SAMSN1 restrains NK cell mediated anti-tumor immunity in hepatocellular carcinoma

Domain: biology
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
NK cells are critical mediators of anti-tumor immunity whose function is frequently compromised in the tumor microenvironment. Here we identify SAM domain, SH3 domain and nuclear localization signals 1 (SAMSN1) as a previously unrecognized immune checkpoint that predominantly regulates NK cell function in hepatocellular carcinoma (HCC). Single-cell RNA sequencing (scRNA-seq) analysis reveals significant SAMSN1 upregulation in intratumoral NK cells from HCC patients, correlating with reduced granzyme B expression and poor prognosis. In orthotopic Hepa1-6 hepatocellular carcinoma models, global Samsn1 knockout (Samsn1−/−) mice exhibits 34% tumor burden reduction with enhanced NK cell granzyme B production (P = 0.0002). Critically, NK cell-specific deletion alone (Samsn1f/f-Ncr1Cre+) recapitulates this therapeutic effect (41% tumor burden reduction, P = 0.0017), demonstrating that SAMSN1 functions predominantly through intratumoral NK cells rather than other immune populations in the HCC microenvironment. Mechanistically, SAMSN1 suppresses NK cell activation, proliferation, and granzyme B production. These findings indicate SAMSN1 as a targetable NK cell checkpoint with direct therapeutic implications for HCC immunotherapy.

Introduction
Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related deaths worldwide1. Despite advances in diagnostic techniques and treatment modalities, the prognosis for patients with HCC remains poor, with a 5-year survival rate of approximately 18%2–4. This outcome is often attributed to late-stage diagnosis, high recurrence rates, and the limited efficacy of available treatments5–7. Current management strategies for HCC include surgical resection, liver transplantation, local ablation, transarterial chemoembolization (TACE), and systemic therapies6–8. Immune checkpoint inhibitors (ICIs) have demonstrated efficacy in several cancer types by reinvigorating exhausted T cells and restoring anti-tumor immunity9–11. In HCC, the combination of atezolizumab (anti-PD-L1) and bevacizumab (anti-VEGF) has become the standard of care for first-line treatment of advanced disease, based on the IMbrave150 trial, which showed improved overall survival compared with sorafenib8,12,13. However, the overall response rate to ICIs in HCC remains low, at 20–30%14,15. The efficacy of immunotherapy in HCC faces several challenges, including an immunosuppressive tumor microenvironment (TME) characterized by regulatory T cells, myeloid-derived suppressor cells, and tumor-associated macrophages14,16. The heterogeneity of HCC, at both the genetic and immunological levels, complicates treatment strategies15. NK cells, which are components of the innate immune system, can recognize and eliminate transformed cells without prior sensitization17,18. In HCC, NK cells constitute a significant proportion of resident lymphocytes19–21. Studies have demonstrated that NK cell infiltration in HCC tumors correlates with patient prognosis22. Tumor-infiltrating NK cells (TINKs) can directly recognize and kill HCC cells through various mechanisms, including the detection of stress-induced ligands and the absence of major histocompatibility complex (MHC) class I molecules22,23. In HCC specifically, NK cells face unique challenges. The liver harbors the largest population of tissue-resident NK cells in the body, comprising up to 30–50% of intrahepatic lymphocytes. However, within the HCC microenvironment, NK cells often exhibit impaired functionality due to the presence of multiple immunosuppressive factors19,20,23. This NK cell exhaustion correlates with HCC progression and poor prognosis, yet the molecular checkpoints driving this dysfunction remain incompletely defined. Identifying targetable NK cell checkpoints specific to the HCC microenvironment represents a critical unmet need for improving immunotherapy efficacy in HCC. SAMSN1, also known as HACS1 or SLY2, is an adaptor protein initially identified in leukemia24. Adaptor proteins facilitate protein-protein interactions and coordinate the assembly of signaling complexes25–27. SAMSN1 has been implicated in various cellular processes, particularly in immune cell regulation28,29. In B cells, SAMSN1 modulates activation and differentiation processes28. It promotes immunosuppression in sepsis by programming macrophages to express coinhibitory molecules via KEAP1-NRF2 signaling, thereby inducing CD8⁺ T cell exhaustion characterized by impaired IFN-γ and TNF-α production and sustained PD1 and TIM3 expression30. In cancer biology, altered SAMSN1 expression has been reported in various malignancies30–32. SAMSN1 exhibits context-dependent tumor-suppressive functions in hematopoietic malignancies31,32. Despite emerging evidence of SAMSN1’s immunoregulatory functions, its role as a potential NK cell checkpoint in solid tumors, particularly HCC, where NK cells comprise up to 30% of tumor-infiltrating lymphocytes, has not been investigated. This knowledge gap is critical given the limited success of current NK cell-based immunotherapies in HCC.

Our study investigates the role of SAMSN1 in NK cell regulation within the HCC tumor microenvironment, filling a gap in understanding innate immune checkpoints for cancer therapy. Our findings reveal that SAMSN1 is specifically upregulated in intratumoral NK cells, where it suppresses cytotoxic activity and correlates with poor patient prognosis. Genetic deletion of SAMSN1 restores NK cell effector functions, resulting in tumor growth inhibition and improved survival in HCC models. These results establish SAMSN1 as a promising target for enhancing NK cell-based immunotherapy, with broader implications for treating malignancies that evade innate immune surveillance.

Results
SAMSN1 expression in tumor microenvironment is associated with a poor prognosis
To validate the correlation between SAMSN1 expression and tumorigenesis, we analyzed Single-cell RNA sequencing (scRNA-seq) data of 18 patients with HCC from published paper33. scRNA-seq analysis revealed heterogeneous SAMSN1 expression across different cell populations within the tumor microenvironment (Fig. 1a, b). To elucidate the relationship between SAMSN1 expression and HCC development, specimens were classified as intratumor (IT; core tumor region) or paratumor (PT; tissue ≤2 cm from tumor margin, pathologically confirmed to contain mixed tumor/stromal cells), respectively (Fig. 1c). We found that the NK cells (1.35-fold) exhibited markedly higher SAMSN1 expression than T cells (1.21-fold), B cells (1.07-fold), myeloid (0.97-fold), parenchymal (1.0-fold), and tumor cells (0.61-fold) in IT compared to PT regions (Fig. 1d). This preferential upregulation in NK cells within the HCC microenvironment suggests a specific role for SAMSN1 in suppressing NK cell-mediated anti-tumor immunity in HCC. Other cell types showed minimal or no SAMSN1 upregulation in IT regions. This suggests that SAMSN1 may be involved in the poor prognosis of HCC.

Next, to elucidate the mechanisms driving the poor prognosis associated with high SAMSN1 expression in HCC, we analyzed The Cancer Genome Atlas (TCGA) data from Liver Hepatocellular Carcinoma samples available in the cBioPortal database. We stratified the cohort into SAMSN1 high and SAMSN1 low groups based on best cut-off option. We found that SAMSN1 expression was correlated with significantly reduced overall survival (OS) (Fig. 1e) and disease-free survival (DFS) (Fig. 1f). Expression analysis demonstrated strong positive correlations between SAMSN1 and the key immune-related genes, KLRC1 (Spearman r=0.59), TIM3 (Spearman r=0.89), TIGIT (Spearman r=0.77), and LAG3 in NK cell subset (Spearman r=0.54) (Fig. 1g). Notably, these correlations were weaker or absent in CD8+ T cells (SAMSN1-TIM3 correlation in T cells), further supporting NK cell-specific regulatory function in the HCC context.

To resolve the tumor ecosystem in HCC, we collected surgical tumor specimens and paired adjacent non-tumor tissues from primary HCC patients (cohort 1) to perform Immunohistochemistry (IHC) and cytometry by time of flight (CyTOF) (Fig. 1h). Detailed clinical information is provided of cohort 1 (Supplementary Table 1). IHC was performed to verify the spatial distribution of SAMSN1 in HCC tissues (Fig. 1i). The results revealed that integrated optical density (IOD) of SAMSN1 staining was significantly higher SAMSN1 expression within IT regions than in PT areas (Fig. 1j). Subsequently, SAMSN1 expression levels were quantified in HCC patient samples via IHC staining. Samples were stratified into SAMSN1 high and SAMSN1 low groups using IHC-based H-scores. Patients were dichotomized by the cohort-wide median H-score (median=120). OS and disease-free survival (DFS) were then analyzed across these groups. The OS (Fig. 1k) and DFS (Fig. 1l) was further validated. These findings highlight SAMSN1’s differential expression in the HCC microenvironment, prognostic potential, and association with immune-related genes, suggesting a potential role in tumor immunity.

SAMSN1 restrains activity of immune cells in IT
To further elucidate the mechanism connecting SAMSN1 to diminished anti-tumor immunity in HCC, we isolated tumor-infiltrating lymphocytes (TILs) from HCC cohort 2 for CyTOF assay. Detailed clinical information is provided of cohort 2 (Supplementary Table 2). The t-SNE analysis revealed distinct clustering of immune cell populations in both PT and IT samples (Fig. 2a). Among the 22 samples processed, 8 samples were excluded from downstream analysis due to failure to meet quality control criteria. Quantification of CD45+ cells showed significant differences in the populations of NK and CD4+ T cell between PT and IT, with NK cells being more abundant in PT (P=0.031) and CD4+ T cells being more abundant in IT (P=0.0035) (Fig. 2b).

To further assess the effector functions of these cells, we examined the expression levels of indicated effectors in CyTOF. The gating strategy was shown (Supplementary Fig. 1a). Examination of effector production revealed that granzyme B (GZMB) expression was markedly higher in PT compared to IT (Fig. 2c). This was particularly evident in NK and CD8+ T cells (Fig. 2d). NK cells in IT area displayed lower GZMB abundance than PT area (Fig. 2e). Further analysis of cell subsets revealed no significant differences in IFN-γ (Supplementary Fig. 1b), Perforin (Supplementary Fig. 1c), TNF-α (Supplementary Fig. 1d), and GM-CSF (Supplementary Fig. 1e). Similarly, the inhibition markers of TIGIT (Supplementary Fig. 1f) and CD96 (Supplementary Fig. 1g) also showed no significant variation between PT and IT samples.

Further, we next explored the SAMSN1 levels in PT and IT samples by flow cytometry. The t-SNE analysis revealed distinct clustering of SAMSN1 in both PT and IT samples (Fig. 2f). SAMSN1 was strongly upregulated in myeloid cells and NK cells in IT samples (Fig. 2g). SAMSN1 expression was significantly elevated in CD45+ cells of IT samples (Fig. 2h). Meanwhile, GZMB expression was significantly decreased in SAMSN1+ NK cells compared with SAMSN1- NK cells (Fig. 2i). Correlation analyses demonstrated significant negative correlations between SAMSN1 and effector molecules, such as GZMB (Spearman r=−0.543) and CXCR3 (Spearman r=−0.02), and positive correlations with immunosuppressive markers, such as LAG3 (Spearman r=0.077), KLRC1 (Spearman r=0.311), TIM3 (Spearman r=0.469), TIGIT (Spearman r=0.457) and at the protein level (Fig. 2j).

In conclusion, our analysis of tumor-specific cells from patients showed upregulation of SAMSN1 in IT area, suggesting GZMB+ cells exhibited a significant reduction, decreasing from 13.8% to 6.1% in NK cells (P=0.0240) and from 19.4% to 8.2% in T cells (P=0.0099) within the IT region of patients. SAMSN1+ NK cells showed 6.5% reduction (from 9.48% to 2.98%) in granzyme B (GZMB) expression (P=0.0055). However, SAMSN1+ CD8+ cells were no difference in IT when compared with PT samples. GZMB+ NK cells showed 5% reduction (from 42% to 37%) in SAMSN1+ cells when compared with SAMSN1− cells (P=0.0340). The correlation between SAMSN1 and inhibitory markers (TIM3, TIGIT) was strongest in NK cells. Taken together, CyTOF analysis identified SAMSN1 upregulated in NK cell populations that contribute to the anti-tumor immune escape. These patient-derived findings provide the clinical rationale for subsequent genetic dissection of SAMSN1’s cell-type-specific functions in preclinical models, with NK cells emerging as the primary therapeutic target based on their predominant expression and functional correlation with SAMSN1.

SAMSN1 mediates NK cell dysfunction
To determine how SAMSN1 regulate the anti-tumor immunology, we enriched NK cells from human peripheral blood mononuclear cells (PBMCs) and performed SAMSN1 knockout using CRISPR-Cas9 technology (Fig. 3a). The GFP+ cells were gated after transfected with Cas9 RNPs complexes (Fig. 3b). The knockout efficiency was detection in mRNA level (Fig. 3c) and protein level by flow cytometry (Fig. 3d). The results indicated that we successfully generated SAMSN1 knockout primary human NK cells using CRISPR-Cas9. Moreover, we recruited SAMSN1 knockout NK cells to perform bulk RNA-seq. Bulk RNA-seq analysis revealed that SAMSN1 knockout dramatically increased surface expression of activation markers while decreasing inhibitory receptors on NK cells (Fig. 3e). This phenotype suggests that SAMSN1 constrains NK cell activation and that its ablation potentiates their anti-tumor function. Gene Ontology (GO) analysis highlighted the involvement of SAMSN1 in various immune-related processes including leukocyte activation, cell adhesion, and lymphocyte activation (Fig. 3f). Gene Set Enrichment Analysis (GSEA) of SAMSN1 knockout human NK cells confirmed that SAMSN1 knockout was associated with positive regulation of NK cell activation (Fig. 3g), suggesting a potential role of SAMSN1 in regulating NK cell function.

Next, to functionally elucidate SAMSN1’s function in NK cells, we generated the NK92 knockout (NK92KO-SAMSN1) cell line with CRISPR-Cas9 technology and overexpression (NK92OE-SAMSN1) cell line using lentivirus infection. Next, we generated a co-culture model of NK92 cells with HepG2 (Fig. 3h). Knockout of SAMSN1 promoted GZMB, Perforin, and IFN-γ secretion in NK92 cells, whereas SAMSN1 overexpression inhibited cell function (Fig. 3i).

To further explore the function of SAMSN1 in NK cells, we generated the Samsn1 knockout (Samsn1−/−) mice and detected the function of SAMSN1 in splenocytes. The workflow is shown (Fig. 3j). The generation of Samsn1−/− mice and genotyping was performed as shown (Supplementary Fig. 2a, b). Analysis of splenocytes from wild-type (WT) and Samsn1−/− mice revealed no significant differences in immune cell populations (Fig. 3k). However, Samsn1−/− mice exhibited increased percentages of Ki67+ NK cells compared with WT mice in spleen (Fig. 3l). In contrast, the percentages of Ki67+ CD4+ T cells, and CD8+ T cells showed no differences (Fig. 3l). Additionally, GZMB+ NK cells were significantly elevated in the spleen of Samsn1−/− mice (Fig. 3m). The gating strategy for flow cytometry is shown (Supplementary Fig. 2c, d). To verify the impact of Samsn1 knockout on GZMB and IFN-γ secretion in splenic NK cells under naive conditions, we compared secretion levels under both naive and PMA/ionomycin-stimulated conditions. The results showed that Samsn1−/− NK cells exhibited increased GZMB and IFN-γ secretion compared to WT NK cells under PMA/Ionomycin stimulation. However, in the naive state, no significant differences were observed due to the inherently low expression levels of GZMB and IFN-γ (Supplementary Fig. 2e, f). These findings suggest that SAMSN1 regulates NK cell cytotoxic activity against tumor cells.

Samsn1 knockout limits HCC tumor growth and enhances NK cell function
To definitively establish SAMSN1’s role in HCC, we employed orthotopic Hepa1-6 liver tumor models, which recapitulate the immunosuppressive hepatic microenvironment characteristic of human HCC. This model was selected over subcutaneous implantation to preserve liver-resident NK cell populations, recreate the fibrotic milieu present in most HCC patients, and enable physiologically relevant immune cell trafficking patterns (Fig. 4a). Samsn1−/− mice demonstrated robust anti-tumor efficacy in this HCC-relevant model. Tumor burden, quantified as liver-to-body weight ratio, decreased from 8.8% in wild-type compared to 5.8% in Samsn1−/− mice (P=0.0024), representing a 34% reduction (Fig. 4b, c). To determine the cellular mechanism underlying this therapeutic effect, we analyzed TILs from liver tumors at day 21 post-implantation. NK cells from Samsn1−/− mice exhibited enhanced effector function, with GZMB expression increasing from 49.9% to 63.6% (P=0.0002, Fig. 4d) and IFN-γ production increasing from 30.9% to 46.7% (P=0.0050, Supplementary Fig. 3a). This translated to significant survival benefit, with median survival increasing from 27.5 days to 36 days (31% extension, P=0.0072, Fig. 4e). Critically, these functional enhancements were specific to NK cells. CD8+ T cells showed no significant changes in GZMB (4.36% vs. 7.27%, P=0.22, Supplementary Fig. 3b) or IFN-γ (5.34% vs. 6.51%, P=0.07, Supplementary Fig. 3c) expression in Samsn1−/− tumors compared to wild-type controls, despite comparable T cell infiltration. NK cell, CD4+ T cell, and CD8+ T cell frequencies in TILs were similar between genotypes (Supplementary Fig. 3d–f), indicating that therapeutic efficacy resulted from enhanced NK cell function rather than altered immune cell recruitment. To exclude potential confounding effects from direct tumor cell targeting, we verified that Hepa1-6 cells lack detectable SAMSN1 expression by western blot (WB) and real time PCR (RT-PCR) (Supplementary Fig. 3g, h). This confirms that observed anti-tumor effects derive from immune modulation rather than tumor cell-intrinsic mechanisms. Collectively, these data establish SAMSN1 as a bonafide NK cell checkpoint in hepatocellular carcinoma, with NK cell-intrinsic SAMSN1 deletion sufficient to suppress HCC tumor growth and prolong survival.

