While the central nervous system is considered an immunoprivileged site and brain tumors display immunosuppressive features, both innate and adaptive immune responses affect glioblastoma (GBM) growth and treatment resistance. However, the impact of the major immune cell population in gliomas, represented by glioma‐associated microglia/macrophages (GAMs), on patients’ clinical course is still unclear. Thus, we aimed at assessing the immunohistochemical expression of selected microglia and macrophage markers in 344 gliomas (including gliomas from WHO grade I–IV). Furthermore, we analyzed a cohort of 241 IDH1R132H‐non‐mutant GBM patients for association of GAM subtypes and patient overall survival. Phenotypical properties of GAMs, isolated from high‐grade astrocytomas by CD11b‐based magnetic cell sorting, were analyzed by immunocytochemistry, mRNA microarray, qRT‐PCR and bioinformatic analyses. A higher amount of CD68‐, CD163‐ and CD206‐positive GAMs in the vital tumor core was associated with beneficial patient survival. The mRNA expression profile of GAMs displayed an upregulation of factors that are considered as pro‐inflammatory M1 (eg, CCL2, CCL3L3, CCL4, PTGS2) and anti‐inflammatory M2 polarization markers (eg, MRC1, LGMN, CD163, IL10, MSR1), the latter rather being associated with phagocytic functions in the GBM microenvironment. In summary, we present evidence that human GBMs contain mixed M1/M2‐like polarized GAMs and that the levels of different GAM subpopulations in the tumor core are positively associated with overall survival of patients with IDH1R132H‐non‐mutant GBMs.

The activation of immune cells by targeting checkpoint inhibitors showed promising results with increased patient survival in distinct primary cancers. Since only limited data exist for human brain metastases, we aimed at characterizing tumor infiltrating lymphocytes (TILs) and expression of immune checkpoints in the respective tumors.Two brain metastases cohorts, a mixed entity cohort (n = 252) and a breast carcinoma validation cohort (n = 96) were analyzed for CD3+, CD8+, FOXP3+, PD-1+ lymphocytes and PD-L1+ tumor cells by immunohistochemistry. Analyses for association with clinico-epidemiological and neuroradiological parameters such as patient survival or tumor size were performed.TILs infiltrated brain metastases in three different patterns (stromal, peritumoral, diffuse). While carcinomas often show a strong stromal infiltration, TILs in melanomas often diffusely infiltrate the tumors. Highest levels of CD3+ and CD8+ lymphocytes were seen in renal cell carcinomas (RCC) and strongest PD-1 levels on RCCs and melanomas. High amounts of TILs, high ratios of PD-1+/CD8+ cells and high levels of PD-L1 were negatively correlated with brain metastases size, indicating that in smaller brain metastases CD8+ immune response might get blocked. PD-L1 expression strongly correlated with TILs and FOXP3 expression. No significant association of patient survival with TILs was observed, while high levels of PD-L1 showed a strong trend towards better survival in melanoma brain metastases (Log-Rank p = 0.0537).In summary, melanomas and RCCs seem to be the most immunogenic entities. Differences in immunotherapeutic response between tumor entities regarding brain metastases might be attributable to this finding and need further investigation in larger patient cohorts.

Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

Melanoma patients carry a high risk of developing brain metastases, and improvements in survival are still measured in weeks or months. Durable disease control within the brain is impeded by poor drug penetration across the blood-brain barrier, as well as intrinsic and acquired drug resistance. Augmented mitochondrial respiration is a key resistance mechanism in BRAF -mutant melanomas but, as we show in this study, this dependence on mitochondrial respiration may also be exploited therapeutically. We first used high-throughput pharmacogenomic profiling to identify potentially repurposable compounds against BRAF -mutant melanoma brain metastases. One of the compounds identified was β-sitosterol, a well-tolerated and brain-penetrable phytosterol. Here we show that β-sitosterol attenuates melanoma cell growth in vitro and also inhibits brain metastasis formation in vivo. Functional analyses indicated that the therapeutic potential of β-sitosterol was linked to mitochondrial interference. Mechanistically, β-sitosterol effectively reduced mitochondrial respiratory capacity, mediated by an inhibition of mitochondrial complex I. The net result of this action was increased oxidative stress that led to apoptosis. This effect was only seen in tumor cells, and not in normal cells. Large-scale analyses of human melanoma brain metastases indicated a significant role of mitochondrial complex I compared to brain metastases from other cancers. Finally, we observed completely abrogated BRAF inhibitor resistance when vemurafenib was combined with either β-sitosterol or a functional knockdown of mitochondrial complex I. In conclusion, based on its favorable tolerability, excellent brain bioavailability, and capacity to inhibit mitochondrial respiration, β-sitosterol represents a promising adjuvant to BRAF inhibitor therapy in patients with, or at risk for, melanoma brain metastases. Electronic supplementary material The online version of this article (10.1186/s40478-019-0712-8) contains supplementary material, which is available to authorized users.

