Mots-Clés
systems biology
spatial biology
tumor heterogeneity
ovary cancer
multimodal models
Description
The Cancer Bioinformatics and Systems Biology team in collaboration with the Precision Imaging as a New Key in Cancer Care team, both at IRCM (Cancer Research Institute of Montpellier), are looking for a talented postdoctoral fellow.
Cellular networks and their spatial heterogeneity play an important role in multiple diseases. In this project, we want to develop new, multimodal methods based on graph neural networks or spatial graphs to capture molecular networks in the microenvironment (TME) of ovary and pancreatic cancer and relate them to in vivo and ex vivo MRI imaging.
The successful candidate will primarily develop models aimed at representing tumor heterogeneity from a signaling and metabolic standpoint. In collaboration with medical imaging experts of the Precision Imaging as a New Key in Cancer Care team, he/she will contribute to the construction of a multimodal linking molecular data with MRI-phenotypic data.
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].
The Precision Imaging as a New Key in Cancer Care (PINKCC) lab is a translational research team based at IRCM and Montpellier Cancer Institute (ICM), led by Prof. Stéphanie Nougaret, recipient of a European Research Council (ERC) Starting Grant. The lab develops advanced imaging, artificial intelligence, and multimodal data integration approaches to better characterize tumor biology and improve precision oncology. Its research focuses particularly on ovarian and pancreatic cancers, combining multiparametric MRI and CT imaging, radiomics and deep learning, and digital pathology.
Preferred qualifications are either a bioinformatics PhD and solid machine learning skills or a mathematics/physics/computer science PhD with application to molecular biology. Deep neural network practical experience would be a plus. The position is funded for 2 years. A first contract will be established for 1 year and extended upon performance.
Interested applicants should e-mail their CV, a letter of motivation and the names and e-mails of 2 references to Prof Jacques Colinge (jacques.colinge@umontpellier.fr).
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