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

National Human Genome Research Institute

Investigators

Linked publications & trials

Abstract

Development of methods and biomarkers for blood-based multi-cancer detection The Elnitski lab previously reported the first known DNA methylation biomarker, at ZNF154, with pan/multi-cancer relevance (Sanchez-Vega et al. 2013). We discriminated circulating tumor DNA from normal samples using simulated blood-based data (Margolin et al. 2014). We next developed an assessment of ZNF154 methylation to measure circulating tumor DNA molecules, using a quasi-digital PCR and melting curve analysis (Miller et al. 2020). This highly sensitive and specific test detected tumor DNA in plasma samples from ovarian cancer patients including serous and endometrioid subtypes of ovarian cancer. ZNF154 methylation performed better than the current gold-standard biomarker, protein antigen CA-125, which does not detect many endometrioid samples and cannot be used for diagnostic purposes. Along with our computational analysis method (known as Epiclass), the ZNF154 assay can be used to detect multiple solid epithelial cancers from patient plasma samples (Miller et al. 2020). We demonstrated the potential for this assay to detect early and late-stage pancreatic cancers, ovarian and colon cancers (Miller et al. 2021). In comparison, blood-based testing of late-stage pancreatic cancer using only KRAS mutations performed worse than ZNF154 DNA methylation and did not detect early-stage tumors. Profiling epigenetic features in up to 20 solid human epithelial cancer types This project aims to subdivide tumors into homogeneous subsets to potentially improve cancer treatment efficacy based on epigenetic landscapes. We developed a computational approach and demonstrated that the CpG island methylator phenotype (CIMP) is present in 14 distinct cancer types (using The Cancer Genome Atlas data) and 23 cancer cell lines (Sanchez-Vega et al. 2015). My lab also published the first report of a CIMP in ovarian tumors, where the altered methylation patterns were similar among ovarian and uterine endometrioid tumors (Kolbe et al. 2012). We also demonstrated that CIMP tumors from different cancers share biological pathways and may have a common underlying etiology (Miller et al. 2016). For example, we identified similarities and differences among CIMP tumors from esophageal, gastric, and colorectal adenocarcinomas (Sanchez-Vega et al. 2017), which narrows the search for common causal mechanisms. To further explore the relationship of aberrant DNA methylation patterns and pathogenic phenotypes, my lab demonstrated associations between cancer methylomes and somatic driver mutations in 18 cancer types from 4000 tumors to illustrate the stable relationships between the epigenome and the genome (Chen et al. 2017). We showed that genome-wide patterns of aberrant hypomethylation or hypermethylation were associated with specific types of somatic mutations, indicating that the methylation patterns are not random. Many driver mutations were associated with global epigenetic disruptions, suggesting numerous genes are involved with epigenetic rewiring. We next demonstrated that DNA methylation patterns in tumor genomes are associated with distinct gene isoform expression patterns. The methylation locations predicted promoter repression, alternative splicing, and alternative terminal exons (Chen and Elnitski 2019). Our discovery of these relationships enables a better understanding of the impact of epigenetics on isoform expression. Profiling epigenetic features in human populations This project identified novel sequence polymorphisms occurring in CpG methylation sites. Sequence polymorphisms occurring in CpG methylation sites can confound methylation analysis by creating a heterogeneous mixture of Cs and Ts at the same position. We developed the algorithm MethylToSNP, to detect characteristic patterns of DNA methylation based on tiered methylation values at the same position in a population of samples, indicative of sequence polymorphisms (LaBarre et al. 2020). This method can be used to filter SNPs from methylation data when no genotype data are available. We identified uncharacterized polymorphisms and distinguished altered methylation in regulatory regions from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent), and KhoeSan (Southern African) populations. Similarly, we helped develop new analysis tools to assess ChIP-seq methylation data (Lichtenberg et al. 2017). We next compared methylation landscapes of KhoeSan individuals from the Kalahari Desert to individuals living in similar geographical, but industrialized locations (Goncearenco et al. 2021). From more than 10,000 differentially methylated sites, we found the top 5% distinguished the KhoeSan samples from ethnic groups worldwide. This finding could reflect both genetic and environmental influences. Methylation in enhancers, gene bodies, and CpG islands showed significant underrepresentation in the expected number of changes, suggesting the epigenetic landscape in the human genome is not subject to random alteration. Our discoveries about the Kalahari KhoeSan epigenomes contribute to the greater understanding of human genome diversity made possible by the KhoeSan community. Insights to mechanisms of epigenetic regulation in the human genome This project addresses altered regulation of enhancers through DNA methylation. Our collaborative study with the Shaw lab (Chen et al. 2018) assessed data from 14 pairs of monozygotic twins discordant for attention deficit hyperactivity disorder (ADHD). 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. The affected twins showed significant differences in DNA methylation patterns of enhancer regions of genes expressed in the altered brain anatomical structures, consistent with the idea that subtle (epigenetic) changes to enhancer elements can be associated with neuroanatomical anomalies. Our studies of epigenetic regulation reflect differential transcription factor activity. For example, noncoding regions of the genome contain enhancer elements as well as repressive elements. Together with the Ovcharenko lab, our research examined characteristics of repressive elements and trained a multivariate model of silencer elements. We validated the silencer elements with a specialized luciferase-expression assay developed in my lab (Petrykowska et al. 2008). We showed a statistically significant loss of gene expression attributed to candidate silencer regions (Huang et al. 2019). We have recently shown that gene repression is involved in shaping ovarian cancer subtypes (Li et al. 2021). The novel regulatory complex similar to the MegaTrans complex, previously known only in breast cancer, distinguishes aggressive ovarian tumor subtypes from premalignant tumors. We showed that the repressive factor PITX1 is a new component of the complex. The co-transcriptional regulatory process of mRNA splicing is also considered epigenetic. Using whole exome mutation data, my lab developed a machine learning classifier to identify novel mutations that disrupt splicing (Gotea et al. 2019). We also built a computational approach to identify alternative splicing important in melanomas. The isoforms originated from kinase genes, known perpetrators of cancer growth (Holland et al. 2022). These studies provide new insights into subtle changes of gene expression that define disease phenotypes. We are currently addressing the factors involved in the surveillance of mRNA splicing necessary to maintain protein integrity. We built a computational pipeline to identify affected genes and demonstrated that the protein NCL plays an essential role in nuclear mRNA surveillance (Shefer et al. 2022). This system may be fundamental to processing all coding genes in the human genome to protect against disease