Andrey Alekseenko

Andrey Alekseenko

Affiliated to Research | Docent
Visiting address: Solnavägen 9, 17165 Stockholm
Postal address: C5 Cell- och molekylärbiologi, C5 CMB Ericson, 171 77 Stockholm
Part of:

About me

  • 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.
    *DEGREES*
    *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).

Research

  • The activity is focused on creating and applying biostatistics, data
    integration, and systems biology methods to biomedicine and clinical projects
    http://www.evistat.se/ [1].
    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[1]. 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[2]
    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[3] 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[4] for network analysis in automated and parallelized
    data pipelines as well as fully functional analytic web suits EviNet[5]
    https://www.evinet.org/ [2] and EviCor https://www.evicor.org/ [3] that
    facilitate machine learning and predictive modelling using public databases
    and in-house data[21]. 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[6]
  • Petrov and
    Alexeyenko, 2022[7]). 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)[8]. Another example of a large team effort was our work in Norway
    spruce genome project[9] 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[10], 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].
    -------- REFERENCES ----------------------------------------------------------
    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 [4]
    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
    [5]
    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 [6]
    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 [7]
    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 [8]
    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 [9]
    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 [10]
    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 [11]
    22. [12]* *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 [13]
    [1] http://www.evistat.se/
    [2] https://www.evinet.org/
    [3] https://www.evicor.org/
    [4] http://www.ncbi.nlm.nih.gov/pubmed/19246318
    [5] http://www.ncbi.nlm.nih.gov/pubmed/22966941
    [6] http://dx.doi.org/10.1038/s41598-019-39019-2
    [7] https://www.ncbi.nlm.nih.gov/pubmed/28361684
    [8] https://doi.org/10.1093/nar/gky485
    [9] http://www.ncbi.nlm.nih.gov/pubmed/25236784
    [10] https://doi.org/10.7554/eLife.74010
    [11] https://doi.org/10.1016/j.jmb.2022.167528
    [12] https://doi.org/10.1016/j.jmb.2022.167528
    [13] https://dx.doi.org/10.21037/tlcr-22-236

Teaching

  • 1) Artificial Intelligence and Machine Learning for Biomedical and Clinical
    Research (Karolinska Institutet (2020-2022): 2 weeks, course responsible
    and lecturer.
    2) Molecular oncology and biostatistics
  • bachelor program in Biomedicine
    (2015): lecturer (2 hours), tutor (16 hours).
    3) 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).
    4) 'Omics' data analysis: from raw data to biological information,
    Karolinska Institute (2012, 2013, 2014, 1-hour lectures at each occasion.
    5) Bioinformatics
  • master program in Biomedicine, Karolinska Institute
    (2009, 2014): lecturer (4 hours), tutor (3 hours), examiner (2 hours).
    6) NatiOn: research school for clinical cancer research (2011, 2013):
    lectures (7 hours) and tutor (20 hours), examiner (3 hours).
    7) “Practical Proteomics” at Tumor biology / Oncology program,
    Karolinska Institute (2010, 2011): 1-hourlectures at each occasion.
    8) "Cancer Systems Biology" at Tumor biology / Oncology program, Karolinska
    Institute (2010) (co-organizer (4 hours), lecturer (4 hours), tutor (4
    hours), examiner (3 hours).
    9) "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).
    10) "Applied statistics" for researchers of Northern Caucasus Research
    Institute for Horticulture and Viticulture, Krasnodar (2002) (1 week,
    organizer and lecturer).

Articles

All other publications

Employments

  • Affiliated to Research, Department of Cell and Molecular Biology, Karolinska Institutet, 2023-2025

Degrees and Education

  • Docent, Karolinska Institutet, 2016

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