Papillary renal neoplasm with reverse polarity (PRNRP) has been proposed as a distinct subtype of renal cell neoplasm with recurrent KRAS mutations and indolent behavior. However, its epigenetic landscape is poorly understood. In this study, 12 PRNRPs and a PRNRP initially diagnosed as "papillary adenoma" were analyzed. All 13 cases underwent targeted next-generation sequencing for driver mutations. Eleven PRNRPs were profiled using the Illumina MethylationEPIC array and compared with a reference cohort of 71 common renal cell tumors. KRAS mutations were identified in 12 of 13 (92%) cases of PRNRP. Copy-number analysis from methylation profiling showed that 9 of 11 (82%) PRNRPs lacked copy-number changes. Two cases showed a focal loss of chromosome 8 and a gain of chromosome 16, respectively. Unsupervised clustering based on methylation data showed that PRNRPs form a distinct epigenetic group, separate from papillary renal cell carcinomas (pRCCs) and other major renal tumors, but with the closest affinity to clear cell papillary renal cell tumors. In addition, DNA methylation analysis suggested PRNRP may arise from the distal nephron, in contrast to pRCC, which appears to recapitulate proximal tubules. These findings support PRNRP as a subtype of renal cell neoplasm with a distinct epigenetic signature.

Abstract Background Systemic immune dysregulation is well-characterized in adult high-grade gliomas but remains underexplored in pediatric population. Among these, H3K27M-mutant diffuse midline gliomas are biologically and clinically distinct, with limited therapeutic options and poor prognosis. While prior studies in medulloblastoma suggest tumor-induced lymphopenia at diagnosis, the immune profile of H3K27M gliomas has not been systematically evaluated. Methods We performed a multicenter retrospective analysis of pretreatment complete blood counts (CBCs) in pediatric patients (≤18 years) with H3K27M-mutant gliomas (n = 37), comparing them to a control cohort with pilocytic astrocytoma (n = 61). Absolute neutrophil count (ANC), monocyte count (AMC), lymphocyte count (ALC), neutrophil-to-lymphocyte count ratio (NLCR), and monocyte-to-lymphocyte ratio (MLR) were evaluated. Patients receiving corticosteroids prior to CBC collection were excluded. Results No significant differences were observed in ANC, ALC, or NLCR between groups. However, the H3K27M group demonstrated significantly elevated AMC (0.70 vs. 0.50 K/μL; p = 0.012) and MLR (0.20 vs. 0.19; p = 0.021) compared to controls. Monocytosis, defined by AMC above age-adjusted means, was present in 78.4% of H3K27M patients versus 46.7% of controls (OR = 4.14, p = 0.003). These findings suggest a distinct pre-treatment immune signature in H3K27M gliomas. Conclusions This is the first study to identify pre-treatment monocytosis and elevated MLR in pediatric H3K27M-mutant gliomas, highlighting a novel systemic immune phenotype with potential implications for biomarker discovery and immunotherapeutic targeting. These results underscore the need for further mechanistic studies and prospective validation in larger, mutation-stratified cohorts.

Abstract Next-generation sequencing (NGS) for the detection of somatic variants has become the method of choice in a variety of molecular oncology fields and in the clinic. Its use ranges from sequencing entire tumor genomes and transcriptomes to targeted clinical diagnostic gene panels. The NYU Langone Genome PACT (Profiling of Actionable Cancer Targets, LG-PACT) assay is a qualitative in vitro diagnostic test that uses targeted next generation sequencing (NGS) of formalin-fixed paraffin-embedded (FFPE) tumor tissue matched with normal specimens from patients to detect gene alterations in a targeted panel covering 606 genes and the TERT promoter. Indications for testing are cancer (solid tumors and hematological malignancies) where a mutational profile from multiple genes would be informative for disease stratification, prognosis, or treatment options including targeted therapies and eligibility for clinical trials. The test is intended to provide information on somatic mutations including point mutations, small insertions/deletions (indels), and copy number aberrations for diagnostic and treatment decisions. LG-PACT is a United States Food and Drug Administration (FDA) cleared diagnostic test (510K: K202304). The clinical interpretation of sequencing data of molecular tumor markers from NGS encompasses automated variant calling tools with human interpretation. This final mostly manual review of data step is intensive, involving highly trained scientists, encompassing literature review, interpretation and clinical tier classification by pathologists, who then provide a complete molecular diagnostic report to the treating oncologists. We provide analysis of 1339 clinical genomic profiles from 31 different cancers and their subtypes, comprising of central nervous system (CNS) 792 (59%) cases (incl. meningioma, glioma and glioblastoma), with 267 (20%) cases predominantly of lung, pancreatic and colorectal and 280 of others (21%). Here, we present the technical challenges of validating an NGS oncological diagnostic targeted assay for clinical grade accuracy and sensitivity for patient care. We show how copy number alterations provide a more comprehensive description of the tumors genomic profile. We then outline the utility of targeted panel sequencing based on certified pathologist selection of reportable variants for our current patient cohort. Where analysis of variant detection has led to 49.4% (661/1339) of our clinical tumor samples containing mutations in known therapy targeted genes, 35.6% (477/1339) with mutation detected in other genes, and 15% (201/1339) cases being negative.

