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adi7764
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Cancer
Immunology
Immunology
Syndecan-1 inhibition promotes antitumor immune response and facilitates the efficacy of anti-PD1 checkpoint immunotherapy
Loss of syndecan-1 enhances antitumor immunity
https://orcid.org/0009-0000-2172-6634
Liu Yun Conceptualization Formal analysis Investigation Methodology Project administration Validation Visualization Writing - original draft Writing - review & editing 1 †
https://orcid.org/0009-0003-2434-4457
Xu Chen Investigation 1 †
https://orcid.org/0000-0002-9835-7846
Zhang Li Methodology Project administration 1 †
Xu Guiqin Investigation Methodology 1
https://orcid.org/0000-0002-5451-1282
Yang Zhaojuan Investigation 1
Xiang Lvzhu Investigation 1
https://orcid.org/0009-0007-3573-3369
Jiao Kun Investigation 1
Chen Zehong Investigation 1
Zhang Xiaoren Supervision 2
https://orcid.org/0000-0002-1477-116X
Liu Yongzhong Conceptualization Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing - original draft Writing - review & editing 1 *
1 State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200032, China.
2 Affiliated Cancer Hospital and Institute, Guangzhou Medical University, Guangzhou, China.
* Corresponding author. Email: liuyzg@shsci.org.
† These authors contributed equally to this work.

13 9 2024
11 9 2024
10 37 eadi776417 5 2023
02 8 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Tumor cell–originated events prevent efficient antitumor immune response and limit the application of anti-PD1 checkpoint immunotherapy. We show that syndecan-1 (SDC1) has a critical role in the regulation of T cell–mediated control of tumor growth. SDC1 inhibition increases the permeation of CD8+ T cells into tumors and triggers CD8+ T cell–mediated control of tumor growth, accompanied by increased proportions of progenitor-exhausted and effector-like CD8+ T cells. SDC1 deficiency alters multiple signaling events in tumor cells, including enhanced IFN-γ–STAT1 signaling, and augments antigen presentation and sensitivity to T cell–mediated cytotoxicity. Combinatory inhibition of SDC1 markedly potentiates the therapeutic effects of anti-PD1 in inhibiting tumor growth. Consistently, the findings are supported by the data from human tumors showing that SDC1 expression negatively correlates with T cell presence in tumor tissues and the response to immune checkpoint blockade therapy. Our findings suggest that SDC1 inhibits antitumor immunity, and that targeting SDC1 may promote anti-PD1 response for cancer treatment.

Ablation of syndecan-1 stimulates antitumor immunity and enhances the efficacy of immune checkpoint therapy.

http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81972579 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82273002 http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 82273005 the Shanghai Rising-Star Program 21QA1408400 The State Key Laboratory of Oncogenes and Related Genes zz-94-2304 Innovative research team of high-level local universities in Shanghai
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pmcINTRODUCTION

Immunotherapy has emerged as one of the breakthroughs in cancer treatment (1). Most current immunomodulatory therapies focus on unleashing antitumor effector CD8+ T cell responses, and a major success has been achieved by immune checkpoint blockade (ICB) (2, 3). However, only a subset of patients respond to the treatment and achieve a sustained response. Therefore, exploring the mechanisms of tumor resistance to ICB is urgently needed for identifying effective targets and developing efficient combination strategies for immunotherapy.

Accumulated studies have demonstrated that several tumor cell–intrinsic and tumor cell–extrinsic mechanisms contribute to the resistance of ICB (4, 5). Cancer cell–intrinsic mechanisms include tumor genetic defects (6, 7), aberration of oncogenic signaling pathways (6–9), loss of the major histocompatibility complex class I (MHC-I) (10), reduced expression of neoantigens (11, 12), and up-regulation of inhibitory checkpoint molecules (13). Cancer cell–extrinsic mechanisms include the poor accumulation and dysfunction of antitumor immune cells (14, 15), the increased infiltration and inhibitory function of immunosuppressive cells (5, 15, 16), abnormal vasculature (17), metabolic status (18, 19), and other biological events (20). Tumor cell–intrinsic events are usually considered as determinants that are casually correlated with cancer cell–extrinsic alterations.

Syndecan-1 (SDC1; also known as CD138), a matrix-binding receptor, is a member of the heparin sulfate proteoglycan family and predominantly expressed in epithelial and plasma cells. It binds to a collection of proteins and regulates the activity of several cell adhesion molecules and growth factors, thereby playing an important role in inflammatory diseases and cancer (21–23). Sdc1-null mice display increased leukocyte recruitment and higher expression of cytokines and chemokines compared with wild-type controls in the murine allergy model of delayed-type hypersensitivity and the dextran sodium sulfate–induced colitis model (24, 25). In addition, SDC1 contributes to tumor initiation, growth, and metastasis. For instance, SDC1 is crucial for Wnt-1–induced mouse mammary gland tumorigenesis (26) and required for micropinocytosis in pancreatic ductal adenocarcinoma (PDAC) (27). In addition, it has been found that SDC1 expression in breast carcinoma fibroblast is required for the organization of parallel collagen fibers and the assembly of extracellular matrix (ECM) architecture (28, 29). However, it remains unknown whether tumor cell SDC1 contributes to immune evasion and ICB resistance.

In this study, we uncovered the role of tumor cell–derived SDC1 in obstructing immune infiltration and regulating T cell–mediated control of tumor growth. We found that deletion of SDC1 increased the accumulation of CD8+ T cells and altered CD8+ cell states in tumors. Moreover, ablation of SDC1 increases the strength of IFN signaling, MHC-I expression, and antigen presentation in tumor cells and improves their sensitivity to the cytotoxic CD8+ T cells. Inhibition of SDC1 elicited an enhanced immune response to anti-programmed cell death protein 1 (PD1) treatment. Collectively, we identify SDC1 as a regulator of tumor immunity and a candidate target for cancer immunotherapy.

RESULTS

SDC1 deletion promotes CD8+ T cell control of tumor growth

Analysis of the expression pattern of Sdc1 using The Cancer Genome Atlas (TCGA) database indicates that the transcriptional expression of Sdc1 is up-regulated in multiple tumor types and negatively associated with favorable clinical outcomes (fig. S1, A and B). We next examined the effects of Sdc1 knockdown on the proliferation of mouse MC38 colon carcinoma cells, B16 melanoma tumor cells, and Panc02 PDAC cells in culture (Fig. 1A and fig. S2A). The results showed that MC38 and B16 cells were invulnerable to Sdc1 silencing (fig. S2, B to F). Consistently, the capability of B16 cells with SDC1 deletion in tumor growth was similar to that of wild-type tumor cells in nude mice (fig. S2G). However, we found that the growth of Sdc1-knockdown MC38 cells was slower than that of wild-type tumors in immune-competent mice, as evidenced by an ~60% reduction in tumor volume (Fig. 1B). We further investigated the function of SDC1 using the B16 tumor model, in which irradiated granulocyte-macrophage colony-stimulating factor (GM-CSF)–secreting tumor cell vaccination (GVAX) was used for eliciting an adaptive immune response before tumor cell inoculation, and the results showed that Sdc1 knockdown resulted in a remarked abrogation of tumor growth and around half of the mice could survive the tumor challenge (Fig. 1, C and D). We confirmed that Sdc1-null B16 tumors had a growth disadvantage in mice with two additional small guide RNAs (sgRNAs) targeting Sdc1 (fig. S2, H to J). Because SDC1 inhibition was effective in immune-competent mice and in the model with GVAX, we further tested whether CD8+ T cells were responsible for the tumor suppression. The results showed that the inhibitory effect of SDC1 ablation was abrogated by depletion of CD8+ T cells (Fig. 1E). More evidently, deletion of beta-2 microglobulin (β2m), leading to loss of MHC-I that is required for tumor antigen presentation to CD8+ T lymphocytes, almost completely attenuated the detrimental influence of SDC1 deletion on tumor growth. We set up MHC-I (H2K/H2D) as a readout for validation, and IFN-γ was used for induction (Fig. 1, F and G). These results indicate that SDC1 deficiency–mediated suppression of tumor growth largely depends on CD8+ cell response.

