Affiliated to research
Expert in systems biology and biostatistics, developed and applies methods for integration of heterogeneous large-scale datasets using global network context and tools.
Andrey can lead or guide in using other methods existing in this field and assist in results interpretation. Andrey can also help with related issues in high-throughput data management, analysis, statistics, functional interpretation, and bridging gaps between different sides of analysis. Andrey received an extensive training in higher education and has vast experience in teaching biostatistics and systems biology at both undergraduate and graduate levels.
The activity is focused on creating and applying biostatistics, data integration, and systems biology methods to biomedicine and clinical projects http://www.evistat.se/.
This work includes statistical analysis of preclinical and clinical datasets, such as candidate drug analysis and evaluation of radio-, chemo-, immuno-, and targeted therapies. The data analysis methods are used for development of e.g. companion diagnostics for anti-cancer therapies and early markers of autoimmune diseases. It requires evaluation of existing and development of novel tools and pipelines for genomics, transcriptomics, and cancer immunology. A major achievement in the past was FunCoup: a machine learning framework for reconstructing gene networks via systematic integration of large public datasets. Due to its robust design, comprehensive data collection and analytic web interface, FunCoup became a biologically sound and useful resource for both online and offline usage. Next followed the development of a new methodology for Network Enrichment Analysis, NEA which served functional exploration and impact evaluation of experimental gene lists. The method was demonstrated to be superior to existing alternatives in e.g. finding molecular determinants of anti-cancer drug response and was applied in a number of collaborative efforts. This was complemented with further development of NEA software and online tools, such as R package NEArender for network analysis in automated and parallelized data pipelines as well as fully functional analytic web suits EviNet https://www.evinet.org/ and EviCor https://www.evicor.org/ that facilitate machine learning and predictive modelling using public databases and in-house data. We develop methods of network analysis in order to investigate high-throughput data with information on drug response and further combine systems biology profiles with clinical covariates to find informative and prognostic markers for patient subsets. Particular focus lays within such areas as:
- raising molecular landscape investigation to the pathway level;
- discovery of novel functional modules in the interactome;
- distinguishing between driver and passenger mutations in cancer genomes;
- inference of causative regulatory networks;
- comparative network analysis under contrast (e.g. pathological vs. normal) conditions;
- evaluation of functional relevance of candidate markers;
- cross-validation of predictive signatures using novel, independent datasets;
- breaking the patient population into sub-types to enable efficient prognostication.
Our contribution to clinical interpretation of tumor sequencing data has been a pipeline for driver mutation analysis (Merid et al., 2014; Petrov and Alexeyenko, 2022). Lately, the most promising was development of marker-based diagnostics for cancer immunotherapy together with researchers of Karolinska Institutet and Istituto Nazionale Tumori IRCCS Pascale (Napoli). Another example of a large team effort was our work in Norway spruce genome project at SciLifeLab.
Otherwise, we use large and complex datasets in order to solve concrete problems, such as the identification of early markers of autoimmune diseases, development of companion diagnostics for checkpoint and targeted therapies, evaluation of candidate disease genes in common and rare diseases[11,12,13,14,22] as well as creation of novel tools for genomics, transcriptomics, and immunology[15,16,17,18,19,20,21].
1. Alexeyenko, A. & Sonnhammer, E. L. L. Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res. 19, 1107–1116 (2009). http://www.ncbi.nlm.nih.gov/pubmed/19246318
2. Alexeyenko, A. et al. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics 13, 226 (2012). http://www.ncbi.nlm.nih.gov/pubmed/22966941
3. Franco, M. et al. Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data. Sci Rep 9, 2379 (2019). http://dx.doi.org/10.1038/s41598-019-39019-2
4. Jeggari, A. & Alexeyenko, A. NEArender: an R package for functional interpretation of ‘omics’ data via network enrichment analysis. BMC Bioinformatics 18, (2017). https://www.ncbi.nlm.nih.gov/pubmed/28361684
5. Jeggari, A. et al. EviNet: a web platform for network enrichment analysis with flexible definition of gene sets. Nucleic Acids Res 46, W163–W170 (2018). https://doi.org/10.1093/nar/gky485
6. Merid, S. K., Goranskaya, D. & Alexeyenko, A. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis. BMC Bioinformatics 15, 308 (2014). http://www.ncbi.nlm.nih.gov/pubmed/25236784
7. Petrov, I. & Alexeyenko, A. Individualized discovery of rare cancer drivers in global network context. eLife 11, e74010 (2022). https://doi.org/10.7554/eLife.74010
8. Mallardo, D. et al. Toward transcriptomics-based prediction of response to immune checkpoint inhibitor in patients with malignant melanoma. in JOURNAL OF TRANSLATIONAL MEDICINE vol. 18 (BMC CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND, 2020).
9. Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).
10. Brink, M., Lundquist, A., Alexeyenko, A., Lejon, K. & Rantapää-Dahlqvist, S. Protein profiling and network enrichment analysis in individuals before and after the onset of rheumatoid arthritis. Arthritis Research & Therapy 21, 288 (2019).
11. Reynolds, C. A. et al. Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk. Hum. Mol. Genet. 19, 2068–2078 (2010).
12. Bennet, A. M. et al. Genetic association of sequence variants near AGER/NOTCH4 and dementia. J. Alzheimers Dis. 24, 475–484 (2011).
13. Hong, M.-G., Alexeyenko, A., Lambert, J.-C., Amouyel, P. & Prince, J. A. Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease. J. Hum. Genet. 55, 707–709 (2010).
14. Brownstein, C. A. et al. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biol 15, R53 (2014).
15. Franzén, B. et al. A fine‐needle aspiration‐based protein signature discriminates benign from malignant breast lesions. Mol Oncol 12, 1415–1428 (2018).
16. Franzén, B. et al. Protein profiling of fine‐needle aspirates reveals subtype‐associated immune signatures and involvement of chemokines in breast cancer. Mol Oncol 13, 376–391 (2019).
17. Bersani, C. et al. Genome-wide identification of Wig-1 mRNA targets by RIP-Seq analysis. Oncotarget 7, 1895–1911 (2016).
18. Lee, W. et al. Identifying and Assessing Interesting Subgroups in a Heterogeneous Population. Biomed Res Int 2015, 462549 (2015).
19. Akan, P. et al. Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines. Genome Med 4, 86 (2012).
20. Giacomello, S. et al. Spatially resolved transcriptome profiling in model plant species. Nat Plants 3, 17061 (2017).
21. Petrov, I. & Alexeyenko, A. EviCor: Interactive Web Platform for Exploration of Molecular Features and Response to Anti-cancer Drugs. Journal of Molecular Biology 434, 167528 (2022). https://doi.org/10.1016/j.jmb.2022.167528
22. Alexeyenko A., ... Hydbring, P., and Ekman, S. Plasma RNA profiling unveils transcriptional signatures associated with resistance to osimertinib in EGFR T790M positive non-small cell lung cancer patients Transl Lung Cancer Res. 11(10):2064-2078. (2022) https://dx.doi.org/10.21037/tlcr-22-236
- Artificial Intelligence and Machine Learning for Biomedical and Clinical Research (Karolinska Institutet (2020-2022): 2 weeks, course responsible and lecturer.
- Molecular oncology and biostatistics; bachelor program in Biomedicine (2015): lecturer (2 hours), tutor (16 hours).
- Summer School in Computational and Systems Biology of Cancer, StratCan-KI-BILS (2014): 1.5hp; co-organizer and director (20 hours), lecturer (2 hours), lab work tutor (5 hours), examiner (3 hours).
- 'Omics' data analysis: from raw data to biological information, Karolinska Institute (2012, 2013, 2014, 1-hour lectures at each occasion.
- Bioinformatics; master program in Biomedicine, Karolinska Institute (2009, 2014): lecturer (4 hours), tutor (3 hours), examiner (2 hours).
- NatiOn: research school for clinical cancer research (2011, 2013): lectures (7 hours) and tutor (20 hours), examiner (3 hours).
- “Practical Proteomics” at Tumor biology / Oncology program, Karolinska Institute (2010, 2011): 1-hourlectures at each occasion.
- "Cancer Systems Biology" at Tumor biology / Oncology program, Karolinska Institute (2010) (co-organizer (4 hours), lecturer (4 hours), tutor (4 hours), examiner (3 hours).
- "Experimental design and statistical analysis" at Cell Biology and Genetics PhD program, Karolinska Institute (2006):2.0hp, organizer, director, lecturer (14 hours), tutor (12.5 hours), examiner (7 hours).
- "Applied statistics" for researchers of Northern Caucasus Research Institute for Horticulture and Viticulture, Krasnodar (2002) (1 week, organizer and lecturer).
2001: Ph.D. in statistical genetics, Vavilov Research Institute for Plant Industry, St. Petersburg.
1989: Diploma of Kuban State University, Krasnodar (Russia) in biology (major) and genetics (minor).