Abstract DNA methylation profiling has become a critical tool in the classification of CNS tumors, helping to refine diagnoses when histology or imaging are inconclusive. In parallel, liquid biopsy approaches analyzing circulating tumor DNA (ctDNA) from cerebrospinal fluid (CSF) offer minimally invasive windows into tumor biology, with applications in diagnosis, treatment monitoring, and relapse detection. A unified method that profiles both methylation and somatic alterations from low-input DNA—such as CSF-ctDNA and FFPE—would provide substantial clinical value. TET-Assisted Pyridine-Borane Sequencing (TAPS+) enables such integrated profiling through a bisulfite-free workflow that preserves DNA integrity while detecting CpG methylation, mutations, indels, copy-number changes, and gene fusions from a single library. We applied TAPS+ to 139 CSF samples and 61 FFPE CNS tumors to evaluate analytical performance and clinical utility. Methods: CSF samples were collected through the Perlmutter Cancer Center liquid biopsy program and FFPE CNS tumors from archival tissue. TAPS+ libraries were prepared from 1-100 ng DNA using the Watchmaker Genomics kit and sequenced to 10-80× depth. Somatic variants were called using the nf-gOS pipeline. The Rastair methylation framework was extended to incorporate strand-discordance logic and probabilistic CpG scoring to distinguish true variants from methylation-derived C>T transitions. Results: Across six matched FFPE tumors, Heidelberg classifier confidence improved after applying our CpG-probability and strand-discordance model, increasing from a mean of 0.20 to 0.81, with all samples gaining 0.50-0.79 and low-confidence calls converted to high-confidence predictions. CpG methylation sensitivity improved from 0.78 to 0.99 while maintaining high specificity (0.998→0.991), enabling accurate discrimination of methylation-derived artifacts from true variants. In CSF-derived ctDNA, excluding 11 QC failures, TAPS+ detected ≥1 tissue-confirmed variant in 43 of 67 evaluable samples (64%), while 24 (36%) were ctDNA-negative despite adequate coverage. TAPS+ also recovered canonical CNS tumor drivers across tissue and CSF, including 1p/19q codeletion, trisomy 7, chromosome 10 loss, EGFR and MET amplification in GBM, ERBB2 amplification, IDH pathway mutations, MN1::CXXC5 and EGFRvIII fusions, and MGMT promoter methylation concordant with array-based calls. Conclusions: TAPS+ enables accurate, bisulfite-free profiling of methylation and somatic alterations from low-input and degraded DNA. Improved classifier performance in FFPE tumors and reliable variant recovery in CSF support TAPS+ as a unified genomic-epigenomic assay. Citation Format: Johnathan Rafailov, Eloise Freitag, Aditya Deshpande, Kevin Hadi, Camila Fang, Alexa Oviedo, Emma Hanley, August Kolb, Neha Dhasmana, Hannah Weiss, Chanel Schroff, Yiying Yang, Jonathan Serrano, Kazimierz Wrzeszczynski, Stergios Zacharoulis, Daniel Orringer, Alexandra M. Miller, Marcin Imielinski, Matija Snuderl. TAPS+ enables direct 5mC/5hmC-resolved genomic and methylation profiling of CNS tumors in CSF-derived ctDNA and FFPE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 102.