Fig. 1. Targeting tumor cell–derived SDC1 induces CD8+ T cell–dependent inhibition of tumor growth.

(A) Expression of SDC1 in MC38 cells transfected with Sdc1-targeting (red solid or dashed line) or control (black line) short hairpin RNAs (shRNAs). (B) Tumor growth of ShSdc1 and ShRFP control subcutaneous (sc) MC38 tumors. n = 6 mice per group. Data are means ± SD. (C) Tumor growth of ShSdc1 and ShRFP control subcutaneous B16 tumors. n = 5 mice per group. Data are means ± SD. Two-way analysis of variance (ANOVA). [(A) to (C)] Data represent a representative experiment from three independent experiments with similar results. (D) Survival analysis for ShSdc1 (red) or ShRFP (black) B16 cells growing in mice. n = 13 animals per group. Two-sided log-rank test. The survival endpoint is when the tumor volume is up to 1500 mm3. (E) Growth of ShSdc1 and ShRFP subcutaneous MC38 tumors in mice systemically treated with anti-CD8 or IgG. n = 6 mice per group. Data represent a representative experiment from two independent experiments with similar results. Data are means ± SD. (F) Expression of H2K(b)/H2D(b) on either β2-microglobulin (β2m)–knockout or control B16 cells with or without IFN-γ stimulation. (G) Tumor growth of the indicated B16 cells is shown. n = 5 mice per group. Data represent a representative experiment from two independent experiments with similar results. Data are means ± SD. (H) Tumor growth of ShSdc1 and ShRFP control subcutaneous B16 tumors in wild-type or Sdc1−/− mice. n = 12 mice per group. Data are pooled from two independent experiments. Data are means ± SD. **P < 0.01, and ****P < 0.0001. [(B), (E), (G), and (H)] One-way ANOVA corrected for multiple comparisons. n.s., not significant.

Because SDC1 is expressed by tumor cells, as well as by stromal and plasma cells, we further verified the impact of tumor-derived SDC1 using the Sdc1−/− mice. We transplanted B16 cells with or without Sdc1-knockdown into Sdc1−/− or wild-type (wt) mice after GVAX. Notably, Sdc1 knockdown in tumor cells yielded a similar inhibition of tumor growth in the recipients with or without Sdc1 knockout, although an inhibition was found in Sdc1−/− mice compared with that in wt mice when control cells were inoculated (Fig. 1H). These results indicate that the effects of tumor cell–derived SDC1 are important in the regulation of tumor growth and that inhibition of tumor cell–derived SDC1 can promote antitumor immune response.

SDC1 loss enhances CD8+ T cell infiltration and function in tumors

Analysis of the TCGA database revealed that the expression of SDC1 was negatively associated with the intratumoral plenitude of CD8+ T cells [evaluated by five algorithms: CIBERSORT (30), EPIC (31), MCPCOUNTER (32), QUANTISEQ (33), and XCELL (34)] (fig. S3). We next investigated whether tumor cell SDC1 inhibition had an impact on the immune landscape of tumors by measuring the infiltration and constitution of immune cells in tumors. The results of fluorescence-activated cell sorting (FACS) showed that tumor-infiltrating CD45+ lymphocytes in Sdc1-knockdown tumors were more abundant compared to their counterparts (Fig. 2, A and B, and fig. S4A). Furthermore, SDC1 loss increased the proportions and numbers of CD8+ T cells and natural killer (NK) cells in CD45+ lymphocytes in both MC38 and B16 tumors (Fig. 2, C and D, and fig. S4, B to D). Consistently, immunofluorescence (IF) staining analyses revealed approximately a 12-fold increase in the number of CD8+ T cells in Sdc1-knockdown tumors compared with control tumors (fig. S4, E and F). SDC1 deficiency did not affect the proportions of myeloid-derived suppressor cells as well as F4/80+ and CD11c+ cells in CD45+ lymphocytes in both MC38 and B16 tumors (fig. S4G). In addition, Sdc1 knockdown resulted in a repression of tumor growth of B16 cells expressing the antigen ovalbumin (OVA) in mice with one dose of GAVX treatment (fig. S4H), accompanied by a larger fraction of CD45+ lymphocytes and SIINFEKL-specific CD8+ T cells in tumors (Fig. 2, E and F). Furthermore, Sdc1-knockdown tumors exhibited higher proportions of CD8+ and CD4+ T cells positive for IFN-γ (Fig. 2, G and H, and fig. S4, I to K). We furthermore analyzed SDC1 expression in tumor cells of lung squamous cell carcinomas (LUSCs) and CD8+ T infiltration in human LUSC tissues using IF staining and found a negative correlation between SDC1 expression in tumor cells and CD8+ T infiltration (fig. S4, L and M).

Fig. 2. SDC1 inhibits infiltration and activation of CD8+ T cells in tumors.

(A) Analysis of tumor-infiltrating CD45+ cells in ShSdc1 or control subcutaneous MC38 tumors. Data are pooled from two independent experiments. (B) Analysis of tumor-infiltrating CD45+ cells in ShSdc1 or control subcutaneous B16 tumors. (C and D) Percentage of CD8+, natural killer (NK), or CD4+ cells in CD45+ cells in ShSdc1 or control subcutaneous MC38 (C) or B16 (D) tumors. Data represent a representative experiment from three independent experiments with similar results [(B) to (D)]. (E) Analysis of tumor-infiltrating CD45+ cells in ShSdc1 or control subcutaneous B16-OVA tumors. (F) Percentage of SIINFEKL-specific tetramer+ cells in CD8+ cells in ShSdc1 or control subcutaneous B16-OVA tumors. Data are pooled from two independent experiments. (G and H) Percentage of IFN-γ+ or TNFα+ in T cells in ShSdc1 or control subcutaneous MC38 (G) or B16 (H) tumors. Data represent a representative experiment from three independent experiments with similar results. (I) Analysis of uniform manifold approximation and projection (UMAP) (left) and proportion (right) of CD8+ T cell clusters using scRNA-seq data. (J) Analysis of effector cytokines expression in CD8+ T cells using scRNA-seq data. (K) Single-sample gene set enrichment analysis (GSEA) analysis of CD8+ T cell subsets using scRNA-seq data. (L) GSEA analysis of CD8+ T cell subsets using scRNA-seq data. (M) Analysis of effector cytokines expression in NK cells using scRNA-seq data. Unpaired two-tailed t tests [(A) to (H)]. Each symbol represents a single mouse, and horizontal bars represent the mean. Error bars, SD. *P < 0.05, **P < 0.01, and ***P < 0.001.

To further illustrate the effects of SDC1 depletion on the tumor immune microenvironment, we performed single-cell RNA sequencing (scRNA-seq) to interrogate phenotypic and functional alterations of immune cells in Sdc1-knockdown versus control tumors. CD45+ population isolated from single cells pooled with six tumors per group was used for scRNA-seq. Five CD8+ T cell population subclusters were identified on the basis of differentially expressed markers (fig. S5, A to C) (35, 36). Sdc1-knockdown tumors displayed higher fractions of the effector-like, progenitor-exhausted, and terminally exhausted subclusters, as well as of the subcluster with increased proliferating activities, but had a low proportion with naïve phenotype in CD8+ T cells (Fig. 2I). In keeping with FACS results, CD8+ T cells in Sdc1-knockdown tumors were more active and cytotoxic based on increased expression of the effector factors, including Ifng, Tnf, Gzmb, and Nkg7, in comparison with those in control tumors (Fig. 2J). Single-sample gene set enrichment analysis (GSEA) analysis of CD8+ T cell subsets showed that IFN-γ production score was increased in CD8+ T cells from SDC1-deficient tumors (Fig. 2K). Furthermore, we performed GSEA of CD8+ T cell subsets and found that several immune processes in CD8+ T cells from SDC1-deficient tumors were altered (Fig. 2L). In addition, NK cells in Sdc1-knockdown tumors expressed higher levels of effector factors, such as Gzmb, Gzmk, and Prf1, than those in control tumors (Fig. 2M).

