Cancer is one of the main health problems in the developed world. One in two men and one in three women will face some form of cancer in their lifetime. Enormous resources have been devoted to biomedical cancer research, but achievements have been modest. For instance, the annual reduction in cancer mortality has been only 1%-2% in recent years. The use of mathematical models and advanced data analysis tools is providing new insights to advance the 'war on cancer'.

Advances in this field provide substantial benefits to both Mathematics and Medicine. Mathematics finds a new field of applications, giving rise to new types of fundamental problems and methodological developments. Oncology benefits from the analytic capabilities that mathematical models provide as well as the application of data-analysis tools incorporating elements of statistics, artificial intelligence (AI) and topological data analysis (TDA).

In this proof-of-concept project MOLAB researchers will focus on transferring knowledge generated in their previous project PID2019-110895RB-I00 that used mathematical modelling as a key conceptual hub linking different areas of Oncology: brain tumours (BTs), leukemias, immunotherapy and evolutionary dynamics of cancer. In AFTERbioMATH MOLAB researchers will focus on providing tools aiding clinical decision-making in pediatric acute lymphoblastic leukemias and brain metastases.