Combining Genetic, Gene Activity, and Gene Regulation Temporal Data to Understand Relapse in Hormone-Dependent Breast Cancer

Breast cancer relapse continues to be a major issue, particularly in hormone-dependent breast cancer. Even after successful treatment, some cancer cells seem to ""hide"" in a dormant state, only to reawaken and cause a relapse years or even decades later. Understanding how this happens is crucial for developing better long-term treatments.

To do this we studied a cell model of hormone-dependent breast cancer that mimics the dormancy and reawakening process across time. We analysed these cells using multiple layers of biological data:
- Genomics (changes in DNA).
- Transcriptomics (gene activity).
- Epigenomics (chemical modifications that regulate genes). In this layer we are particularly interested in histone modifications, a special kind of chemical modifications that affect the elements around which DNA wraps around.

My project's objective is to combine all the data in a single analysis, instead of looking at each layer separately, as this can give us more information about how the whole dormancy-reawakening process works. There are two aspects to this analysis:
- A temporal analysis, as we want to model the patterns of the data through time to detect changes and unify the data format of all biological layers.
- A multi-omic analysis, which refers to the actual combination of the different biological data layers. We are interested in techniques that help us visualise the data, as well as techniques that allow us to spot groups of data with similar patterns, to see what is changing together in the dormancy-reawakening process.

We aim for the outcomes of this research to highlight potential targets for new drugs and diagnostic methods to prevent relapse. This research brings us one step closer to answering a critical question: How do we stop breast cancer from coming back?