Additional validation in other solid tumor models (MC38 colon cancer and B16F10 melanoma) confirmed enhanced NK cell function upon Samsn1−/− mice (Fig. 4 and Supplementary Fig. 4). In these models, Samsn1−/− mice exhibited significantly reduced tumor size, lower liver-to-body weight ratio, and increased overall survival compared to WT mice (Fig. 4i–o and Supplementary Fig. 4a–c). NK cells from Samsn1−/− mice showed elevated GZMB (Fig. 4j), IFN-γ (Supplementary Fig. 4d), and Perforin secretion (Supplementary Fig. 4e), with no significant changes in CD8+ T cells (Supplementary Fig. 4f–h). These findings establish that Samsn1 knockout enhances NK cell-mediated anti-tumor immunity in HCC.

NK cell-specific Samsn1 knockout alone is sufficient to suppress HCC tumor growth
While global Samsn1−/− mice demonstrate anti-HCC efficacy, critical questions remain: Is NK cell-intrinsic SAMSN1 deletion sufficient, or do other immune cells contribute? Could developmental defects in germline knockout mice confound interpretation?

To definitively address these concerns, we generated NK cell-specific conditional knockout mice with Samsn1 floxed alleles (Samsn1f/f) crossed with Ncr1-2A-Cre mice, restricting Cre recombinase activity exclusively to NK cells (Fig. 5a and Supplementary Fig. 5a, b). Critically, NK cell frequencies were identical between Samsn1f/f-Ncr1Cre+ and control littermates in blood (7.1% vs. 7.3%, P=0.85) and spleen (5.4% vs. 5.5%, P=0.79) (Fig. 5b–d), excluding developmental artifacts that might confound tumor studies.

To further confirm the function of SAMSN1 in HCC model, we generated the orthotopic mouse model to validate the function of Samsn1 in vivo (Fig. 5e). Samsn1f/f-Ncr1Cre+ mice exhibited markedly reduced tumor size (Fig. 5f), with the liver-to-body weight ratio showing a 41% reduction in tumor burden compared to Samsn1f/f-Ncr1Cre− mice (Fig. 5g, P=0.0017). Analysis of NK cells (Fig. 5h) and CD8+ T cells (Supplementary Fig. 5c) in TILs of liver from Hepa1-6 orthotopic model revealed no significant differences in immune cell populations. NK cells from Samsn1f/f-Ncr1Cre+ tumors also displayed increased secretion of GZMB (Fig. 5i) and IFN-γ (Fig. 5j). In accordance with prevailing predictions, no alterations were observed in the levels of GZMB (Supplementary Fig. 5d) and IFN-γ (Supplementary Fig. 5e) in CD8+ T cells. Samsn1f/f-Ncr1Cre+ mice was also associated with increased OS compared to Samsn1f/f-Ncr1Cre− mice, with median survival increasing from 27 days to 36 days (33% extension, P=0.0101, Fig. 5k). To further validate the NK cell-dependent nature of this phenotype, we generated NK-specific Samsn1 knockout metastasis mice model (Fig. 5l). However, one mouse in WT group died within 24 h after tumor cell inoculation and was excluded from statistical analysis. There was no significant different in radiance intensity (Fig. 5m, n). However, Samsn1f/f-Ncr1Cre+ resulted in significantly longer OS (Fig. 5o). In summary, the phenotypic concordance between global Samsn1−/− and Samsn1f/f-Ncr1Cre+ mice establishes that Samsn1 knockout in NK cells alone is both necessary and sufficient for the observed anti-tumor effects. The comparable tumor reduction (35% in Samsn1−/− vs. 41% in Samsn1f/f-Ncr1Cre+) and survival benefit (median survival 36 days in Samsn1−/− vs. 36 days in Samsn1f/f-Ncr1Cre+) demonstrate that other immune cell types contribute minimally to the therapeutic efficacy, definitively positioning NK cells as the primary functional target of SAMSN1 in HCC.

Enforced expression of SAMSN1 in NK92 cells induces cell exhaustion
To determine whether SAMSN1 is associated with poor prognosis in human, we detected the phonotypes of NK92KO-SAMSN1 cell line and NK92OE-SAMSN1 cell line (Fig. 6a). The SAMSN1 knockout efficiency was showed (Supplementary Fig. 6a–c). Given the high baseline and transduction efficiencies, the SAMSN1 expression level was showed by Mean fluorescence intensity (MFI) values in flow cytometry (Fig. 6b). The SAMSN1 expression level was also showed in WB (Supplementary Fig. 6d) and RT-PCR (Supplementary Fig. 6e). The proliferation of cells was found to be significantly inhibited in NK92OE-SAMSN1 cells (Fig. 6c). In contrast, the knockout of SAMSN1 in NK92 cells was found to be a stimulant of cell proliferation (Fig. 6d). Meanwhile, SAMSN1 exhibited a marked propensity to elicit apoptosis (Supplementary Fig. 6f). NK92KO-SAMSN1 cells promoted GZMB (Fig. 6e), and IFN-γ (Fig. 6f) secretion when compared with control cell line (NK92Control), whereas SAMSN1 overexpression in NK92 cells hindered the secretion of GZMB (Fig. 6e). And thesecretion of IFN-γ (Fig. 6f), Perforin (Fig. 6g) was no significantly different, when compared with NK92Control with PMA/Ionomycin stimulation. In contrast, the inhibition marker of TIGIT was significantly increased in NK92OE-SAMSN1 cells, when compared with NK92Control with PMA/Ionomycin stimulation (Fig. 6h). Importantly, NK92OE-SAMSN1 cells showed reduced lower oxygen consumption rate (OCR) (Supplementary Fig. 6g, h) and increased extracellular acidification rate (ECAR) (Supplementary Fig. 6i, j), indicating metabolic alteration.

To further characterize the function of SAMSN1 in NK cells in anti-tumor immunity, we generated a humanized mouse model (Fig. 6i) and investigated whether the exhaustion of NK92OE-SAMSN1 correlated with the anti-tumor capacity of NK92 cells. We adoptively transferred NK92OE-SAMSN1 cells or NK92Control into immunodeficient mice bearing HepG2 xenografts. NK92OE-SAMSN1 cells exhibited significantly reduced tumor growth suppression compared to NK92Control cells (Fig. 6j–m). Specifically, tumor volumes in mice treated with NK92OE-SAMSN1 cells were significantly larger than those in the control group by day 16 post-transfer, indicating a diminished anti-tumor effect. This finding is consistent with our in vitro cytotoxicity assays, where NK92OE-SAMSN1 cells showed reduced specific lysis of tumor targets compared to NK92KO-SAMSN1 and NK92Control cells (Supplementary Fig. 6k). Taken together, the present study investigates the regulatory role of SAMSN1 expression levels in modulating NK cell activity and its implications in HCC (Fig. 6n). Collectively, these data implicate SAMSN1 as a potential biomarker of dysfunctional NK cell states and a contributor to poor clinical outcomes, possibly through its dual effects on immune effector function and metabolic fitness. The targeting of SAMSN1 or its downstream pathways may represent a novel therapeutic strategy to restore NK cell-mediated anti-tumor immunity in patients with SAMSN1-driven immunosuppression.

Methods
Study design
The primary objective of this study was to elucidate the role of SAMSN1 in HCC and the underlying mechanisms involved. To achieve this goal, we investigated the expression and distribution of SAMSN1 in HCC patient specimens and Samsn1 knockout mice, and explored the mechanism by which SAMSN1 contributes to tumor growth.

Human samples
Human samples were obtained under the ethical approvals detailed in the “Ethics statement” above. For the retrospective HCC cohort (Cohort 1), we collected a total of 90 samples from patients diagnosed with HCC who underwent liver resection surgery without neoadjuvant chemotherapy. These samples comprised: (1) 80 retrospective paraffin-embedded tissues obtained under approval KY2021204 (consent waiver); and (2) 10 additional retrospective samples obtained under approval 2023-A-(H)-048 (written informed consent). All 90 samples were used for IHC staining experiments (Fig. 1i–l). However, 2 of the 10 additional samples lacked complete survival follow-up data and were therefore excluded from the survival analysis, resulting in n=88 for the prognostic analyses shown in Fig. 1i–l. All samples were anonymized, and data were handled in compliance with privacy regulations.

Cell lines
Mouse MC38 colorectal cancer cell line was purchased from Cell bank of Chinese Academy of Sciences. Human HepG2, Hepa1-6 murine hepatoma, and B16-F10 mouse melanoma cell lines were purchased from the American Type Culture Collection (ATCC). A B16-F10-luciferase cell line was established by transducing B16-F10 cells with a lentiviral vector encoding the luciferase gene. B16-F10 and B16-F10-luc cell lines were maintained in DMEM containing 10% FBS. NK92 cells were purchased from ATCC (Cat: CRL-2407) and maintained according to the protocol provided by ATCC. NK92 overexpression and knockout cell lines were established by transducing NK92 cells with lentiviruses. K562 cell line was also purchased from ATCC (Cat: CCL-243) and maintained in IMEM medium containing 10% FBS. All cells were cultured in a humidified, 5% CO2 incubator at 37°C, and grown in respective medium and 100 U/ml penicillin–streptomycin (Gibco, Cat: 15070063). All cell lines were tested and were negative for mycoplasma contamination.

Mice
The primary tumor models used for mechanistic validation were HCC-relevant orthotopic Hepa1-6 mice models. Additional exploratory validation was performed in MC38 and B16F10 models to assess generalizability. All mice used in this study were of the species Mus musculus. C57BL/6 (C57BL/6Smoc) (Cat. NO. SM-001), M-NSG (NOD-PrkdcscidIl2rgem1/Smoc) (Cat. NO. SM-001), and Ncr1-2A-Cre (C57BL/6Smoc-Ncr1em2(2A-iCre)Smoc) (Cat: NM-KI-190027) mice were purchased from Shanghai Model Organisms Center, Inc. Samsn1f/f and Samsn1 knockout (Samsn1−/−) mice were also generated on a C57BL/6 background in Shanghai Model Organisms Center, Inc. Mice used in the cancer model (Samsn1+/+ and Samsn1−/−) were crosses of Samsn1+/− bred in-house carrying the following genotype: Samsn1+/+, Samsn1+/−, and Samsn1−/−, and mice used in the cancer model (Samsn1f/f Ncr1cre− and Samsn1f/f Ncr1cre+) were crosses of Samsn1f/f and Ncr1cre+ bred in-house carrying the following genotype: Samsn1f/f Ncr1cre+, Samsn1f/+ Ncr1cre+, and Samsn1f/f Ncr1cre−, where the plus (+) and cre indicates presence of the mutant/transgenic allele and a minus (−) indicates allele absence. Samsn1f/f Ncr1cre− and Ncr1cre+ mice had a similar tumor growth profile (not shown), and we preferentially used the Samsn1f/f Ncr1cre− mice as controls. All the mice were maintained in a specific pathogen-free (SPF) environment with controlled temperature (22±2°C) and humidity (50±15%) under 12 h light/dark cycle at the University of Science and Technology of China (USTC) Animal Facility. Mice aged 6–8 weeks were used for experiments. Group sizes are specified per experiment in figure legends and Source Data. Breeding mice were housed separately by sex prior to grouping. After grouping, experimental and control animals were not co-housed; they were placed in separate cages within the facility and bred separately. The maximum tumor burden permitted by our institutional ethical board is 1500 mm³. We strictly adhered to these size limits, and no tumor exceeded this volume in our experiments. PCR primers used in this study can be found in Supplementary Table 3.

Validation of Ncr1-Cre conditional knockout system
Ncr1-2A-Cre mice were validated for NK cell-specific recombination through multiple approaches. Ncr1-2A-Cre mice were crossed with Rosa26-tdTomato reporter mice. Flow cytometric analysis confirmed tdTomato expression exclusively in NKp46+ NK cells in spleen (2.97%) and blood (2.51%)38, with negligible recombination in CD3+ T cells (<0.5%), CD11b+ myeloid cells (<0.1%), or CD19+ B cells (<0.3%). Ncr1-2A-Cre+ mice (without floxed Samsn1 alleles) showed no differences in tumor growth, survival, or NK cell function compared to Cre-negative littermates in Hepa1-6 models (data available upon request), excluding Cre toxicity artifacts. In addition, SAMSN1 protein levels were reduced specifically in NK cells (78% reduction by flow cytometry) but unchanged in CD4+ or CD8+ T cells from Samsn1f/f-Ncr1Cre+ mice. These validations confirm that observed anti-tumor effects result from NK cell-intrinsic SAMSN1 deletion rather than off-target recombination or Cre expression artifacts.

In vivo mouse models and analysis
To evaluate tumor growth in Samsn1 knockout mice, Samsn1−/− or Samsn1f/f-Ncr1cre+ mice (C57BL/6 background, 6-to 8-week-old females) and littermate control mice (C57BL/6; 8-week-old females) were anesthetized via intraperitoneal injection of ketamine (100/10 mg/kg), and were inoculated subcutaneously in the right flank at a final volume of 100 μL with MC38 cells (2×105) or B16-F10 cells. Tumor dimensions were measured every 2–3 days using digital calipers, and tumor volume was calculated as 0.5×length×width×width. Mice were monitored daily for health status. Criteria for early termination (humane endpoints) included tumor volume reaching 1500 mm³, tumor ulceration, weight loss >20%, or signs of severe distress. Mice were euthanized by carbon dioxide inhalation when these endpoints were reached.

For the metastatic B16-F10-luc model, B16F10-luc cells (1×10⁶) were injected into the left hepatic lobe of anesthetized control and Samsn1−/− or Samsn1f/f-Ncr1cre+ mice (6-to 8-week-old females). Tumor growth was monitored by bioluminescence imaging using an IVIS system. For internal tumor models where tumor size could not be externally measured, the criteria for early endpoints included weight loss >20%, lethargy, hunched posture, abdominal distension, or when bioluminescence signal exceeded 1×10⁸ photons/s/cm²/sr. Euthanasia was performed by carbon dioxide inhalation immediately upon reaching these endpoints.

For orthotopic Hepa1-6 mouse models, 6-to 8-week-old female control and Samsn1−/− or Samsn1f/f-Ncr1cre+ mice were anesthetized. A aseptic midline laparotomy (~1.5 cm) exposed the left hepatic lobe. Hepa1-6 cells (1×10⁶) suspended in 1:1 (v/v) PBS/matrigel (Corning, Cat: 356231) were injected into the subcapsular parenchyma (50 μL total) using a 30-gauge needle. The abdomen was closed in layers (5-0 Vicryl sutures for muscle; staples for skin). For tumor burden assessment, mice (n=8/group) were euthanized at 21 days by carbon dioxide inhalation; livers were weighed, imaged, and liver index calculated as (liver weight/body weight)×100. A survival cohort (n=8/group) was monitored daily. Humane endpoints for this non-measurable tumor model included weight loss >20%, lethargy, or palpable abdominal masses >1 cm.

For subcutaneous tumor model in M-NSG mice, 6-to 8-week-old female M-NSG mice received subcutaneous injections of HepG2 cells (5×106) and IL-2-primed NK92 cells (2×106) suspended in PBS/matrigel (1:1 v/v; total volume 100 μL) in the right flank. Control groups included HepG2-only injections. Tumor dimensions were measured three times weekly by blinded operators using digital calipers; volume was calculated as 0.5×length×width×width. Humane endpoints (tumor volume >1500 mm³, weight loss >20%, or ulceration) were strictly enforced, and mice were euthanized by carbon dioxide inhalation.

Single-cell RNA sequencing and data analysis
scRNA-seq data from datasets from published paper33 were used for analysis of SAMSN1 expression in lymphocytes of PT and IT samples. Seurat package (version: 4.0.3, https://satijalab.org/seurat/) was used for cell normalization and regression based on the expression table according to the UMI counts of each sample and percent of mitochondrial rate to obtain the scaled data. PCA was constructed based on the scaled data with top 2000 high variable genes and top 10 principals were used for tSNE construction. Utilizing graph based cluster method, the unsupervised cell cluster result based the PCA top 10 principal was acquired and the marker genes were calculated by FindAllMarkers function with Wilcox rank sum test algorithm under following criteria: 1. lnFC > 0.25; 2. p-value < 0.05; 3. min.pct > 0.1. In order to identify the cell type detailed, the clusters of same cell type were selected for re-tSNE analysis, graph-based clustering, and marker analysis. For pseudotime analysis, the Single-Cell Trajectories analysis utilizing Monocle 2 (http://cole-trapnell-lab.github.io/monocle-release) using DDR-Tree and default parameter. Based on the pseudo-time analysis, branch expression analysis modeling (BEAM Analysis) was applied for branch fate determined gene analysis.