We further analyzed the impact of SDC1 expression on the phenotype of other cell subsets in tumor immune infiltrates. The cell clusters were annotated by major lineage markers (37). On the whole, Sdc1-knockdown tumors exhibited higher proportions of CD8+ T, CD4+ T, and NK cells but a low proportion of tumor-associated macrophages (TAMs), in which the subsets of SPP1+Arg1+ and Chil3+ cells were deceased, but C1qc+ portion was relatively increased, based on the definition by differential gene expression (fig. S5, D to F) (38–41). Whether SDC1 affects the phenotype and function of TAM and dendritic cells (DC) needs further study. Of note, analysis of the human TCGA database demonstrated that SDC1 expression negatively correlated with cytolytic effector factors, including IFNG, GZMB, and TNFA in several tumor types (fig. S5G). Overall, these results suggest that SDC1 not only obstructs infiltration of CD8+ T cells in tumors but also regulates their state and cytotoxic functions.

SDC1-mediated immune exclusion depends on its extracellular portion

We next explored the structural basis of SDC1 required for the regulation of CD8+ T cell presence in tumors with function recovery assay. The truncated forms of Sdc1 were constructed and expressed in Sdc1-knockdown tumor cells (Fig. 3A). As expected, enforced expression of the full-length SDC1 in SDC1-deficient tumor cells abrogated their growth disadvantage in vivo (Fig. 3B and fig. S6). The SDC1 (ΔC30) mutant, which lacks the PSD-95/Dlg/ZO-1(PDZ) binding domain, a region responsible for binding to the PDZ domain of syntenin, also rescued the tumor growth of SDC1-deficient cells (Fig. 3B and fig. S6). Syntenin functions as an adaptor that is involved in coupling syndecans to cytoskeletal proteins or intracellular signal factors (42). However, the SDC1 (ΔEct) mutant, which lacks the extracellular domain, could not increase the growth of SDC1-deficient tumor cells (Fig. 3B and fig. S6). SDC1 (ΔGAG), in which the modification sites of glycosaminoglycan chain in the extracellular portion were mutated, failed to rescue the tumor growth of SDC1-depleted cells, suggesting that the glycosaminoglycan chains attached to the core protein play a critical role in promoting tumor growth (Fig. 3B and fig. S6). Accordingly, enforced expression of the full-length SDC1 or SDC1 (ΔC30) mutant but not the SDC1 (ΔEct) or SDC1 (ΔGAG) mutant in SDC1-deficient tumor cells decreased the infiltration of CD8+ T cells in tumors (Fig. 3, C and D). These results strongly suggest that the extracellular portion and the glycosaminoglycan modification of SDC1 are critical for the regulation of antitumor immune response.

Fig. 3. The extracellular domain of SDC1 is essential for the immune exclusion.

(A) B16 tumor cells stably expressing empty vector, wild-type SDC1, or mutant constructs of SDC1 were transfected with control ShRFP or ShSdc1. The expression of SDC1 was detected by flow cytometry. (B) Tumor volume for indicated groups was shown. Data are pooled from two independent experiments. Each symbol represents a single mouse, and horizontal bars represent the mean. Error bars, SD. One-way ANOVA corrected for multiple comparisons. (C) Representative CD8 (green) image in tumor tissues as indicated in (B). 4′,6-Diamidino-2-phenylindole (blue) was used to stain the nuclei. (D) Quantification of CD8+ T cells in tumor tissues from (C). HPF, high-power-field. One-way ANOVA corrected for multiple comparisons. (E) GSEA terms enriched or depleted in Sdc1-knockdown B16 cells compared with control B16 cells. **P < 0.01 and ***P < 0.001. NF-κB, nuclear factor κB.

The transcriptomic expression analysis of Sdc1-knockdown and control tumor cells showed that the enriched genes caused by loss of SDC1 were referred to ECM organization within GSEA terms (Fig. 3E). Multiple ECM-related genes were changed in Sdc1-knockdown tumor cells compared to those in control cells (log2 fold change > 2 and Padj < 0.05) (fig. S7A). We observed an enrichment of other pathways including “oxidative stress-induced senescence,” “cytokine_cytokine receptor interaction,” and “mTORC1 signaling” in Sdc1-knockdown tumor cells compared to those in wild-type control tumor cells. Moreover, immune-related pathways included “TNFα signaling via NF-κB,” “inflammatory response,” and “cytokine signaling in immune systems” were also enriched in Sdc1-knockdown tumor cells (Fig. 3E). These results indicate that SDC1 inhibition in tumor cells alters several intrinsic signaling pathways in tumor cells.

Loss of SDC1 enhances the sensitivity of tumor cells to CD8+ T cell cytotoxicity

Our results above showed that tumor cell–derived SDC1 contributed to immune exclusion and was deleterious for the maintenance of functional states of CD8+ T cells in tumors. We next extended our study by observing whether SDC1 in tumor cells influenced their sensitivity to T cell–mediated elimination because several intrinsic pathways were affected in tumor cells by SDC1 inhibition. We performed in vivo competitive experiments to compare the relative growth advantage of mixed SDC1-deficient and control B16 cells in mice treated with GVAX and anti-PD1 immunotherapy. By FACS analyzing the distributions of the cells with the ZsGreen and TdTomato markers in culture and tumors, we found that the ratio of SDC1-deficient cells to control cells in tumor tissues was remarkedly decreased compared with that in culture, suggesting that SDC1-deficient cells have a growth disadvantage over control tumor cells upon CD8+ T cell response (Fig. 4, A and B). To examine whether SDC1 deficiency directly sensitizes tumor cells to CD8+ T cell–mediated killing, we cocultured OVA-expressing Sdc1-knockdown or control B16 cells with OVA-specific CD8+ T cells (OT-I cells) and found that SDC1-deficient tumor cells were preferentially eliminated in the coculture (Fig. 4C).

Fig. 4. Loss of SDC1 enhances the sensitivity of tumor cells to CD8+ T cell cytotoxicity.

(A) Schematic overview of in vivo competitive assay. (B) Flow plots showing ratios of control cells versus control cells and Sdc1-knockdown cells versus control cells for conditions indicated. Data represent a representative experiment from two independent experiments with similar results. (C) B16-OVA cells deficient in SDC1 or control tumor cells were cocultured with OVA-specific CD8+ T cells (OT-I cells) for 3 days. The fold change was calculated by comparing the number of tumor cells in indicated coculture conditions with that in the matched-control group (condition without T cells). Data represent a representative experiment from two independent experiments with similar results. Data are means ± SD. Paired two-tailed t tests. (D) Data are shown as ratios of B16-OVA cells lacking SDC1 or control cells stimulated with IFN-γ versus unstimulated cells at the indicated time. Tumor cell growth was detected by MTT. Data represent a representative experiment from two independent experiments with similar results. Two-way ANOVA. (E) Expression of H2K(b)/H2D(b) on either Sdc1-knockdown (purple) or control (blue) B16 tumors (left) and quantification of mean fluorescence intensity (MFI) (right). Data represent a representative experiment from two independent experiments with similar results. Data are means ± SD. (F) Quantification of H2K(b)/H2D(b) MFI on either Sdc1-knockdown or control MC38 tumor cells with or without IFN-γ stimulation. Data are pooled from six independent experiments with similar results. (G) Quantification of SIINFEKL H2K(b) MFI on either Sdc1-knockdown or control MC38-OVA tumor cells with or without IFN-γ stimulation. Data are pooled from four independent experiments with similar results. Data are means ± SEM. Paired two-tailed t tests [(F) and (G)]. *P < 0.05, **P < 0.01, and ***P < 0.001.

