On the multi-scale causality of human cancer
Sala de Grados, Edificio Politécnico
Thursday March 30, 2017
Mathematical Oncology Laboratory

Cancer is a highly heterogeneous disease: more than 200 distinct types of human cancer have been described, and various tumor subtypes can be found within specific organs. It encompasses different cellular entities and a wide range of clinical behaviors, and is underpinned by a complex array of gene alterations that affect supra-molecular processes. This genetic variability is what primarily determines the self-progression of neoplastic disease and its response to therapy. Additionally, the asynchrony and self-progression of a cancer cell population suggests that the extent to which each neoplastic cell shares the properties of a non-tumoral cell may differ in time and in space. The complexity of alterations in cancer presents a daunting problem with respect to treatment: how can we effectively treat cancers arising from such variability? A tumor consists of genetically distinct subpopulations of cancer cells, each with its own characteristic sensitivity profile to a given therapeutic agent. Each cancer therapy can be viewed as a “filter” that remove a portion of cancer cells that are sensitive to this treatment while allowing other insensitive subpopulations to escape. It is indubitable that the conception of anatomical entities as a hierarchy of graduated forms and the increase in the number of observed sub-entities and structural variables has generated a growing complexity, thus highlighting new properties of tumoral cells. The need to tackle system complexity has become even more evident since completion of the various genome projects. One of the pre-eminent characteristics of the entire living world is its tendency to form multi-level structures of "systems within systems", each of which forms a Whole in relation to its parts and is simultaneously part of a larger Whole. Anatomical entities, when viewed at microscopic as well as macroscopic level of observation, show a different degree of complexity. The still unsolved central question is how to transform molecular knowledge into an understanding of complex phenomena in cells, tissues, organs and organisms. In order to understand cancer as a complex system that involves so many interacting components, we need to determine the type of data that needs to be collected at each level of organization, the boundary conditions to use when describing the disease, and the technologies and approaches best suited to reveal its underlying biological behavior. Critical analysis of traditional concepts is needed, as is reinterpretation of the clinical significance of failed therapies from the perspective of complexity. Two main concepts, multi-scale causality and heterogeneity need to be considered when generating new medical interventions. The need to find a new way of classifying tumoral entities, and objectively quantifying their different structural changes, prompted us to investigate the theory of “complex systems”, and to apply their concepts to human cancer. It is known that mathematical methods have proved to be practical in oncology, but the current models struggle to resolve the 10-12 order-of-magnitude span of the timescales of systemic events, be they molecular, cellular or physiological.

This Research Topic is aimed at discussing the molecular, cellular, clinical, epidemiological and imaging findings that are fundamental for identifying the complexity underlying human cancer. Viewing cancer as a system that is complex in time and space might reveals more about its underlying behavioral characteristics. It is encouraging that clinicians, biologists and mathematicians contribute together towards a common understanding of cancer complexity. This multi-disciplinary view may help to clarify old concepts, categorize the actual knowledge, and suggest alternative approaches to discover new biomarkers with potential clinical value. The combined efforts of such a multidisciplinary approach will help to clarify existing concepts, categorize current knowledge, and suggest alternative approaches to the discovery of new biomarkers and predictive factors that urgently need to be translated into clinical practice.