TCGA data analysis
We analyzed the Cancer Genome Atlas (TCGA) data from Liver Hepatocellular Carcinoma samples available in the cBioPortal database and used to analyze the gene expression correlations between SAMSN1 and other immune checkpoints as well as the overall survival of HCC patients based on their SAMSN1 gene expression. Correlations were analyzed by UCSC Xena (https://xena.ucsc.edu). The Kaplan–Meier survival of patients with HCC was analyzed using OncoLnc (http://www.oncolnc.org).

Bulk RNA sequencing
Total RNA was isolated using RNeasy Mini Kit (QIAGEN, Germany) from purified human NK cells derived from PBMCs, following SAMSN1 knockout via the CRISPR-Cas9 system. Total RNAs (2 μg) were used for stranded RNA sequencing library preparation by means of a Stranded mRNA Library Prep Kit from DR08502 (Bioyigene) according to the manufacturer’s instructions. The library products corresponding to 200–500 bp were enriched, quantified, and finally sequenced on DNBSEQ-T7. The gene expression profiles of NK cells from WT and SAMSN1 knockout were determined by RNA-Seq data analysis (Bioyigene). In brief, raw sequencing data were first filtered by FastQC; low-quality reads were discarded, and adaptor sequences were trimmed. RNA-seq reads were aligned to the human GRCh38 reference genome (GENCODE v43 annotation) using HISAT2 v2.2.1. Only samples with >90% uniquely mapped reads were retained. Significantly differentially expressed transcripts were screened by applying the criteria FC ≥ 2 or ≤−2 and p-value < 0.05.

Gene set and pathway enrichment analyses
GSEA was performed using gsea v4.2.3, by comparing single cells annotated as sgSAMSN1 vs. sgControl; all genes with log2FC threshold ≥0.1 were included. Pathway enrichment analysis was performed using the top 20 most upregulated genes by log2FC for both single cells annotated as sgSAMSN1 vs. sgControl for single cells within the leukemia cluster, with CytoTRACE > 0.95 vs < 0.95. These gene sets were compared against the ChEA and ENCODE transcription factor targets databases via the enrichr platform.

Isolation of human PBMCs and NK cells
Peripheral blood samples from healthy controls were collected from The Affiliated Hospital of University of Science and Technology of China (Cohort 2, n=4). And PBMCs were isolated by Ficoll Paque (GE Healthcare, Cat: 17-1440-02) density gradient centrifugation according to the manufacturer’s instructions. NK cells were purified by negative selection using human NK Cell Isolation Kit (Miltenyi Biotec, Cat: 130-092-657) following the manufacturer’s protocol. The purity of NK cells was >90% as determined by flow cytometry. Human PBMCs and purified NK cells were cultured in RPMI-1640 medium with 10% FBS and 100 IU/ml recombinant human IL2 (Proteintech, Cat: Hz-1015).

In vitro knockout of SAMSN1 with CRISPR/Cas9
NK cells magnetically enriched (Mitenyi) from PBMCs were cultured in RPMI-1640 supplemented with recombinant human IL2 (Proteintech, Cat: Hz-1015) for expansion. To disrupt the target gene, we employed the type II CRISPR-Cas9 system from S. pyogenes. Specifically, cells (1×106) were resuspended in 20 μl P4 solution with NLS-Cas9-EGFP Nuclease (Novoprotein, Cat: E379-02A) and guide RNA (gRNA) (0.1 nmol, GenScript) to make RNP complex, followed by nucleofection using Lonza 4D Nucleofector with program CM137 (Lonza). The single-guide RNA (sgRNA) designed using the CRISPR Design Tool (Zhang Lab, MIT) to target exon 6 of SAMSN1. This is a widely used, well-characterized version of CRISPR-Cas9 with established protocols for in vitro applications. The cells were cultured post-electroporation for 72 h. Flow cytometry was used to gate GFP+ cells and verify the knockout efficiency by CHANGE-seq, and mRNA was purified to perform bulk RNA-seq. Sanger sequence was used for indel efficiency analysis. gRNA sequences used in this study can be found in Supplementary Table 3.

Generation of SAMSN1 overexpression and knockout cell lines
The following lentiviral plasmids were used for this experiment: pLVX-EGFP-IRES-puro was a gift from Qingming Fang (Addgene, Cat: 128652) and modified by replacing the puro sequence with EGFP and inserting SAMSN1 in the original EGFP position to generated the pLVX-SAMSN1-IRES-EGFP and pLVX-SAMSN1-IRES-puro plasmids. lentiCRISPR v2 (Addgene, Cat: 52961) and lentiCRISPR v2GFP (Addgene, Cat: 82416) were inserted with sgRNA sequences to knockout SAMSN1 in NK92 cells. HEK293T cells were used to produce lentiviral particles using psPAX2 (Addgene, Cat: 12260) and pMD2.G (Addgene, Cat: 12259) plasmids. Once the presence of lentivirus was confirmed, the supernatant was divided and stored in −80°C until use. PCR primers used in this study can be found in Supplementary Table 3.

Transduction of NK92 cells with concentrated lentiviral particles
NK92 cells (3×105) were resuspended with 2 ml lentivirus (6×106 PFU/ml) and supplemented with 8 μg/ml polybrene. The mixture was subjected to centrifugation at 30°C for 90 min in 6-well plate, after which it was transferred to a 37°C incubator for 12 h. Thereafter, centrifugation was performed once more with the objective of replacing the liquid. The cells were validated after 72 h.

CHANGE-seq
The CHANGE-seq was performed on genomic DNA isolated from human NK cells using the Gentra Puregene Kit (Qiagen, Cat: 158767). The DNA was tagmented with a custom Tn5-transposome to an average length of 400 bp, followed by gap repair using Kapa HiFi HotStart Uracil+ DNA Polymerase (Roche, Cat: KK2601) and Taq DNA ligase (NEB, Cat: M0208V). The tagmented DNA was then treated with USER enzyme (NEB, Cat: M5505S) and T4 polynucleotide kinase (NEB, Cat: M5505S), circularized intramolecularly with T4 DNA ligase (NEB, Cat: M0202V), and residual linear DNA was degraded using a cocktail of exonucleases (Plasmid-Safe ATP-dependent DNase [Lucigen], Lambda exonuclease [NEB], and Exonuclease I [NEB]). In vitro cleavage was conducted with 125 ng circularized DNA, 90 nM SpCas9 protein, NEB buffer 3.1, and 270 nM gRNA in a 50 μL reaction. Cleaved products were A-tailed, ligated with a hairpin adaptor, treated with USER enzyme, and PCR-amplified using barcoded primers (NEBNext Multiplex Oligos for Illumina, Cat: E6444S) and Kapa HiFi Polymerase. Libraries were quantified by qPCR and sequenced with 151 bp paired-end reads on an Illumina NextSeq. Data analysis used open-source CHANGE-seq software. For CHANGE-seq-BE analysis, adapters were trimmed with cutadapt (v1.18) using parameters. to remove Tn5 adapters. Paired-end reads were mapped via bwa mem, filtered for outward orientation and self-overlap (default min_overlap=0 and max_overlap=15), and start positions were tabulated to identify enriched intervals in nuclease-treated samples. Flanking sequences (±30 bp) were searched for off-target sites with edit distance ≤6 (allowing gaps). Percentage was calculated as the ratio of deamination-containing reads to total valid reads per off-target.

Cytotoxicity assay
The tumor cell lines were labeled with CFSE (ThermoFisher, Cat: C34554) according to the manufacturer’s instructions. Labeled tumor cells were co-cultured with effector cells in 96-well plates for 4 h at different effector/target ratios. For the spontaneous death control, CFSE-labeled target cells were cultured alone, followed by the addition of 7AAD (Biolegend, Cat: 420403), and lysed cells (CFSE+7AAD+) were identified using flow cytometry. Details information of antibody used in this study can be found in Supplementary Table 4.

Immunohistochemistry
Formalin-fixed paraffin-embedded HCC tissue sections (4 μm) were deparaffinized and rehydrated. Antigen retrieval was performed using citrate buffer (pH 6.0) at 95°C for 20 min. Endogenous peroxidase was blocked with 3% H2O2 for 10 min. Sections were incubated with anti-SAMSN1 antibody (1:200, Atlas Antibodies, Cat: HPA059729) overnight at 4°C. After washing, the sections were incubated with an HRP-conjugated secondary antibody (1:500, Abcam, Cat: ab205718) for 1 h at room temperature. 3,3′-Diaminobenzidine (DAB) was used for color development. Lymphocytes were identified based on their characteristic morphological features, including small, round cell morphology, hyperchromatic nuclei, scant cytoplasm, and a high nuclear-to-cytoplasmic ratio, with cell diameters typically less than 10 μm. In the IHC images, blue staining (hematoxylin) denotes nuclei, while brown staining (DAB) indicates SAMSN1 expression. The IHC score was calculated as the integrated optical density (IOD) divided by the area (IOD/area) to quantify SAMSN1 expression. SAMSN1 expression was quantified using ImageJ software (NIH, USA) in five random high-power fields (400×) per region. The staining intensity was scored as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong). The percentage of positive cells was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), or 4 (76–100%). The average IOD/area was calculated for each sample. Wilcoxon rank-sum test was used in the statistical analysis. The final score was calculated by multiplying intensity and percentage scores.

Metabolism assays
Extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were measured with the Seahorse XF96 Flux Analyser (Agilent) and Agilent Wave 2.6.1 software according to the manufacturer’s instructions. 1 h before testing, the media was replaced with assay media, and cells were seeded on plates. For ECAR test, 1 mmol/L oligomycin (O), 0.5 mmol/L carbonyl cyanide p-trifluoromethoxyphenylhydrazone (FCCP), and 0.1 mmol/L antimycin A plus 0.5 mmol/L rotenone (A&R) were injected to the wells. For OCR test, 10 mmol/L glucose, 1 mol/L oligomycin (O), and 50 mmol/L 2-deoxyglucose (2-DG) were added to the wells.

Preparation of cell suspensions
Single-cell suspensions were prepared from mouse spleens or tumors as previously described39. In brief, tumors were dissociated mechanically and digested with 1 mg/ml collagenase A and 0.1 mg/ml DNase I for 20 min at 37°C. Spleens were mechanically dissociated, digested with 0.1 mg/ml collagenase A and 0.01 mg/ml DNase I for 20 min at 37°C, and passed through a 40 μm cell strainer and lysed of red blood cells (using ACK buffer), then washed with cold PBS and centrifuged.

Flow cytometry
All antibodies used in this study can be found in Supplementary Table 4. For flow cytometry, tumor samples were collected from The First Affiliated Hospital of Anhui Medical University (Hefei, China). Tumor tissues were finely minced and digested at 37°C in digestion buffer: RPMI-1640 medium with 1 mg/ml collagenase IV (Worthington Biochemical, Cat: 9001-12-1) and 0.1 mg/ml DNase I (Sigma-Aldrich, Cat: AMPD1-1KT). All organs were processed into a single-cell suspension over a 70-μm filter and washed with PBS and 5 mM EDTA. cells were left untreated or stimulated with 30 ng/ml phorbol 12-myristate 13-acetate (PMA) (Sigma-Aldrich, Cat: P1585) and 1 μg/ml ionomycin, followed by 2.5 μg/ml monensin (Sigma-Aldrich, Cat: 22373-78-0). After washing with FACS buffer (PBS+0.5% FBS+2 mM EDTA), the cells were stained with a Zombie Aqua Fixable Viability Kit (BioLegend, Cat: 423101) or a Horizon Fixable Viability Stain 700 (BD Biosciences, Cat: 564997). The reaction was terminated by washing with FACS buffer. Subsequently, the cells were stained for 30 min at 4°C in the dark with fluorophore-conjugated antibodies specific for panels of cell-surface markers. Intracellular staining was performed using the FoxP3/Transcription Factor Fix/Perm Kit (eBioscience, Cat: 00-5523-00). Antibodies used in this study were diluted at 1:200 unless otherwise specified. The anti-SAMSN1 antibody used for FACS was sourced from Boster Biological Technology (Cat: A08977-2, 1:100 dilution) and labeled with PE Donkey anti-rabbit IgG (Biolegend, Cat: 406421, 1:100 dilution). The following cell populations were identified on the basis of cell marker expression: CD4+ T cells (CD45+CD3+CD4+), CD8+ T cells (CD45+CD3+CD8+), B cells (CD45+CD3−CD19+), natural killer (NK) cells (CD45+NK1.1+) for mice and (CD45+CD56+) for human samples, NKT cells (CD45+CD3+NK1.1+) or (CD45+CD3+ CD56+), monocytes (CD45+CD3+CD11b+). All samples were acquired on an LSR Fortessa (BD, Franklin Lakes, USA) and were analyzed using FlowJo software (BD, Franklin Lakes, USA).

Cytometry by time of flight (CyTOF) analysis
For mass cytometry, Single-cell suspensions were prepared from tumors as described above. All antibodies used in this study can also be found in Supplementary Table 4. Antibodies were purchased unlabeled and conjugated to heavy metals in-house. Antibody conjugation to the heavy metals tags was performed using the MaxPar Antibody Conjugation Kit (Fluidigm), according to the manufacturer’s protocol. After labeling, antibodies were diluted to 0.2–0.5 mg/ml in antibody stabilization solution (Candor Bioscience) and stored at 4°C until use. Before experimental use, conjugated antibodies were titrated against tissue to determine the optimal staining concentration. Before sample staining, all antibodies were pooled into either the surface or intracellular master mix and stained.

Cells were resuspended at a 1:1 ratio in PBS+5 mM EDTA and 100 μM cisplatin (Sigma-Aldrich; diluted in PBS+5 mM EDTA) for 60 s before quenching at a 1:1 ratio in FACS buffer to determine viability. Cells were then treated with an Fc receptor blocking reagent to minimize nonspecific binding, followed by incubation with a mixture of surface antibodies for 30 min on ice. Cells were fixed overnight at 4°C in Fix/Perm Buffer (eBioscience). Samples were washed with permeabilization buffer (eBioscience) prior to intracellular staining. Intracellular antibody cocktails were applied for 30 min on ice, after which cells were washed, resuspended in ultrapure water, and mixed with EQ calibration beads (20%, Fluidigm) for instrument standardization. Data acquisition was performed on a Helios mass cytometer (Fluidigm). Among the 22 samples processed, 8 samples were excluded from downstream analysis due to failure to meet quality control criteria. Raw data were preprocessed by debarcoding using a mass-tag-specific method that incorporated doublet filtering. Bead-mediated normalization was applied to standardize signals between acquisition runs. Viable singlet immune cells were selected through manual gating in FlowJo software, with exclusion of debris, non-viable cells, and multiplets. Cellular subtypes were delineated via the PhenoGraph clustering method, which partitioned cells on the basis of their marker profiles. Subtype annotations were determined from standard marker signatures. Low-dimensional projections of the clustered dataset were produced with the t-SNE technique to aid in assessing subtype abundance, marker levels, and inter-group variations. Variations in subtype proportions across conditions were analyzed with unpaired t-tests.

Western blotting
NK92 cell lines were harvested and washed with cold PBS, then lysed with RIPA lysis buffer (Solarbio) on ice with addition of protease inhibitor cocktail (Sigma) and subsequently heated at 98°C for 10 min. After total protein normalized, protein samples were separated by 8–10% SDS-PAGE gel electrophoresis and transferred onto 0.22 mm NC membranes. After blocking with 5% BSA diluted in Tris buffer saline plus 0.1% Tween 20 (TBST) for 1 h at room temperature, membranes were incubated overnight with SAMSN1 antibody (1:1000, Boster Biological Technology, Cat: A08977-2), and 2 h with Anti-rabbit IgG, HRP-linked antibody (1:5000, CST, Cat: 7074S). Blots were visualized by the Bio-Rad system.

RT-qPCR assay
To determine the expression levels of SAMSN1, NKp30, CCR3, and CXCR3 in NK92 cells, total RNA was isolated using a MolPure Cell/Tissue Total RNA Kit (YEASEN, Cat: 19221ES50) and converted to cDNAs using the Superscript III First Strand Synthesis System (YEASEN, China). RT-qPCR was performed using a Bio-Rad IQ5 System. PCR reactions were performed in 20 μL reactions using the SYBR Green PCR master mix (YEASEN, Cat: 11201ES) and 0.2 μM specific primers. The relative expression levels of mRNAs were calculated using the comparative CT method normalized to GAPDH. Primers used for RT-qPCR are shown in Supplementary Table 3.

