Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a poor prognosis, despite surgical resection combined with radio- and chemotherapy. The major clinical obstacles contributing to poor GBM prognosis are late diagnosis, diffuse infiltration, pseudo-palisading necrosis, microvascular proliferation, and resistance to conventional therapy. These challenges are further compounded by extensive inter- and intra-tumor heterogeneity and the dynamic plasticity of GBM cells. The complex heterogeneous nature of GBM cells is facilitated by the local inflammatory tumor microenvironment, which mostly induces tumor aggressiveness and drug resistance. An immunosuppressive tumor microenvironment of GBM provides multiple pathways for tumor immune evasion. Infiltrating immune cells, mostly tumor-associated macrophages, comprise much of the non-neoplastic population in GBM. Further understanding of the immune microenvironment of GBM is essential to make advances in the development of immunotherapeutics. Recently, whole-genome sequencing, epigenomics and transcriptional profiling have significantly helped improve the prognostic and therapeutic outcomes of GBM patients. Here, we discuss recent genomic advances, the role of innate and adaptive immune mechanisms, and the presence of an established immunosuppressive GBM microenvironment that suppresses and/or prevents the anti-tumor host response.

ObjectiveDesmoplasia and hypovascularity are thought to impede drug delivery in pancreatic ductal adenocarcinoma (PDAC). However, stromal depletion approaches have failed to show clinical responses in patients. Here, we aimed to revisit the role of the tumour microenvironment as a physical barrier for gemcitabine delivery.DesignGemcitabine metabolites were analysed in LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre (KPC) murine tumours and matched liver metastases, primary tumour cell lines, cancer-associated fibroblasts (CAFs) and pancreatic stellate cells (PSCs) by liquid chromatography-mass spectrometry/mass spectrometry. Functional and preclinical experiments, as well as expression analysis of stromal markers and gemcitabine metabolism pathways were performed in murine and human specimen to investigate the preclinical implications and the mechanism of gemcitabine accumulation.ResultsGemcitabine accumulation was significantly enhanced in fibroblast-rich tumours compared with liver metastases and normal liver. In vitro, significantly increased concentrations of activated 2′,2′-difluorodeoxycytidine-5′-triphosphate (dFdCTP) and greatly reduced amounts of the inactive gemcitabine metabolite 2′,2′-difluorodeoxyuridine were detected in PSCs and CAFs. Mechanistically, key metabolic enzymes involved in gemcitabine inactivation such as hydrolytic cytosolic 5′-nucleotidases (Nt5c1A, Nt5c3) were expressed at low levels in CAFs in vitro and in vivo, and recombinant expression of Nt5c1A resulted in decreased intracellular dFdCTP concentrations in vitro. Moreover, gemcitabine treatment in KPC mice reduced the number of liver metastases by >50%.ConclusionsOur findings suggest that fibroblast drug scavenging may contribute to the clinical failure of gemcitabine in desmoplastic PDAC. Metabolic targeting of CAFs may thus be a promising strategy to enhance the antiproliferative effects of gemcitabine.

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Introduction:The WHO classification of pulmonary neuroendocrine tumors (PNETs) is also used to classify thymic NETs (TNETs) into typical and atypical carcinoid (TC and AC), large cell neuroendocrine carcinoma (LCNEC), and small cell carcinoma (SCC), but little is known about the usability of alternative classification systems.Methods: One hundred seven TNET (22 TC, 51 AC, 28 LCNEC, and 6 SCC) from 103 patients were classified according to the WHO, the European Neuroendocrine Tumor Society, and a grading-related PNET classification. Low coverage whole-genome sequencing and immunohistochemical studies were performed in 63 cases. A copy number instability (CNI) score was applied to compare tumors. Eleven LCNEC were further analyzed using targeted next-generation sequencing. Morphologic classifications were tested against molecular features.Results: Whole-genome sequencing data fell into three clusters: CNI low , CNI int , and CNI high . CNI low and CNI int comprised not only TC and AC, but also six LCNECs. CNI high contained all SCC and nine LCNEC, but also three AC. No morphologic classification was able to predict the CNI cluster. Cases where primary tumors and metastases were available showed progression from low-grade to higher-grade histologies. Analysis of LCNEC revealed a subgroup of intermediate NET G3 tumors that differed from LCNEC by carcinoid morphology, expression of chromogranin, and negativity for enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2).Conclusions: TNETs fall into three molecular subgroups that are not reflected by the current WHO classification. Given the large overlap between TC and AC on the one hand, and AC and LCNEC on the other, we propose a

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