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Article ; Online: ME-Class2 reveals context dependent regulatory roles for 5-hydroxymethylcytosine.

Schlosberg, Christopher E / Wu, Dennis Y / Gabel, Harrison W / Edwards, John R

2019 Volume 47, Issue 5, Page(s) e28

Abstract: Since the discovery of 5-hydroxymethylcytosine (5hmC) as a prominent DNA modification found in mammalian genomes, an emergent question has been what role this mark plays in gene regulation. 5hmC is hypothesized to function as an intermediate in the ... ...

Abstract	Since the discovery of 5-hydroxymethylcytosine (5hmC) as a prominent DNA modification found in mammalian genomes, an emergent question has been what role this mark plays in gene regulation. 5hmC is hypothesized to function as an intermediate in the demethylation of 5-methylcytosine (5mC) and in the reactivation of silenced promoters and enhancers. Further, weak positive correlations are observed between gene body 5hmC and gene expression. We previously demonstrated that ME-Class is an effective tool to understand relationships between whole-genome bisulfite sequencing data and expression. In this work, we present ME-Class2, a machine-learning based tool to perform integrative 5mCG, 5hmCG and expression analysis. Using ME-Class2 we analyze whole-genome single-base resolution 5mCG and 5hmCG datasets from 20 primary tissue and cell samples to reveal relationships between 5hmCG and expression. Our analysis indicates that conversion of 5mCG to 5hmCG within 2 kb of the transcription start site associates with distinct functions depending on the summed level of 5mCG + 5hmCG. Unchanged levels of 5mCG + 5hmCG (conversion from 5mCG to stable 5hmCG) associate with repression. Meanwhile, decreases in 5mCG + 5hmCG (5hmCG-mediated demethylation) associate with gene activation. Our results demonstrate that ME-Class2 will prove invaluable to interpret genome-wide 5mC and 5hmC datasets and guide mechanistic studies into the function of 5hmCG.
MeSH term(s)	5-Methylcytosine/analogs & derivatives ; 5-Methylcytosine/metabolism ; Animals ; Brain/metabolism ; Databases, Genetic ; Datasets as Topic ; Genes/genetics ; Genome/genetics ; Humans ; Machine Learning ; Methylation ; Mice ; Organ Specificity/genetics ; Promoter Regions, Genetic/genetics ; Sequence Analysis, RNA/methods ; Sulfites/chemistry ; Sulfites/metabolism
Chemical Substances	Sulfites ; 5-hydroxymethylcytosine (1123-95-1) ; 5-Methylcytosine (6R795CQT4H) ; hydrogen sulfite (OJ9787WBLU)
Language	English
Publishing date	2019-02-01
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkz001
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Article ; Online: Modeling complex patterns of differential DNA methylation that associate with gene expression changes.

Schlosberg, Christopher E / VanderKraats, Nathan D / Edwards, John R

Nucleic acids research

2017 Volume 45, Issue 9, Page(s) 5100–5111

Abstract: Numerous genomic studies are underway to determine which genes are abnormally regulated by DNA methylation in disease. However, we have a poor understanding of how disease-specific methylation changes affect expression. We thus developed an integrative ... ...

Abstract	Numerous genomic studies are underway to determine which genes are abnormally regulated by DNA methylation in disease. However, we have a poor understanding of how disease-specific methylation changes affect expression. We thus developed an integrative analysis tool, Methylation-based Gene Expression Classification (ME-Class), to explain specific variation in methylation that associates with expression change. This model captures the complexity of methylation changes around a gene promoter. Using 17 whole-genome bisulfite sequencing and RNA-seq datasets from different tissues from the Roadmap Epigenomics Project, ME-Class significantly outperforms standard methods using methylation to predict differential gene expression change. To demonstrate its utility, we used ME-Class to analyze 32 datasets from different hematopoietic cell types from the Blueprint Epigenome project. Expression-associated methylation changes were predominantly found when comparing cells from distantly related lineages, implying that changes in the cell's transcriptional program precede associated methylation changes. Training ME-Class on normal-tumor pairs from The Cancer Genome Atlas indicated that cancer-specific expression-associated methylation changes differ from tissue-specific changes. We further show that ME-Class can detect functionally relevant cancer-specific, expression-associated methylation changes that are reversed upon the removal of methylation. ME-Class is thus a powerful tool to identify genes that are dysregulated by DNA methylation in disease.
MeSH term(s)	Base Sequence ; Colonic Neoplasms/genetics ; DNA Methylation ; Epigenomics ; Gene Expression Regulation ; Gene Expression Regulation, Neoplastic ; Genome, Human ; Hematopoiesis/genetics ; Humans ; Models, Genetic ; Promoter Regions, Genetic ; RNA, Messenger ; Sequence Analysis, DNA ; Sequence Analysis, RNA
Chemical Substances	RNA, Messenger
Language	English
Publishing date	2017-01-19
Publishing country	England
Document type	Evaluation Study ; Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkx078
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Article ; Online: Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Schlosberg Christopher E / Schwantes-An Tae-Hwi / Duan Weimin / Saccone Nancy L

BMC Proceedings, Vol 5, Iss Suppl 9, p S

2011 Volume 109

Abstract: Abstract Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated ... ...

