Driving cancer evolution

Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

Objective

The treatment plans designed with the guidance of the mathematical model and adaptive strategy can trap tumor subpopulations in a periodic and controllable loop. But this process requires detailed information about the tumor system, which is difficult to obtain. Therefore, we wondered whether the fixed periodic treatment plans designed with the typical values of population parameters could be applied to a similar tumor system without complete information.

Methods

A binary tumor system constructed by an EGFR-mutant and a KRAS-mutant cell line was used to explore the applicability of the fixed periodic treatment plans. The dynamics of this system were described by combining the Lotka-Volterra model with the framework of the nonlinear mixed-effects model. The typical values of population parameters were used to design the plans, and the robust plans were screened through parameter variation. These screened plans were examined their applicability in animal experiments and simulations.

Results

In animal experiments where system parameters vary from -30% to 30%, the "osimertinib administration, withdrawal, FK866 administration and withdrawal" plan can trap subpopulations of the system in periodic cycles. In simulation, when there was an unknown resistant subpopulation, the screened fixed periodic treatment plans can still delay the evolution of resistance. The median outcomes of screened plans were better than conventional sequential treatment in most cases. There was no significant difference between the outcomes of the screened plan with median stability and the optimal therapy. The evolutionary trajectories of these two plans were similar.

Conclusion

According to the results, these fixed periodic plans should be tried in treatment even the information of the tumor system was incomplete.

Cancer: An unknown territory; rethinking before going ahead

Cancer is a disease of altered signaling and metabolism, causing uncontrolled division and survival of transformed cells. A host of molecules, factors, and conditions have been designated as underlying causes for the inception and progression of the disease. An enormous amount of data is available, system-wide interaction networks of the genes and proteins are generated over the years and have now reached up to a level of saturation, where we need to shift our focus to the more advanced and comprehensive methods and approaches of data analysis and visualization. Even with the availability of enormous literature on this one of the most pressing pathological conditions, a successful cure of the disease seems to be obscure. New treatment plans, like immunotherapy and precision medicine, are being employed for different studies. Nevertheless, their actual benefits to the patients would be known only after the evaluation of clinical data over the next few years. Therefore, we need to look at few fundamental challenges that should be addressed in more depth before we could devise better, rigorous, and comprehensive treatment plans and may successfully reach a possible cure of the disease. This article aims at bringing attention towards some fundamental gaps in our approach towards the disease that leads to failure in devising successful therapeutics.

What is mutation? A chapter in the series: How microbes "jeopardize" the modern synthesis

Mutations drive evolution and were assumed to occur by chance: constantly, gradually, roughly uniformly in genomes, and without regard to environmental inputs, but this view is being revised by discoveries of molecular mechanisms of mutation in bacteria, now translated across the tree of life. These mechanisms reveal a picture of highly regulated mutagenesis, up-regulated temporally by stress responses and activated when cells/organisms are maladapted to their environments-when stressed-potentially accelerating adaptation. Mutation is also nonrandom in genomic space, with multiple simultaneous mutations falling in local clusters, which may allow concerted evolution-the multiple changes needed to adapt protein functions and protein machines encoded by linked genes. Molecular mechanisms of stress-inducible mutation change ideas about evolution and suggest different ways to model and address cancer development, infectious disease, and evolution generally.

Rethinking Causation in Cancer with Evolutionary Developmental Biology

Despite the productivity of basic cancer research, cancer continues to be a health burden to society because this research has not yielded corresponding clinical applications. Many proposed solutions to this dilemma have revolved around implementing organizational and policy changes related to cancer research. Here I argue for a different solution: a new conceptualization of causation in cancer. Neither the standard molecular biomarker approaches nor evolutionary biology approaches to cancer fully capture its complex causal dynamics, even when considered jointly. These approaches map on to Ernst Mayr's proximate-ultimate distinction, which is an inadequate conceptualization of causation in biological systems and makes it difficult to connect developmental and evolutionary viewpoints. I propose looking to evolutionary developmental biology (EvoDevo) to overcome the distinction and integrate the proximate and ultimate causal frameworks. I use the concepts of modularity and evolvability to show how an EvoDevo perspective can be manifested in cancer translational research. This perspective on causation in cancer is better suited for integrating the complexity of current empirical results and can facilitate novel developments in the investigation and clinical treatment of cancer.