Besides the increased sensitivity of tumor cells with SDC1 inhibition to CD8+ T cells, SDC1-deficient tumor cells showed a declined growth rate relative to control tumor cells exposed to IFN-γ in vitro, suggesting a role for SDC1 in regulating the strength of IFN signaling in tumor cells (Fig. 4D). The results promote us to hypothesize that the increased sensitivity of SDC1-deficient tumor cells to immune attack is possibly related to enhanced antigen presentation and tumor cell recognition. As expected, analyses of tumor cells grown in vivo showed that Sdc1-knockdown cells exhibited a higher level of MHC-I expression compared to control tumor cells (Fig. 4E). To exclude the possibility that the up-regulation of MHC-I expression in SDC1-deleted tumor cells might be caused by increased infiltration of immune cells that probably increases IFN level in local, we further corroborated the results with in vitro cultured cells and found that SDC1 inhibition could directly enhance MHC-I expression in MC38 tumor cells upon IFN-γ stimulation (Fig. 4F). Furthermore, Sdc1 knockdown in MC38-OVA cells showed higher expression of H2K(b) specific to the SIINFEKL, a specific peptide of OVA, compared to that in the control cells in the presence of IFN-γ, indicating that the tumor cell–intrinsic function of SDC1 exists in the regulation of antigen processing and presentation in tumor cells (Fig. 4G). Collectively, these results indicate that deletion of SDC1 sensitizes tumor cells to the cytotoxicity of CD8+ T cells and increases their capability to present antigens.

SDC1 down-regulates IFN-γ signaling in tumor cells

We next sought to further validate whether SDC1 modulated the strength of IFN-γ signaling in tumor cells. We analyzed the transcriptomic profiles of Sdc1-knockdown and control B16 cells stimulated with IFN-γ. GSEA analysis revealed an enriched IFN response in tumor cells with SDC1 inhibition (Fig. 5A). Consistently, SDC1 deletion increased the expression of genes within Gene Ontology terms conferred to “response to IFN-γ,” “cytokine production,” and “cytokine-mediated signaling pathway” (Fig. 5B). We found that loss of SDC1 led to a notably increased expression of the IFN-γ–stimulated genes, such as Ifit1, Ifit3, Oas1, Oas2, Oas3, and Isg15 (Fig. 5C and fig. S7B). In addition, increased expression of the cell cycle– and apoptosis-related genes, including Cdkn1a, Casp12, and Casp4, was detected in SDC1-deficient B16 tumor cells compared to that in control cells upon IFN-γ exposure (fig. S7C). Furthermore, in agreement with the improved infiltration of CD8+ T cells in tumors, the chemokines including Cxcl9 and Cxcl10, which are associated with CD8+ T cell accumulation, were up-regulated in their expression upon SDC1 inhibition (Fig. 5C). We further confirmed the increased expression of CXCL9 in SDC1-deleted tumor cells at the protein level using a protein array (Fig. 5D). In addition, Sdc1-knockdown tumors had an increased expression of Cxcl9 mRNA compared to control tumors (Fig. 5E). Moreover, levels of the Sdc1-knockdown–related gene signature were positively correlated with abundances of CXCL9 transcripts in melanoma within the human TCGA database (Fig. 5F).

Fig. 5. Loss of SDC1 enhances IFN-γ signaling.

(A) GSEA terms up-regulated by Sdc1-knockdown B16 cells compared with control cells with IFN-γ stimulation. (B) Enrichment of the indicated GSEA signatures in Sdc1-knockdown B16 cells compared with control cells with IFN-γ stimulation. (C) Validation of RNA-seq results. Selected ISG expression was detected by reverse transcription polymerase chain reaction (PCR). Unpaired two-tailed t tests. (D) Detection of CXCL9 expression in Sdc1-knockdown B16 cells or control cells stimulated with IFN-γ by protein array. (E) Detection of Cxcl9 transcriptional expression in Sdc1-knockdown or control B16 tumors. Data represent a representative experiment from three independent experiments. Each symbol represents a single tumor. Data are means ± SD. Unpaired two-tailed t tests. (F) Scatter plots showing the correlation between CXCL9 mRNA expression in melanoma within the human TCGA database and Sdc1-knockdown signature scores in B16 cells. (G and H) Detection of IFN-γR1 in Sdc1-knockdown or control B16 (G) or MC38 (H) tumor cells. (I and J) Detection of surface expression of IFN-γR1 in Sdc1-knockdown or control B16 (I) and MC38 (J) tumor cells by flow cytometry. Data represent a representative experiment from three [(G) and (H)] or five [(I) and (J)] independent experiments. (K) p-STAT1 expression in Sdc1-knockdown or control B16 tumors. (L and M) Detection of JAK-STAT pathway protein in Sdc1-knockdown or control tumor cells without or with IFN-γ stimulation. (N) Tumor volume for indicated groups was shown. n = 6 mice per group. Data are means ± SD. One-way ANOVA corrected for multiple comparisons. Data represent a representative experiment from two independent experiments [(K) to (N)]. (O) Dot plots depicting negatively enriched pathways [Padj < 0.05 and normalized enrichment score (NES) < −1] in GSEA, which was used to compare Sdc1 high-expressing patients versus Sdc1 low-expressing patients across several TCGA tumors. ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; PAAD, pancreatic adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; STAD, stomach adenocarcinoma. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

We confirmed the results by analyzing the transcriptomic profiling of Sdc1-knockdown cells expressing the full-length Sdc1 or empty vector with IFN-γ stimulation. GSEA analysis revealed a decreased IFN response in tumor cells with SDC1 expression (fig. S8A). We further performed the transcriptomic expression analysis of Sdc1-knockdown tumor cells expressing the SDC1 mutants with distinct activities in rescuing tumor growth. Analysis of the enrichment of related signaling pathways showed that enforced expression of the SDC1 (ΔC30) mutants but not the SDC1 (ΔEct) mutant (fig. S8B) decreased the “IFN signaling” in SDC1-deficient tumor cells, emphasizing the importance of the extracellular region of SDC1 in the regulation of IFN signaling.

To explore the mechanism underlying the increased IFN-γ response induced by SDC1 deficiency, we detected the expression of IFN-γ receptor 1 (IFN-γR1) and IFN-γR2 in the Sdc1-knockdown and control tumor cells. The results showed that SDC1 ablation increased the expression of IFN-γR1 but not IFN-γR2 in B16 and MC38 cells (Fig. 5, G and H, and fig. S8, C and D). Furthermore, the detection of surface IFN-γR1 showed that SDC1 deficiency improved the expression of IFN-γR1 on the tumor cell surface (Fig. 5, I and J). We corroborated the findings in human tumor cells and found that, consistent with the observation in mouse tumor cell lines, SDC1 ablation in SW480 human colorectal adenocarcinoma cells or AsPC1 human pancreatic adenocarcinoma cells increased expression of IFN-γR1 and enhanced CXCL9 and human leukocyte antigen (HLA)-ABC in the presence of IFN-γ stimulation (fig. S9).

We further examined the expression of IFN-γR1 and MHC-I on the cells expressing the full-length SDC1 and SDC1 mutants in Sdc1-knockdown tumor cells. The results showed that enforced expression of the full-length SDC1 and SDC1 (ΔC30) mutant but not the SDC1 (ΔEct) or SDC1 (ΔGAG) mutant in SDC1-deficient tumor cells decreased the surface expression of IFN-γR1 and MHC-I (fig. S10, A and B). These results suggest that the extracellular portion of SDC1 might decrease the IFN-γ response of tumor cells and inhibit tumor immunity by regulating the expression of IFN-γR1 and MHC-I on tumor cells. The molecular mechanism regarding how SDC1 affects IFN-γR1 abundance is still unknown and warrants our further study.

