Natural killer (NK) cells and T cells are key effectors of antitumor immune responses and major targets of checkpoint inhibitors. In multiple cancer types, we find that the expression of Wnt signaling potentiator R-spondin genes (e.g., RSPO3) is associated with favorable prognosis and positively correlates with gene signatures of both NK cells and T cells. Although endothelial cells and cancer-associated fibroblasts comprise the R-spondin 3–producing cells, NK cells and T cells correspondingly express the R-spondin 3 receptor LGR6 within the tumor microenvironment (TME). Exogenous expression or intratumor injection of R-spondin 3 in tumors enhanced the infiltration and function of cytotoxic effector cells, which led to tumor regression. NK cells and CD8+ T cells independently and cooperatively contributed to R-spondin 3–induced control of distinct tumor types. The effect of R-spondin 3 was mediated in part through upregulation of MYC and ribosomal biogenesis. Importantly, R-spondin 3 expression enhanced tumor sensitivity to anti–PD-1 therapy, thereby highlighting new therapeutic avenues.

Significance:

Our study identifies novel targets in enhancing antitumor immunity and sensitizing immune checkpoint inhibition, which provides a rationale for developing new immunotherapies against cancers. It also offers mechanistic insights on Wnt signaling–mediated modulation of anticancer immunity in the TME and implications for a putative R-spondin–LGR6 axis in regulating NK-cell biology.

This article is highlighted in the In This Issue feature, p. 2945

Natural killer (NK) cells are essential innate immune effector cells that can recognize and rapidly kill oncogenically transformed target cells. More important, NK-cell interactions with other immune cells in the tumor microenvironment (TME), such as dendritic cells (DC) and T cells, are crucial to magnifying the overall immune response against cancer (1, 2). Supporting this notion, elevated numbers and enhanced functionality of NK cells are associated with better responses to immune checkpoint blockade therapies that largely target T cells (2), and emerging evidence indicates that direct targeting on NK cells may also exist (3, 4). However, NK cells typically exhibit poor capacity to infiltrate tumors and frequently become functionally exhausted within tumors due to various immunosuppressive facets of the TME (5). Thus, the identification of molecular targets and developing therapeutic strategies to promote infiltration and maintain or restore antitumor functions of NK cells in the TME have been an outstanding clinical priority to improve cancer outcomes.

Wnt signaling pathways control a wide range of cellular processes and are delicately regulated by a variety of positive and negative regulators with temporospatial specificity (6). Several recent studies have highlighted an important role that Wnt signaling plays in regulating NK-cell antitumor functionality (7). Activation of the Wnt/β-catenin pathway via inhibition of GSK3β enhances the maturation and function of NK cells (8). Cancer cell secretion of the Wnt signaling antagonists Dickkopf 1/2 (DKK1/2) facilitates the evasion of NK cell–mediated antitumor responses in certain contexts (9, 10). On the other hand, hyperactivation of the Wnt/β-catenin pathway is often a hallmark of cancer cells and crucial in tumor formation. In addition, evidence is also mounting for an immune cell exclusion phenotype associated with tumor cell–intrinsic aberrant β-catenin signaling activation across cancers (11, 12). Thus, the TME is controlled by an intricate interplay of Wnt agonists, antagonists, and antiantagonists, and there could be certain components in the Wnt signaling pathway that play critical roles in tuning the activity and infiltration of NK cells in the TME.

The R-spondin gene family, RSPO1 through RSPO4, encodes four evolutionarily conserved secreted proteins. R-spondins can potentiate canonical Wnt signaling at a low dose of Wnt following binding to the leucine-rich repeat-containing G-protein–coupled receptors (LGR) LGR4, LGR5, and LGR6 with high affinity (13, 14). Previous studies have described functions for R-spondins mainly in embryonic development, adult stem cell maintenance, and tumorigenesis (15, 16). However, the roles of R-spondins in modulating antitumor immunity remain ill-defined and largely unexplored.

LGR6 shares a similar structural basis with LGR4/5, and together they belong to the B-type LGR subfamily that is characterized by a long ectodomain containing 17 leucine-rich repeats (16). These three LGRs were considered obligate high-affinity receptors for R-spondins (13). Although LGR-independent enhancement of Wnt signaling has also been reported recently for RSPO2 and RSPO3 (17, 18), LGR6 was shown to have a unique expression pattern and has been extensively reported to mark distinct types of adult stem cells in actively self-renewing tissues, such as epidermis and mammary glands (19–22). However, the expression and function of LGR6 in cell types other than the stem/progenitor cell populations remain unspecified.

Here we identify the R-spondin family members R-spondin 3 and R-spondin 1, which are mainly expressed by endothelial cells (EC) and cancer-associated fibroblasts (CAF) in the TME, as potential modulators of antitumor immunity in cancers. Exogenous expression of R-spondin 3 in the TME promotes tumor suppression largely through NK cells, as well as CD8+ T cells. The mechanism of R-spondin 3 enhancement of effector cell responses involves enhanced expression of the Wnt target gene MYC in NK cells. Importantly, R-spondin 3 and PD-1 blockade therapy cooperatively enhance immune control of tumors. These findings provide molecular and mechanistic insights on Wnt signaling components in modulating antitumor immunity and a strong rationale for developing novel anticancer immunotherapeutic strategies using R-spondins.

RSPO3 and RSPO1 Levels Positively Correlate with Anticancer Immune Cell Signatures and Better Prognosis in Multiple Cancers

To explore Wnt signaling components that may regulate the antitumor immune responses of NK cells in the TME, we generated correlation matrices with The Cancer Genome Atlas (TCGA) data sets using 60 genes encoding Wnt signaling components (Supplementary Table S1) and 9 NK-cell signature genes (KIR2DL4, NCR1, KLRD1, KLRC1, KLRC2, KLRC3, KLRC4, KLRB1, KLRK1; ref. 23). These genes are enriched in NK cells and can be used to indirectly infer the abundance of NK cells within tumor tissue. RSPO1 and RSPO3 recurrently showed positive associations with the NK-cell signature genes in four cancer types: skin cutaneous melanoma (SKCM), pancreatic adenocarcinoma (PAAD), lung squamous carcinoma (LUSC), and head and neck squamous cell carcinoma (HNSC; Fig. 1A). Notably, all four members of the R-spondin family are downregulated in most cancer types compared with matched normal tissues (Fig. 1B), suggesting a potential for a shared underlying mechanistic role of R-spondin in different cancers. We then performed further correlation analyses specifically for the NK-cell signature genes with RSPO3 or RSPO1 in a total of 33 cancer types from TCGA data sets. Nine cancer types showed positive correlations (R > 0.45, P < 0.05) for RSPO3, with SKCM, PAAD, breast invasive carcinoma (BRCA), and cholangiocarcinoma (CHOL) showing strong correlations (R > 0.5; Fig. 1C). Four cancer types showed positive correlations (R > 0.45, P < 0.05) for RSPO1, with PAAD and CHOL showing strong correlations (R > 0.5; Supplementary Fig. S1A). In contrast, RSPO2 and RSPO4 did not show positive correlations with the NK-cell signature in the TCGA data sets (R > 0.45, P < 0.05; Supplementary Fig. S1B and S1C). Together, these data suggest positive correlations between the expression of RSPO3 or RSPO1 with NK-cell signature across different cancers, particularly in SKCM, PAAD, BRCA, CHOL, and LUSC.

Figure 1.

EC- and CAF-derived R-spondins correlate with anticancer immune cell signatures and prognosis in multiple cancers. A, Hierarchically clustered Kendall correlation matrices using the indicated data sets from the TCGA database based on components of the Wnt signaling pathway and NK-cell signature genes. B, Heatmap visualization of the gene expression levels of RSPO1, RSPO2, RSPO3, and RSPO4 in different cancer tissues (T) and the matched normal tissues (N) in the TCGA data sets. Data are shown as log2 (TPM+1). TPM, transcripts per million. C, Results of Spearman rank correlation analyses of RSPO3 with NK-cell signature genes using TCGA data sets plotted with coefficient R and –log10 (P). Cancer types with correlation P < 0.01 and R > 0.45 are marked red. D, Overall comparison of Spearman rank correlation coefficients for RSPO3 with NK-cell signature for a total of 19 types of tumors and normal tissues. Wilcoxon tests were performed. E, Spearman rank correlation plots for RSPO3 with CD69, GZMA, GZMB, and IFNG in SKCM and PAAD of TCGA data sets. F, Kaplan–Meier curves showing the prognostic impact of RSPO3 expression levels for the overall survival of patients with SKCM and classic PAAD of TCGA data sets. HRs and P values of log-rank tests are shown. Patients were grouped by the median expression levels of RSPO3. G and H, The expression of the detectable RSPO genes in the single-cell RNA-sequencing data sets of patients with melanoma (G) and pancreatic carcinoma (H) reported previously. I, Representative pictures of human melanoma tissues with immunohistochemical staining of RSPO3 and CD31 in serial sections. Pictures are shown as 200×. Abbreviations for the cancer types are listed in Supplementary Table S1.

Figure 1.

