PD-1 blockade plus tyrosine kinase inhibitor remodels the tumor microenvironment in advanced renal cell carcinoma

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
The combination of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) has improved clinical outcomes in advanced renal cell carcinoma (aRCC), though therapeutic resistance remains a major challenge. We conducted single-cell transcriptomic analysis on 61 tumor samples from 34 aRCC patients treated with TKIs alone or in combination with ICIs. Non-responders exhibited increased neutrophil infiltration and enrichment of SAA+ tumor cells following treatment. We identified a VEGFA+CEACAM1+ neutrophil subset exhibiting immunosuppressive properties that is associated with poor clinical response. In vivo, pharmacologic inhibition of SAA enhanced sensitivity to ICI plus TKI combination therapy, while blockade of the CEACAM1–TIM-3 axis in humanized PD-1 mouse models significantly potentiated anti–PD-1 efficacy. Our study establishes a single-cell atlas of the aRCC tumor microenvironment under treatment pressure and identifies a previously unrecognized SAA–CEACAM1–TIM-3 axis driving drug resistance, highlighting potential targets to improve therapy efficacy.

Introduction
Renal cell carcinoma (RCC) accounts for approximately 2–3% of adult malignancies worldwide, with clear cell RCC (ccRCC) representing 70–80% of cases1. Around 30% of patients present with metastatic disease, and 20–40% of those with localized tumors experience recurrence post-nephrectomy2. Advanced RCC (aRCC) remains a lethal disease with a 5-year survival rate of only 11.7%3. The discovery of von Hippel–Lindau mutations and the central role of angiogenesis in ccRCC led to the establishment of tyrosine kinase inhibitors (TKIs) as first-line therapy4. Meanwhile, the immune-rich and pro-inflammatory nature of ccRCC supports the use of immune checkpoint inhibitors (ICIs). Recently, combination therapy with TKIs and ICIs has transformed aRCC treatment, achieving survival benefits in both advanced2 and neoadjuvant settings5.

However, drug resistance remains a major clinical challenge, with 5–20% of patients failing to respond to combination therapies2. Current clinical tools fail to reliably identify patients at risk of resistance. Traditional ICI biomarkers, including PD-L1, tumor mutational burden, and microsatellite instability, have limited utility in ccRCC. Transcriptomic studies in aRCC have revealed molecular subtypes associated with differential response—such as effector T cell and angiogenic signatures—but these findings require further validation6. A deeper understanding of the dynamic, multifactorial mechanisms underlying resistance, particularly the complex cellular crosstalk within the tumor microenvironment (TME), is essential for precision intervention. Single-cell RNA sequencing (scRNA-seq) offers an unprecedented opportunity to dissect TME heterogeneity and track treatment-induced changes across cell types. Yet, an integrated single-cell–level analysis of resistance mechanisms in aRCC is lacking.

Here, we conduct comprehensive scRNA-seq analyses on 61 tumor samples from 34 aRCC patients treated with TKIs alone or in combination with ICIs. Our analyses reveal a previously unrecognized SAA⁺ tumor–CEACAM1+ neutrophil–CD8+ T cell axis that promotes immune suppression and therapeutic resistance. These findings provide a mechanistic framework for treatment failure and nominate actionable targets to overcome resistance in aRCC.

Results
TME dynamics before and after ICI plus TIKI combination therapy in aRCC
We performed scRNA-seq on tumor samples from 18 patients in the PLAGH cohort, including three treated with TKI monotherapy, and 15 with ICI plus TKI combination therapy. A total of 30 tumor samples were analyzed: nine were pre-treatment samples (six from non-responders [PreNR], three from responders [PreR]) and 21 post-treatment samples (17 from non-responders [PostNR], four from responders [PostR]). The samples were obtained from primary tumors (n = 17), tumor thrombi (n = 9), adrenal metastatic lesion (n = 1) or mixed tissue (n = 3). One post-treatment sample (P6) was excluded due to poor sequencing quality (Supplementary Datasets 1,2).

To improve the analytical robustness of our study, we integrated two publicly available scRNA-seq datasets of aRCC. The first (HRA004711)7, including tumor and adjacent normal tissues from four patients treated with TKI monotherapy and the second (GSE264586)8, included samples from 12 patients treated with combination therapy, encompassing both primary and metastatic sites (Fig. 1a). After integration, the samples were classified as PreNR (n = 17), PreR (n = 8), PostNR (n = 26), and PostR (n = 10). After quality control, we profiled 333,169 high-quality single cells and constructed a comprehensive aRCC atlas, capturing the dynamic transcriptomic landscape across treatment (Fig. 1b, Supplementary Fig. 1a). Unsupervised clustering identified nine major cell types (Supplementary Dataset 3): normal epithelial cells (n = 5,544; Supplementary Dataset 4), tumor cells (n = 91,451), T/NK cells (n = 92,929; Supplementary Dataset 5), B cells (n = 10,407; Supplementary Dataset 6), plasma cells (n = 4,508; Supplementary Dataset 6), monocytic cells (n = 78,895; Supplementary Dataset 7), neutrophils (n = 14,779; Supplementary Dataset 8), mast cells (n = 2,303; Supplementary Dataset 9), and stromal cells (n = 32,353; Supplementary Dataset 10), based on canonical marker genes (Supplementary Figs. 1b–i and ; Supplementary Datasets 3–10).

