In vivo CRISPR screens identify CBX4 as an epigenetic regulator for cancer immunotherapy
Zhibo Ma, Wenlong Jia, Xi Zhou, Jing Liu, Qingwen Li, Ruizhi Chang, Gu Shiqi, Naonao Yuan, Zhishui Chen, Peixiang Lan
Zhibo Ma, Wenlong Jia, Xi Zhou, Jing Liu, Qingwen Li, Ruizhi Chang, Gu Shiqi, Naonao Yuan, Zhishui Chen, Peixiang Lan
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Research Article Immunology Oncology

In vivo CRISPR screens identify CBX4 as an epigenetic regulator for cancer immunotherapy

Abstract

Epigenetic dysregulation is associated with immune evasion and immune checkpoint blockade (ICB) resistance. Here, using in vivo CRISPR/Cas9 screens targeting epigenetics-related factors in mouse tumor models treated with ICB, we identified chromobox 4 (CBX4) as a key negative regulator of the immune tumor microenvironment (TME). Single-cell RNA-seq and spatial transcriptomics analyses of patients receiving neoadjuvant anti–programmed cell death protein 1 (anti–PD-1) therapy revealed high CBX4 expression in both tumor cells and immunosuppressive tumor-associated macrophage subpopulations, with preferential accumulation in nonresponders. Deficiency of CBX4 in macrophages or tumor cells induced robust antitumor immunity and increased infiltration and the cytotoxic activity of CD8+ T cells and NK cells, thereby heightening the sensitivity of ICB treatment. Mechanistically, CBX4 targeted H3K9me3- and H3K27me3-marked endogenous retroelements such as RLTR4-Mm-int. Loss of CBX4 derepressed retrotransposons, activating cytosolic RNA-sensing pathways and triggering the type I IFN response, ultimately leading to a robustly inflamed TME. Moreover, we uncovered a negative correlation between CBX4 expression, immune responses, and retrotransposon levels, and were able to determine the prognosis of patients with hepatocellular carcinoma (HCC) undergoing ICB therapy. Our study establishes CBX4 as an epigenetic immune checkpoint through the epigenetic silencing of retrotransposons, remodeling the immune TME and thus providing a promising therapeutic target to enhance tumor immunogenicity and overcome immunotherapy resistance.

Authors

Zhibo Ma, Wenlong Jia, Xi Zhou, Jing Liu, Qingwen Li, Ruizhi Chang, Gu Shiqi, Naonao Yuan, Zhishui Chen, Peixiang Lan

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Figure 6

Deletion of CBX4 derepresses endogenous retroelements.

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Deletion of CBX4 derepresses endogenous retroelements. (A) Representativ...
(A) Representative images from clinical HCC tissues with responsive or nonresponsive to anti–PD-1 treatment. Scale bars: 50 μm. (B) Representative images from Hepa1-6 tumor tissues. Scale bars: 50 μm. (C) Representative images from control and sgCbx4 Hepa1-6 tumor cells, WT and Cbx4-cKO TAMs, treated with or without 1 μM UNC3866 BMDMs. Scale bars: 20 μm. (D) Volcano plot showing retroelement loci with increased expression (red) and decreased expression (blue) in Cbx4-cKO CD11bhiF4/80hi TAMs versus WT CD11bhiF4/80hi TAMs, combining both forward and reverse strands. (E) Violin plots showing differentially expressed retroelement classes comparing Cbx4-cKO CD11bhiF4/80hi TAMs with WT CD11bhiF4/80hi TAMs. (F) Heatmap of scaled retroelement loci expression in differential retroelement classes comparing Cbx4-cKO CD11bhiF4/80hi TAMs with WT CD11bhiF4/80hi TAMs. (G and H) Western blots showing the expression of H3K9me3 and H3K27me3 in control and sgCbx4 Hepa1-6 tumor cells (G) and WT and Cbx4-cKO CD11bhiF4/80hi TAMs (H). (I) Integrative Genomic Viewer (IGV) screenshots of Cbx4, H3K9me3, and H3K27me3 CUT&Tag signals from WT and Cbx4-cKO CD11bhiF4/80hi TAMs. (J and K) sgCbx4-Hepa1-6 and MC38 tumor volumes, tumor growth curves, and tumor weights were assessed for siNegative, si RLTR4_Mm_int1, and si RLTR4_Mm_int2 groups (n = 5). (L) Schematic of dsRNA production. (M and N) Hepa1-6 and MC38 tumor volumes, tumor growth curves, and tumor weights were assessed for control and ERV: RLTR4_Mm_int (n = 6) groups. (O and P) Hepa1-6 and MC38 tumor volumes, tumor growth curves, and tumor weights were assessed for IgG, anti–PD-1, ERV: RLTR4_Mm_int, and ERV: RLTR4_Mm_int + anti–PD-1 groups (n = 6). Data represent the mean ± SEM. Tumor growth curve data were analyzed by 2-way ANOVA with Tukey’s multiple-comparison test (J, K, and M–P). Other data were analyzed by 1-way ANOVA (J, K, O, and P) and 2-tailed, unpaired Student’s t test (M and N). **P < 0.01, ***P < 0.001, and ****P < 0.0001.