Malignant melanotic nerve sheath tumor with PRKAR1A, KMT2C, and GNAQ mutations.

Malignant melanotic nerve sheath tumor (MMNST) is a rare and potentially aggressive lesion defined in the 2021 WHO Classification of Tumors of the Central Nervous System. MMNST demonstrate overlapping histologic and clinical features of schwannoma and melanoma. MMNST often harbor PRKAR1A mutations, especially within the Carney Complex. We present a case of aggressive MMNST of the sacral region in a 48-year-old woman. The tumor contained PRKAR1A frameshift pR352Hfs*89, KMT2C splice site c.7443-1G>T and GNAQ p.R183L missense mutations, as well as BRAF and MYC gains. Genomic DNA methylation analysis using the Illumina 850K EpicBead chip revealed that the lesion did not match an established methylation class; however, uniform manifold approximation and projection (UMAP) placed the tumor very near schwannomas. The tumor expressed PD-L1, and the patient was treated with radiation and immune checkpoint inhibitors following en bloc resection. Although she had symptomatic improvement, she suffered early disease progression with local recurrence, and distant metastases, and died 18 months after resection. It has been suggested that the presence of GNAQ mutations can differentiate leptomeningeal melanocytic neoplasms and uveal melanoma from MMNST. This case and others demonstrate that GNAQ mutations may exist in malignant nerve sheath tumors; that GNAQ and PRKAR1A mutations are not always mutually exclusive and that neither can be used to differentiate MMNST or MPNST from all melanocytic lesions.

Tracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers.

Epidemiologic studies often rely on questionnaire data, exposure measurement tools, and/or biomarkers to identify risk factors and the underlying carcinogenic processes. An emerging and promising complementary approach to investigate cancer etiology is the study of somatic "mutational signatures" that endogenous and exogenous processes imprint on the cellular genome. These signatures can be identified from a complex web of somatic mutations thanks to advances in DNA sequencing technology and analytical algorithms. This approach is at the core of the Sherlock-Lung study (2018-ongoing), a retrospective case-only study of over 2,000 lung cancers in never-smokers (LCINS), using different patterns of mutations observed within LCINS tumors to trace back possible exposures or endogenous processes. Whole genome and transcriptome sequencing, genome-wide methylation, microbiome, and other analyses are integrated with data from histological and radiological imaging, lifestyle, demographic characteristics, environmental and occupational exposures, and medical records to classify LCINS into subtypes that could reveal distinct risk factors. To date, we have received samples and data from 1,370 LCINS cases from 17 study sites worldwide and whole-genome sequencing has been completed on 1,257 samples. Here, we present the Sherlock-Lung study design and analytical strategy, also illustrating some empirical challenges and the potential for this approach in future epidemiologic studies.