Expanding the spectrum of low-grade sinonasal adenocarcinoma with biphasic seromucinous differentiation and activating HRAS/AKT1 mutations.

AIMS: Low-grade non-intestinal-type sinonasal adenocarcinoma (LGSNAC) is a rare heterogeneous and poorly characterised group of tumours, distinct from intestinal- and salivary-type neoplasms. Therefore, further characterisation is needed for clearer biological understanding and classification. METHODS AND RESULTS: Clinical, histological and molecular characterisation of four cases of biphasic, low-grade adenocarcinomas of the sinonasal tract was performed. All patients were male, aged between 48 and 78 years, who presented with polypoid masses in the nasal cavity. Microscopically, virtually all tumours were dominated by tubulo-glandular biphasic patterns, microcystic, focal (micro)papillary, oncocytic or basaloid features. Immunohistochemical staining confirmed biphasic differentiation with an outer layer of myoepithelial cells. Molecular profiling revealed HRAS (p.G13R, p.Q61R) mutations, and concomitant AKT1 (p.E17K, p.Q79R) mutations in two cases. Two cases showed potential in-situ/precursor lesions adjacent to the tumour. Follow-up periods ranged from 1 to 30 months, with one case relapsing locally after 12 and > 20 years. CONCLUSION: This study further corroborates a distinct biphasic low-grade neoplasm of the sinonasal tract with seromucinous differentiation. Although morphological and molecular features overlap with salivary gland epithelial-myoepithelial carcinoma, several arguments favour categorising these tumours within the spectrum of LGSNAC.

Rare case of tentorial cavernous malformation mimicking a meningioma: illustrative case.

BACKGROUND: Cavernous malformations of the dura, especially of the tentorium, are exceedingly rare. In the available literature, only 10 cases have been described to date. OBSERVATIONS: The authors present the case of a 46-year-old male patient with a 1-cm infratentorial lesion suspicious for meningioma that was found on routine magnetic resonance imaging (MRI) performed for vertigo. The lesion was followed for 1.5 years with no change in signal and size. Nevertheless, the patient was concerned about the lesion and requested removal. The removal was successful and without any neurological sequelae. However, histological evaluation demonstrated a cavernous malformation. Postoperative computed tomography and MRI showed complete removal. Preoperative MRI characteristics, intraoperative images, and a video, as well as histological evaluation, are shown. The case is discussed with respect to the literature. LESSONS: Cavernous malformations of the tentorium are extremely rare and mimic meningiomas; thus, they need to be taken into account. DOTATOC positron emission tomography may help to differentiate in these cases. Considering the cases reported in the literature, in cases of large tumors, preoperative angiography and possibly embolization may be helpful. https://thejns.org/doi/10.3171/CASE24168.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.