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H 1 Part 2 - Dynamic logic models complement machine learning for personalized medicine

Auteurs : Saez-Rodriguez, Julio (Auteur de la Conférence)
CIRM (Editeur )

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    Résumé : In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to infer logic models from phosphoproteomic and phenotypic data (CellNOpt and PHONEMeS). We have recently adapted these tools to single-cell data. I will illustrate their utility in cases of biomedical relevance, in particular to improve our understanding of cancer and to develop novel therapeutic opportunities. As main application I will discuss our work analysing, as a model for personalized medicine, large pharmaco-genomic screenings in cell lines. These screenings provide rich information about alterations in tumours that confer drug sensitivity or resistance. Integration of this data with prior knowledge provides biomarkers and offer hypotheses for novel combination therapies. Our own analysis as well as the results of a crowdsourcing effort (as part of a DREAM
    challenge) reveals that prediction of drug efficacy from basal omics data is that discussed above is far from accurate, implying important limitations for personalised medicine. An important aspect that deserves detailed attention is the dynamics of signaling networks and how they response to perturbations such as drug treatment.
    I will present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities not noticeable by static molecular characterisation.

    Keywords : biological network; bio-informatics

    Codes MSC :
    92-08 - Computational methods
    92B05 - General biology and biomathematics
    92C42 - Systems biology, networks
    92-10 - Mathematical modeling or simulation for problems pertaining to biology

    Informations sur la rencontre

    Nom de la rencontre : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire
    Organisateurs de la rencontre : Baudot, Anais ; Hubert, Florence ; Mossé, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu
    Dates : 02/03/2020 - 06/03/2020
    Année de la rencontre : 2020
    URL Congrès : https://conferences.cirm-math.fr/2305.html

    Citation Data

    DOI : 10.24350/CIRM.V.19622103
    Cite this video as: Saez-Rodriguez, Julio (2020). Part 2 - Dynamic logic models complement machine learning for personalized medicine. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19622103
    URI : http://dx.doi.org/10.24350/CIRM.V.19622103

    Voir aussi


    1. MENDEN, Michael P., IORIO, Francesco, GARNETT, Mathew, et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS one, 2013, vol. 8, no 4. - https://dx.doi.org/10.1371%2Fjournal.pone.0061318

    2. IORIO, Francesco, KNIJNENBURG, Theo A., VIS, Daniel J., et al. A landscape of pharmacogenomic interactions in cancer. Cell, 2016, vol. 166, no 3, p. 740-754. - https://doi.org/10.1016/j.cell.2016.06.017

    3. MENDEN, Michael P., CASALE, Francesco Paolo, STEPHAN, Johannes, et al. The germline genetic component of drug sensitivity in cancer cell lines. Nature communications, 2018, vol. 9, no 1, p. 1-8. - https://doi.org/10.1038/s41467-018-05811-3

    4. MENDEN, Michael P., WANG, Dennis, MASON, Mike J., et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 2019, vol. 10, no 1, p. 1-17. - https://doi.org/10.1038/s41467-019-09799-2

    5. SCHUBERT, Michael, KLINGER, Bertram, KLÜNEMANN, Martina, et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature communications, 2018, vol. 9, no 1, p. 1-11. - https://doi.org/10.1038/s41467-017-02391-6

    6. GARCIA-ALONSO, Luz, IORIO, Francesco, MATCHAN, Angela, et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer research, 2018, vol. 78, no 3, p. 769-780. - http://dx.doi.org/10.1158/0008-5472.CAN-17-1679

    7. GARCIA-ALONSO, Luz, HOLLAND, Christian H., IBRAHIM, Mahmoud M., et al. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome research, 2019, vol. 29, no 8, p. 1363-1375. - http://dx.doi.org/10.1101/gr.240663.118

    8. SAEZ-RODRIGUEZ, Julio, COSTELLO, James C., FRIEND, Stephen H., et al. Crowdsourcing biomedical research: leveraging communities as innovation engines. Nature Reviews Genetics, 2016, vol. 17, no 8, p. 470. - https://doi.org/10.1038/nrg.2016.69

    9. CHOOBDAR, Sarvenaz, AHSEN, Mehmet E., CRAWFORD, Jake, et al. Assessment of network module identification across complex diseases. Nature methods, 2019, vol. 16, no 9, p. 843-852. - https://doi.org/10.1038/s41592-019-0509-5

    10. COSTELLO, James C., HEISER, Laura M., GEORGII, Elisabeth, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nature biotechnology, 2014, vol. 32, no 12, p. 1202. - https://doi.org/10.1038/nbt.2877

    11. EDUATI, Federica, DOLDÀN-MARTELLI, Victoria, KLINGER, Bertram, et al. Drug Resistance mechanisms in colorectal cancer dissected with cell type–specific dynamic logic models. Cancer research, 2017, vol. 77, no 12, p. 3364-3375. - http://dx.doi.org/10.1158/0008-5472.CAN-17-0078

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