We then further examined the activation status of the components of the IFN-γ signaling in cells with or without SDC1 inhibition. We found that expression of the phosphorylated (p)–signal transducer and activator of transcription 1 (STAT1) was increased in SDC1-deficient tumor tissues compared with that in control tumors (Fig. 5K). Consistently, SDC1 deletion expressed enhanced levels of p–Janus kinase 1 (JAK1) and p-STAT1 in cultured tumor cells upon IFN-γ treatment (Fig. 5, L and M). Consistently, SDC1 silencing also increased p-STAT1 expression in SW480 and AsPC1 cells (fig. S10C). To verify whether the growth disadvantage caused by SDC1 deficiency in tumor cells was dependent on the regulation of IFN signaling, we comparatively analyzed the growth of SDC1-deficient B16 tumor cells with or without Stat1 knockout in vivo. The results showed that concomitant interference with Stat1 could alleviate the growth disadvantage of tumor cells with Sdc1 knockdown (fig. S10D and Fig. 5N). STAT1 inhibition might also influence the type I IFN signaling, which possibly has an impact on tumor growth in the setting. GSEA analysis of the human TCGA database showed that the “response to IFN-γ” pathway is down-regulated in various types of tumors with SDC1 high expression (Fig. 5O). Therefore, the underlying mechanism by which loss of SDC1 impedes tumor progression is at least partially related to the modulation of the IFN-γ–STAT1 signaling.

SDC1 inhibition improves the efficacy of anti-PD1 immunotherapy

We further investigated the impact of tumor cell SDC1 on the efficacy of anti-PD1 immunotherapy. We compared the growth of SDC1-deficient MC38 tumors to control tumors in mice treated with or without PD1 blockade. SDC1-deficient MC38 tumors treated with anti-PD1 showed complete regression, a sign of a more robust response, in comparison with anti-PD1–treated control tumors (Fig. 6A). Similar results were observed for the B16 model in combination treatment of GVAX and PD1 blockade (Fig. 6B). In addition, the highest levels of CD45+ and CD8+ infiltration were observed in the SDC1-deficient B16 tumors with anti-PD1 treatment relative to other groups (Fig. 6, C and D). Furthermore, the presence of tumor-infiltrating IFN-γ+ or tumor necrosis factor–α (TNFα)+ in CD8+ T cells was up-regulated most notably in SDC1-deficient B16 tumors with anti-PD1 treatment (Fig. 6E), and SDC1-deficient tumors with anti-PD1 treatment showed the highest proportion of IFN-γ+TNFα+ cells in CD8+ T cells as well among the groups examined (Fig. 6F).

Fig. 6. Targeting SDC1 improves the efficacy of anti-PD1 immunotherapy.

(A and B) Tumor volume average for ShRFP or ShSdc1 subcutaneously MC38 (A) or B16 (B) tumors untreated or treated with anti-PD1. n = 6 mice per group. Data represent a representative experiment from two independent experiments with similar results. Data are means ± SD. (C) Flow cytometry analysis of the percentage of CD45+ in ShRFP or ShSdc1 subcutaneously B16 tumors untreated or treated with anti-PD1. (D) Flow cytometry analysis of the percentage of CD8+ T cells in tumor-infiltrating CD45+ lymphocytes in ShRFP or ShSdc1 subcutaneously B16 tumors untreated or treated with anti-PD1. (E) Percentage of IFN-γ+ or TNFα+ in CD8+ T cells in ShSdc1 or control subcutaneous or B16 tumors untreated or treated with anti-PD1. Data are pooled from two independent experiments [(C) to (E)]. Each symbol represents a single mouse, and horizontal bars represent the mean. Error bars, SD. (F) The cytokine expression pattern of CD8+ T cells in ShSdc1 or control subcutaneous or B16 tumors untreated or treated with anti-PD1. Data represent a representative experiment from two independent experiments with similar results. (G) SDC1 relative expression in patients who were predicted to be responders or nonresponders in various types of tumors using the tumor immune dysfunction and exclusion (TIDE) algorithm. BRCA, breast invasive carcinoma; LGG, brain lower grade glioma; OV, ovarian serous cystadenocarcinoma; PRAD, prostate adenocarcinoma; TGCT, testicular germ cell tumors. (H) SDC1 relative expression in patients who were responders or nonresponders to ICB treatment in clinical trials. One-way ANOVA corrected for multiple comparisons [(A) to (E)]. Unpaired two-tailed t tests [(G) and (H)]. ▪ 0.05 < P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

We further applied the “tumor immune dysfunction and exclusion” to predict the response of TCGA tumors to ICB treatment based on SDC1 expression (43). In various types of tumors, the predicted responders displayed lower levels of SDC1 transcripts in tumors compared to those predicted as nonresponders (Fig. 6G). We further interrogated transcriptional expression data of the tumor biopsies from several clinical trials, including renal cell carcinoma [dbGap: phs001493.v1.p1 (44) and Braun_2020 (45)], melanoma [GSE91061 (46) and GSE78220 (47)], non–small cell lung cancer [GSE126044 (48)], and glioblastoma [PRJNA482620 (49)], in which patients with cancers underwent ICB treatment. We found that tumors from the responders expressed low levels of SDC1 mRNA compared to those from nonresponders in these clinical trials (Fig. 6H). Further analysis indicated that the lower expression of SDC1 was correlated with longer survival times in patients with glioblastoma, melanoma, and renal cell carcinoma who underwent ICB treatment (fig. S11).

DISCUSSION

Understanding how tumor-intrinsic events contribute to the resistance to ICB treatment is crucial for improving immunotherapy. In this study, we demonstrated that tumor cell–derived SDC1 not only inhibited the accumulation and function of tumor-infiltrating CD8+ T cells but also decreased tumor cell vulnerability to immune elimination by regulating the strength of IFN signaling in tumor cells. SDC1 inhibition in tumor cells amplified the antitumor immune response and potentiated the efficiency of anti-PD1 checkpoint blockade.

Independent transcriptome data from patients with different types of cancer showed that tumor SDC1 expression correlates with T cell presence in the tumor. We found that loss of SDC1 in tumor cells increased the infiltration of immune cells, including CD8+ T cells, in the mouse tumor microenvironment. Consistently, ablation of SDC1 did not affect the growth rates of tumor cells in culture but restrained tumor growth in transplantation models in vivo, indicating that tumor cell–extrinsic clues probably involving T cell response participate in SDC1-mediated regulation of tumor growth. This speculation was confirmed by the results showing that the effects of SDC1 deletion were abrogated by systemic depletion of CD8+ T cells or β2m loss in tumor cells. Multiple investigations have presented evidence that ECM-lymphocyte interactions vitally affect leukocyte motility and infiltration. SDC1 is involved in the assembly of ECM and correlated with a parallel and organized ECM fiber architecture (28, 29), which is reportedly crucial for immune exclusion in human breast carcinoma (50). Of note, deletion of tumor cell SDC1 also increased expression of the chemokine CXCL9, which may contribute to the recruitment of CD8+ T cells into tumors. Thus, SDC1 inhibition enhances the permeation of CD8+ T cells in tumors possibly through affecting different processes.

Besides the alterations in immune cell presence in tumors caused by loss of SDC1, CD8+ T cell state and the composition of immune cells were remarkedly influenced. Our results revealed that loss of SDC1 in tumor cells increased the proportions of progenitor-exhausted and effector-like CD8+ T cells, accompanied by a reduction in the proportion of naïve CD8+ T cells. Both progenitor-exhausted and effector-like CD8+ T cells are crucial for an effective antitumor immune response. Recently, it has been demonstrated that a rise in the subpopulation of progenitor-exhausted CD8+ T cells correlates with improved efficacy of anti-PD1 for cancer treatment (51, 52). Furthermore, our results demonstrated that CD8+ T cells in SDC1-deletion tumors expressed high levels of cytotoxic cytokines and related factors, including IFN-γ, Gzmb, and Tbx21. IFN-γ produced by activated bystander CD8+ T cells is crucial to the maturation and function of NK/ILLC1 (53). Concomitantly, our scRNA-seq results showed that NK cells in SDC1-deficient tumors displayed increased expression of the effector cytokines, such as Gzmb, Gzmk, and Prf1. These observations suggest that SDC1 inhibition efficiently potentiates the presence and functional state of effector immune cells that may enhance antitumor immune response.

