Mining Biological Intelligence From Cells: a Network Based Approach

Time: Friday, 3rd October at 10.00

Speaker: Andrey Alexeyenko, Associate Professor at Karolinska Institutet, Stockholm, with expertise in bioinformatics, biostatistics and systems biology

Mining Biological Intelligence From Cells: a Network Based Approach

Despite the widening range of high-throughput platforms and exponential growth of data volumes, the discovery of disease drivers and biomarker validation remain challenging tasks. The research team led by Prof. Alexeyenko proposed tackling cancer heterogeneity and data dimensionality via a sensitive and robust network-based approach to pathway analysis. This approach transforms the original omics space into a pathway dimension which is both more compact and relevant to the underlying biology. The new coordinates can then be used in downstream analyses. The method proved superior to various alternative algorithms in terms of 1) applicability to different data types, 2) reproducibility across datasets, and 3) ability to explain patient response. The method discovered predictors valid both in vitro and for patients treated with the same drug¹. Similarly, they applied the approach to finding novel cancer driver genes². Due to the scarcity of mutation patterns, such drivers were poorly detectable via mutation frequency but could be discovered by accounting for co-occurrence in tumor genomes. The ability to identify previously unnoticed candidate drivers emerged from combining individual genomic context with a pathway and network perspective. The discovered drivers were shown to have low error rates, to be informative regarding cancer outcomes, and to be related to cancer biology domains poorly covered by previous analyses.

Prof. Alexeyenko will present the team’s web resources available for network-assisted exploration of omics data, EviNet and EviMark^3,4 as well as latest results from both the method development and application to biological problems⁵.

1. Franco M, Jeggari A, … Alexeyenko A (2019) Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data. Scientific Reports,

2. Petrov I, Alexeyenko A (2022) Individualized discovery of rare cancer drivers in global network context. eLife.

3. Jeggari A. … Alexeyenko A (2019) EviNet: a web platform for network enrichment analysis with flexible definition of gene sets. Nucleic Acids Res.

4. Petrov I, Alexeyenko A (2022) EviCor: interactive web platform for exploration of molecular features and response to anti-cancer drugs. J Mol Biol.

5. Alexeyenko A, Brustugun OT, Eide IJZ, Gencheva R, … Ekman S (2022) Plasma RNA profiling unveils transcriptional signatures associated with resistance to osimertinib in EGFR T790M positive non-small cell lung cancer patients. Translational Lung Cancer Research.