Back

Sensitization with an allogeneic MHC class I molecule induces anti-tumor immunity in the absence of PD-1 in mice

Paramitasari, K. A.; Ishida, Y.

2023-08-28 immunology
10.1101/2023.08.26.554968 bioRxiv
Show abstract

To investigate the effect of a major histocompatibility complex class I (MHC-I) overexpression to augment immune sensitivity against tumors, we have generated the murine colorectal carcinoma cell line MC38 (with the endogenous H-2b haplotype) overexpressing the allogeneic mouse MHC-I cell surface molecule H-2Kd (MC38 H-2Kd). The tumorigenicity of unmodified parental cells (MC38 PT) and MC38 H-2Kd was tested in vivo by subcutaneous injection into the flank of wild-type (WT) and programmed death-1 (PD-1) knockout (KO) mice in a C57BL/6 (H-2b) genetic background. MC38 PT cells readily formed tumors and grew progressively in both WT and PD-1 KO mice. The speed of MC38 PT tumor growth was slower in PD-1 KO mice than in WT mice. In contrast, MC38 H-2Kd cells showed full sensitivity to rejection by the immune system in both naive WT and PD-1 KO mice, indicated by spontaneous tumor regression. Next, we sought to determine the extent to which H-2Kd-overexpressing tumors could protect the mice against unmodified cancers. PD-1 KO mice were first sensitized with highly immunogenic MC38 H-2Kd cells and then challenged with weakly immunogenic MC38 PT cells. Intriguingly, all PD-1 KO mice gained immunity against the aggressive MC38 tumor and became tumor-free. Sensitizing PD-1 KO mice with growth-arrested (by the pre-treatment with mitomycin C, MMC) and the debris of MC38 H-2Kd tumors also provided full protection against the growth of secondary MC38 PT tumors. Most notably, sensitization with the debris of MC38 H-2Kd cells provided the long-term immunological memory against MC38 PT carcinoma cells. This finding implies that MC38 H-2Kd cells retain highly efficient and durable immunogenicity.

Matching journals

The top 3 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.