While the changes in the immune cell landscape and properties are important for the enhancement in the immune control of tumor growth, increased vulnerability of tumor cells to CD8+ T cell cytotoxicity caused by SDC1 inhibition may also represent as a vital determinant for facilitating antitumor response. This might be at least partially due to the increase in the strength of IFN singling in tumor cells that accounts for the enhancement of MHC-I expression and antigen presentation in SDC1-deficient tumor cells. These results were reinforced by the data that SDC1-depleted tumor cells exhibited a reduced growth rate compared to control tumor cells upon IFN-γ treatment in vitro. SDC1 deficiency caused increased expression of cell cycle–related genes such as Cdkn1a and several genes associated with apoptosis, such as Casp12, Casp8ap2, Casp4, and Ripk1, in B16 tumor cells compared to that in control cells upon IFN-γ exposure, indicating that the tumor susceptibility to IFN-γ–mediated cytostatic effects is increased by SDC1 deletion.

Accumulated studies revealed that activation of IFN-γ signaling in tumor cells up-regulates antigen presentation and enhances antitumor immunity. Recently, two unbiased CRISPR-Cas9 screens demonstrated that decreased antigen presentation and IFN-γ signaling contribute to the resistance to ICB therapy (54, 55). In addition, it has been documented that acquired resistance to cancer immunotherapy in patients with JAK1 or JAK2 mutations is associated with defects in antigen presentation, suggesting that the impairment in IFN-γ signaling pathway accounts for immune escape and ICB resistance (56). Paradoxically, IFN-γ signaling in tumor cells was reported to inhibit both adaptive and innate immune responses, and loss of IFN-γ signaling sensitized tumor cells to immunotherapy (57, 58). The discrepancy of these conclusions may not be easily reconciled due to differences in tumor models or types of cancer studied. In addition, the observations based on the entire inactivation of the IFN-γ signaling, intact presence, and enhanced exposure of the signaling in tumor cells may also yield different conclusions, because both the spectrums of and the thresholds in the induction of IFN-stimulated genes (ISGs) vary if the strength of IFN-γ signaling transduced in cells is different (59). It is worth noting that, in addition to the up-regulation of IFN-γ signaling, loss of SDC1 readily resulted in transcriptional alterations associated with the rapamycin (mTOR), retinoic acid, Notch signaling, as well as cytokine signaling. These pathways may act in a coordinated manner to regulate the sensitivity of tumor cells to immune attack in response to SDC1 inhibition. Furthermore, our results demonstrate that the extracellular segment of SDC1 functions essentially in SDC1-mediated inhibition of immune response. It is certainly an interesting question of whether the extracellular segment of SDC1 affects the interaction of tumor cells with immune cells. Given the complexity of the tumor microenvironment, further elucidation of the mechanism underlying the SDC1-mediated immune evasion in tumors is needed in future.

Increased accumulation and function of tumor-infiltrating CD8+ T cells coupled with enhanced sensitivity of tumor cells to CD8+ cytotoxic T cells suggest a potential of SDC1 inhibition in potentiating the efficacy of ICB treatment. The hypothesis was confirmed by our results showing that SDC1-deficient tumors were highly sensitive to anti-PD1 treatment, in association with improved infiltration and functional state of CD8+ cells in tumor. Our finding from experimental therapy in mice is recapitulated by the data of several clinical trials showing that patients with cancer with low SDC1 expression have improved responses to anti-PD1 checkpoint blockade treatment. Furthermore, it has been reported that elevated SDC1 expression is associated with increased malignant cellular invasion and metastasis in breast cancer, pancreatic cancer, and multiple myeloma (60–62). Our analysis of clinical data from patients with metastatic melanoma treated with anti-PD1 therapy (GSE78220) showed that SDC1 expression was negatively correlated with poor prognosis. Whether SDC1 inhibition in combination with ICB therapy benefits the control of metastatic progression still needs to be further explored. Of note, our results showed a reduction of tumor growth in Sdc1−/− mice compared with that in wild-type mice, indicating that the absence of SDC1 in host tissues may affect tumor growth and antitumor immune response. Further elucidation of the specific function of SDC1 in tumor cells, as well as in stromal and immune cells, is needed in future studies. On the basis of the phenotype of systemic knockout of SDC1 in mice, targeting of SDC1 may represent a rational means for tumor treatment. Furthermore, designing antibodies more specifically against SDC1 on tumor cells with effective technologies may enhance its therapeutic applications. Overall, we propose that a combination of SDC1 inhibition and anti-PD1 may serve as a potential strategy for cancer immunotherapy.

MATERIALS AND METHODS

Mice

Sdc1 knockout mouse model was created by GemPharmatech. In brief, this model used CRISPR-Cas9 technology to edit the Sdc1 gene. Exon2-exon5 of the Sdc1-201 transcript was chosen as the knockout region. sgRNA targeting exon2-exon5 of the Sdc1-201 transcript was transcribed in vitro. Cas9 and sgRNA were microinjected into the fertilized eggs of C57BL/6J mice. Fertilized eggs were transplanted to obtain positive F0 mice, which were confirmed by polymerase chain reaction (PCR) and sequencing. A stable F1 generation mouse model was obtained by mating positive F0 generation mice with C57BL/6J mice. Genotyping was performed by PCR with the standard protocol. The primers used to detect the deleted Sdc1 are as follows: (i) forward (F): 5′-CTCCAGCTTTCCTTCCTATGGAG-3′ and reverse (R): 5′-AACCTGAGACTAGAAAGGCTGTGGG-3′; and (ii) F: 5′-TGGGACTCTAGCACATCTTGACCTAG-3′ and R: 5′-AGCCTGAGGCTTGGACACTGT-3′. The designs and procedures of the animal experiments were approved by the Shanghai Jiaotong University Animal Care Committee. Six- to 8-week-old wild-type C57BL/6 mice were obtained from LingChang Biotechnology Co. Ltd. All mice were housed under specific pathogen–free conditions.

Cell lines

MC38 murine colon adenocarcinoma cell line was a gift from L. Deng (School of Pharmacy, Shanghai Jiao Tong University, Shanghai). B16/F10 murine melanoma, Panc02 murine PDAC, SW480 human colorectal adenocarcinoma, AsPC1 human pancreatic adenocarcinoma cell lines, and human embryonic kidney (HEK) 293T cells were purchased from the American Type Culture Collection (ATCC; Manassas VA, USA). All the cell lines were cultured in Dulbecco’s modified Eagle medium (DMEM) with 10% fetal bovine serum (FBS) and antibiotics. HEK293T cell line was authenticated using the GenePrint10 System (Promega Biotech Co.) and verified with the ATCC STR database. Other cell lines were not authenticated in our laboratory but routinely examined for mycoplasma contamination.

Mouse models and animal treatment

Two different GM-CSF vaccination strategies were used for the B16 model.

For prophylactic vaccination, 6- to 8-week-old C57BL/6 mice were subcutaneously immunized with 1 × 107 B16-GM-CSF cells that had been irradiated with 100 gray (Gy) on the left flank 12 days before tumor implantation (day −12). On day 0, 1 × 106 B16 cells were injected into the right flank subcutaneously.

For therapeutic vaccination, 6- to 8-week-old C57BL/6 mice were subcutaneously inoculated with 1 × 106 B16 cells on the right flank on day 0. Mice were immunized with 1 × 107 B16-GM-CSF cells that had been irradiated with 100 Gy into the left flank on days 1, 3, and 6.

For MC38 inoculation model (except for CD8 neutralization experiments), 6- to 8-week-old C57BL/6 mice were subcutaneously inoculated with 1 × 106 MC38 cells on the right flank.

