dr. Louise de Schaetzen van Brienen (PhD)
Scientific staff IMEC, scientific staff FWO, post-doctoral researcher (Faculty of Sciences, Department of Plant Biotechnology and Bioinformatics & Faculty of Engineering and Architecture, Department of Information technology - UGent)
Principal investigator: prof. Kathleen Marchal (PhD)
My research aims at developing a data-driven integrative framework for the identification of cancer driver pathways and their mode of action. Pharmaceutical partners expressed the need for:
- integrative strategies that allow leveraging in-house proprietary data with other available data from the public domain in order to better prioritize druggable pathways
- integrative strategies for genotype-phenotype mapping in the context of cancer subtyping, biomarker and driver identification and the design of combinatorial therapies
The goal of my project is to develop advanced analytics to answer those needs. For this purpose, we will rely on networks. Networks allow for comprehensively summarizing omics-derived prior knowledge, they are an intuitive scaffold to drive the integrative analysis of in-house data and allow studying pathways rather than genes individually, hereby providing a mechanistic view of the studied phenotype.
The latter is an interesting property in the context of drug target prioritization as a particular disease gene might not be druggable but other genes that act in the same pathway might be. In the context of cancer cohort analysis, it allows identifying the less frequent drivers. In the context of precision medicine, it allows not only prioritizing actionable mutations but explains why mutations are actionable as well. This facilitates evidence-based medical decision-making.
- Halvade somatic: Somatic variant calling with Apache Spark, 2022
- Extracting functional insights from loss-of-function screens using deep link prediction, 2022
- Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic, 2021
- Comparative analysis of somatic variant calling on matched FF and FFPE WGS samples. BMC Medical Genomics, 2020. (10.21203/rs.2.15860/v3)