Martin Kerick

Data from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

<div>AbstractProstate cancer is the second most common cancer among men worldwide. Alterations in the DNA methylation pattern can be one of the leading causes for prostate cancer formation. This study is the first high-throughput sequencing study investigating genome-wide DNA methylation patterns in a large cohort of 51 tumor and 53 benign prostate samples using methylated DNA immunoprecipitation sequencing. Comparative analyses identified more than 147,000 cancer-associated epigenetic alterations. In addition, global methylation patterns show significant differences based on the TMPRSS2–ERG rearrangement status. We propose the hypermethylation of miR-26a as an alternative pathway of ERG rearrangement-independent EZH2 activation. The observed increase in differential methylation events in fusion–negative tumors can explain the tumorigenic process in the absence of genomic rearrangements.Significance: In contrast to TMPRSS2–ERG-rearranged tumors, the pathomechanism for gene fusion–negative tumors is completely unclear. Using a sequencing-based approach, our work uncovers significant global epigenetic alterations in TMPRSS2–ERG gene fusion–negative tumors and provides a mechanistic explanation for the tumor formation process. Cancer Discov; 2(11); 1024–35. ©2012 AACR.Read the Commentary on this article by Alumkal and Herman, p. 979.This article is featured in Highlights of This Issue, p. 961</div>

TMPRSS2

10.1158/2159-8290.c.6545723

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Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

medRxiv (Cold Spring Harbor Laboratory) (2020)

Guillermo Barturen Sepideh Babaei Francesc Català‐Moll Manuel Martínez‐Bueno Zuzanna Makowska

SUMMARY Background Clinical heterogeneity, a hallmark of systemic autoimmune diseases (SADs) impedes early diagnosis and effective treatment, issues that may be addressed if patients could be grouped into a molecular defined stratification. Methods With the aim of reclassifying SADs independently of the clinical diagnoses, unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data of 918 patients with 7 SADs and 263 healthy controls was undertaken. In addition, an inception cohort was prospectively followed for 6 and 14 months to validate the results and analyze if cluster assignment changed or not with time. Results Four clusters were identified. Three clusters were aberrant, representing ‘inflammatory’, ‘lymphoid’, and ‘interferon’ patterns each including all diagnoses and defined by genetic, clinical, serological and cellular features. A fourth cluster showed no specific molecular pattern and accumulated also healthy controls. An independent inception cohort showed that with time, the molecular clusters remain stable, showing that single aberrant molecular signatures characterize each individual patient. Conclusions Patients with SADs can be jointly stratified into three stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of therapy non-responsiveness marking a paradigm shift in the view of SADs.

10.1101/2020.02.21.20021618

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Supplementary Figures 1-5 from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

PDF file - 947K, Supplementary Fig. S1. Differential methylations in PCa validated by BS-MS experiments. Supplementary Fig. S2. DNA-Methylomes of prostate cancer differ significantly from normal tissues. Supplementary Fig. S3. Global differences in DNA methylation patterns between tumor, normal, FUS+ and FUS- samples. Supplementary Fig. S4 Patientwise patterns of promoter hypermethylation and corresponding gene expression of EZH2 target genes. Supplementary Fig. S5 Methylation of the miRNA-26a genomic region

TMPRSS2

CpG site

10.1158/2159-8290.22528505.v1

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Supplementary Figures 1-5 from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

PDF file - 947K, Supplementary Fig. S1. Differential methylations in PCa validated by BS-MS experiments. Supplementary Fig. S2. DNA-Methylomes of prostate cancer differ significantly from normal tissues. Supplementary Fig. S3. Global differences in DNA methylation patterns between tumor, normal, FUS+ and FUS- samples. Supplementary Fig. S4 Patientwise patterns of promoter hypermethylation and corresponding gene expression of EZH2 target genes. Supplementary Fig. S5 Methylation of the miRNA-26a genomic region

TMPRSS2

10.1158/2159-8290.22528505

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Primary Biliary Cirrhosis, Primary Sclerosing Cholangitis, and Autoimmune Hepatitis

Rare diseases of the immune system (2019)

David González‐Serna Martin Kerick Javier Martı́n

Primary Sclerosing Cholangitis

Primary Biliary Cirrhosis

Autoimmune Hepatitis

Genome-wide Association Study

Liver disease

10.1007/978-3-030-03934-9_9

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Supplementary Figure Legends 1-5, Table Legends 1-4 from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

PDF file - 64K

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Erg

10.1158/2159-8290.22528508.v1

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Supplementary Tables 1-4 from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

PDF file - 175K, Table S1 Clinical data and sequencing statistics. Table S2: Oligonucleotides for library construction and quantification and BS-MS-primers used for validation of MeDIP-Seq data. Table S3 Top 100 differentially methylated regions between tumor and normal (a) and between FUS+ and FUS- samples (b). Table S4 Correlation of EZH2 expression to expression of homeobox (Holland et al. 2007) and target genes (Yu et al. 2007) and target promoter methylation (+-2kb)

TMPRSS2

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10.1158/2159-8290.22528502

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Data from Genome-wide DNA Methylation Events in TMPRSS2–ERG Fusion-Negative Prostate Cancers Implicate an EZH2-Dependent Mechanism with miR-26a Hypermethylation

Stefan T. Börno Axel Fischer Martin Kerick Maria Fälth Mark Laible

<div>AbstractProstate cancer is the second most common cancer among men worldwide. Alterations in the DNA methylation pattern can be one of the leading causes for prostate cancer formation. This study is the first high-throughput sequencing study investigating genome-wide DNA methylation patterns in a large cohort of 51 tumor and 53 benign prostate samples using methylated DNA immunoprecipitation sequencing. Comparative analyses identified more than 147,000 cancer-associated epigenetic alterations. In addition, global methylation patterns show significant differences based on the TMPRSS2–ERG rearrangement status. We propose the hypermethylation of miR-26a as an alternative pathway of ERG rearrangement-independent EZH2 activation. The observed increase in differential methylation events in fusion–negative tumors can explain the tumorigenic process in the absence of genomic rearrangements.Significance: In contrast to TMPRSS2–ERG-rearranged tumors, the pathomechanism for gene fusion–negative tumors is completely unclear. Using a sequencing-based approach, our work uncovers significant global epigenetic alterations in TMPRSS2–ERG gene fusion–negative tumors and provides a mechanistic explanation for the tumor formation process. Cancer Discov; 2(11); 1024–35. ©2012 AACR.Read the Commentary on this article by Alumkal and Herman, p. 979.This article is featured in Highlights of This Issue, p. 961</div>

TMPRSS2

Erg

10.1158/2159-8290.c.6545723.v1

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EXPRESSION QUANTITATIVE TRAIT LOCI-EQTL- ANALYSIS IN SYSTEMIC SCLEROSIS

Martin Kerick David Gonzalez Serna María Teruel Sepideh Babaei Marialbert Acosta‐Herrera

Trait

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Metaanálisis del genoma completo revela nuevos loci compartidos en enfermedades reumáticas seropositivas sistémicas