Arie Perry

Background: Tumor invasion and malignant transformation are spatiotemporally coordinated processes that shape clinical behavior, diagnostic classification, and treatment response. Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive tumors that can arise sporadically or in people with neurofibromatosis type 1 (NF-1), yet how distinct spatial niches underlie MPNST intratumor heterogeneity, local invasion, or malignant transformation from benign precursors is not fully understood. Methods: We integrate spatial analysis of 50,896 transcriptomes from 15 human MPNSTs with deep learning image analysis to define the interplay between genetic alterations, the immune microenvironment, and epigenetic cell fate regulation in MPNST invasion and transformation. Results: Spatial transcriptomics reveal distinct infiltrating immune cells and immunomodulatory spatial clusters in sporadic compared to NF-1–associated MPNSTs. Local invasion is marked by spatially defined immunomodulatory and dedifferentiation programs. Analysis of 2 NF-1–associated MPNSTs with contiguous morphologically defined neurofibroma and MPNST components demonstrates that NF-1–associated tumors exhibit a similar malignant transformation process associated with dynamic immune infiltration, chromosome 9p loss, and dedifferentiation concomitant with increased cell proliferation. Finally, deep learning morphology encoders were trained on spatial transcriptomic data to predict spatial transcriptomic cluster identity and chromosome 9p loss from hematoxylin and eosin–stained images although the generalizability of such encoders remains a challenge. Conclusions: Our data provide a spatial framework for peripheral nerve sheath tumor transformation although the limited sample size of the present cohort will require additional validation of the proposed model.

Background: High-grade astrocytoma with piloid features (HGAP) is a recently defined tumor type that is not well understood. Prognostic factors of clinical outcomes are not well established. Methods: Methylation profiling was performed on tumor samples, many at the National Cancer Institute (NCI) Laboratory of Pathology, and others from publicly available sources. Methylation classifier scores of ≥0.90 to the HGAP class on the NCI-Bethesda classifier version 3 were included. Clinical features were collected from the medical record. Survival analyses were performed using the Kaplan-Meier and Cox-proportional hazards methods. Results: The cohort comprised 421 patients. There were high rates of ATRX alteration (62%), CDKN2A/B homozygous loss (78%) and MGMT promoter methylation (53%). MAPK alterations were identified in 74% of evaluable samples. The median age was 46 years, and posterior fossa location was predominant (52%). The median overall survival was 88 months. Older age (P = .01) and the presence of an ATRX alteration (P = .04) were found to be negative prognostic factors. The presence of cystic features on magnetic resonance imaging (MRI) was found to be favorably prognostic (P = .01). Factors that were not significantly associated with prognosis included histologic high-grade features, CDKN2A/B homozygous deletion, MGMT promoter methylation, extent of resection, and presence of NF1 syndrome. Conclusions: This large cohort establishes relative frequencies of several important markers. Additionally, older age, the presence of an ATRX alteration, and cystic features on MRI were found to be prognostic. Our work may aid in optimizing treatment regimens for patients with this tumor type.

Background: Molecular profiling is increasingly important for risk stratification and individualization of treatment recommendations for patients with meningioma. Prior investigations have established the genomic architecture of meningioma, but the distribution of meningioma copy number alterations (CNAs) and short somatic variants (SSVs) across demographic groups is incompletely understood. Here we define a prospective, consecutive benchmark for meningioma CNAs and SSVs across demographic groups. Methods: Prospective next-generation DNA sequencing was performed on 1,104 tumor samples from 1,044 consecutive patients who were treated for meningioma or whose meningiomas underwent molecular testing at a large academic medical center in a major US city from 2019 to 2025. Univariate and multivariate regression analyses were performed to evaluate associations between demographic characteristics, CNAs, and SSVs. Results: Meningiomas from Asian males were enriched in high-risk molecular features, including (1) copy number deletion of chromosomes 1p, 6q, and 14q, (2) copy number gain of chromosome 1q, (3) chromosome 1p/22q codeletion or co-occurrent 1p loss/1q gain, and (4) CDKN2A/B homozygous deletion. In contrast, meningiomas from females were enriched in low-risk molecular features, including SSVs in TRAF7, AKT1, and PIK3CA. The molecular architecture of meningiomas from Hispanic and non-Hispanic White individuals appeared similar, though Hispanic ethnicity was a unique independent predictor of low-grade disease. Conclusions: In this US-based cohort, the distribution of meningioma CNAs and SSVs varies across demographic groups. Asian males appear to have an increased risk of clinically aggressive meningioma due to enrichment in high-risk molecular features that were previously unappreciated in this patient population. Molecularly favorable meningiomas are more common in females, and low-grade meningiomas are more common in Hispanic individuals.

