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

By Julio Saez-Rodriguez

Appears in collection : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire

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.

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Citation data

  • DOI 10.24350/CIRM.V.19622103
  • Cite this video Saez-Rodriguez Julio (3/2/20). Part 2 - Dynamic logic models complement machine learning for personalized medicine. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19622103
  • URL https://dx.doi.org/10.24350/CIRM.V.19622103


  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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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