EC- and CAF-derived R-spondins correlate with anticancer immune cell signatures and prognosis in multiple cancers. A, Hierarchically clustered Kendall correlation matrices using the indicated data sets from the TCGA database based on components of the Wnt signaling pathway and NK-cell signature genes. B, Heatmap visualization of the gene expression levels of RSPO1, RSPO2, RSPO3, and RSPO4 in different cancer tissues (T) and the matched normal tissues (N) in the TCGA data sets. Data are shown as log2 (TPM+1). TPM, transcripts per million. C, Results of Spearman rank correlation analyses of RSPO3 with NK-cell signature genes using TCGA data sets plotted with coefficient R and –log10 (P). Cancer types with correlation P < 0.01 and R > 0.45 are marked red. D, Overall comparison of Spearman rank correlation coefficients for RSPO3 with NK-cell signature for a total of 19 types of tumors and normal tissues. Wilcoxon tests were performed. E, Spearman rank correlation plots for RSPO3 with CD69, GZMA, GZMB, and IFNG in SKCM and PAAD of TCGA data sets. F, Kaplan–Meier curves showing the prognostic impact of RSPO3 expression levels for the overall survival of patients with SKCM and classic PAAD of TCGA data sets. HRs and P values of log-rank tests are shown. Patients were grouped by the median expression levels of RSPO3. G and H, The expression of the detectable RSPO genes in the single-cell RNA-sequencing data sets of patients with melanoma (G) and pancreatic carcinoma (H) reported previously. I, Representative pictures of human melanoma tissues with immunohistochemical staining of RSPO3 and CD31 in serial sections. Pictures are shown as 200×. Abbreviations for the cancer types are listed in Supplementary Table S1.

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To further investigate whether the correlations for RSPO3 or RSPO1 with NK-cell signature are unique in tumor tissues relative to normal tissues, we analyzed the available TCGA normal tissues or the Genotype-Tissue Expression data set. Results showed no or weaker positive correlations were observed in the counterpart normal tissues in most cancer types analyzed except for bladder urothelial carcinoma and thyroid carcinoma (Supplementary Fig. S1D and S1E). A generally stronger correlation with NK-cell signature in tumor tissues relative to counterpart normal tissues was observed for RSPO3 but not for RSPO1 (Fig. 1D; Supplementary Fig. S1F). The impact of confounding clinical factors on these associations was assessed by multivariant analysis, including age, sex, history of neoadjuvant therapy, tumor grade, and tumor stage. The results showed that RSPO3 level remained an independent factor that correlated with the expression of NK-cell signature in both SKCM and PAAD (Supplementary Fig. S1G). These data suggest a positive correlation between the expression of RSPO3 and NK-cell signature in tumor tissues.

NK cells exhibit tight interactions with DCs and T cells and subsequently affect the overall anticancer immune responses (1). To further explore whether the positive correlation seen for R-spondin genes with NK-cell signature could also be observed for DC or T-cell signature, we did correlation analyses for RSPO3 with a conventional type 1 dendritic cell (cDC1) signature (KIT, CCR7, BATF3, FLT3, ZBTB46, IRF8, BTLA, MYCL, CLEC9A, and XCR1; ref. 24) and a T-cell signature (CD8A, CD8B, and CD3E). Fourteen cancer types showed positive correlations (R > 0.45, P < 0.05) between RSPO3 and cDC1 signature, whereas 10 cancer types showed positive correlations between RSPO3 and T-cell gene signatures (Supplementary Fig. S2A and S2B). We next checked whether RSPO3 or RSPO1 correlated with the expression of an immune cell activation marker (CD69) or cytotoxic functional genes (GZMA, GZMB, and IFNG) in the TME. Strong positive correlations (R > 0.55, P < 0.05) with RSPO3 were observed for CD69, GZMA, and GZMB in SKCM and PAAD (Fig. 1E). Overall positive correlations (R = 0.3–0.75, P < 0.1) were observed for these markers with RSPO3 in CHOL, BRCA, and LUSC (Supplementary Fig. S2C) and with RSPO1 in PAAD and CHOL (Supplementary Fig. S2D). Together, these data suggest that high expression levels of RSPO3 or RSPO1 are associated with better anticancer immunity in the TME.

Importantly, survival analyses revealed that patients with higher levels of RSPO3 had a better prognosis in SKCM, PAAD (classic subtype), CHOL, and BRCA (nonluminal, triple-negative, HER2+; Fig. 1F; Supplementary Fig. S2E). Patients with higher levels of RSPO1 had better survival in CHOL (Supplementary Fig. S2F). Collectively, this body of evidence suggests that expression of RSPO3 or RSPO1 is associated with better NK- and T-cell activity as well as improved prognosis in cancers.

RSPO3 Is Expressed by ECs and CAFs in the TME

To explore the source of R-spondins in the TME, we analyzed available single-cell RNA sequencing (scRNA-seq) data sets of human melanoma and pancreatic carcinoma (25, 26). Results showed that ECs and CAFs were the major cell populations that expressed RSPO1 and RSPO3 in the TME, with RSPO3 being much more abundantly expressed (Fig. 1G and H). Low to negligible expression levels of RSPO2 and RSPO4 were observed in the TME of the two cancer types (Fig. 1G and H). Furthermore, IHC staining of R-spondin 3 and EC marker CD31 showed an abundant protein level of R-spondin 3 in the melanoma TME with regional overlap with CD31 staining (Fig. 1I). These data indicate that ECs and CAFs are among the major cell sources of R-spondin 3 in the TME.

R-spondin Receptor LGR6 Is Prominently Expressed by Human NK Cells

We next interrogated the expression of R-spondin receptors in the TME. LGR4, LGR5, and LGR6 are considered the obligate high-affinity receptors for R-spondins 1 to 4 (13, 17). By analyzing the scRNA-seq data set of human melanoma (25), we found that NK cells were the predominant cell population that expressed LGR6 in the TME, whereas ECs and CAFs expressed an appreciable amount of LGR4 and LGR5, respectively (Fig. 2A). To further investigate the expression pattern of LGRs 4 to 6 in normal NK cells and other immune cells, we analyzed the Database of Immune Cell Expression (DICE), which covers 15 immune cell subtypes that include NK cells, B cells, T cells, and monocytes from 91 healthy donors (27). Among these cell populations, NK cells showed the most pronounced expression of LGR6, whose transcript level [median transcripts per million (TPM) = 88.55; 95% confidence interval (CI), 76.52–102.5] was comparable to that of NCAM1 (median TPM = 84.04; 95% CI, 81.15–86.93), which encodes for the canonical NK-cell marker CD56 (Fig. 2B). Meanwhile, CD4+ Th1 cells displayed a varied but overall lower level of LGR6 compared with NK cells (Fig. 2B). For the other two receptors, appreciable expression of LGR4 could be observed in B cells, and LGR5 was not abundantly expressed by any of these immune cells (Fig. 2B). Another data set from the Primary Cell Atlas in BioGPS Dataset Library (28, 29), a meta-analysis of microarray data sets of 745 human primary cell samples, also showed that NK cells and, to a lesser extent, CD8+ T cells were the two cell populations that had prominent expression levels of LGR6 (Supplementary Fig. S3). The other two known R-spondin receptors—LGR4 and LGR5—are mainly expressed by embryonic stem cells and mesoderm-mesenchymal stem cell–derived cell lineages (Supplementary Fig. S3). Interestingly, analysis of a transcriptome data set of human peripheral blood NK-cell subsets (30) suggested that the mature and cytotoxic subset of NK cells (CD56dimCD57+) had higher levels of LGR6 compared with the less mature and less cytotoxic subsets (CD56bright; log2FC = 5.69, P < 0.0001; Fig. 2C). Together, this evidence consistently suggests that NK cells have a high transcriptional level of LGR6.

Figure 2.

LGR6 is prominently expressed by human NK cells. A, The expression of LGR4, LGR5, and LGR6 in the scRNA-seq data set of human melanoma reported previously, accessed from Broad Institute's Single Cell Portal. B, Visualization of LGR4, LGR5, LGR6, and NCAM1 expression in human immune cell subtypes in DICE data sets. TH, T helper; TREG, regulatory T cell; TFH, T follicular helper. C, Heatmap visualization of the gene expression levels of LGR4, LGR5, and LGR6 in a bulk RNA-seq data set of human circulating NK-cell subsets reported previously. D, qRT-PCR analysis of LGR4 expression for different immune cell types isolated from human peripheral blood pooled by two different donors. Gran, granulocyte. E, Protein expression of LGR4 in different immune cell types isolated from human peripheral blood from two different donors. F, qRT-PCR analysis of LGR6 expression for different immune cell types isolated from human peripheral blood pooled by two different donors. G, Protein expression of LGR6 in different immune cell types isolated from human peripheral blood from two different donors. H, Spearman rank correlation plot for RSPO3 with LGR6 using the TCGA SKCM data set. Data are shown as mean ± SD for D and F.

Figure 2.

LGR6 is prominently expressed by human NK cells. A, The expression of LGR4, LGR5, and LGR6 in the scRNA-seq data set of human melanoma reported previously, accessed from Broad Institute's Single Cell Portal. B, Visualization of LGR4, LGR5, LGR6, and NCAM1 expression in human immune cell subtypes in DICE data sets. TH, T helper; TREG, regulatory T cell; TFH, T follicular helper. C, Heatmap visualization of the gene expression levels of LGR4, LGR5, and LGR6 in a bulk RNA-seq data set of human circulating NK-cell subsets reported previously. D, qRT-PCR analysis of LGR4 expression for different immune cell types isolated from human peripheral blood pooled by two different donors. Gran, granulocyte. E, Protein expression of LGR4 in different immune cell types isolated from human peripheral blood from two different donors. F, qRT-PCR analysis of LGR6 expression for different immune cell types isolated from human peripheral blood pooled by two different donors. G, Protein expression of LGR6 in different immune cell types isolated from human peripheral blood from two different donors. H, Spearman rank correlation plot for RSPO3 with LGR6 using the TCGA SKCM data set. Data are shown as mean ± SD for D and F.

