Mots-Clés
Spatial Transcriptomics
Multiplex imaging
Tumor microenvironment
Machine learning
Cancer-Associated Fibroblasts
Artificial Intelligence
Description
Project description
The tumor microenvironment (TME) plays a critical role in cancer development and is found to be associated with treatment response. Cancer-associated fibroblasts (CAF) are one of the TME key components. They produce the main structure of the extracellular matrix (ECM), including collagen fibers, are involved in the ability of immune cells to infiltrate the tumor and can directly modulate tumor cell growth and invasiveness. Our team recently identified distinct CAF subtypes, two of which are associated with T-cell exclusion (Grout et al., Cancer Discovery 2022). This PhD project aims to investigate the spatial and molecular interplay between CAFs, immune cells, and tumor cells, at various stages of lung tumor progression, using state-of-the-art spatial transcriptomics (ST) data. Datasets are already available and were generated using advanced imaging-based (MERFISH, Xenium) and sequencing-based (VisiumHD) ST technologies.
Project Objectives
The goals of the study are to:
(1) Develop robust ST analytical methods: Implement statistical pipelines for data filtering and normalization, and apply machine learning/deep learning techniques for cell type annotation and cell-cell communications.
(2) Uncover biological insights: Explore how spatial architecture and cell-cell interactions influence tumor progression and treatment response variability among patients.
(3) Develop predictive model: Develop AI/deep learning-based model to identify and quantify CAF subtypes and fibrillar architecture of the tumor from Hematoxylin Eosin Saffron (HES) stained histology slides.
Wet lab experiments (e.g., 3D spheroid co-culture, imaging, tumor cell killing assays) can be integrated into the project, depending on the candidate’s background and interests; or will be performed in collaboration with wet-lab researchers of the team.
Research Environment
The project will be conducted at Institut Curie Research Center, in the “Stroma and immunity” team led by Hélène Salmon. Institut Curie offers a unique environment combining a leading cancer Hospital with a top-tier Research Center, facilitating close collaboration between basic research and clinical applications, including access to primary tumor samples. The Salmon Lab is part of the “Immunity and Cancer” Department (Inserm U932), headed by Ana-Maria Lennon-Duménil, including experts in cell biology, immunology, clinical immunotherapy, as well as a large working group of bioinformaticians. The Salmon laboratory combines wet lab and computational approaches to understand the contribution of stromal cells, especially CAFs, on shaping immune responses against cancer. The PhD student will also be part of the Curie Bioinformatics HUB, a dynamic structure supported by the Curie Bioinformatics Core Facility (led by N. Servant, P. Hupé and E. Barillot), that promotes best practices and peer exchange among bioinformaticians and computational researchers.
Supervision
Primary Supervisor: Hélène Salmon - with weekly one-to-one meetings and participation in team meetings.
Bioinformatics Supervision: Mamy Andrianteranagna – senior bioinformatician in the team.
Community Engagement: The PhD student will participate in the activities of the Curie Bioinformatics HUB for ongoing support and collaboration.
Candidate Profile
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Solid background in Bioinformatics, including a good knowledge in statistics and machine learning (Deep learning is a plus)
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Strong interest in Biology, Oncology, and/or Immunology questions
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Prior experience in single-cell and/or ST data analysis is highly desirable
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Ability to clearly present scientific approach and results, both orally and in writing
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(Optional) Wet lab experience (e.g. cell culture, immunology assays)
References
Grout et al, Cancer Discovery 2022, https://doi.org/10.1158/2159-8290.
Landragin et al, Molecular Cancer 2025. https://doi.org/10.1186/s12943-025-02331-9
Ackermann J, eLife 2025. https://doi.org/10.7554/eLife.101885.2
Captier N, Nature Communications 2025. https://doi.org/10.1038/s41467-025-55847-5