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Purpose: Classification and treatment of WHO grade II/III gliomas have dramatically changed. Implementing molecular markers into the WHO classification raised discussions about the significance of grading and clinical trials showed overall survival (OS) benefits for combined radiochemotherapy. As molecularly stratified treatment data outside clinical trials are scarce, we conducted this retrospective study.
Methods: We identified 343 patients (1995–2015) with newly diagnosed WHO grade II/III gliomas and analyzed molecular markers, patient characteristics, symptoms, histology, treatment, time to treatment failure (TTF) and OS.
Results: IDH-status was available for all patients (259 mutant, 84 IDH1-R132H-non-mutant). Molecular subclassification was possible in 173 tumors, resulting in diagnosis of 80 astrocytomas and 93 oligodendrogliomas. WHO grading remained significant for OS in astrocytomas/IDH1-R132H-non-mutant gliomas (p < 0.01) but not for oligodendroglioma (p = 0.27). Chemotherapy (and temozolomide in particular) showed inferior OS compared to radiotherapy in astrocytomas (median 6.1/12.1 years; p = 0.03) and oligodendrogliomas (median 13.2/not reached (n.r.) years; p = 0.03). While radiochemotherapy improved TTF in oligodendroglioma (median radiochemotherapy n.r./chemotherapy 3.8/radiotherapy 7.3 years; p < 0.001/ = 0.06; OS data immature) the effect, mainly in combination with temozolomide, was weaker in astrocytomas (median radiochemotherapy 6.7/chemotherapy 2.3/radiotherapy 2.0 years; p < 0.001/ = 0.11) and did not translate to improved OS (median 8.4 years).
Conclusion: This is one of the largest retrospective, real-life datasets reporting treatment and outcome in low-grade gliomas incorporating molecular markers. Current histologic grading features remain prognostic in astrocytomas while being insignificant in oligodendroglioma with interfering treatment effects. Chemotherapy (temozolomide) was less effective than radiotherapy in both astrocytomas and oligodendrogliomas while radiochemotherapy showed the highest TTF in oligodendrogliomas.
Background: The epidermal growth factor receptor (EGFR) signaling pathway is genetically activated in approximately 50% of glioblastomas (GBs). Its inhibition has been explored clinically but produced disappointing results, potentially due to metabolic effects that protect GB cells against nutrient deprivation and hypoxia. Here, we hypothesized that EGFR activation could disable metabolic adaptation and define a GB cell population sensitive to starvation.
Methods: Using genetically engineered GB cells to model different types of EGFR activation, we analyzed changes in metabolism and cell survival under conditions of the tumor microenvironment.
Results: We found that expression of mutant EGFRvIII as well as EGF stimulation of EGFR-overexpressing cells impaired physiological adaptation to starvation and rendered cells sensitive to hypoxia-induced cell death. This was preceded by adenosine triphosphate (ATP) depletion and an increase in glycolysis. Furthermore, EGFRvIII mutant cells had higher levels of mitochondrial superoxides potentially due to decreased metabolic flux into the serine synthesis pathway which was associated with a decrease in the NADPH/NADP+ ratio.
Conclusions: The finding that EGFR activation renders GB cells susceptible to starvation could help to identify a subgroup of patients more likely to benefit from starvation-inducing therapies.
DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma
(2021)
Background: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. Methods: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). Results: We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. Conclusions: These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.
Linking epigenetic signature and metabolic phenotype in IDH mutant and IDH wildtype diffuse glioma
(2020)
Aims: Changes in metabolism are known to contribute to tumour phenotypes. If and how metabolic alterations in brain tumours contribute to patient outcome is still poorly understood. Epigenetics impact metabolism and mitochondrial function. The aim of this study is a characterisation of metabolic features in molecular subgroups of isocitrate dehydrogenase mutant (IDHmut) and isocitrate dehydrogenase wildtype (IDHwt) gliomas. Methods: We employed DNA methylation pattern analyses with a special focus on metabolic genes, large-scale metabolism panel immunohistochemistry (IHC), qPCR-based determination of mitochondrial DNA copy number and immune cell content using IHC and deconvolution of DNA methylation data. We analysed molecularly characterised gliomas (n = 57) for in depth DNA methylation, a cohort of primary and recurrent gliomas (n = 22) for mitochondrial copy number and validated these results in a large glioma cohort (n = 293). Finally, we investigated the potential of metabolic markers in Bevacizumab (Bev)-treated gliomas (n = 29). Results: DNA methylation patterns of metabolic genes successfully distinguished the molecular subtypes of IDHmut and IDHwt gliomas. Promoter methylation of lactate dehydrogenase A negatively correlated with protein expression and was associated with IDHmut gliomas. Mitochondrial DNA copy number was increased in IDHmut tumours and did not change in recurrent tumours. Hierarchical clustering based on metabolism panel IHC revealed distinct subclasses of IDHmut and IDHwt gliomas with an impact on patient outcome. Further quantification of these markers allowed for the prediction of survival under anti-angiogenic therapy. Conclusion: A mitochondrial signature was associated with increased survival in all analyses, which could indicate tumour subgroups with specific metabolic vulnerabilities.