Molecular reductionism has been the reigning paradigm in cancer research for the last decades. Although very useful, this approach is limited in explaining the emerging complexity of this life-threatening disease. Often, cancer biology suffers the lack of a theoretical framework of reference that is instead established in other fields, such as in physics.
The paradigm of evolutionary cancer biology has gained importance as a reference model of tumour biology, introducing into oncology quantitative concepts such as clonal selection and population bottlenecks, which have a solid foundation in population genetics. This paradigm offers the basis for the development of a new set of tools that are quantitative in nature, with the aim of understanding malignant processes in a rigorous fashion.
Our lab maintains a strong interdisciplinary focus, bringing together ideas and tools from oncology, molecular biology, bioinformatics, mathematics, statistics and evolutionary biology, with the aim of deciphering the mechanisms of cancer progression, metastasis and treatment resistance. In particular, recent accumulating evidence from us and others have exposed the uniqueness of each cancer in terms of inter- and intra-patient molecular variation, thus emphasizing the urgent need for personalized medicine.
Since every tumour may follow a different set of rules to grow, metastasise and develop resistance to therapy, our aim is to identify those rules in each single patient, to reveal the events that drive the tumour progression and anticipate the course of the disease.
Although finding those specific rulesets in the vast range of possibilities may sound unfeasible, our approach based on the integration of genomic data and mathematical modelling allows measuring patient-specific malignant dynamics directly from human samples in a quantitative manner, with the goal of identifying characteristics of the tumour that will inform prognosis and novel treatment options.
We tackle cancer as a complex system, using genomics and rational sampling as the basis for data generation, ultimately oriented at formulating model-driven hypotheses and test predictions in a rigorous mathematical fashion.