Cancer Risk Stratification of Anal Intraepithelial Neoplasia in Human Immunodeficiency Virus-Positive Men by Validated Methylation Markers Associated With Progression to Cancer

BACKGROUND: High-grade anal intraepithelial neoplasia (HGAIN; AIN2-3) is highly prevalent in HIV+ men, but only a minority of these lesions progress towards cancer. Currently, cancer progression risk cannot be established; therefore, no consensus exists on whether HGAIN should be treated. This study aimed to validate previously identified host cell DNA methylation markers for detection and cancer risk stratification of HGAIN. METHODS: A large independent cross-sectional series of 345 anal cancer, AIN3, AIN2, AIN1, and normal control biopsies of HIV+ men was tested for DNA methylation of 6 genes using quantitative methylation-specific PCR. We determined accuracy for detection of AIN3 and cancer (AIN3+) by univariable and multivariable logistic regression analysis, followed by leave-one-out cross-validation. Methylation levels were assessed in a series of 10 anal cancer cases with preceding HGAIN at similar anatomic locations, and compared with the cross-sectional series. RESULTS: Methylation levels of all genes increased with increasing severity of disease (P < .05). HGAIN revealed a heterogeneous methylation pattern, with a subset resembling cancer. ZNF582 showed highest accuracy (AUC = 0.88) for AIN3+ detection, slightly improved by addition of ASCL1 and SST (AUC = 0.89), forming a marker panel. In the longitudinal series, HGAIN preceding cancer displayed high methylation levels similar to cancers. CONCLUSIONS: We validated the accuracy of 5 methylation markers for the detection of anal (pre-) cancer. High methylation levels in HGAIN were associated with progression to cancer. These markers provide a promising tool to identify HGAIN in need of treatment, preventing overtreatment of HGAIN with a low cancer progression risk

Relationships between the species composition of forest field-layer vegetation and environmental drivers, assessed using a national scale survey

Simulation models of forest stand dynamics have increased understanding of over-storey vegetation functioning, and have facilitated the development of tools capable of assessing possible successional trajectories. However, few models incorporate the response of the field layer vegetation despite it being another key component of forest ecosystems. Our main objective was to assess the degree to which field-layer vegetation composition in forests is determined by variables operating at different scales, from regional (e.g. climate, location) to local factors (e.g. basal area of canopy trees, management). We used data gathered during a nationwide forest survey to assess the relative effects of a broad spectrum of environmental variables on species composition. Variation partitioning was used to examine the relative contribution of subsets of environmental variables such as site spatial variation, boundary type and presence of herbivores. Ordination confirmed hypotheses that field layer vegetation is primarily structured by two composite geo-climatic gradients. However, variation partitioning demonstrated that site- and plot-scale management factors also strongly influence the floristic composition of forest patches. Disturbance variables (site boundary type/regional presence of deer) accounted for considerable species variation, exceeding that due to either site spatial variation or forest structure. This is the first time variation attributable to such a comprehensive range of environmental variables has been quantified for forests surveyed at a national scale. We thus provide a context within which regional studies, or analyses considering a more limited range of factors, can be viewed, and a framework from which robust models of floristic response to gradual and episodic natural and anthropogenic disturbances may be developed. The methodology we present, including a novel technique for the identification and removal of outliers in large data sets, provides a unique and standardized means of assessing the relative importance of diverse environmental drivers across a range of habitat types at the landscape scale, and is readily applicable elsewher