Our research focus is centered on tumour evolution and heterogeneity and understanding the mutational processes leading to aggressive disease.
Cancer is a disease of the genome, fueled by the accumulation of genomic alterations that improve the fitness of the cell. We use computational genomics approaches to study clonal evolution from clinical cancer genome sequencing. Our overarching goal to understand the mutational processes that allow tumour cells to escape and evolve in time and space to cause treatment-resistance. We have a particular interest in the larger complex structural variants, their presence and impact on cancer cells and clonal evolution. We use inter-disciplinary computational and molecular approaches including cancer genome analysis of single cell sequencing, short and long-read sequencing and in vitro drug-testing to achieve our aims.
Merits of the lab:
- Analysis of evolutionary and mechanistic forces shaping cancer genomes (Li et al, Nature, 2020; PCAWG consortium, Nature, 2020; Hansen et al, Blood, 2019; Gerhäuser et al, Cancer Cell, 2018; Gröbner et al, Nature ,2018; Rausch et al, Cell, 2012)
- Methodologies to analyse structural variant-mediated chromatin alterations leading to enhancer hijacking mechanisms in cancer genomes (Sidiropoulos, Mardin et al, Genome Research 2022; Rheinbay et al, Nature, 2020; Weischenfeldt et al, Nature Genetics, 2017; Northcott et al, Nature, 2017)
- Development of tumour evolution prediction methodologies and analysis of age-dependent mutational processes in cancer genomes (Gerhäuser et al, Cancer Cell, 2018; Weischenfeldt, J. et al, Cancer Cell, 2013).
Why do we want medical doctors?
We strive to bridge the gap between cancer genomics and the clinic, and all our projects involve interactions with collaborating medical doctors, primarily oncologists, pathologists, and surgeons. We have currently an MD PhD student in the group and another MD PhD closely affiliated.
Prostate cancer is a heterogeneous and slow-glowing disease, and distinguishing patients who will develop aggressive disease is of pivotal importance. We have previously identified several markers of aggressive disease in prostate cancer, including driver mutations, epigenetic changes and changes in hormone levels. What are the orders of mutational events in prostate cancer, and how does it impact tumour evolution? The overarching aim of the EMERALD project is to address this essential question using large-scale clinical and cancer genomics data.
How we will do it?
The EMERALD PhD fellow will be part of a team of computational biologists and medical doctors, with different expertises in clinical cancer genomics. The project involves mutational analysis of multi-region cancer sequencing and circulating tumour DNA from cancer patients. The EMERALD fellow will be involved in the analysis of medical records and integration with genomic data to identify molecular trajectories of the disease. The fellow will learn and develop machine learning methods that can be utilised to identify how a series of mutations affect the outcome of the cancer patient.
Why is this important?
Prostate cancer is highly heterogeneous and typically develops over decades. Identifying patients who will need aggressive treatment is essential. In a metastatic setting, obtaining biopsy material for precision oncology approaches from all metastatic sites is impossible. Tracing and forecasting the evolution of patients using clinical data and mutational information from liquid biopsies has tremendous potential to stratify patients that need definite treatment.
Who is a good fit for the project?
We are looking for a medical doctor, preferably with a background in oncology, and with the following qualities:
– Keen interest in cancer and genomics.
– Strong drive, curiosity and desire to contribute to scientific discoveries.
– Bioinformatic skills can be taught, but interest and prior experience will be useful.
IDIBAPS#1 – Developing and investigating computing, machine learning and physiological modelling for understanding each individual heart towards personalised medicineDavid Brena2022-05-17T10:37:53+00:00