Dysembryoplastic neuroepithelial tumors (DNTs) are low-grade glioneuronal tumors with FGFR1 alterations. They show significant histologic and molecular overlap with other glioneuronal tumors, complicating diagnosis. We analyzed 44 tumors that were either classified as DNT by DNA methylation (n = 37), or were diagnosed histologically as DNT but did not classify as DNT by DNA methylation (n = 7). 13/37 (35%) DNT-classifying tumors were histologically diagnosed as DNTs. High-confidence DNTs (score >0.9, 23 cases, 62%) demonstrated variable histology, most frequently DNT (39%), oligodendroglioma, and ganglioglioma and most frequently harbored FGFR1 alterations. Lower-confidence DNTs (score < 0.9, 14 cases, 38%) showed greater heterogeneity; their histologic diagnoses included papillary glioneuronal tumor, extraventricular neurocytoma, and pilocytic astrocytoma. Tumors with low confidence score exhibited diverse molecular alterations including BRAF V600E mutations, PDGFRA amplification, or multiple gene fusions. Among 7 histologically diagnosed DNTs that did not classify as DNT by methylation, most grouped with the myxoid glioneuronal PDGFRA-mutant class despite lacking canonical PDGFRA mutations. Thus, DNTs with high confidence scores are relatively homogenous but DNTs with low methylation confidence scores are heterogenous, highlighting the importance of integrated molecular profiling. Our findings also suggest that the myxoid glioneuronal tumor methylation class may require further classification of underlying drivers.

Supplementary Table 1 includes the metadata on all subjects, including basic demographics and medical history. Supplementary Table 2 provides statistics on chrom 8 gene expression among tumors with H3K27me3 loss. Note: no changes have been made to this file since our last resubmission.

Purpose:Sarcomas pose a severe diagnostic challenge. A wide variety of these distinct entities need to be distinguished from each other and from less aggressive types of mesenchymal tumors, to ensure correct clinical management. A machine learning based classifier for sarcomas utilizing DNA methylation data from 1077 tumors recognizing 62 sarcoma types has already been developed and termed the sarcoma classifier, which we published in 2021. Here we present a major advancement of the scale and precision of the sarcoma classifier. Methods:DNA methylation profiles and histologic data from an unprecedented multi-institutional cohort of mesenchymal tumors were collected and analyzed. Utilizing a machine learning approach, the classifier was rigorously validated through five-fold nested cross-validation, achieving a 98% class-level accuracy and a Brier score of 0.017, indicative of well-calibrated probability estimates. Results:The sarcoma classifier v13.1 was developed based on a training set of 4377 methylation profiles from sarcomas and less aggressive mesenchymal tumors comprising 116 tumor sub-classes and 4 control groups forming 93 distinct methylation classes. Performance was validated using four independent cohorts, comprising a total of 1547 mesenchymal tumors. A methylation-based classifier prediction was obtained in 73% of cases in the validation sets, of which 91% matched the original histopathology diagnosis, thereby increasing diagnostic confidence. The classifier enabled a definitive molecular diagnosis or tumor reclassification in 6% of cases with inconclusive or ambiguous histological findings. Conclusion:Adding new sarcoma types and expanding tumor sample numbers in each methylation class in the new sarcoma classifier decisively increased the number of diagnostic predictions and improved match with histologic evaluation. This substantial advancement will promote clinical implementation of the tool for the diagnosis of mesenchymal tumor lesions.

Supplementary figures provide additional details on the genomic landscape of MPNST and interesting cases within the cohort.

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands recognized entities from 91 classes in version 11 (v11) to 184 subclasses. This expansion is a result of newly identified tumor types discovered through our large online repository and global collaborations, underscoring CNS tumor heterogeneity. The random forest-based classifier achieves 95% subclass-level accuracy, with its well-calibrated probabilistic scores providing a reliable measure of confidence for each classification. Its hierarchical output structure enables interpretation across subclass, class, family, and superfamily levels, thereby supporting clinical decisions at multiple granularities. Comparative analyses demonstrate that v12.8 surpasses previous versions and conventional WHO-based approaches. These advances highlight the improved precision and practical utility of the updated classifier in personalized neuro-oncology.

