Yuri Kogan

Yuri Kogan holds a BA degree in Theoretical Mathematics from Tel-Aviv University (TAU). Over many years of scientific research at IMBM, he has been involved in biomathematical modeling of several cancer indications, including Prostate cancer, lung cancer, melanoma, as well as in the research of cancer stem cells, intracellular signal transduction, immune system functioning, and clinical immunotherapy, drug treatment optimization and others.

Scientific activity in 2021

In the last year, Yuri has continued to develop the predictive personalization algorithm of patients with melanoma undergoing immunotherapy. He was involved in the establishment of cooperation with Tubingen University hospital (Germany) with the aim of retrospectively and prospectively validating the algorithm. Yuri is leading the computational effort in this project.
In addition, Yuri has led the development of the novel personalization algorithm predicting imminent deterioration for hospitalized patients with Covid-19, based on their measured cellular and molecular parameters. To do this, cooperation with the Covid-19 division in Sheba Hospital was established, having obtained real-world patient data for the analysis. Our model employs advanced Machine Learning methods and has retrospectively evaluated ROC AUC metric of 0.8 by cross-validation.

Scientific activity in 2022

Yuri will continue working on the Covid-19 project, aiming to develop and prospectively validate a practical tool the will help physicians to timely evaluate patients’ prognoses and take the proper action.
Further, an additional collaboration we have established with Barzilai Hospital will allow rendering the algorithm more robust and versatile. We will consider additional prediction targets and using of additional types of data.
Further work on melanoma treatment personalization is planned, contingent on receiving the required data from our partners in Tubingen. Additional advanced cancer indications will be considered too using similar approaches.

Publications

    1. Perlstein D, Shlagman O, Kogan Y, et al. Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug. Plos One. 2019;14(12):e0226869.

    2. Tsur N, Kogan Y, Rehm M, Agur Z. Response of Patients with Melanoma to Immune Checkpoint Blockade – Insights Gleaned from Analysis of a New Mathematical Mechanistic Model. Journal of Theoretical Biology 2019:110033. doi: 10.1016/j.jtbi.2019.110033. PubMed PMID: 31580835.

    3. Tsur N, Kogan Y, Avizov-Khodak E, Vaeth D, Vogler N, Utikal J, Lotem M, Agur Z. Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm. Journal of Translational Medicine 2019;17(1):338. doi: 10.1186/s12967-019-2081-2. PubMed PMID: 31590677.

    4. Hochman G, Halevi-Tobias K, Kogan Y, Agur Z. Extracellular inhibitors can attenuate tumorigenic Wnt pathway activity in adenomatous polyposis coli mutants: Predictions of a validated mathematical model. PLoS One 2017 Jul 14;12(7):e0179888.

    5. Agur Z, Halevi-Tobias K, Kogan Y, et al. Employing Dynamical Computational Models for Personalizing Cancer Immunotherapy. J Expert Opinion on Biological Therapy , 2016.

    6. Forys U, Bodnar M, Kogan Y.Asymptotic dynamics of some t-periodic one-dimensional model with application to prostate cancer immunotherapy. J Math Biol 2016.

    7. Kogan Y, Agur Z, Elishmereni M. A mathematical model for the immunotherapeutic control of the Th1/Th2 imbalance in melanoma. Discrete and Continuous Dynamics – Series B 2013, 18(4) pp. 1017-1030.

    8. Kogan Y, Halevi-Tobias K, Elishmereni M, Vuk-Pavlović S, Agur Z. 2012. Reconsidering the Paradigm of Cancer Immunotherapy by Computationally Aided Real-Time Personalization. Cancer Research, Published OnlineFirst March 15, 2012; doi: 10.1158/0008-5472.

    9. Kogan Y, Halevi-Tobias KE, Hochman G, Baczmanska AK, Leyns L, Agur, Z. A new validated mathematical model of the Wnt signaling pathway predicts effective combinational therapy by sFRP and Dkk Biochem J 2012, 443, doi:10.1042/BJ20111887.

    10. Vainstein V, Kirnasovsky O, Kogan Y, Agur Z, Strategies for cancer stem cell elimination: Insights from mathematical modeling. J Theor Bol 2012, vol. 298, pp. 32–41.

    11. Kronik N, Kogan Y, Schlegel PG, Wölfl M. Improving T-cell Immunotherapy for Melanoma Through a Mathematically Motivated Strategy: Efficacy in Numbers? J of Immunotherapy 2012 35(2).