Clinical trials including KEYNOTE-426, CheckMate 9ER, and RENOTORCH have demonstrated the superior efficacy of ICI plus TKI combination therapy compared to TKI monotherapy2. We compared changes in the proportions of major immune cell populations before and after treatment under these two therapeutic strategies. Notably, T cells, natural killer (NK) cells, B cells, plasma cells, and neutrophils exhibited distinct shifts in abundance. Neutrophils decreased following TKI monotherapy but increased with combination therapy (Supplementary Fig. 2a). A similar pattern was also observed for B cells and plasma cells (Supplementary Fig. 2a). In contrast, NK cells increased after TKI monotherapy but declined with combination therapy (Supplementary Fig. 2a). T cells increased after both treatments, especially in the combination group (Supplementary Fig. 2a). Within this group, responders had greater T cell enrichment, while macrophages and neutrophils were more predominant in non-responders (Supplementary Figs. 2b, c). Notably, responders exhibited higher T cell infiltration and reduced infiltration of myeloid cells across both treatment regimens (Supplementary Figs. 2b, c).

To quantify associations between immune cell populations and treatment outcomes, we applied a predictive index (Pi) and therapeutic index (Ti) frameworks9. T cells had the highest Pi, while neutrophils had the lowest (Fig. 1c). This finding was supported by multiplex immunohistochemistry (mIHC) staining, which revealed elevated neutrophil infiltration in the PreNR group (Fig. 1d). Across multiple RCC cohorts receiving ICI monotherapy, TKI monotherapy, or combination therapy, higher neutrophil signature scores were associated with shorter overall and progression-free survival (Supplementary Fig. 2d).

Ti analysis also associated B cells and plasma cells with favorable treatment responses, in line with previous studies10,11. Conversely, macrophages and neutrophils were consistently enriched in non-responders (Fig. 1c). Notably, macrophage enrichment in resistant RCC has been previously reported12. Neutrophils were also enriched in the PostNR group in our cohorts (Fig. 1d), and the same patterns were also observed when comparing major pathologic response (MPR) and non-MPR (NMPR) obtained from the post-treatment samples in the non-small cell lung cancer (NSCLC) dataset (Supplementary Figs. 2e–g).

In summary, our findings reveal significant alterations in the TME landscape of aRCC patients undergoing TKI monotherapy or ICI plus TKI combination therapy. Specifically, these results suggest the involvement of neutrophils, which may potentially contribute to therapeutic resistance in aRCC patients.

Distinct neutrophils confer resistance to TKI with or without ICI
Tumor-associated neutrophils have been linked to immunotherapy resistance, with higher infiltration predicting poorer survival13. A pan-cancer analysis also revealed substantial transcriptional heterogeneity among neutrophils14. We performed high-resolution profiling of intratumoral neutrophils and identified six transcriptionally distinct subclusters: Neu_01_S100A8, Neu_02_CD74, Neu_03_Stress, Neu_04_ZFP36L1, Neu_05_IRF1, and Neu_06_VEGFA (Fig. 1b, Fig. 2a, and Supplementary Fig. 1i). The Neu_01_S100A8 exhibited enrichment in chemotaxis-related pathways and high expression of S100A12 (Supplementary Fig. 1i, 3a–b), resembling previously characterized S100A12+ 14 and CD10+ALPL+ neutrophils15. The Neu_02_CD74 displayed elevated CD74 expression and enrichment in antigen presentation pathways (Supplementary Fig. 1i, 3a, b), consistent with the HLA-DR+CD74+ neutrophils associated with favorable prognosis and improved PD-1 response14. The Neu_05_IRF1 expressed the immune checkpoint molecule CD274 (Fig. 2b), aligning with the PD-L1+ TANs that suppress T cell cytotoxicity16 (Supplementary Fig. 1i, 3a, b). Notably, the Neu_06_VEGFA subcluster (VEGFA+ neutrophils) expressed high expression of VEGFA, CXCR4, and LDHA (Fig. 2b), with enrichment in glycolysis and hypoxia-related pathways, similar to the VEGFA+SPP1+ neutrophils linked to poor prognosis14 (Fig. 2c, Supplementary Fig. 3a). This subset also exhibited high CXCR4 expression (Fig. 2b), consistent with CXCR4hi neutrophils characterized by increased neutrophil extracellular traps (NETs) formation and inflammatory cytokine production, accompanied by lactate-driven vascular remodeling17.

