BACKGROUND:Recently, we developed AT101, an IgM-class mouse monoclonal antibody directed against glypican-1 (GPC1), a proteoglycan that can be considered as useful target for glioblastoma multiforme (GBM) treatment being specifically and highly expressed on GBM cell surface. Here, we proposed the use of AT101 as targeting agent in a drug delivery nanoplatfom to effectively deliver chitosan nanobubbles (NBs) for GBM treatment. METHODS:Chitosan NBs were prepared and conjugated with AT101 or left unconjugated as control. RESULTS:The ability of AT101 to bind the GPC1 protein was demonstrated by flow cytometry and immunofluorescence analysis in the "GBM-like" GPC1-expressing cell lines U-87 MG and T98G. AT101 was shown to bind GPC1-expressing GBM tumor samples by immunofluorescence. In-vivo experiments in the U-87 MG xenograft model showed that AT101 was able to bind GPC1 on cell surface and accumulate in U-87 MG tumor masses (p = 0.0002 respect to control). Moreover, in-vivo experiments showed that AT101 is able to target GPC1 when conjugated to chitosan NBs, thus increasing their specific deliver to GPC1-expressing cells of U-87 MG tumor, as compared to chitosan NBs not conjugated to AT101 (p = 0.02). CONCLUSIONS:AT101 is an useful targeting agent for the development of drug delivery nanoplatforms for GBM treatment.

Glioma grade 4 (GG4) tumors, including astrocytoma IDH-mutant grade 4 and the astrocytoma IDH wt are the most common and aggressive primary tumors of the central nervous system. Surgery followed by Stupp protocol still remains the first-line treatment in GG4 tumors. Although Stupp combination can prolong survival, prognosis of treated adult patients with GG4 still remains unfavorable. The introduction of innovative multi-parametric prognostic models may allow refinement of prognosis of these patients. Here, Machine Learning (ML) was applied to investigate the contribution in predicting overall survival (OS) of different available data (e.g. clinical data, radiological data, or panel-based sequencing data such as presence of somatic mutations and amplification) in a mono-institutional GG4 cohort. By next-generation sequencing, using a panel of 523 genes, we performed analysis of copy number variations and of types and distribution of nonsynonymous mutations in 102 cases including 39 carmustine wafer (CW) treated cases. We also calculated tumor mutational burden (TMB). ML was applied using eXtreme Gradient Boosting for survival (XGBoost-Surv) to integrate clinical and radiological information with genomic data. By ML modeling (concordance (c)- index = 0.682 for the best model), the role of predicting OS of radiological parameters including extent of resection, preoperative volume and residual volume was confirmed. An association between CW application and longer OS was also showed. Regarding gene mutations, a role in predicting OS was defined for mutations of BRAF and of other genes involved in the PI3K-AKT-mTOR signaling pathway. Moreover, an association between high TMB and shorter OS was suggested. Consistently, when a cutoff of 1.7 mutations/megabase was applied, cases with higher TMB showed significantly shorter OS than cases with lower TMB. The contribution of tumor volumetric data, somatic gene mutations and TBM in predicting OS of GG4 patients was defined by ML modeling.

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

Glioblastoma (GBM) is the most frequent and aggressive primary central nervous system tumor. Surgery followed by radiotherapy and chemotherapy with alkylating agents constitutes standard first-line treatment of GBM. Complete resection of the GBM tumors is generally not possible given its high invasive features. Although this combination therapy can prolong survival, the prognosis is still poor due to several factors including chemoresistance. In recent years, a comprehensive characterization of the GBM-associated molecular signature has been performed. This has allowed the possibility to introduce a more personalized therapeutic approach for GBM, in which novel targeted therapies, including those employing tyrosine kinase inhibitors (TKIs), could be employed. The GBM tumor microenvironment (TME) exerts a key role in GBM tumor progression, in particular by providing an immunosuppressive state with low numbers of tumor-infiltrating lymphocytes (TILs) and other immune effector cell types that contributes to tumor proliferation and growth. The use of immune checkpoint inhibitors (ICIs) has been successfully introduced in numerous advanced cancers as well as promising results have been shown for the use of these antibodies in untreated brain metastases from melanoma and from non-small cell lung carcinoma (NSCLC). Consequently, the use of PD-1/PD-L1 inhibitors has also been proposed in several clinical trials for the treatment of GBM. In the present review, we will outline the main GBM molecular and TME aspects providing also the grounds for novel targeted therapies and immunotherapies using ICIs for GBM.

Background: Adipose tissue derived mesenchymal stromal/stem cells (ASC) can be expanded using supernatant rich in growth factors (SRGF) as Good Manufacturing Practice compatible additive, instead of fetal bovine serum (FBS). After transendothelial migration, ASC can migrate to cancer masses where they can release active substances. Due to their homing and secretion properties ASC can be used as targeted drug delivery vehicles. Nevertheless, the fraction of ASC actually reaching the tumor target is limited. The impact of culture conditions on ASC homing potential on cancer cells is unknown. Methods: In dynamic in vitro conditions, we perfused FBS or SRGF ASC in flow chambers coated with collagen type I and fibronectin or seeded with endothelial cells or with HT1080, T98G and Huh7 cancer cells. Expression of selected adhesion molecules was evaluated by standard cytofluorimetry. Dynamic intracellular calcium concentration changes were evaluated in microfluidic and static conditions. Results: When compared to FBS ASC, not specific adhesion of SRGF ASC on collagen type I and fibronectin was lower (-33.9%+/- 12.2% and -45.3%+/- 16.9%), while on-target binding on HT1080 and 198G was enhanced (+147%+/- 8% and 120.5%+/- 5.2%). Adhesion of both FBS and SRGF ASC on Huh7 cells was negligible. As confirmed by citofluorimetry and by function-blocking antibody, SRGF mediated decrease of CD49a expression accounted for lower SRGF-ASC avidity for matrix proteins. Upon stimulation with calcium ionophore in static conditions, mobilization of intracellular calcium in SRGF ASC was greater than in FBS ASC. In dynamic conditions, upon adhesion on matrix proteins and HT1080 cells, SRGF ASC showed marked oscillatory calcium concentration changes. Conclusions: SRGF can enhance specific ASC binding capacity on selected cancer cells as HT1080 (fibrosarcoma) and T98G (glioblastoma) cells. Upon cell-cell adhesion, SRGF ASC activate intracellular responses potentially improving cell secretion functions. SRGF ASC could be considered as suitable drug delivery vehicle for cancer therapy.

The development of nanostructures for therapeutic purpose is rapidly growing, following the results obtained in vivo in animal models and in the clinical trials. Unfortunately, the potential therapeutic efficacy is not completely exploited, yet. This is mainly due to the fast clearance of the nanostructures in the body. Nanoparticles and the liver have a unique interaction because the liver represents one of the major barriers for drug delivery. This interaction becomes even more relevant and complex when the drug delivery strategies employing nanostructures are proposed for the therapy of liver diseases, such as hepatocellular carcinoma (HCC). In this case, the selective delivery of therapeutic nanoparticles to the tumor microenvironment collides with the tendency of nanostructures to be quickly eliminated by the organ. The design of a new therapeutic approach based on nanoparticles to treat HCC has to particularly take into consideration passive and active mechanisms to avoid or delay liver elimination and to specifically address cancer cells or the cancer microenvironment. This review will analyze the different aspects concerning the dual role of the liver, both as an organ carrying out a clearance activity for the nanostructures and as target for therapeutic strategies for HCC treatment.

Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10×5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF-β signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.

