A metapopulation model of social group dynamics and disease applied to Yellowstone wolves.

The population structure of social species has important consequences for both their demography and transmission of their pathogens. We develop a metapopulation model that tracks two key components of a species' social system: average group size and number of groups within a population. While the model is general, we parameterize it to mimic the dynamics of the Yellowstone wolf population and two associated pathogens: sarcoptic mange and canine distemper. In the initial absence of disease, we show that group size is mainly determined by the birth and death rates and the rates at which groups fission to form new groups. The total number of groups is determined by rates of fission and fusion, as well as environmental resources and rates of intergroup aggression. Incorporating pathogens into the models reduces the size of the host population, predominantly by reducing the number of social groups. Average group size responds in more subtle ways: infected groups decrease in size, but uninfected groups may increase when disease reduces the number of groups and thereby reduces intraspecific aggression. Our modeling approach allows for easy calculation of prevalence at multiple scales (within group, across groups, and population level), illustrating that aggregate population-level prevalence can be misleading for group-living species. The model structure is general, can be applied to other social species, and allows for a dynamic assessment of how pathogens can affect social structure and vice versa.

Challenges in Developing a Real-Time Bee-Counting Radar.

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life.

Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an 'epidemiological landscape' connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a 'movement-pathogen pace of life' heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.

Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data.

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.

Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem.

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age-specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late-stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade-offs from a management perspective-that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations.

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas (Gorilla beringei beringei) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Body size and digestive system shape resource selection by ungulates: A cross-taxa test of the forage maturation hypothesis.

The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.

Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem.

Wildlife migrations provide important ecosystem services, but they are declining. Within the Greater Yellowstone Ecosystem (GYE), some elk Cervus canadensis herds are losing migratory tendencies, which may increase spatiotemporal overlap between elk and livestock (domestic bison Bison bison and cattle Bos taurus), potentially exacerbating pathogen transmission risk. We combined disease, movement, demographic and environmental data from eight elk herds in the GYE to examine the differential risk of brucellosis transmission (through aborted foetuses) from migrant and resident elk to livestock. For both migrants and residents, we found that transmission risk from elk to livestock occurred almost exclusively on private ranchlands as opposed to state or federal grazing allotments. Weather variability affected the estimated distribution of spillover risk from migrant elk to livestock, with a 7%-12% increase in migrant abortions on private ranchlands during years with heavier snowfall. In contrast, weather variability did not affect spillover risk from resident elk. Migrant elk were responsible for the majority (68%) of disease spillover risk to livestock because they occurred in greater numbers than resident elk. On a per-capita basis, however, our analyses suggested that resident elk disproportionately contributed to spillover risk. In five of seven herds, we estimated that the per-capita spillover risk was greater from residents than from migrants. Averaged across herds, an individual resident elk was 23% more likely than an individual migrant elk to abort on private ranchlands. Our results demonstrate links between migration behaviour, spillover risk and environmental variability, and highlight the utility of integrating models of pathogen transmission and host movement to generate new insights about the role of migration in disease spillover risk. Furthermore, they add to the accumulating body of evidence across taxa that suggests that migrants and residents should be considered separately during investigations of wildlife disease ecology. Finally, our findings have applied implications for elk and brucellosis in the GYE. They suggest that managers should prioritize actions that maintain spatial separation of elk and livestock on private ranchlands during years when snowpack persists into the risk period.

Group density, disease, and season shape territory size and overlap of social carnivores.

The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography. Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization. Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models. We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups and shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology. We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks.

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

"One Health" or Three? Publication Silos Among the One Health Disciplines.

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation--especially along the veterinary/ecological research interface--remains.

Audit of Endometrial Cancer Pathology for a Regional Gynecological Oncology Multidisciplinary Meeting.