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To validate the findings from these bioinformatics analyses, we purified human NK cells, CD19+ B cells, CD8+ T cells, CD4+ T cells, CD14+ monocytes, and granulocytes from the peripheral blood of healthy donors and performed qRT-PCR or Western blot assays. Supporting the results shown in Fig. 2B, qRT-PCR analysis revealed over threefold greater level of LGR4 in B cells compared with that in the rest of the peripheral blood cell populations (Fig. 2D). Protein levels of LGR4 showed no dramatic differences between the peripheral blood mononuclear cell fractions but showed a much lower level in the granulocytes (Fig. 2E). Importantly, although LGR6 mRNA was detected in NK cells and, to a lesser extent, in CD8+ T cells, the LGR6 protein was substantially expressed by NK cells but not by any other peripheral blood mononuclear cells in a decent amount (Fig. 2F and G). These results suggest LGR6 is prominently expressed by human NK cells. Consistent with these data, a moderate positive correlation (R = 0.33, P < 0.05) was observed for LGR6 with RSPO3 in the TCGA data set of melanoma (Fig. 2H), indicating that active R-spondin ligand–LGR6 interactions possibly existed within the tumor tissue. Collectively, these data suggest that LGR6 is highly expressed by human NK cells, and the R-spondin 3/LGR6 axis may serve as a signaling axis in the TME to regulate NK cell–mediated antitumor immunity in human cancers.

Interestingly in mouse NK cells, as revealed by the ImmGen project data set (31), a higher level of Lgr6, as well as Lgr4, was observed in the mature and cytotoxic subset (CD11b+CD27) relative to the less mature and less cytotoxic subset (CD11bCD27+; Lgr6: log2FC = 4.09, P < 0.05, Lgr4: log2FC = 3.03, P < 0.05; Supplementary Fig. S4A), suggesting a conserved regulatory mechanism for the expressions of R-spondin receptors that may exist between mouse and human NK cells. Notably, unlike in human samples, the overall transcription level of mouse Lgr6 in bulk NK cells, as revealed by the Haemopedia RNA sequencing (RNA-seq) data sets (32), was not as abundant as that of mouse NK-cell marker genes, such as Ncr1 and Klrb1c (Supplementary Fig. S4B), implying differential functional significance for the LGR6-mediated signaling pathway in the associated biological processes between the two species.

Exogenous Expression of R-spondin 3 in the TME Inhibits Tumor Progression

To investigate whether an increased level of R-spondin 3 in the TME affects tumor progression, we generated mouse melanoma tumor cell line B16F10 overexpressing R-spondin 3. The endogenous expression level of RSPO3 in B16F10 was low compared with several other tumor cell lines analyzed (Supplementary Fig. S5A). B16F10 cells were transduced with empty vector (B16F10-EV) or vector expressing R-spondin 3 (B16F10-Rspo3; Supplementary Fig. S5B and S5C). The two lines showed marginal growth difference in vitro (Supplementary Fig. S5D) and no growth difference in vivo in the immunodeficient NRG mice (NOD-Rag1nullIL2rgnull, NOD rag gamma) that lack both innate and adaptive immunity (Fig. 3A and B; Supplementary Fig. S5E). Notably, in the immune-competent syngeneic mice, the B16F10-Rspo3 group showed substantially impaired tumor progression (Fig. 3C and D; Supplementary Fig. S5F) and prolonged overall survival (Fig. 3E) relative to the B16F10-EV group. Similar effects were recapitulated in a mouse pancreatic carcinoma cell line, Pan02, in which the overexpression of R-spondin 3 (Supplementary Fig. S5G and S5H) showed marginal effects on in vitro growth (Supplementary Fig. S5I) and in vivo growth in NRG mice (Fig. 3F and G; Supplementary Fig. S5J) but inhibited tumor progression in syngeneic B6 mice (Fig. 3HJ; Supplementary Fig. S5K). The tumors of Pan02-Rspo3 were also more movable and with clearer boundaries from surrounding tissues compared with Pan02-EV tumors, indicating less invasiveness. To further confirm the role of R-spondin 3 in inhibiting tumor progression, we performed intratumor injections of R-spondin 3 protein in B16F10 syngeneic tumor models (Fig. 3K). The treatment was effective in suppressing tumor growth and extending survival (Fig. 3LN). Importantly, in the Lgr6−/− mice, the exogenously expressed R-spondin 3–mediated tumor suppression could still be observed in both tumor models, whereas the effect was diminished in the Pan02 model, as shown by an enhanced tumor progression of Pan02-Rspo3 tumors in the Lgr6−/− mice relative to that in the wild-type mice (Supplementary Fig. S5L–S5O), suggesting that LGR6 partially mediated the tumor suppression caused by enhanced R-spondin 3 levels in the TME in a tumor-specific fashion.

Figure 3.

Exogenous expression of R-spondin 3 in the TME inhibits tumor progression. A and B, Growth curves of B16F10-EV and B16F10-Rspo3 in NRG mice (n = 8 mice per group; A) and representative pictures of tumors dissected 18 days after inoculation (B). C and D, Growth curves of B16F10-EV and B16F10-Rspo3 in syngeneic B6 mice (n = 8 mice per group; C) and representative pictures of tumors dissected 18 days after inoculation (D). E, Survival curves and result of the log-rank test for growth of B16F10-EV and B16F10-Rspo3 in syngeneic B6 mice (n = 11 for each group). F and G, Growth curves of Pan02-EV and Pan02-Rspo3 in NRG mice (n = 8 mice per group; F) and representative pictures of tumors dissected 33 days after inoculation (G). H and I, Growth curves of Pan02-EV and Pan02-Rspo3 in syngeneic B6 mice (n = 8 mice per group; H) and representative pictures of tumors dissected 33 days after inoculation (I).J, Survival curves and result of the log-rank test for growth of Pan02-EV and Pan02-Rspo3 in syngeneic B6 mice (n = 12 for each group). K, Experimental design for LN. Recombinant mouse R-spondin 3 (10 μg) or the same volume of PBS was intratumorally injected to B16F10 tumors in syngeneic B6 mice (n = 6 mice per group). L, Tumor volumes measured before and 2 days after the first dose of R-spondin 3 intratumoral therapy were compared. Two-way ANOVA test with Sidak multiple comparisons. M, Tumor growth curves. N, Survival curves and result of the log-rank test. For A, C, F, H, and M, two-way ANOVA test with or without Tukey multiple comparisons test was performed. Data are shown as mean ± SEM or individual sample results. Data are representative of at least two independent experiments. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 3.

Exogenous expression of R-spondin 3 in the TME inhibits tumor progression. A and B, Growth curves of B16F10-EV and B16F10-Rspo3 in NRG mice (n = 8 mice per group; A) and representative pictures of tumors dissected 18 days after inoculation (B). C and D, Growth curves of B16F10-EV and B16F10-Rspo3 in syngeneic B6 mice (n = 8 mice per group; C) and representative pictures of tumors dissected 18 days after inoculation (D). E, Survival curves and result of the log-rank test for growth of B16F10-EV and B16F10-Rspo3 in syngeneic B6 mice (n = 11 for each group). F and G, Growth curves of Pan02-EV and Pan02-Rspo3 in NRG mice (n = 8 mice per group; F) and representative pictures of tumors dissected 33 days after inoculation (G). H and I, Growth curves of Pan02-EV and Pan02-Rspo3 in syngeneic B6 mice (n = 8 mice per group; H) and representative pictures of tumors dissected 33 days after inoculation (I).J, Survival curves and result of the log-rank test for growth of Pan02-EV and Pan02-Rspo3 in syngeneic B6 mice (n = 12 for each group). K, Experimental design for LN. Recombinant mouse R-spondin 3 (10 μg) or the same volume of PBS was intratumorally injected to B16F10 tumors in syngeneic B6 mice (n = 6 mice per group). L, Tumor volumes measured before and 2 days after the first dose of R-spondin 3 intratumoral therapy were compared. Two-way ANOVA test with Sidak multiple comparisons. M, Tumor growth curves. N, Survival curves and result of the log-rank test. For A, C, F, H, and M, two-way ANOVA test with or without Tukey multiple comparisons test was performed. Data are shown as mean ± SEM or individual sample results. Data are representative of at least two independent experiments. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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Exogenous Expression of R-spondin 3 in the TME Enhances Antitumor Immunity

To investigate whether R-spondin 3 in the TME affects the NK-cell and overall antitumor immunity, we analyzed the tumor-infiltrating immune cells from both B16F10-EV and B16F10-Rspo3 tumors. An increased infiltration of CD45+ immune cells and NK cells was observed by both flow cytometry analysis and immunohistochemistry (Fig. 4A; Supplementary Fig. S6A–S6C). In addition, enhanced expressions of cytotoxic molecules granzyme B and perforin and activation marker CD69 were observed in the NK cells from B16F10-Rspo3 tumors (Fig. 4B). In vitro killing capacity against B16F10 cells or YAC-1 cells, an NK-cell sensitive MHC-I low–expressing lymphoma cell line, of the NK cells derived from B16F10-Rspo3 tumors was stronger compared to that derived from the B16F10-EV tumors (Fig. 4C; Supplementary Fig. S6D), indicating a better NK-cell functionality. Furthermore, in the B16F10-Rspo3 tumors, we observed an increased proportion of CD103+ cDC1s (Fig. 4D; Supplementary Fig. S6E) and CD8+ T cells (Fig. 4E; Supplementary Fig. S6F and S6G), whose expression of granzyme B, perforin, CD69, and IFNγ were also increased (Fig. 4F; Supplementary Fig. S6H) compared with those in the B16F10-EV tumors, indicating a better overall anticancer immunity. Similarly, in the Pan02 pancreatic cancer model, increased percentages of NK cells and CD8+ T cells in the CD45+ immune cell population, with enhanced expression of granzyme B, were detected in the Pan02-Rspo3 group relative to the Pan02-EV group (Fig. 4G and H), although enhanced infiltrations could not be observed when measured as absolute infiltrating cell number per tumor weight (Supplementary Fig. S6I and S6J), which could be related to an increased amount of stromal tissue observed in the Pan02-Rspo3 tumors (Supplementary Fig. S6K). Collectively, these data indicate that increasing R-spondin 3 levels in the TME enhances NK-cell antitumor immunity and promotes better overall antitumor responses.