Statistical analyses
Data are presented as mean±standard deviation. All statistics were carried out using GraphPad Prism 10.0. Student’s t-test was used to compare the differences between two groups. One-way analysis of variance (ANOVA) and Tukey’s multiple comparison test were used for the three or more groups comparisons. At least three independent biological replicates have been performed for each experiment. The number of independent experiments is indicated. P<0.05 was considered to be statistically significant. All statistical was labeled as shown in figure unless otherwise indicated.

Ethics statement
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Anhui Provincial Hospital, China (approval numbers KY2021204 and KY2022222) and the Biomedical Ethics Committee of the University of Science and Technology of China (approval numbers 2019-N-(H)-121 and 2023-A-(H)-048).

Written informed consent was obtained for: (1) 4 healthy donor blood samples (Cohort 2, n=4) for bulk RNA-seq under approval 2019-N-(H)-121, using the approved informed consent form (version dated March 15, 2019); and (2) 10 retrospective HCC samples (Cohort 1, n=10) for expanded IHC validation under approval 2023-A-(H)-048, using the approved informed consent form (version T2.2023-09, dated September 17, 2023). Informed consent was waived by the ethics committees for: (1) 80 retrospective paraffin-embedded HCC tissues (Cohort 1, n=80) for IHC staining under approval KY2021204; and (2) 22 fresh HCC samples (Cohort 2, n=22) for CyTOF under approval KY2022222, as all samples were de-identified and used for observational research.

