Back

Cellular and molecular events organizing the assembly of tertiary lymphoid structures in glioblastoma

Vaccaro, A.; Yang, F.; van de Walle, T.; Franke, S.; Lugano, R.; Denes, A.; Magoulopoulou, A.; Cid-Farina, I.; Smits, A.; Uhrbom, L.; Libard, S.; Latini, F.; Essand, M.; Nilsson, M.; He, L.; Olsson Bontell, T.; Jakola, A. S.; Ramachandran, M.; Dimberg, A.

2024-07-06 cancer biology
10.1101/2024.07.04.601824 bioRxiv
Show abstract

Tertiary lymphoid structures (TLS) are ectopic lymphoid aggregates associated with improved prognosis in numerous tumors. To date, their prognostic value in central nervous system cancers and the events underlying their formation remain unclear. Here, we find that TLS correlate with improved survival in glioblastoma patients. Furthermore, combining spatial transcriptomics of human tissues with longitudinal studies in murine glioma, we establish that the assembly of T cell-rich clusters is a prerequisite for the development of canonical TLS, and show that CD4 T cells play a central role in this process. Indeed, we provide evidence that IL7R+CCR7+ Th1 lymphocytes with lymphoid tissue-inducing potential are recruited to TLS nucleation sites, where they can initiate TLS assembly by expressing lymphotoxin {beta}. Our work defines TLS as a potential prognostic factor in glioblastoma and identifies cellular and molecular mechanisms of TLS formation. These findings have broader implications for the development of TLS-inducing therapies for cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/601824v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@78be05org.highwire.dtl.DTLVardef@1037706org.highwire.dtl.DTLVardef@7b64e1org.highwire.dtl.DTLVardef@11bfd5c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 8 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.