed >10% EZH2 staining. EZH2 (≥80% staining) had a sensitivity of 81% for thymic carcinoma and a specificity of 100% for thymic carcinoma versus type A thymoma and MNTLS but had poor specificity (46%) for thymic carcinoma versus B3 thymoma. Adding EZH2 to a panel of CD117, TdT, BAP1, and MTAP increased cases with informative results from 67/81 (83%) to 77/81 (95%). Overall, absent EZH2 staining may be useful for excluding thymic carcinoma, diffuse EZH2 staining may help to exclude type A thymoma and MNTLS, and ≥10% POU2F3 staining has excellent specificity for thymic carcinoma versus thymoma.

SUBMITTER: Naso JR

PROVIDER: S-EPMC10137080 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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EZH2 and POU2F3 Can Aid in the Distinction of Thymic Carcinoma from Thymoma.

Naso Julia R JR Vrana Julie A JA Koepplin Justin W JW Molina Julian R JR Roden Anja C AC

Cancers 20230413 8

Thymic carcinoma is an aggressive malignancy that can be challenging to distinguish from thymoma using histomorphology. We assessed two emerging markers for these entities, EZH2 and POU2F3, and compared them with conventional immunostains. Whole slide sections of 37 thymic carcinomas, 23 type A thymomas, 13 type B3 thymomas, and 8 micronodular thymomas with lymphoid stroma (MNTLS) were immunostained for EZH2, POU2F3, CD117, CD5, TdT, BAP1, and MTAP. POU2F3 (≥10% hotspot staining), CD117, and CD5 ...[more]

PMID: 37190202

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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.

Project description:BackgroundThymoma and thymic carcinoma are the most common tumors of the anterior mediastinum. However, there are little research on applying machine learning (ML) approaches to the prognostic prediction of thymoma and thymic carcinoma. The study aims to develop predictive models utilizing ML techniques to accurately forecast the 5-year survival of patients with thymoma and thymic carcinoma.MethodsPatients with malignant thymic neoplasms were identified in the Surveillance, Epidemiology, and End Results (SEER) 17 database, and their demographic and clinicopathological characteristics were collected. ML classifiers, including elastic net regularized logistic regression, random forest (RF), non-linear support vector machine (SVM), extreme gradient boosting (XGBoost) machine, and categorical boosting (CatBoost) were trained. The hyper-parameter of the algorithms was optimized by a grid search with five repeats of 10-fold cross-validation. Ensemble models were built based on the three algorithms with the highest area under the receiver operator characteristic (ROC) curve (AUC) in the validation set. The best model among the single models and ensemble model was selected as the final model. Calibration curve and decision curve were adopted to evaluate the calibration performance and clinical utility. For comparison, we constructed a baseline model consisting of age and Masaoka stages using logistic regression.ResultsAfter data cleaning, 1,363 patients and 841 patients were included in the overall survival (OS) dataset and disease-specific survival (DSS) dataset, respectively. CatBoost [AUC: 0.755; 95% confidence interval (CI): 0.698-0.811] had the best performance in the OS prediction for the original dataset. The ensemble model achieved the highest prognostic efficiency for the original dataset, with an AUC of 0.833 (95% CI: 0.765-0.901). Calibration showed favorable goodness of fit and was further verified with the Hosmer-Lemeshow test (CatBoost: χ2=12.63, P=0.13; ensemble model: χ2=7.61, P=0.47). The decision curve showed that the final model provided a high net benefit. The model could significantly distinguish the prognosis of patients (all P values <0.001). Finally, World Health Organization (WHO) histological classification, Masaoka stage, and age were the variables that significantly contributed to the models' prediction of OS and DSS.ConclusionsWe trained ML-based predictive models that could accurately predict the 5-year OS and DSS of patients with thymoma and thymic carcinoma.

Project description:BackgroundNo standard treatments are available for advanced thymic epithelial tumours after failure of platinum-based chemotherapy. We investigated the activity of sunitinib, an orally administered tyrosine kinase inhibitor.MethodsBetween May 15, 2012, and Oct 2, 2013, we did an open-label phase 2 trial in patients with histologically confirmed chemotherapy-refractory thymic epithelial tumours. Patients were eligible if they had disease progression after at least one previous regimen of platinum-containing chemotherapy, an Eastern Cooperative Oncology Group performance status of two or lower, measurable disease, and adequate organ function. Patients received 50 mg of sunitinib orally once a day, in 6-week cycles (ie, 4 weeks of treatment followed by 2 weeks without treatment), until tumour progression or unacceptable toxic effects arose. The primary endpoint was investigator-assessed best tumour response at any point, which we analysed separately in thymoma and thymic carcinoma cohorts. Patients who had received at least one cycle of treatment and had their disease reassessed were included in the analyses of response. The trial was registered with ClinicalTrials.gov, number NCT01621568.Findings41 patients were enrolled, 25 with thymic carcinoma and 16 with thymoma. One patient with thymic carcinoma was deemed ineligible after enrolment and did not receive protocol treatment. Of patients who received treatment, one individual with thymic carcinoma was not assessable because she died. Median follow-up on trial was 17 months (IQR 14.0-18.4). Of 23 assessable patients with thymic carcinoma, six (26%, 90% CI 12.1-45.3, 95% CI 10.2-48.4) had partial responses, 15 (65%, 95% CI 42.7-83.6) achieved stable disease, and two (9%, 1.1-28.0) had progressive disease. Of 16 patients with thymoma, one (6%, 95% CI 0.2-30.2) had a partial response, 12 (75%, 47.6-92.7) had stable disease, and three (19%, 4.1-45.7) had progressive disease. The most common grade 3 and 4 treatment-related adverse events were lymphocytopenia (eight [20%] of 40 patients), fatigue (eight [20%]), and oral mucositis (eight [20%]). Five (13%) patients had decreases in left-ventricular ejection fraction, of which three (8%) were grade 3 events. Three (8%) patients died during treatment, including one individual who died of cardiac arrest that was possibly treatment-related.InterpretationSunitinib is active in previously treated patients with thymic carcinoma. Further studies are needed to identify potential biomarkers of activity.FundingNational Cancer Institute (Cancer Therapy Evaluation Program).

Dataset Information

EZH2 and POU2F3 Can Aid in the Distinction of Thymic Carcinoma from Thymoma.

Publications

EZH2 and POU2F3 Can Aid in the Distinction of Thymic Carcinoma from Thymoma.

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