All mouse experiments were conducted in accordance with animal protocols approved by the Laboratory Animal Centre of the University of Science and Technology of China (USTCACUC192201040, USTCACUC25030123013), and every effort was made to minimize suffering.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The presented study addresses a significant clinical issue, identifying SAMSN1 as a molecule that impairs NK cell antitumor activity. However, the work in its current form has several limitations from both a scientific perspective and in terms of manuscript writing. For instance, insufficient information on the datasets analyzed, the patients, the healthy controls, experimental details, and statistical analysis makes it challenging to evaluate the manuscript adequately. Consequently, the scientific relevance of the paper is limited.\nThe main limitations include the following:\n1. The authors state in the title that SAMSN1 negatively regulates NK cell-mediated antitumor immunity in hepatocellular carcinoma (HCC). However, this seems not to be specific for NK cells, as both CD4 and CD8 T cells appear to be affected too (as also stated in the result paragraph “The regulation of immune function by SAMSN1 is not NK-specific”). Moreover, only some of the results are related to HCC, the others refer to other tumor types.\n2. The abstract contains many errors. Some phrases are not correctly written. Moreover, the authors affirm that SAMSN1 expression in NK cells inversely correlates with immune checkpoint blockade responsiveness, but this has not been demonstrated in the present manuscript.\n3. Is SAMSN1 associated with a specific subset of NK cells (both in mice and humans)? The authors evaluated the expression of SAMSN1 in the tumor setting, which is its expression on circulating NK cells?\n4. Some results are not properly described.\n5. The authors reported the analysis of scRNA-seq data from published data, however the papers are not cited. Moreover, there are no information about the patients included in the analysis and about the analysis performed. How were the populations annotated? Is SAMSN1 differentially expressed between PT and IT? The authors indicate that SAMSN1 expression is higher in lymphocytes compared to other populations: how were the statistics calculated?\n6. Which is the HCC cohort analyzed for the survival analysis? There is no information at all about it. How has SAMSN1low from SAMSN1high groups been defined? How has SAMSN1 expression been normalized on PTPRC?\n7. The authors reported that the analysis of the TCGA cohort is of a scRNA-seq dataset. However, the TCGA contains bulk RNA-sequencing data. Moreover, they reported that they identified prognostic biomarkers in the blood, but no results were shown, moreover TCGA does not contain data on peripheral blood.\n8. The resolution of immunohistochemistry images is not good. In the figure legend, the authors indicate that SAMSN1 staining is shown in lymphocytes, however, there is no indication of how lymphocytes are identified. Moreover, they state that the negative control shows no significant staining, but no images are reported. How is the immunohistochemistry score (IOD/area) calculated?\n9. It is not described how the t-SNE analysis has been performed. The authors state that the t-SNE analysis revealed distinct clustering of immune cell populations in both PT and IT samples, however, the two t-SNE maps are very similar and no clustering analysis has been performed.\n10. The cytokines expression has been evaluated only upon stimulation, which is their expression at basal levels?\n11. The authors in the results of CyTOF experiments reported only some of the markers included in the panel, what about the other markers? Especially those that correlated with SAMSN1 expression from the TCGA analysis, as NKG2A, TIM-3 and LAG-3-. What are the differences between SAMSN1+ and SAMSN1- cells?\n12. It is not clear whether the difference in GZMB expression between SAMSN1+ and SAMSN1- cells shown in figure 2H refer to PT or IT.\n13. In the genetic deletion of SAMSN1 experiments, HCC was not evaluated as tumor model.\n14. In the result paragraph “The regulation of immune function by SAMSN1 is not NK-specific” the authors state that SAMSN1 regulation is not NK-specific, but as summary of the paragraph they state that SAMSN1 negatively regulated NK cell function and metabolism, this is contradictory with the results showed. Moreover, up to this point data regarding the metabolism have not been shown.\n15. In the same result paragraph, the authors refer to “conditional knockout in other immune cells”, however, nothing is reported about this experimental design.\n16. It is not reported the gating strategy used for the sorting of GFP+ NK cells as well as there is not description in the methods section of it.\n17. In the analysis of NK92 cells overexpressing SAMSN1, the authors reported only the MFI of SAMSN1, what about its frequency (%)? The evaluation has been performed by FACS or CyTOF? If it has been performed with FACS, what is the antibody used for SAMSN1?\n18. Are NK92 cells overexpressing SAMSN1 cytotoxic in vitro against tumor cells?\n19. What is the effect of SAMSN1 silencing on NK cells from HCC patients (both peripheral blood and tumor specimens)?\n20. The authors state that in vivo experiments demonstrated that NK92 cells overexpressing SAMSN1 were more effective at controlling tumor growth, but it should be the opposite.\n21. Sample size and statistical analyses are not provided for figures S4B-C.\n22. In the discussion, the authors state that SAMSN1 targeting leads to NK cell exhaustion and reduced GZMB and perforin production. This is confusing, as SAMSN1 expression leads to this. Moreover, the reported results refer mainly to GZMB and not perforin.\n23. The discussion should be rewritten as the results of the study were poorly discussed and some points are not clear, in fact, the authors came to conclusions without any evidence from their results. Moreover, some repetitions should be avoided.\n24. The methods section does not have a logic. Moreover, on one hand, several details are lacking, and, on the other one, reported experiments and reagents are not shown in the results.\n25. In the table of patients are reported several patients without HCC. Why have they been included? Moreover, clinical information of the patient is completely absent.\n26. The statistical tests have only been indicated without reporting how they have been used based on the distribution of the data. Moreover, unpaired tests have been used on matched datasets and ANOVA has been used for the comparison of 2 groups.\n27. It is not clear the difference between figures 1H and 1I. Moreover, in the text, it has been indicated that 14 patients are included in the analysis, but they seem to be more. The same is valid for figure 2I.\n28. Some references are not correctly cited or in some points are not cited at all.\nMinor points:\n1. Several typing errors are present.\n2. There are several discrepancies between the figures and the text.\n3. The figure or tables are not in the order of appearance in the text.\n4. The authors refer not correctly to “cytokine expression”, as usually other activation markers that are not cytokines are included in the analysis.\n5. The figure legends often report the description of the results. They should contain only the description of the figures and the statistics. There are repetitions and some phrases are not correctly written.\n6. The authors often refer to geneset signatures when they refer to only one gene.\n7. There is no figure description in the figure caption for figure 1I and figures descriptions are not correctly reported.\n8. The authors use the word “signature” not correctly.\n9. It is not clear which population is shown in the figure 2E.\n10. The authors indicated that NK cells were sorted, but it is an enrichment.\n11. For figure 2D it is not clear how many samples have been analyzed.\n12. In the legend of figure S1A it is reported that the comparison is between control and SAMSN1 knockout, but it should be between peritumor and intratumor.\n13. The schematic representations of the alleles for the generation of the knockouts are usually repeated.\n14. The figure S4A is not cited.\n15. The tables do not have the legend.\n16. For table 2 it could be useful to specify for which experiments the antibodies are used. The same for table 3 for the primers and DNA sequences. Moreover, in table 3, more primers and DNA sequences are reported than shown in the results.\n17. The authors reported that the study included cryopreserved tumor cells, but they have analyzed all the tumor microenvironment, especially the immune compartment.\n18. The antibodies are reported in supplementary table 2 and not 3 as indicated in the text.\n19. Monocytes are identified as CD3-, not CD3+. This should also be specified for NK cells.\n20. The “statistical analysis” methods section is reported both in the main text and as supplementary material.\n21. The first time that authors use an abbreviation in the text, they should present both the spelled-out version and the short form. They can use only the abbreviated form from then on. Some examples are: MHC, TILs, the extended form is not present.", "reviewer_2": "Summary\nThe authors have elucidated the role of the SAMSN1 gene in NK cell-mediated anti-tumor immunity related to HCC. By utilizing the CRISPR-Cas9 system, they implemented a knockout (K/O) of the SAMSN1 gene in human NK cells and a mouse model to conduct an in-depth study. This paper presents intriguing findings that could contribute to advancements in next-generation cell-based cancer therapies. If several essential experiments are additionally performed, I would recommend this manuscript for publication in Nature Communications.\nMajor\nTo investigate the function of the SAMSN1 gene, the authors used the CRISPR-Cas9 system to generate SAMSN1 knockout (K/O) human NK cells and mouse models and analyzed their phenotypes. Although CRISPR technology has advanced rapidly in recent years, the authors did not specify which version of the CRISPR-Cas9 system was used in their methods. While the consistency in phenotypes observed between human NK cells and mouse models enhances the reliability of the findings, the potential for off-target effects when targeting the SAMSN1 gene using a standard CRISPR-Cas9 system cannot be entirely ruled out. The authors did not provide sufficient off-target analysis for SAMSN1, leaving open the possibility that the observed effects may have been influenced by modifications in off-target genes other than SAMSN1.\nMinor\nThe authors demonstrated the K/O of the SAMSN1 gene using CRISPR-Cas9 through an SAMSN1 expression profile. However, please provide direct evidence of editing within the target SAMSN1 gene (e.g., indel formation).", "reviewer_3": "Chen et al. present an intriguing study on the role of SAMSN1 in regulating NK cell-mediated anti-tumor immunity, particularly in hepatocellular carcinoma (HCC). The manuscript highlights SAMSN1 as a potential immune checkpoint that suppresses NK cell activation and cytotoxicity, thereby promoting tumor immune evasion. The study identifies elevated SAMSN1 expression as a marker of poor prognosis in HCC patients and demonstrates, through experimental models, that SAMSN1 deletion enhances NK cell functionality, limits tumor growth, and prevents immune cell exhaustion. These findings position SAMSN1 as a promising therapeutic target for improving NK cell-based cancer immunotherapy and enhancing the efficacy of immune checkpoint blockade (ICB). While the study presents novel insights, there are several areas requiring clarification and improvement. Below, I outline both major and minor concerns.\nMajor concerns\n1. Introduction: The introduction lacks depth and precision. Several references appear inappropriate or irrelevant, suggesting a cursory literature review. For example, Reference 8 cites a low-profile nursing journal unrelated to the topic. Reference 11 is from an MDPI journal, which raises questions about quality; it should be replaced with a higher-impact reference. References 18–20 are reviews rather than original research, which should be prioritized when citing specific findings. Reference 23 (uterine NK cells) and Reference 24 (liver NK cells during homeostasis) are cited in contexts where they appear irrelevant or inaccurate. The paragraph spanning lines 87–99 is particularly vague. For instance: \"May function as a tumor suppressor in certain contexts\"—this requires specifying the relevant mechanisms described in the cited papers. \"Modulation of signaling pathways related to cell proliferation and survival in certain contexts\"—similarly, this statement should be clarified with detailed findings. \"Suggested a role for SAMSN1 in regulating inflammatory responses\"—again, what was specifically discovered in the referenced studies? Additional references are missing for claims such as \"reported in various malignancies, including multiple myeloma and lung cancer.\"\n2. Figure 1: The rationale behind Figure 1B is unclear. The absence of t-SNE plots showing SAMSN1 expression across population clusters weakens the analysis. Terminology like \"Peritumor (PT)\" and \"Intratumor (IT)\" is confusing. According to the legend (lines 512–513), both PT and IT are tumor zones, making it unclear what is meant by \"adjacent normal tissue.\" Correlating SAMSN1 expression with NK cell scoring signatures (e.g., PMID: 31088844) would significantly enrich this figure.\n3. Lines 144-146 mentioned Figure 1A-C, which analyzes scRNAseq data from published studies; however, the corresponding references are missing and should be included. Additionally, Figure 1F illustrates the correlation between SAMSN1 and other immune checkpoint receptors. It is unclear whether the expression of SAMSN1 and the immune checkpoint receptors analyzed here is for the whole cell population in the dataset or specific on NK cells. If the analysis does not specifically target NK cells, the findings presented may not directly support the role of SAMSN1 in NK cells concerning tumor immunity, which is your focus of this paper.\n4. Figure 3: CRISPR-Cas9 knockout efficiency in NK cells is insufficiently validated. While there was a 3-fold reduction in mRNA, protein reduction (81.8% to 56.3%) is marginal and lacks statistical comparison in Figure 3C. Key data are missing, such as FACS plots showing GFP+ NK cells (CD3⁻, CD56⁺, NKp46⁺) after GFP-Cas9 expression. Figure 3D appears underpowered, with only two biological replicates. Additional validations, such as intracellular Granzyme B staining (Figure 3F), and comprehensive immune phenotyping (e.g., liver ILC1 numbers, NK cell maturation phenotype) are required to substantiate claims.\n5. Figures 4 and 5: The use of B16F10 melanoma and MC38 colorectal models is incongruent with the HCC focus of the study. Including HCC-relevant models, such as aflatoxin B1-related HCC or intra-splenic MC38 for liver metastases, would enhance relevance. For B16F10, standard lung metastasis quantification (e.g., macroscopic metastasis counting) should be performed instead of relying solely on luciferase-based assays. Validation of the Ncr1^cre SAMSN1^fl/fl model is insufficient. Authors should: cross this model with Rosa26-YFP and validate Cre specificity to NKp46⁺ cells; address potential differences in NK cell function by using Ncr1^cre without flox as a control; include phenotypic data (e.g., Granzyme B, IFN-γ, immune cell counts). Figures 5D and 5E require additional data. For example, Figure 5E should differentiate between apoptosis and necrosis, and tumor homogenates should quantify NK92 cell infiltration.\nMinor concerns\n1. The ethics section lacks clarity on whether approvals for both animal and human experiments were obtained. Numbers corresponding to approvals should be explicitly stated.\n2. Figure legends are excessively long and should be succinctly rewritten for readability.\n3. Significant grammar and syntax errors are present throughout the manuscript. Examples include inconsistent hyphenation (e.g., in vivo vs. in-vivo), capitalization (e.g., \"Re-exploring\"), and typographical errors (e.g., \"jane.auteur@example.com\"). A thorough English language review is essential.\n4. Materials and methods. The section is overly high-level and should include detailed experimental protocols rather than relying on supplementary materials.\n5. Missing references. For example, lines 145–146 mention \"published papers\" without citations. Proper referencing is necessary."}, "author_response": "Response to Reviewer’s comments\nWe thank the reviewers for their constructive feedback and positive assessment of our study. We have carefully considered the concerns and suggestions raised and have made revisions to enhance the quality and impact of the manuscript. The major changes from our revision process can be summarized as follows:\nRevision overview\nMajor changes from our revision process can be summarized as follows:\n1. We have added Hepa1-6 orthotopic HCC models in Samsn1 knockout (Samsn1-/-) and Samsn1 conditional knockout (Samsn1f/f-Ncr1Cre+) mice, demonstrating reduced tumor growth and enhanced intratumoral NK cell function in liver including cytotoxicity, IFN-γ, and GzmB production.\n2. We generated a SAMSN1-knockout NK92 cell line using CRISPR-Cas9 and confirmed the knockout efficiency through Sanger sequencing of the genomic locus and flow cytometric analysis at the protein level. We also performed a co-culture assay to assess the functional role of SAMSN1 in NK cell cytotoxicity against the HCC cell line HepG2 in vitro.\n3. We optimized NK cell adoptive transfer models in NSG mice, adjusting tumor cell numbers, NK:HCC ratios, and timepoints to better recapitulate human tumor microenvironments.\n4. We clarified human scRNA-seq datasets from published paper (PMID: 33357445; CNSA: CNP0000650). We also upgraded IHC image quality and scoring methods.\nThese revisions robustly confirm that SAMSN1 suppresses NK cell function to promote HCC immune evasion, addressing the reviewers’ concerns about specificity, methodology, and clinical relevance.\nThe extensive revisions made in light of the reviewer’s comments have bolstered the case that SAMSN1 is associated with prognosis in HCC patients. The following point-by-point responses detail how we have addressed each comment.\nREVIEWER #1 - SUMMARY OF KEY REVISIONS\nIssue Category Original Concern Our Response Evidence Location\nHCC Specificity Lack of HCC-specific Added Hepa1-6 orthotopic models in Main: Fig. 4a-e, Fig.\ntumor models both global KO and NK-specific KO 5f-k\nmice\nNK Cell Effects extend to NK-specific deletion alone sufficient Main: Fig. 5f-k;\nSpecificity CD4/CD8 T cells; not (41% tumor reduction); T cells show no Extended Data Fig. 3a-\nNK-exclusive GZMB change in KO mice f, 4f-h\nPatient Cohort Missing clinical Added complete clinical data for Cohort Supplementary Table\ninformation and 1 (n=90) and Cohort 2 (n=22) 1-2\nsample details\nStatistical Unpaired tests on Corrected to paired tests where Methods p.17-27; All\nMethods matched data; appropriate; proper test selection figure legends\nANOVA for 2 groups throughout\nscRNA-seq Missing citations and Added full citation (Cell 2021, Results p.5 line 89;\nAnalysis patient information PMID:33357445); detailed methods and Methods p.18 line 416;\npatient demographics Supp. Table 1\nCRISPR Insufficient knockout Added Sanger sequencing, TIDE Extended Data Fig. 6a-\nValidation verification analysis (65% indels), off-target c\nvalidation via CHANGE-seq\nRelevance HCC focus exploratory proof-of-concept; main Fig. 5l-o\nfigure focuses on HCC\nReviewer #1\nThe presented study addresses a significant clinical issue, identifying SAMSN1 as a molecule that impairs NK cell\nantitumor activity. However, the work in its current form has several limitations from both a scientific perspective and\nin terms of manuscript writing. For instance, insufficient information on the datasets analyzed, the patients, the healthy\ncontrols, experimental details, and statistical analysis makes it challenging to evaluate the manuscript adequately.\nConsequently, the scientific relevance of the paper is limited.\nThe main limitations include the following:\n1. The authors state in the title that SAMSN1 negatively regulates NK cell-mediated antitumor immunity in\nhepatocellular carcinoma (HCC). However, this seems not to be specific for NK cells, as both CD4 and CD8 T cells\nappear to be affected too (as also stated in the result paragraph “The regulation of immune function by SAMSN1 is not\nNK-specific”). Moreover, only some of the results are related to HCC, the others refer to other tumor types.\nReply:We express our gratitude to the reviewers for their insightful and valuable feedback. In response to the concerns,\nwe have conducted additional experiments and analyses to clarify the predominant role of SAMSN1 in NK cells while\naddressing its effects on other immune cell types and tumor contexts. Below, we summarize the key findings from our\nrevised experiments, including single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and in vivo studies\nusing NK cell-specific conditional knockout mouse models. Our experiments evidence that:\n1) scRNA-seq (n = 22 in HCC cohort): NK cells exhibited markedly higher SAMSN1 expression than T cells,\nmyeloid, parenchymal, and tumor cells\n2) CyTOF (n = 22): SAMSN1 was strongly upregulated in NK cells of IT samples.\n3) HCC model: Samsn1-/- mice show reduced tumor burden compared with NK cell-specific SAMSN1 conditional\nknockout mice (Samsn1f/f-Ncr1Cre+) (41% vs 34%, P < 0.001) and Similar survival benefit (ns)\n4) Cell-specific effects in TILs: Enhanced GzmB (48.1%→65.8%, P = 0.0001) in NK cells of Samsn1-/- mice; but\nCD8+ T cells were no significant difference (ns)\nDetailed information is provided as follows:\n1) SAMSN1 Expression Predominantly Elevated in NK Cells in HCC cohort:\nAnalysis of scRNA-seq data from 18 HCC patients (published paper, PMID 33357445) revealed significantly higher\nSAMSN1 expression in NK cells compared to T cells, myeloid cells, parenchymal cells, and tumor cells. Specifically,\nSAMSN1 expression in NK cells was 1.35-fold higher in intratumoral (IT) compared to para tumor (PT) regions,\nwhereas T cells exhibited a modest 1.21-fold increase (Fig. 1a-d). This indicates a preferential upregulation of\nSAMSN1 in NK cells within the HCC tumor microenvironment.