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The current paradigm views cancer as arising clonally from a degradation of genetic information in single cells. A complementary perspective, originating at the dawn of modern developmental biology, is that cancer is the result of a system disorder of algorithms that normally orchestrate individual cell activities toward specific anatomical structures and away from tumorigenesis. A view of cancer as a disease of geometry focuses on the pathways that allow cells to cooperate to build and maintain large-scale anatomical patterning. Cancer may result when cells stop maintaining higher-order structures and reduce the boundary of their computational selves to a single-cell level, reverting to a unicellular lifestyle in which the rest of the organism is merely part of the environment at the expense of which all living things survive. While this view has been widely discussed, little progress has been made in providing a quantitative, mechanistic framework within which this perspective's specific and unique implications for treatment strategies can be tested and biomedically exploited. Here, we highlight two recent areas of progress which may facilitate much-needed progress on the cancer problem. First, we review the roles that endogenous bioelectrical networks, operating across many tissues in vivo, play as a medium of information processing in tumor suppression, progression, and reprogramming. Second, we provide a primer to the development of computational theory and tools for quantifying the information and causal control structures in cancer and other complex biological systems. Rigorous mathematical formalisms now exist to measure and analyze the extent to which "a whole is more than the sum of its parts", applications of which could lead to new strategies for cancer reprogramming. Here, we review the basic landscape of these related subfields, and sketch specific ways in which a synthesis of novel integrative biophysics and mathematical analysis may contribute to novel ways to understand and address cancer in vivo.

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem