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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.
The immune suppressive microenvironment affects efficacy of radio-immunotherapy in brain metastasis
(2021)
The tumor microenvironment in brain metastases is characterized by high myeloid cell content associated with immune suppressive and cancer-permissive functions. Moreover, brain metastases induce the recruitment of lymphocytes. Despite their presence, T-cell-directed therapies fail to elicit effective anti-tumor immune responses. Here, we seek to evaluate the applicability of radio- immunotherapy to modulate tumor immunity and overcome inhibitory effects that diminish anti-cancer activity. Radiotherapy- induced immune modulation resulted in an increase in cytotoxic T-cell numbers and prevented the induction of lymphocyte-mediated immune suppression. Radio-immunotherapy led to significantly improved tumor control with prolonged median survival in experi- mental breast-to-brain metastasis. However, long-term efficacy was not observed. Recurrent brain metastases showed accumula- tion of blood-borne PD-L1+ myeloid cells after radio-immunother- apy indicating the establishment of an immune suppressive environment to counteract re-activated T-cell responses. This finding was further supported by transcriptional analyses indicat- ing a crucial role for monocyte-derived macrophages in mediating immune suppression and regulating T-cell function. Therefore, selective targeting of immune suppressive functions of myeloid cells is expected to be critical for improved therapeutic efficacy of radio-immunotherapy in brain metastases.
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.