Prognostic factors to predict postoperative survival in patients with recurrent glioblastoma.

Background: There are no generally accepted criteria for selecting patients with recurrent glioblastoma for surgery. This retrospective study in a Danish population-based cohort aimed to identify prognostic factors affecting postoperative survival after repeated surgery for recurrent glioblastoma and to test if the preoperative New Scale for Recurrent Glioblastoma Surgery (NSGS) developed by Park CK et al could assist in the selection of patients for repeat glioblastoma surgery. Methods: Clinical data from 66 patients with recurrent glioblastoma and repeated surgery were analyzed. Kaplan-Meier plots were produced to illustrate survival in each of the three NSGS prognostic groups, and Cox proportional hazard regression was used to identify prognostic variables. Multivariable analysis was used to identify differences in survival in the three prognostic groups. Results: Six variables significantly affected postoperative survival: preoperative Karnofsky Performance Status (KPS) < 70 (p = 0.002), decreased KPS after second surgery (p = 0.012), ependymal involvement (p = 0.002), tumor volume â‰§ 50 cm3 (p = 0.021), age (p = 0.033) and Ki-67 (p = 0.005). Retrospective application of the criteria previously published by Park CK et al showed that median postoperative survival for the three prognostic groups was 390 days (0 points), 279 days (1 point), and 80 days (2 points), respectively. Conclusion: Several prognostic variables to predict postoperative survival in patients with recurrent glioblastoma were identified and should be considered when selecting patient for repeat surgery. The NSGS scoring system was useful as there were significant differences in postoperative survival between its three prognostic groups.

Genome-Wide DNA Methylation Profiling as a Prognostic Marker in Pituitary Adenomas-A 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.

Non-metabolic functions of phosphofructokinase-1 orchestrate tumor cellular invasion and genome maintenance under bevacizumab therapy.

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.

Prognostic role of Ki-67 in glioblastomas excluding contribution from non-neoplastic cells.

Survival of glioblastoma patients varies and prognostic markers are important in the clinical setting. With digital pathology and improved immunohistochemical multiplexing becoming a part of daily diagnostics, we investigated the prognostic value of the Ki-67 labelling index (LI) in glioblastomas more precisely than previously by excluding proliferation in non-tumor cells from the analysis. We investigated the Ki-67 LI in a well-annotated population-based glioblastoma patient cohort (178 IDH-wildtype, 3 IDH-mutated). Ki-67 was identified in full tumor sections with automated digital image analysis and the contribution from non-tumor cells was excluded using quantitative double-immunohistochemistry. For comparison of the Ki-67 LI between WHO grades (II-IV), 9 IDH-mutated diffuse astrocytomas and 9 IDH-mutated anaplastic astrocytomas were stained. Median Ki-67 LI increased with increasing WHO grade (median 2.7%, 6.4% and 27.5%). There was no difference in median Ki-67 LI between IDH-mutated and IDH-wildtype glioblastomas (p = 0.9) and Ki-67 LI was not associated with survival in glioblastomas in neither univariate (p = 0.9) nor multivariate analysis including MGMT promoter methylation status and excluding IDH-mutated glioblastomas (p = 0.2). Ki-67 may be of value in the differential diagnostic setting, but it must not be over-interpreted in the clinico-pathological context.

[Integrated diagnostics of brain tumours based on histological and molecular features].

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.