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Simple Summary: Pituitary adenomas often regrow after surgery, posing a treatment challenge. By studying the DNA methylation patterns of these adenomas, we aimed to identify different subtypes that may predict regrowth potential. Samples from 33 patients with nonfunctioning pituitary adenomas were analyzed, revealing distinct DNA methylation clusters. While trends suggesting certain clusters might be associated with higher regrowth potential emerged, overall significant differences in regrowth rates between these clusters were not found. DNA methylation profiling could help identify adenomas prone to regrowth, potentially improving treatment strategies in the future. Further larger studies are needed to confirm the findings from this explorative pilot study. Background: The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been proposed, but no study has linked this epigenetic variance with regrowth potential and the clinical heterogeneity of nonfunctioning pituitary adenomas. This study aimed to investigate whether DNA methylation profiling can be useful as a clinical prognostic marker. Methods: A DNA methylation analysis by Illumina's MethylationEPIC array was performed on 54 pituitary macroadenomas from patients who underwent transsphenoidal surgery during 2007–2017. Twelve patients were excluded due to an incomplete postoperative follow-up, degenerated biobank-stored tissue, or low DNA methylation quality. For the quantitative measurement of the tumor regrowth rate, we conducted a 3D volumetric analysis of tumor remnant volume via annual magnetic resonance imaging. A linear mixed effects model was used to examine whether different DNA methylation clusters had different regrowth patterns. Results: The DNA methylation profiling of 42 tissue samples showed robust DNA methylation clusters, comparable with previous findings. The subgroup of 33 nonfunctioning pituitary adenomas of an SF1-lineage showed five subclusters with an approximately unbiased score of 86%. There were no overall statistically significant differences when comparing hazard ratios for regrowth of 100%, 50%, or 0%. Despite this, plots of correlated survival estimates suggested higher regrowth rates for some clusters. The mixed effects model of accumulated regrowth similarly showed tendencies toward an association between specific DNA methylation clusters and regrowth potential. Conclusion: The DNA methylation profiling of nonfunctioning pituitary adenomas may potentially identify adenomas with increased growth and recurrence potential. Larger validation studies are needed to confirm the findings from this explorative pilot study. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is a highly lethal malignancy for which neoangiogenesis serves as a defining hallmark. The anti-VEGF antibody, bevacizumab, has been approved for the treatment of recurrent GBM, but resistance is universal., Methods: We analyzed expression data of GBM patients treated with bevacizumab to discover potential resistance mechanisms. Patient-derived xenografts (PDXs) and cultures were interrogated for effects of phosphofructokinase-1, muscle isoform (PFKM) loss on tumor cell motility, migration, and invasion through genetic and pharmacologic targeting., Results: We identified PFKM as a driver of bevacizumab resistance. PFKM functions dichotomize based on subcellular location: cytosolic PFKM interacted with KIF11, a tubular motor protein, to promote tumor invasion, whereas nuclear PFKM safeguarded genomic stability of tumor cells through interaction with NBS1. Leveraging differential transcriptional profiling, bupivacaine phenocopied genetic targeting of PFKM, and enhanced efficacy of bevacizumab in preclinical GBM models in vivo., Conclusion: PFKM drives novel molecular pathways in GBM, offering a translational path to a novel therapeutic paradigm., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Diagnostics of tumours of the central nervous system has for decades been based entirely on microscopy. A con-siderable degree of diagnostic interobserver variability has been observed due to imprecise histological criteria. In the revised WHO classification for central nervous system tumours from 2016, several diagnoses are now defined by both histological and molecular features and constitute "integrated diagnoses". The development based on new technologies like next-generation sequencing and DNA methylation profiling is discussed in this review as well as its implication for daily diagnostics and the patient.