Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer.
Sanghoon LeeYoungbin ChoYiting LiRuxuan LiDaniel BrownPriscilla McAuliffeAdrian V LeeSteffi OesterreichIoannis K ZervantonakisHatice Ulku OsmanbeyogluPublished in: bioRxiv : the preprint server for biology (2024)
Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.
Keyphrases
- estrogen receptor
- adipose tissue
- papillary thyroid
- single cell
- endoplasmic reticulum
- squamous cell
- breast cancer cells
- poor prognosis
- squamous cell carcinoma
- rna seq
- childhood cancer
- metabolic syndrome
- type diabetes
- lymph node metastasis
- skeletal muscle
- long non coding rna
- transcription factor
- binding protein
- climate change
- artificial intelligence
- electronic health record
- deep learning
- dengue virus