Pharmacogenomics is the study of molecular profiles and identification of personalised treatments for patients based on their individual molecular characterisation. Matching the right drug with the right patient does not only increase the treatment success, but also saves the patient from unpleasant side-effects of suboptimal treatments. For delivering such personalised treatments, we use as models cancer cell lines in large-scale pharmacological screens and their genomic profiles (Garnett et al Nature, 2012; Barretina et al Nature, 2012; Iorio et al Cell, 2016; Seashore-Ludlow et al Cancer Discovery, 2015). These studies enable the identification of genes that separate patients into responders versus non-responders that are commonly known as biomarkers of drug response.Identifying biomarkers is a statistically challenging task and we focus on the development of novel methods. For example, we developed machine learning methods that do not only leverage the genomic aspect, but also the chemistry of drugs to predict the drug response (Menden et al PLoS One, 2013). A main challenge is that the number of samples is relatively small when compared to the number of genetic features to consider. We are currently exploring various methods that leverage mechanistic knowledge of the underlying signaling pathways and regulatory modules to limit the number of features. The lower number of features increases statistical power and at the same time provides understandable and actionable biomarkers (Iorio et al Cell, 2016, accepted).
We also study the aspect of drug repositioning, which enables the in silico identification of compounds that were not necessarily studied in the context of cancer yet (Iorio et al Drug Discovery Today, 2012). Drug repositioning enables quick delivery of novel treatments to patients. For example, a drug may be already proven to be not toxic in humans, but unfortunately inefficient for the originally studied disease; nevertheless, those early clinical trials increase the paste of FDA approval and delivery to clinics.
Targeted therapies and personalised treatments are the most promising assets to treat cancer. However, in many patients, a tumor’s innate or acquired resistance to a given therapy will render the treatment ineffective. To increase therapeutic options and to overcome drug resistance, cancer researchers have been actively investigating drug combinations. The rationale is that by targeting multiple mechanisms simultaneously, the potency of the treatment is increased and tumor cells are less likely to develop resistance and become refractory to treatment. We are involved in an evaluation of computational approaches to predict drug combinations in the context of the DREAM challenges.
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).