Functional Genome Analysis  (B070)
Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580
D-69120 Heidelberg, Germany.
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  Epigenetics
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DNA & RNA Methylation

microRNA-Based Regulation
   - Promoter methylation promotes
   - oncogenic gene activation
   - METTL3-based RNA methylation
   - microRNA-192 promoter in PDAC

   - Pan-cancer hypermethylation marker    - Common cancer biomarker GHSR
   - lncRNA 299 in peripheral TNBC blood
Archive

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DNA-methylation studies

Changes in genomic DNA methylation patterns are early and consistent features of tumourigenesis. Aberrant DNA methylation profiles can thus be used as a valuable markers for clinical tumour characterisation. Technically, we initially applied microarray technology toward a genome-wide and high-resolution analysis of DNA methylation patterns. Meanwhile, next-generation sequencing and other techniques are used.
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The detection of methylation variations is performed by using bisulfite treatment to uncover the methylation status. Sodium bisulfite induces methylation-dependent single-nucleotide polymorphisms by converting unmethylated cytosine to uracil and, upon PCR amplification, to thymine (see figure below). 5-Methylcytosine is not affected by sodium bisulfite treatment and thus amplified as cytosine. The conversion can be identified by any means of sequence analysis.

scheme of bisulfite treatment

The data are evaluated in combination with available clinical data and information from other analyses, such as transcript analyses. This allows insights into the role of DNA methylation during tumourigenesis. We study in some detail the functional consequences of variations of particularly promoter methylation, and have uncovered relevant functional mechanisms in several cancer entities.



Early analysis results: Methylation pattern at CpG dimers in promoters of cancer-relevant genes. The type of pancreas tissue analysed is shown at the top; the degree of methylation is indicated by a colour-code ranging from green (no methylation) to red (hypermethylation).










Bermejo et al. (2019) Epigenomics 11, 81. pdf icon






Wu et al. (2021) Cancers 13, 4569 pdf icon
Bure et al. (2018) Genes Chrom. Cancer 57, 584. pdf icon
Bubnov et al. (2012) Exp. Oncol. 34, 370. pdf icon



Manoocherhri et al. (2021) Int. J. Cancer 52, 1025-1035. pdf icon
Meder et al. (2017) Circulation 136, 1528. pdf icon
Botla et al. (2012) Breast Canc. Res. Treat. 135, 705. pdf icon



Manoocherhri et al. (2021) Clin. Epigenetics 13, 207. pdf icon
Botla et al. (2016) Cancer Res. 76, 4149.  pdf icon
Moskalev et al. (2012) BMC Cancer 12, 213. pdf icon



Kapsner et al. (2021) Int. J. Cancer 149, 1150-1165. pdf icon
Moskalev et al. (2015) Oncotarget 6, 4418. pdf icon
Moskalev et al. (2012) Genes Chrom. Cancer 51, 105. pdf icon



Manoocherhri et al. (2020) Sci. Rep. 10, 11762. pdf icon
Jandaghi et al. (2015) Cell Cycle 14, 689. pdf icon
Moskalev et al. (2011) Nucleic Acids Res. 39, e77. pdf icon



Manoocherhri et al. (2020) Mol. Oncol. 14, 1252-1267. pdf icon
Haller et al. (2014) Int. J. Cancer 136, 1013. pdf icon
de Souza Rocha Simonini et al. (2010) Cancer Res. 70, 9175. pdf icon



Visvanathan et al. (2019) Genes 10, 141. pdf icon
Dutruel et al. (2014) Oncogene 33, 3401. pdf icon
Böttcher et al. (2010) PloS ONE 5, e11002. pdf icon



Amini et al. (2019) J. Cell. Physiol. 234, 15320-15329.  pdf icon
Haas et al. (2013) EMBO Mol. Med. 5, 413. pdf icon
Riazalhosseini & Hoheisel (2008) Genome Biol. 9, 405. pdf icon





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Epigenetic signature that differentiates pancreatic cancer from chronic pancreatitis
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Diagnostic differentiation between PDAC and chronic pancreatitis (CP) is challenging and currently only 65% accurate in clinical practice. Misdiagnoses in both directions have severe consequences for the patients. We set out to define molecular markers for a clear distinction of PDAC and CP. We established a random-forest based machine-learning approach to identify markers for patient classification comparing the performance of data from genome-wide DNA methylation, mRNA and miRNA transcriptional analyses as well as combinations thereof. The approach succeeded in defining accurate markers, while low-dimensional embedding and cluster analysis failed to do so. Variations in DNA methylation yielded the best diagnostic accuracy, dwarfing the importance of transcript levels. Identified changes were validated with data taken from public repositories and confirmed in an independent sample set. A signature made of few DNA methylation sites achieved a validated diagnostic accuracy of 100%, which even included some degree of redundancy for diagnostic robustness.

The success of the machine-learning process to identify a highly effective marker signature documents the power of this approach. The substantially improved diagnostic accuracy in patients with suspected pancreatic cancer could have tremendous consequences for patient management.
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Promoter methylation promotes the binding of transcrption factor NFATc1, triggering oncogenic gene activation in pancreatic cancer

Studies have indicated that some genes involved in carcinogenesis are highly methylated in their promoter regions but nevertheless strongly transcribed. It has been proposed that transcription factors could bind specifically to methylated promoters and trigger transcription. We looked at this rather comprehensively for pancreatic ductal adenocarcinoma (PDAC) and studied some cases in more detail. Some 2% of regulated genes in PDAC exhibited higher transcription coupled to promoter hypermethylation in comparison to healthy tissue.

