Pharmacogenomics is based on (A) pharmacological screens of cell lines and (B) their deep molecular characterisation. (C) Both different data sources enable the identification of sensitivity biomarkers, e.g. a cancer somatic mutation might render cell lines sensitive to treatment X. (D) Shows a computational approach, which additionally to the deep molecular characterisation of cell lines, also considers the chemistry of the compounds [adapted from Menden et al. PLoS One, 2013]. (E) Using a microfluidic platform, we can screen cells derived from tumor samples resected from patients. Cells are co-encapsulated along with single drugs and drug combinations and resulting data are used to analyze drug interactions.
State of the art screening technologies allow high-throughput screening of a large panel of drugs and cell lines but they cannot be applied to primary tumor freshly resected from patients, mainly due to the limited amount of malignant cells that can be recovered from a biopsy or a resection. For these reasons, we are collaborating with Christoph Merten at EMBL to develop a droplet-based microfluidics platform to perform combinatorial drug perturbation screening on patient samples. The main advantages of using microfluidics are that: 1. it allows to perform automatic combinatorial drug screening experiments with low amount of reagents and 2. it requires only a reduced number of cells per droplet (thus per experiment) allowing the application also to primary cells, including patient-derived samples, without need for intermediate culturing steps. We are applying this technology to identify combination therapies for pancreatic cancer, for which very limited therapeutic opportunities exist, in collaboration with Thorsten Cramer (Molecular Tumor Biology and Department of Surgery, RWTH Aachen University Hospital).
Eduati, F., et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 33(9), 933–940 (2015).
Iorio, F al. . A landscape of pharmacogenomic interactions in cancer. Cell, 166: 740-754 (2016).
Menden, M. P., et al. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties. PLoS ONE, 8(4), e61318 (2013).