Damien Kaukonen
Postdoctoral researcher
A bioinformatician focusing on HER2+ breast cancer. My interests are patient outcomes, machine learning, and Multi -omic approaches (RNA, Exome, and epiegentics). Worked in CA, FI, IE, FR, and now SE.
About me
Originally started my science career in Canada in astrophysics, but changed to molecular biology halfway through my BSc when someone close to me was diagnosed with breast cancer. I completed my BSc thesis combining the mathematics and biology sides of my degree to model the treatment of ER+ cell lines with various different anti-estrogens.
Carrying the momentum of cross-disciplinary work, I went to the University of Turku in Finland to do my MSc in Bioinformatics, with a focus in machine learning. My MSc project utilized machine learning to identify gene expression patterns from key pathways across several breast cancer cell lines that correlated with sensitivity to different chemotheraputics. I then went on to the Royal College of Surgeons in Ireland, where I did my PhD in translational bioinformatics. Here I developed workflows, analyzed data from clinical trials, and helped to develop and optimize our own laboratory method to better study co-locating epigenetic marks. All of my work here was done taking a multi-omic approach to understanding the phenomenon that drives HER2+ breast cancer. This included genomic, epigenomic, transcriptomic, and proteomic data, combined with clinical outcomes, to better understand HER2+ breast cancer. During my PhD, I did a secondment at INSERM in Paris, working in a lab that focused on multi-omic research.
2020-2021 was spent working as a postdoc in COVID-19 and neurological diseases at Åbo Akademi in Finland. Now I am at Karolinska, at the Medical Epidemiology and Biostatistics department working on HER2+ breast cancer.
Research description
Currently, my interests are focused around HER2+ breast cancer. While I am a bioinformatician at heart, I am currently integrating epidemiology into my skill set. My focus continues the investigation of HER2 and clinical outcomes, especially with regards to the Estrogen receptor and other diagnostic/prognostic factors. I am also interested in the family history of HER2+ breast cancer as not much is known about it.
Education
BSc- Molecular Biology (Molecular Genetics, Immunology, Oncology)
MSc- Bioinformatics (Machine learning, transcriptomics, pharmacokinetics)
PhD- Bioinformatics (Translational bioinformatics, workflow development, multi-omics)