Inherent evolutionary unpredictability in cancer model system

With the advent of precision cancer medicine, a lot of focus is now on characterization of genomic variations in each individual tumor instead of describing all cancers of the same site under one umbrella. This has resulted in a fine-tuned prognostication of many tumors by predicting the probable course of the disease based on each cancers molecular signature. Yet, the threat of a relapse and a consequent treatment resistant runaway disease loom in many cases. Tracing cellular lineages to elucidate clonal evolution has revealed how tumors evolve in different evolutionary trajectories and resistant clones often appear due to excessive branching early in the disease and remain dormant only to clonally fixate afterwards. Multiregional sequencing has further revealed how the genomic signature carries spatial stochasticity. Disorganized genomic rearrangements was theorized as genome chaos. Shifting our vision away from the deterministic nature of Somatic Mutation Theory it is important to reconcile that cancer is not a purely stochastic phenomenon, rather result of runaway dysregulations in a complex and dynamic system. Generalized logistic model is a commonly used construct in simulating spatially constricted growth of cancer cell populations. Simulations show that evolutionary trajectories of cancers are highly dependent on how cell populations grow and how they interact with the stromal boundary and just so it happens, logistic map produces chaotic oscillation under certain growth parameters. We here probe the question whether the logistic function as a mechanism for tumor growth can explain emergent diversity in clonal geographies in tumorigenesis and if it invokes chaos. ### Competing Interest Statement The authors have declared no competing interest.