Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: implications for biopsy targeting, classification and therapy.

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.

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression.

PURPOSE: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. MATERIAL AND METHODS: At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier's performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. RESULTS: In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77-0.93 (sensitivity 0.91, 95% CI 0.81-0.97; specificity 0.71, 95% CI 0.44-0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72-0.9), 0.81 for [18F]-FET-PET (95% CI 0.7-0.89), and 0.81 for expert consensus (95% CI 0.7-0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. CONCLUSION: Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Position sensitive measurement of trace lithium in the brain with NIK (neutron-induced coincidence method) in suicide.

Mood disorder is the leading intrinsic risk factor for suicidal ideation. Questioning any potency of mood-stabilizers, the monovalent cation lithium still holds the throne in medical psychiatric treatment. Furthermore, lithium`s anti-aggressive and suicide-preventive capacity in clinical practice is well established. But little is still known about trace lithium distribution and any associated metabolic effects in the human body. We applied a new technique (neutron-induced coincidence method "NIK") utilizing the 6Li(n,α)3H reaction for the position sensitive, 3D spatially resolved detection of lithium traces in post-mortem human brain tissue in suicide versus control. NIK allowed, for the first time in lithium research, to collect a three dimensional high resolution map of the regional trace lithium content in the non lithium-medicated human brain. The results show an anisotropic distribution of lithium, thus indicating a homeostatic regulation under physiological conditions as a remarkable link to essentiality. In contrast to suicide we could empirically prove significantly higher endogenous lithium concentrations in white compared to gray matter as a general trend in non-suicidal individuals and lower lithium concentrations in emotion-modulating regions in suicide.

Discrimination of Different Brain Metastases and Primary CNS Lymphomas Using Morphologic Criteria and Diffusion Tensor Imaging.

Purpose: Brain metastases are a common complication of cancer and occur in about 15 - 40 % of patients with malignancies. The aim of this retrospective study was to differentiate between metastases from different primary tumors/CNS lymphyomas using morphologic criteria, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Materials and Methods: Morphologic criteria such as hemorrhage, cysts, pattern of contrast enhancement and location were reported in 200 consecutive patients with brain metastases/primary CNS lymphomas. FA and ADC values were measured in regions of interest (ROIs) placed in the contrast-enhancing tumor part, the necrosis and the non-enhancing peritumoral region (NEPTR). Differences between histopathological subtypes of metastases were analyzed using non-parametric tests, decision trees and hierarchical clustering analysis. Results: Significant differences were found in morphologic criteria such as hemorrhage or pattern of contrast enhancement. In diffusion measurements, significant differences between the different tumor entities were only found in ADC analyzed in the contrast-enhancing tumor part. Among single tumor entities, primary CNS lymphomas showed significantly lower median ADC values in the contrast-enhancing tumor part (ADClymphoma 0.92 [0.83 - 1.07] vs. ADCno_lymphoma 1.35 [1.10 - 1.64] P = 0.001). Further differentiation between types of metastases was not possible using FA and ADC. Conclusion: There were morphologic differences among the main subtypes of brain metastases/CNS lymphomas. However, due to a high variability of common types of metastases and low specificity, prospective differentiation remained challenging. DTI including FA and ADC was not a reliable tool for differentiation between different histopathological subtypes of brain metastases except for CNS lymphomas showing lower ADC values. Biopsy, surgery and staging remain essential for diagnosis. Key Points: • Histopathological subtypes of brain metastases/CNS lymphomas show different morphologic features on MRI• Primary CNS lymphomas show significantly reduced ADC values• DTI is not a reliable tool for differentiation between brain metastases Citation Format: • Bette S, Wiestler B, Delbridge C et al. Discrimination of Different Brain Metastases and Primary CNS Lymphomas Using Morphologic Criteria and Diffusion Tensor Imaging. Fortschr Röntgenstr 2016; 188: 1134 - 1143.

Polymorphous low-grade adenocarcinoma of seromucous glands of the nasopharynx. A report of a case and a discussion of the morphologic and immunohistochemical features.

Polymorphous low-grade adenocarcinoma (PLGA) is a minor salivary gland neoplasm that is characterized by morphologic variability, cytologic uniformity, and an infiltrating growth pattern. To date, this entity has been identified either within the confines of the oral cavity or, rarely, in the nasal cavity. The authors report a case of PLGA that arose in the nasopharynx of a 44-year-old female, representing the first documented occurrence of this tumor outside the oral cavity or the sinonasal tract. The histopathologic and immunohistochemical differentiation from pleomorphic adenoma and adenoid cystic carcinoma is discussed.

Monoclonal antibody for identification of human duct cell carcinoma of pancreas.

Monoclonal antibody to duct cell surface marker (HP-DU-1), produced by hybridoma, was shown to be specific to duct cell surface antigen when tested against normal adult and 12-20-week-old fetal human pancreases. There were no common HLA antigens for locus A, B, C and DR among the 10 pancreas donors; nor did HP-DU-1 reveal any affinity for lymphocytes of donors whose pancreases were used as antigen for immunization of BALB/c mice. Seven out of 7 human pancreas adenocarcinomas including a poorly differentiated human pancreatic tumor expressed surface determinants detectable by HP-DU-1. Cell lines CAPAN-1 and CAPAN-2, derived from human pancreatic adenocarcinoma, also revealed the same antigenic determinants.