Abstract	Abstract Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2011-11-01T00:00:00Z
Publisher	BioMed Central
Document type	Article ; Online
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Article ; Online: Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data.

Schlosberg, Christopher E / Schwantes-An, Tae-Hwi / Duan, Weimin / Saccone, Nancy L

BMC proceedings

2011 Volume 5 Suppl 9, Page(s) S109

Abstract: Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the ... ...

Abstract	Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.
Language	English
Publishing date	2011-11-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2411867-9
ISSN	1753-6561 ; 1753-6561
ISSN (online)	1753-6561
ISSN	1753-6561
DOI	10.1186/1753-6561-5-S9-S109
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Article: A functional genomic screen in vivo identifies CEACAM5 as a clinically relevant driver of breast cancer metastasis.

NPJ breast cancer

2018 Volume 4, Page(s) 9

Abstract: Tumor cells disseminate early in tumor development making metastasis-prevention strategies difficult. Identifying proteins that promote the outgrowth of disseminated tumor cells may provide opportunities for novel therapeutic strategies. Despite multiple ...

Abstract	Tumor cells disseminate early in tumor development making metastasis-prevention strategies difficult. Identifying proteins that promote the outgrowth of disseminated tumor cells may provide opportunities for novel therapeutic strategies. Despite multiple studies demonstrating that the mesenchymal-to-epithelial transition (MET) is critical for metastatic colonization, key regulators that initiate this transition remain unknown. We serially passaged lung metastases from a primary triple negative breast cancer xenograft to the mammary fat pads of recipient mice to enrich for gene expression changes that drive metastasis. An unbiased transcriptomic signature of potential metastatic drivers was generated, and a high throughput gain-of-function screen was performed in vivo to validate candidates. Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) was identified as a metastatic driver. CEACAM5 overproduction enriched for an epithelial gene expression pattern and facilitated tumor outgrowth at metastatic sites. Tissues from patients with metastatic breast cancer confirmed elevated levels of CEACAM5 in lung metastases relative to breast tumors, and an inverse correlation between CEACAM5 and the mesenchymal marker vimentin was demonstrated. Thus, CEACAM5 facilitates tumor outgrowth at metastatic sites by promoting MET, warranting its investigation as a therapeutic target and biomarker of aggressiveness in breast cancer.
Language	English
Publishing date	2018-04-30
Publishing country	United States
Document type	Journal Article
ISSN	2374-4677
ISSN	2374-4677
DOI	10.1038/s41523-018-0062-x
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Article ; Online: p53 deficiency linked to B cell translocation gene 2 (BTG2) loss enhances metastatic potential by promoting tumor growth in primary and metastatic sites in patient-derived xenograft (PDX) models of triple-negative breast cancer.

Powell, Emily / Shao, Jiansu / Yuan, Yuan / Chen, Hsiang-Chun / Cai, Shirong / Echeverria, Gloria V / Mistry, Nipun / Decker, Keith F / Schlosberg, Christopher / Do, Kim-Anh / Edwards, John R / Liang, Han / Piwnica-Worms, David / Piwnica-Worms, Helen

Breast cancer research : BCR

2016 Volume 18, Issue 1, Page(s) 13

Abstract: Background: Despite advances in early diagnosis and treatment of cancer patients, metastasis remains the major cause of mortality. TP53 is one of the most frequently mutated genes in human cancer, and these alterations can occur during the early stages ... ...

