New Molecular and Computational Approaches to Study Immune and Metabolic Interactions in Cancer Biology

Dear Colleagues,

Cancer, together with cardiovascular diseases, represents one of the main causes of death worldwide, with healthcare costs estimated at billions of euros every year. For all these reasons, fighting cancer has been one of the biggest challenges for medical research in the last century.
Cancer research is committed to meeting this huge medical need by proposing new experimental and computational approaches integrating different research fields in order to identify promising features to be used to detect, prevent, and treat cancer, the four objectives around which cancer research is built. Therefore, the integration of multiple points of view can be used to better describe and characterize the complex tumor biology.

Studies on cancer metabolism, arising from Warburg's theory, have exponentially grown in recent decades and currently represent one of the main hallmarks of cancer. Recently, on the basis of the “Seed and Soil” theory proposed by Paget, there have been numerous research papers studying the complex metabolic symbiotic relationship between the microenvironment and tumor cells. In fact, it is now widely acknowledged that the tumor microenvironment plays an important role in tumor progression as well as in responses to therapies, which determine patient survival. In this context, immune cells are deeply investigated, and the amounts and types, commonly referred to as the immune landscape, have been robustly underlined to have prognostic value based on the evidence showing that specific cell types are associated with distinct disease outcomes in patients.

Integration of multiple research fields is nowadays helped by artificial intelligence (AI) and digital pathology, which are gaining interest in cancer research since they allow to streamline and ameliorate diagnostic and prognostic processes. The deep learning models that are being built can extract features from huge and various datasets (AI) or from images (digital pathology) even beyond the visual perception, with the ultimate aim of improving disease detection, prevention, and treatment.

Original research articles and reviews on such research fields, as well as the integration of those that represent a step forward in understanding the complex molecular biology of cancer are welcome.

Dr. Federica Mornata
Dr. Luca De Feo
Dr. Rebecca Polidori
Dr. Matteo Brindisi
Guest Editors

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• solid and liquid cancer
• cancer metabolism
• tumor microenvironment
• digital pathology
• immunoncology