| Literature DB >> 36162919 |
Kok Haw Jonathan Lim1,2, Evangelos Giampazolias1, Oliver Schulz1, Neil C Rogers1, Anna Wilkins3,4, Erik Sahai3, Jessica Strid2, Caetano Reis E Sousa5.
Abstract
Type 1 conventional dendritic cells (cDC1) play a critical role in priming anticancer cytotoxic CD8+ T cells. DNGR-1 (a.k.a. CLEC9A) is a cDC1 receptor that binds to F-actin exposed on necrotic cancer and normal cells. DNGR-1 signaling enhances cross-presentation of dead-cell associated antigens, including tumor antigens. We have recently shown that secreted gelsolin (sGSN), a plasma protein, competes with DNGR-1 for binding to dead cell-exposed F-actin and dampens anticancer immunity. Here, we investigated the effects of loss of sGSN on various anticancer therapies that are thought to induce cell death and provoke an immune response to cancer. We compared WT (wildtype) with Rag1-/- , Batf3-/- , Clec9agfp/gfp , sGsn-/- or sGsn-/- Clec9agfp/gfp mice implanted with transplantable tumor cell lines, including MCA-205 fibrosarcoma, 5555 BrafV600E melanoma and B16-F10 LifeAct (LA)-ovalbumin (OVA)-mCherry melanoma. Tumor-bearing mice were treated with (1) doxorubicin (intratumoral) chemotherapy for MCA-205, (2) BRAF-inhibitor PLX4720 (oral gavage) targeted therapy for 5555 BrafV600E, and (3) X-ray radiotherapy for B16 LA-OVA-mCherry. We confirmed that efficient tumor control following each therapy requires an immunocompetent host as efficacy was markedly reduced in Rag1-/- compared with WT mice. Notably, across all the therapeutic modalities, loss of sGSN significantly enhanced tumor control compared with treated WT controls. This was an on-target effect as mice deficient in both sGSN and DNGR-1 behaved no differently from WT mice following therapy. In sum, we find that mice deficient in sGsn display enhanced DNGR-1-dependent responsiveness to chemotherapy, targeted therapy and radiotherapy. Our findings are consistent with the notion some cancer therapies induce immunogenic cell death (ICD), which mobilizes anticancer T cells. Our results point to cDC1 and DNGR-1 as decoders of ICD and to sGSN as a negative regulator of such decoding, highlighting sGSN as a possible target in cancer treatment. Further prospective studies are warranted to identify patients who may benefit most from inhibition of sGSN function. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.Entities:
Keywords: antigen presentation; dendritic cells; immunity
Mesh:
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Year: 2022 PMID: 36162919 PMCID: PMC9516286 DOI: 10.1136/jitc-2022-005245
Source DB: PubMed Journal: J Immunother Cancer ISSN: 2051-1426 Impact factor: 12.469
Figure 1Loss of sGSN enhances response to immunogenic chemotherapy, radiotherapy, and targeted therapy. (A) Graphical illustration of the chemotherapy model for data shown in (B, C). Mice were subcutaneously inoculated with MCA-205 fibrosarcoma cells, and on day 6 or 7, randomized to receive either chemotherapy with doxorubicin or PBS vehicle control (as indicated by the red arrows in the plots). (D) Tumor growth profile in mice receiving either doxorubicin (WT, n=8; Rag1, n=8), or vehicle control (WT, n=8; Rag1, n=8). (C) Doxorubicin was administered to n=9 sGsn mice vs n=11 WT littermates, and compared with n=4 sGsn mice vs n=7 WT littermates receiving vehicle control. (D) Graphical illustration of the radiotherapy model for data shown in (E, F). Tumors were derived from subcutaneous inoculation of B16-F10 LA-OVA-mCherry melanoma cells into the shaved right flanks of mice. Mice were then randomized to receive a single fraction of x-ray irradiation to the target tumor area or sham irradiation (untreated), on day 7 following tumor implantation (as indicated by the red arrows in the plots). (E) Growth profile of tumors in WT (X-ray, n=10; sham, n=8), Rag (X-ray, n=10; sham, n=10) and Batf3 (X-ray, n=10; sham, n=6) mice. (F) Growth profile of tumors in co-housed WT (X-ray, n=15; sham, n=12) and sGsn (X-ray, n=14; sham, n=10) mice. (G) Graphical illustration of the targeted therapy model for data shown in (H, I). Mice were subcutaneously inoculated with 5555 BRAFV600E melanoma cells, and on day 6, randomized to receive treatment with either the Braf-inhibitor PLX4720 or 5% DMSO as vehicle control, for a total of 14 days (as indicated by the treatment bars below the x-axes in the plots). (H) Growth profile of tumors in Rag1 mice (PLX4720, n=8; control, n=8) vs co-housed WT (PLX4720, n=7; control, n=6) mice. (I) Growth profile of tumors in co-housed WT (PLX4720, n=13; control, n=10) and sGsn (PLX4720, n=14; control, n=12) mice. Data are represented as tumor volume (mm3) SEM, and groups were compared using two-way ANOVA with post hoc Bonferroni correction. where indicated, *p0.05, **p<0.01, ***p<0.001, ****p<0.0001; NS, not significant. Error bars are depicted in all plots; when not visible, errors are small. Data are representative of one experiment respectively for (C, F, H); one of two independent experiments for (B, E); and one of three independent experiments for (I). The data in (C, F) are further replicated in figure 2A, B, respectively. ANOVA, analysis of variance; LA, LifeAct; PBS, phosphate-buffered saline; OVA, ovalbumin; sGSN, secreted gelsolin; WT, wild type.
Figure 2Enhanced therapeutic response in the loss of secreted gelsolin settings is dependent on DNGR-1. (A) Tumor growth profile in mice bearing MCA-205 fibrosarcoma receiving chemotherapy. Doxorubicin was administered to cohoused WT (n=10), sGsn (n=10) and sGsn (n=10) mice, and compared with cohoused WT (n=10), sGsn (n=3) and sGsn (n=3) mice receiving vehicle control. (B) Growth profile of B16-F10 LA-OVA-mCherry melanoma in co-housed WT (X-ray, n=14; sham, n=10), sGsn (X-ray, n=11; sham, n=10) and sGsn (X-ray, n=12; sham, n=11) mice. Data are plotted as tumor volume (mm3) SEM, and mean tumor volumes were compared using two-way ANOVA with post hoc Bonferroni correction. Where indicated, *p0.05, **p<0.01, ***p<0.001, ****p<0.0001; NS, not significant. Error bars are depicted in all plots; when not visible, errors are small. Data are representative of one experiment respectively. ANOVA, analysis of variance; LA, LifeAct; OVA, ovalbumin; WT, wild type.