After treatment, responders exhibited reduced proportions of Neu_01_S100A8, Neu_06_VEGFA, Neu_04_ZFP36L1, Neu_05_IRF1, and Neu_03_Stress, but a higher proportion of Neu_02_CD74 than non-responders (Supplementary Fig. 3c). Notably, while Neu_06_VEGFA was enriched in responders prior to treatment, its expansion was predominantly observed in non-responders after treatment (Supplementary Fig. 3c). This post-treatment enrichment was further validated by mIHC (Fig. 2d). Trajectory analysis revealed two primary differentiation routes from Neu_01_S100A8 towards either Neu_02_CD74 or Neu_06_VEGFA. Neutrophils predominantly differentiated along the Neu_02_CD74 lineage in responders and the Neu_06_VEGFA lineage in non-responders (Supplementary Figs. 3d–g). Pi and Ti analyses further supported these observations, with Pi identifying Neu_01_S100A8 as a baseline predictor of poor response, and Ti highlighting Neu_06_VEGFA as a treatment-associated mediator of therapeutic resistance (Fig. 2e). To assess neutrophil reprogramming following therapy, we compared the transcriptional profiles between PostR and PostNR samples. Neutrophils from PostNR samples exhibited upregulation of immune checkpoints including carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), VSIG4 and CD300A (Fig. 2f, Supplementary Dataset 11), along with enrichment of pathways related to angiogenesis, neutrophil chemotaxis and negative regulation of T cell-mediated immunity (Fig. 2g). Among all neutrophil subclusters, Neu_06_VEGFA showed the highest expression of LGALS3 and CEACAM1 (Figs. 2b, h)—ligands for LAG-3 and TIM-3, respectively, which are predominantly expressed on CD8+ T cells18. LGALS3 is widely expressed in the TME, including normal epithelial cells, tumor cells, monocytes, macrophages and mast cells19 (Supplementary Fig. 3h), its elevated expression in the Neu_06 subset likely reflects an acquired, stress-induced phenotype rather than a canonical neutrophil lineage marker. In contrast, CEACAM1 is mainly expressed in neutrophils and endothelial cells and is a key component of NETs20,21 (Fig. 2h, Supplementary Fig. 3i), underscoring its subset specificity and immunomodulatory relevance in resistance-associated neutrophils.

Disruption of CEACAM1–TIM-3 interactions has been shown to enhance PD-1 efficacy22,23. Given these observations, we investigated the association of CEACAM1+ neutrophils with non-responsiveness. In our cohort and three additional pan-cancer datasets, including NSCLC and colorectal cancer (CRC), CEACAM1+ neutrophils were significantly enriched in the PostNR group (Fig. 2i, Supplementary Figs. 3j–m), a finding further validated by mIHC (Fig. 2j). Post-treatment bulk RNA-seq revealed elevated CEACAM1 expression in non-responders, suggesting its association with hepatocellular carcinoma (HCC) treated with ICI plus TKI (Fig. 2k).

We identified distinct neutrophil subpopulations linked to resistance against both TKI monotherapy and ICI plus TKI therapy. Among them, Neu_06_VEGFA stood out as a key immunosuppressive subset, potentially mediating resistance through CEACAM1–driven immunomodulatory effects.

SAA+ tumor cells associated with resistance to TKI with or without ICI
Tumor cells, as central components of the TME, play a pivotal role in shaping immune responses. Accumulating evidence demonstrates that tumor-derived signals recruit neutrophils, thereby fostering an immunosuppressive niche that facilitates tumor progression and therapy resistance24. To investigate the source of neutrophil recruitment in aRCC, we reconstructed cellular interaction networks through ligand-receptor analysis. In non-responders with aRCC, monocytes, macrophages and dendritic cells more frequently expressed the chemokines (CXCL2, CXCL3 and CXCL8) to recruit CXCR2+ neutrophils compared to those in responders (Fig. 3a). Notably, tumor cells from post-treatment non-responders exhibited increased expression of CXCL14, a chemokine known to recruit CXCR4+ neutrophils25 (Fig. 3a). This is consistent with the Neu_06_VEGFA enrichment seen in PostNR samples (Fig. 2d).

To compare the differences in the cellular states of malignant cells between postR and postNR groups, we directly compared malignant cells from these two groups treated with ICI plus TKI to identify differentially expressed genes (DEGs) (Fig. 3b, Supplementary Dataset 12). Notably, ferroptosis-inhibiting genes like SLC7A11 were enriched in malignant cells from the postNR group (Fig. 3b). Their upregulation, driven by interleukin 6 via the JAK2–STAT3 pathway, was previously reported in patients with aRCC receiving TKI monotherapy7. Our data indicate that the same pathway is activated during combination therapy, potentially driving resistance in non-responders. DEGs were also significantly enriched in pathways associated with acute inflammatory response and neutrophil infiltration, including SAA1 and SAAS2 (Figs. 3b, c).