Meningiomas in pediatric and adolescent/young adult patients are poorly characterized biologically and clinically, and risk stratification is largely extrapolated from adult tumors. We analyze 293 tumors from patients aged 0-39 years using integrated histopathological and molecular profiling. Youth-onset meningiomas are enriched for NF2 and SMARCE1 alterations and exhibit a gain-dominated copy-number landscape, including recurrent chr17q gain, whereas canonical adult high-risk features, such as chr1p loss, lack prognostic significance. Adult-derived prognostic frameworks, including WHO grade, methylation-based stratification and integrated risk scores, fail to predict progression in patients ≤21 years of age. Tumors segregate into age-enriched epigenetic clusters defined by SMARCE1, NF2 and BAP1 alterations. Among NF2-altered tumors, patterns of Merlin inactivation, shaped by germline status and co-occurring copy-number variations, delineate biologically divergent subsets. In patients ≤21 years, extent of resection is the dominant predictor of outcome, while molecular features further refine risk assessment. These findings define pediatric and young adult meningiomas as a distinct molecular entity and support age-adapted risk refinement that integrates molecular features with strong clinical determinants.

Supplementary Fig. 12. Single-cell RNA sequencing of human and mouse cells from meningioma xenografts. A, Single-cell counts across experimental conditions, biological replicates (samples), and UMAP clusters from CH-157MN meningioma xenograft human cells analyzed using single-cell RNA sequencing. B, Single-cell counts across experimental conditions, biological replicates, and phases of the cell cycle from meningioma xenograft human cells analyzed using single-cell RNA sequencing. Colors and rows (samples) as in A. C, Single-cell counts across experimental conditions, biological replicates, and UMAP clusters from meningioma xenograft mouse cells analyzed using single-cell RNA sequencing. D, Stacked bar plots showing the distribution of meningioma xenograft human single-cell RNA sequencing clusters across biological replicates. Colors as in A. E, Stacked bar plots showing the distribution of meningioma xenograft mouse single-cell RNA sequencing clusters across biological replicates. Colors as in C.

Supplementary Fig. 6. WHO grade 3 meningioma immunofluorescence microscopy for NOTCH3 and the mural cell marker SMA. NOTCH3 is expressed in and out of the perivascular niche, and colocalizes with mural cells in the perivascular niche, in meningiomas with WHO grade 3 histology. SF numbers indicate individual meningiomas. Representative of n=10 WHO grade 3 meningiomas. WHO grade defined using histological criteria. Scale bars, 10µm.

Supplementary Fig. 8. WHO grade 2 meningioma immunofluorescence microscopy for NOTCH3 and the endothelial cell marker VWF. NOTCH3 expression is primarily restricted to the perivascular niche adjacent to endothelial cells in meningiomas with WHO grade 2 histology. SF number indicates meningioma identifier. Representative of n=10 WHO grade 2 meningiomas. WHO grade defined using histological criteria. Scale bar, 10µm.

Supplementary Fig. 7. WHO grade 1 meningioma immunofluorescence microscopy for NOTCH3 and the endothelial cell marker VWF. NOTCH3 expression is restricted to the perivascular niche adjacent to endothelial cells in meningiomas with WHO grade 1 histology. SF numbers indicate individual meningiomas. Representative of n=10 WHO grade 1 meningiomas. WHO grade defined using histological criteria. Scale bars, 10µm.

Supplementary Fig. 4. WHO grade 1 meningioma immunofluorescence microscopy for NOTCH3 and the mural cell marker SMA. NOTCH3 expression is restricted to the perivascular niche and colocalizes with mural cells in meningiomas with WHO grade 1 histology. SF numbers indicate individual meningiomas. WHO grade defined using histological criteria. Representative of n=10 WHO grade 1 meningiomas. Scale bars, 10µm.

Supplementary Fig. 1. Single-cell RNA sequencing of human meningiomas with loss of chromosome 22q. A, Single-cell counts across UMAP clusters from human meningioma samples with loss of chromosome 22q analyzed using single-cell RNA sequencing. B, UMAP showing single-cell RNA sequencing of human meningioma samples shaded by sample of origin. C, Stacked bar plots showing the distribution of chromosome 22q loss (red) across single-cell RNA sequencing clusters from human meningioma samples. D, UMAP and stacked bar plot showing single-cell RNA sequencing cell cycle analysis across human meningioma samples. E-G, Heatmap and feature plots showing differentially expressed genes across UMAP clusters from human meningioma samples analyzed using single-cell RNA sequencing. Colors as in A. See also Supplementary Table 1. G, Feature plots showing expression of meningioma (SSTR2, PTGDS) or cancer stem-cell markers (NOTCH3, THY1) across UMAP clusters from human meningioma samples analyzed using single-cell RNA sequencing.