DNASE1L3 enhances antitumor immunity and suppresses tumor progression in colon cancer
Wenling Li, … , Xiaoling Li, Leping Li
Wenling Li, … , Xiaoling Li, Leping Li
Published August 15, 2023
Citation Information: JCI Insight. 2023;8(17):e168161. https://doi.org/10.1172/jci.insight.168161.
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Research Article Oncology

DNASE1L3 enhances antitumor immunity and suppresses tumor progression in colon cancer

Abstract

DNASE1L3, an enzyme highly expressed in DCs, is functionally important for regulating autoimmune responses to self-DNA and chromatin. Deficiency of DNASE1L3 leads to development of autoimmune diseases in both humans and mice. However, despite the well-established causal relationship between DNASE1L3 and immunity, little is known about the involvement of DNASE1L3 in regulation of antitumor immunity, the foundation of modern antitumor immunotherapy. In this study, we identify DNASE1L3 as a potentially new regulator of antitumor immunity and a tumor suppressor in colon cancer. In humans, DNASE1L3 is downregulated in tumor-infiltrating DCs, and this downregulation is associated with poor patient prognosis and reduced tumor immune cell infiltration in many cancer types. In mice, Dnase1l3 deficiency in the tumor microenvironment enhances tumor formation and growth in several colon cancer models. Notably, the increased tumor formation and growth in Dnase1l3-deficient mice are associated with impaired antitumor immunity, as evidenced by a substantial reduction of cytotoxic T cells and a unique subset of DCs. Consistently, Dnase1l3-deficient DCs directly modulate cytotoxic T cells in vitro. To our knowledge, our study unveils a previously unknown link between DNASE1L3 and antitumor immunity and further suggests that restoration of DNASE1L3 activity may represent a potential therapeutic approach for anticancer therapy.

Authors

Wenling Li, Hideki Nakano, Wei Fan, Yuanyuan Li, Payel Sil, Keiko Nakano, Fei Zhao, Peer W. Karmaus, Sara A. Grimm, Min Shi, Xin Xu, Ryushin Mizuta, Daisuke Kitamura, Yisong Wan, Michael B. Fessler, Donald N. Cook, Igor Shats, Xiaoling Li, Leping Li

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Figure 5

Dnase1l3 deficiency promotes tumor progression and impairs activity of cDCs in a syngenic tumor model.

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Dnase1l3 deficiency promotes tumor progression and impairs activity of ...
(A) Dnase1l3 deficiency increases the growth of MC38 tumors. In total, 1 × 105 MC38 colon cancer cells were s.c. injected into 8- to 12-week-old immunocompetent WT or Dnase1l3-KO mice (n = 10 WT and 11 KO; Mann-Whitney U test, *P < 0.05). (B) Representative images of final dissected tumors. Scale bar: 2 cm. (C) Weights of final MC38 tumors (n = 30 tumors from WT and 39 tumors from KO from 3 independent experiments; Mann-Whitney U test, *P < 0.05). (D) UMAP analysis of cDC single cells sampled from MC38 tumors from Dnase1l3 WT and Dnase1l3-KO mice (analyzed by scRNA-Seq). (E) DCs from MC38 tumors in Dnase1l3-KO mice have reduced cDC2 but increased cDC1 populations. (F) The relative abundance of different subgroups of cDC2 cells. (G) Dnase1l3-deficient cDC2 cells in cluster 3 have reduced expression of chemokines and cytokines (Mann-Whitney U test, *P < 0.05, ***P < 0.001). (H) Dnase1l3-deficient cDC2 cells in cluster 3 have reduced expression of various activation marker genes (Mann-Whitney U test, **P < 0.01, ***P < 0.001, ****P < 0.0001). (I) Dnase1l3-KO mice have reduced abundance of CD86+CD11b+ cDCs in the MC38 tumors at an early stage of tumor development (10 days after inoculation) (n = 21 WT and 21 KO from 3 independent experiments; *P < 0.05). Representative FACS plots from WT and KO mice are shown. Biological replicates were combined in the analysis using a linear regression model to remove the batch effects (see Methods). (J) Dnase1l3-KO mice have reduced abundance of activated cDCs in tumor draining lymph nodes (dLNs) at an early stage of tumor development (10 days after inoculation). Indicated immune cell population in dLNs was analyzed by flow cytometry (n = 16 WT and 16 KO from 2 independent experiments). Biological replicates were combined in the analysis using a linear regression model to remove the batch effects (see Methods).