Dimensionality reduction clustering of aRCC malignant cells revealed substantial inter-patient heterogeneity (Fig. 1b). To identify conserved functional programs while minimizing transcriptional variability driven by gene dosage effects, we applied non-negative matrix factorization. This approach defined nine metaprograms (MPs) in malignant cells, which were subsequently annotated into eight functional modules based on signature genes and enriched pathways (Fig. 3d, Supplementary Figs. 4a, b and Supplementary Dataset 13). These modules corresponded to: (1) cell cycle (MP1; TOP2A and STMN1), (2) neuronal-like26 (MP2; NR2F2 and PTPRG), (3) chemokines regulation (MP3; CXCL1 and CXCL2), (4) Proximal tubule (MP4; NAT8 and SLC17A3), (5) epithelial-mesenchymal transition (EMT I, MP5; LOX, PLOD2 and TGM2), (6) EMT II (MP6; SAA1, SAA2 and CXCL14), (7) MHC-II (MLP7; CD74 and HLA-DPA1), and (8) stress response processes (MP8; HSP90AA1 and HSPA1A). Module similarity analysis between our aRCC cohort and a recent study of treatment-naive RCC27 revealed strong concordance, suggesting that the functional architecture of tumor programs is largely preserved following treatment (Supplementary Fig. 4b). External validation of these findings using the TCGA-KIRC cohort confirmed a significant association between the activity of MP1, MP4, MP5, and MP6 tumor cell signatures and both overall survival and progression-free survival in patients with ccRCC (Supplementary Fig. 4c).

We annotated MP5 and MP6 as EMT-related modules based on their strong enrichment for hypoxia and EMT pathways (Supplementary Fig. 4a). MP5 incorporates extracellular matrix (ECM)-mediating factors, including LOX, PLOD2, TGM2 and is enriched in functions related to ECM organization (Fig. 3d). Its expression was significantly elevated in post-treatment samples compared to pre-treatment ones and was notably higher in PreNR tumor cells than in PreR cells (Fig. 3e). Additionally, MP6 contains SAA1, SAA2, CXCL14, and is enriched in functions related to the acute phase response, neutrophil chemotaxis, and migration (Supplementary Fig. 4a). Tumor cell scoring across our dataset and external single-cell datasets showed that MP6 was significantly upregulated in PostNR samples compared to PostR (Fig. 3e).

To further investigate tumor cell-intrinsic drivers of drug resistance in aRCC, we intersected genes from the postNR-enriched MP6 module with those consistently upregulated in both postNR samples and TKI-resistant ccRCC cell lines (A-498 and 786-O), identifying SAA1 and SAA2 as shared candidates (Fig. 3f and Supplementary Datasets 14,15). Serum amyloid A (SAA, comprising the SAA1/SAA2 isoforms in humans) is rapidly induced during acute-phase inflammatory responses28, and has been implicated in immune suppression and RCC progression29-31. Transcriptomic data from patients with HCC receiving ICI plus TKI therapy revealed higher SAA expression in postNR versus postR samples (Fig. 3g), suggesting a broader role for SAA in mediating resistance across cancers. Moreover, we validated that high SAA expression was significantly associated with poor survival outcomes in five previously published pan-cancer ICI datasets (Fig. 3i, Supplementary Figs. 4d, e). To assess the clinical utility of SAA as a predictive biomarker for ICI plus TKI therapy, we performed enzyme-linked immunosorbent assay-based quantification of pre-treatment plasma samples and found significantly elevated circulating SAA levels in patients with preNR tumors (Fig. 3j). At the tissue level, mIHC revealed increased SAA+ tumor cell abundance not only in preNR compared to preR samples, but also in post-treatment samples relative to pre-treatment, suggesting that therapy may further amplify the SAA+ tumor cell population enriched in NR groups (Fig. 3h). This enrichment was further confirmed in postNR versus postR tumors by both mIHC and scRNA-seq analyses (Fig. 3h; Supplementary Fig. 4f).

Collectively, these findings suggest that SAA+ tumor cells may constitute a persistent, treatment-amplified resistance population in aRCC.

SAA blockade enhances the combination therapy efficacy
SAA has been demonstrated to induce PD-L1 expression in neutrophils via LDHA/STAT3-mediated glycolytic activation and oncostatin M release, thereby attenuating cytotoxic T cell function in HCC32. Our data revealed significant enrichment of SAA+ tumor cells and Neu_06_ VEGFA in PostNR tumors, suggesting a symbiotic regulatory dynamic between these cell types. To clarify the spatial relationship between SAA+ tumor cells and VEGFA+ neutrophils, we divided TME into tumor core and margin (Supplementary Fig. 5a-f). In non-responders, SAA+ cells mainly localize in the core with a few scattered in the margin, where VEGFA⁺ neutrophils are enriched (Supplementary Fig. 5g-h). Co-localization index analysis showed non-responders have higher indices than responders, supporting their potential interaction in non-responders’ tumor margin (Supplementary Fig. 5i). Furthermore, cell–cell communication analysis revealed enhanced interactions between SAA+ tumor cells and Neu_06_VEGFA in PostNR samples (Supplementary Fig. 6a). To functionally validate the SAA–CEACAM1 axis, we performed an in vitro perturbation experiment showing that SAA stimulation upregulates CEACAM1 expression in neutrophils (Fig. 4a, b; Supplementary Fig. 6b). Given the symbiotic interaction between SAA+ tumor cells and VEGFA+ neutrophils, we evaluated the therapeutic potential of targeting this axis in RCC. In preclinical syngeneic orthotopic RCC mouse models, triple therapy combining anti-SAA1/2 antibody, axitinib, and anti–PD-1 antibody produced a synergistic antitumor effect (Figs. 4c, d), without causing notable toxicity and outperforming either anti–PD-1 plus axitinib or anti-SAA1/2 monotherapy (Fig. 4e). mIHC analysis demonstrated that triple therapy increased CD8+ T cell infiltration (Fig. 4f) and significantly reduced neutrophil infiltration (Fig. 4g).