\n2) CyTOF Analysis Confirms NK-Specific SAMSN1 Upregulation:\nCyTOF data from 22 HCC patients demonstrated a significant increase in SAMSN1+ NK cells in IT regions\n(16.6% to 27.8%, P = 0.024) compared to PT regions. In contrast, SAMSN1+ CD8+ T cells showed no significant\ndifference (ns). Furthermore, GZMB+ NK cells were reduced in SAMSN1+ NK cells (42% to 37%, P = 0.034) in\nIT samples, underscoring the negative regulatory role of SAMSN1 in NK cell effector function. This NK cell-\nspecific upregulation is functionally significant, as demonstrated by a strong negative correlation between\nSAMSN1 and GZMB expression in NK cells (r = -0.543, P = 0.0106, Fig. 2j), whereas weaker or no correlations\nwere observed in T cell subsets.\n3) In vivo Validation in HCC Models (Samsn1-/- and Samsn1f/f-Ncr1Cre+ mice):\nTo investigate the functional role of SAMSN1 in NK cells, we utilized Samsn1-/- and Samsn1f/f-Ncr1Cre+ mice in\northotopic Hepa1-6 HCC models. In Samsn1-/- mice, liver weight relative to body weight decreased from 8.8% to\n5.8% (P = 0.0024), with a median survival increase from 27.5 to 36 days (P = 0.0091) compared to controls (Fig.\n4c, e). Similarly, in Samsn1f/f-Ncr1Cre+ mice, liver weight relative to body weight decreased from 8.8% to 5.2% (P\n= 0.0017), and median survival increased from 27 to 36 days (P = 0.0101) (Fig. 5g, k). Notably, Samsn1f/f-Ncr1Cre+\n2\nachieved 41% tumor burden reduction compared to 34% in Samsn1-/- mice (P < 0.001), with comparable survival\nbenefits (ns), highlighting the critical role of SAMSN1 in NK cells.\n4) NK-Specific Effector Function Enhancement in Tumor-infiltrating lymphocytes (TILs):\nTILs from Samsn1-/- and Samsn1f/f-Ncr1Cre+ mice showed significantly enhanced NK cell effector functions.\nGzmB expression in NK cells increased from 49.9% to 63.6% (P = 0.0002) and IFN-γ production from 30.9% to\n46.7% (P = 0.0050) in Samsn1-/- mice (Fig. 4d, Extended Data Fig. 3d). Similarly, in Samsn1f/f-Ncr1Cre+ mice,\nintratumoral NK cells displayed elevated GzmB (48.1% to 65.8%, P = 0.0001) and IFN-γ (31.5% to 41.6%, P =\n0.0395) production (Fig. 5i, j). In contrast, CD8+ T cells showed no significant changes in GzmB or IFN-γ\nexpression (ns) in either model (Extended Data Fig. 5d, e).\nThese findings collectively demonstrate that SAMSN1 predominantly regulates NK cell function in the HCC tumor\nmicroenvironment, with minimal impact on CD8+ T cells. While our study initially included data from other tumor\ntypes to provide a broader context, the revised manuscript emphasizes HCC-specific results to address the reviewer’s\nconcern. Detailed data and analyses are provided in the revised figures and extended data sections. We believe these\nclarifications strengthen the specificity of SAMSN1’s role in NK cell-mediated antitumor immunity in HCC.\n2. The abstract contains many errors. Some phrases are not correctly written. Moreover, the authors affirm that\nSAMSN1 expression in NK cells inversely correlates with immune checkpoint blockade responsiveness, but this\nhas not been demonstrated in the present manuscript.\nReply:We thank the reviewer for their valuable feedback.The abstract has been revised for accuracy and clarity. We\nalso address the description regarding errors in the abstract and the claim about SAMSN1 expression in NK cells\ncorrelating with immune checkpoint blockade (ICB) responsiveness.\n1) Abstract Errors: We have thoroughly revised the abstract to improve clarity, accuracy, and grammatical correctness\n(page2 line23-37). And removed the claim about “immune checkpoint blockade” entirely from the revised abstract.\n2) The B16F10 data has been repositioned following the HCC model as exploratory validation to support the broader\napplicability of SAMSN1 in other tumor types, explicitly labeled as hypothesis-generating for future studies.\n3) Reframed our conclusions to focus exclusively on HCC, where we provide definitive evidence. Patient samples\n(n=90 IHC, n=22 CyTOF) establish clinical relevance. Hepa1-6 orthotopic models (Samsn1-/- and Samsn1f/f-\nNcr1Cre+) prove causality. All mechanistic conclusions derive from HCC-specific experiments.\nWe acknowledge that any implications for ICB therapy require dedicated clinical studies. Our current work establishes\nSAMSN1 as a targetable NK cell checkpoint in HCC; its role in ICB resistance remains a hypothesis for future\ninvestigation. The B16F10 data are retained solely as supplementary material outside the scope of HCC research to\ndocument exploratory findings in alternative tumor models that support the potential broader applicability of SAMSN1\nas an NK cell checkpoint, though definitive conclusions in these contexts require dedicated studies, with explicit\nnotation that they do not form the basis for our HCC-related conclusions.\n3. Is SAMSN1 associated with a specific subset of NK cells (both in mice and humans)? The authors evaluated\nthe expression of SAMSN1 in the tumor setting, which is its expression on circulating NK cells?\nReply: We thank the reviewer for their insightful question. Below, we address these points:\n1) SAMSN1 and NK Cell Subsets: In our current study, the data from our Samsn1-/- mice show enhanced NK cell\nproliferation (Ki67 expression) and cytokine secretion (GzmB) in spleen NK cells. However, the T cells were no\nsignificant difference. In addition, the sc-RNA seq and CyTOF dada showed that antitumor function of NK cells\nwas significant influent in the IT region of HCC patients (question1). These data suggests that SAMSN1 broadly\ninfluence NK cell function across different compartments. However, in our current study, we did not directly assess\nSAMSN1 expression in specific NK cell subsets (e.g., CD56bright vs. CD56dim in humans or Ly49+ subsets in mice).\nTo address this limitation, we have added a discussion in the revised manuscript (page 16, lines 365) stating that\nfuture studies should further explore SAMSN1 expression in different subsets.\n2) SAMSN1 Expression in Circulating NK Cells: Our current study focusses on pan NK cells in liver. In addition,\nthe data showed significant increased GzmB production in spleen (Fig. 3m) and GzmB production and IFN-γ\nproduction in blood (Extended Data Fig. 2e. f) of Samsn1-/- mice. These data demonstrate that SAMSN1\npredominantly regulates NK cell function, with its effects most pronounced in tumor-infiltrating NK cells within\n3\nthe HCC microenvironment, while showing minimal impact on circulating lymphocytes.\nFig. 3j-m In C57BL/6J WT and Samsn1-/- mice, spleens were analyzed via flow cytometry to compare frequencies of CD4+, CD8+ T, and NK cells, Ki-\n67+ staining, and GzmB+ lymphocytes following PMA/ionomycin stimulation. Extended Data Fig. 2e. f Quantification of immune cell subsets\nexpressing various effector molecules in TME stimulated with and without PMA/ionomycin stimulation.\n4. Some results are not properly described.\nReply: We thank the reviewers for their feedback. As the reviewer’s comment does not specify which results were\nunclear, we conducted a thorough review of the manuscript to identify and address potential issues, and we also use a\nhighlight revision manuscript to discriminate our revision. If the reviewer could provide specific examples of results\nthat remain unclear, we would be happy to address them further.\n5. The authors reported the analysis of scRNA-seq data from published data, however the papers are not cited.\nMoreover, there are no information about the patients included in the analysis and about the analysis performed.\nHow were the populations annotated? Is SAMSN1 differentially expressed between PT and IT? The authors\nindicate that SAMSN1 expression is higher in lymphocytes compared to other populations: how were the statistics\ncalculated?\nReply: The scRNA-seq data source and analysis details have been added:\n1) Citation of scRNA-seq Data Source: In the revised manuscript, we have included a proper citation for the\nscRNA-seq data from Yunfan Sun et al. (Cell, 2021; 184(2):404–421) in the results,this published dataset\ncomprises 18 HCC patients with matched tumor and peritumoral samples, providing a foundation for identifying\nSAMSN1's expression patterns across the HCC immune landscape. The corresponding modifications can be found\ninthe results (page 6, lines 95), method (page 21, lines 459), and reference 34.\n2) Patient Information and Analysis Details: To address the lack of information about the patients included in the\nscRNA-seq analysis, we have added a detailed description in the methods (page 21, lines 459) and supplementary\ntable1.\n3) Population Annotation Methodology: The methodology for annotating cell populations is now described in the\nmethod section (page 21, lines 459). Briefly, cell populations were annotated based on canonical marker gene\nexpression using Seurat’s clustering and differential expression analysis, with validation against reference datasets.\n4) SAMSN1 Differential Expression Between PT and IT: The scRNA-seq data for SAMSN1 expression between\nPT and IT have provided in fig.1a-d for reply 1.\n5) Statistical Analysis of SAMSN1 Expression in Lymphocytes: We have clarified the statistical methods used for\nthis comparison in method section (page 29, lines 656) and figure legend.\nThese revisions, including updated citations, detailed methods, and clarified statistical analyses, are reflected in the\nrevised manuscript and supplementary materials.\n6. Which is the HCC cohort analyzed for the survival analysis? There is no information at all about it. How has\nSAMSN1low from SAMSN1high groups been defined? How has SAMSN1 expression been normalized on\nPTPRC?\nReply: We thank the reviewer for highlighting these critical issues. We have now extensively revised the manuscript to\naddress all concerns, with key clarifications below:\n1) HCC Cohort for Survival Analysis: In the revised manuscript, we have provided a detailed description of Cohort\n1 (n = 90)prospectively enrolled at Anhui Provincial Hospital, with clinical characteristics such as ethnicity, tumor\ntype, tumor stage, age, gender, overall survival (OS), and disease-free survival (DFS) in supplementary figure table\n1 and methods (page 22, lines 473).\n2) Definition of SAMSN1-low and SAMSN1-high Groups: “Patients were divided into SAMSN1-high and\nSAMSN1-low groups and we identify the best cut-off value for distinguishing between high and low expression\nusing R language.” This stratification method is now explicitly described in the revised manuscript figure legend\n(page 36, lines 798–799).\n3) Normalization of SAMSN1 Expression on PTPRC: We apologize for the misleading terminology. SAMSN1\nprotein levels were quantified solely by H-score in IHC staining (page 36, lines 808–810).\n7. The authors reported that the analysis of the TCGA cohort is of a scRNA-seq dataset. However, the TCGA\ncontains bulk RNA-sequencing data. Moreover, they reported that they identified prognostic biomarkers in the\nblood, but no results were shown, moreover TCGA does not contain data on peripheral blood.\nReply: We sincerely apologize for this error which resulted from version control issues during manuscript preparation.\nThis error does not affect our conclusions as the corrected bulk RNA-seq analysis yields similar results. To ensure data\nintegrity, we have:\n1) Re-analyzed all TCGA data with independent verification by two team members\n2) Implemented a systematic audit of data descriptions (page 6, lines 106–107) and method (page 22, lines 473).\n3) Deposited our analysis scripts in GitHub (https://github.com/RuifengWan/HCC-scSeq.git) for full transparency.\n8. The resolution of immunohistochemistry images is not good. In the figure legend, the authors indicate that\nSAMSN1 staining is shown in lymphocytes, however, there is no indication of how lymphocytes are identified.\nMoreover, they state that the negative control shows no significant staining, but no images are reported. How is\nthe immunohistochemistry score (IOD/area) calculated?\nReply: We thank the reviewer for their valuable feedback regarding the immunohistochemistry (IHC) analysis. Below,\nwe address each point raised to improve clarity and transparency:\n1) Resolution of IHC Images: In the revised manuscript, we have replaced these with high-resolution images to\nenhance clarity and better visualize SAMSN1 staining (figure 1i).\n2) Identification of Lymphocytes in IHC Staining: Lymphocytes were identified based on their characteristic\nmorphological features, including small, round cell morphology, hyperchromatic nuclei, scant cytoplasm, and a\nhigh nuclear-to-cytoplasmic ratio, with cell diameters typically less than 10 μm. In the IHC images, blue staining\n(hematoxylin) denotes nuclei, while brown staining (DAB) indicates SAMSN1 expression (page 26, lines 589).\n3) Calculation of Immunohistochemistry Score (IOD/area): The IHC score was calculated as the integrated optical\ndensity (IOD) divided by the area (IOD/area) to quantify SAMSN1 expression. Five random fields per slide were\nanalyzed at 400x magnification using image analysis software (ImageJ). The average IOD/area was calculated for\neach sample. Statistical analysis (Wilcoxon rank-sum test) confirmed that SAMSN1 expression in IT region is\nsignificantly higher than PT region. This methodology is now described in the revised method (page 26, lines 589).\nFig. 1i. IHC staining to validate the presence of SAMSN1+ aggregates in the tumor of HCC patients from cohort1.\n9. It is not described how the t-SNE analysis has been performed. The authors state that the t-SNE analysis\nrevealed distinct clustering of immune cell populations in both PT and IT samples, however, the two t-SNE maps\nare very similar and no clustering analysis has been performed.\nReply: We appreciate the reviewer’s insightful comment regarding the t-SNE analysis. To address this concern, we have\nincluded a detailed description of the t-SNE analysis methodology in the revised manuscript (Page 21, Line 458).\nAdditionally, we have incorporated clustering analysis to better delineate immune cell populations in both PT and IT\nsamples. These analyses are now thoroughly described in the Methods section, ensuring clarity and reproducibility.\nThe updated t-SNE maps, combined with the clustering analysis, demonstrate distinct immune cell populations while\naddressing the observed similarity between PT and IT samples, further supporting our findings.\nFig. 1a-d Single-cell RNA sequencing (scRNA-seq) analysis of 18 HCC patients from China National GeneBank DataBase.\n10. The cytokines expression has been evaluated only upon stimulation, which is their expression at basal levels?\nReply: We thank the reviewer for their comment regarding cytokine expression. In response, we clarify that cytokine\nexpression was evaluated under basal (unstimulated) conditions in our mouse models. The results demonstrate that in\nunstimulated cells, GZMB and IFN-γ expression levels are significantly elevated in SAMSN1 knockout mice compared\nto controls, indicating a consistent regulatory role of SAMSN1 under basal conditions (Extended Data Fig. 2d-f).\ne f\nUnstimula\nP\nte\nM\nd A/Ionomycin Unstimula\nP\nte\nM\nd A/Ionomycin\nExtended Data Fig. 2e-f Flow cytometry analysis of GzmB+ NK cells in the spleen of Samsn1-/- and WT mice.\n11. The authors in the results of CyTOF experiments reported only some of the markers included in the panel, what\nabout the other markers? Especially those that correlated with SAMSN1 expression from the TCGA analysis, as\nNKG2A, TIM-3 and LAG-3-. What are the differences between SAMSN1+ and SAMSN1- cells?\nReply: To address the reviewer’s concern, we address each point raised to enhance clarity and transparency:\n1) Reporting of CyTOF Markers: we have expanded the analysis to include additional markers, specifically Lag3\nand CXCR3 (Fig. 2j), which support conclusion that SAMSN1 is associated with inhibitory receptor signatures in\n2) Differences Between SAMSN1+ and SAMSN1- Cells: Our correlation analysis focused on the relationship\nbetween SAMSN1 and other key NK cell functional markers, such as NKG2A, TIM-3, and LAG-3. Therefore, the\nanalysis was designed to assess co-expression patterns in cells where SAMSN1 is present, rather than directly\ncomparing SAMSN1-positive with SAMSN1-negative subsets.\nj\nr=-0.543 P=0.0106 r=-0.02 P=0.928 r=0.077P=0.734 r=0.311 P=0.1707 r=0.469 P=0.028 r=0.457 P=0.033\nFig. 2 j Mass cytometry (CyTOF) was used to analyse correlations between SAMSN1 and proteins (GZMB, KLRC1, TIGIT, TIM3).\n12. It is not clear whether the difference in GZMB expression between SAMSN1+ and SAMSN1- cells shown in\nfigure 2H refer to PT or IT.\nReply: To clarify, the data presented in Figure 2H (now Figure 2i in the revised manuscript), we stratified total NK cells\nfrom IT into SAMSN1+ and SAMSN1- subsets. The results demonstrate that SAMSN1+ NK cells exhibit significantly\nlower GZMB expression compared to SAMSN1- NK cells (P = 0.0340). This finding is now explicitly stated in the\nrevised manuscript (page 8, lines 151–152) and figure legend for Figure 2i.\n13. In the genetic deletion of SAMSN1 experiments, HCC was not evaluated as tumor model.\nReply: Thank you for this valuable observation. We have now included HCC tumor model as addressed in our response\nto Comment 1. To investigate the functional role of SAMSN1 in NK cells, we utilized Samsn1-/- and Samsn1f/f-Ncr1Cre+\nmice in orthotopic Hepa1-6 HCC models.The Hepa1-6 model recapitulates key features of human HCC, including the\nimmunosuppressive hepatic microenvironment and high NK cell infiltration. Critically, both genetic approaches yielded\ncomparable therapeutic efficacy:\n1) In Samsn1-/- mice, liver weight relative to body weight decreased from 8.8% to 5.8% (P = 0.0024), with a median\nsurvival increase from 27.5 to 36 days (P = 0.0091) compared to controls (Fig. 4c, e).\n2) Similarly, in Samsn1f/f-Ncr1Cre+ mice, liver weight relative to body weight decreased from 8.8% to 5.2% (P\n= 0.0017), and median survival increased from 27 to 36 days (P = 0.0101) (Fig. 5g, k).\n3) Notably, Samsn1f/f-Ncr1Cre+ achieved 41% tumor burden reduction compared to 34% in Samsn1-/- mice (P <\n0.001), with comparable survival benefits (ns), highlighting the critical role of SAMSN1 in NK cells.\nThis quantitative concordance definitively establishes that SAMSN1 functions primarily through NK cells in the HCC\ncontext. Furthermore, verification that Hepa1-6 cells do not express SAMSN1 (Extended Data Fig. 3a,b) confirms that\nobserved effects result from immune modulation rather than direct tumor cell targeting.\nThe Hepa1-6 orthotopic liver tumor model in Samsn1-/-(Fig. 4) andSamsn1f/f-Ncr1Cre+mice (Fig. 5),\n14. In the result paragraph “The regulation of immune function by SAMSN1 is not NK-specific” the authors state\nthat SAMSN1 regulation is not NK-specific, but as summary of the paragraph they state that SAMSN1 negatively\nregulated NK cell function and metabolism, this is contradictory with the results showed. Moreover, up to this\npoint data regarding the metabolism have not been shown.\nReply: We thank the reviewer for their critical feedback.\n1) Clarification of NK-Specificity Contradiction: As addressed in our response to Comment 1.\n7\n2) Metabolism Data: In the revised manuscript, we have introduced metabolism-related results earlier, specifically\nreferencing Extended Data Fig.6e-h, which show that NK92OE-SAMSN1 cells exhibit reduced OCR and increased\nECAR compared to control cells. These findings suggest that SAMSN1 suppresses NK cell metabolism,\npotentially contributing to its inhibitory effects on NK cell function (page 13, lines 288).\nExtended Data Fig.6g-j The experiment measured the OCR and ECAR in NK92Control and NK92OE-SAMSN1cells.\n15. In the same result paragraph, the authors refer to “conditional knockout in other immune cells”, however,\nnothing is reported about this experimental design.\nReply: We thank the reviewer for their careful attention to detail. This phrase was unintentionally included during\nmanuscript preparation and does not reflect experiments conducted in this study. We have corrected this error in the\nrevised manuscript by removing the phrase and clarifying that our experiments specifically targeted NK cells (page13.\nline 265-271).\n16. It is not reported the gating strategy used for the sorting of GFP+ NK cells as well as there is not description in\nthe methods section of it.\nReply: We thank the reviewer for their valuable suggestion. In the revised manuscript, we have added new results\nillustrating the gating strategy of GFP+ NK cells and presented the SAMSN1+ NK cells in Figure 3b (page 9, lines 173)\nand method (page 24, lines 541). Generally, NK Cells (1 × 106) were resuspended in RNP complex (NLS-Cas9-EGFP\nNuclease and guide RNA), followed by nucleofection using Lonza 4D Nucleofector with program CM137 (Lonza). The\ncells were cultured post-electroporation for 72h. Flow cytometry was used to gate GFP+ cells and verify the knockout\nefficiency in protein level.\nFig.3b, d GFP+ cells were gated after 72h of transfection. SAMSN1 mRNA level was shown.\n17. In the analysis of NK92 cells overexpressing SAMSN1, the authors reported only the MFI of SAMSN1,\nwhat about its frequency (%)? The evaluation has been performed by FACS or CyTOF? If it has been performed with\nFACS, what is the antibody used for SAMSN1?\nReply: We thank the reviewer for their critical feedback, and we address each aspect of the comment below:\n1) Frequency of SAMSN1 Expression: SAMSN1 is constitutively expressed at detectable levels in nearly all\nparental NK92 cells (baseline frequency >95%, as determined by flow cytometry). Given the high baseline and\ntransduction efficiencies, we focused on reporting MFI to quantify the relative increase in SAMSN1 expression\nlevels. This rationale is now clarified in the revised manuscript.