Evidence map›Paper›PMID 37993930›Full record

ArticleClinical epigenetics2023

Targeting oncogenic TERT promoter variants by allele-specific epigenome editing.

Alexandra G Kouroukli, Nivethika Rajaram, Pavel Bashtrykov, Helene Kretzmer, Reiner Siebert, Albert Jeltsch, Susanne Bens

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Crosstalk BetweenCancers · 2026
    Review
  2. Epigenetic editing: from concept to clinic.Nature reviews. Drug discovery · 2026
    Review
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Alexandra G KouroukliInstitute of Human Genetics, Ulm University and Ulm University Medical Center, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
Nivethika RajaramDepartment of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
Pavel BashtrykovDepartment of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
Helene KretzmerComputational Genomics, Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Reiner SiebertInstitute of Human Genetics, Ulm University and Ulm University Medical Center, Albert-Einstein-Allee 11, 89081, Ulm, Germany.
Albert JeltschDepartment of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
Susanne BensInstitute of Human Genetics, Ulm University and Ulm University Medical Center, Albert-Einstein-Allee 11, 89081, Ulm, Germany. susanne.bens@uni-ulm.de.
Universität Ulm · DEUniversity of Stuttgart · DEMax Planck Institute for Molecular Genetics · DE

Funding

Baden-Württemberg Stiftung gGmbH ID09
6 · The paper itself

Abstract

backgroundActivation of dominant oncogenes by small or structural genomic alterations is a common driver mechanism in many cancers. Silencing of such dominantly activated oncogenic alleles, thus, is a promising strategy to treat cancer. Recently, allele-specific epigenome editing (ASEE) has been described as a means to reduce transcription of genes in an allele-specific manner. In cancer, specificity to an oncogenic allele can be reached by either targeting directly a pathogenic single-nucleotide variant or a polymorphic single-nucleotide variant linked to the oncogenic allele. To investigate the potential of ASEE in cancer, we here explored this approach by targeting variants at the TERT promoter region. The TERT promoter region has been described as one of the most frequently mutated non-coding cancer drivers.

resultsSequencing of the TERT promoter in cancer cell lines showed 53% (41/77) to contain at least one heterozygous sequence variant allowing allele distinction. We chose the hepatoblastoma cell line Hep-G2 and the lung cancer cell line A-549 for this proof-of-principle study, as they contained two different kinds of variants, namely the activating mutation C228T in the TERT core promoter and the common SNP rs2853669 in the THOR region, respectively. These variants were targeted in an allele-specific manner using sgRNA-guided dCas9-DNMT3A-3L complexes. In both cell lines, we successfully introduced DNA methylation specifically to the on-target allele of the TERT promoter with limited background methylation on the off-target allele or an off-target locus (VEGFA), respectively. We observed a maximum CpG methylation gain of 39% and 76% on the target allele when targeting the activating mutation and the common SNP, respectively. The epigenome editing translated into reduced TERT RNA expression in Hep-G2.

conclusionsWe applied an ASEE-mediated approach to silence TERT allele specifically. Our results show that the concept of dominant oncogene inactivation by allele-specific epigenome editing can be successfully translated into cancer models. This new strategy may have important advantages in comparison with existing therapeutic approaches, e.g., targeting telomerase, especially with regard to reducing adverse side effects.

Indexed as

Lung NeoplasmsTelomeraseAllelesDNA MethylationEpigenomeHumansMutationNucleotidesPromoter Regions, GeneticRNA, Guide, CRISPR-Cas SystemsNucleotidesRNA, Guide, CRISPR-Cas SystemsTelomeraseTERT protein, humanAllele-specific epigenome editing (ASEE)CancerDNA methylationSingle-nucleotide variantsTelomerase reverse transcriptaseTERT

Identifiers

PMID37993930
PMCPMC10666398
OpenAlexW4388899914

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.