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Context.--Dedifferentiated chondrosarcoma is a rare adult bone tumor with a dismal prognosis and is composed of a conventional chondrosarcoma juxtaposed to high-grade nonchondrogenic sarcoma. Dedifferentiated chondrosarcomas may represent tumor progression from a differentiated to a primitive histotype. Objective.--To determine the genetic and molecular events that drive progression from a conventional chondrosarcoma to high grade nonchondrogenic sarcoma. Design.--We analyzed the genomic landscape of paired conventional and dedifferentiated components of 11 dedifferentiated chondrosarcoma using targeted next-generation DNA sequencing with immunohistochemical validation. Clinical, radiographic, and pathologic features of tumors were reviewed. Capture-based DNA sequencing targeting the coding regions of 479 cancer genes and select introns was performed. Results.--The tumors arose in the femur (n = 4; 36%), scapula (n = 3; 27%), pelvis (n = 3; 27%), and humerus (n = 1; 9%) of 7 men (64%) and 4 women (36%; median age, 61 years). DNA was adequate for sequencing from all 11 dedifferentiated components (100%) and 9 paired conventional chondrosarcoma components (82%). All tumors (100%) harbored either IDH1 p.R132 or IDH2 p.R172S hotspot mutations. Seven tumors (64%) displayed COL2A1 alterations. TERT promoter mutations were present in 5 of 9 pairs (56%) and 2 (22%) additional unpaired dedifferentiated components. IDH1/2, COL2A1, and TERT mutations were identical in both components of the paired samples. Pathogenic missense or truncating mutations in TP53 and large-scale copy number alterations were more common in dedifferentiated components than in those of matched conventional components. Conclusions.--The results support IDH1/2, COL2A1, and TERT promoter mutations being common in dedifferentiated chondrosarcoma and as likely early events in progression, whereas inactivating mutation of TP53 and high-level copy number alterations may be later events in the dedifferentiated phenotype. doi: 10.5858/arpa.2020-0379-OA

Chromosome instability leading to aneuploidy and accumulation of copy number gains or losses is a hallmark of cancer. Copy number alteration (CNA) signatures are increasingly used for cancer risk stratification, but size thresholds for defining CNAs across cancers are variable and the biological and clinical implications of CNA size heterogeneity and co-occurrence are incompletely understood. Here we analyze CNA and clinical data from 691 meningiomas and 10,383 tumors from The Cancer Genome Atlas to develop cancer- and chromosome-specific size-dependent CNA and CNA co-occurrence models to predict tumor control and overall survival. Our results shed light on technical considerations for biomarker development and reveal prognostic CNAs with optimized size thresholds and co-occurrence patterns that refine risk stratification across a diversity of cancer types. These data suggest that consideration of CNA size, focality, number, and co-occurrence can be used to identify biomarkers of aggressive tumor behavior that may be useful for individualized risk stratification. Currently, the biological and clinical implications of copy number alteration (CNA) size heterogeneity and co-occurrence are incompletely understood. Here, the authors use 691 meningiomas and 29 cancer types from The Cancer Genome Atlas to develop size-dependent CNA and CNA co-occurrence models to optimize individualized pan-cancer risk stratification.

Schwann cell tumors are the most common cancers of the peripheral nervous system and can arise in patients with neurofibromatosis type-1 (NF-1) or neurofibromatosis type-2 (NF-2). Functional interactions between NF1 and NF2 and broader mechanisms underlying malignant transformation of the Schwann lineage are unclear. Here we integrate bulk and single-cell genomics, biochemistry, and pharmacology across human samples, cell lines, and mouse allografts to identify cellular de-differentiation mechanisms driving malignant transformation and treatment resistance. We find DNA methylation groups of Schwann cell tumors can be distinguished by differentiation programs that correlate with response to the MEK inhibitor selumetinib. Functional genomic screening in NF1-mutant tumor cells reveals NF2 loss and PAK activation underlie selumetinib resistance, and we find that concurrent MEK and PAK inhibition is effective in vivo. These data support a de-differentiation paradigm underlying malignant transformation and treatment resistance of Schwann cell tumors and elucidate a functional link between NF1 and NF2. The molecular mechanisms underlying malignant transformation of the Schwann lineage in Schwann cell tumours remain to be explored. Here, the authors suggest that NF2 inactivation leads to PAK activation leading to NF1-mutant Schwann cell tumour de-differentiation and resistance to selumetinib.

