An Emergence Framework of Carcinogenesis

Experimental paradigms provide the framework for the understanding of cancer, and drive research and treatment, but are rarely considered by clinicians. The Somatic Mutation Theory (SMT), in which cancer is considered a genetic disease, has been the predominant traditional model of cancer for over fifty years. More recently alternative theories have been proposed, such as Tissue Organization Field Theory (TOFT), evolutionary models and inflammatory models. Key concepts within the various models have led to them being difficult to reconcile. Progressively, it has been recognized that biological systems cannot be fully explained by the physicochemical properties of their constituent parts. There is an increasing call for a ‘systems’ approach. Incorporating the concepts of ‘emergence’, ‘systems’, ‘thermodynamics’ and ‘chaos’, a single integrated framework for carcinogenesis has been developed, enabling existing theories to become compatible as alternative mechanisms, facilitating the integration of bioinformatics and providing a structure in which translational research can flow from both ‘benchtop to bedside’ and ‘bedside to benchtop’. In this review, a basic understanding of the key concepts of ‘emergence’, ‘systems’, ‘system levels’, ‘complexity’, ‘thermodynamics’, ‘entropy’, ‘chaos’ and ‘fractals’ is provided. Nonlinear mathematical equations are included where possible to demonstrate compatibility with bioinformatics. Twelve postulates that define the ‘emergence framework of carcinogenesis’ are developed, with postulates 1–10 encapsulating the key concepts upon which the framework is built and their application to carcinogenesis. Postulate 11 relates the framework to cancer progression. The 12th postulate relates to the application of the framework to translational research. The ‘emergence framework of carcinogenesis’ collates current paradigms, concepts and evidence around carcinogenesis into a single framework that incorporates previously incompatible viewpoints and ideas. Any researcher, scientist or clinician involved in research, treatment or prevention of cancer can employ this framework.