Figure 4.

Exogenous expression of R-spondin 3 in the TME enhances antitumor immunity. A, Absolute numbers of tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) in B16F10-EV and B16F10-Rspo3 tumors (n = 5–6 mice per group) by flow cytometry analysis. B, Representative flow plots (left) and summary (right) of the percentages of tumor-infiltrating NK cells expressing granzyme B, perforin, and CD69 in the B16F10-EV and B16F10-Rpso3 tumors (n = 5–7 mice per group). C, Cytotoxicity of freshly isolated tumor-infiltrating NK cells from B16F10-EV and B16F10-Rspo3 tumors was measured with chromium (51Cr)-release assays using YAC-1 cells as target cells with the indicated effector/target (E:T) ratios. Purified NK cells were pooled from four mice per group. D, Percentages of CD103+ DC [lineage (CD90.2, CD45R, Ly6G, NK1.1), CD45+, Ly6C, MHC-II+, F4/80, CD24+] in the CD45+ cell population in the B16F10-EV and B16F10-Rpso3 tumors by flow cytometry analysis (n = 6 mice per group). E, Absolute numbers of tumor-infiltrating CD8+ cells (CD45+CD3+CD8+) in B16F10-EV and B16F10-Rspo3 tumors by flow cytometry analysis (n = 5–6 mice per group). F, Representative flow plots (left) and summary (right) of the percentages of tumor-infiltrating CD8+ cells expressing granzyme B, perforin, and CD69 in the B16F10-EV and B16F10-Rpso3 tumors (n = 4–8 mice per group). G, Percentages of tumor-infiltrating NK cells and CD8+ T cells in the CD45+ cell population by flow cytometry analysis of Pan02-EV and Pan02-Rspo3 tumors (n = 9–11 mice per group). H, Representative flow plots (left) and summary (right) of the percentages of granzyme B–expressing tumor-infiltrating NK cells and CD8+ T cells in the Pan02-EV and Pan02-Rpso3 tumors (n = 9–11 mice per group). I, Growth curves of B16F10-EV and B16F10-Rspo3 cells in syngeneic B6 mice treated with isotype control (ISO), anti-NK1.1 depletion antibody, anti-CD8a depletion antibody, or both (n = 5–8 mice per group). J, Growth curves of B16F10-EV and B16F10-Rspo3 cells in Rag1−/− mice (n = 6 mice per group). K, Growth curves of Pan02-EV and Pan02-Rspo3 cells in syngeneic B6 mice treated with isotype control, anti-NK1.1 depletion antibody, anti-CD8a depletion antibody, or both antibodies (n = 5–8 mice per group). L, Growth curves of Pan02-EV and Pan02-Rspo3 cells in Rag1−/− mice (n = 6 mice per group). Data are shown as mean ± SEM and are representative of at least two independent experiments. For A, B, and D–H, Student t tests or Welch t tests were performed. For IL, two-way ANOVA tests were performed. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

Figure 4.

Exogenous expression of R-spondin 3 in the TME enhances antitumor immunity. A, Absolute numbers of tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) in B16F10-EV and B16F10-Rspo3 tumors (n = 5–6 mice per group) by flow cytometry analysis. B, Representative flow plots (left) and summary (right) of the percentages of tumor-infiltrating NK cells expressing granzyme B, perforin, and CD69 in the B16F10-EV and B16F10-Rpso3 tumors (n = 5–7 mice per group). C, Cytotoxicity of freshly isolated tumor-infiltrating NK cells from B16F10-EV and B16F10-Rspo3 tumors was measured with chromium (51Cr)-release assays using YAC-1 cells as target cells with the indicated effector/target (E:T) ratios. Purified NK cells were pooled from four mice per group. D, Percentages of CD103+ DC [lineage (CD90.2, CD45R, Ly6G, NK1.1), CD45+, Ly6C, MHC-II+, F4/80, CD24+] in the CD45+ cell population in the B16F10-EV and B16F10-Rpso3 tumors by flow cytometry analysis (n = 6 mice per group). E, Absolute numbers of tumor-infiltrating CD8+ cells (CD45+CD3+CD8+) in B16F10-EV and B16F10-Rspo3 tumors by flow cytometry analysis (n = 5–6 mice per group). F, Representative flow plots (left) and summary (right) of the percentages of tumor-infiltrating CD8+ cells expressing granzyme B, perforin, and CD69 in the B16F10-EV and B16F10-Rpso3 tumors (n = 4–8 mice per group). G, Percentages of tumor-infiltrating NK cells and CD8+ T cells in the CD45+ cell population by flow cytometry analysis of Pan02-EV and Pan02-Rspo3 tumors (n = 9–11 mice per group). H, Representative flow plots (left) and summary (right) of the percentages of granzyme B–expressing tumor-infiltrating NK cells and CD8+ T cells in the Pan02-EV and Pan02-Rpso3 tumors (n = 9–11 mice per group). I, Growth curves of B16F10-EV and B16F10-Rspo3 cells in syngeneic B6 mice treated with isotype control (ISO), anti-NK1.1 depletion antibody, anti-CD8a depletion antibody, or both (n = 5–8 mice per group). J, Growth curves of B16F10-EV and B16F10-Rspo3 cells in Rag1−/− mice (n = 6 mice per group). K, Growth curves of Pan02-EV and Pan02-Rspo3 cells in syngeneic B6 mice treated with isotype control, anti-NK1.1 depletion antibody, anti-CD8a depletion antibody, or both antibodies (n = 5–8 mice per group). L, Growth curves of Pan02-EV and Pan02-Rspo3 cells in Rag1−/− mice (n = 6 mice per group). Data are shown as mean ± SEM and are representative of at least two independent experiments. For A, B, and D–H, Student t tests or Welch t tests were performed. For IL, two-way ANOVA tests were performed. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

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To dissect the roles of different immune cells in the TME in R-spondin 3–mediated tumor suppression, we inoculated B16F10-EV and B16F10-Rspo3 tumors in mice lacking CD8+ T cells and/or NK cells by injecting the anti-CD8a and/or anti-NK1.1 depleting antibodies (Supplementary Fig. S7A–S7C). Depletion of both CD8+ T and NK cells, but not by depletion of either cell type alone, abrogated the tumor-suppressive effect of R-spondin 3 exogenous expression (Fig. 4I; Supplementary Fig. S7D), indicating that NK cells and CD8+ T cells are the major populations mediating tumor suppression, and both can act at least partially independently of each other. Further functional dissection using Rag1−/− mice, which lack mature B cells and T cells, also abrogated tumor suppression (Fig. 4J; Supplementary Fig. S7E), suggesting that other T-cell subsets or B cells may also play roles here. Interestingly, in the Pan02 pancreatic cancer model, depleting NK cells alone was sufficient to abrogate the tumor suppression, and depleting CD8+ T cells alone or tumor growth in Rag1−/− mice could just modestly but noticeably reduce R-spondin 3–mediated tumor suppression (Fig. 4K and L; Supplementary Fig. S7F–S7H). These results suggested that both NK cells and CD8+ T cells contributed to R-spondin 3–mediated tumor suppression in this pancreatic carcinoma tumor model, and there existed a T cell–independent effect. Together, these data indicate that both NK cells and CD8+ T cells mediate the tumor-suppressive effects of R-spondin 3.

R-spondin 3 Promotes MYC Expression in NK Cells in the TME

R-spondins are able to potentiate canonical Wnt signaling activity, which upregulates the expression of a series of target genes in a cell type– and context-specific manner (6, 33). To explore the mechanism by which R-spondin 3 promotes antitumor immunity in the TME, we sorted NK cells from the B16F10-EV and B16F10-Rspo3 tumors and measured the expression levels of several known Wnt target genes, including Myc, Axin2, Cd44, Lef1, Tcf7, Ppard, Mmp7, and Ccnd1. Among the detectable genes, Myc is significantly upregulated in NK cells from the B16F10-Rspo3 tumors relative to the B16F10-EV tumors (Fig. 5A). Using syngeneic recipients of MycG/G mice, in which the endogenous Myc locus has been modified to encode a GFP-MYC fusion protein enabling the measurement of MYC expression with GFP signal intensities, we further confirmed an increased MYC protein level in the tumor-infiltrating NK cells from the B16F10-Rspo3 tumors compared with B16F10-EV tumors (Fig. 5B). Thus, R-spondin 3 promotes MYC expression in NK cells in the TME.

Figure 5.

R-spondin 3 promotes MYC expression in NK cells in the TME. A, qRT-PCR results of Wnt target genes of flow-sorted CD11b+CD27 NK cells from tumor tissues (n = 4 for each group). B, Flow analysis of tumor-infiltrating NK cells from B16F10-EV or B16F10-Rspo3 tumors inoculated subcutaneously to MycG/G mice (n = 5 for each group). Median fluorescence intensities of GFP for the indicated NK-cell subpopulations are shown. C and D, qRT-PCR results of rRNA (C) or ribosomal protein mRNA (D) of flow-sorted tumor-infiltrating NK cells from B16F10-EV or B16F10-Rspo3 tumor tissues. Data are shown as mean ± SD. Expression levels were normalized by cell numbers sorted. E, Median forward scatter (FSC) intensities of flow-analyzed tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) from B16F10-EV and B16F10-Rspo3 tumors (n = 4–5 per group). F, Tumor growth curves of B16F10-EV and B16F10-Rspo3 in Ncr1Cre and MycΔ/Δ/Ncr1Cre mice (n = 4–5 per group). G, Tumor growth curves of B16F10-EV and B16F10-Rspo3 cells in MycΔ/Δ/Ncr1Cre mice depleted of NK cells (left) or CD8+ T cells (right; n = 4–5 per group). For A–D and F–G, two-way ANOVA with or without Sidak multiple comparisons tests was performed. For E, Student t test was performed. Data represent at least two independent experiments. Data are shown as mean ± SEM unless otherwise noted. P < 0.05 is considered statistically significant. ns, not significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

Figure 5.