For in vivo depletion of CD8+ T cells, mice were treated with 100 μg of anti-CD8 or anti–immunoglobulin G (IgG) isotype control via intraperitoneal injection on days −3, 0, 3, 6, 9, 12, 15, and 18. Mice were subcutaneously inoculated with 0.5 × 106 MC38 cells on the right flank on day 0. The depletion efficiency was up to 90%.

For in vivo anti-PD1 treatment, mice were treated with 100 μg of anti-PD1 or anti-IgG isotype control via intraperitoneal injection twice a week from day 9 after tumor implantation.

Tumor volumes were measured twice or thrice a week and estimated using the formula: (length × width2)/2. The survival endpoint is when the tumor volume is up to 1500 mm3.

In vivo competitive assay

B16 tumor cells were engineered to express ZsGreen or TdTomato by lentiviral transduction. ShSdc1 and control B16 cells were mixed and grown in vitro for two passages before injection into mice treated with GVAX and anti-PD1 immunotherapy. The proportion of the mixtures was analyzed on the day of tumor injection by flow cytometry. Tumors were dissected and minced on ice and incubated in collagenase I (50 μg ml−1, Sigma-Aldrich), collagenase IV (50 μg ml−1, Sigma-Aldrich), hyaluronidase (25 μg ml−1, Sigma-Aldrich), and deoxyribonuclease (DNase) I (10 μg ml−1, Roche) supplemented DMEM for 15 min at 37°C. After incubation, digested tissues were passed through 70-μm filters. Tumor cells were washed with ice-cold phosphate-buffered saline (PBS) and stained with Live/Dead (1:1000, eBioscience) for 30 min on ice. Then, the cells were washed and resuspended in PBS with 1% FBS. The ZsGreen:TdTomato tumor cell ratios were detected by flow cytometry.

Flow cytometry analysis of tumor-infiltrating lymphocytes

Tumors were dissected, mechanically minced and incubated in DMEM mixing with 5% FBS, collagenase I (50 μg ml−1, Sigma-Aldrich), collagenase IV (50 μg ml−1, Sigma-Aldrich), hyaluronidase (25 μg ml−1, Sigma-Aldrich), and DNase I (10 μg ml−1, Roche) for 15 min at 37°C. Digested tissues were filtered through 70-μm filters, and red blood cells (RBCs) were lysed with RBC lysis buffer (BioLegend) for 5 min. For cytokine examination, cells were stimulated with PMA (20 ng ml−1, Sigma-Aldrich) and ionomycin (1 μg ml−1, Beyotime Biotechnology) in the presence of brefeldin A (1:1000, eBioscience) for 4 hours. Cells were stained with Live/Dead (1:1000, eBioscience) for 30 min on ice, blocked with monoclonal antibody to mouse CD16/CD32 (BD Biosciences PharMingen) for 30 min, and then stained with surface antibodies for 20 min. For intracellular staining, cells were fixed and permeabilized with a Foxp3 staining buffer set (eBioscience) according to the manufacturer’s recommendations and then stained with intracellular antibodies. Flow cytometry was performed with LSR Fortessa (BD Biosciences). Results were analyzed with FlowJo software (TreeStar).

Analysis of scRNA-seq data

We processed the scRNA-Seq data with the Cell Ranger pipeline (10x Genomics), using binary base call files as inputs, and generated a digital expression matrix for each sample. Afterward, we analyzed the matrix using Seurat, an R package specialized for scRNA-seq analysis. To filter the low-quality libraries, we removed cells containing fewer than 200 detected genes and those with over 15% mitochondrial RNA content. Apart from this, genes that were not expressed or expressed in less than three cells were eliminated. Subsequently, the filtered matrix was normalized by using the Seurat function NormalizeData, and the Seurat function FindVariableGenes was applied to identify the variable genes. To regress out the impact of sequencing depth variation for each cell, the matrix was scaled. Principal components analysis (PCA) was performed using the previously identified variable genes to decrease the dimensions of the data. Following this, t-distributed stochastic neighbor embedding and uniform manifold approximation and projection projections were generated on the basis of the top 30 PCA dimensions. The clusters were then characterized using known marker genes, including those for Cd3d, Cd3e, Cd3g, Cd8a, Cd8b1, Cd4 (T cell), Ncr1 (NK cell), Cd14 (myeloid), Flt3 (dendritic cell), Igtam, Csf1r, Cd163, Cd68 (monocyte/macrophage), S100a8, S100a9 (neutrophil), Cd19, and Cd79a (B cell). Cells that co-expressed marker genes from multiple cell types were referred to as doublets and subsequently excluded from further analysis. Next, cell subpopulations that were identified by broad clustering analysis were isolated, and reclustering analysis was performed on them individually to identify more refined subpopulations. Using the previously described method, cells were reclustered, and distinct functional subpopulations were inferred and compared between the Sdc1-knockdown and control tumors. scRNA-seq data were uploaded to the Gene Expression Omnibus (GEO) database (accession code GSE231357).

RNA sequencing

ShSdc1- or control short hairpin RNA (shRNA)–transfected B16 tumor cells, Sdc1-knockdown cells expressing the full-length SDC1 or empty vector B16 tumor cells, and Sdc1-knockdown cells expressing the SDC1 mutants or empty vector MC38 tumor cells were stimulated with or without IFN-γ (10 ng ml−1) for 24 hours. Total RNA was extracted by TRIzol and subjected to RNA sequencing (RNA-seq) analysis. Illumina TruSeq paired-end sequencing libraries were generated according to the manufacturer’s instructions of the Tiangen Biotech (Beijing) Co. Ltd. Samples were sequenced on the Illumina HiSeq 2500. Approximately 50 million clean reads were obtained and mapped to the mouse genome mm10. The mapped fragments were evaluated using HTSeq followed by trimmed mean of M value normalization to calculate gene expression by normalized reads per kilobase of exon per megabase of library size. Significant differential expression genes were identified as those with log2 fold change > 2 and Padj < 0.05 using DEseq2 software. RNA sequencing data were uploaded to the GEO database (accession codes GSE231357 and GSE254619).

Antibodies and reagents

The following fluorochrome-conjugated anti-mouse antibodies were used for flow cytometry: Anti-SDC1 (558626) was obtained from BD Biosciences. Anti-CD45 (53-0451-82 and 45-0451-82), anti-CD8α (12-0081-85 and 53-0081-82), anti-NK1.1 (17-5491-82), anti-CD4 (17-0041-82 and 15-0041-82), anti–IFN-γ (17-7311-82 and 12-7311-82), anti-TNFα (17-7321-82 and 25-7321-82), anti-Foxp3 (17-5773-82, 53-5773-82, and 12-5773-82), anti-CD11b (25-0112-82), anti–Ly-6G/Ly6C (17-5931-82), anti-F4/80 (11-4801-82), anti-CD11c (11-0114-82 and 12-0114-81), and anti–IFN-γR1 (12-1191-82) were obtained from eBioscience. Anti-H2K(b)/H2D(b) (116518), anti-SIINFEKL-H2K(b) (141606), and anti-CD45 (103110, 103106, and 103108) were obtained from BioLegend. Anti–T-Select H-2Kb OVA Tetramer-SIINFEKL (TS-5001-2C) and anti-CD8 (D271-5) were obtained from Medical & Biological Laboratories Co. Ltd. The following fluorochrome-conjugated anti-human antibodies were used for flow cytometry: Anti-SDC1 (352308) was obtained from BioLegend. Anti–HLA-ABC (11-9983-42) was obtained from eBioscience. Anti–IFN-γR1 (A23817) was obtained from ABclonal. The following primary antibodies were used for Western blotting: Anti-STAT1 (14994T), anti-JAK1 (50996s), and anti–phospho-JAK1 (74129) were all obtained from Cell Signaling Technology. Anti–phospho-STAT1 (Tyr701) (AP0135), anti–IFN-γR1 (A11653), and anti–IFN-γR2 (A7558) were obtained from ABclonal. Anti–β-actin (sc-47778) antibody was obtained from Santa Cruz Biotechnology. Secondary antibodies: Anti-rabbit IRDye800CW (926-32211) and anti-mouse IRDye680LT (926-68070) were obtained from LI-COR. The following primary antibodies were used for IF staining: Anti-mouse CD8α (ab217344), anti-human CD8α (ab178089), and anti-SDC1 (ab128936) were obtained from Abcam.