ScRNA-seq re-analysis of Saa-/- and wild-type (WT) mice33, revealed 11 major cell types using canonical markers (Supplementary Figs. 6c, d). Focusing on neutrophils, we identified eight distinct subpopulations (Supplementary Figs. 6e, f). Among them, mNeu_07_Ldha demonstrated notably higher expression levels of both Ldha and Hilpda and exhibited a transcriptional pattern highly comparable to that of Neu_06_VEGFA (Supplementary Fig. 6f). A significantly higher proportion of mNeu_07_Ldha was observed in WT mice (Figs. 4h, i).

Neutrophils from WT mice exhibited upregulation of angiogenesis- and chemotaxis-related genes and pathways (Fig. 4j, Supplementary Fig. 6g). Moreover, CD8+ T cells from Saa-/- mice exhibited enrichment in NK cell activation and immune-stimulatory pathways (Supplementary Fig. 6h). A comparison of transcriptomic differences between Saa-/- and WT neutrophils revealed upregulation of Ceacam1 expression (Fig. 4j). Similarly, SAA blockade using an anti-SAA1/2 antibody led to elevated Ceacam1 expression in neutrophils within the orthotopic RCC model (Fig. 4k).

Collectively, SAA+ tumor cells orchestrate a pro-tumor microenvironment by promoting Neu_06_VEGFA infiltration and CD8+ T cell dysfunction.

Diverse T cells distinguish therapy sensitivity and resistance
Numerous studies have underscored the importance of T cell characteristics within the TME and their influence on the response to anti-tumor therapies34. Our mIHC data revealed increased infiltration of CD8+ T cells in the PostR group following combination therapy (Fig. 5a). Conversely, a higher density of CD8+ T cells was observed in the PreNR group prior to treatment (Fig. 5a). This observation aligns with our previous report, in which biopsy specimens from non-responders exhibited enhanced infiltration of CD8+ cells5. Combination therapy also significantly enhanced T cell-mediated anti-tumor response activity in CD8+ T cells compared to TKI monotherapy (Supplementary Figs. 7a, b). Using high-resolution T/NK cell profiling in aRCC tumors, we identified four CD4+ T cell subsets, six CD8+ T cell subsets, and two NK cell subsets (Fig. 1b; Supplementary Figs. 1d, 7c and d). The CD8+ T cells were further classified into six subsets: cycling CD8+ T cells (CD8T_06_Cycling, MKI67+), memory-like CD8+ T cells (CD8T_01_CXCR4_preEX, TCF7+CCR7+CXCR4+), intermediate exhausted CD8+ T cells (CD8T_03_CCL5_Temra, GZMK+CCL5+CD74+, Exh-Pre), stress-state CD8+ T cells (CD8T_02_Stress, HSPA1A+, Exh-Prog), and two terminally exhausted subsets (CD8T_04_HAVCR2_EX, CCL5+ENTPDE1+HAVCR2+; Exh-Term and CD8T_05_CCL4_EX, CCL4+CCL4L2+CCL5+, Exh-KLR) (Figs. 1b, 5b; Supplementary Figs. 1d, 7e and f)35-37. Trajectory and proportion analyses indicated that, compared to TKI monotherapy, combination therapy appeared to facilitate the transition from Exh-Pre to Exh-Prog (Supplementary Figs. 7g-j). However, in the PostNR group receiving combination therapy, Exh-Prog cells accumulated, suggesting a potential blockade in their progression to terminally exhausted states (Supplementary Fig. 7h). This finding is consistent with the previously described significant enrichment of the terminally exhausted gene signature in cells from responders compared to non-responders in RCC immunotherapy35,38. The CD4+ T cells were reclustered into four subsets: naive CD4+ T cells (CD4T_01_Tn_CCR7, CCR7+), stress-state CD4+ T cells (CD4T_02_Stress, HSPA1A+), memory CD4+ T cells (CD4T_03_Tm_ANXA1, ANXA1+ CD40LG+), and regulatory T cells (Tregs, CD4T_04_Treg_FOXP3, FOXP3+) (Supplementary Figs. 1d, 7c and d). Compared to TKI monotherapy, combination therapy significantly increased the proportion of Tregs, particularly in the PostNR group (Supplementary Figs. 8a, b).