Screening 661 transcription factors, several were found to bind specifically to methylated promoters, in particular molecules of the NFAT family. One of them - NFATc1 - was substantially more expressed in PDAC than control tissue and exhibited a strong oncogenic role. Functional studies combined with computational analyses allowed determining affected genes. A prominent one was gene ALDH1A3, which accelerates PDAC metastasis and correlates with a bad prognosis. Further studies confirmed the direct up-regulation of ALDH1A3 transcription by NFATc1 promoter binding in a methylation-dependent process, providing insights into the oncogenic role of transcription activation in PDAC that is promoted by DNA methylation
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Wu et al. (2021) Cancers 13, 4569. pdf icon
 
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SST hypermethylation acts as a pan-cancer marker

Aberrant DNA methylation is often involved in carcinogenesis. A genome-wide methylation study was performed on DNA from pancreatic ductal adenocarcinoma (PDAC) and endocrine pancreas tumours. Validation of DNA methylation patterns and concomitant alterations in expression of gene candidates was performed on patient samples and pancreatic cancer cell lines. Furthermore, validation was done on independent data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Finally, droplet digital PCR was employed to detect DNA methylation marks in cell-free (cf) DNA isolated from plasma samples of PDAC patients and cancer-free blood donors.
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Hypermethylation of the SST gene (encoding somatostatin) and concomitant down-regulation of its expression was discovered in PDAC and endocrine tumour tissues while not being present in chronic pancreatitis (inflamed) tissues and normal pancreas. Fittingly, treatment with a somatostatin agonist (Octreotide) reduced cell proliferation and migration of pancreatic cancer cells. Diagnostic performance of SST methylation in a receiver operating characteristic (ROC) curve analysis was 100% and 89% for tissue and plasma samples, respectively.
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A large body of TCGA and GEO data confirmed SST hypermethylation and down-regulation in PDAC and showed a similar effect in a broad spectrum of other tumour entities. SST promoter methylation represents a sensitive and promising molecular, pan-cancer biomarker detectable in tumour tissue and liquid biopsy samples..

Manoocherhri et al. (2020) Mol. Oncol. 14, 1252-1267. pdf icon


Figure legend: External validation of SST expression and methylation. (A) The SST gene methylation was looked at in two independent datasets about pancreatic cancer obtained from GEO and TCGA (left panel). The central panel shows the down-regulation of SST gene expression in PDAC in three independent datasets from GEO. In the right panel, the Pearson correlation of DNA methylation and gene expression levels of the SST gene are shown (data from TCGA-PAAD). (B) Investigation of SST gene methylation and expression in esophageal, stomach, colon, and rectal adenocarcinomas. The β values of two probes that were common to the 450k and 27k methylation arrays were applied for comparison. (C) SST gene methylation results are shown as in panel B but for breast, lung, prostate, head & neck, liver, bladder and kidney cancer. TPM: transcripts per million; *: p ≤ 0.05; ****: p ≤ 0.0001.
  
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M6-Methyladenoadenosine landscape of glioma stem-like cells: METTL3 is essential for the expression of actively transcribed genes and sustenance of the oncogenic signalling

Despite recent advances in m6A biology, the regulation of crucial RNA processing steps by the RNA methylase METTL3 in glioma stem-like cells (GSCs) remains obscure. An integrated analysis of m6A-RNA-immunoprecipitation and total RNA-sequencing of METTL3-silenced GSCs identified that m6A modification in GSCs is principally carried out by METTL3. The m6A-modified transcripts showed higher abundance compared to non-modified transcripts. Further, METTL3 is essential for the expression of GSC-specific actively transcribed genes. Silencing METTL3 resulted in an elevation of several aberrant, alternative splicing events. Putative m6A reader proteins play a key role in the RNA stabilization function of METTL3. METTL3 altered A-to-I and C-to-U RNA editing events by differentially regulating RNA editing enzymes ADAR and APOBEC3A. Similar to protein-encoding genes, lincRNAs with m6A-marks showed METTL3-dependent high expression. m6A modification of 3’-UTRs appears to result in a conformation-dependent hindrance of miRNA binding to their targets. The integrated analysis of the m6A regulome in METTL3-silenced GSCs showed global disruption in tumorigenic pathways that are indispensable for GSC maintenance and glioma progression. We conclude that METTL3 plays a vital role in many steps of RNA processing and orchestrates successful execution of oncogenic pathways in GSCs.

Visvanathan et al. (2019) Genes 10, 141. pdf icon
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Methylation in the gene encoding the long intergenic non-coding RNA 299 serves as biomarker in peripheral blood for triple-negative breast cancer

Worldwide, breast cancer was the most frequently diagnosed cancer and cause of cancer death among women in 2012. Triple-negative breast cancer (TNBC), defined by lack of (i) tumour expression of estrogen receptor (ER), (ii) progesterone receptor (PR), and (iii) human epidermal growth factor receptor 2 (HER2), is the most aggressive subtype of breast cancer and accounts for 10% to 20% of all diagnoses. We screened the epigenome-wide methylation profiles of 233 TNBC patients and 233 age-matched controls (discovery set) and validated the most promising methylation probes in 57 TNBC patients and 124 controls (validation set) with the aim of identifying epigenetic biomarkers based on DNA from peripheral blood leukocytes.
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Figure legend: Manhattan plot showing the chromosomal distribution of the p-values of 370,706 methylation variations in TNBC. The dashed line indicates the 0.001 significance threshold.
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Bermejo et al. (2019) Epigenomics 11, 81. pdf icon

  

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