Untangling the model muddle: Empirical tumour growth in Tasmanian devil facial tumour disease

Complex associations between cancer progression and immune gene expression reveals early influence of transmissible cancer on Tasmanian devils

The world's largest extant carnivorous marsupial, the Tasmanian devil, is challenged by Devil Facial Tumor Disease (DFTD), a fatal, clonally transmitted cancer. In two decades, DFTD has spread across 95% of the species distributional range. A previous study has shown that factors such as season, geographic location, and infection with DFTD can impact the expression of immune genes in Tasmanian devils. To date, no study has investigated within-individual immune gene expression changes prior to and throughout the course of DFTD infection. To explore possible changes in immune response, we investigated four locations across Tasmania that differed in DFTD exposure history, ranging between 2 and >30 years. Our study demonstrated considerable complexity in the immune responses to DFTD. The same factors (sex, age, season, location and DFTD infection) affected immune gene expression both across and within devils, although seasonal and location specific variations were diminished in DFTD affected devils. We also found that expression of both adaptive and innate immune genes starts to alter early in DFTD infection and continues to change as DFTD progresses. A novel finding was that the lower expression of immune genes MHC-II, NKG2D and CD8 may predict susceptibility to earlier DFTD infection. A case study of a single devil with regressed tumor showed opposite/contrasting immune gene expression patterns compared to the general trends observed across devils with DFTD infection. Our study highlights the complexity of DFTD's interactions with the host immune system and the need for long-term studies to fully understand how DFTD alters the evolutionary trajectory of devil immunity.

The tumour is in the detail: Local phylogenetic, population and epidemiological dynamics of a transmissible cancer in Tasmanian devils

Infectious diseases are a major threat for biodiversity conservation and can exert strong influence on wildlife population dynamics. Understanding the mechanisms driving infection rates and epidemic outcomes requires empirical data on the evolutionary trajectory of pathogens and host selective processes. Phylodynamics is a robust framework to understand the interaction of pathogen evolutionary processes with epidemiological dynamics, providing a powerful tool to evaluate disease control strategies. Tasmanian devils have been threatened by a fatal transmissible cancer, devil facial tumour disease (DFTD), for more than two decades. Here we employ a phylodynamic approach using tumour mitochondrial genomes to assess the role of tumour genetic diversity in epidemiological and population dynamics in a devil population subject to 12 years of intensive monitoring, since the beginning of the epidemic outbreak. DFTD molecular clock estimates of disease introduction mirrored observed estimates in the field, and DFTD genetic diversity was positively correlated with estimates of devil population size. However, prevalence and force of infection were the lowest when devil population size and tumour genetic diversity was the highest. This could be due to either differential virulence or transmissibility in tumour lineages or the development of host defence strategies against infection. Our results support the view that evolutionary processes and epidemiological trade-offs can drive host-pathogen coexistence, even when disease-induced mortality is extremely high. We highlight the importance of integrating pathogen and population evolutionary interactions to better understand long-term epidemic dynamics and evaluating disease control strategies.

The ecology and evolution of wildlife cancers: Applications for management and conservation

Ecological and evolutionary concepts have been widely adopted to understand host-pathogen dynamics, and more recently, integrated into wildlife disease management. Cancer is a ubiquitous disease that affects most metazoan species; however, the role of oncogenic phenomena in eco-evolutionary processes and its implications for wildlife management and conservation remains undeveloped. Despite the pervasive nature of cancer across taxa, our ability to detect its occurrence, progression and prevalence in wildlife populations is constrained due to logistic and diagnostic limitations, which suggests that most cancers in the wild are unreported and understudied. Nevertheless, an increasing number of virus-associated and directly transmissible cancers in terrestrial and aquatic environments have been detected. Furthermore, anthropogenic activities and sudden environmental changes are increasingly associated with cancer incidence in wildlife. This highlights the need to upscale surveillance efforts, collection of critical data and developing novel approaches for studying the emergence and evolution of cancers in the wild. Here, we discuss the relevance of malignant cells as important agents of selection and offer a holistic framework to understand the interplay of ecological, epidemiological and evolutionary dynamics of cancer in wildlife. We use a directly transmissible cancer (devil facial tumour disease) as a model system to reveal the potential evolutionary dynamics and broader ecological effects of cancer epidemics in wildlife. We provide further examples of tumour-host interactions and trade-offs that may lead to changes in life histories, and epidemiological and population dynamics. Within this framework, we explore immunological strategies at the individual level as well as transgenerational adaptations at the population level. Then, we highlight the need to integrate multiple disciplines to undertake comparative cancer research at the human-domestic-wildlife interface and their environments. Finally, we suggest strategies for screening cancer incidence in wildlife and discuss how to integrate ecological and evolutionary concepts in the management of current and future cancer epizootics.

Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modeling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long-period oscillations. Our findings show that pathogen extirpation or host-pathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modeling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

Large-effect loci affect survival in Tasmanian devils (Sarcophilus harrisii) infected with a transmissible cancer

Identifying the genetic architecture of complex phenotypes is a central goal of modern biology, particularly for disease-related traits. Genome-wide association methods are a classical approach for identifying the genomic basis of variation in disease phenotypes, but such analyses are particularly challenging in natural populations due to sample size difficulties. Extensive mark-recapture data, strong linkage disequilibrium and a lethal transmissible cancer make the Tasmanian devil (Sarcophilus harrisii) an ideal model for such an association study. We used a RAD-capture approach to genotype 624 devils at ~16,000 loci and then used association analyses to assess the heritability of three cancer-related phenotypes: infection case-control (where cases were infected devils and controls were devils that were never infected), age of first infection and survival following infection. The SNP array explained much of the phenotypic variance for female survival (>80%) and female case-control (>61%). We found that a few large-effect SNPs explained much of the variance for female survival (~5 SNPs explained >61% of the total variance), whereas more SNPs (~56) of smaller effect explained less of the variance for female case-control (~23% of the total variance). By contrast, these same SNPs did not account for a significant proportion of phenotypic variance in males, suggesting that the genetic bases of these traits and/or selection differ across sexes. Loci involved with cell adhesion and cell-cycle regulation underlay trait variation, suggesting that the devil immune system is rapidly evolving to recognize and potentially suppress cancer growth through these pathways. Overall, our study provided necessary data for genomics-based conservation and management in Tasmanian devils.