Despite its well-characterized side effects, dexamethasone is widely used in the pre-, peri- and postoperative neurosurgical setting due to its effective relief of tumor-induced symptoms through the reduction of tumor-associated edema. However, some patients show laboratory-defined dexamethasone induced elevation of white blood cell count, and its impact on glioblastoma progression is unknown. We retrospectively analyzed 113 patients with newly diagnosed glioblastoma to describe the incidence, risk factors and clinical features of dexamethasone-induced leukocytosis in primary glioblastoma patients. We further conducted an immunohistochemical analysis of the granulocyte and lymphocyte tumor-infiltration in the available corresponding histological sections. Patient age was identified to be a risk factor for the development of dexamethasone-induced leukocytosis (p < 0.05). The presence of dexamethasone-induced leukocytosis decreased overall survival (HR 2.25 95% CI [1.15-4.38]; p < 0.001) and progression-free survival (HR 2.23 95% CI [1.09-4.59]; p < 0.01). Furthermore, patients with dexamethasone-induced leukocytosis had significantly reduced CD15 + granulocytic- (p < 0.05) and CD3 + lymphocytic tumour infiltration (p < 0.05). We identified a subgroup of glioblastoma patients that are at particularly high risk for poor outcome upon dexamethasone treatment. Therefore, restrictive dosage or other edema reducing substances should be considered in patients with dexamethasone-induced leukocytosis.

In Europe extracts from Viscum album L., the European white-berry mistletoe, are widely used as a complementary cancer therapy. Viscumins (mistletoe lectins, ML) have been scrutinized as important active components of mistletoe and exhibit a variety of anticancer effects such as stimulation of the immune system, induction of cytotoxicity, reduction of tumor cell motility as well as changes in the expression of genes associated with cancer development and progression. By microarray expression analysis, quantitative RT-PCR and RT-PCR based validation of microarray data we demonstrate for the Viscum album extract Iscador Qu and for the lectins Aviscumine and ML-1 that in glioma cells these drugs differentially modulate the expression of genes involved in the regulation of cell migration and invasion, including processes modulating cell architecture and cell adhesion. A variety of differentially expressed genes in ML treated cells are associated with the transforming growth factor (TGF)-β signaling pathway or are targets of TGF-β. ML treatment downregulated the expression of TGF-β itself, of the TGF-β receptor II (TGFBR2), of the TGF-β intracellular signal transducer protein SMAD2, and of matrix-metalloproteinases (MMP) MMP-2 and MMP-14. Even if the changes in gene expression differ between Aviscumine, Iscador Qu and ML-1, the overall regulation of motility associated gene expression by all drugs showed functional effects since tumor cell motility was reduced in a ML-dependent manner. Therefore, ML containing compounds might provide clinical benefit as adjuvant therapeutics in the treatment of patients with invasively growing tumors such as glioblastomas.

Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.

Dual-energy CT (DECT) iodine maps enable quantification of iodine concentrations as a marker for tissue vascularization. We investigated whether iodine map radiomic features derived from staging DECT enable prediction of breast cancer metastatic status, and whether textural differences exist between primary breast cancers and metastases. Seventy-seven treatment-naïve patients with biopsy-proven breast cancers were included retrospectively (41 non-metastatic, 36 metastatic). Radiomic features including first-, second-, and higher-order metrics as well as shape descriptors were extracted from volumes of interest on iodine maps. Following principal component analysis, a multilayer perceptron artificial neural network (MLP-NN) was used for classification (70% of cases for training, 30% validation). Histopathology served as reference standard. MLP-NN predicted metastatic status with AUCs of up to 0.94, and accuracies of up to 92.6 in the training and 82.6 in the validation datasets. The separation of primary tumor and metastatic tissue yielded AUCs of up to 0.87, with accuracies of up to 82.8 in the training, and 85.7 in the validation dataset. DECT iodine map-based radiomic signatures may therefore predict metastatic status in breast cancer patients. In addition, microstructural differences between primary and metastatic breast cancer tissue may be reflected by differences in DECT radiomic features.