Previous studies have implicated TIM-3 and other immune checkpoints in adaptive resistance to PD-1 blockade39. Building on our findings of CEACAM1 enrichment in neutrophils from PostNR tumors, we noted that its ligand, TIM-3 (HAVCR2), is predominantly expressed on CD8+ T cells23, prompting investigation of the CEACAM1–TIM-3 axis as a potential mediator of resistance. Across both therapeutic regimens, PD-1-TIM-3+ CD8+ T cells were significantly enriched in PostNR samples (Fig. 5c, Supplementary Fig. 9a and Supplementary Datasets 16,17). This finding was validated in three independent pan-cancer datasets (Fig. 5d, Supplementary Figs. 9b, c). PD-1-TIM-3+ CD8+ T cells exhibited elevated exhaustion scores (second only to PD-1+TIM-3+ cells) and reduced PD-1 response signatures (Supplementary Figs. 9d, e). Consistent with the reduction of PD-1+CD8+ T cells after treatment, the relative enrichment of PD-1-TIM-3+ CD8+ T cells suggests that TIM-3 upregulation, rather than sustained PD-1 signaling, represents the dominant exhaustion pathway in PostNR samples (Fig. 5c). Transcriptomic and pathway enrichment analyses demonstrated that TIM-3 upregulation was associated with enhanced immune evasion programs (Supplementary Figs. 9f-i) indicating a functional role for TIM-3 in adaptive resistance to PD-1 blockade.

Notably, spatial transcriptome-based cell abundance analysis revealed the co-occurrence of Neu_06_VEGFA and CD8⁺ T cell subsets (Fig. 5e, Supplementary Fig. 10a). Specifically, Neu_06_VEGFA and PD-1⁻TIM-3⁺ CD8⁺ T cells exhibited spatial co-localization across multiple samples (Fig. 5e, Supplementary Fig. 10a). Intriguingly, in PostNR samples, Neu_06_VEGFA cells were positioned significantly closer to PD-1⁻TIM-3⁺ CD8⁺ T cells than in PostR samples (Fig. 5e, Supplementary Fig. 10a). This observation was further supported by a higher co-localization index between these two populations in PostNR samples (Supplementary Fig. 10b), indicating a preferential spatial association of Neu_06_VEGFA with PD-1⁻TIM-3⁺ CD8⁺ T cells in non-responders. Considering the limitations of low-resolution in Visium spot-level data, we further analyzed scRNA-seq data and found enhanced interactions between Neu_06_VEGSFA and PD-1- TIM-3+ CD8+ T cells (Figs. 5f, g). NicheNet analysis indicated that Neu_06_VEGFA may promote exhaustion in PD-1-TIM-3+ CD8+ T cells by upregulating the expression of exhaustion-associated transcription factors BHLHE40, PRDM1, and STAT3 via CEACAM1-HAVCR2 signaling (Fig. 5h).

In summary, combination therapy enhances CD8+ T cell infiltration but also accelerates their progressive exhaustion, especially in non-responders. The accumulation of Tregs and Exh-Prog CD8+ T cells fosters a more immunosuppressive TME following combination therapy. Our results indicate that the CEACAM1–TIM-3 axis serves as a crucial driver of adaptive resistance, providing a potential therapeutic target to improve treatment efficacy.

CEACAM1–TIM-3 blockade alleviates anti–PD-1 resistance
CEACAM1 mediates immune evasion during the precancerous stage of lung adenocarcinoma (LUAD) via TIM-3 binding, and that TIM-3 blockade can attenuate tumor progression40. Notably, the anti-CEACAM1 monoclonal antibody exhibits synergistic anti-tumor activity with anti–PD-1 in NSCLC41, aligning with reports that disrupting CEACAM1–TIM-3 interactions enhances the efficacy of PD-1 blockade22,23. To evaluate the therapeutic potential of this axis, we utilized a humanized PD-1 mouse model bearing orthotopic murine RCC tumors, which inherently display resistance to PD-1 monotherapy42. ML-T7, a CEACAM1–TIM-3 blocking agent43, effectively suppressed tumor growth both as monotherapy and in combination with the human anti–PD-1 antibody Toripalimab (Figs. 6a–c). While Toripalimab monotherapy alone had no effect on tumor regression, the combination therapy achieved superior tumor control, with no significant weight loss, indicating a favorable safety profile (Figs. 6a–c). These findings are in line with previous studies in liver cancer, which showed that blockade of the CEACAM1–TIM-3 axis enhances the efficacy of PD-1 inhibition22.

To explore the mechanism by which TIM-3 blockade overcomes PD-1 resistance in RCC, we conducted 10x GEM-X single-cell transcriptomic profiling on tumors harvested from mouse specimens (Fig. 6a). Unsupervised clustering identified 12 major cell types (Fig. 6d, Supplementary Fig. 10c and Supplementary Dataset 18). ML-T7 plus Toripalimab markedly reduced the proportion of Ceacam1+ neutrophils compared to all other groups (Supplementary Fig. 10d), while increasing the proportion of Pd-1-Tim-3+ CD8+ T cells relative to the control group (Supplementary Fig. 10d). In contrast, PD-1+TIM-3+ CD8+ T cells were elevated in all treatment groups compared to the control (Supplementary Fig. 10d). This finding suggests that while combination therapy effectively targets myeloid-mediated immunosuppression, it does not fully prevent the accumulation of highly exhausted CD8+ T cells co-expressing both checkpoints. Interestingly, differential abundance analysis revealed a significant enrichment of macrophages in the ML-T7 plus Toripalimab group compared to the control group, while neutrophils were predominantly enriched in the control group (Figs. 6e, f). Notably, the ML-T7 monotherapy group displayed a similar macrophage enrichment pattern to that of the combination therapy (Supplementary Fig. 10e, f). In contrast, CD4+ T cells, CD8+ T cells, and NK cells were more abundant in the control group relative to ML-T7 monotherapy (Supplementary Figs. 10e, f). Toripalimab monotherapy resulted in enrichment of CD4+, CD8+, and NK cells, as well as neutrophils, while showing macrophage infiltration patterns comparable to those of the control (Supplementary Figs. 10g, h).

