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Hepatocellular carcinoma (HCC) is the typical inflammation-induced neoplasia. It often prospers where a chronic liver disease persists, thus leading a strong rationale for immune therapy. Several immune-based treatments, including immune checkpoint inhibitors (ICI), cytokines, adoptive cell transfer, and vaccines, have been tested in the treatment of HCC. In this review, we summarize the role of the ICI in HCC patients in various sets of treatment. As for advanced HCC, the anti-Programmed cell Death protein 1 (PD1) antibodies and the anti-Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) antibodies have been examined in patients with enthusiastic results in phase I-II-III studies. Overall, this led the Food and Drug Administration (FDA) to approve pembrolizumab, nivolumab, and nivolumab + ipilimumab in the second-line setting. The anti- Programmed Death-Ligand 1 (PDL-1) antibodies have also been evaluated. Thanks to the results obtained from phase III IMbrave study, atezolizumab + bevacizumab is now the standard of care in the first-line advanced setting of HCC. As for localized HCC, the putative immunological effect of locoregional therapies led to evaluate the combination strategy with ICI. This way, chemoembolization, ablation with radiofrequency, and radioembolization combined with ICI are currently under study. Likewise, the study of adjuvant immunotherapy following surgical resection is underway. In addition, the different ICI has been studied in combination with other ICI as well as with multikinase inhibitors and anti-angiogenesis monoclonal antibody. The evidence available suggests that combining systemic therapies and locoregional treatments with ICI may represent an effective strategy in this context.

The interplay between neoplastic cells and surrounding extracellular matrix (ECM) is one of the determinant elements for cancer growth. The remodeling of the ECM by cancer-associated fibroblasts (CAFs) shapes tumor microenvironment by depositing and digesting ECM proteins, hence promoting tumor growth and invasion. While for epithelial tumors CAFs are well characterized, little is known about the stroma composition of mesenchymal cancers, such as in rhabdomyosarcoma (RMS), the most common soft tissue sarcoma during childhood and adolescence. The aim of this work is to identify the importance of CAFs in specifying RMS microenvironment and the role of these stromal cells in RMS growth.

We assessed in two dimensional (2D) and three dimensional (3D) systems the attraction between RMS cells and fibroblasts using epithelial colon cancer cell line as control. CAFs were studied in a xenogeneic mouse model of both tumor types and characterized in terms of fibroblast activation protein (FAP), mouse PDGFR expression, metalloproteases activation, and ECM gene and protein expression profiling.

In 2D model, the rate of interaction between stromal and malignant cells was significantly lower in RMS with respect to colon cancer. Particularly, in 3D system, RMS spheroids tended to dismantle the compact aggregate when grown on the layer of stromal cells.

, despite the well-formed tumor mass, murine CAFs were found in low percentage in RMS xenogeneic samples.

Our findings support the evidence that, differently from epithelial cancers, RMS cells are directly involved in their own ECM remodeling, and less dependent on CAFs support for cancer cell growth.

Our findings support the evidence that, differently from epithelial cancers, RMS cells are directly involved in their own ECM remodeling, and less dependent on CAFs support for cancer cell growth.

To measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.

Pre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman’s rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.

Longer word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. AZD1390 ATM inhibitor IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC 0.89, 95% CI 0.88-0.90).

Report length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

Report length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.In magnetic resonance metabolic imaging, signal from the water content is frequently used for normalization to derive quantitative or semi-quantitative values of metabolites in vivo or ex vivo tumors and tissues. Ex vivo high-resolution metabolic characterization of tumors with magnetic resonance spectroscopy (MRS) provides valuable information that can be used to drive the development of noninvasive MRS biomarkers and to identify metabolic therapeutic targets. Variability in the water content between tumor and normal tissue can result in over or underestimation of metabolite concentrations when assuming a constant water content. Assuming a constant water content can lead to masking of differences between malignant and normal tissues both in vivo and ex vivo. There is a critical need to develop biomarkers to detect pancreatic cancer and to develop novel treatments. Our purpose here was to determine the differences in water content between pancreatic tumors and normal pancreatic tissue as well as other organs to accurately quantify metabolic differences when using the water signal for normalization.