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Systems Level Analysis of Immune Cell Subsets and Intercellular Communication Networks in Human Breast Cancer

Authors
  • Noël, Floriane
Publication Date
Oct 29, 2018
Source
HAL-ENAC
Keywords
Language
English
License
Unknown
External links

Abstract

Cell-to-cell communication is at the basis of the higher order organisation observed in tissues, organs, and organism. Understanding cell-to-cell communication, and its underlying mechanisms that drive the development of cancer is essential. Breast tumor microenvironment (TME) is composed of a great cellular diversity, such as endothelial, stromal or immune cells that can influence tumor progression as well as its response to treatment. Among the different immune cell populations, dendritic cells (DCs) subsets integrate signals from their microenvironment and are subsequently essential in orchestrating specific immune response through T cell activation. However, the differential function of these subsets, and their interactions within the TME remain poorly described. My main thesis objective was to understand the impact of the breast TME on DC subsets using systems-level analysis. We used RNA sequencing to systematically analyze the transcriptomes of tumor-infiltrating plasmacytoid pre-DCs (pDCs), cell populations enriched for type 1 classical DCs (cDC1e), type 2 classical DCs (cDC2s), CD14+DCs, and monocytes-macrophages from human primary luminal breast cancer and triple-negative breast cancer. We found that transcriptional reprogramming of tumor-infiltrating antigen-presenting cells is subset-specific. These results suggest a complex interplay between ontogeny and tissue imprinting in conditioning DC diversity and function in cancer.As a second objective, I aimed at studying the cellular communications in order to understand how cells integrate signals from their environment. I developed ICELLNET, a tool to reconstruct intercellular communication networks. This original quantitative method, integrating ligand-receptor interactions and cell type specific gene expression, can be automatically applied to any cell population level transcriptomic profile opening perspectives of application in several disease contexts and biology fields.

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