The Computational Cancer Biology group led by Lodewyk Wessels develops novel computational approaches and performs state-of-the-art analyses of a wide array of data types to further our basic understanding of cancer and to translate these findings to the clinic. We follow a three-pronged approach:
- In Theme 1, we focus on employing computational approaches to chart the cancer landscape by integrating heterogeneous molecular data sets.
- In Theme 2, we model transcriptional, signalling and metabolic networks to better understand the determinants of the cancer landscape. Finally, in
- In Theme 3 we employ patient response data, where available, and drug response data from model systems to map the cancer landscape to (combination) treatments most likely to effectively combat the cancer.
We have developed several computational approaches an applied these in collaborative studies. In Theme 1, we developed PropSeg, a novel method for estimating copy number ratios from capture sequencing data (Rigaill et al., 2012). To identify driver genes, we developed RUBIC, an approach to detect recurrent focal copy number alterations from tumour copy number profiles (van Dyk et al. 2016), while OncoScape identifies putative oncogenes and tumour suppressors by the integration of multiple data types (Schlicker et al, 2016).
We also focused extensively on developing approaches to find interacting loci from mutation and copy number data, See e.g. Klijn et al, 2010, de Ridder et al, 2010 and Canisius et al, 2016. In Theme 2, we have studied gene regulatory mechanisms in the TRIP system (Akhtar et al, 2013) as well as miRNA regulation in breast cancer (Farazi et al, 2014). In Theme 3 we developed approaches to detect biomarkers of drug response in patients (de Ronde et al, 2013) and cell lines (Iorio et al, 2016) and performed subtyping of colorectal cancer (Schlicker et al, 2013) and Invasive Lobular Breast Cancer (Michaut et al, 2016).