Background Treatment for patients with malignant peripheral nerve sheath tumors (MPNST) is an unmet clinical need. Loss of NF1 in MPNST leads to hyperactivation of RAS, however little is known about relevant downstream oncogenic signaling through RAF paralogs and effective targeted therapies in MPNST are still lacking. Methods Conditional gene expression, CRISPR-CAS9, and shRNA-mediated knockdown were used to perform gain/loss-of-function experiments to explore the effect of reconstituting the GTPase-activating protein-related domain of NF1 or knockdown of A/B/CRAF kinases on ERK signaling output and MPNST cell growth. Colony formation, cell proliferation and live cells imaging assays were performed to assess cell growth in response to genetic manipulations or drug treatments. Pathway enrichment analysis on RNA sequencing following drug perturbation, efficacy studies in cell-line-derived and patient-derived xenograft models, and immunoblotting/immunohistochemistry were conducted to assess tumor growth and ERK pathway activity in cells or in pharmacodynamic analyses of tumor xenografts. Results NF1 loss activates RAS/ERK signaling through B/CRAF, and cell growth and ERK signaling of NF1-MPNST are dependent on B/CRAF, but not ARAF. Genetic or pharmacological inhibition of B/CRAF using a paralog-selective RAF inhibitor (RAFi) significantly potentiates MEK inhibitor (MEKi) treatment through more effective suppression of ERK signaling and proliferation. This is shown in multiple traditional and patient-derived cell line and xenograft models, including those with acquired resistance to MEKi. Conclusions These findings contribute preclinical evidence that the combination of paralog-selective B/CRAFi and MEKi is effective in NF1-MPNST and can overcome resistance to single agent MEKi.

Recent advances in artificial intelligence (AI) and computer vision empower deep-learning models to infer molecular features from histopathological images to classify CNS tumours. The aim of this study was to test the classification accuracy of a molecular inference-based AI assistant for CNS tumour diagnosis. In this multi-institutional, retrospective study, we used data from whole slide images of samples from patients aged 0–95 years, diagnosed with primary or recurrent CNS tumours. Reference diagnostic labels were determined by DNA methylation-based tumour classification to match one of 52 tumour types selected to encompass most types of gliomas, embryonal tumours, and meningeal and mesenchymal tumours encountered in clinical practice. The Neuropath-AI model was trained on 5835 samples from the National Cancer Institute (NCI; USA), the Children's Brain Tumor Network (USA), and the Digital Brain Tumour Atlas (Austria) to infer molecular features from whole slide images and to use these to predict tumour types with associated confidence scores. The test cohort comprised 5516 samples identified in laboratory archives between May 17, 2024, and May 13, 2025, from the NCI, Northwestern Medicine (USA), University of Pittsburgh Medical Center (USA), and University College London (UK). There were 2753 (50%) female and 2763 (50%) male patients, median age 43 years (IQR 25–59). The primary objective was to measure the classification accuracy of the model family-level and terminal classification predictions in test samples, with coprimary endpoints of sample coverage and prediction and balanced accuracy. Sample coverage was defined as samples receiving a model prediction with a confidence score above a specified threshold. Prediction accuracy and balanced accuracy were analysed in the covered samples (ie, those meeting the confidence criterion) and evaluated by comparing the top-1 or top-2 predictions with reference labels. Family-level classifications were reached in 5299 (96%) of 5516 samples. Predictions to one of the terminal classifications with at least moderate confidence were reached for 4772 (87%) samples. The single highest-scoring classification matched the reference label in 3817 (95% CI 3770–3865; 80% [95% CI 79–81]) of 4772 samples (balanced accuracy 66% [95% CI 63–70]). The two highest-scoring classifications contained the reference label in 4103 (95% CI 4056–4152; 86% [95% CI 85–87]) of 4772 samples (balanced accuracy 75% [95% CI 71–78]). Our model provides the basis for a clinically applicable deep-learning assistant to improve human efficiency and accuracy of CNS tumour diagnosis. The model will be made publicly available and could be implemented to assist human pathologists in future prospective studies. The Intramural Research Program of the National Institutes of Health.

Primary central nervous system lymphoma (PCNSL) is a rare form of extranodal non-Hodgkin lymphoma that primarily arises in the brain, spinal cord, leptomeninges, and vitreoretinal compartment of the eye. The term is sometimes used interchangeably with primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) because DLBCL comprises a great majority (90–95%) of PCNSL. Although rare, other types of lymphomas can be seen in the central nervous system (CNS), and familiarity with these entities will help their recognition and further workup in order to establish the diagnosis. The latter is especially important in the case of PCNSL where procurement of diagnostic specimen is often challenging and yields scant tissue. In this review, we will discuss the most common types of primary lymphomas that can be seen in the CNS with emphasis on the diagnostic histomorphologic, immunophenotypic, and molecular genetic features. The differential diagnostic approach to these cases and potential pitfalls will also be discussed.