Gliomas are the most common primary brain tumors and a major source of mortality and morbidity in adults and children. Recent genomic studies have identified multiple molecular subtypes; however metabolic characterization of these tumors has thus far been limited. We performed metabolic profiling of 114 adult and pediatric primary gliomas and integrated metabolomic data with transcriptomics and DNA methylation classes. We identified that pediatric tumors have higher levels of glucose and reduced lactate compared to adult tumors regardless of underlying genetics or grade, suggesting differences in availability of glucose and/or utilization of glucose for downstream pathways. Differences in glucose utilization in pediatric gliomas may be facilitated through overexpression of SLC2A4, which encodes the insulin-stimulated glucose transporter GLUT4. Transcriptomic comparison of adult and pediatric tumors suggests that adult tumors may have limited access to glucose and experience more hypoxia, which is supported by enrichment of lactate, 2-hydroxyglutarate (2-HG), even in isocitrate dehydrogenase (IDH) wild-type tumors, and 3-hydroxybutyrate, a ketone body that is produced by oxidation of fatty acids and ketogenic amino acids during periods of glucose scarcity. Our data support adult tumors relying more on fatty acid oxidation, as they have an abundance of acyl carnitines compared to pediatric tumors and have significant enrichment of transcripts needed for oxidative phosphorylation. Our findings suggest striking differences exist in the metabolism of pediatric and adult gliomas, which can provide new insight into metabolic vulnerabilities for therapy.

List of all methylGSA results for (Gene Ontology,GO, gene sets) with differential methylation comparing patients in cluster 1 vs cluster 2 from unsupervised clustering of the tissue DNA methylation results.

Abstract Purpose: Noninvasive prognostic biomarkers to inform clinical decision-making are an urgent unmet need for the management of patients with glioblastoma (GBM). We previously showed that higher circulating cell-free DNA (ccfDNA) concentration is associated with worse survival in GBM. However, the biology underlying this is unknown. Experimental Design: We prospectively enrolled 129 patients with treatment-naïve GBM with blood drawn prior to initial resection (baseline) and at the time of the first postradiotherapy MRI. We performed ccfDNA methylation deconvolution to determine cellular sources of ccfDNA. ELISA was performed to detect citrullinated histone 3 (citH3), a marker of neutrophil extracellular traps (NET). Multiplex proteomic analysis was used to measure soluble inflammatory proteins. Results: We found that neutrophils contributed the highest proportion of prognostic ccfDNA. The percentage of ccfDNA derived from neutrophils was correlated with total [ccfDNA] but only in patients receiving preoperative corticosteroids. At baseline and on therapy, [citH3] was significantly higher in the plasma of patients with GBM receiving corticosteroids compared with corticosteroid-naïve GBM or no-cancer controls. Unsupervised hierarchical clustering of ccfDNA methylation patterns yielded two clusters, with one enriched for patients with the NETosis phenotype and who received corticosteroids. Unsupervised clustering of circulating inflammatory proteins yielded similar results. Conclusions: These data suggest neutrophil-mediated NETosis is the dominant source of prognostic ccfDNA in patients with GBM and may be associated with glucocorticoid exposure. If further studies show that pharmacological inhibition of NETosis can mitigate the deleterious effects of corticosteroids, these plasma markers will have important clinical utility as noninvasive correlative biomarkers.

High-grade astrocytoma with piloid features (HGAP) is a rare glial tumor defined by an astrocytic morphology and a distinct DNA methylation profile. The clinical behavior, histological grading, and response to treatment for these tumors are not well established. We analyzed 59 HGAPs by DNA methylation and Heidelberg classifier 12.5 at NYU Langone Health. Copy number variations (CNV) were analyzed using Conumee package. Clinical, pathological, molecular, and survival data were retrieved from medical records. Overall survival was analyzed by Survival pack in R. The median age at diagnosis was 38.5 years. The most common location was the posterior fossa, followed by cerebral hemispheres. DNA methylation showed that all cases clustered with the methylation class “HGAP.” The copy number profile was complex in 51 cases, and simple in 8. 52/59 cases demonstrated homozygous loss of CDKN2A/B. Recurrent amplifications were noted in MDM2(10), CDK4(8), PPM1D(5), PDGFRA(5), and TERT(3) genes. DNA and RNA NGS was available for 24 patients and showed recurrent alterations of ATRX(12), NF1(6), FGFR1(6), PIK3CA(3), BRAF(2), TSC2(2), H3K27M(1), ERBB2(1). The median overall survival rate was 22 months. Prolonged progression free survival was recorded in only one patient (12 years). Their tumor was characterized by a favorable histology and simple copy number profile. Repeat analysis by DNA methylation was available for three cases: an HGAP that progressed to GBM_CBM after 3 years, a pilocytic astrocytoma that progressed to HGAP, then to pedHGG_RTK1, and a stable HGAP on 2 resections 4 months apart. We show that HGAPs are high-grade, molecularly complex tumors that frequently occur in nonresectable sites. They may represent a successor for pilocytic astrocytoma, and a precursor for glioblastoma, especially in NF1 patients. Although rare cases with favorable histology and simple copy number profiles may show a prolonged progression free survival, the median overall survival is comparable to other high-grade gliomas.

Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are never assigned a primary site. DNA methylation array profiling is a reliable method for tumor classification but tumor-type-specific classifier development requires many reference samples. This is difficult to accomplish for CUP as many cases are never assigned a specific diagnosis. Recent studies identified subsets of methylation quantitative trait loci (mQTLs) unique to specific organs, which could help increase classifier accuracy while requiring fewer samples. We performed a retrospective genome-wide methylation analysis of 759 carcinoma samples from formalin-fixed paraffin-embedded tissue samples using Illumina EPIC array. Utilizing mQTL specific for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) (185k total probes), we developed a deep learning-based methylation classifier that achieved 93.12% average accuracy and 93.04% average F1-score across a 10-fold validation for BLOCKT organs. Our findings indicate that our organ-based DNA methylation classifier can assist pathologists in identifying the site of origin, providing oncologists insight on a diagnosis to administer appropriate therapy, improving patient outcomes.

Gynecologic neuroectodermal tumors either exhibit central nervous system (CNS) differentiation (CNS-like) or represent Ewing sarcoma (EWS), which lacks CNS features and harbors FET-ETS gene fusions. DNA methylation profiling reclassified CNS primitive neuroectodermal tumors into common CNS neoplasms or embryonal tumors with specific epigenetic/genetic characteristics. Its utility in classifying gynecologic neuroectodermal tumors is unknown. Whole-genome DNA methylation profiling was performed on 26 gynecologic neuroectodermal tumors (22 CNS-like tumors, 4 EWS) arising in the ovary, paratubal soft tissue, uterus, and vulva, which were classified by using sarcoma and CNS tumor DNA methylation classifiers. Sarcoma-related gene fusions were confirmed by fluorescence in situ hybridization or targeted RNA next-generation sequencing. Tumor-only whole-exome sequencing (WES) was performed in 13 cases. Copy number alterations and zygosity were inferred from DNA methylation array and WES data. Methylation abnormalities associated with imprinting were examined. The sarcoma methylation classifier identified EWS (n = 3) and high-grade endometrial stromal sarcoma (n = 1), confirmed by fluorescence in situ hybridization or next-generation sequencing detection of EWSR1 and YWHAE rearrangements, respectively. The remaining CNS-like tumors were classified by DNA methylation with positive/valid (n = 4), indeterminate (n = 9), and negative (n = 9) scores at the family level. Methylation subclasses included teratoma; embryonal tumor with multilayered rosettes, atypical; medulloblastoma, SHH-activated, subtype 3; medulloblastoma, group 3; intraocular medulloepithelioma; supratentorial ependymoma, ZFTA::RELA fused, subclass A; and diffuse pediatric-type high-grade glioma, MYCN subtype. Male biological sex was predicted in 54% of methylation-confirmed CNS-like tumors and none of the sarcomas. Among CNS-like tumors, copy number analyses identified genome-wide chromosomal gains and losses, and WES revealed genome-wide allelic imbalance suggestive of genome-wide duplications. Epigenetic imprinting analyses showed increased paternal or maternal imprinting signal across multiple chromosomes, suggesting uniparental duplication. DNA methylation profiling successfully classified gynecologic neuroectodermal tumors as known CNS tumors or sarcoma entities. Epigenetic and exomic studies indicate a male genome and increased maternal allelic contribution in CNS-like tumors, suggesting development via conception or chimerism.

BACKGROUND:Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories. METHODS:In this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes. RESULTS:CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation. CONCLUSION:Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.

Correlation statistics for Olink-detected proteins compared to [citH3], % neutrophil cfDNA, and pre-operative steroid dose ranked from highest to lowest rho for association with [CitH3].