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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.
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.
Immune profile and radiological characteristics of progressive multifocal leukoencephalopathy
(2021)
Background and purpose: Progressive multifocal leukoencephalopathy (PML) constitutes a severe disease with increasing incidence, mostly in the context of immunosuppressive therapies. A detailed understanding of immune response in PML appears critical for the treatment strategy. The aim was a comprehensive immunoprofiling and radiological characterization of magnetic resonance imaging (MRI) defined PML variants.
Methods: All biopsy-confirmed PML patients (n = 15) treated in our department between January 2004 and July 2019 were retrospectively analysed. Data from MRI, histology as well as detailed clinical and outcome data were collected. The MRI-defined variants of classical (cPML) and inflammatory (iPML) PML were discriminated based on the intensity of gadolinium enhancement. In these PML variants, intensity and localization (perivascular vs. parenchymal) of inflammation in MRI and histology as well as the cellular composition by immunohistochemistry were assessed. The size of the demyelinating lesions was correlated with immune cell infiltration.
Results: Patients with MRI-defined iPML showed a stronger intensity of inflammation with an increased lymphocyte infiltration on histological level. Also, iPML was characterized by a predominantly perivascular inflammation. However, cPML patients also demonstrated certain inflammatory tissue alterations. Infiltration of CD163-positive microglia and macrophage (M/M) subtypes correlated with PML lesion size.
Conclusions: The non-invasive MRI-based discrimination of PML variants allows for an estimation of inflammatory tissue alterations, although exhibiting limitations in MRI-defined cPML. The association of a distinct phagocytic M/M subtype with the extent of demyelination might reflect disease progression.