    12. Agur Z, Bloch N, Gorelik B, Kleiman M, Kogan Y, Sagi Y, Sidreansky D, Ronen Y. Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases. In Systems Biology in Drug Discovery and Development, Young DL, Michelson S. (eds). Wiley, 2011, pp 203-231.

    13. Agur Z, Kirnasovsky OU, Vasserman G, Tencer-Hershkowicz L, Kogan Y, Harrison H, et al. Dickkopf1 regulates fate decision and drives breast cancer stem cells to differentiation: an experimentally supported mathematical model. PLoS One 2011 6(9) e24225.

    14. Kronik N., Kogan Y., Elishmereni M., Halevi-Tobias K., Vuk Pavlović S., Agur Z. Predicting Effect of Prostate Cancer Immunotherapy by Personalized Mathematical Models PLoS One 2010 5(12) e15482.

    15. Agur Z., Kogan Y., Levi L., Harrison H., Lamb R., Kirnasovsky O.U., Clarke R.B. Disruption of a Quorum Sensing Mechanism Triggers Tumorigenesis: a Simple Discrete Model Corroborated by Experiments in Mammary Cancer Stem Cells. Biol Direct 2010 5(1) pp.20-41.

    16. Kogan Y., Forys U., Shukron O., Kronik N., Agur Z. Cellular immunotherapy for high grade Gliomas: mathematical analysis deriving efficacious infusion rates based on patient requirements. SIAM J. Appl. Math. 2010 70(6) pp. 1953-1976.

    17. Agur Z, Elishmereni M, Kogan Y, Kheiffetz Y, Ziv I, Shoham M, Vainstein V. Mathematical modeling as a new approach for improving the efficacy/toxicity profile of drugs: the thrombocytopenia case study, Preclinical Development Handbook, Shayne Gad Ed., John Wiley and Sons, USA. 2008, pp 1229-1266.

    18. Kirnasovsky O, Kogan Y, Agur Z. Analysis of a Mathematical Model for the Molecular Mechanism of Fate Decision in Mammary Stem Cells, Mathematical Modelling of Natural Phenomena. 2008 3(7) pp. 78-89.

    19. Agur Z, Elishmereni M, Kogan Y, Kheifetz Y, Ziv I, Shoham M, Vainstein V, Mathematical modeling as a new approach for improving the efficacy/toxicity profile of drugs: the thrombocytopenia case study. In: Preclinical Development Handbook, John Wiley and Sons. 2008.

    20. Kogan Y, Ribba B, Marron K, Dahan N, Vainshtein V, Agur Z. 2004. Intensified Doxorubicin-Based Regimen Efficacy in Residual Non-Hodgkin’s Limphoma Disease: Towards a Computationally Supported Treatment Improvement. Mathematical Modelling of Natural Phenomena Vol. 2, No. 3, 2007, pp. 47-68.

    21. Vainstein V., Ginosar Y., Shoham M., Ianovski A., Rabinovich A., Kogan Y., Selitser V., Agur Z. Improving cancer therapy by Doxorubicin and Granulocyte colony-stimulating factor: Insights from a Computerized Model of Human Granulopoiesis. Mathematical Modelling of Natural Phenomena 2006 1(2), pp.70-80.

    22. Kheifetz Y, Kogan Y, Agur Z, Long-range predictability in models of cell populations subjected to phase-specific drugs: growth-rate approximation using properties of positive compact operators, M3AS, 16(7) Supp, July 2006-11-15.

    23. Arakelyan L, Merbl Y, Daugulis P, Ginosar Y, Vainstain V, Kogan Y, Selitser V, Harpak H and Agur Z. 2002. Using multi-scale mathematical modeling in anti-angiogenic therapy, Chap. 7 in Cancer Modeling and Simulation Mathematical Biology and Medicine Series, Chapman & Hall/CRC.

    24. Kheifetz Y, Kogan Y, Agur Z, Long-range predictability in models of cell populations subjected to phase-specific drugs: growth-rate approximation using properties of positive compact operators, M3AS, 16(7) Supp, July 2006-11-15..

    25. Kheifetz Y, Kogan Y, Agur Z, Long-range predictability in models of cell populations subjected to phase-specific drugs: growth-rate approximation using properties of positive compact operators, M3AS, 16(7) Supp, July 2006-11-15..