INTRODUCTION:The introduction of immune checkpoint inhibitors has been lately proposed for the treatment of hepatocellular carcinoma (HCC) with respect to other cancer types. Several immunotherapeutic approaches are now under evaluation for HCC treatment including: i) antibodies acting as immune checkpoint inhibitors; ii) antibodies targeting specific tumor-associated antigens; iii) chimeric antigen receptor redirected T (CAR-T) cells targeting specific tumor-associated antigens; iv) vaccination strategies with tumor-specific epitopes. Areas covered: The review provides a wide description of the clinical trials investigating the efficacy of the main immunotherapeutic approaches proposed for the treatment of patients affected by HCC. Expert opinion: The balancing between immunostimulative and immunosuppressive factors in the context of HCC tumor microenvironment results in heterogeneous response rates to immunotherapeutic approaches such as checkpoint inhibitors, among HCC patients. In this context, it becomes crucial the identification of predictive factors determining the treatment response. A multiple approach using different biomarkers could be useful to identify the subgroup of HCC patients responsive to the treatment with a checkpoint inhibitor (as an example, nivolumab) as single agent, and to identify those patients in which other treatment regimens, such as the combination with sorafenib, or with locoregional therapies, could be more effective.

Hepatocellular carcinoma (HCC) is a malignancy characterized by neoangiogenesis that is determined by an augmented production of proangiogenesis factors by tumor and adjacent cells. This unbalanced angiogenesis process is a key feature of HCC carcinogenesis and progression. Proangiogenic factors also have a relevant role in the generation and maintenance of an immunosuppressive tumor microenvironment. Several therapeutic options for HCC treatment are based on the inhibition of angiogenesis, both in the early/intermediate stages of the disease and in the late stages of the disease. Conventional treatment options employing antiangiogenic approaches provide for the starving of tumors of their blood supply to avoid the refueling of oxygen and nutrients. An emerging alternative point of view is the normalization of vasculature leading to enhance tumor perfusion and oxygenation, potentially capable, when proposed in combination with other treatments, to improve delivery and efficacy of other therapies, including immunotherapy with checkpoint inhibitors. The introduction of novel biomarkers can be useful for the definition of the most appropriate dose and scheduling for these combination treatment approaches. The present review provides a wide description of the pharmaceutical compounds with an antiangiogenic effect proposed for HCC treatment and investigated in clinical trials, including antibodies and small-molecule kinase inhibitors.

Initial lymphatic vessel endothelial cells are connected to the surrounding elastic fibers by fibrillin anchoring filaments that have been hypothesized to favor interstitial fluid drainage in edema pulling apart interendothelial junctions. We hypothesized a biochemical mechanism involving mechanotransduction. This study was designed to verify whether a relation exists between focal adhesion molecules and anchoring filaments and whether they may transduce extracellular forces to the nucleus. We first performed an immunohistochemical study on human skin cryostat sections to evaluate whether fibrillin and alpha v-beta 3 integrins, FAK and fibrillin, or alpha v-beta 3 integrins and FAK co-localize in lymphatic endothelium. We observed that integrins and FAK co-localize and that fibrillin filament attachment sites to endothelial cells merge with these molecules. These data may suggest that fibrillin anchoring filaments are connected to endothelial cells through focal adhesions. Mechanotransduction was investigated applying static stretching to bovine thoracic duct segments and lymphatic endothelial cells cultured on elastic membranes and immunohistochemically evaluating the expression of ERK1/2. Under stretching conditions, ERK1/2 labels the nucleus. Western blotting on cultured cells confirmed the presence of ERK1/2 in stretched cells. Based on our data we speculate that anchoring filaments may trigger a focal adhesion-mediated cascade of mechanotransduction toward the nucleus for genetic modulation and thus contribute to endothelial adaptation to interstitial requirements.

The performance of dental or orthopedic implants is closely dependent on surface properties in terms of topography and chemistry. A phosphated carboxymethylcellulose containing one phosphate group for each disaccharide unit was synthesized and used to functionalize titanium oxide surfaces with the aim to improve osseointegration with the host tissue. The modified surfaces were chemically characterized by means of X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. The investigation of the surface topography was performed by atomic force microscopy measurements before and after the polysaccharide coating. In vitro biological tests using osteoblast-like cells demonstrated that functionalized TiO2 surfaces modulated cell response, in terms of adhesion, proliferation, and morphology. Phosphated carboxymethylcellulose promoted better cell adhesion and significantly enhanced their proliferation. The morphology of cells was polygonal and more spread on this type of modified surface. These findings suggest that the presence of a phosphate polysaccharide coating promotes osteoblast growth on the surface potentially improving biomaterial osseointegration.