Mathematical modeling of homeostasis and oncogenesis in mature T cells

In this project we apply mathematical modelling techniques to consistently explain processes that potentially lead to malignant transformations of T cells. Specifically, we establish a mathematical framework, which allows to study effect of cellular interactions of multiple T-cell receptor(TCR)-specific clones on the expansion and/or control of malignant T-cell clones. Based on the modelling work, we will be able to derive experimentally testable predictions, addressing particular regulatory mechanisms, but also proposing potential therapeutic strategies to treat T-cell related malignancies.

TCR quasi-monoclonal T-cell populations transduced with potent T-cell oncogenes developed mature T-cell lymphoma/ leukemia in RAG1-deficient recipient mice © IMB TCR quasi-monoclonal T-cell populations transduced with potent T-cell oncogenes developed mature T-cell lymphoma/ leukemia in RAG1-deficient recipient mice © IMB

Phenomenon which motivated the modelling work (Figure from Gerdes et al., Front. Immunol. 2013)

TCR quasi-monoclonal T-cell populations transduced with potent T-cell oncogenes developed mature T-cell lymphoma/ leukemia in RAG1-deficient recipient mice

Phenomenon which motivated the modelling work (Figure from Gerdes et al., Front. Immunol. 2013) © IMB

The modelling approach is a central component to link the experimental efforts within CONTROL-T and to achieve a quantitative, scale-bridging understanding of proliferation and survival of mature T-cell clones in the normal homeostatic and in the malignant situation. Beyond the mathematical modelling, our group also supports the bioinformatical and statistical analysis of next generation sequencing data to identify genetic lesions that are potentially related to the malignant transformation of T cells.

Abundance of healthy and pre-leukemic T cells for two different situations © IMB Abundance of healthy and pre-leukemic T cells for two different situations © IMB

Simulation results. Abundance of healthy and pre-leukemic T cells for two different situations. Top: In the oligo-clonal situation, i.e. number of different TCR-specific clones (M=33) below a critical threshold, the healthy T cells are outcompeted by the (malignant( pre-leukemic T cells. Bottom: In case of a more poly-clonal situation, i.e. number of different TCR-specific clones (M=66) above a critical threshold, the pre-leukemic T cells can be controlled. For details see Diebner et al. J. Theor. Biol. 2016.

Abundance of healthy and pre-leukemic T cells for two different situations

Simulation results. Abundance of healthy and pre-leukemic T cells for two different situations. Top: In the oligo-clonal situation, i.e. number of different TCR-specific clones (M=33) below a critical threshold, the healthy T cells are outcompeted by the (malignant( pre-leukemic T cells. Bottom: In case of a more poly-clonal situation, i.e. number of different TCR-specific clones (M=66) above a critical threshold, the pre-leukemic T cells can be controlled. For details see Diebner et al. J. Theor. Biol. 2016. © IMB

Involved scientists (* directly funded by the project)

  • Hans Diebner*

  • Matthias Kuhn

  • Ingo Roeder

Publications

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Funding

DFG (RO3500/4-1) within the DFG research group FOR1961 „Control-T“

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Katja Tampe
Last modified: Apr 06, 2017