Person:

Allen, Benjamin

Tumor growth is caused by the acquisition of driver mutations, which enhance the net reproductive rate of cells. Driver mutations may increase cell division, reduce cell death, or allow cells to overcome density-limiting effects. We study the dynamics of tumor growth as one additional driver mutation is acquired. Our models are based on two-type branching processes that terminate in either tumor disappearance or tumor detection. In our first model, both cell types grow exponentially, with a faster rate for cells carrying the additional driver. We find that the additional driver mutation does not affect the survival probability of the lesion, but can substantially reduce the time to reach the detectable size if the lesion is slow growing. In our second model, cells lacking the additional driver cannot exceed a fixed carrying capacity, due to density limitations. In this case, the time to detection depends strongly on this carrying capacity. Our model provides a quantitative framework for studying tumor dynamics during different stages of progression. We observe that early, small lesions need additional drivers, while late stage metastases are only marginally affected by them. These results help to explain why additional driver mutations are typically not detected in fast-growing metastases.

In solid tumors, targeted treatments can lead to dramatic regressions, but responses are often short-lived because resistant cancer cells arise. The major strategy proposed for overcoming resistance is combination therapy. We present a mathematical model describing the evolutionary dynamics of lesions in response to treatment. We first studied 20 melanoma patients receiving vemurafenib. We then applied our model to an independent set of pancreatic, colorectal, and melanoma cancer patients with metastatic disease. We find that dual therapy results in long-term disease control for most patients, if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden, triple therapy is needed. We also find that simultaneous therapy with two drugs is much more effective than sequential therapy. Our results provide realistic expectations for the efficacy of new drug combinations and inform the design of trials for new cancer therapeutics. DOI: http://dx.doi.org/10.7554/eLife.00747.001

Microorganisms have been cooperating with each other for billions of years: by sharing resources, communicating with each other, and joining together to form biofilms and other large structures. These cooperative behaviors benefit the colony as a whole; however, they may be costly to the individuals performing them. This raises the question of how such cooperation can arise from natural selection. Mathematical modeling is one important avenue for exploring this question. Evolutionary experiments are another, providing us with an opportunity to see evolutionary dynamics in action and allowing us to test predictions arising from mathematical models. A new study in this issue of PLOS Biology investigates the evolution of a cooperative resource-sharing behavior in yeast. Examining the competition between cooperating and “cheating” strains of yeast, the authors find that, depending on the initial mix of strains, this yeast society either evolves toward a stable coexistence or collapses for lack of cooperation. Using a simple mathematical model, they show how these dynamics arise from eco-evolutionary feedback, where changes in the frequencies of strains are coupled with changes in population size. This study and others illustrate the combined power of modeling and experiment to elucidate the origins of cooperation and other fundamental questions in evolutionary biology.

In this Formal Comment, the authors challenge the claims of a recent theoretical study that genetic relatedness is important in the evolution of eusociality.

Social structure affects the emergence and maintenance of cooperation. Here, we study the evolutionary dynamics of cooperation in fragmented societies, and show that conjoining segregated cooperation-inhibiting groups, if done properly, rescues the fate of collective cooperation. We highlight the essential role of intergroup ties, which sew the patches of the social network together and facilitate cooperation. We point out several examples of this phenomenon in actual settings. We explore random and non-random graphs, as well as empirical networks. In many cases, we find a marked reduction of the critical benefit-to-cost ratio needed for sustaining cooperation. Our finding gives hope that the increasing worldwide connectivity, if managed properly, can promote global cooperation.

Protein evolution is a critical component of organismal evolution and a valuable method for the generation of useful molecules in the laboratory. Few studies, however, have experimentally characterized how fundamental parameters influence protein evolution outcomes over long evolutionary trajectories or multiple replicates. In this work, we applied phage-assisted continuous evolution (PACE) as an experimental platform to study evolving protein populations over hundreds of rounds of evolution. We varied evolutionary conditions as T7 RNA polymerase evolved to recognize the T3 promoter DNA sequence and characterized how specific combinations of both mutation rate and selection stringency reproducibly result in different evolutionary outcomes. We observed significant and dramatic increases in the activity of the evolved RNA polymerase variants on the desired target promoter after 96 hours of selection, confirming positive selection occurred under all conditions. We used high-throughput sequencing to quantitatively define convergent genetic solutions, including mutational “signatures” and non-signature mutations that map to specific regions of protein sequence. These findings illuminate key determinants of evolutionary outcomes, inform the design of future protein evolution experiments, and demonstrate the value of PACE as a method to study protein evolution.