Dynamic interplay between tumour, stroma and immune system can drive or prevent tumour progression

Immunotherapy: enhancing the efficacy of this promising therapeutic in multiple cancers

Cancer treatments often reach a refractory period leading to treatment failure and patients developing disease recurrence. This can be due to tumour cells escaping the immune response and creating an immunosuppressive microenvironment enhancing cancer progression. Immunotherapy has become a promising tool for cancer treatment as it restores the anti-tumour response of the patient's immune system. Immune checkpoint inhibitors are the most widely studied immunotherapies worldwide and are now approved for multiple cancers. However, chimeric antigen receptor (CAR)-T cell therapy has also shown promise by targeting T lymphocytes that are genetically modified ex vivo to express CARs and this is now approved to treat some haematological cancers. Although immunotherapy has shown successful treatment outcomes in multiple cancers, some patients do not respond to this treatment. Therefore, approaches to enhance the efficacy of immunotherapies are likely to be the key to improve their effectiveness. Therefore, combination therapies of checkpoint inhibitors +/- chemotherapy are at the forefront of current research. Furthermore, biomarkers that predict treatment response are now beginning to emerge. Additionally, utilising nanoparticles as a newly targeted drug delivery system to enhance CAR-T cell therapy may enhance the efficacy of the cells when re-infused within the patient. Even if efficacy is enhanced, severe immune-related adverse events (irAEs) occur that are life-threatening and could lead to therapy being stopped. Therefore, predictive biomarkers for toxicity are also needed to improve both the patient's quality of life and treatment outcomes. This review will look at the current immunotherapies in clinical trials and discuss how to enhance their efficacy.

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

Tumor-Associated T-Lymphocytes and Macrophages are Decreased in Endometrioid Endometrial Carcinoma with MELF-Pattern Stromal Changes

Microcystic, elongated, fragmented (MELF)-pattern is an unusual morphology of myometrial invasive front in endometrioid endometrial carcinoma (EA). The aim of the study was to investigate potential correlation between MELF-pattern and peritumoral inflammatory immune response. A total of 96 out of 368 patients were included in this study. CD3, CD20, CD57. CD68 and S100 markers were used for the detection of tumor-associated T-lymphocytes (TAT), tumor-associated B-lymphocytes (TAB), tumor-associated NK-lymphocytes (NK), tumor-associated macrophages and dendritic cells respectively. Mann-Whitney tests, receiver operating characteristic (ROC) curve analysis, and Spearman correlation were used as methods for statistical analyses. Odds ratio with 95% confidence interval (95% CI) was determined with the use of a logistic regression model. A p < 0.05 was considered statistically significant. Our results suggested that the number of CD3 and CD68 cells were significantly lower (p < 0.001) in cases of endometrioid carcinoma with MELF-pattern. A significant correlation between the presence of MELF-pattern and decrease of CD3 positive T-lymphocytes (r = 0.691; p < 0.001) was also observed. Additionally, we found an inverse correlation between the presence of MELF-pattern and TAM (r = 0.568; p = 0.001). Therefore, our data suggest that MELF-pattern may be associated with EA stroma fibrosis that contains immune cells infiltration and demonstrated a decrease in the number of TAT and TAM cells. This may indicate the poor clinical prognosis of this disease.