Faculty Bibliography

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: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.

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.

[This corrects the article DOI: 10.3389/fonc.2022.969812.].

BACKGROUND/UNASSIGNED:Methylation class pleomorphic xanthoastrocytoma (mcPXA) comprises tumors with the DNA methylation signature of classical PXA but with a wider histologic spectrum, including overlap with glioblastoma (GBM). METHODS/UNASSIGNED:To clarify the histologic and molecular scope of mcPXA and characterize its clinical behavior, a cohort of 469 tumor samples from 458 patients matching to mcPXA by the DKFZ classifier (v12.6 score ≥0.85) was interrogated. RESULTS/UNASSIGNED:promoter mutations. CONCLUSION/UNASSIGNED:Tumors in mcPXA share molecular characteristics with histologically defined PXA, and high-grade histologic features can help predict their clinical behavior. The use of an epigenetic classification of PXA reveals that this group of tumors is more common than previously appreciated and warrants in-depth study to identify efficacious therapeutic options.

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.

Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor-specific mutations or gene fusions, there is an unmet need to identify additional diagnostic biomarkers for entities lacking specific alterations. In this study, we collected a comprehensive cohort of 363 cases encompassing 20 different salivary gland tumor entities and explored the potential of DNA methylation to classify these tumors. We were able to show that most entities show specific epigenetic signatures and present a machine learning algorithm that achieved a mean balanced accuracy of 0.991. Of note, we showed that cribriform adenocarcinoma is epigenetically distinct from classical polymorphous adenocarcinoma, which could support risk stratification of these tumors. Myoepithelioma and pleomorphic adenoma form a uniform epigenetic class, supporting the theory of a single entity with a broad but continuous morphologic spectrum. Furthermore, we identified a histomorphologically heterogeneous but epigenetically distinct class that could represent a novel tumor entity. In conclusion, our study provides a comprehensive resource of the DNA methylation landscape of salivary gland tumors. Our data provide novel insight into disputed entities and show the potential of DNA methylation to identify new tumor classes. Furthermore, in future, our machine learning classifier could support the histopathologic diagnosis of salivary gland tumors.

Sudden unexplained death in childhood (SUDC) is death of a child ≥ 12 months old that is unexplained after autopsy and detailed analyses. Among SUDC cases, ~ 30% have febrile seizure (FS) history, versus 2-5% in the general population. SUDC cases share features with sudden unexpected death in epilepsy (SUDEP) and sudden infant death syndrome (SIDS), in which brainstem autonomic dysfunction is implicated. To understand whether brainstem protein changes are associated with FS history in SUDC, we performed label-free quantitative mass spectrometry on microdissected midbrain dorsal raphe, medullary raphe, and the ventrolateral medulla (n = 8 SUDC-noFS, n = 11 SUDC-FS). Differential expression analysis between SUDC-FS and SUDC-noFS at p < 0.05 identified 178 altered proteins in dorsal raphe, 344 in medullary raphe, and 100 in the ventrolateral medulla. These proteins were most significantly associated with increased eukaryotic translation initiation (p = 3.09 × 10^-7, z = 1.00), eukaryotic translation elongation (p = 6.31 × 10^-49, z = 6.01), and coagulation system (p = 1.32 × 10^-5, z = 1.00). The medullary raphe had the strongest enrichment for altered signaling pathways, including with comparisons to three other brain regions previously analyzed (frontal cortex, hippocampal dentate gyrus, cornu ammonus). Immunofluorescent tissue analysis of serotonin receptors identified 2.1-fold increased 5HT2A in the medullary raphe of SUDC-FS (p = 0.025). Weighted gene correlation network analysis (WGCNA) of case history indicated that longer FS history duration significantly correlated with protein levels in the medullary raphe and ventrolateral medulla; the most significant gene ontology biological processes were decreased cellular respiration (p = 9.8 × 10^-5, corr = - 0.80) in medullary raphe and decreased synaptic vesicle cycle (p = 1.60 × 10^-7, corr = - 0.90) in the ventrolateral medulla. Overall, FS in SUDC was associated with more protein differences in the medullary raphe and was related with increased translation-related signaling pathways. Future studies should assess whether these changes result from FS or may in some way predispose to FS or SUDC.

BACKGROUND AND OBJECTIVES/OBJECTIVE:The management of World Health Organization (WHO) grade 2 meningiomas is complicated by their diverse clinical behaviors. Stereotactic radiosurgery (SRS) can be an effective management option. Literature on SRS dose selection is limited but suggests that a higher dose is better for tumor control. We characterize the predictors of post-SRS outcomes that can help guide planning and management. METHODS:We reviewed a cohort of consecutive patients with pathologically-proven WHO grade 2 meningiomas who underwent SRS at a single institution between 2011 and 2023. RESULTS:Ninety-nine patients (median age 62 years) underwent SRS, 11 of whom received hypofractionated SRS in 5 fractions. Twenty-two patients had received previous irradiation. The median follow-up was 49 months. The median overall survival was 119 months (95% CI 92-NA) with estimated 5- and 10-year survival of 83% and 27%, respectively. The median progression-free survival (PFS) was 40 months (95% CI 32-62), with 3- and 5-year rates at 54% and 35%, respectively. The median locomarginal PFS was 63 months (95% CI 51.8-NA) with 3- and 5-year rates at 65% and 52%. Nine (9%) patients experienced adverse events, 2 Common Terminology Criteria for Adverse Events grade 3 and 7 grade 2, consisting of worsening neurologic deficit from edema. In the single-session cohort, Ki-67 significantly predicted both overall survival and intracranial PFS. Tumors with Ki-67 >10% had 2.17 times the risk of locomarginal progression compared with Ki-67 ≤10% (P = .018) adjusting for covariates. Sex, prescription dose, tumor volume, and location also predicted tumor control. In tumors with Ki-67 >10%, margin dose ≥14 Gy was associated with significantly better tumor control but not for tumors with Ki-67 ≤10%. CONCLUSION/CONCLUSIONS:The management of WHO grade 2 meningiomas requires a multimodality approach. This study demonstrates the value of a targeted SRS approach in patients with limited disease and further establishes predictive biomarkers that can guide planning through a personalized approach.