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Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.

ABSTRACT: Further characterization of thymic epithelial tumors (TETs) is needed. Genomic information from 102 evaluable TETs from The Cancer Genome Atlas (TCGA) dataset and from the IU-TAB-1 cell line (type AB thymoma) underwent clustering analysis to identify molecular subtypes of TETs. Six novel molecular subtypes (TH1-TH6) of TETs from the TCGA were identified, and there was no association with WHO histologic subtype. The IU-TAB-1 cell line clustered into the TH4 molecular subtype and in vitro testing of candidate therapeutics was performed. The IU-TAB-1 cell line was noted to be resistant to everolimus (mTORC1 inhibitor) and sensitive to nelfinavir (AKT1 inhibitor) across the endpoints measured. Sensitivity to nelfinavir was due to the IU-TAB-1 cell line's gain-of function (GOF) mutation in PIK3CA and amplification of genes observed from array comparative genomic hybridization (aCGH), including AURKA, ERBB2, KIT, PDGFRA and PDGFB, that are known upregulate AKT, while resistance to everolimus was primarily driven by upregulation of downstream signaling of KIT, PDGFRA and PDGFB in the presence of mTORC1 inhibition. We present a novel molecular classification of TETs independent of WHO histologic subtype, which may be used for preclinical validation studies of potential candidate therapeutics of interest for this rare disease.

SUBMITTER: Padda SK

PROVIDER: S-EPMC8202771 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.

Padda Sukhmani K SK Gökmen-Polar Yesim Y Hellyer Jessica A JA Badve Sunil S SS Singh Neeraj K NK Vasista Sumanth M SM Basu Kabya K Kumar Ansu A Wakelee Heather A HA

Oncotarget 20210608 12

Further characterization of thymic epithelial tumors (TETs) is needed. Genomic information from 102 evaluable TETs from The Cancer Genome Atlas (TCGA) dataset and from the IU-TAB-1 cell line (type AB thymoma) underwent clustering analysis to identify molecular subtypes of TETs. Six novel molecular subtypes (TH1-TH6) of TETs from the TCGA were identified, and there was no association with WHO histologic subtype. The IU-TAB-1 cell line clustered into the TH4 molecular subtype and <i>in vitro</i> t ...[more]

PMID: 34136086

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Project description:BackgroundThymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients.MethodsTotally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots.ResultsTotally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications.ConclusionA prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.

Project description:Background and purposeMeningiomas are very commonly diagnosed intracranial primary neoplasms, of which the chordoid subtype is seldom encountered. Our aim was to retrospectively review preoperative MR imaging of intracranial chordoid meningiomas, a rare WHO grade II variant, in an effort to determine if there exist distinguishing MR imaging characteristics that can aid in differentiating this atypical variety from other meningioma subtypes.Materials and methodsTen cases of WHO grade II chordoid meningioma were diagnosed at our institution over an 11-year span, 8 of which had preoperative MR imaging available for review and were included in our analysis. Chordoid meningioma MR imaging characteristics, including ADC values and normalized ADC ratios, were compared with those of 80 consecutive cases of WHO grade I meningioma, 21 consecutive cases of nonchordoid WHO grade II meningioma, and 1 case of WHO grade III meningioma.ResultsPreoperative MR imaging revealed no significant differences in size, location, signal characteristics, or contrast enhancement between chordoid meningiomas and other meningiomas. There were, however, clear differences in the ADC values and normalized ADC ratios, with a mean absolute ADC value of 1.62 ± 0.33 × 10(-3) mm(2)/s and a mean normalized ADC ratio of 2.22 ± 0.47 × 10(-3) mm(2)/s in chordoid meningiomas compared with mean ADC and normalized ADC values, respectively, of 0.88 ± 0.13 × 10(-3) mm(2)/s and 1.17 ± 0.16 × 10(-3) mm(2)/s in benign WHO grade I meningiomas, 0.84 ± 0.11 × 10(-3) mm(2)/s and 1.11 ± 0.15 × 10(-3) mm(2)/s in nonchordoid WHO grade II meningiomas, and 0.57 × 10(-3) mm(2)/s and 0.75 × 10(-3) mm(2)/s in the 1 WHO grade III meningioma.ConclusionsChordoid meningiomas have statistically significant elevations of ADC and normalized ADC values when compared with all other WHO grade I, II, and III subtypes, which enables reliable preoperative prediction of this atypical histopathologic diagnosis.

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Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.

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Genomic clustering analysis identifies molecular subtypes of thymic epithelial tumors independent of World Health Organization histologic type.

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