Back

RELB Reprograms Exhausted Tumor-Infiltrating Lymphocytes for Improved Adoptive Cell Therapy

McRoberts Amador, C. D.; Conover, R. E.; Brown, M. C.; Lyniv, L. S.; Noldner, P. K.; Zhou, Y.; Gao, A. R.; McCutcheon, S. R.; Antonia, S. J.; Gersbach, C. A.

2025-10-12 bioengineering
10.1101/2025.10.11.681829 bioRxiv
Show abstract

Tumor-infiltrating lymphocytes (TILs) are a promising autologous cell therapy to treat solid tumors. TILs are manufactured by expanding and reinfusing tumor-reactive T cells from tumor biopsies. Efficacy of TIL therapies has been limited by the heterogeneity of expanded TIL products and the high prevalence of dysfunctional exhausted CD8+ T cells (TEX). While a subset of CD8+ TILs co-expressing CD103 and CD39 are enriched for tumor-reactive TILs across multiple cancer types, these cells are often in the TEX state with low proliferative potential. To identify regulators of human TIL proliferation, we screened an open reading frame library encoding for all human transcription factors (TFs). RELB emerged as the dominant driver of human TIL expansion with a skew towards CD8+ cells. TCR diversity was maintained after multiple days of in vitro expansion driven by RELB. Transcriptome profiling of multiple RELB-expressing TIL subtypes revealed a shift towards a memory/costimulatory-like phenotype. Using a HER2-targeting CAR and tumor co-culture model, RELB conferred improved persistence after multiple tumor challenges in vitro and improved solid tumor control in mouse xenografts in vivo. Finally, co-culture of RELB-overexpressing TILs with patient-matched tumor organoids showed an increase in TIL product polyfunctionality, tumor reactivity, and tumor killing. Collectively these results support promoting RELB expression as a strategy for broadly enabling TIL therapy for treating solid tumors.

Matching journals

The top 7 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.