Biology subjects

Coetzee, S. G.

Publications and source records attributed to Coetzee, S. G..

6 recordsLinked to original sources

bioRxiv2020

A Molecular Taxonomy of Tumors Independent of Tissue-of-Origin

Cancer is a complex disease involving disrupted cellular metabolism, basic biochemical processes, and the microenvironment. However, despite some generally agreed upon unifying principles (Hanahan and Weinberg 2000, 2011), molecular signatures remain largely indistinguishable from tissue-of-origin, presenting a major barrier for precision health and individualized medicine. To address this challenge, we reduce mutation data to disruptions in a select set of pathways relevant to basic cell biology, from DNA replication to cellular communication. Using dimensionality reduction techniques, we assign tumor samples into ten clusters distinct from tissue-of-origin and largely free of bias from mutational burden or clinical stage. We show that the clusters vary in prognosis by modeling relative risk of death by cancer type and cluster. We identify cluster-specific mutations in different tissues, demonstrating that tissue-specific signatures contribute to common cellular phenotypes. Moreover, germline risk genes involved in replication fidelity and genome stability are equally distributed among clusters, contrary to the expectation that such genes are avatars of molecular subtype. We investigate metastatic and non-metastatic pathways, and show that most differences are cluster-specific. Some metastatic pathways from one cluster are cluster-specific pathways from non-metastatic tumors of another cluster, suggesting phenotypic convergence. Taken as a whole, our observations suggest that common driver genes combine with tissue-specific disruptions in tumor-promoting pathways to produce a limited number of distinct molecular phenotypes. Thus, we present a coherent view of global tumor biology, and explain how common cellular dysfunction might arise from tissue-specific mutations.

cancer biology↗

bioRxiv2020

DNA methylation landscapes of matched primary and recurrent high grade serous ovarian cancers are preserved throughout disease progression and chemoresistance.

Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC). We performed whole genome bisulfite sequencing (WGBS) and whole transcriptome sequencing (RNA-seq) to establish methylation and gene expression signatures in 62 primary and recurrent tumors from 28 patients diagnosed with stage III/IV HGSOC. Eleven of these patients carried pathogenic germline BRCA1/BRCA2 mutations. Genome-wide methylation and transcriptomic features identified in primary tumors were largely preserved in matched recurrent tumors from the same patient (P-value = 7.16 x 10-7 and 1.41 x 10-3 in BRCA1/2 and non-BRCA1/2 cases respectively). Tumors from BRCA1/2 carriers displayed high levels of heterogeneity, with significantly more shared methylation changes identified between primary and recurrent tumors from non-BRCA1/2 patients, which may be related to the poorer survival we observe in HGSOCs from non-BRCA1/2 carriers (P-value = 0.0056). Partially methylated domains (PMDs) dominated the epigenetic variation across all tumors, and were more hypomethylated in BRCA1/2 than non-BRCA1/2 cases. Differential gene expression analysis identified upregulation of genes from immune pathways including antigen processing and presentation in tumors from BRCA1/2 carriers, implicating increased immune response in the improved survival observed in these patients. In summary, this study shows a previously unreported conservation of methylation and gene expression in recurrent HGSOCs. These data have implications for the possible effectiveness of epigenetic based therapies to treat both primary and recurrent ovarian cancers.

genomics↗

bioRxiv2020

Ovarian Cancer Risk Variants are Enriched in Histotype-Specific Enhancers that Disrupt Transcription Factor Binding Sites

Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome wide association studies (GWAS) have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, suggesting they mediate their function through the regulation of gene expression by their interaction with tissue specific regulatory elements (REs). In this study, by intersecting germline genetic risk data with regulatory landscapes of active chromatin in ovarian cancers and their precursor cell types, we first estimated the heritability explained by known common low penetrance risk alleles. The narrow sense heritability [Formula] of both EOC overall and high grade serous ovarian cancer (HGSOCs) was estimated to be 5-6%. Partitioned SNP-heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from public resources (Roadmap Epigenomics and ENCODE), and H3K27Ac ChIP-Seq data generated in 26 ovarian cancer-relevant cell types. We identified significant enrichment of risk SNPs in active REs marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active REs influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that break TF motifs (enrichment P-Value < 1x10-5 compared to control variants). The most frequently broken motif was REST (P-Value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. These systematic functional annotations with epigenomic data highlight the specificity of the regulatory landscape and demonstrate functional annotation of germline risk variants is most informative when performed in highly relevant cell types.

genetics↗

bioRxiv2018

HiCAGE: an R package for large-scale annotation and visualization of 3C-based genomic data

Chromatin interactions measured by the 3C-based family of next generation technologies are becoming increasingly important for measuring the physical basis for regulatory interactions between different classes of functional domains in the genome. Software is needed to streamline analyses of these data and integrate them with custom genome annotations, RNA-seq, and gene ontologies. We introduce a new R package compatible with Bioconductor--Hi-C Annotation and Graphics Ensemble (HiCAGE)--to perform these tasks with minimum effort. In addition, the package contains a shiny/R web app interface to provide ready access to its functions.\n\nAvailability and ImplementationThe software is implemented in R and is freely available under GPLv3. HiCAGE runs in R (version 3.4) and is freely available through github (https://github.com/mworkman13/HiCAGE) or on the web (https://junkdnalab.shinyapps.io/hicage).

bioinformatics↗

bioRxiv2017

Enhancer Linking by Methylation/Expression Relationships with the R package ELMER version 2

MotivationDNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.\n\nResultsWe present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that this approach can identify GRNs associated with many new Master Regulators including KLF5 in breast cancer.\n\nAvailabilityELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/

bioinformatics↗

bioRxiv2017

StateHub-StatePaintR: rules-based chromatin state annotations.

Genome annotation is critical to understand the function of disease variants, especially for clinical applications. To meet this need there are segmentations available from public consortia reflecting varying unsupervised approaches to functional annotation based on epigenetics data, but there remains a need for transparent, reproducible, and easily interpreted genomic maps of the functional biology of chromatin. We introduce a new methodological framework for defining a combinatorial epigenomic model of chromatin state on a web database, StateHub. In addition, we created an annotation tool for bioconductor, StatePaintR, which accesses these models and uses them to rapidly (on the order of seconds) produce chromatin state segmentations in standard genome browser formats. Annotations are fully documented with change history and versioning, authorship information, and original source files. StatePaintR calculates ranks for each state from next-gen sequencing peak statistics, facilitating variant prioritization, enrichment testing, and other types of quantitative analysis. StateHub hosts annotation tracks for major public consortia as a resource, and allows users to submit their own alternative models.

bioinformatics↗