To further investigate the effect of TIM-3 blockade on CD8+ T cells, we compared exhaustion, oxidative phosphorylation (OXPHOS), and mitochondrial organization scores across treatment groups. CD8+ T cells from the ML-T7 plus Toripalimab group exhibited significantly lower exhaustion and tolerance scores but higher OXPHOS and mitochondrial function scores than other groups (Fig. 6g), consistent with the known metabolic rewiring during T cell activation44. In contrast, Toripalimab monotherapy exhibited the highest exhaustion scores and upregulation of checkpoint genes like Havcr2 and transcription factors such as Stat3 and Entpd1 (Fig. 6h). The combination treatment was enriched for mitochondrial metabolism genes including Tpi1 and Fabp5 (Fig. 6h).

Our results aslo showed that ML-T7 combined with Toripalimab markedly increased macrophage abundance (Figs. 6e, f). Further analysis revealed that macrophages from the combination group exhibited marked downregulation of genes associated with NLRP3 inflammasome activation relative to controls (Fig. 6i), accompanied by downregulation of key inflammasome genes such as Nlrp3, Il1a, and Il1b (Fig. 6j). These data are consistent with accumulating evidence that macrophage-intrinsic NLRP3 inflammasome activation drives tumor progression and undermines immunotherapeutic efficacy45.

Collectively, the ML-T7 plus anti–PD-1 combination may overcome resistance by reinvigorating CD8+ T cells and reducing myeloid-driven immunosuppression through inhibition of macrophage NLRP3 inflammasome activity.

Methods
Study design
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Chinese PLA General Hospital (PLAGH, Beijing, China; approval numbers: S2017-100 and S2019-349). All patients provided written informed consent for the collection and use of clinical data and specimens. Treatment response was assessed using Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 criteria. Patients whose best overall response was a partial response (PR) or complete response (CR) were classified as responders (R). The patients with stable disease (SD) or progressive disease (PD) were classified as non-responders (NR). Detailed clinicopathological information is summarized in Supplementary Datasets 1 and 2.

Animal experiments were performed in strict accordance with institutional guidelines and were approved by the Institutional Animal Care and Use Committee of the PLAGH. All mice were housed under specific pathogen-free conditions. The maximal permitted tumor burden was limited to no more than 10% of the animal’s body weight, in accordance with institutional regulations. Animals were monitored regularly for body weight, physical condition, and signs of distress. No animals exceeded the approved tumor burden or humane endpoint criteria during the study.

SeekOne® Single Cell 3’ Transcriptome (PLAGH cohort)
Fresh pre-treatment biopsies and post-treatment surgical specimens from the PLAGH cohort were processed using the SeekOne® Single Cell 3’ Transcriptome platform (SeekGene, China). Tissues were minced and enzymatically dissociated using the Multi-tissue Dissociation Kit (Miltenyi Biotec, #130-110-203), with DNase I adjusted according to sample viscosity. After red blood cell lysis (Miltenyi Biotec, #130-094-183), single-cell suspensions were filtered, counted, and assessed for viability using acridine orange/propidium iodide (AO/PI) staining on the Countstar® Rigel system. Where necessary, dead cells and debris were removed (Miltenyi Biotec, #130-090-101/#130-109-398). Cells were resuspended in PBS containing 0.04% BSA at 1×106 cells/mL. Single-cell libraries were generated using either the SeekOne® MM (#K00301-0401) or DD (#K00202-0801) kit, following manufacturer protocols. Briefly, cells were loaded onto 170,000-well chips, followed by magnetic bead capture, lysis, reverse transcription, exonuclease digestion, second-strand synthesis, PCR amplification, and indexing. Libraries were purified, quantified (KAPA Biosystems, #SKK4824), and sequenced using an Illumina NovaSeq 6000 system with PE150 reads.

Data processing and quality control for scRNA-seq (PLAGH cohort)
The raw Fastq sequencing data for each sample were processed using the SeekSoulTools software (version 1.2.2). During this processing, the data were aligned against the human hg38 reference genome, resulting in the generation of a raw gene expression count matrix. Subsequently, we utilized the CreateSeuratObject function in Seurat R package (version 4.1.0) to create a Seurat object with the default settings. Low-quality cells were then filtered out if they did not meet the following three criteria: (1) the total number of unique molecular identifiers (UMIs) per cell (library size) was greater than 300 and less than 30,000; (2) the number of detected genes per cell was above 100 and below 6,000; (3) the percentage of mitochondrial genes per cell was below 50. Notably, given the characteristically low transcript counts of neutrophils as documented in publications16, we adjusted the acceptable range of the number of detected genes for neutrophils to be between 100 and 6,000. Next, for each sample, we employed the scDblFinder R package (version 1.8.0) to identify and remove potential doublets. After the removal of low-quality cells and doublets, we employed regularized negative binomial regression using the SCTransform function in Seurat, to normalize the UMI counts, while regressing out the influence of the percentage of mitochondrial genes for each sample. Subsequently, we performed principal component analysis (PCA) for linear dimensionality reduction on the scaled data. The RunPCA function was used, and we retained 50 principal components. All the details regarding the Seurat analysis carried out in this study can be accessed through the online tutorial available at the following website: https://satijalab.org/seurat/articles/get_started.html.

