Back

Transcriptional profiling reveals a previously unknown population of Cdkn2a-positive tumor-associated macrophages in aggressive brain cancer

Melo dos Santos, L. S.; Kich Grun, L.; Nathali Scholl, J.; Barbe-Tuana, F. M.; Demaria, M.

2024-10-21 cancer biology
10.1101/2024.10.17.618451 bioRxiv
Show abstract

Microglia, the resident immune cells of the Central Nervous System (CNS), play crucial roles in homeostasis, immune responses, and the modulation of neuronal functions. In brain tumors, microglia are difficult to distinguish from infiltrating macrophages and are collectively called Tumor-Associated Macrophages (TAMs). While these cells exhibit plasticity, support tumor growth, and suppress antitumor immune responses, their identity and function remain largely unknown. Cellular senescence, a state of growth arrest associated with the upregulation of the cell cycle inhibitor p16INK4A (Cdkn2a), can both suppress and promote tumor progression via the secretion of inflammatory and tissue remodeling factors, collectively known as Senescence-Associated Secretory Phenotype (SASP). By leveraging existing single-cell RNA sequencing (scRNA-seq) data of murine brain tumors, we identified a population of Cdkn2a-positive TAMs involved in immune regulation and neurogenesis, characterized by high expression of Emb and Cd93, which we named MECC (Microglia-like, and Emb-, CD93- and Cdkn2a-positive) cells. In humans, these cells were more prevalent in recurrent glioblastoma and associated with poorer prognosis. Comparative analysis of brain samples from patients with neurodegeneration and healthy older individuals revealed the specificity of MECCs for brain tumors. These findings suggest that MECCs are a unique and distinct cell population associated with aggressive brain tumors, which could potentially serve as targets for novel treatments.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.