Network-based multi-drug response prediction using multi-omics data

Our research focuses on multi-drug response prediction using multi-omics networks. Our goal is to develop a pipeline to predict the response to multiple drugs in diseases such as cancer.

Networks allow for visualizing and analyzing interactions between entities in complex systems, e.g., people in social networks or genes in biological networks. Networks are modeled as graphs with a given set of nodes and edges. The nodes represent the investigated entities, while the edges represent the relationships between these entities. Based on this, networks are utilized to predict the response to perturbations of one or more nodes or edges.

Biological systems are very complex, with thousands of genes, proteins, and other molecules interacting in a directed or undirected manner. These systems can be modeled with networks [1]. Different types of analyses of the biological system can be done based on the networks, such as predicting the system’s response to a drug [2-4]. In vivo and in vitro testing of drugs and their combination is costly and time-consuming. Therefore, in silico testing of these drugs and consequently reducing the number of potential drugs for a specific disease is beneficial for medical research. The administration of drugs causes perturbations of the underlying system that can be analyzed using the representative biological network. Several different information layers, i.e., omics (genomics, proteomics, and metabolomics) levels, can be utilized for drug response prediction.

Network-based (multi)-drug response prediction using multi-omics data:

Biological multi-omics networks combine multiple layers of biological information like gene expression levels, protein quantities, and other available biological information. Using multi-omics networks, we predict the response to drugs in diseases such as cancer. Our biological networks include data from gene expression analyses, protein quantifications, and metabolite quantifications. We are working on a workflow that predicts the differential drug response of two different phenotypes employing multi-omics networks. Two multi-layer networks are constructed for the two different phenotypes based on the correlation of the respective biological omics level. After combining the two networks to a differential network, the drug response is predicted. As a case study, we analyze the differential drug response in breast cancer data of ER+ (Estrogen Receptor positive) and ER- (Estrogen Receptor negative) patients. The subsequent goal is to develop a framework for multi-drug response prediction based on our single-drug prediction workflow.

Winter Semester 2021/2022

• Implementing information flow in complex networks (project seminar)
• Introduction to programming (Schülerkolleg 7^th-8^th grade)

1. Ramos, P., Arge, L., Lima, N., Fukutani, K. F., & de Queiroz, A. (2019). Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets. Frontiers in genetics, 10, 1120. https://doi.org/10.3389/fgene.2019.01120

2. Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., et al. (2016). A Landscape of Pharmacogenomic Interactions in Cancer. Cell, 166(3), 740–754. https://doi.org/10.1016/j.cell.2016.06.017

3. Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., et al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 10(1), 2674. https://doi.org/10.1038/s41467-019-09799-2

4. Cheng, F., Kovács, I. A., & Barabási, A. L. (2019). Network-based prediction of drug combinations. Nature communications, 10(1), 1197. https://doi.org/10.1038/s41467-019-09186-x