\n2) FACS vs. CyTOF: Flow cytometry was exclusively used for SAMSN1 detection in NK92 cells (page 9, lines\n174). The experimental process is described in detail in the methods (page 28, lines 636-637).\n3) Antibody Details: The SAMSN1 antibody used for FACS was sourced from Boster Biological Technology (Cat#\nA08977-2, 1:100 dilution). This information is included in Supplementary figure Table 4.\n18. Are NK92 cells overexpressing SAMSN1 cytotoxic in vitro against tumor cells?\n8\n)nim/lomp(RCO\nNK92Control NK92Control\nNK92OE-SAMSN1 NK92OE-SAMSN1\nRotenone 2-DG\nOligomycin & Oligomycin\nantimycin FCCP A Glucose\nNK92\nC\nN\non\nK\ntr\n9\no\n2\nl OE-SAMSN1\n)nim/lomp(RCO\nh i j k\nNK92\nC\nN\non\nK\ntr\n9\no\n2\nl OE-SAMSN1\nReply: We have now conducted cytotoxicity assays and provide complete experimental data with full statistical\nanalysis. We performed standard 4-hour Flow cytometry-based killing assays using NK92 cells (NK92Control, NK92 KO-\nSAMSN1, and NK92 OE-SAMSN1) against HepG2 hepatocellular carcinoma targets at multiple effector:target (E:T)\nrT) ratios.\nExtended Data Fig.6i NK92 cell lines mediated killing of HepG2 cells at varying effector-to-target (E:T) ratios.\n19. What is the effect of SAMSN1 silencing on NK cells from HCC patients (both peripheral blood and tumor\nspecimens)?\nReply:We agree this is an important validation that would strengthen clinical translatability. While direct SAMSN1\nsilencing experiments in patient NK cells are technically challenging and beyond the scope of the current study,\nwe provide multiple lines of converging evidence from patient samples:\n1) CyTOF analysis demonstrates that SAMSN1+ NK cells exhibit significantly reduced GZMB expression compared\nto SAMSN1- NK cells in the same patients (Fig. 2i, 42% vs 37%, P = 0.0340, n=14 paired samples).\n2) This functional correlation is specific to NK cells, as SAMSN1+ CD8+ T cells show no significant GZMB\ndifference (ns).\n3) The phenotypic association observed in patients (SAMSN1+ cells show reduced GZMB) is directly validated by\nour gain-of-function and loss-of-function studies in human NK92 cells (Fig. 6e, Extended Data Fig. 6i).\nWe acknowledge that future studies using patient-derived NK cells with SAMSN1 knockout would provide additional\nvalidation. However, the concordance between patient correlative data and functional validation in human NK cell\nlines, combined with definitive causal evidence from mouse models, provides support for SAMSN1's role in\nsuppressing patient NK cell function.\n20. The authors state that in vivo experiments demonstrated that NK92 cells overexpressing SAMSN1 were more\neffective at controlling tumor growth, but it should be the opposite.\nReply: We thank the reviewer for identifying the inaccurate statement. As emphasized throughout our study, SAMSN1\nacts as a negative regulator of NK cell anti-tumor activity. We have revised the manuscript to correct this error, clearly\nstating that NK92OE-SAMSN1 cells exhibited significantly reduced tumor growth suppression compared to NK92Control\ncells (P < 0.0001) (n=4). Specifically, tumor volumes in mice treated with NK92OE-SAMSN1 cells were significantly larger\nthan those in NK92Control cells by day 16 post-transfer (page 14, lines 291-296).\nFig.6 The humanized mouse model was used to evaluate the function of NK92OE-SAMSN1 or NK92OE-control cells in anti-tumor activity.\n21. Sample size and statistical analyses are not provided for figures S4B-C.\nReply: We thank the reviewer for pointing out the omission of sample size and statistical analysis details for Figures\nS4B–C (revised as Extended Data Fig. 6e–h). To address this, sample sizes and statistical analysis methods for the\nresults of OCR and ECAR in NK92OE-SAMSN1 cells are detailed in the method section (page 27, line 607) and the\ncorresponding figure legend for Extended Data Fig.6e-h. This has addressed in question 14 of review 1.\n9\n)%(gnilliK\ni\nE:T Ratio NK92Control NK92KO-SAMSN1 NK92OE-SAMSN1 Statistics\n20:1 18.3 11.7 25.7 P< 0.001\n10:1 17.1 8.6 20.6 P< 0.001\n5:1 12.3 8.1 17.2 P< 0.001\n2:1 9.1 6.8 13.4 P< 0.001\nHepG2+NK92OE-SAMSN1\nCR: 0%\nPR: 100%\nP< 0.05\n)3mm(emulovromuT\nP<0.0001\nP=0.0025\nP<0.0001\nj k l m\nPBS HepG2 HepG2+NK92Control\nNK92Control\nNK92OE-SAMSN1 C\nPR\nR\n:\n:\n0\n0\n%\n%\nC\nPR\nR\n:\n:\n2\n7\n5\n5\n%\n%\nP= ns P< 0.05\n22. In the discussion, the authors state that SAMSN1 targeting leads to NK cell exhaustion and reduced GZMB\nand perforin production. This is confusing, as SAMSN1 expression leads to this. Moreover, the reported results\nrefer mainly to GZMB and not perforin.\nReply: We thank the reviewer for identifying this inaccurate statement. SAMSN1 expression in NK cells is associated\nwith an exhaustion phenotype, characterized by reduced GZMB production. We have revised the Discussion section to\nensure accurate description of our findings, focusing specifically on the GZMB results while removing the reference to\nperforin.\n23. The discussion should be rewritten as the results of the study were poorly discussed and some points are not\nclear, in fact, the authors came to conclusions without any evidence from their results. Moreover, some repetitions\nshould be avoided.\nReply: We thank the reviewer for this valuable feedback regarding the Discussion section. To address the concerns\nabout inadequate discussion of results, unclear points, unsupported conclusions, and repetitions, we have substantially\nrewritten the Discussion. Our revisions focus on tightly linking interpretations to experimental evidence, eliminating\nredundant statements (e.g., repeated mentions of SAMSN1 as a checkpoint and its therapeutic potential without new\ncontext), and improving clarity through a more structured narrative. Specifically, we have:\n1) Enhanced evidence-based discussions: All conclusions are now explicitly tied to our results, such as the NK-\nspecific effects supported by conditional knockout data (e.g., 41% vs. 34% tumor burden reduction),\nCyTOF findings (e.g., GZMB reduction from 42% to 37% in SAMSN1+ NK cells), and correlations (e.g., r=0.469 for\nTIM3). Unsupported claims, like broad references to perforin without data, have been removed.\n2) Improved clarity and structure: The section now begins with a concise summary of key findings, followed by\nmechanistic insights, comparisons to prior literature, therapeutic implications, and limitations. This logical flow\navoids ambiguity and ensures each point builds on the previous one.\n3) Eliminated repetitions: Redundant phrases (e.g., multiple iterations of SAMSN1's role in exhaustion or\nimmunotherapy) have been consolidated into single, focused paragraphs. For instance, prognostic and therapeutic\ndiscussions are now streamlined without overlap.\nThe revised Discussion is provided in the manuscript for reference. These changes strengthen the section's alignment\nwith our results while enhancing overall readability.\n24. The methods section does not have a logic. Moreover, on one hand, several details are lacking, and, on the\nother one, reported experiments and reagents are not shown in the results.\nReply: We thank the reviewer for highlighting these critical issues with the Methods section. To comprehensively\naddress the concerns regarding logical organization, missing details, and discrepancies between described methods and\npresented results, we have completely restructured the Methods section (pages 18-30). Specifically, we have:\n1) Established logical organization: Methods are now organized sequentially by experimental workflow (e.g.,\nStudy design→ Patient samples →Mouse models →Single-cell analysis → Functional assays → Statistical\nanalysis), with clear subheadings and cross-references to corresponding Results/Figures.\n2) Supplemented all missing technical details: Complete protocols have been added for all analyses, including\nspecific reagents (e.g., antibody clones, catalog numbers), instrument settings (e.g., CyTOF parameters),\nsoftware versions (e.g., Seurat v4.3.0), and statistical thresholds.\n3) Ensured rigorous correspondence: Every described method now directly corresponds to data in the\nResults/Figures, with explicit figure references. Experiments/reagents not presented in Results have been\nremoved.\n4) Eliminated extraneous content: Unused methodologies (e.g., unrelated staining protocols) have been excised to\nenhance focus and readability.\nThese revisions ensure the Methods section is comprehensive, logical, and fully aligned with our results, facilitating\nreproducibility. The restructured section is provided in the revised manuscript (pages 18-30).\n25. In the table of patients are reported several patients without HCC. Why have they been included? Moreover,\nclinical information of the patient is completely absent.\nReply: We thank the reviewer for identifying these important issues with the patient cohort table. To address the\n10\ninclusion of non-HCC patients and the absence of clinical information, we have thoroughly revised Supplementary\nTable 1 (Supplementary page 9, line 85). Specifically, we have:\n1) Excluded all non-HCC patients: The cohort now consists exclusively of 22 confirmed HCC patients, removing\nany cases with other liver malignancies or benign conditions.\n2) Added comprehensive clinical information: The revised table now includes essential details for each patient,\nincluding ethnicity, tumor type, tumor stage, age, gender, overall survival (OS), and disease-free survival (DFS).\n3) Enhanced cohort description: A summary of cohort description has been added to the Results text (page 6, line\n94-97) to provide context for the analyses.\nThese revisions ensure the patient cohort is HCC-specific and fully clinically annotated, improving the transparency and\nrelevance of our findings. The updated Supplementary Table 1 is provided in the revised Supplementary manuscript.\n26. The statistical tests have only been indicated without reporting how they have been used based on the\ndistribution of the data. Moreover, unpaired tests have been used on matched datasets and ANOVA has been used\nfor the comparison of 2 groups.\nReply: We thank the reviewer for identifying these critical statistical concerns. We have thoroughly revised our\nstatistical approach, including:\n1) Updated figure legends: Each now specifies test type, n, normality, and rationale (e.g., paired t-test for matched\nPT/IT samples).\n2) Added Methods justification: New \"Statistical Analysis\" subsection details test selection criteria, normality\ntesting, and multiple comparison corrections (page 29, lines 656).\n3) Corrected errors: Paired tests for matched data; t-tests replacing ANOVA for 2-group comparisons;\nnon-parametric tests where needed (Comments 12 updates).\n4) Added Supplementary Table 3: Summarizes all tests by figure panel with P-values and effect sizes.\nThese revisions ensure appropriate, justified analyses. Updated figures, Methods, and Table 3 are in the revised\nmanuscript.\n27. It is not clear the difference between figures 1H and 1I. Moreover, in the text, it has been indicated that 14\npatients are included in the analysis, but they seem to be more. The same is valid for figure 2I.\nReply: We thank the reviewer for identifying these important issues. Figures 1H and 1I are from same data with\ndifferent presentation. To resolve the confusion, we have removed Figure 1i. For Figure 2H (revived Figure 2i),\nwe now explicitly clarify it analyzes a standalone cohort of 22 HCC patients (distinct from the 14 paired samples)\nto assess SAMSN1's clinical correlations, with this distinction rigorously documented in both the Methods section\nand Figure 2i legend (page 37, line 829-830).\n28. Some references are not correctly cited or in some points are not cited at all.\nReply:We have conducted a thorough review of all references, correcting inaccuracies such as mismatched\ncitations, incomplete bibliographic details, and instances where citations were omitted. Specifically, we ensured\nthat all claims, data sources (e.g., TCGA analysis in Comment 1), and figures (e.g., Fig. 1c, 1d, 2i, 2j) are now\nappropriately cited with updated reference numbers (e.g., ref[34], ref[35–45], ref[43], ref[52]) aligned with the\nrevised reference list. We have also added citations where previously absent, particularly in introduction and\ndiscussion.\nMinor points:\n1. Several typing errors are present.\nReply: We have meticulously proofread the entire text through triple-checking by both native English-speaking\ncollaborators and automated editing tools (Grammarly/EndNote). This rigorous process corrected grammatical\ninaccuracies, standardized terminology, fixed formatting inconsistencies, and eliminated all identified typing oversights.\nThis revision has highlighted in revised manuscript.\n2. There are several discrepancies between the figures and the text.\nReply: We thank the reviewer for this observation. We have systematically cross-verified every figure, table, and result\nstatement throughout the manuscript to eliminate all discrepancies, ensuring perfect alignment between visual data and\ntextual descriptions.\n11\n3. The figure or tables are not in the order of appearance in the text\nReply: We have systematically reordered all figures and tables to strictly follow their sequence of first mention in\nthe main text, enhancing navigational clarity for readers.\n4. The authors refer not correctly to “cytokine expression”, as usually other activation markers that are not\ncytokines are included in the analysis.\nReply: We have systematically replaced all instances of \"cytokine expression\" with precise terminology (eg., GZMB\nexpression and IFN-γ production) throughout the manuscript to accurately reflect the analyzed effector molecules\n(page 8, line 151; page 10, line 214).\n5. The figure legends often report the description of the results. They should contain only the description of the\nfigures and the statistics. There are repetitions and some phrases are not correctly written.\nReply: We have thoroughly revised all figure legends to include only methodological descriptions and statistical details\n(n, test type, P-values), eliminating interpretive results, redundancies, and phrasing errors for concise, technical accuracy.\n6. The authors often refer to geneset signatures when they refer to only one gene.\nReply: We have carefully reviewed the entire text and replaced all inaccurate references to \"geneset signatures\" with\nappropriate terms when describing individual gene analyses (page 6, line 112; page 8, line 163).\n7. There is no figure description in the figure caption for figure 1I and figures descriptions are not correctly\nreported.\nReply: We have thoroughly revised all figure captions, including removal of Figure 1I and correction of inaccuracies\nacross all panels, to ensure complete, clear, and technically precise descriptions.\n8. The authors use the word “signature” not correctly.\nReply:We have replaced all instances of \"signature\" with \"genes and proteins\" when referring to individual genes (page\n6, line 112; page 8, line 163), eliminating misuse while preserving scientific accuracy.\n9. It is not clear which population is shown in the figure 2E.\nReply: We have clarified in the revised manuscript and legend that Figure 2e presents the gating strategy used to identify\nthe NK cell population analyzed in Figure 2d, ensuring unambiguous interpretation.\n10. The authors indicated that NK cells were sorted, but it is an enrichment.\nReply: We confirm that Figure 3a depictsenrichment(not sorting) of NK cells, and we have corrected this terminology\nthroughout the revised manuscript (Figure 3a legend) to ensure accuracy (page 37, line 834).\n11. For figure 2D it is not clear how many samples have been analyzed.\nReply: Thank you for highlighting this point. We confirm that Figure 2d analyzed paired samples (n=14 pairs of IT and\nPT samples), consistent with the samples used in Figures 2b and 2c. To clarify this, we have explicitly added the\nfollowing details to the Figure 2d legend in the revised manuscript (page 37, line 823).\n12. In the legend of figure S1A it is reported that the comparison is between control and SAMSN1 knockout, but it\nshould be between peritumor and intratumor.\nReply: The comparison in Figure S1A (revived Extended Data Fig.1a) is indeed between PT and IT groups, not control\nvs. SAMSN1 knockout. We have corrected the figure legend in the revised manuscript to accurately reflect this\n(supplementary page 3, line 20).\n13. The schematic representations of the alleles for the generation of the knockouts are usually repeated.\nReply: We agree that the allele schematics for knockout generation were redundantly presented. We have removed\nduplicate panels to eliminate repetition and refined the schematic description to clearly illustrate the knockout strategy\nwithout redundancy.\n14. The figure S4A is not cited.\nReply: We have now thoroughly checked the manuscript and added appropriate citations for Figure S4A (revived\nExtended Figure 6d) (page 13, line 278).\n15. The tables do not have the legend.\nReply: We have carefully reviewed all tables and added comprehensive legends to ensure clarity. Each table now\nincludes essential descriptions of content, statistical methods, and sample details in a standardized format.\n16. For table 2 it could be useful to specify for which experiments the antibodies are used. The same for table 3\n12\nfor the primers and DNA sequences. Moreover, in table 3, more primers and DNA sequences are reported than\nshown in the results.\nReply: We have carefully revised Tables 2 and 3 as follows:\n1) Table 2 (revised Supplementary Table 4): Added explicit descriptions of experimental applications.\n2) Table 3: Removed primers/sequences not utilized in the final results to avoid redundancy.\n17. The authors reported that the study included cryopreserved tumor cells, but they have analyzed all the tumor\nmicroenvironment, especially the immune compartment.\nReply: We appreciate your query and have carefully re-examined the manuscript. We believe there may be a\nmisunderstanding: our study did not explicitly state the analysis of \"cryopreserved tumor cells\" in isolation. Instead, we\nfocused exclusively on characterizing immune cell dynamics within the tumor microenvironment (TME) of HCC\nsamples, specifically comparing PTandIT regions. To prevent ambiguity, we have revised all relevant sections to\nclarify this scope.\n18. The antibodies are reported in supplementary table 2 and not 3 as indicated in the text.\nReply: We have corrected all in-text citations to accurately Table 2 (revised Supplementary Table 4) Verified cross-\nreferences for all tables/figures to ensure consistency throughout the manuscript.\n19. Monocytes are identified as CD3-, not CD3+. This should also be specified for NK cells.\nReply:Thank you for this critical correction. As rightly noted, monocytes are CD3-negative (CD3⁻) and NK cells are\nCD3⁻CD56⁺. We have revised the manuscript to explicitly state this in the Methods and Results sections: \"Monocytes\n(CD3⁻CD14⁺) and NK cells (CD3⁻CD56⁺) were gated using flow cytometry\" (page 28, line 631).\n20. The “statistical analysis” methods section is reported both in the main text and as supplementary material.\nReply: We agree that the duplication of the \"Statistical Analysis\" section in both the main text and supplementary\nmaterial was redundant. We have merged and streamlined this section exclusively into the revived manuscript (page 29,\nline 656).\n21. The first time that authors use an abbreviation in the text, they should present both the spelled-out version and\nthe short form. They can use only the abbreviated form from then on. Some examples are: MHC, TILs, the\nextended form is not present.\nReply: We have thoroughly revised the manuscript to ensure all abbreviations (e.g., MHC, TILs) are explicitly defined\nat first use (e.g., \"major histocompatibility complex (MHC),\" \"tumor-infiltrating lymphocytes (TILs)\") and consistently\nabbreviated thereafter (page 3, line 60 for MHC, and page 7, line 132 for TILs).\nREVIEWER #2 - SUMMARY OF KEY REVISIONS\nIssue Category Original Concern Our Response Evidence Location\nCRISPR System Version of Standard SpCas9 RNP system; detailed Methods p.21 lines\nSpecification CRISPR-Cas9 not methodology provided 491\nspecified\nOff-target Effects Insufficient off- (1) CHOPCHOP/Cas-OFFinder prediction; (2) Extended Data Fig.\ntarget analysis CHANGE-seq validation showing no 6a-c; Point-by-\nsignificant off-targets; (3) Phenotypic rescue point for Major\nIndel Validation Direct evidence of Sanger sequencing + TIDE analysis confirms Extended Data Fig.\ngene editing ~65% indel efficiency at SAMSN1 locus 6a\nmissing\nProtein-Level Only expression Added Western blot and flow cytometry Main: Fig. 6b-c;\nConfirmation profile shown showing reduced mRNA/protein levels Extended Data Fig.\n6d-e\nReviewer #2\nThe authors have elucidated the role of the SAMSN1 gene in NK cell-mediated anti-tumor immunity related to\nHCC. By utilizing the CRISPR-Cas9 system, they implemented a knockout (K/O) of the SAMSN1 gene in human\nNK cells and a mouse model to conduct an in-depth study. This paper presents intriguing findings that could\ncontribute to advancements in next-generation cell-based cancer therapies. If several essential experiments are\nadditionally performed, I would recommend this manuscript for publication in Nature Communications.\nMajor\nTo investigate the function of the SAMSN1 gene, the authors used the CRISPR-Cas9 system to generate\nSAMSN1 knockout (K/O) human NK cells and mouse models and analyzed their phenotypes. Although CRISPR\ntechnology has advanced rapidly in recent years, the authors did not specify which version of the CRISPR-Cas9\nsystem was used in their methods. While the consistency in phenotypes observed between human NK cells and\nmouse models enhances the reliability of the findings, the potential for off-target effects when targeting the\nSAMSN1 gene using a standard CRISPR-Cas9 system cannot be entirely ruled out. The authors did not provide\nsufficient off-target analysis for SAMSN1, leaving open the possibility that the observed effects may have been\ninfluenced by modifications in off-target genes other than SAMSN1.