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Introduction: Chromatin accessibility and cell-free DNA fragmentation patterns can be used to identify epigenomic mechanisms (Sharma et al. 2010) and infer cell-types contributing to cfDNA in pathological states such as cancer (Snyder et al. 2016; Ulz et al. 2017). We describe results from a novel blood-based cell-free DNA (cfDNA) assay using epigenomic signatures that have high sensitivity for detecting early stages of breast cancer, a cancer type that is characterized by low tumor burden (Phallen et al. 2017). We present the results from a prospective, case-control study demonstrating improved sensitivity to the screening mammogram and other published blood-based assays. Methods: Assay performance was evaluated using a case-control study design enrolling 123 total subjects (58% Healthy, 18% Stage I, 13% Stage II, 11% Stage III). Cases were defined as subjects with a confirmatory diagnosis of invasive breast cancer, at any stage, by tissue biopsy. Controls were composed of subjects with either a negative finding by mammography (BI-RADS 1 or 2) or self-declared cancer-free. Whole blood samples were collected in Streck BCT tubes and shipped to a central laboratory for processing. Total cell-free DNA was extracted from plasma and prepped for next-generation sequencing. Sequencing libraries were enriched using a custom panel targeting genomic regions with distinct epigenomic activity in breast cancer. We trained a neural net to predict regulatory events in each of these regions, and then identified those events that were predictive of the presence of breast cancer. Final classification was performed by logistic regression over the predicted regulatory events. Results: Performance was tested using a held-out test set and achieved an overall sensitivity of 92.5% (95% CI: 88.1%, 97%) at specificity of 88.9% with an overall AUC of 95.8%. Performance of screening mammography is reported to be 86.9% (95% CI: 86.3%, 87.6%) sensitive at 88.9% specificity on data obtained from six Breast Cancer Surveillance Consortium (BCSC) registries on 792808 women (Lehman et al. 2017). Conclusion: These results support the utility for detecting epigenomic signals from cell-free DNA to enhance early detection of breast cancer. A prospective breast cancer screening study in a larger cohort is needed to further validate performance. Citation Format: Erik Gafni, Adam Harvey, Artur Jaroszewicz, Omid Shams Solari, Jane Landolin, Mouadh Barbirou, Amanda Miller, Peter J. Tonellato, Anshul Kundaje, Stefanie S. Jeffrey, Christina Curtis, George W. Sledge, Paul Giresi, Nathan Boley. Cell-free DNA fragments inform epigenomic mechanisms for early detection of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2105.

Significance We demonstrate a high-accuracy method for predicting enhancers genome-wide with >85% precision as validated by transgenic reporter assays in Drosophila embryos. This accuracy in a metazoan system enables us to predict with high confidence 1,640 enhancers genome-wide that participate in body segmentation during early development. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classic indicators of activity, including H3K27ac, but are bound by key transcription factors., Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

Natural habitats are exposed to an increasing number of environmental stressors that cause important ecological consequences. However, the multifarious nature of environmental change, the strength and the relative timing of each stressor largely limit our understanding of biological responses to environmental change. In particu- lar, early response to unpredictable environmental change, critical to survival and fitness in later life stages, is largely uncharacterized. Here, we characterize the early transcriptional response of the keystone species Daphnia magna to twelve environmental perturbations, including biotic and abiotic stressors. We first perform a differential expression analysis aimed at identifying differential regulation of individual genes in response to stress. This preliminary analysis revealed that a few individual genes were responsive to environmental perturbations and they were modulated in a stressor and genotype-specific manner. Given the limited number of differentially regulated genes, we were unable to identify pathways involved in stress response. Hence, to gain a better understanding of the genetic and functional foundation of tolerance to multiple environmental stressors, we leveraged the correlative nature of networks and performed a weighted gene co-expression network analysis. We discovered that approximately one-third of the Daphnia genes, enriched for metabo- lism, cell signalling and general stress response, drives transcriptional early response to environmental stress and it is shared among genetic backgrounds. This initial response is followed by a genotype- and/or condition-specific transcriptional response with a strong genotype-by-environment interaction. Intriguingly, genotype- and condition-specific transcriptional response is found in genes not conserved beyond crustaceans, suggesting niche-specific adaptation. ispartof: Molecular Ecology vol:2018 issue:27 pages:886-897 ispartof: location:England status: published

Identifying functional enhancers elements in metazoan systems is a major challenge. For example, large-scale validation of enhancers predicted by ENCODE reveal false positive rates of at least 70%. Here we use the pregrastrula patterning network ofDrosophila melanogasterto demonstrate that loss in accuracy in held out data results from heterogeneity of functional signatures in enhancer elements. We show that two classes of enhancer are active during earlyDrosophilaembryogenesis and that by focusing on a single, relatively homogeneous class of elements, over 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well predicted elements is composed predominantly of enhancers driving multi-stage, segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome, 916 of which are novel. An analysis of 32 novel SDEs using wholemount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.Significance StatementWe demonstrate a high accuracy method for predicting enhancers genome wide with > 85% precision as validated by transgenic reporter assays inDrosophilaembryos. This is the first time such accuracy has been achieved in a metazoan system, allowing us to predict with high-confidence 1640 enhancers, 916 of which are novel. The predicted enhancers are demarcated by heterogeneous collections of epigenetic marks; many strong enhancers are free from classical indicators of activity, including H3K27ac, but are bound by key transcription factors. H3K27ac, often used as a one-dimensional predictor of enhancer activity, is an uninformative parameter in our data.

The full exploration of gene-environment interactions requires model organisms with well-characterized ecological interactions in their natural environment, manipulability in the laboratory and genomic tools. The waterflea Daphnia magna is an established ecological and toxicological model species, central to the food webs of freshwater lentic habitats and sentinel for water quality. Its tractability and cyclic parthenogenetic life-cycle are ideal to investigate links between genes and the environment. Capitalizing on this unique model system, the STRESSFLEA consortium generated a comprehensive RNA-Seq data set by exposing two inbred genotypes of D. magna and a recombinant cross of these genotypes to a range of environmental perturbations. Gene models were constructed from the transcriptome data and mapped onto the draft genome of D. magna using EvidentialGene. The transcriptome data generated here, together with the available draft genome sequence of D. magna and a high-density genetic map will be a key asset for future investigations in environmental genomics.