Endometrial cancer is a common disease, and in England all cancer cases are discussed at a central multidisciplinary meeting (MDT) with pathology review. We reviewed cases discussed/reviewed at a regional Gynecology MDT comparing (i) original referral histology with review histology and (ii) final review histology with the final hysterectomy histology. Cases identified as potentially eligible for the study (n=884) were found over a 4-yr period. This was reduced to 630 due to data and other issues for the primary biopsy review, and to 488 for both biopsy and hysterectomy sample. Cases were classed by agreement by grade/type and compared by clinical management (low grade vs. high grade). Of the original biopsies, central review agreed exactly with 67% and disagreed with 33%. A total of 11.6% of low-grade cancers were upgraded to high grade on review, and 6.1% of high-grade cancers were downgraded. For the biopsy/hysterectomy comparison, this was 72.5% agreement and 27.5% disagreement, with 3.5% upgraded to high from low grade, and 7.5% downgraded from high to low grade. The main areas of significant change was the identification of high-grade serous carcinoma from low-grade endometrial cancers, as well some other high grade types (clear cell and carcinosarcoma) and the confident diagnosis of cancer as opposed to an atypical hyperplasia. Central pathology review for MDT discussion does highlight significant areas of pathologic disagreement that would affect clinical management. The audit highlights that a significant disagreement rate in reporting such material between pathologists may be inescapable, but can be reduced by review.

Estimating greenhouse gases emissions from horticultural peat soils using a DNDC modelling approach.

Peat soils represent an important global carbon (C) sink, but can also provide a highly fertile medium for growing horticultural crops. Sustainable crop production on peat soils involves a trade-off between ensuring food security and mitigating typically high greenhouse gas (GHG) emissions and rates of soil C loss. An alternative approach to resource intensive field-based monitoring of GHG fluxes for all potential management scenarios is to use a process-based model driven by existing field data to estimate emissions. The aim of this study was to evaluate the suitability of the Denitrification-Decomposition (DNDC) model for estimating emissions of CO2, N2O and CH4 from horticultural peat soils. The model was parameterised using climatic, soil, and crop management data from two intensively cultivated sites on soils of contrasting soil organic matter (SOM) contents (∼35% and ∼70% SOM content). Simulated emissions of CO2, N2O and CH4, and simulated soil physical and crop output values, were compared to actual GHG, soil and crop measurements. Model performance was assessed using baseline parameterisation (i.e. model defaults), then calibrated using pre-simulation and sensitivity analysis processes. Under baseline parameterisation conditions, DNDC proved poor at predicting GHG emissions and soil/crop variables. Calibration and validation improved DNDC performance in estimating the annual magnitude of emissions, but model refinement is still required for reproducing seasonal GHG patterns in particular. Key constraints on model functioning appear to be its ability to reliably model soil moisture and some aspects of C and nitrogen dynamics, as well as the quality of input data relating to water table dynamics. In conclusion, our results suggest that the DNDC (v. 9.5) model cannot accurately reproduce or be used to replace actual field measurements for estimation of GHG emission factors under different management scenarios for horticultural peat soils, but may be able to do so with further modification.

The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts.

Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly ad hoc Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.

Inferring infection hazard in wildlife populations by linking data across individual and population scales.

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Contact and contagion: Probability of transmission given contact varies with demographic state in bighorn sheep.

Understanding both contact and probability of transmission given contact are key to managing wildlife disease. However, wildlife disease research tends to focus on contact heterogeneity, in part because the probability of transmission given contact is notoriously difficult to measure. Here, we present a first step towards empirically investigating the probability of transmission given contact in free-ranging wildlife. We used measured contact networks to test whether bighorn sheep demographic states vary systematically in infectiousness or susceptibility to Mycoplasma ovipneumoniae, an agent responsible for bighorn sheep pneumonia. We built covariates using contact network metrics, demographic information and infection status, and used logistic regression to relate those covariates to lamb survival. The covariate set contained degree, a classic network metric describing node centrality, but also included covariates breaking the network metrics into subsets that differentiated between contacts with yearlings, ewes with lambs, and ewes without lambs, and animals with and without active infections. Yearlings, ewes with lambs, and ewes without lambs showed similar group membership patterns, but direct interactions involving touch occurred at a rate two orders of magnitude higher between lambs and reproductive ewes than between any classes of adults or yearlings, and one order of magnitude higher than direct interactions between multiple lambs. Although yearlings and non-reproductive bighorn ewes regularly carried M. ovipneumoniae, our models suggest that a contact with an infected reproductive ewe had approximately five times the odds of producing a lamb mortality event of an identical contact with an infected dry ewe or yearling. Consequently, management actions targeting infected animals might lead to unnecessary removal of young animals that carry pathogens but rarely transmit. This analysis demonstrates a simple logistic regression approach for testing a priori hypotheses about variation in the odds of transmission given contact for free-ranging hosts, and may be broadly applicable for investigations in wildlife disease ecology.

