Mots-Clés
spatial biology
deep learning
cellular networks
cancer
microenvironment
Description
The Cancer Bioinformatics and Systems Biology team at IRCM (Cancer Research Institute of Montpellier) are looking for a talented postdoctoral fellow.
The reprograming of its microenvironment by the tumor leads to the alteration of a multitude of cellular networks. In this project, we want to map such disruptive events for the purpose of better understanding the microenvironment heterogeneity, and to identify potential new targets against tumors. We will focus on breast and colorectal cancers.
The successful candidate will primarily develop deep learning models aimed at representing the spatial tumor heterogeneity with respect to cell signaling. This work will be done in close collaboration with experts of the biology of the chosen tumors.
The Cancer Bioinformatics and Systems Biology team has developed a number of algorithms and machine learning models to infer both intra-cellular and cellular networks [1–4], and to integrate data over such networks to extract actionable biological information such as candidate targets or biomarkers [5–6], including in single-cell and spatial transcriptomics [7,8].
Preferred qualifications are either a bioinformatics PhD and solid deep learning skills or a mathematics/physics/computer science PhD with application to molecular biology. The position is funded for 3 years. A first contract will be established for 1 year and extended upon performance.
References
1. Villemin J-P, Bassaganyas L, Pourquier D, Boissière F, Cabello-Aguilar S, Crapez E, et al. Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res. 2023; gkad352. doi:10.1093/nar/gkad352
2. Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020. doi:10.1093/nar/gkaa183
3. Villemin J-P, Giroux P, Maillard M, et al., Colinge J. Addressing multiple facets of ligand-receptor network inference including single-cell proteomics. bioRxiv, 2025. doi:10.1101/2025.10.05.680519
4. Borg J-P, Colinge J, Ravel P. Testing and overcoming the limitations of modular response analysis. Brief Bioinform. 2025. doi:10.1093/bib/bbaf098.
5. Alame M, Cornillot E, Cacheux V, Tosato G, Four M, Oliveira LD, et al. The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities. Theranostics. 2020;10: 4383–4394. doi:10.7150/thno.42986
6. Blomen VA, Majek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science. 2015;350: 1092–1096. doi:10.1126/science.aac7557
7. Giguelay A, Turtoi E, Khelaf L, Tosato G, Dadi I, Chastel T, et al. The landscape of cancer-associated fibroblasts in colorectal cancer liver metastases. Theranostics. 2022;12: 7624–7639. doi:10.7150/thno.72853
8. Honda CK, Kurozumi S, Fujii T, Pourquier D, Khellaf L, Boissiere F, et al. Cancer-associated fibroblast spatial heterogeneity and EMILIN1 expression in the tumor microenvironment modulate TGF-β activity and CD8+ T-cell infiltration in breast cancer. Theranostics. 2024;14: 1873–1885. doi:10.7150/thno.90627