Season, weight, and age, but not transmissible cancer, affect tick loads in the endangered Tasmanian devil.

The Tasmanian devil (Sarcophilus harrisii) is a carnivorous marsupial threatened by a transmissible cancer, devil facial tumour disease (DFTD). While we have a good understanding of the effect of the transmissible cancer on its host, little information is available about its potential interactions with ectoparasites. With this study, we aimed to determine the factors driving tick loads in a DFTD affected Tasmanian devil population, using long-term mark-recapture data. We investigated the effect of a range of life history traits (age, weight, sex, body condition) and of DFTD (time since DFTD arrival and presence of tumours) on the ectoparasitic tick load of the devils. Mixed effect models revealed that tick load in Tasmanian devils was primarily driven by season, weight, body condition and age. Young devils had more ticks compared to older or healthier devils. The reduction in Tasmanian devil population size over the past 14 years at the studied site had little effect on tick infestation. We also found that devils infected by DFTD had a similar tick load compared to those free of observable tumours, suggesting no interaction between the transmissible cancer and tick load. Our study highlights seasonality and life cycle as primary drivers of tick infestation in Tasmanian devils and the need for further investigations to integrate devil stress and immune dynamics with ectoparasite counts.

Darwinian Approaches for Cancer Treatment: Benefits of Mathematical Modeling.

One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka-Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.