\nReply: We thank the reviewer for their insightful comments regarding the specificity of our CRISPR-Cas9 methodology\nand the potential for off-target effects in SAMSN1 knockout studies. We fully acknowledge the importance of rigorous\nvalidation in CRISPR applications, and we have strengthened our manuscript accordingly by incorporating additional\ndetails, experimental validations, and references to support the on-target specificity of our findings. Below, we address\neach point systematically, with revisions reflected in the updated Methods section (page 23, lines 522) and new\nExtended Data Fig. 6a-c.\n1) CRISPR-Cas9 System Specification: For human NK cell knockouts (NK92 line), we employed the standard\nStreptococcus pyogenes Cas9 (SpCas9) ribonucleoprotein (RNP) complex (NLS-SpCas9-EGFP, novoprotein Cat#\n1081061), delivered via electroporation. sgRNAs targeted SAMSN1 exon 2 and were designed using the Zhang\nLab CRISPR Design Tool (MIT) for optimal specificity. We selected RNP delivery based on established evidence\nthat it reduces off-target activity through transient expression compared to plasmid-based methods (e.g., Kim et al.,\nNat Biotechnol 2013), thereby enhancing reliability.\n2) sgRNA Design and Initial Off-Target Mitigation: To minimize off-target risks from the outset, we designed and\nvalidated two independent sgRNAs using multiple prediction algorithms (CHOPCHOP v3 and Cas-OFFinder),\nprioritizing those with high on-target scores (>85%) and low predicted off-target sites (≤3 mismatches). This\nmulti-sgRNA approach, combined with phenotypic consistency across guides, supports SAMSN1-specific effects.\n3) Empirical Off-Target Validation via CHANGE-seq: To directly rule out off-target contributions, we conducted\nCHANGE-seq analysis on NK92KO-SAMSN1 cells. This genome-wide assay detected no cleavage events at the top 10\npredicted off-target sites above background threshold (>0.1% editing frequency). The data are summarized in\nExtended Data Fig. 6b, c.\n4) On-Target Confirmation: Sanger sequencing of the SAMSN1 locus, analyzed via TIDE software, confirmed\nefficient indel formation (~65% editing rate) specifically at the target site (Extended Data Fig. 6a). No unintended\nedits were observed in flanking regions.\n5) Mouse Model Validation: In Samsn1-/- mice, biallelic indels were verified by Sanger sequencing at the on-target\nlocus, with no detectable disruptions in closely homologous genes, further corroborating specificity.\nThese comprehensive measures, including spanning design, empirical testing, and functional rescue, collectively\ndemonstrate that the observed phenotypes are attributable to SAMSN1 knockout alone, without confounding off-target\neffects. We believe these enhancements address the reviewer's concerns and bolster the robustness of our conclusions.\nThe revised manuscript includes all new data and references for full reproducibility.\nMinor\nThe authors demonstrated the K/O of the SAMSN1 gene using CRISPR-Cas9 through an SAMSN1 expression\nprofile. However, please provide direct evidence of editing within the target SAMSN1 gene (e.g., indel\nformation).\nReply: We thank the reviewer for highlighting the need for direct evidence of SAMSN1 gene editing. To address this\nconcern, we have performed:\nA. Sanger sequencing of the target region in the SAMSN1 gene following sgRNA transfection in human NK92 cells,\nwhich confirms the presence of insertions/deletions (indels) indicative of successful CRISPR-Cas9 editing\n(Extended Data Fig. 6a-c).\nB. We have verified the functional knockout by quantifying reduced SAMSN1 mRNA levels and protein expression\nin the edited NK92 cells compared to controls (Figure 6b; Extended Data Fig. 6d, e).\nThese data have been incorporated into the revised manuscript and supplementary figure.\nExtended Data Fig. 6d, e validation of overexpression and knockout cells.\nREVIEWER #3 - SUMMARY OF KEY REVISIONS\nIssue Category Original Concern Our Response Evidence Location\nHCC Model B16F10/MC38 Added Hepa1-6 orthotopic HCC Main: Fig. 4a-e, Fig. 5a-k\nRelevance models incongruent models;\nwith HCC focus\nValidation validation of validate Cre specificity; (2) Used Ncr1- Data Fig. 5a-b; Point-to-\nconditional KO mice Cre without flox as control; (3) Added point for Major concerns\nphenotypic data (GzmB, IFN-γ, cell 5 of reviewer#3\ncounts)\nIntroduction Inappropriate Replaced low-impact refs with primary Introduction p.3-4;\nQuality references; vague high-impact sources\nstatements\nscRNA-seq Missing citations for Added citation: Sun Y et al., Cell 2021; Results p.5 line 89;\nReferences Fig. 1A-C 184(2):404-421 Methods p.18 line 416\nFigure 1 Lack of t-SNE plots; (1) Added t-SNE showing SAMSN1 Main: Fig. 1c; Results p.5\nAnalysis unclear PT/IT expression across clusters; (2) Clarified lines 94\nterminology PT (≤2cm margin) vs IT (core tumor);\n(3) Added NK signature correlation\nCRISPR Marginal protein (1) Added statistics (P<0.01); (2) Main: Fig. 3b-d\nValidation in reduction; Included GFP+ gating strategy; (3)\nNK Cells underpowered Expanded functional validation\nreplicates (GZMB/IFN-γ/Perforin)\nEthics Unclear approval Explicitly stated: Animal (KY2021204, Methods p.16 line 350;\nApproval status KY2021212); Human (2019-N-(H)- p.17 line 386-390\n121, 2023-A-(H)-048)\nReviewer #3\nChen et al. present an intriguing study on the role of SAMSN1 in regulating NK cell-mediated anti-tumor\nimmunity, particularly in hepatocellular carcinoma (HCC). The manuscript highlights SAMSN1 as a potential\nimmune checkpoint that suppresses NK cell activation and cytotoxicity, thereby promoting tumor immune\nevasion. The study identifies elevated SAMSN1 expression as a marker of poor prognosis in HCC patients and\ndemonstrates, through experimental models, that SAMSN1 deletion enhances NK cell functionality, limits tumor\ngrowth, and prevents immune cell exhaustion. These findings position SAMSN1 as a promising therapeutic target\nfor improving NK cell-based cancer immunotherapy and enhancing the efficacy of immune checkpoint blockade\n(ICB). While the study presents novel insights, there are several areas requiring clarification and improvement.\nBelow, I outline both major and minor concerns.\nMajor concerns\n1. Introduction: The introduction lacks depth and precision. Several references appear inappropriate or irrelevant,\nsuggesting a cursory literature review. For example, Reference 8 cites a low-profile nursing journal unrelated to\nthe topic. Reference 11 is from an MDPI journal, which raises questions about quality; it should be replaced with\na higher-impact reference. References 18–20 are reviews rather than original research, which should be prioritized\nwhen citing specific findings. Reference 23 (uterine NK cells) and Reference 24 (liver NK cells during\nhomeostasis) are cited in contexts where they appear irrelevant or inaccurate. The paragraph spanning lines 87–99\nis particularly vague. For instance: \"May function as a tumor suppressor in certain contexts\"—this requires\nspecifying the relevant mechanisms described in the cited papers. \"Modulation of signaling pathways related to\ncell proliferation and survival in certain contexts\"—similarly, this statement should be clarified with detailed\nfindings. \"Suggested a role for SAMSN1 in regulating inflammatory responses\"—again, what was specifically\ndiscovered in the referenced studies? Additional references are missing for claims such as \"reported in various\nmalignancies, including multiple myeloma and lung cancer.\"\nReply: We have revised the Introduction with appropriate primary research citations:\n16\n1) Reference Replacements: Replaced low-impact or irrelevant citations with high-impact, topic-specific\noriginal research. Specifically, Reference 8 (nursing journal) was replaced with Reference 6 (Llovet JM,\net al) and Reference 11 (MDPI journal) with Reference 8 (Reig M, et al) (page 3, line 44). Reference 23\n(uterine NK cells) also replaced with Reference 18 (Caligiuri MA) (page 3, line 55), and Reference 24 (liver\nNK cells during homeostasis) with Reference 19 (Sun J, et al.) (page 3, line 55). These replacements\nprioritize primary studies over reviews (e.g., References 17-18).\n2) New References: Added citations to support claims of SAMSN1 in various malignancies, including multiple\nmyeloma (Noll JE, et al.) and lung cancer (Yao L, et al.).\n3) Mechanistic Clarity: Rewrote the paragraph of SAMSN1 to remove ambiguous terms (e.g., \"certain\ncontexts\") and provide detailed mechanistic insights, supported by referenced research in introduction (page\n4, line 71).\n2. Figure 1: The rationale behind Figure 1B is unclear. The absence of t-SNE plots showing SAMSN1 expression\nacross population clusters weakens the analysis. Terminology like \"Peritumor (PT)\" and \"Intratumor (IT)\" is\nconfusing. According to the legend (lines 512–513), both PT and IT are tumor zones, making it unclear what is\nmeant by \"adjacent normal tissue.\" Correlating SAMSN1 expression with NK cell scoring signatures (e.g., PMID:\n31088844) would significantly enrich this figure.\nReply: We thank the reviewer for their insightful and valuable suggestions to strengthen Figure 1. We have\ncarefully addressed each concern to enhance the clarity, rigor, and interpretability of our analysis as follows:\n1) Rationale for Figure 1B: To clarify the rationale for Figure 1b, we have revised the manuscript to explicitly state\nits purpose (page 6, lines 95–97): Figure 1b aims to quantify SAMSN1 expression in TILs, particularly NK cells,\nwithin the HCC tumor microenvironment using scRNA-seq data. This analysis establishes SAMSN1 as a\ndifferentially expressed gene in NK cells, supporting its role as a negative regulator of NK cell function in HCC.\nThis has addressed in question 1 of review 1.\n2) Addition of t-SNE Plots: In response to the reviewer’s suggestion, we have added t-SNE plots in Figure 1c to\nvisualize SAMSN1 expression across immune cell clusters (B cells, T cells, NK cells, myeloid cells) in both PT\nand IT samples. These plots reveal significantly elevated SAMSN1 expression in the IT NK cell subset compared\nto other immune cell types and PT regions, consistent with our hypothesis that SAMSN1 plays a critical role in\nNK cell dysfunction within the TME. This addition strengthens the visual representation of SAMSN1’s expression\npatterns and addresses the reviewer’s concern about the lack of such plots. This has addressed in question 1 of\nreview1.\n3) Terminology Clarification: In the revised manuscript (page 5, lines 97–100), we have clarified these definitions:\nPT refers to tissue within 2 cm of the tumor margin, pathologically confirmed to contain a mixture of tumor and\nstromal cells, while IT represents the core tumor region enriched in malignant cells. The term \"adjacent normal\ntissue\" has been removed from the figure legend and replaced with \"non-tumor liver tissue\" to clearly denote liver\ntissue outside the tumor margin (>2 cm), pathologically verified as non-malignant.\n4) NK Cell Signature Correlation Analysis: We performed the requested NK cell signature analysis (Cursons et al.,\n2019). While some signatures showed variable correlation due to inter-patient heterogeneity, we identified\nconsistent patterns：(1) Cytotoxicity signature: inverse correlation with SAMSN1 (r = -0.42, P = 0.0331); (2)\nExhaustion signature: positive correlation (r = 0.38, P = 0.047); (3) We acknowledge variability and have included\nall raw data for transparency. The heterogeneity itself is informative, suggesting patient stratification potential.\nFig. 1a-d Single-cell RNA sequencing (scRNA-seq) analysis of 18 HCC patients from China National GeneBank DataBase.\n17\nj\nr=-0.543 P=0.0106 r=-0.02 P=0.928 r=0.077P=0.734 r=0.311 P=0.1707 r=0.469 P=0.028 r=0.457 P=0.033\nFig. 2 j Mass cytometry (CyTOF) was used to analyse correlations between SAMSN1 and proteins (GZMB, KLRC1, TIGIT, TIM3).\n3. Lines 144-146 mentioned Figure 1A-C, which analyzes scRNAseq data from published studies; however, the\ncorresponding references are missing and should be included. Additionally, Figure 1F illustrates the correlation\nbetween SAMSN1 and other immune checkpoint receptors. It is unclear whether the expression of SAMSN1 and\nthe immune checkpoint receptors analyzed here is for the whole cell population in the dataset or specific on NK\ncells. If the analysis does not specifically target NK cells, the findings presented may not directly support the role\nof SAMSN1 in NK cells concerning tumor immunity, which is your focus of this paper.\nReply: We thank the reviewer for their meticulous feedback regarding the scRNA-seq data and correlation analysis in\nFigure 1. We have addressed the concerns as follows:\n1) Missing References for Figure 1a–c: Added the relevant references for the scRNA-seq data to line 148 of the\nrevised manuscript, citing Yunfan Sun et al. (Cell, 2021; 184(2):404–421, DOI: 10.1016/j.cell.2020.11.041), to\nprovide full attribution for the published studies.\n2) Specificity of Figure 1f Analysis: Confirmed that the correlation analysis between SAMSN1 and immune\ncheckpoint receptors in Figure 1F (revived Figure1g) was conducted specifically within the NK cell subset of the\ndataset. This targeted analysis ensures that the findings directly support SAMSN1’s role in NK cell-mediated tumor\nimmunity, aligning with the study’s focus.\n4. Figure 3: CRISPR-Cas9 knockout efficiency in NK cells is insufficiently validated. While there was a 3-fold\nreduction in mRNA, protein reduction (81.8% to 56.3%) is marginal and lacks statistical comparison in Figure 3C.\nKey data are missing, such as FACS plots showing GFP+ NK cells (CD3⁻, CD56⁺, NKp46⁺) after GFP-Cas9\nexpression. Figure 3D appears underpowered, with only two biological replicates. Additional validations, such as\nintracellular Granzyme B staining (Figure 3F), and comprehensive immune phenotyping (e.g., liver ILC1\nnumbers, NK cell maturation phenotype) are required to substantiate claims.\nReply: We thank the reviewer for their detailed feedback on Figure 3, which has prompted significant improvements.\nWe have addressed each concern as follows:\n1) Statistical Analysis for Figure 3C: Added a paired t-test to the protein-level data in Figure 3C （revived Fig.\n3D）, confirming a significant reduction in SAMSN1 expression from 81.8% to 56.3% (n = 3, P < 0.01).\n2) FACS Gating Strategy: Since SAMSN1 knockout was performed in pre-sorted primary human NK cells (isolated\nfrom PBMCs using CD3⁻/CD56⁺ selection), we did not re-gate for CD3⁻/CD56⁺/NKp46⁺ in flow plots. However,\nwe have included a supplemental figure (Figure 3B) showing the gating strategy for GFP⁺ NK cells to validate\ntransduction efficiency.\n3) Statistical Power for Figure 3D: Acknowledged the underpowered design with only two biological replicates in\nFigure 3D (revived Fig. 3E). We are repeating these assays (Figure 3D and 3F) with ≥ 3 additional biological\nreplicates, and raw data will be supplement.\n4) Immune Phenotyping: Expanded the intracellular staining in Figure 3F to evaluate the functional impact on\nGZMB, IFN-γ, and Perforin expression. In SAMSN1 knockout NK92 cells, we observed increased release of these\ncytokines, supporting the functional enhancement post-knockout.\n5. Figures 4 and 5: The use of B16F10 melanoma and MC38 colorectal models is incongruent with the HCC focus\nof the study. Including HCC-relevant models, such as aflatoxin B1-related HCC or intra-splenic MC38 for liver\nmetastases, would enhance relevance. For B16F10, standard lung metastasis quantification (e.g., macroscopic\nmetastasis counting) should be performed instead of relying solely on luciferase-based assays. Validation of the\nNcr1^cre SAMSN1^fl/fl model is insufficient. Authors should: cross this model with Rosa26-YFP and validate\nCre specificity to NKp46⁺ cells; address potential differences in NK cell function by using Ncr1^cre without flox\n18\nas a control; include phenotypic data (e.g., Granzyme B, IFN-γ, immune cell counts). Figures 5D and 5E require\nadditional data. For example, Figure 5E should differentiate between apoptosis and necrosis, and tumor\nhomogenates should quantify NK92 cell infiltration.\nReply: We have added HCC-specific models as requested.\n1) HCC models validation: (1) We have now performed orthotopic Hepa1-6 liver tumor models in both Samsn1\nknockout (Samsn1-/-) mice and Samsn1 conditional knockout (Samsn1f/f-Ncr1Cre+) mice. Consistent with our\noriginal findings, these new data demonstrate that Samsn1 deletion significantly suppresses tumor growth and\nextends survival in both models. These data are now included in Figure 4 and Figure 5.\n(2) We acknowledge the\nlimitation of murine models. However, due to ethical constraints, we cannot access patient-derived xenograft HCC\nmodels. (3) Meanwhile, we performed meta-analysis of 5 published HCC studies (n=847 patients) confirming\nSAMSN1-survival association (HR=4.9, P < 0.001)\n2)\n3) Validation of the Samsn1 conditionalknockout (Samsn1f/f-Ncr1Cre+) mice: Ncr1-2A-Cre mice (Cat. NO. NM-\nKI-190027) were purchased from Shanghai Model Organisms Center, Inc., which has validated cross this model\nwith Rosa26 tdTomato+/-in figure1. We have already compared control groups Ncr1-2A-Cre mice vs. Samsn1f/f in\nall experiments and found no significant differences in tumor growth or immune phenotypes. This confirms that\nobserved effects are due to Samsn1 deletion rather than Cre expression.\nValidation Data Cre-mediated recombination in NK cells derived from spleen and peripheral blood of Ncr1-(2A-iCre); Rosa26 tdTomato mice.\n4) B16F10 Metastasis Quantification: Agreed that standard lung metastasis counting would complement luciferase-\nbased assays.\n5) Figures 5D and 5E Data: Recognized the importance of distinguishing apoptosis versus necrosis for mechanistic\ninsight. Current data suggest Samsn1 knockout enhances tumor cell killing capacity (Extended Data Fig. 6k), but\nthe exact contribution of death pathways remains unclear.\nMinor concerns\n1. The ethics section lacks clarity on whether approvals for both animal and human experiments were obtained.\nNumbers corresponding to approvals should be explicitly stated.\nReply: We thank the reviewer for highlighting the need for greater clarity in the Ethics section regarding approvals for\nanimal and human experiments. To address this, we have expanded the Ethics Approval section in the revised\nmanuscript (page 19, lines 416-420) to explicitly state that all studies were conducted in compliance with relevant\nguidelines. All the experiments were conducted in accordance with animal protocols approved by by Anhui Provincial\nHospital (KY2021204, KY2021212) and the Laboratory Animal Centre of the University of Science and Technology of\nChina (USTCACUC192201040, USTCACUC25030123013).\n19\n2. Figure legends are excessively long and should be succinctly rewritten for readability.\nReply: We thank the reviewer for this constructive suggestion. All figure legends have now been revised to eliminate\nredundant methodological details.\n3. Significant grammar and syntax errors are present throughout the manuscript. Examples include inconsistent\nhyphenation (e.g., in vivo vs. in-vivo), capitalization (e.g., \"Re-exploring\"), and typographical errors (e.g.,\n\"jane.auteur@example.com\"). A thorough English language review is essential.\nReply: To ensure linguistic precision throughout the manuscript, we implemented standardized formatting rules. All\ncorrections were applied during full manuscript proofreading—not merely as selective fixes.We confirm enhanced\nreadability and technical accuracy in the revised version.\n4. Materials and methods. The section is overly high-level and should include detailed experimental protocols\nrather than relying on supplementary materials.\nReply: We thank the reviewer for emphasizing the need for methodological rigor and detailed protocols in the Materials\nand Methods section. To address this concern, we have substantially expanded the Methods in the revised manuscript\n(pages 18-30), incorporating comprehensive experimental details previously relegated to supplementary materials.\nSpecifically, we have:\n1) Added full protocols: Included step-by-step procedures for key experiments, such as scRNA-seq library\npreparation (e.g., 10x Genomics Chromium protocols with specific reagent concentrations and incubation times),\nCyTOF staining (antibody panels, metal tags, and acquisition parameters), and mouse model generation (CRISPR\ntargeting strategies, genotyping primers).\n2) Integrated reagent details: Listed all reagents with catalog numbers, vendors, and concentrations (e.g., PMA at 50\nng/mL for stimulation assays).\n3) Enhanced reproducibility: Added sections on data processing pipelines (e.g., Seurat v4.3.0 parameters for\nclustering), statistical software (R v4.2.1), and quality control thresholds.\n4) Removed supplementary reliance: All essential methods are now self-contained in the main text, with\nsupplementary materials limited to raw data and extended figures.\nThese revisions ensure the section is detailed, standalone, and fully reproducible, aligning with best practices for\nscientific reporting. The updated Methods section is provided in the revised manuscript for reference.\n5. Missing references. For example, lines 145–146 mention \"published papers\" without citations.\nReply: We have included a proper citation for the scRNA-seq data from Yunfan Sun et al. (Cell, 2021; 184(2):404–421)\nin the results section (page 6, lines 95) and references section.\n20"}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised version of the manuscript, authored properly addressed mu queries and the overall quality of this paper remarkably increased. Hence, I think that the present revised version of this work os now suitable to be published on Nature Communications", "reviewer_2": "The authors have addressed all my questions regarding the issues in this paper. I recommend its publication.", "reviewer_3": "The authors have satisfactorily addressed all previous comments and concerns. The revised manuscript is improved and meets the necessary standards for consideration."}, "author_response": ""}]

Decision letter