Figure 2.
Efficacy of G47Δ in combination with CTLA-4 or PD-1 inhibition in a murine subcutaneous AKR tumor model
(A−D) Effects of G47Δ or CTLA-4 inhibition, either alone or in combination, on tumor growth in the murine subcutaneous AKR tumor model. (A) Experimental design. C57BL/6 mice harboring unilateral subcutaneous AKR tumors were given intratumoral injections with G47Δ (5 × 106 PFUs on days 0 and 3) or mock in combination with intraperitoneal injections with the anti-CTLA-4 antibody (25 μg on days 0, 3, and 6) as indicated. (B) Delayed tumor growth was observed with either G47Δ (p < 0.05) or CTLA-4 inhibition (p < 0.01) alone, but the combination treatment was associated with a significant decrease in tumor growth compared with each monotherapy (versus G47Δ, p < 0.001; versus αCTLA-4, p < 0.01). The results are presented as the mean ± SEM (n = 8 per group). (C) Individual tumor growth curves of AKR tumors. The combination therapy achieved a cure in 5/8 animals. (D) Kaplan-Meier survival curves. The combination therapy resulted in significantly prolonged survival (p < 0.001 versus control; G47Δ, p < 0.01 versus αCTLA-4). (E−H) Efficacies of G47Δ and PD-1 inhibition, either alone or in combination, in the murine subcutaneous AKR tumor model. (E) Experimental design. C57BL/6 mice harboring subcutaneous AKR tumors were treated with G47Δ (5 × 106 PFUs) or mock together with intraperitoneal injections with the anti-PD-1 antibody (100 μg) as indicated. (F) Tumor growth was significantly inhibited by the anti-PD-1 antibody alone (versus control, p < 0.001). The efficacy of G47Δ combined with systemic PD-1 inhibition was equivalent to that of PD-1 inhibition alone. The results are presented as the mean ± SEM (n = 7 per group). (G) Individual tumor growth curves. The combination therapy did not yield a cure. (H) Kaplan-Meier survival curves. One-way ANOVA followed by Dunnett’s test was used for the comparisons of tumor growth. For survival analysis, the log-rank test followed by Holm’s sequential Bonferroni corrections was used to determine statistical significance (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ns, not significant).