R-spondin 3 promotes MYC expression in NK cells in the TME. A, qRT-PCR results of Wnt target genes of flow-sorted CD11b+CD27 NK cells from tumor tissues (n = 4 for each group). B, Flow analysis of tumor-infiltrating NK cells from B16F10-EV or B16F10-Rspo3 tumors inoculated subcutaneously to MycG/G mice (n = 5 for each group). Median fluorescence intensities of GFP for the indicated NK-cell subpopulations are shown. C and D, qRT-PCR results of rRNA (C) or ribosomal protein mRNA (D) of flow-sorted tumor-infiltrating NK cells from B16F10-EV or B16F10-Rspo3 tumor tissues. Data are shown as mean ± SD. Expression levels were normalized by cell numbers sorted. E, Median forward scatter (FSC) intensities of flow-analyzed tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) from B16F10-EV and B16F10-Rspo3 tumors (n = 4–5 per group). F, Tumor growth curves of B16F10-EV and B16F10-Rspo3 in Ncr1Cre and MycΔ/Δ/Ncr1Cre mice (n = 4–5 per group). G, Tumor growth curves of B16F10-EV and B16F10-Rspo3 cells in MycΔ/Δ/Ncr1Cre mice depleted of NK cells (left) or CD8+ T cells (right; n = 4–5 per group). For A–D and F–G, two-way ANOVA with or without Sidak multiple comparisons tests was performed. For E, Student t test was performed. Data represent at least two independent experiments. Data are shown as mean ± SEM unless otherwise noted. P < 0.05 is considered statistically significant. ns, not significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

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We next explored the functional impact of altered MYC expression of NK cells in the R-spondin 3–mediated tumor suppression. Implications from a Myc conditional knockout mouse model—MycΔ/Δ/Ncr1Cre mice—which depleted MYC expression in NK cells and restricted subsets of innate lymphoid cells (34), indicated that ribosome and NK cell–mediated cytotoxicity were the two most enriched pathways in the differentially downregulated genes (FDR < 0.05) by RNA-seq analysis of the isolated splenic NK cells from MycΔ/Δ/Ncr1Cre mice (Supplementary Fig. S8A; Supplementary Table S2). The translation-associated gene set was also shown to be negatively enriched in the NK cells with Myc deletion (Supplementary Fig. S8B). These results suggested impaired ribosomal biogenesis as a feature of the NK cells with MYC deficiency. To determine whether the tumor-infiltrating NK cells from B16F10-Rspo3 tumors, which have enhanced MYC expression, also have enhanced ribosomal biogenesis compared with that from B16F10-EV tumors, qRT-PCR was performed using the NK cells sorted from these tumors. Results showed increased expression of rRNAs and mRNA levels of ribosomal proteins by the NK cells from B16F10-Rspo3 tumors compared with that from the B16F10-EV tumors (Fig. 5C and D). An increased forward scatter intensity revealed in flow cytometric analysis is a phenomenon usually observed in cells with enhanced ribosomal biogenesis associated with larger cell sizes, and was also seen for the tumor-infiltrating NK cells in B16F10-Rspo3 tumors relative to B16F10-EV tumors (Fig. 5E). Together, these indicate a stronger ribosomal biogenesis capacity of the NK cells in TME with a higher level of R-spondin 3.

To determine whether MYC expression in NK cells is required for R-spondin 3–mediated tumor suppression, B16F10 tumors were inoculated into MycΔ/Δ/Ncr1Cre mice and Ncr1Cre controls. Although R-spondin 3–mediated tumor suppression could still be observed in the MycΔ/Δ/Ncr1Cre mice (Fig. 5F), which corresponded to the results shown in Fig. 4E that depleting NK cells alone was not enough to abrogate tumor suppression in wild-type mice, we found the tumor suppression was abrogated in the MycΔ/Δ/Ncr1Cre mice with CD8+ T cells depleted (Fig. 5G), suggesting that MYC expression in NK cells is required for the contribution of NK cells to the R-spondin 3–mediated effects.

R-spondin 3 Sensitizes Tumors to PD-1 Blocking Therapy

Given that NK-cell activity and immune cell frequencies associate with patient responses to immune checkpoint inhibitors (2), we next tested the therapeutic efficacy of anti–PD-1 antibody in the R-spondin 3–overexpressing B16F10 tumors, whose parental line is known to be resistant to anti–PD-1 antibody therapy (Fig. 6A). Results showed a sensitization of B16F10 tumors with R-spondin 3 overexpression to anti–PD-1 antibody treatment (Fig. 6B), with some B16F10-Rspo3 tumors nearly completely rejected (Fig. 6C). The response rate of B16F10-Rspo3 tumors to anti–PD-1 therapy was significantly higher compared with B16F10-EV tumors (Supplementary Fig. S9A and S9B). Survival of mice inoculated with B16F10-Rspo3 tumors with anti–PD-1 therapy was substantially extended, with some achieving durable tumor remission (Fig. 6D). Importantly, the percentages of tumor-infiltrating NK cells and CD8+ T cells were both increased in the B16F10-Rspo3 tumors with anti–PD-1 antibody treatment compared with other groups (Fig. 6E; Supplementary Fig. S9C), indicating a better infiltration of cytotoxic cells. Furthermore, therapeutic merits could also be observed in the Pan02-Rspo3 tumors with anti–PD-1 antibody treatment (Fig. 6FH), indicating R-spondin 3 and anti–PD-1 therapy cooperatively enhance tumor control.

Figure 6.

R-spondin 3 sensitizes tumors to PD-1 blocking therapy. A, Experimental design for BE. B16F10-EV or B16F10-Rspo3 cells (5 × 105 cells) were inoculated subcutaneously (S.C.) to B6 mice. Then, 200 μg anti–PD-1 antibody or isotype antibody was intraperitoneally (i.p.) injected at days 8, 11, and 14. B, Tumor growth curves of B16F10-EV and B16F10-Rspo3 cells with isotype (iso) or anti–PD-1 therapy (n = 6–8 mice per group). C, Representative tumor pictures (left) and summary of tumor weights (right) of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy dissected 18 days after inoculation. D, Survival curves and results of the log-rank test of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy (n = 6–8 mice for each group). E, Absolute numbers of tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) and CD8+ cells (CD45+CD3+CD8+) in B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 antibody therapy (n = 3–6 mice per group). F, Experimental design for G and H. Pan02-EV or Pan02-Rspo3 cells (5 × 105 cells) were inoculated subcutaneously to B6 mice. Then, 200 μg anti–PD-1 antibody or isotype antibody was intraperitoneally injected at days 8, 11, 14 and 17. G, Tumor growth curves of Pan02-EV and Pan02-Rspo3 cells with isotype or anti–PD-1 therapy (n = 6–8 mice per group). H, Survival curves and results of the log-rank test of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy (n = 6–8 mice for each group). I, Growth curves of B16F10-EV and B16F10-Rspo3 cells with or without anti–PD-1 antibody therapy in mice treated with isotype control, anti-NK1.1 depletion antibody, or anti-CD8a depletion antibody (n = 5–8 mice per group) are shown. J, Growth curves of Pan02-EV and Pan02-Rspo3 cells in mice treated with isotype control, anti-NK1.1 depletion antibody, or anti-CD8 depletion antibody (n = 6–8 mice per group). For B, G, I, and J, two-way ANOVA tests were performed. For C and E, two-way ANOVA tests with Tukey multiple comparisons were performed. Data are representative of at least two independent experiments. Data are shown as mean ± SEM. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 6.

R-spondin 3 sensitizes tumors to PD-1 blocking therapy. A, Experimental design for BE. B16F10-EV or B16F10-Rspo3 cells (5 × 105 cells) were inoculated subcutaneously (S.C.) to B6 mice. Then, 200 μg anti–PD-1 antibody or isotype antibody was intraperitoneally (i.p.) injected at days 8, 11, and 14. B, Tumor growth curves of B16F10-EV and B16F10-Rspo3 cells with isotype (iso) or anti–PD-1 therapy (n = 6–8 mice per group). C, Representative tumor pictures (left) and summary of tumor weights (right) of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy dissected 18 days after inoculation. D, Survival curves and results of the log-rank test of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy (n = 6–8 mice for each group). E, Absolute numbers of tumor-infiltrating NK cells (CD45+CD3NK1.1+DX5+) and CD8+ cells (CD45+CD3+CD8+) in B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 antibody therapy (n = 3–6 mice per group). F, Experimental design for G and H. Pan02-EV or Pan02-Rspo3 cells (5 × 105 cells) were inoculated subcutaneously to B6 mice. Then, 200 μg anti–PD-1 antibody or isotype antibody was intraperitoneally injected at days 8, 11, 14 and 17. G, Tumor growth curves of Pan02-EV and Pan02-Rspo3 cells with isotype or anti–PD-1 therapy (n = 6–8 mice per group). H, Survival curves and results of the log-rank test of B16F10-EV and B16F10-Rspo3 tumors with isotype or anti–PD-1 therapy (n = 6–8 mice for each group). I, Growth curves of B16F10-EV and B16F10-Rspo3 cells with or without anti–PD-1 antibody therapy in mice treated with isotype control, anti-NK1.1 depletion antibody, or anti-CD8a depletion antibody (n = 5–8 mice per group) are shown. J, Growth curves of Pan02-EV and Pan02-Rspo3 cells in mice treated with isotype control, anti-NK1.1 depletion antibody, or anti-CD8 depletion antibody (n = 6–8 mice per group). For B, G, I, and J, two-way ANOVA tests were performed. For C and E, two-way ANOVA tests with Tukey multiple comparisons were performed. Data are representative of at least two independent experiments. Data are shown as mean ± SEM. P < 0.05 is considered statistically significant. ns, not statistically significant. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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We next dissected the contribution of NK cells and CD8+ T cells to the combinatory effect of R-spondin 3 with anti–PD-1 therapy. Results showed that depletion of CD8+ T cells completely abrogated the exceptional outcome achieved by anti–PD-1 treatment in Rspo3 tumors in both tumor models (Fig. 6I and J), suggesting a dependence on CD8+ T cells for the combinatory effects of anti–PD-1 therapy with increased tumor R-spondin 3 level. Importantly, although this combinatory effect could still be observed after NK-cell depletion (Fig. 6I and J), the strength was much diminished in the B16F10 model (Supplementary Fig. S9D), suggesting NK cells also play a role here. Together, these data suggest a robust sensitization for PD-1 blocking therapy by enhanced R-spondin 3 level in the TME.

