Back

The costimulatory domain influences CD19 CAR-T cell resistance development in B-cell malignancies.

Krawczyk, M.; Fernandez-Fuentes, N.; Fidyt, K.; Winiarski, T.; Pepek, M.; Graczyk-Jarzynka, A.; Davis, J.; Bousquets-Munoz, P.; Puente, X. S.; Menendez, P.; Benard, E.; Walchli, S.; Thomas-Tikhonenko, A.; Winiarska, M.

2025-03-05 immunology
10.1101/2025.02.28.640707 bioRxiv
Show abstract

CD19-CAR-T-cells emerge as a major therapeutic option for relapsed/refractory B-cell-derived malignancies, however approximately half of patients eventually relapse. To identify resistance-driving factors, we repeatedly exposed B-cell lymphoma/B-cell acute lymphoblastic leukemia to 4-1BB/CD28-based CD19-CAR-T-cells in vitro. Generated models revealed costimulatory domain-dependent differences in CD19 loss. While CD19-4-1BB-CAR-T-cells induced combination epitope/total CD19 protein loss, CD19-CD28-CAR-T-cells did not drive antigen-escape. Consistent with observations in patients relapsing after CD19-4-1BB-CAR-T-cells, we identified CD19 frameshift/missense mutations affecting residues critical for FMC63 epitope recognition. Mathematical simulations revealed that differences between CD19-4-1BB- and CD19-CD28-CAR-T-cells activity against low-antigen-expressing tumor contribute to heterogeneous therapeutic responses. By integrating in vitro and in silico data, we propose a biological scenario where CD19-4-1BB-CAR-T-cells fail to eliminate low-antigen tumor cells, fostering CAR-resistance. These findings offer mechanistic insight into the observed clinical differences between axi-cel (CD28-based) and tisa-cel (4-1BB-based)-treated B-cell lymphoma patients and advance our understanding on CAR-T resistance. Furthermore, we underscore the need for specific FMC63 epitope detection to deliver information on antigen levels accessible for CD19-CAR-T-cells. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/640707v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1728c4org.highwire.dtl.DTLVardef@759660org.highwire.dtl.DTLVardef@1e3a0deorg.highwire.dtl.DTLVardef@15653e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 11 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.