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Impact of Docetaxel on blood-brain barrier function and formation of breast cancer brain metastases
(2019)
Background: Breast cancer (BC) is the most frequent malignant tumor in females and the 2nd most common cause of brain metastasis (BM), that are associated with a fatal prognosis. The increasing incidence from 10% up to 40% is due to more effective treatments of extracerebral sites with improved prognosis and increasing use of MRI in diagnostics. A frequently administered, potent chemotherapeutic group of drugs for BC treatment are taxanes usually used in the adjuvant and metastatic setting, which, however, have been suspected to be associated with a higher incidence of BM. The aim of our study was to experimentally analyze the impact of the taxane docetaxel (DTX) on brain metastasis formation, and to elucidate the underlying molecular mechanism.
Methods: A monocentric patient cohort was analyzed to determine the association of taxane treatment and BM formation. To identify the specific impact of DTX, a murine brain metastatic model upon intracardial injection of breast cancer cells was conducted. To approach the functional mechanism, dynamic contrast-enhanced MRI and electron microscopy of mice as well as in-vitro transendothelial electrical resistance (TEER) and tracer permeability assays using brain endothelial cells (EC) were carried out. PCR-based, immunohistochemical and immunoblotting analyses with additional RNA sequencing of murine and human ECs were performed to explore the molecular mechanisms by DTX treatment.
Results: Taxane treatment was associated with an increased rate of BM formation in the patient cohort and the murine metastatic model. Functional studies did not show unequivocal alterations of blood-brain barrier properties upon DTX treatment in-vivo, but in-vitro assays revealed a temporary DTX-related barrier disruption. We found disturbance of tubulin structure and upregulation of tight junction marker claudin-5 in ECs. Furthermore, upregulation of several members of the tubulin family and downregulation of tetraspanin-2 in both, murine and human ECs, was induced.
Conclusion: In summary, a higher incidence of BM was associated with prior taxane treatment in both a patient cohort and a murine mouse model. We could identify tubulin family members and tetraspanin-2 as potential contributors for the destabilization of the blood-brain barrier. Further analyses are needed to decipher the exact role of those alterations on tumor metastatic processes in the brain.
Simple Summary
Seizures are among the most common symptoms of meningioma patients even after surgery. This study sought to identify risk factors for early and late seizures in meningioma patients and to evaluate a modified version of a score to predict postoperative seizures on an independent cohort. The data underline that there are distinct factors identifying patients with a high risk of postoperative seizures following meningioma surgery which has been already shown before. We could further show that the high proportion of 43% of postoperative seizures occur as late seizures which are more dangerous because they may happen out of hospital. The modified STAMPE2 score could predict postoperative seizures when reaching very high scores but was not generally transferable to our independent cohort.
Abstract
Seizures are among the most common symptoms of meningioma. This retrospective study sought to identify risk factors for early and late seizures in meningioma patients and to evaluate a modified STAMPE2 score. In 556 patients who underwent meningioma surgery, we correlated different risk factors with the occurrence of postoperative seizures. A modified STAMPE2 score was applied. Risk factors for preoperative seizures were edema (p = 0.039) and temporal location (p = 0.038). For postoperative seizures preoperative tumor size (p < 0.001), sensomotory deficit (p = 0.004) and sphenoid wing location (p = 0.032) were independent risk factors. In terms of postoperative status epilepticus; sphenoid wing location (p = 0.022), tumor volume (p = 0.045) and preoperative seizures (p < 0.001) were independent risk factors. Postoperative seizures lead to a KPS deterioration and thus an impaired quality of life (p < 0.001). Late seizures occurred in 43% of patients with postoperative seizures. The small sub-cohort of patients (2.7%) with a STAMPE2 score of more than six points had a significantly increased risk for seizures (p < 0.001, total risk 70%). We concluded that besides distinct risk factors, high scores of the modified STAMPE2 score could estimate the risk of postoperative seizures. However, it seems not transferable to our cohort
Despite multidisciplinary local and systemic therapeutic approaches, the prognosis for most patients with brain metastases is still dismal. The role of adaptive and innate anti-tumor response including the Human Leukocyte Antigen (HLA) machinery of antigen presentation is still unclear. We present data on the HLA class II-chaperone molecule CD74 in brain metastases and its impact on the HLA peptidome complexity.
