Litcius/Paper detail

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

Rikke Hedegaard Dahlrot, Julie A. Bangsø, Jeanette Krogh Petersen, Ann Mari Rosager, Mia Dahl Sørensen, Guido Reifenberger, Steinbjørn Hansen, Bjarne Winther Kristensen

2021Scientific Reports61 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Ki-67Anaplastic astrocytomaUnivariate analysisImmunohistochemistryPathologyContext (archaeology)Proliferative indexMedicineProliferation indexIDH1PopulationIsocitrate dehydrogenaseAstrocytomaGlioblastomaOncologyCancer researchBiologyInternal medicineMultivariate analysisMutationGeneBiochemistryPaleontologyEnzymeEnvironmental healthGlioma Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingMathematical Biology Tumor Growth