Background and activities
My research activity is in the translational field between computational transcriptomics, metabolomics, epigenetics and gene regulation to improve disease outcome predictions and treatment decisions for prostate cancer. This is achieved through a collaboration between BiGR and the MR Cancer Group at NTNU and Department of Urology at St. Olavs Hospital. In our research, I and my colleagues are trying to understand what separates aggressive from indolent prostate cancers at the molecular level through integration of data from various high throughput technologies.
Due to increased focus on men’s health through campaigns and public debate, it is expected that the number of men tested for prostate cancer will increase. A challenge in current prostate cancer diagnostics is the separation of prostate cancer in low, intermediate and high risk groups based on the acquired clinical data, which includes MR-images, tissue biopsies and blood-samples. Limitations in the classification system sometimes lead to undertreatment of high risk cancers. However, a more common problem is probably the tendency to overtreat low risk cancers resulting in unwanted side-effects from treatment for a large group of patients. The goal of our research is to improve the detection of high and low risk prostate cancer in clinical care, both at initial diagnosis and during active surveillance of the disease.
We are currently developing robust classifiers for aggressive prostate cancer based on molecular data from tissue biopsies. However, our long term vision is that aggressive prostate cancer should be classified through minimally invasive blood-samples or non-invasively through MR-imaging. To achieve this we need to focus on extensive analysis of patient tissue samples using state-of-the-art technology improve our understanding on how prostate tissue characteristics, molecular signals and image features are connected. This is currently the focus of the ongoing ProstOmics project.
Current research directions:
Development and validation of a gene expression signature which can robustly separate indolent and aggressive prostate cancer from clinical patient biopsies.
Inegrating data produced in the Prostomics project with other huge resources of publicly available data to connect molecular data with tissue and image features. The long-term goal is to improve non-invasive classification of aggressive prostate cancer
Investigating the potential of epigenetic markers for detection of aggressive prostate cancer in blood, urine and prostatic fluid.
In addition I am also involved in the Bioinformatics Core Facility and the Bioinformatics Helpdesk for Elixir Norway, where I am assisting other researchers with bioinformatics analysis, particularly involving Next Generation Sequencing (NGS) data. I am also responsible for an annual course in NGS analysis, hosted biannually by NORBIS.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2019) MACPET: model-based analysis for ChIA-PET. Biostatistics.
- (2018) Integrative metabolic and transcriptomic profiling of prostate cancer tissue containing reactive stroma. Scientific Reports. vol. 8:14269.
- (2018) Cholesterol synthesis pathway genes in prostate cancer are transcriptionally downregulated when tissue confounding is minimized. BMC Cancer. vol. 18 (478).
- (2018) APIM-peptide targeting PCNA improves the efficacy of docetaxel treatment in the TRAMP mouse model of prostate cancer. OncoTarget. vol. 9 (14).
- (2018) Norwegian e-Infrastructure for Life Sciences (NeLS). F1000 Research. vol. 7:968.
- (2017) SFRP4 gene expression is increased in aggressive prostate cancer. Scientific Reports. vol. 7.
- (2016) Presence of TMPRSS2-ERG is associated with alterations of the metabolic profile in human prostate cancer. OncoTarget.
- (2016) A novel non-canonical Wnt signature for prostate cancer aggressiveness. OncoTarget. vol. 8 (6).
- (2016) A balanced tissue composition reveals new metabolic and gene expression markers in prostate cancer. PLoS ONE. vol. 11 (4).
- (2016) Phosphatase of regenerating liver 3 (PRL-3) is overexpressed in human prostate cancer tissue and promotes growth and migration. Journal of Translational Medicine. vol. 14 (71).
- (2015) ClusTrack: Feature extraction and similarity measures for clustering of genome-wide data sets. PLoS ONE. vol. 10 (4).
- (2015) The constrained maximal expression level owing to haploidy shapes gene content on the mammalian X chromosome. PLoS biology. vol. 13:e1002315 (12).
- (2014) A promoter-level mammalian expression atlas. Nature. vol. 507 (7493).
- (2014) Gene signatures ESC, MYC and ERG-fusion are early markers of a potentially dangerous subtype of prostate cancer. BMC Medical Genomics. vol. 7 (50).
- (2014) Chromatin states reveal functional associations for globally defined transcription start sites in four human cell lines. BMC Genomics. vol. 15 (1).
- (2013) The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research. vol. 41 (W1).
- (2013) Identification of serum microRNA profiles in colon cancer. British Journal of Cancer. vol. 108 (8).
- (2012) Cell-type specificity of ChIP-predicted transcription factor binding sites. BMC Genomics. vol. 13.
- (2012) The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding. BMC Bioinformatics. vol. 13.
- (2012) Preprocessing of electrophoretic images in 2-DE analysis. Chemometrics and Intelligent Laboratory Systems. vol. 117 (August).