We analyzed CD74 and HLA class II expression on tumor cells in a subset of 236 human brain metastases, primary tumors and peripheral metastases of different entities in association with clinical data including overall survival. Additionally, we assessed whole DNA methylome profiles including CD74 promoter methylation and differential methylation in 21 brain metastases. We analyzed the effects of a siRNA mediated CD74 knockdown on HLA-expression and HLA peptidome composition in a brain metastatic melanoma cell line.
We observed that CD74 expression on tumor cells is a strong positive prognostic marker in brain metastasis patients and positively associated with tumor-infiltrating T-lymphocytes (TILs). Whole DNA methylome analysis suggested that CD74 tumor cell expression might be regulated epigenetically via CD74 promoter methylation. CD74high and TILhigh tumors displayed a differential DNA methylation pattern with highest enrichment scores for antigen processing and presentation. Furthermore, CD74 knockdown in vitro lead to a reduction of HLA class II peptidome complexity, while HLA class I peptidome remained unaffected.
In summary, our results demonstrate that a functional HLA class II processing machinery in brain metastatic tumor cells, reflected by a high expression of CD74 and a complex tumor cell HLA peptidome, seems to be crucial for better patient prognosis.
Aim: The cytokine receptor tumor necrosis factor receptor superfamily member 9 (TNFRSF9) is mainly considered to be a co-stimulatory activation marker in hematopoietic cells. Several preclinical models have shown a dramatic beneficial effect of treatment approaches targeting TNFRSF9 with agonistic antibodies. However, preliminary clinical phase I/II studies were stopped after the occurrence of several severe deleterious side effects. In a previous study, it was demonstrated that TNFRSF9 was strongly expressed by reactive astrocytes in primary central nervous system (CNS) tumors, but was largely absent from tumor or inflammatory cells. The aim of the present study was to address the cellular source of TNFRSF9 expression in the setting of human melanoma brain metastasis, a highly immunogenic tumor with a prominent tropism to the CNS.
Methods: Melanoma brain metastasis was analyzed in a cohort of 78 patients by immunohistochemistry for TNFRSF9 and its expression was correlated with clinicopathological parameters including sex, age, survival, tumor size, number of tumor spots, and BRAF V600E expression status.
Results: Tumor necrosis factor receptor superfamily member 9 was frequently expressed independently on both melanoma and endothelial cells. In addition, TNFRSF9 was also present on smooth muscle cells of larger vessels and on a subset of lymphomonocytic tumor infiltrates. No association between TNFRSF9 expression and patient survival or other clinicopathological parameters was seen. Of note, several cases showed a gradual increase in TNFRSF9 expression on tumor cells with increasing distance from blood vessels, an observation that might be linked to hypoxia-driven TNFRSF9 expression in tumor cells.
Conclusion: The findings indicate that the cellular source of TNFRSF9 in melanoma brain metastasis largely exceeds the lymphomonocytic pool, and therefore further careful (re-) assessment of potential TNFRSF9 functions in cell types other than hematopoietic cells is needed. Furthermore, the hypothesis of hypoxia-driven TNFRSF9 expression in brain metastasis melanoma cells requires further functional testing.
Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.
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.
Higher grade meningiomas tend to recur. We aimed to evaluate protein levels of vascular endothelial growth factor (VEGF)-A with the VEGF-receptors 1-3 and the co-receptors Neuropilin (NRP)-1 and -2 in WHO grade II and III meningiomas to elucidate the rationale for targeted treatments. We investigated 232 specimens of 147 patients suffering from cranial meningioma, including recurrent tumors. Immunohistochemistry for VEGF-A, VEGFR-1-3, and NRP-1/-2 was performed on tissue micro arrays. We applied a semiquantitative score (staining intensity x frequency). VEGF-A, VEGFR-1-3, and NRP-1 were heterogeneously expressed. NRP-2 was mainly absent. We demonstrated a significant increase of VEGF-A levels on tumor cells in WHO grade III meningiomas (p = 0.0098). We found a positive correlation between expression levels of VEGF-A and VEGFR-1 on tumor cells and vessels (p < 0.0001). In addition, there was a positive correlation of VEGF-A and VEGFR-3 expression on tumor vessels (p = 0.0034). VEGFR-2 expression was positively associated with progression-free survival (p = 0.0340). VEGF-A on tumor cells was negatively correlated with overall survival (p = 0.0084). The VEGF-A-driven system of tumor angiogenesis might still present a suitable target for adjuvant therapy in malignant meningioma disease. However, its role in malignant tumor progression may not be as crucial as expected. The value of comprehensive testing of the ligand and all receptors prior to administration of anti-angiogenic therapy needs to be evaluated in clinical trials.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
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.