In this study, we identified R-spondin 3 and R-spondin 1 derived from ECs/CAFs in the TME as critical regulators of antitumor immunity to affect cancer outcomes and sensitivity to immune checkpoint inhibitors. The expression of LGR6, a high-affinity receptor for R-spondins, is prominently expressed in human NK cells. Mechanistically, R-spondin 3 enhances MYC and ribosomal biogenesis gene expression in NK cells in the tumor tissue.

Wnt signaling is delicately regulated by a variety of positive and negative regulators with temporospatial specificity. DKK1 is a secreted Wnt signaling negative regulator. As a target gene of Wnt signaling, DKK1 is highly secreted by cancer cells with Wnt signaling aberrant activation (35). On the other hand, previous literature has shown cancers with aberrant β-catenin activation present immune deserts lacking infiltration of immune cells (11, 12). Interestingly, R-spondins synergize with Wnt proteins to activate canonical Wnt signaling with particular potency in the presence of DKK1 (16). These findings collectively suggest that the levels of R-spondins are reasonable with the capacity to modulate the activity of canonical Wnt signaling in the TME. This could particularly be the case for noncancer cells in the TME, including antitumor immune cells, whose activation of Wnt signaling is not like in cancer cells that are determined by intrinsic mutations or aberrant activation but are affected, to a greater extent, by the alterations of signals in the TME. These may also partially explain why numerous drugs inhibiting Wnt/MYC signaling showed good efficacy in vitro but were compromised in vivo (36). Therefore, our study provides new clues for these important questions.

The gene expression of R-spondins are widely reduced across multiple cancers shown in TCGA data, while the underlying mechanism remains unclear. It is possible that R-spondin proteins are essential sustaining factors for immune cells in the TME, and downregulating the expression levels of R-spondins may be a key mechanism for cancer cells to evade antitumor immunity. Our data showed positive correlations of RSPO3 and, to a lesser extent, RSPO1 with immune cell signatures and cancer outcomes, although we did not observe significant correlations for RSPO2 and RSPO4. This selectivity could be related to a more abundant expression of RSPO3 in tumor tissues compared with other RSPO genes (Fig. 1B). In addition, although all four R-spondins are able to potentiate Wnt signaling, their expression patterns and phenotypes shown in knockout mice have striking differences (37), suggesting distinct roles for the four R-spondin members in modulating a variety of biological processes.

Our data showed a pronounced expression of LGR6 in human NK cells, particularly by the more mature and cytotoxic subsets. The finding is of notable interest given a wide belief that the three B-type LGRs (LGR4/5/6) mainly regulate embryonic development and adult stem cell self-renewal as determined by their unique expression patterns primarily observed in stem/progenitor cell populations (16). Interestingly, in contrast to the neonatal lethality found in both Lgr4- null and Lgr5-null mutation mice, Lgr6 knockout mice are healthy and fertile (38), implying an essential difference for the biological functions of LGR6 from the other two LGRs. Future studies will be of great interest to clarify whether LGR6, like its expression in other tissues, marks specific NK-cell populations with self-renewing capacities, such as memory NK cells, or whether it functions, like most other G-protein coupled receptors (GPCR) that are highly specialized, to regulate certain NK-cell biological functions through G-protein–mediated signaling, a general mechanism used by GPCRs but not yet identified to be used by the three B-type LGRs (13).

The R-spondins bind to LGR4/5/6 with high affinity through their furin 2 repeat, which allows the other furin repeat in R-spondin to interact with RNF43/ZNRF3, a membrane E3 ubiquitin ligase complex that removes Wnt receptors from the cell surface. The subsequent endocytosis of the R-spondin–LGR–RNF43/ZNRF3 complex in turn leads to membrane clearance of the E3 ligases and persistence of Wnt receptors on the cell surface, thereby promoting Wnt signaling strength and duration (16, 39). Of note, LGR-independent enhancement of Wnt signaling has also been reported recently for RSPO2 and RSPO3, which was determined by direct interaction of R-spondins with RNF43/ZNRF3 (17, 18). Interestingly, although RNF43/ZNRF3 homologs exist in invertebrates, the R-spondin/LGR/RNF43 module is considered a relatively recent evolutionary “add-on” seen only in vertebrates and largely dedicated to adult stem cells (40). In this regard, although the pronounced LGR6 expression in human NK cells is highly likely to mediate profound biological functions in human cancers, the R-spondin 3–mediated tumor suppression revealed by our study in mouse tumor models is not necessarily mediated through LGR6, as shown in our data of a minor or no rescue of R-spondin 3–mediated tumor suppression in Lgr6−/− mice (Supplementary Fig. S5L–S5O). In contrast, LGR-independent signaling or LGR4/5-mediated signaling in other cell components in the TME may play roles here. Our observation of a small peak expression bar shown in the mouse NK cells relative to other cell types by RNA-seq analysis (Supplementary Fig. S4B) may provide evidence for the evolution of this LGR-mediated exquisite modulation of Wnt signaling in an early phase shown in mice.

MYC, as the key target gene of the canonical Wnt signaling pathway, is a master regulator controlling a variety of cellular processes, including ribosomal biogenesis that regulates mRNA translation. The expression of MYC has been shown to be essential for NK-cell metabolism and functional status (41, 42). Reduced MYC expression in the peripheral blood NK cells of patients with cancers was also reported (43). Of note, one hallmark of NK cells is that they maintain abundant mRNA levels of cytotoxic molecules at rest, and a ready-to-go ribosomal biogenesis machinery that ensures prompt translation of cytotoxic molecules when encountering target cells is critically needed for their innate killing capacity (44). Thus, our data showing enhanced MYC and ribosomal biogenesis gene expression in NK cells with increased R-spondin 3 in the TME provide mechanistic insights on how R-spondin 3 promotes antitumor immunity. Importantly, because the exact molecular basis of R-spondin–mediated signaling modulation in this context was unspecified, further studies may be performed to interrogate whether the enhanced MYC expression is a direct target of enhanced canonical Wnt signaling potentiated by R-spondin or a secondary consequence of an improved TME by R-spondin through activating other signaling pathways, such as noncanonical signaling. Given the use of a constitutive Ncr1Cre in our study that could result in an altered NK-cell compartment and function, potential confounding factors could be involved to affect the observed phenotypes. Thus, an inducible Ncr1-iCreER allele that has been reported recently (45) could be a better tool to be used in the future to study the NK-cell biology in cancers.

Inhibition of the PD-1/PD-L1 pathway has become a very powerful therapeutic strategy that has remarkably improved the prognosis of patients with cancers. However, resistance remains a hurdle for broader application, with multiple mechanisms proposed to contribute, which include inadequate T- or NK-cell infiltration. Regarding this, in our tumor models, enhanced R-spondin 3 in the TME promoted better infiltration of both T and NK cells in tumor tissues, which is likely to be one mechanism for the sensitization to anti–PD-1 therapy. Importantly, this combinatory effect is not only dependent on CD8+ T cells; depletion of NK cells also reduced the sensitivity to anti–PD-1 antibody observed in the B16F10-Rspo3 tumor model, which could be due to a diminished tumor suppression by R-spondin 3 and/or a consequence of immune checkpoint inhibitors to directly target NK cells (3, 4). Furthermore, depletion of CD8+ T cells completely eliminated any antitumor benefits observed in the B16F10-Rspo3 tumors, indicating complexity of the CD8+ T cell–independent mechanism by anti–PD-1 antibody combined with R-spondin 3. Future studies to investigate whether there is altered expression of PD-1 or PD-L1 in NK cells and CD8+ T cells will be of interest and informative to clarify underlying mechanisms.

Our study provides support for a translational potential of R-spondin proteins as immunotherapeutic agents to treat cancers, although the safety of R-spondins being therapeutic agents should take into consideration their roles in regulating the differentiation and proliferation of adult stem cells and tumorigenesis (46, 47). Gene fusions involving RSPO3 or RSPO2 were previously identified in colon cancers, and anti-RSPO3 treatment was demonstrated to inhibit tumor growth in PTPRK–RSPO3 fusion–positive human tumor xenografts through mechanisms including regulating intestinal stem cell function and promoting differentiation (48). Of note, although transgenic overexpression of RSPO3 or RSPO3 fusion genes could result in adenomatous growth of the intestine, this alone was not sufficient to promote continued tumor growth (49, 50), supporting the observation that RSPO fusion genes always co-occur with either BRAF or KRAS mutation in colon cancers (47). Therefore, although care should be taken, the strategy of using R-spondins as immunotherapeutic agents remains promising.

To conclude, our study identified a novel role of R-spondins in promoting antitumor immunity in the TME (Supplementary Fig. S10). Although R-spondin 3 showed the broadest expression across tissue types, further studies are warranted to elucidate which R-spondin is the most robust in providing therapeutic benefits. Further studies are also needed to determine the molecular or immunologic features of the cancers that may benefit from R-spondin–based therapy. Future studies integrating nanoparticles or other bioengineering technologies with the administration of R-spondin proteins will be valuable in advancing the translation of these new findings to clinical use in cancer therapies.

