Faculty Bibliography

BACKGROUND:Diffuse hemispheric glioma, histone 3 (H3) G34-mutant, has been newly defined in the 2021 WHO classification of central nervous system tumors. Here we sought to define the prognostic roles of clinical, neuroimaging, pathological, and molecular features of these tumors. METHODS:We retrospectively assembled a cohort of 114 patients (median age 22 years) with diffuse hemispheric glioma, H3 G34-mutant, CNS WHO grade 4 and profiled the imaging, histological and molecular landscape of their tumors. RESULTS:Compared with glioblastoma, H3 G34-mutant diffuse hemispheric gliomas exhibited less avid contrast enhancement, necrosis and edema on MRI. Comprehensive analyses of mutational and DNA copy number profiles revealed recurrent mutations in TP53 and ATRX, homozygous deletions of CDKN2A/B, and amplifications of PDGFRA, EGFR, CCND2, and MYCN. MGMT promoter methylation was detected in 79 tumors (75%); 11 tumors (13%) showed DNA copy number profiles suggestive of circumscribed deletions on 10q26.3 involving the MGMT locus. Median survival was 21.5 months. Female sex, gross total resection, and MGMT promoter methylation were positive prognostic factors on univariate analysis. Among radiological, pathological and molecular features, absence of pial invasion, and presence of microvascular proliferation and CDK6 amplification were positive prognostic factors on univariate analyses. CONCLUSIONS:This study refines the clinical and molecular landscape of H3 G34-mutant diffuse hemispheric gliomas. Dedicated trials for this novel tumor type are urgently needed.

BACKGROUND:The diagnosis and treatment of tumors often depend on molecular-genetic data. However, rapid and iterative access to molecular data is not currently feasible during surgery, complicating intraoperative diagnosis and precluding measurement of tumor cell burdens at surgical margins to guide resections. METHODS:Here, we introduce Ultra-Rapid droplet digital PCR (UR-ddPCR), a technology that achieves the fastest measurement, to date, of mutation burdens in tissue samples, from tissue to result in 15 min. Our workflow substantially reduces the time from tissue biopsy to molecular diagnosis and provides a highly accurate means of quantifying residual tumor infiltration at surgical margins. FINDINGS/RESULTS: = 0.995). CONCLUSIONS:The technology and workflow developed here enable intraoperative molecular-genetic assays with unprecedented speed and sensitivity. We anticipate that our method will facilitate novel point-of-care diagnostics and molecularly guided surgeries that improve clinical outcomes. FUNDING/BACKGROUND:This study was funded by the National Institutes of Health and NYU Grossman School of Medicine institutional funds. Reagents and instruments were provided in kind by Bio-Rad.

IDH-mutant astrocytomas are diffuse gliomas that are defined by characteristic mutations in IDH1 or IDH2 and do not have complete 1p/19q co-deletion. The established grading criteria include histological features of brisk mitotic activity (grade 3) and necrosis and/or microvascular proliferation (grade 4). In addition, homozygous deletion of the CDKN2A/B locus has recently been implemented as a molecular marker for grade 4 IDH-mutant astrocytomas. Here, we describe a subgroup of high-grade IDH-mutant astrocytomas characterised by a primitive neuronal component based on histology and a distinct DNA methylation profile (n = 51, ASTRO PNC). Misinterpretation as carcinoma metastasis was common, since GFAP expression was absent in the primitive neuronal component, whereas TTF-1 expression was detected in 15/19 cases (79%) based on immunohistochemistry. Apart from mutations in IDH1, TP53, and ATRX, we observed enrichment for alterations in RB1 (n = 19/51, 37%) and MYCN (n = 14/51, 27%). Homozygous CDKN2A/B deletion (n = 1/51, 2%) and CDK4 amplification (n = 3/51, 6%) were relatively rare events. Clinical (n = 31 patients) and survival data (n = 23 patients) indicate a clinical behaviour similar to other CNS WHO grade 4 IDH-mutant astrocytomas, however with an increased risk for leptomeningeal (n = 7) and extra-axial (n = 2) spread. Taken together, ASTRO PNC is defined by a distinct molecular and histological appearance that can mimic metastatic disease and typically follows an aggressive clinical course.

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

PURPOSE/OBJECTIVE:Non-invasive 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 concentration [ccfDNA] is associated with worse survival in GBM. However, the biology underlying this is unknown. EXPERIMENTAL DESIGN/METHODS:We prospectively enrolled 129 patients with treatment-naïve GBM with blood drawn prior to initial resection (baseline) and at time of first post-radiotherapy MRI. We performed ccfDNA methylation deconvolution to determine cellular sources of ccfDNA. ELISA was performed to detect citrullinated H3 (citH3), a marker of neutrophil extracellular traps (NETs). 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 pre-operative corticosteroids. At baseline and on-therapy, [citH3] was significantly higher in the plasma of patients with GBM receiving corticosteroids compared to corticosteroid-naïve GBMs 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 non-invasive correlative biomarkers.

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.

