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Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered., (© 2023 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Temozolomide (TMZ) is standard treatment for glioblastoma (GBM); nonetheless, resistance and tumor recurrence are still major problems. In addition to its association with recurrent GBM and TMZ resistance, ALDH1A3 has a role in autophagy-dependent ferroptosis activation. In this study, we treated TMZ-resistant LN229 human GBM cells with the ferroptosis inducer RSL3. Remarkably, TMZ-resistant LN229 clones were also resistant to ferroptosis induction, although lipid peroxidation was induced by RSL3. By using Western blotting, we were able to determine that ALDH1A3 was down-regulated in TMZ-resistant LN229 cells. Most intriguingly, the cell viability results showed that only those clones that up-regulated ALDH1A3 after TMZ withdrawal became re-sensitized to ferroptosis induction. The recovery of ALDH1A3 expression appeared to be regulated by EGFR-dependent PI3K pathway activation since Akt was activated only in ALDH1A3 high clones. Blocking the EGFR signaling pathway with the EGFR inhibitor AG1498 decreased the expression of ALDH1A3. These findings shed light on the potential application of RSL3 in the treatment of glioblastoma relapse.

The fatal clinical course of human glioblastoma (GBM) despite aggressive adjuvant therapies is due to high rates of recurrent tumor growth driven by tumor cells with stem-cell characteristics (glioma stem cells, GSCs). The aldehyde dehydrogenase 1 (ALDH1) family of enzymes has been shown to be a biomarker for GSCs, and ALDH1 seems to be involved in the biological processes causing therapy resistance. Ferroptosis is a recently discovered cell death mechanism, that depends on iron overload and lipid peroxidation, and it could, therefore, be a potential therapeutic target in various cancer types. Since both ALDH1 and ferroptosis interact with lipid peroxidation (LPO), we aimed to investigate a possible connection between ALDH1 and ferroptosis. Here, we show that RSL3-induced LPO and ferroptotic cell death revealed RSL3-sensitive and -resistant malignant glioma cell lines. Most interestingly, RSL3 sensitivity correlates with ALDH1a3 expression; only high ALDH1a3-expressing cells seem to be sensitive to ferroptosis induction. In accordance, inhibition of ALDH1a3 enzymatic activity by chemical inhibition or genetic knockout protects tumor cells from RSL3-induced ferroptotic cell death. Both RSL-3-dependent binding of ALDH1a3 to LC3B and autophagic downregulation of ferritin could be completely blocked by ALDH inhibition. Therefore, ALDH1a3 seems to be involved in ferroptosis through the essential release of iron by ferritinophagy. Our results also indicate that ferroptosis induction might be a particularly interesting clinical approach for targeting the highly aggressive cell population of GSC.

Background: Intratumoral heterogeneity at the cellular and molecular level is a hallmark of glioblastoma (GB) that contributes to treatment resistance and poor clinical outcome. Little is known regarding epigenetic heterogeneity and intratumoral phylogeny and their implication for molecular classification and targeted therapies., Patients and Methods: Multiple tissue biopsies (238 in total) were sampled from 56 newly-diagnosed, treatment-naive GB patients from a prospective in-house cohort and publicly available data and profiled for DNA methylation using the Illumina MethylationEPIC array. Methylation-based classification using the glioma classifier developed by Ceccarelli et al. and estimation of the MGMT promoter methylation status via the MGMT-STP27 model were carried out. In addition, copy number variations (CNVs) and phylogeny were analyzed., Results: Almost half of the patients (22/56, 39%) harbored tumors composed of heterogeneous methylation subtypes. We found two predominant subtype combinations: classic-/mesenchymal-like, and mesenchymal-/pilocytic astrocytoma-like. Nine patients (16%) had tumors composed of subvolumes with and without MGMT promoter methylation, whereas 20 patients (36%) were homogeneously methylated, and 27 patients (48%) were homogeneously unmethylated. CNV analysis revealed high variations in many genes, including CDKN2A/B, EGFR, and PTEN. Phylogenetic analysis correspondingly showed a general pattern of CDKN2A/B loss and gain of EGFR, PDGFRA, and CDK4 during early stages of tumor development., Conclusions: (Epi)genetic intratumoral heterogeneity is a hallmark of GB, both at DNA methylation and CNV level. This intratumoral heterogeneity is of utmost importance for molecular classification as well as for defining therapeutic targets in this disease, as single biopsies might underestimate the true molecular diversity in a tumor., Competing Interests: Disclosure The authors have declared no conflicts of interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: For recurrent glioblastoma (GB) patients, several therapy options have been established over the last years such as more aggressive surgery, re-irradiation or chemotherapy. Age and the Karnofsky Performance Status Scale (KPSS) are used to make decisions for these patients as these are established as prognostic factors in the initial diagnosis of GB. This study's aim was to evaluate preoperative patient comorbidities by using the age-adjusted Charlson Comorbidity Index (ACCI) as a prognostic factor for recurrent GB patients., Methods: In this retrospective analysis we could include 123 patients with surgery for primary recurrence of GB from January 2007 until December 2016 (43 females, 80 males, mean age 57 years (range 21-80 years)). Preoperative age, sex, ACCI, KPSS and adjuvant treatment regimes were recorded for each patient. Extent of resection (EOR) was recorded as a complete/incomplete resection of the contrast-enhancing tumor part., Results: Median overall survival (OS) was 9.0 months (95% CI 7.1-10.9 months) after first re-resection. Preoperative KPSS > 80% (P < 0.001) and EOR (P = 0.013) were associated with significantly improved survival in univariate analysis. Including these factors in multivariate analysis, preoperative KPSS < 80 (HR 2.002 [95% CI: 1.246-3.216], P = 0.004) and EOR are the only significant prognostic factor (HR 1.611 [95% CI: 1.036-2.505], P = 0.034). ACCI was not shown as a prognostic factor in univariate and multivariate analyses., Conclusion: For patients with surgery for recurrent glioblastoma, the ACCI does not add further information about patient's prognosis besides the well-established KPSS and extent of resection., (© 2022. The Author(s).)

Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.

Aims: O (6)-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a high predictive factor for therapy results of temozolomide in patients with glioma. The objective of this work was to analyse the impact of MGMT promoter methylation in patients with primary diagnosed glioblastoma (GBM) relating to survival using a quantitative method (methylation quantification of endonuclease-resistant DNA, MethyQESD) by verifying a cut-off point for MGMT methylation provided by the literature (

Background: Wavelet transformed reconstructions of dynamic susceptibility contrast (DSC) MR perfusion (wavelet-MRP) are a new and elegant way of visualizing vascularization. Wavelet-MRP maps yield a clear depiction of hypervascular tumor regions, as recently shown., Objective: The aim of this study was to elucidate a possible connection of the wavelet-MRP power spectrum in glioblastoma (GBM) with local vascularity and cell proliferation., Methods: For this IRB-approved study 12 patients (63.0+/-14.9y; 7m) with histologically confirmed IDH-wildtype GBM were included. Target regions for biopsies were prospectively marked on tumor regions as seen on preoperative 3T MRI. During subsequent neurosurgical tumor resection 43 targeted biopsies were taken from these target regions, of which all 27 matching samples were analyzed. All specimens were immunohistochemically analyzed for endothelial cell marker CD31 and proliferation marker Ki67 and correlated to the wavelet-MRP power spectrum as derived from DSC perfusion weighted imaging., Results: There was a strong correlation between wavelet-MRP power spectrum (median = 4.41) and conventional relative cerebral blood volume (median = 5.97 ml/100g) in Spearman's rank-order correlation (κ = .83, p < .05). In a logistic regression model, the wavelet-MRP power spectrum showed a significant correlation to CD31 dichotomized to no or present staining (p = .04), while rCBV did not show a significant correlation to CD31 (p = .30). No significant association between Ki67 and rCBV or wavelet-MRP was found (p = .62 and p = .70, respectively)., Conclusion: The wavelet-MRP power spectrum derived from existing DSC-MRI data might be a promising new surrogate for tumor vascularity in GBM., Competing Interests: The authors have read the journal's policy and have the following competing interests: LR: Medical consultant for Smart Reporting GmbH. BW: speaker honoraria from Bayer AG. CP: No disclosures. FL: No disclosures. TP: No disclosures. DN: Head of Medical Content Smart Reporting GmbH. SB: Former Consultant for Brainlab AG. KT: No disclosures. JG: Consultant for Brainlab AG. CZ: has served on scientific advisory boards for Philips and Bayer Schering; serves as co-editor on the Advisory Board of Clinical Neuroradiology; has received speaker honoraria from Bayer-Schering and Philips and has received research support and investigator fees for clinical studies from Biogen Idec, Quintiles, M.S.D. Sharp & Dome, Boehringer Ingelheim, Inventive Health Clinical U.K. Ltd., Advance Cor, Brainsgate, Pfizer, BayerSchering, Novartis, Roche, Servier, Penumbra, W.C.T. GmbH, Syngis, S.S.S. Internartional Clinical Research, P.P.D. Germany GmbH, Worldwide Clinical Trials Ltd., Phenox, Covidien, Actelion, Medivation, Medtronic, Harrison Clinical Research, Concentric, Penumbra, Pharmtrace, Reverse Medical Corp., Premier Research Germany Ltd., Surpass Medical Ltd. and GlaxoSmithKline. TH: Head of Scientific Cooperations Smart Reporting GmbH, Speaker for Bayer AG, Former Consultant for Brainlab AG This does not affect our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development, or marketed products to declare.