Cancer Epigenomics

Overview

Our group investigates the role of epigenetic alterations in the development and progression of cancer. The focus is on acute myeloid leukemia and chronic lymphocytic leukemia. We utilize state-of-the-art epigenomic profiling technologies for both bulk samples and single-cell analysis to identify cancer-specific alterations. Multiomic data analysis is employed to deconvolute events specific to cell type and genotype. These strategies have enabled us to characterize the epigenomes of their cells of origin and to define epigenomic alterations associated with recurrent genomic alterations. A significant observation was the heterogeneity in epigenetic patterns at the single-cell level, which forms the basis for several ongoing projects investigating therapy resistance.

Projects

The image illustrates three processes: A shows the transition from normal to tumor cells, B depicts different normal tissue cell types, and C highlights various developmental stages, aging, and microenvironment effects on cell composition, represented through changing colors and shapes of cells.

The detailed molecular mechanisms that lead to epigenetic changes in the tumor genome are not understood. This is, however, of upmost importance if one considers the development of novel therapies. We have ongoing research projects that will allow us to decipher the epigenome (DNA methylation, nucleosome position and histone marks) of cells identified as potential tumor origin and in comparison with the tumor epigenome, will identify cancer-specific epigenetic changes. This information is of upmost importance in order to understand the epigenomic contributions in a cancer cell to development and progression of tumorigenesis, as well as to therapy response. We utilize unique existing resources and expertise (tissues resources, cell biology assays, epigenetic profiling protocols, bioinformatical analysis pipelines, and clinical/pathology resources). Based on our current knowledge, we work on the following hypothesis: The epigenome of cancer cells is highly variable and reflects patterns preexisting in the cell-of-origin in addition to cancer-specific events.

Recurrent deletions in cancer genomes overlap with the chromosomal locations of tumor-suppressor genes. Knudson's two-hit hypothesis has successfully guided cancer biologists for the past fifty years in the identification of such tumor-suppressor genes. However, in many cases monoallelic loss can only explain haploinsufficiency of tumor-suppressor genes. Preliminary work in the division indicates that altered chromosome topology and epigenetic gene regulation can also affected by deletions, resulting in the activation of oncogenes located outside of the deleted segment.

We aim to establish a novel paradigm in interpreting (epi)genomic data in cancer. We hypothesize that oncogene activation, in concert with haploinsufficient tumor-suppressor genes, deregulated because of a single genetic event, could lead to the discovery of novel intertwined oncogenic pathways.

Using acute myeloid leukemia as a model, we aim:

to reveal oncogene activation through novel molecular pathways in cases carrying deletions of 5q and 7q by molecular sequencing-based profiling and to validate the requirement of the novel oncogene(s) for leukemic growth.
to determine the molecular mechanisms resulting from oncogene overexpression and to test the accelerated tumorigenesis dependent on haploinsufficient tumor suppressor genes.
to determine the contribution of oncogene activation through structural variation in combination with haploinsufficiency in a pan-cancer setting.

A significant clinical problem and unmet need in cancer therapy represents the fact that almost all cancer patients treated, develop resistance to current therapeutic drugs. Cancer therapy resistance is intimately linked to tumor heterogeneity, either preexisting or acquired. Resistance is characterized by transcriptomic and phenotypic changes in both tumor cells and their tumor microenvironment (TME) as well as growth of cells that tolerate the treatment. In this context, drug resistance could either be mediated by the outgrowth of preexisting-refractory tumor-cell subpopulations present in a heterogeneous tumor cell pool, or alternatively the consequence of acquired alterations in the neoplastic cells leading to the generation of resistant cells (Fig.1). Insights into genomic and transcriptomic data from tumor cells contributed partly to a mechanistic understanding of treatment failure. However, in over 35% of cases non-genetically determined resistance mechanisms were postulated.

Non-genetic mechanisms of drug resistance could either represent the outgrowth of preexisting-refractory tumor-cell subpopulations (persister-cells), which carry an epigenetic pattern mediating a resistant phenotype or alternatively resistance could be the consequence of acquired epigenetic, intra-tumor heterogeneity with the generation of resistant cells. The molecular pathways leading to non-genetic alteration driving cancer therapies are not understood. This is however, of upmost importance since an improved mechanistic understanding of therapy resistance could support the discovery of novel vulnerabilities providing directions for novel therapeutic avenues.

We will focus our work on acute myeloid leukemia (AML) and lung cancer.

Job openings

Project description: Epigenetic heterogeneity as a novel driving force of therapy resistance

Acute Myeloid Leukemia (AML) is an aggressive blood cancer with overall low survival and high relapse rates. Epigenetic dysregulation plays a major role in disease progression, since epigenetic regulators are frequently lost due to deletions and enter into haploinsufficiency. Furthermore, epigenetic drugs are now used as standard treatment [1]. A recent study showed that around 40 % of relapses can be attributed to epigenetic heterogeneity in the pool of leukemic cells at diagnosis (Figure 1). While epigenomic heterogeneity can be quantified using bulk approaches, understanding the relationship between a specific epigenetic configuration and relapse requires single-cell approaches. Understanding how epigenomic heterogeneity, together with the activation of oncogenes such as MNX1 [2, 3, 4], results in accelerated tumorigenesis or therapy resistance is crucial for improving AML treatment.

We hypothesise that haploinsufficiency of epigenetic regulators causes elevated levels of epigenomic heterogeneity, leading to accelerated tumour formation and resistance to therapy. To understand the specific epigenetic configuration driving relapse, we will profile samples from AML patients at diagnosis and relapse. Using various readouts of the epigenetic configuration — including single-cell ATAC-seq for profiling chromatin accessibility and scTAM-seq [5] for profiling DNA methylation — we will analyse the molecular mechanisms driving heterogeneity. The project will entail running single-cell assays and computational analysis of the generated data in relation to the bulk data. Ultimately, we will explore the role of epigenetic regulation in therapy resistance in cancers beyond AML, such lung and prostate cancer, paving the way for precision medicine.

References:

1. Brocks D, et al: DNMT and HDAC inhibitors induce cryptic transcription start sites encoded in long terminal repeats. Nat Genet 2017.

2. Weichenhan D, et al: Altered enhancer-promoter interaction leads to MNX1 expression in pediatric acute myeloid leukemia with t(7;12)(q36;p13). Blood Adv 2023.

3. Weichenhan D, et al: Translocation t(6;7) in AML-M4 cell line GDM-1 results in MNX1 activation through enhancer-hijacking. Leukemia 2023.

4. Sollier E, Riedel A, Toprak UH: Enhancer hijacking discovery in acute myeloid leukemia by pyjacker identifies MNX1 activation via deletion 7q. Blood Cancer Discovery 2025

5. Scherer M, et al: Somatic epimutations enable single - cell lineage tracing in native hematopoiesis across the murine and human lifespan. Nature 2025.

Job requirements

We are looking for a highly motivated Molecular Cancer Biologist with a PhD degree, with an interest in cancer research and the ability to work in teams with computational scientists.

The candidate should have the following skills: