Digital image analysis workflows for evaluation of cell behavior and tumor microenvironment to aid therapeutic assessment in high-risk neuroblastoma.

Digital pathology and artificial intelligence are promising emerging tools in precision oncology as they provide more robust and reproducible analysis of histologic, morphologic and topologic characteristics of tumor cells and the surrounding microenvironment. This study aims to develop digital image analysis workflows for therapeutic assessment in preclinical in vivo models. For this purpose, we generated pipelines that enable automatic detection and quantification of vitronectin and αvß3 in heterotopic high-risk neuroblastoma xenografts, demonstrating that digital analysis workflows can be used to provide robust detection of vitronectin secretion and αvß3 expression by malignant neuroblasts and to evaluate the possibility of combining traditional chemotherapy (etoposide) with extracellular matrix-targeted therapies (cilengitide). Digital image analysis added evidence for the relevance of territorial vitronectin as a therapeutic target in neuroblastoma, since its expression is modified after treatment, with a mean percentage of 60.44% in combined therapy tumors vs 45.08% in control ones. In addition, the present study revealed the efficacy of cilengitide for reducing αvß3 expression, with a mean αvß3 positivity of 34.17% in cilengitide treated material vs 66.14% in control and with less tumor growth when combined with etoposide, with a final mean volume of 0.04 cm3 in combined therapy vs 1.45 cm3 in control. The results of this work highlight the importance of extracellular matrix-focused therapies in preclinical studies to improve therapeutic assessment for high-risk neuroblastoma patients.

The complexity of cancer immunotherapy illustrated through skin tumors.

Skin tumours are among the cancer types most sensitive to immunotherapy, due to their unique immunogenic features including skin-associated lymphoid tissue, high mutational load, overexpression of tumour antigens, and high frequency of viral antigens. Despite this high immunotherapy response rate, however, ultimately most skin tumours develop similar treatment resistance to most other malignant tumours, which highlights the need for in-depth study of mechanisms of response and resistance to immunotherapy. A bibliographic review of the most recent publications regarding currently in use and emerging biomarkers on skin tumors has been done. Predictive biomarkers of treatment response, biomarkers that warn of possible resistance, and emerging markers, the majority of a systemic nature, are described. Including factors affecting not only genomics, but also the immune system, nervous system, microbiota, tumour microenvironment, metabolism and stress. For accurate diagnosis of tumour type, knowledge of its functional mechanisms and selection of a comprehensive therapeutic protocol, this inclusive view of biology, health and disease is fundamental. This field of study could also become a valuable source of practical information applicable to other areas of oncology and immunotherapy.