Abstract	Background: Despite advances in early diagnosis and treatment of cancer patients, metastasis remains the major cause of mortality. TP53 is one of the most frequently mutated genes in human cancer, and these alterations can occur during the early stages of oncogenesis or as later events as tumors progress to more aggressive forms. Previous studies have suggested that p53 plays a role in cellular pathways that govern metastasis. To investigate how p53 deficiency contributes to late-stage tumor growth and metastasis, we developed paired isogenic patient-derived xenograft (PDX) models of triple-negative breast cancer (TNBC) differing only in p53 status for longitudinal analysis. Methods: Patient-derived isogenic human tumor lines differing only in p53 status were implanted into mouse mammary glands. Tumor growth and metastasis were monitored with bioluminescence imaging, and circulating tumor cells (CTCs) were quantified by flow cytometry. RNA-Seq was performed on p53-deficient and p53 wild-type tumors, and functional validation of a lead candidate gene was performed in vivo. Results: Isogenic p53 wild-type and p53-deficient tumors metastasized out of mammary glands and colonized distant sites with similar frequency. However, p53-deficient tumors metastasized earlier than p53 wild-type tumors and grew faster in both primary and metastatic sites as a result of increased proliferation and decreased apoptosis. In addition, greater numbers of CTCs were detected in the blood of mice engrafted with p53-deficient tumors. However, when normalized to tumor mass, the number of CTCs isolated from mice bearing parental and p53-deficient tumors was not significantly different. Gene expression profiling followed by functional validation identified B cell translocation gene 2 (BTG2), a downstream effector of p53, as a negative regulator of tumor growth both at primary and metastatic sites. BTG2 expression status correlated with survival of TNBC patients. Conclusions: Using paired isogenic PDX-derived metastatic TNBC cells, loss of p53 promoted tumor growth and consequently increased tumor cell shedding into the blood, thus enhancing metastasis. Loss of BTG2 expression in p53-deficient tumors contributed to this metastatic potential by enhancing tumor growth in primary and metastatic sites. Furthermore, clinical data support conclusions generated from PDX models and indicate that BTG2 expression is a candidate prognostic biomarker for TNBC.
MeSH term(s)	Animals ; Apoptosis/genetics ; Cell Line, Tumor ; Cell Proliferation/genetics ; Disease Models, Animal ; Female ; Humans ; Immediate-Early Proteins/biosynthesis ; Immediate-Early Proteins/genetics ; Mice ; Neoplasm Metastasis ; Neoplastic Cells, Circulating/pathology ; Triple Negative Breast Neoplasms/genetics ; Triple Negative Breast Neoplasms/pathology ; Tumor Suppressor Protein p53/genetics ; Tumor Suppressor Proteins/biosynthesis ; Tumor Suppressor Proteins/genetics ; Xenograft Model Antitumor Assays
Chemical Substances	Immediate-Early Proteins ; TP53 protein, human ; Tumor Suppressor Protein p53 ; Tumor Suppressor Proteins ; BTG2 protein, human (141490-22-4)
Language	English
Publishing date	2016-01-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2015059-3
ISSN	1465-542X ; 1465-5411
ISSN (online)	1465-542X
ISSN	1465-5411
DOI	10.1186/s13058-016-0673-9
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Article ; Online: Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.

Li, Shunqiang / Shen, Dong / Shao, Jieya / Crowder, Robert / Liu, Wenbin / Prat, Aleix / He, Xiaping / Liu, Shuying / Hoog, Jeremy / Lu, Charles / Ding, Li / Griffith, Obi L / Miller, Christopher / Larson, Dave / Fulton, Robert S / Harrison, Michelle / Mooney, Tom / McMichael, Joshua F / Luo, Jingqin /

Tao, Yu / Goncalves, Rodrigo / Schlosberg, Christopher / Hiken, Jeffrey F / Saied, Laila / Sanchez, Cesar / Giuntoli, Therese / Bumb, Caroline / Cooper, Crystal / Kitchens, Robert T / Lin, Austin / Phommaly, Chanpheng / Davies, Sherri R / Zhang, Jin / Kavuri, Megha Shyam / McEachern, Donna / Dong, Yi Yu / Ma, Cynthia / Pluard, Timothy / Naughton, Michael / Bose, Ron / Suresh, Rama / McDowell, Reida / Michel, Loren / Aft, Rebecca / Gillanders, William / DeSchryver, Katherine / Wilson, Richard K / Wang, Shaomeng / Mills, Gordon B / Gonzalez-Angulo, Ana / Edwards, John R / Maher, Christopher / Perou, Charles M / Mardis, Elaine R / Ellis, Matthew J

Cell reports

2013 Volume 4, Issue 6, Page(s) 1116–1130

Abstract: To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained ... ...

Abstract	To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation.
MeSH term(s)	Adaptor Proteins, Signal Transducing/genetics ; Alleles ; Animals ; Breast Neoplasms/drug therapy ; Breast Neoplasms/genetics ; Breast Neoplasms/metabolism ; Breast Neoplasms/pathology ; Drug Resistance, Neoplasm ; Estradiol/pharmacology ; Estrogen Receptor alpha/genetics ; Female ; Gene Amplification ; Genomic Instability ; Heterografts ; Humans ; Mice ; Neoplasm Proteins/biosynthesis ; Neoplasm Proteins/genetics ; Neoplasm Staging ; Phosphoproteins/genetics ; Point Mutation ; RNA, Neoplasm/biosynthesis ; RNA, Neoplasm/genetics ; Transcription Factors ; Translocation, Genetic
Chemical Substances	Adaptor Proteins, Signal Transducing ; ESR1 protein, human ; Estrogen Receptor alpha ; Neoplasm Proteins ; Phosphoproteins ; RNA, Neoplasm ; Transcription Factors ; YAP1 protein, human ; Estradiol (4TI98Z838E)
Language	English
Publishing date	2013-09-19
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2013.08.022
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Article ; Online: ME-Class2 reveals context dependent regulatory roles for 5-hydroxymethylcytosine.

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Article ; Online: Modeling complex patterns of differential DNA methylation that associate with gene expression changes.

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Article ; Online: Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

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Article ; Online: Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data.

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Article: A functional genomic screen in vivo identifies CEACAM5 as a clinically relevant driver of breast cancer metastasis.

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Article ; Online: p53 deficiency linked to B cell translocation gene 2 (BTG2) loss enhances metastatic potential by promoting tumor growth in primary and metastatic sites in patient-derived xenograft (PDX) models of triple-negative breast cancer.

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Article ; Online: Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.

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