Professor Inge Jonassen and part of his team from the CBU (Computational Bioinformatics Unit, Department of Informatics, UiB) are working on the development and application of bioinformatics methods for analysing data descending from high-throughput measurement technologies applied to cancer samples.
The primary focus of the group, in collaboration with the Akslen group, is the development and application of computational deconvolution methods for decomposing transcriptome data from samples composed of a combination of tumor cells and the surrounding and supporting microenvironment. The research aims to decompose computationally the signal into that originating from the tumor cells and those originating from other tissues/cell types in the sample. This will be an enabling step towards studying the interactions between tumor cells and the environment and integrating them into the research along the continuum from diagnosis to treatment and outcome.
The group’s first objective was to analyse several public benchmark transcriptome datasets (microarray and RNA-Seq) with a variety of deconvolution methods, test their performance and find out which of the mathematical assumptions reflect biological reality to a sufficient degree and can lead to more robust results. On a second level, they developed a new computational method that addresses several computational challenges and they show extensively the performance of the group’s method relative to other state-of-the-art methods (manuscript under review) based on the benchmark data. The group’s proposed approach on breast cancer data was further tested and biologically consistent results were found. In this framework, they also developed SelGenes, a tool for selecting marker genes (i.e. genes highly specific for a tissue/cell type) for the cell types included in heterogeneous samples (published in master thesis). The performance of the group’s approach was tested both on the benchmark data and on cancer data and biologically consistent results were found.
Another focus of the group is the development and application of systems biology integrative approaches. In collaboration with the Akslen group, they applied a subpathway enrichment analysis approach to reveal mechanisms that change between tumor samples with high and low Nestin expression, associated with the basal-like phenotype in breast cancer. Moreover, in collaboration with the Biosignal Lab (Professor Anastasios Bezerianos, University of Patras, Greece), the group developed a time-varying method for microRNA-mediated subpathway enrichment analysis. The tool was tested on interferon-gamma (IFN-g) stimulated melanoma cells.
One manuscript has been submitted in collaboration with the Akslen group on the development of a novel computational method for in silico deconvolution of complex gene expression data. Results on subpathway enrichment analysis in relation to Nestin expression in breast cancer subtypes was published by Krüger et al. (Sci Rep 2017).
Plans for the future
The group is working toward utilizing single cell mass cytometry data together and integrate this with transcriptional data in order to understand cells’ signaling status and downstream effects on gene transcription. For this they have sought collaboration with systems biology groups to jointly develop a systems understanding of relevant signaling pathways and link this with therapy response in experimental model systems aiming to personalized systems medicine.
Current challenges in the field
Capitalizing on novel experimental approaches giving molecular biology data on different levels of resolution through innovative data analysis and modelling approaches, and to perform this in close collaboration with the domain experts to enable tight integration between our work and experimental follow-up and verification.