BACKGROUND:Intracerebral schwannomas are rare tumors resembling their peripheral nerve sheath counterparts but localized in the CNS. They are not classified as a separate tumor type in the 2021 WHO classification. This study aimed to compile and characterize these rare neoplasms morphologically and molecularly. METHODS:We analyzed 20 tumor samples by histology, RNA Next-Generation Sequencing, DNA-methylation profiling, copy number analyses, and single nucleus RNA sequencing (snRNA-seq). Clinical data, including age, sex, and disease progression, were collected. MRI series were included when available. RESULTS:All cases with tissue available for histology review (n=13) were morphologically consistent with intracerebral schwannoma, but differed in their extent of GFAP staining. All (n=20) shared DNA-methylation profiles distinct from other CNS tumors, as well as from VGLL-altered peripheral nerve sheath tumors. Most cases (n=14/17) harbored fusions of either VGLL3 or VGLL1 (CHD7::VGLL3 (n=9/17) and EWSR1::VGLL1 (n=5/17)). In two cases the presence of a VGLL3 fusion was also confirmed by CNA analyses (n=2/17). MRI (n=4) showed well-defined, nodular tumors with strong, homogeneous enhancement and no diffusion restriction. Tumors were located throughout the neuroaxis [supratentorial (n=15), infratentorial (n=4), and spinal (n=1)]. snRNA-seq of a VGLL1-fused tumor indicated VGLL1 upregulation in 28.6% of tumor cells (n=1). During median follow-up of 1.8 years (range 3 months-9 years), none of the tumors recurred (n=10). CONCLUSIONS:We identify and define a new benign tumor class, designated VGLL-altered intraparenchymal CNS schwannomas. These tumors feature VGLL alterations and a specific DNA-methylation profile, with schwannoma-like histopathology and CNS localization, akin to previously classified intracerebral schwannomas.

IMPORTANCE/UNASSIGNED:Molecular techniques, including next-generation sequencing, genomic copy number profiling, fusion transcript detection, and genomic DNA methylation arrays, are now indispensable tools for the workup of central nervous system (CNS) tumors. Yet there remains a great deal of heterogeneity in using such biomarker testing across institutions and hospital systems. This is in large part because there is a persistent reluctance among third-party payers to cover molecular testing. The objective of this Review is to describe why comprehensive molecular biomarker testing is now required for the accurate diagnosis and grading and prognostication of CNS tumors and, in so doing, to justify more widespread use by clinicians and coverage by third-party payers. OBSERVATIONS/UNASSIGNED:The 5th edition of the World Health Organization (WHO) classification system for CNS tumors incorporates specific molecular signatures into the essential diagnostic criteria for most tumor entities. Many CNS tumor types cannot be reliably diagnosed according to current WHO guidelines without molecular testing. The National Comprehensive Cancer Network also incorporates molecular testing into their guidelines for CNS tumors. Both sets of guidelines are maximally effective if they are implemented routinely for all patients with CNS tumors. Moreover, the cost of these tests is less than 5% of the overall average cost of caring for patients with CNS tumors and consistently improves management. This includes more accurate diagnosis and prognostication, clinical trial eligibility, and prediction of response to specific treatments. Each major group of CNS tumors in the WHO classification is evaluated and how molecular diagnostics enhances patient care is described. CONCLUSIONS AND RELEVANCE/UNASSIGNED:Routine advanced multidimensional molecular profiling is now required to provide optimal standard of care for patients with CNS tumors.

BACKGROUND:Accurate intraoperative diagnosis is crucial for differentiating between primary CNS lymphoma (PCNSL) and other CNS entities, guiding surgical decision-making, but represents significant challenges due to overlapping histomorphological features, time constraints, and differing treatment strategies. We combined stimulated Raman histology (SRH) with deep learning to address this challenge. METHODS:We imaged unprocessed, label-free tissue samples intraoperatively using a portable Raman scattering microscope, generating virtual H&E-like images within less than three minutes. We developed a deep learning pipeline called RapidLymphoma based on a self-supervised learning strategy to (1) detect PCNSL, (2) differentiate from other CNS entities, and (3) test the diagnostic performance in a prospective international multicenter cohort and two additional independent test cohorts. We trained on 54,000 SRH patch images sourced from surgical resections and stereotactic-guided biopsies, including various CNS neoplastic/non-neoplastic lesions. Training and test data were collected from four tertiary international medical centers. The final histopathological diagnosis served as ground-truth. RESULTS:In the prospective test cohort of PCNSL and non-PCNSL entities (n=160), RapidLymphoma achieved an overall balanced accuracy of 97.81% ±0.91, non-inferior to frozen section analysis in detecting PCNSL (100% vs. 77.77%). The additional test cohorts (n=420, n=59) reached balanced accuracy rates of 95.44% ±0.74 and 95.57% ±2.47 in differentiating IDH-wildtype diffuse gliomas and various brain metastasis from PCNSL. Visual heatmaps revealed RapidLymphoma's capabilities to detect class-specific histomorphological key features. CONCLUSIONS:RapidLymphoma proves reliable and valid for intraoperative PCNSL detection and differentiation from other CNS entities. It provides visual feedback within three minutes, enabling fast clinical decision-making and subsequent treatment strategy planning.