Bioinformatics Analysis of Patient Transcriptome Data

Kendall correlation matrix analysis was performed based on a total of 60 components of the Wnt signaling pathway (Supplementary Table S1) and NK-cell signature of SKCM, PAAD, LUSC, and HNSCC and visualized as a heatmap after hierarchical clustering in Phantasus v1.5.1 (https://genome.ifmo.ru/phantasus). For the correlation analyses between genes or gene signatures, Spearman correlation analyses were performed in GEPIA2 using TCGA data sets (51) and summarized in Prism 8.0.1. Survival analyses were performed with TCGA data sets in GEPIA2. The gene expressions in the scRNA-seq data sets of patients with melanoma or pancreatic carcinoma were visualized with R or in Broad Institute's Single Cell Portal (http://singlecell.broadinstitute.org/single_cell) using the previously reported data sets (52). The DICE data set was downloaded from https://dice-database.org. Data presented with the Primary Cell Atlas in BioGPS Dataset were obtained from BioGPS portal (http://biogps.org/#goto=welcome). Statistical significance of gene expressions between NK-cell subsets was performed with EdgeR algorithm by Galaxy tool (53). Heatmaps were visualized with Phantasus v1.5.1.

Cell Lines

The B16F10 cell line was purchased from ATCC. The Pan02 cell line was purchased from the Division of Cancer Treatment and Diagnosis, National Cancer Institute. Both cell lines were actively cultured for less than 4 months after purchase and not further authenticated. Mycoplasma testing was performed at least every 2 months by the Universal Mycoplasma Detection Kit (ATCC, 30–1012K), with the latest testing date on January 5, 2021. The B16F10 cell line was cultured in DMEM (Thermo Fisher Scientific, 12430054) including 10% fetal bovine serum (Thermo Fisher Scientific, 16140–071) and 1× penicillin and streptomycin (Thermo Fisher Scientific, 15140–122). The Pan02 cell line was cultured in RPMI-1640 (Thermo Fisher Scientific, 21870–076), including 10% fetal bovine serum (Thermo Fisher Scientific, 16140–071) and 1× penicillin and streptomycin (Thermo Fisher Scientific, 15140–122). All cells were cultured at 37°C, 5% CO2.

Transient Transfection and Retrovirus Infection

The ORF clone of mouse Rspo3 (NM_028351.3) in the pcDNA3.1 vector was purchased from GenScript. The full length of the ORF region was amplified with PCR using the primers 5′-CTTGTCGACGCCACCATGCACTTGCGACTG-3′ (forward) and 5′-GTCGAGAATTCTTATCACTTATCGTCGTCATC-3′ (reverse) and cloned into the pMSCV-hpGK-GFP vector using the SalI and EcoRI restriction enzyme. Retroviruses were generated by calcium phosphate transient cotransfection of the retroviral vectors (MSCV-Rspo3-hpGK-eGFP or MSCV-hpGK-eGFP) with the packaging plasmids Gag and Eco-env into 293T cells. The supernatant was harvested at 48 hours and 72 hours and filtrated with a 0.45-μm filter. B16F10 or Pan02 cells were plated in a 6-well plate 1 day before the transduction. On the day of transduction, 1 mL of the original media was kept and 2 mL of the retroviral supernatant was added. Polybrene was used at the final concentration of 6 μg/mL. Cells were centrifuged at 800 × g for 90 minutes at room temperature. The GFP-positive cells were sorted using flow cytometry 2 weeks after the transduction for further use.

Mice

All mice were bred and housed in specific pathogen–free conditions in the animal barrier facility at the Cincinnati Children's Hospital Medical Center (CCHMC). All animal studies were conducted in accordance with an approved Institutional Animal Care and Use Committee protocol and federal regulations. Lgr6−/− mice (Jackson stock #016934), NRG mice, Rag1−/− mice (Jackson stock #002216), C57BL/6 mice (Jackson stock #000664), and C57BL/6 congenic BoyJ mice were purchased from Jackson or the Comprehensive Mouse and Cancer Core of CCHMC. All BoyJ mice used were confirmed with the expression of NKp46 by flow cytometry analysis with peripheral blood samples. The MycG/G mice were a kind gift from Dr. H. Leighton Grimes at CCHMC. Mycf/f mice and Ncr1Cre mice were backcrossed to C57BL/6 background at our laboratory. All mice used were 8 to 12 weeks old. Age and sex matching were performed for each independent experiment. The MycG/G and MycΔ/Δ/Ncr1Cre mice were born at the expected Mendelian ratios and showed normal white blood cell, hemoglobin, and platelet counts.

Syngeneic Mouse Tumor Models

Seven- to 12-week-old mice were used to establish syngeneic mouse tumor models. Mice were subcutaneously inoculated with B16F10 or Pan02 lines (5 × 105 cells/mouse) into the right flank of the mouse. A caliper was used to measure the length and width of the tumor, and tumor volumes were estimated using the following formula: [(length) × (width) × (width)] × 0.52. The tumor volumes were monitored. Mice were sacrificed before the tumor reached the maximum permitted size. Anti–PD-1 antibody (29F.1A12) and isotype (2A3) were purchased from Bio X Cell and administered 200 μg/mouse intraperitoneally at the indicated time point as described. Recombinant carrier-free mouse R-spondin 3 protein (R&D, 4120-RS-025/CF) was used for intratumor injection with the regimen indicated. For immune cell depletion studies, antibodies against CD8a (YTS 169.4, Bio X Cell) and NK1.1 (PK136, Bio X Cell) were used. CD8a depletion (400 μg) was administered by intraperitoneal injection started on day –1 and day 1 and was continued weekly for the duration of the experiment. NK1.1 depletion (100 μg) was administered by intraperitoneal injection started on day 0 and was continued weekly for the duration of the experiment. Lymphocyte depletions were confirmed in peripheral blood lymphocytes and tumor-infiltrating lymphocytes by flow cytometry with the following antibodies: CD8a (53–6.7) and NKp46 (29A1.4).

Flow Cytometry and Cell Sorting

Flow cytometry analysis and cell sorting were performed with FACS Canto, LSR Fortessa, or FACSAria instruments (BD Biosciences). Single-cell suspensions of mouse peripheral blood, bone marrow, spleen, and lymph node were obtained by forcing organs through a 70-μm cell strainer. Single-cell suspensions of tumors were digested in HBSS buffer in the presence of collagenase D (Sigma, 2 mg/mL), hyaluronidase (Sigma, 0.75 mg/mL), and DNaseI (Sigma, 0.4 mg/mL) for 45 minutes at 37°C before passing through the cell strainer. Erythrocytes were then eliminated by red blood cell lysis buffer. Single-cell suspensions were used for surface staining in PBS containing 2% FBS and followed by intracellular staining or secondary staining if necessary. Fixation/Permeabilization Solution Kit (BD Biosciences) was used for intracellular staining of perforin, granzyme B, and IFNγ. The following antibodies were purchased from Biolegend, BD Biosciences, eBioscience, or Thermo Fisher Scientific: CD3 (145–2C11 or 17A2), NK1.1 (PK136), CD49b (DX5), CD11b (M1/70), CD27 (LG.3A10), NKp46 (29A1.4), CD107a (1-D4B), IFNγ (XMG1.2), Ly6G (1A8), B220 (RA3–6B2), CD8 (53–6.7), CD4 (GK1.5), CD115 (AFS98), CD25 (PC61), CD11c (HL3), MHC-II (M5/114.15.2), CD19 (6D5), mouse CD45 (30-F11), Ly6C (HK1.4), CD24 (30-F1), F4/80 (BM8), CD103 (2E7), CD69 (H1.2F3), MYC (Y69), perforin (eBioOMAK-D), granzyme B (QA16A02), BV421 goat anti-rabbit IgG, Alexa Fluor 488 donkey anti-rabbit IgG (H+L), and streptavidin. 7-AAD (BD Biosciences, 559925) or Zombie Aqua Fixable Viability Kit (Biolegend, 423101) was used to exclude dead cells during analysis. For measurement of absolute cell number of tumor-infiltrating immune cells per tumor weight, CountBright Absolute Counting Beads (Thermo Fisher Scientific, C36950) were used. Data were analyzed using FlowJo software. All flow cytometric data were acquired using equipment maintained by the Research Flow Cytometry Core in the Division of Rheumatology at CCHMC.

RNA Preparation and Real-Time qPCR

Bone marrow or spleen single-cell suspensions were prepared and stained as stated above before being sorted into different populations using a FACSAria Cell Sorter (BD Biosciences). The purity of sorted cell populations was >95%. Sorted cells were lysed directly in RLT buffer from the RNeasy Micro Kit (QIAGEN), and total RNA was extracted according to the manufacturer's instructions. Amounts of total RNA were measured using NanoDrop (Thermo Scientific) according to the manufacturer's instructions. cDNA was synthesized using the SuperScript III First-Strand Synthesis System for the RT-PCR Kit (Invitrogen). The cDNA was amplified using SYBR Green Master Mix (Life Technologies) with an Applied Biosystems Step One Plus thermal cycler. Expression of target genes was determined using Atcb as internal control unless otherwise noted. Specific primers for each gene are shown in Supplementary Table S3.

