Regulatory and epigenetic landscapes in biological discovery, diagnostics and disease mechanisms

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Development of methods and biomarkers for blood-based detection of ovarian cancer The Elnitski lab previously reported the first methylation biomarker, ZNF154, as having pan/multi-cancer relevance (Sanchez-Vega et al. 2013) and showed that it could discriminate tumor from normal samples in simulations of dilute blood-based analysis of circulating tumor DNA (Margolin et al. 2014). Over the past several years, I have developed diagnostic markers and assessment methods that use DNA methylation at the ZNF154 locus to detect a myriad of epithelial cancers from patient plasma samples (Miller et al., manuscript submitted). Our semi-quantitative PCR assay uses melting curve temperatures to assess DNA methylation in amplified products (Miller et al. 2020, in press). The test is highly sensitive and specific for circulating tumor DNA and detects tumor DNA in plasma samples from ovarian cancer patients. As a biomarker, ZNF154 detects both serous and endometrioid subtypes of late stage ovarian cancer making it better than the current gold-standard biomarker, protein antigen CA-125. Moreover, using these complementary biomarkers in tandem for ovarian cancer screening may be a promising, more sensitive approach than can be achieved with either marker alone. Profiling epigenetic features in up to 20 solid human epithelial cancer types To potentially improve cancer treatment efficacy, I previously sought to identify attributes that subdivide tumors into more homogeneous subsets using their epigenetic landscape profiles, in contrast to classification approaches that utilize mutational signatures or gene expression patterns. Building a computational approach allowed my group to demonstrate that the CpG island methylator phenotype (CIMP) is present in 14 distinct cancer types from TCGA (the Cancer Genome Atlas) and 23 cancer cell lines (Sanchez-Vega et al. 2015). My lab also published the first report of a CIMP in ovarian endometrioid tumors, which showed consistency with altered DNA methylation seen in uterine endometrioid tumors (Kolbe et al. 2012). We have shown that CIMP tumors from different cancers have shared biological pathways and may have a common underlying etiology (Miller at el. 2016). For example, my lab identified similarities and differences among CIMP tumors from esophageal, gastric, and colorectal adenocarcinomas (Sanchez-Vega et al. 2017), which further addresses how aberrant methylation may occur in multiple tumor types. In further exploration of aberrant DNA methylation patterns, my work demonstrated that associations between cancer methylomes in 18 cancer types and somatic driver mutations illustrate the relationships between the epigenome and the genome in cancers (Chen et al. 2017). Using principal component analyses of methylation-mutation associations applied at the CpG (i.e., nucleotide) level, my labs work showed that genome-wide patterns of aberrant hypomethylation or hypermethylation were associated with a small number of somatic mutations in specific cancer types, suggesting global regulation. In contrast, site-specific methylation changes were associated with an extensive number of mutated driver genes, suggesting local regulation. This year we expanded on our previous findings by showing that DNA methylation patterns in tumor genomes are associated with distinct isoform expression patterns predictive of sites of promoter repression, intragenic exon inclusion, and 3-terminal exon inclusion (Chen and Elnitski 2019). Our discovery of these relationships allows for the interpretation of which mRNA isoform is expressed in a tumor based on the analysis of the epigenome. Profiling epigenetic features in human populations The gold standard used to detect DNA methylation, bisulfite conversion, can result in data ambiguity or interpretation errors caused by polymorphisms at CpG methylation sites. We developed the algorithm MethylToSNP, which detects characteristic patterns of nucleotides that are likely to be confounded by polymorphisms (Goncearenco et al. 2020a), in the absence of corroborating genotype data. Using this tool, we distinguished altered DNA methylation indicative of SNP sites from samples of YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent), and KhoeSan (Southern African) populations. Furthermore, in these populations we identified uncharacterized polymorphisms and determined locations in the genome that are affected by altered methylation or sequence polymorphisms, including CTCF sites and enhancers. Similarly, I have also worked to develop new analysis tools to assess methylation enrichment of ChIP-seq data and integrate it with other types of genome-wide data (Lichtenberg et al. 2017). To address the diversity of DNA methylation in individuals living in distinct environments, my lab compared the epigenomes of KhoeSan individuals from the Kalahari Desert to individuals living in similar geographical locations, but from within industrialized societies of Bantu speaking individuals (Goncearenco et al. 2020b). Between these populations, our analysis identified more than 10,000 differentially methylated sites. The top 5% of differentially methylated sites distinguished the KhoeSan samples from various ethnic groups around the world, even after the data was cleaned for any confounding artifacts. We examined the sites of altered methylation across the KhoeSan genomes (in enhancers, gene bodies, and CpG shores and shelves) and found significant underrepresentation in the expected number of changes, suggesting the presence of selective pressure maintaining the integrity of the epigenetic landscape. Our discoveries about the Kalahari Desert KhoeSan epigenome contribute to the greater understanding of human genome diversity. Insights to mechanisms of epigenetic regulation in the human genome Altered regulation of enhancers can occur through DNA methylation. Our recent study (Chen et al. 2018) on 14 pairs of monozygotic twins discordant for attention deficit hyperactivity disorder found neuroanatomic and epigenetic differences between the siblings. Despite a lack of causal gene mutations, the ADHD-affected twins had a significantly smaller volume in the striatum and thalamus and a trend toward a larger cerebellum. Our analyses found no causal germline mutations, indels, or deletions that would explain the disparate results. However, the affected twins showed significant differences in DNA methylation patterns associated with some enhancer regions of genes expressed in the altered brain anatomical structures. These findings are consistent with the idea that subtle changes, such as the loss or alteration of enhancer elements in the genome, may be associated with discrete neuroanatomical anomalies. In addition to enhancer elements, noncoding regions of the genome also carry repressive functions. Our research examined characteristics of repressive elements and trained a multivariate model of silencer elements. We validated our model results with expression-based experiments. With this combined approach we showed a statistically significant loss of gene expression was attributed to our candidate silencers (Huang et al. 2019). Finally, splicing is an important post-transcriptional regulatory process in gene expression. Using mutation data, my lab predicted mutations that affect splicing as part of the CAGI (Critical Assessment of Genome Interpretation) competition. We used a machine learning classifier and accurately identified mutations that cause aberrant mRNA splicing (Gotea et al. 2019). We also addressed mRNA splicing fidelity by building a computational pipeline to identify factors that suppress latent splicing in intronic regions. This mechanism is fundamental to all genes in the human genome, by preventing the incorporation of premature stop codons into open reading frames (manuscript in preparation).