Therapy optimization in glioblastoma: An integrative human data-based approach using mathematical models
Mathematical models are used routinely in many areas of Science and Engineering as the standard way of providing conceptual frameworks to understand Nature and provide solutions to real-world problems. It has been only recently that this methodology, beyond the classical statistical analysis, is starting to be used in medicine and specifically in cancer research. Many concepts of potential relevance in cancer have been proposed coming from mathematical approaches ranging from evolutionay dynamics concepts to adaptive and metronomic therapies, among others. However, despite their enormous potential, none of those general ideas have been translated to any specific cancer type in the clinic. The main reason is that the clinical implementation of ideas generated by in-silico modelling goes far beyond collaboration between mathematicians and biologist to test ideas “in vitro” or even in animal models and requires the key involvement of clinicians and the use of patient data. The goal of the collaborative activity is to make that happen for glioblastoma (GBM). We intend to create a cooperative framework to design strategies for the collection of a broad range of human data. Those data will be used to construct efficient mathematical algorithms able to interpret that diverse information as a whole. That understanding will help in making knowledge-based decisions on which specific therapeutics, combinations and timings should be used for each GBM patient. The focus will be on GBM but the methodology to be developed goes beyond that. Achieving this goal requires the integration of a vast amount of human clinical data coming from multimodal imaging MRI, PET, omics, immunohistochemical and molecular biology characterization of tumor cells and their microenvironment, fresh solid samples and liquid biopsies. The integration of all that information, together with the kind of conceptual frameworks that mathematical models provide, may allow us to get a “big picture” of the disease natural history and understand and predict its response to therapies. Thus, this project intends to acquire data from patients and integrate those data into mathematical approaches able to characterize the disease evolution. The final goal of the collaborative activity is to use that knowledge to improve GBM treatments.
James S. Mc. Donnell Foundation (2015-2018)
Universidad de Castilla-La Mancha, Insititut de Cancérologie de l'Ouest (Nantes), Cambridge University, Bern Inselspital, Hospital de Ciudad Real, Hospital Virgen de la Salud, Hospital de Malaga, ...
Albillo Labarra, José David. Arana, Estanislao. Belmonte Beitia, Juan. Benavides, Manuel. Bodnar, Marek. Bogdanska, Magdalena. Clairambault, Jean. Doblaré, Manuel. Fernández, Luis J. Fernández Calvo, Gabriel. García, Ana M. Henares Molina, Araceli. Hernández-Lain, Aurelio. Herruzo, Ismael. Luque, Belén. Martínez González, Alicia. Meléndez-Asensio, Bárbara. Molina García, David. Ochoa, Ignacio. Olivier, Lisa. Pérez Beteta, Julián. Pérez García, Victor M.. Pérez Moraga, María Jesús. Pérez Romasanta, Luis. Pesic, Milica. Piotrowska, Monika Joanna. Ramis Conde, Ignacio. Ramos, Ana. Rosa Durán, María. Sánchez-Gómez, Pilar. Sepúlveda, Juan M.. Vallette, François M.. Velazquez, Carlos. Vera, Luis.