A new amplicon-based gene panel for next generation sequencing characterization of meningiomas

A Cohort Study of CNS Tumors in Multiple Endocrine Neoplasia Type 1

PURPOSE

Multiple endocrine neoplasia type 1 (MEN1) is thought to increase the risk of meningioma and ependymoma. Thus, we aimed to describe the frequency, incidence, and specific clinical and histological features of central nervous system (CNS) tumors in the MEN1 population (except pituitary tumors).

EXPERIMENTAL DESIGN

The study population included patients harboring CNS tumors diagnosed with MEN1 syndrome after 1990 and followed up in the French MEN1 national cohort. The standardized incidence ratio (SIR) was calculated based on the French Gironde CNS Tumor Registry. Genomic analyses were performed on somatic DNA from seven CNS tumors, including meningiomas and ependymomas from patients with MEN1, and then on 50 sporadic meningiomas and ependymomas.

RESULTS

A total of 29 CNS tumors were found among the 1,498 symptomatic patients (2%; incidence = 47.4/100,000 person-years; SIR = 4.5), including 12 meningiomas (0.8%; incidence = 16.2/100,000; SIR = 2.5), 8 ependymomas (0.5%; incidence = 10.8/100,000; SIR = 17.6), 5 astrocytomas (0.3%; incidence = 6.7/100,000; SIR = 5.8), and 4 schwannomas (0.3%; incidence = 5.4/100,000; SIR = 12.7). Meningiomas in patients with MEN1 were benign, mostly meningothelial, with 11 years earlier onset compared with the sporadic population and an F/M ratio of 1/1. Spinal and cranial ependymomas were mostly classified as World Health Organization grade 2. A biallelic MEN1 inactivation was observed in 4/5 ependymomas and 1/2 meningiomas from patients with MEN1, whereas MEN1 deletion in one allele was present in 3/41 and 0/9 sporadic meningiomas and ependymomas, respectively.

CONCLUSIONS

The incidence of each CNS tumor was higher in the MEN1 population than in the French general population. Meningiomas and ependymomas should be considered part of the MEN1 syndrome, but somatic molecular data are missing to conclude for astrocytomas and schwannomas.

Angiosarcoma on Top of a Meningioma Mimicking a Transosseous Meningioma: an Interdisciplinary Point of View

Cutaneous angiosarcoma is a rare type of sarcoma with poor prognosis. Meningioma is the most frequent benign intracranial tumor. Despite the fact that meningiomas are mostly benign, bone and skin can be infiltrated. We report the rare case of an angiosarcoma on top of a meningioma with hyperostosis at exactly the same location mimicking a transosseous growth of a meningioma. An 84-year-old man presented with progressive swelling and ulcerous lesion of the forehead. The patient underwent surgery in an interdisciplinary setting together with a plastic surgeon, including resection of the intracranial tumor and infiltrated bone and skin. To the best of our knowledge, this is the only reported case of a meningioma and angiosarcoma in direct neighborhood. A preoperative biopsy of the skin tumor would have led initially to the correct diagnosis of an angiosarcoma and would have allowed a better planning of the operation and extent of resection.

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas

PURPOSE

In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment.

METHODS

Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting.

RESULTS

A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance.

CONCLUSIONS

Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.