Mouse IFN-γ was from PeproTech. InVivoMAb anti-mouse CD8a (BE0061), InVivoMAb rat IgG2b Isotype control (BE0090), InVivoMAb anti-mouse PD1 (BE0273), and InVivoMAb rat IgG2a Isotype control (BE0089) were obtained from BioXcell.

Cytokine arrays

ShSdc1- or control shRNA-transfected B16 tumor cells were treated with or without IFN-γ (10 ng ml−1) for 48 hours. Concentrations of cytokine production in the culture supernatants were measured by mouse cytokine array (R&D Systems) (eBioscience) according to the manufacturer’s instructions. The absorbance was detected by Infinite M1000 Pro (Tecan).

Immunostaining

IF: B16 tumors were fixed in 10% neutral formalin for 24 hours. The fixed tissues were embedded in paraffin, cut into slices, and mounted onto slides for mouse CD8α staining. The slides were de-paraffinized, rehydrated, immersed in 3% hydrogen peroxide solution to block endogenous peroxidase activity, and boiled in a microwave oven in 10 mM citrate buffer (pH 6.0) for antigen retrieval. The primary antibodies against mouse CD8α were used. The human LUSC tissue microarrays were de-paraffinized and rehydrated and immersed in a 3% hydrogen peroxide solution. Antigen retrieval was performed by boiling the slide. The primary antibodies against human SDC1 and CD8α were used. IF was performed using an IF kit (G1236-100T, Servicebio) according to the manufacturer’s instructions. Nuclei were counterstained using 4′,6-diamidino-2-phenylindole (Molecular Probes, Invitrogen). The images were captured by a Panoramic DESK, P-MIDI, P250 slice digital scanner (3D HISTECH, Hungary), and images were analyzed using Case Viewer software (3D HISTECH, Hungary). The SDC1 area density and number of CD8+ T cells in the image of the human LUSC tissue microarrays were calculated using Aipathwell digital pathology image analysis software (Servicebio).

The LUSC tissue microarrays were purchased from Shanghai Outdo Biotechnology Co. Ltd. All patients provided written informed consent for sampling and research. This study was approved by the Ren Ji Hospital Ethics Committee.

Quantitative real-time PCR

RNA was extracted with RNAiso Plus (Takara Bio Inc.) and converted to single-strand cDNA using the PrimeScript RT Master Mix (Takara Bio Inc.) according to the manufacturer’s protocol. Amplification was performed with the ViiA 7 Quantitative PCR System (Applied Biosystems) using SYBR Green Master Mix (YEASEN). Relative gene expression was quantitated by normalization to glyceraldehyde-3-phosphate dehydrogenase. The primer sequences were as follows: Cxcl9 (mouse) F: 5′-TCCTTTTGGGCATCATCTTCC-3′ and R: 5′-TTTGTAGTGGATCGTGCCTCG-3′; Cxcl10 (mouse) F: 5′-CCAAGTGCTGCCGTCATTTTC-3′ and R: 5′-GGCTCGCAGGGATGATTTCAA-3′; Ccl5 (mouse) F: 5′-GCTGCTTTGCCTACCTCTCC-3′ and R: 5′-TCGAGTGACAAACACGACTGC-3′; Mx1 (mouse) F: 5′-GACCATAGGGGTCTTGACCAA-3′ and R: 5′-AGACTTGCTCTTTCTGAAAAGCC-3′; Oas3 (mouse) F: 5′-TCTGGGGTCGCTAAACATCAC-3′ and R: 5′-GATGACGAGTTCGACATCGGT-3′; Tnfsf10 (mouse) F: 5′-ATGGTGATTTGCATAGTGCTCC-3′ and R: 5′-GCAAGCAGGGTCTGTTCAAGA-3′; Isg15 (mouse) F: 5′-GGTGTCCGTGACTAACTCCAT-3′ and R: 5′-TGGAAAGGGTAAGACCGTCCT-3′; Isg20 (mouse) F: 5′-TGGGCCTCAAAGGGTGAGT-3′ and R: 5′-CGGGTCGGATGTACTTGTCATA-3′; Irf7 (mouse) F: 5′-GAGACTGGCTATTGGGGGAG-3′ and R: 5′-GACGAAATGCTTCCAGGG-3′; Ifit1 (mouse) F: 5′-CTGAGATGTCACTTCACATGGAA-3′ and R: 5′-GTGCATCCCCAATGGGTTCT-3′; Ifit35 (mouse) F: 5′-GTGACCCTGCAAACTGTCCTC-3′ and R: 5′-TTCAGGTACTGAGAATGGGATCT-3′; CXCL9 (human) F: 5′-CCAGTAGTGAGAAAGGGTCGC-3′ and R: 5′-AGGGCTTGGGGCAAATTGTT-3′; CCL5 (human) F: 5′-CCAGCAGTCGTCTTTGTCAC-3′ and R: 5′-CTCTGGGTTGGCACACACTT-3′; and ISG15 (human) F: 5′-CCAGCAGTCGTCTTTGTCAC-3′ and R: 5′-CTCTGGGTTGGCACACACTT-3′.The formula for calculations of relative gene expression for quantitative reverse transcription PCR is as follows: Fold gene expression = 2−(∆∆Ct), ∆∆Ct = ∆Ct (treated sample) − ∆Ct (untreated sample), ∆Ct = Ct (gene of interest) − Ct (housekeeping gene).

Western blotting

Cells were lysed with radioimmunoprecipitation assay buffer (Thermo Fisher Scientific) supplemented with protease inhibitor and phosphatase inhibitor cocktail (both from Roche Diagnostics). Protein concentrations were measured using a bicinchoninic acid protein assay kit (Thermo Fisher Scientific). Equal amounts of protein were loaded on SDS–polyacrylamide gel electrophoresis and then transferred to nitrocellulose membranes (Pall Corporation) for immunoblotting. Membranes were blocked and incubated with the indicated primary and secondary antibodies. Images were visualized by the Odyssey Sa Infrared Imaging System (LI-COR). The band intensity of Western blotting was analyzed by Odyssey Sa Imaging System Application Software (version 1.1.7).

Statistical analysis

Significance assessments were measured by GraphPad Prism and SPSS. One-way analysis of variance (ANOVA) corrected for multiple comparisons was used for experiments with more than two groups. The Tukey test was used for conducting post hoc tests on one-way ANOVA. An unpaired or paired two-tailed Student’s t test or two-way ANOVA was used for experiments with two groups. Survival analysis was performed two-sided log-rank test. P < 0.05 was considered significant.

Acknowledgments

We thank L. Deng for providing the MC38 tumor cell line.

Funding: This work was supported by the National Natural Science Foundation of China, grants 81972579 (Yongzhong Liu), 82273002 (Yongzhong Liu), and 82273005 (L.Z.); the State Key Laboratory of Oncogenes and Related Genes, zz-94-2304 (Yongzhong Liu); the Shanghai Rising-Star Program, 21QA1408400 (L.Z.); and Innovative Research Team of High-Level Local Universities in Shanghai.

Author contributions: Conceptualization: Yun Liu and Yongzhong Liu. Methodology: Yun Liu, L.Z., G.X., and Yongzhong Liu. Investigation: Yun Liu, C.X., L.Z., G.X., Z.Y., L.X., K.J., and Z.C. Supervision: Yongzhong Liu and X.Z. Writing—original draft: Yun Liu and Yongzhong Liu. Writing—review and editing: Yun Liu and Yongzhong Liu.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The scRNA-seq and RNA-seq data were uploaded to the GEO database (accession codes GSE231357, GSE231357, and GSE254619).

Supplementary Materials

This PDF file includes:

Figs. S1 to S11
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