Abstract Composite pleomorphic xanthoastrocytoma-ganglioglioma (PXA-GG) is an extremely rare central nervous system neoplasm with 2 distinct but intermingled components. Whether this tumor represents a “collision tumor” of separate neoplasms or a monoclonal neoplasm with divergent evolution is poorly understood. Clinicopathologic studies and capture-based next generation sequencing were performed on extracted DNA from all available PXA-GG at 2 medical centers. Five PXA-GG were diagnosed in 1 male and 4 female patients ranging from 13 to 25 years in age. Four arose within the cerebral hemispheres; 1 presented in the cerebellar vermis. DNA was sufficient for analysis in 4 PXA components and 3 GG components. Four paired PXA and GG components harbored BRAF p.V600E hotspot mutations. The 4 sequenced PXA components demonstrated CDKN2A homozygous deletion by sequencing with loss of p16 (protein product of CDKN2A) expression by immunohistochemistry, which was intact in all assessed GG components. The PXA components also demonstrated more frequent copy number alterations relative to paired GG components. In one PXA-GG, shared chromosomal copy number alterations were identified in both components. Our findings support divergent evolution of the PXA and GG components from a common BRAF p.V600E-mutant precursor lesion, with additional acquisition of CDKN2A homozygous deletion in the PXA component as is typically seen in conventional PXA.

Meningiomas are the most common primary intracranial tumors. There are no effective medical therapies for meningioma patients, and new treatments have been encumbered by limited understanding of meningioma biology. Here, we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic and single-cell approaches to show meningiomas are composed of three DNA methylation groups with distinct clinical outcomes, biological drivers and therapeutic vulnerabilities. Merlin-intact meningiomas (34%) have the best outcomes and are distinguished by NF2 /Merlin regulation of susceptibility to cytotoxic therapy. Immune-enriched meningiomas (38%) have intermediate outcomes and are distinguished by immune infiltration, HLA expression and lymphatic vessels. Hypermitotic meningiomas (28%) have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. To translate these findings into clinical practice, we show cytostatic cell cycle inhibitors attenuate meningioma growth in cell culture, organoids, xenografts and patients. DNA methylation profiling of 565 meningiomas highlights three groups associated with distinct molecular, clinical and therapeutic features.

Background NF1 inactivation is associated with sensitivity to MEK inhibitor targeted therapy in low-grade and some high-grade gliomas. NF1 loss may also be a harbinger of exploitable vulnerabilities in IDH-wildtype glioblastoma (GBM). Accurate and consistent detection of NF1 loss, however, is fraught given the large gene size, challenges with complete coverage and variant calling upon sequencing, and mechanisms of mRNA and protein regulation that result in early degradation in the absence of genomic alterations. Here, we seek to perform a composite analysis for NF1 loss accounting for genomic alterations and protein expression via immunohistochemistry. We also characterize the landscape of NF1 alterations in GBM. Methods We assembled a single-institution, retrospective cohort of 542 IDH-wildtype GBM with somatic next generation sequencing to investigate the frequency and nature of detected NF1 alterations. We selected 69 GBMs from which to build a tissue microarray (TMA) of 44 NF1 -wildtype and 25 NF1 -mutant cases. We performed NF1 immunohistochemistry using two different NF1 antibodies (NFC, Sigma-Aldrich; and iNF-07E, iNFixion Bioscience) and correlated results with clinical, genomic, and other immunohistochemical features. Results In our retrospective cohort, we identified 88 IDH-wildtype GBM with NF1 alterations (16%). NF1 alterations were mutually exclusive with EGFR and MDM2 alterations ( p -adj < 0.001, 0.05, respectively), but co-occurred with PIK3R1 alterations (Log 2 (OR) = − 1.6, p-adj = 0.03). Of the 63 scorable sporadic GBMs in the TMA, 14 harbored NF1 inactivating alterations and of those, 12 (86%) demonstrated minimal NF1 immunoreactivity by NFC antibody, compared to 8 (57%) by iNF-07E antibody. Among the 42 scorable NF1 -wildtype GBM in the TMA, NF1 immunostaining was minimal in 18 (43%) by NFC antibody compared to 4 (10%) by iNF-07E antibody, potentially reflecting false positives or differential protein regulation. Minimal immunoreactivity by NFC antibody was associated with decreased median overall survival (8.5 vs. 16.4 months, p  = 0.011). Cox proportional hazards model correcting for prognostic variables in this subset revealed HR 3.23 (95% CI 1.29–8.06, p  = 0.01) associated with decreased NF1 expression by IHC. Conclusion NF1 immunostaining may serve as a sensitive surrogate marker of NF1 genomic inactivation and a valuable extension to next-generation sequencing for defining NF1 status. Minimal NF1 immunoreactivity is a poor prognostic marker, even in IDH-wildtype glioblastoma without apparent NF1 genomic alterations, but the underlying molecular mechanism requires further investigation.