Modelling of transcriptional and epigenetic network to understand, predict and overcome treatment resistance
Therapeutic failures are often due to a phenomenon of resistance that the cells acquire following a treatment. Here, we will address the mechanism underlying treatment resistant by studying the PLZF-RARA retinoic acid (RA) resistant acute promyelocytic leukemia (APL) model. Acute Promyelocytic Leukemia (APL) is characterized by clonal growth and evolution of undifferentiated hematopoietic cells and is an excellent model to study the interplay between epigenetic and transcriptional regulation in treatment resistance. We have already generated a comprehensive set of omics data from human cells and mouse models, but we still lack proper integrative approaches to fully exploit these data. In order to characterize the relapse-initiating cells and their vulnerabilities, we will combine two approaches. First, we will identify and characterize the mechanisms on which resistant cells rely. Second, we will develop a model to integrate the underlying transcriptional and epigenetic network to generate testable predictions.
Regulatory networks, mathematical and logical modelling, treatment resistance, data integration, epigenetic and transcriptional regulation
In this project, we aim to decipher the interplay between transcriptional and epigenetic regulatory mechanisms in the context of treatment resistance. To this purpose, we propose to use a combination of multi-omics data analysis and network modelling approaches to identify key regulatory mechanisms and target molecules which lead to the the resistance to ATRA treatment of leukemic cells.
Proposed approach (experimental / theoretical / computational)
This thesis project will combine two approaches: 1) Identifying and characterizing cell populations that are treatment resistant and responsible to relapse based on their joint transcriptional and epigenetic profiles.
Relying on existing multi-omics data (bulk and single-cell RNA-seq and ATAC-seq), the student will use data integration approaches, and then use network inference methods to decipher the underlying regulatory mechanisms. The results will then be combined with cis-regulatory motif analysis to predict novel transcriptional and epigenetic regulatory interactions associated with specific populations. 2) Developping a mechanistic model for the transcriptional, signalling and epigenetic network associated with therapy resistance. Building on pre-existing models, the student will progressively introduce newly inferred regulatory interactions into a comprehensive logical model, perform dynamical analyses, compare the results with existing data, and finally predict novel intervention points to unlock cell differentiation or promote cell death.
This is a collaborative project between a mathematical team and a biological team. MABioS (Mathematics and Algorithms for Systems Biology) team is located at Luminy, in the Mathematical Institute of Marseille (I2M) and gathers expertise in discrete mathematics, graph theory and combinatorics to model and analyze gene regulatory networks. The research team “ Epigenetics in normal and malignant hematopoiesis » leaded by Estelle Duprez is hosted in the Marseille Cancer Research Center. The team is specialized in epigenetic and leukemia and develops project to understand resistant therapy mechanisms. It has recently generated single cell multi-omics data related to retinoic acid resistant leukemia, which contain a lot of information and needs to be synthetized. Here, we propose to analyze the data using a system biology approach that required multidisciplinary skills. To succeed, the PhD student will integrate biology, bio informatics and mathematics tools and will benefit from the expertise of the two teams, from experimental biologists to computer scientists and mathematicians.
The proposed project can be tackled from mathematical or computational biology perspectives. We hence seek for PhD student with either: i) A master degree in mathematics and interest in discrete dynamical systems, combinatorics and graph theory, or ii) A master degree in an area related to Computational Biology with interest in data analysis, genetics and single-cell multiomics . The candidate should have a broad interest in cellular biology and be willing to cross discipline.
Is this project the continuation of an existing project or an entirely new one? In the case of an existing project, please explain the links between the two projects
The two teams have been collaborating on aging mechanisms of the hematopoietic stem cell. This PhD proposition is a new project that is focusing on resistance therapy mechanisms.
2 to 5 references related to the project
- Aibar, S., Gonzalez-Blas, C.B., Moerman, T., Huynh-Thu, V.A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.C., Geurts, P., Aerts, J., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14, 1083-1086. 10.1038/nmeth.4463.
- Geoffroy, M.C., Esnault, C., and de The, H. (2021). Retinoids in hematology: a timely revival? Blood 137, 2429- 2437. 10.1182/blood.2020010100.
- Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W.M., 3rd, Zheng, S., Butler, A., Lee, M.J., Wilk, A.J., Darby, C., Zager, M., et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587 e3529.
3 main publications from each PI over the last 5 years
NCommon publications between the two teams.
• * Hérault L, et al. (2021). Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells. BMC Biol 19(1):1
• Cantini L, et al. (2020). Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun 12(1): 124.
• E. Remy, et al. (2015) A modelling approach to explain mutually exclusive and co- occurring genetic alterations in bladder tumorigenesis. Cancer research, 75 (19) : 4042-52.
• Garciaz, S., N’Guyen, L., Finetti P., Chevalier C., Vernerey, J., Poplineau M., Platet N., Audebert, S., Pophillat M., Bertucci, F., Calmels, B, Recher, C., Birnbaum, D., Chabannon, Vey, N., Duprez, E. Epigenetic down regulation of the HIST1 locus predicts better prognosis in acute myeloid leukemia with NPM1 mutation. Clinical Epigenetics 11:141(2019). PMID: 31606046
• 36. Poplineau M., Vernerey J., Platet N., N'guyen L., Hérault L., Esposito M., Saurin A., Guillouf C., Iwama A., Duprez E. PLZF limits enhancer activity during hematopoietic progenitor aging. Nucleic Acids Research, 2019 May 21;47(9):4509-4520. (2019) PMID:30892634
• Koubi, M., Poplineau M., Vernerey, J., N’Guyen L., El-Kaoutari, A., Garciaz, S., Tiberi, G., Maqbool, MA, Andrau, JC, Guillouf, C., Saurin, A., Duprez E. Regulation of the positive transcriptional effect of PLZF through a non canonical EZH2 activity. Nucleic Acids Research 46: 3339-3350. (2018) PMID: 29425303