RNA-seq and Data Analysis

CD3NK1.1+DX5+ NK cells were sorted by flow cytometry from the splenic cells of three Mycf/f and two MycΔ/Δ/Ncr1Cre mice using FACSAria Cell Sorter (BD Biosciences). Total RNA was prepared as described above and submitted for RNA-seq analysis. Directional RNA-seq was performed by the Genomics, Epigenomics and Sequencing Core at the University of Cincinnati. The RNA quality was determined by Bioanalyzer (Agilent). NEBNext Poly(A) mRNA Magnetic Isolation Module (New England BioLabs) was used to isolate the polyA RNA. A total of 1 μg of good-quality total RNA was used as input. The dUTP-based stranded library was prepared using the NEBNext Ultra II Directional RNA Library Prep Kit (New England BioLabs). The library was indexed and amplified under eight PCR cycles. After library Bioanalyzer quality control analysis and quantification, individually indexed and compatible libraries were proportionally pooled and sequenced using the Hiseq 1000 (Illumina). About 25 million pass filter reads per sample were generated under the sequencing setting of single read 1 × 51 bp.

Sequence reads were aligned to the reference genome using the TopHat aligner (54), and aligned reads to each known transcript were counted using Bioconductor packages and were used for further data analysis (55). The analysis of differentially expressed genes between the Mycf/f and MycΔ/Δ/Ncr1Cre group was performed using the negative binomial statistical model of read counts as implemented in the edgeR Bioconductor package (56). The pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery gene functional classification tool (57). Enrichment analysis of translation-associated gene sets (REACTOME_TRANSLATION) from MSigDB (Broad Institute, Massachusetts Institute of Technology, and Regents of the University of California) was performed using gene set enrichment analysis (58). The number of permutations was 1,000. The signal-to-noise method was used. The raw RNA-seq data reported in this article have been deposited in the Gene Expression Omnibus database under GSE142685.

Immunohistochemistry Staining

The formalin-fixed, paraffin-embedded (FFPE) tumor tissue sections were used for immunohistochemistry staining or hematoxylin and eosin staining. For the former, samples were stained with anti-CD8 antibody (EPR20305, ab209775; Abcam) or anti-NK1.1 antibody (ab197979; Abcam). A Biotin Link was used as a secondary antibody followed by the streptavidin–peroxidase method, visualized with the DAB chromogen, and finally counterstained with hematoxylin. The percentages of positive-staining cells were counted with at least four representative fields at 400× magnification by two individual researchers independently. Scoring of tumor stroma area was based on methods reported before (59). Human melanoma FFPE tissues were purchased from BioCore USA and stained with anti-RSPO3 (17193–1-AP; ProteinTech) and anti-CD31 (ab28364; Abcam).

Isolation of Lymphocytes

Human peripheral blood samples of healthy donors were obtained from the Cell Processing Core, and studies were approved by the Institutional Review Board at CCHMC. Written informed consent was obtained from each donor. Peripheral blood mononuclear cells and granulocytes were obtained by Ficoll (07801; STEMCELL) processing based on the manufacturer's instruction. Lymphocytes were purified by magnetically labeling with the human NK Cell Isolation Kit (130–092–657), human CD19 MicroBeads (130–050–301), human CD4+ T Cell Isolation Kit (130–096–533), and human CD8+ T Cell Isolation Kit (130–096–495) purchased from Miltenyi Biotec and sorted with an autoMACS Pro Separator.

Western Blotting

Human peripheral blood immune cells were purified with magnetic selection. The cell pellets were then lysed in SDS sample buffer containing 10 mmol/L NaF, 10 mmol/L β-glycerophosphate, 1 mmol/L phenylmethylsulfonyl fluoride, 0.2 mmol/L Na3VO4, 2.5 mmol/L dithiothreitol, 5% 2-mercaptoethanol, 1 mmol/L 4-amidinophenylmethanesulfonyl fluoride hydrochloride, and proteinase inhibitors followed by sonication. Samples were boiled at 95°C for 5 minutes and loaded to SDS-PAGE. The separated proteins were transferred to polyvinylidene fluoride membranes (Millipore, Merck KGaA) and blocked with 5% BSA in PBST for 1 hour at room temperature. The membranes were further probed with the indicated primary antibodies overnight at 4°C. The following primary antibodies were used: anti-LGR6 antibody (ab126747; Abcam), anti-RSPO3 antibody (17193–1-AP; ProteinTech), and anti–β-actin antibody (ab49900; Abcam). Horseradish peroxidase–conjugated antibody to rabbit (NA934V; GE Healthcare) was used to detect primary antibodies using the Super Signal West Dura Chemiluminescent Substrate (Pierce) for ECL detection. Band intensity quantification was determined using Image Lab (version 5.2.1) software. All images presented are representative of two to three independent experiments.

NK-Cell Cytotoxicity Assay

Tumor tissues were digested into a single-cell suspension as shown in the section on flow cytometry and cell sorting and further processed with Ficoll (07801; STEMCELL) to remove dead cells. Tumor-infiltrating NK cells were isolated using mouse CD49b (DX5) MicroBeads (Miltenyi Biotec, 130–052–501) according to the manufacturer's instructions by an autoMACS Pro Separator (Miltenyi Biotec). B16F10 or YAC1 target cells were labeled for 2 hours with 2 μCi 51Cr per 1 × 104 target cells at 37°C, 5% CO2. Washing procedures were performed to remove excess 51Cr. Labeled target cells were added to 96-well round-bottom plates (1 × 104 cells/well). Isolated NK cells were added to the plates with effector/target ratios ranging between 50:1 and 6:1. The amount of 51Cr released, which corresponds to target cell death, was measured by a gamma scintillation counter. The percent age of cytotoxicity against target cells was calculated as ((experimental lysis – spontaneous lysis)/(maximal lysis – spontaneous lysis)) × 100. To determine maximal lysis, 51Cr-labeled target cells were treated with 3% Triton X for 4 hours. To determine spontaneous release, target cells without effector cells were used for the assay.

Cell Viability Assays

Cells were seeded in 96-well plates in triplicate at a density of 4,000 cells/100 μL/well. Cell viability was assayed with Cell Counting Kit-8 reagent (Dojindo) based on the manufacturer's instruction, and the relative growth was calculated by normalizing to day 0 results.

Statistical Analysis

Statistical analyses were performed using Prism 8.0.1 software. Selections of all statistical analysis methods met the assumptions of the tests. Equality of variances between the groups was statistically compared. Student t test or Welch t test was used for comparisons of two groups. ANOVA with multiple comparisons was used for three or more groups and tumor growth profiles. The log-rank test was used to determine statistical significance for overall survival data. Multivariant regression analysis was used to determine whether RSPO3 level is an independent factor affecting NK-cell signature in TCGA data sets. Unless specifically noted, all data are representative of more than two independent experiments. Data are shown as mean ± SD unless otherwise noted. P < 0.05 was considered statistically significant. P value is shown if 0.05 < P < 0.1.

Y. Tang reports other support from Pelotonia Fellowship Program and an Arnold W. Strauss Fellow Award during the conduct of the study, as well as a patent for 63/034,010 pending. E. Vivier reports other support from Innate Pharma outside the submitted work. S.N. Waggoner reports grants from NIH and American Heart Association during the conduct of the study, as well as grants from NIH and Clinical Biosafety Services, and nonfinancial support from Synchronicity and Atomwise Inc. outside the submitted work. G. Huang reports grants from NIH, CCTST Pilot Collaborative Studies, and Taub Foundation and EvansMDS Foundation during the conduct of the study, as well as a patent for 63/034,010 pending. No disclosures were reported by the other authors.

Y. Tang: Conceptualization, formal analysis, funding acquisition, validation, investigation, visualization, writing–original draft, writing–review and editing. Q. Xu: Formal analysis, validation, investigation, visualization, writing–original draft. L. Hu: Investigation, visualization, writing–review and editing. X. Yan: Investigation. X. Feng: Investigation. A. Yokota: Investigation. W. Wang: Formal analysis, visualization. D. Zhan: Investigation. D. Krishnamurthy: Validation, investigation. D.E. Ochayon: Validation, investigation. L. Wen: Validation, investigation. L. Huo: Validation, investigation. H. Zeng: Investigation. Y. Luo: Investigation. L.F. Huang: Investigation, visualization. M. Wunderlich: Investigation. J. Zhang: Resources. E. Vivier: Resources. J. Zhou: Funding acquisition, writing–review and editing. S.N. Waggoner: Resources, supervision, funding acquisition, project administration, writing–review and editing. G. Huang: Conceptualization, supervision, funding acquisition, writing–original draft, project administration.

The authors thank G. Freudiger, M. Rife, A. Woeste, P. Seig, L. Tilton, and C. Sexton (Cincinnati Children's Hospital Medical Center) for experiment assistance; X. Pan for bioinformatics analysis inquiry; Dr. H.L. Grimes (Cincinnati Children's Hospital Medical Center) for kindly providing the MycG/G mice; and Dr. J.S. Palumbo (Cincinnati Children's Hospital Medical Center) for kindly providing the YAC-1 cell line. They thank Cincinnati Children's Research Flow Cytometry Core in the Division of Rheumatology, Cincinnati Children's Veterinary Services, and J. Bailey and V. Summey (Cincinnati Children's Comprehensive Mouse and Cancer Core) for experiment assistance. RNA-seq was conducted by the Genomics, Epigenomics and Sequencing Core, Department of Environmental Health, University of Cincinnati. A part of RNA-seq analyses was conducted by X. Zhang, J. Chen, and M. Medvedovic at the Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati. The results shown in this work are in part based upon data generated by the TCGA Research Network(https://www.cancer.gov/tcg). This work was supported by the NIH (R01DK105014 and 1R01CA248019, to G. Huang; DA038017, AI148080, and AR073228, to S.N. Waggoner), the CCTST Pilot Collaborative Studies Grant (to G. Huang), the Taub Foundation and EvansMDS Foundation (to G. Huang), National Natural Science Foundation of China (81570196, to J. Zhou), the Arnold W. Strauss Fellow Award (to Y. Tang), a Pelotonia postdoctoral fellowship (to Y. Tang; any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the Pelotonia Fellowship Program or The Ohio State University), and a postdoctoral fellowship from the American Heart Association (to D.E. Ochayon).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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