ER+ breast tumours undergo genome-wide epigenetic alterations, and we have previously shown that enhancer methylation results in oncogenic estrogen signalling (published in Fleischer et al., Genome Biol 2014, Fleischer & Tekpli et al., Nat Commun 2017, and Ankill et al., NAR Cancer 2022).

Altered DNA methylation and chromatin interactions are crucial in the development of resistance to hormone therapy; a key treatment plan for post-menopausal ER+ breast cancer patients. We aim to identify and characterize treatment resistance-driving epigenetic alterations found in human breast tumour samples using single-cell ATAC-seq. Single-cell ATAC-seq explores the evolving epigenetic landscapes of both tumour and non-tumour cell populations of a patient biopsy (Figure 1), offering novel insight into cell biology, disease etiology and treatment response.

Identification of epigenetically regulated pathways in cancer is possible using the expression methylation quantitative trait loci (emQTL) approach published in Fleischer, Tekpli et al., Nat Commun 2017. Using an improved version of this method, we have now identified epigenetic dysregulation associated with increased activity of the cell cycle, showing that epigenetic dysregulation in triple negative breast cancer may provide a growth advantage in these tumors. 

The aim of this project is to assess the functional and causal effects of the identified epigenetic alterations identified through the emQTL approach. To identify the top candidates, we're using a supervised version of the emQTL approach, where candidate CpG-gene pairs need fulfill several criteria. 1) a CpG needs to be in an enhancer region, 2) a CpG needs to be in the transcription factor (TF) binding region of a TF of interest, 3) a CpG-gene pair must be on opposite sides of a chromatin loop, and 4) a gene needs to be part of a gene set of interest (see Figure 1 for example). 

We have previously shown that DNA methylation linked to proliferation is altered both during treatment and between responders and non-responders [Klajic et al., CCR 2014]. Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to (optimally) select patients for treatment. The aim of this study is to use DNA methylation in a multiomic approach to predict response to treatment, as well as to understand the molecular alterations associated with good or poor response.

Using diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) it is possible to assess parameters such as cellular density, blood flow, and growth patterns of tumors. In this project we are using MRI in combination with 'omics data to create "portraits" of tumors that can help predict treatment response. This project is part of the PerCaThe convergence environment funded by UiO Life Science. These data has already been used as part of a pharmacokinetic and pharmacodynamic N-of-1 in silico model showing proof-of-concept that we can predict and improve treatment response (Lai et al. 2019)

miR-34a is a tumor supressor lowly expressed in triple negative breast cancer, and miR-34a replacement therapy has been suggested for patients with this disease. In this project we confirm that miR-34a is highly associated with regulation of the cell cycle, and we show that miR-34a expression is also associated with altered production of amino acids.