Restaurant Cooking Trends and Increased Risk for Campylobacter Infection.

In the United Kingdom, outbreaks of Campylobacter infection are increasingly attributed to undercooked chicken livers, yet many recipes, including those of top chefs, advocate short cooking times and serving livers pink. During 2015, we studied preferences of chefs and the public in the United Kingdom and investigated the link between liver rareness and survival of Campylobacter. We used photographs to assess chefs' ability to identify chicken livers meeting safe cooking guidelines. To investigate the microbiological safety of livers chefs preferred to serve, we modeled Campylobacter survival in infected chicken livers cooked to various temperatures. Most chefs correctly identified safely cooked livers but overestimated the public's preference for rareness and thus preferred to serve them more rare. We estimated that 19%-52% of livers served commercially in the United Kingdom fail to reach 70°C and that predicted Campylobacter survival rates are 48%-98%. These findings indicate that cooking trends are linked to increasing Campylobacter infections.

Disease introduction is associated with a phase transition in bighorn sheep demographics.

Ecological theory suggests that pathogens are capable of regulating or limiting host population dynamics, and this relationship has been empirically established in several settings. However, although studies of childhood diseases were integral to the development of disease ecology, few studies show population limitation by a disease affecting juveniles. Here, we present empirical evidence that disease in lambs constrains population growth in bighorn sheep (Ovis canadensis) based on 45 years of population-level and 18 years of individual-level monitoring across 12 populations. While populations generally increased (λ = 1.11) prior to disease introduction, most of these same populations experienced an abrupt change in trajectory at the time of disease invasion, usually followed by stagnant-to-declining growth rates (λ = 0.98) over the next 20 years. Disease-induced juvenile mortality imposed strong constraints on population growth that were not observed prior to disease introduction, even as adult survival returned to pre-invasion levels. Simulations suggested that models including persistent disease-induced mortality in juveniles qualitatively matched observed population trajectories, whereas models that only incorporated all-age disease events did not. We use these results to argue that pathogen persistence may pose a lasting, but under-recognized, threat to host populations, particularly in cases where clinical disease manifests primarily in juveniles.

Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data.

Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004-2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods-leaving out individual clusters, or leaving out individual bears-showed that correct prediction of bear visitation to large-biomass carcasses was 78-88 %, whereas the false-positive rate was 18-24 %. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002-2011) and examined trends in carcass visitation during fall hyperphagia (September-October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.

Advanced Ovarian Cancer Displays Functional Intratumor Heterogeneity That Correlates to Ex Vivo Drug Sensitivity.

INTRODUCTION: Epithelial ovarian cancer is recognized to be heterogeneous but is currently treated with a single treatment strategy. Successful patient stratification of emerging chemotherapy agents is dependent upon the availability of reliable biomarkers indicative of the entire tumor. AIM: The aim of this study was to evaluate intertumor and intratumor heterogeneity within a series of epithelial ovarian cancer using homologous recombination (HR) DNA repair status. METHODS: Primary cultures generated from ascites and solid tumor from multiple intra-abdominal sites were characterized by their morphology and expression of protein markers. Results were compared with Formalin fixed paraffin embedded tissue pathology.Homologous recombination function was determined by quantification of nuclear Rad51 foci. Growth inhibition (sulforhodamine B) assays were used to calculate the GI50 for cisplatin and rucaparib. RESULTS: Ascites with matched solid tumor were cultured from 25 patients.Concordance in functional HR status between ascites and solid tumor subcultures was seen in only 13 (52%) of 25 patients. Heterogeneity in HR status was seen even in patients with homogeneous histological subtype. Homologous recombination defective cultures were significantly more sensitive to cisplatin and rucaparib.Additionally, intertumor and intratumor heterogeneity was seen between the expression of epithelial and ovarian markers (EpCAM, cytokeratin, CA125, MOC-31, and vimentin). There was no relationship between heterogeneity of HR functional status and antigen expression. CONCLUSIONS: Intertumor and intratumor functional HR heterogeneity exists that cannot be detected using histological classification. This has implications for biomarker-directed treatment.