Multiple scRNA-seq datasets integration and cell type annotation
A total of three scRNA-seq datasets associated with aRCC treatment were included in this study: the PLAGH scRNA-seq dataset (in-house data), the Chen et al.7 scRNA-seq dataset (HRA004711), and the Msaouel et al.8 scRNA-seq dataset (GSE264586). Subsequently, we employed the reciprocal principal components analysis (RPCA) pipeline in Seurat to integrate a total of 61 samples from these different studies. In detail, for each sample independently, we first performed normalization and identified 2,000 features with high cell-to-cell variation as described above. Next, we selected features that were repeatedly variable across samples for integration using the SelectIntegrationFeatures function. Subsequently, we utilized the PrepSCTIntegration function to identify conserved features for integrating the datasets. Following this, we identified “anchors” between individual samples with the FindIntegrationAnchors function. These identified anchors were then input into the IntegrateData function, which generated a “batch-corrected’’ expression matrix of all cells. This enabled the integration and joint analysis of cells originating from different datasets. Finally, we utilized the top 50 principal components and set the resolution parameter to 0.8 as inputs for the FindNeighbors and FindClusters functions to cluster the cells. Non-linear dimensional reduction was carried out using the RunUMAP function with default settings. This process led to the identification of thirteen major cell clusters, which were further validated using known cell-lineage-specific marker genes listed in Supplementary Dataset 3.

Sub-clustering of major cell types
We further identified subpopulations within each major cell cluster by employing the same procedure as previously described53. Initially, we extracted cells corresponding to each major cell type from the integrated dataset (normal epithelial cells, stromal cells, T cells, B cells and plasma cells, monocytic cells, neutrophils and mast cells), respectively. Next, for each major cell type, we re-integrated the corresponding cells and conducted further sub-clustering, following the aforementioned methodology. During each sub-clustering step, we adjusted the resolution parameter in the Louvain algorithm within a range of 0.3 to 0.9 to optimize the clustering granularity. Following sub-clustering, we identified cluster-specific marker genes using Seurat’s FindAllMarkers function and annotated the clusters based on well-established canonical cell type markers16. Additionally, we performed differential analysis using the MAST method, treating sample identity as a latent variable. Subcluster-specific marker genes are listed in Supplementary Datasets 4–10.

Identification and analysis of malignant cells
Single-cell copy number variants (CNVs) were detected using the CopyKAT R package (version 1.1.0) with the raw count matrix of scRNA-seq expression serving as the input54. Taking the counts of T cells, monocytes, macrophages, and dendritic cells (DCs) as references, the copycat function was used to infer CNVs in tumor cells (epithelial cells), with parameters set as ngene.chr = 5 and win.size = 25. Subsequently, each cell was categorized as either predicted diploid, aneuploid or not.defined.

Furthermore, we performed sub-clustering analysis for all epithelial cells55. We applied the FindAllMarkers function in Seurat to identify specific genes for each cell subset, which was conducted based on the MAST method56, with sample identity treated as a latent variable to account for potential sample-specific variations. Additionally, we obtained DEGs between tumor and normal samples from bulk RNA-seq data of the TCGA-KIRC dataset using the limma R package (version 3.50.0). Subsequently, we extracted the top 3,000 signature genes from the TCGA dataset, comparing tumor and normal samples. Specifically, malignant signature genes were selected as the top 1,500 highly expressed genes (adjusted p < 0.01) in tumor tissues, while non-malignant signature genes were chosen as the top 1,500 highly expressed genes (adjusted p < 0.01) in normal tissues.

For diploid, aneuploid, or undefined cells within a single sample, further grouping based on cell subgroups (Epi0 to Epi7) generated new groups with distinct ploidy and transcriptional states, which were collectively referred to as “cell state”. Next, we generated gene-by-cell state expression matrix by using the AverageExpression function. To classify tumor cells, we took the log-transformed gene expression values of the malignant and non-malignant signature genes from the TCGA-KIRC dataset to construct a predictor by calculating the centroids of these selected features for both subtypes in the feature space.

Malignant and non-malignant cells were assigned to the nearest centroid based on the Pearson correlation coefficient. This analysis was carried out using the cancerclass R package (version 1.40.0). Cells expressing PTPRC were excluded from the analysis.

Non-negative matrix factorization (NMF)
We investigated the expression programs underlying intratumor heterogeneity within malignant cells derived from PreR, PreNR, PostR, and PostNR groups using NMF. Only patients with at least 100 malignant cells were included in the analysis to ensure robust results. Briefly, for each tumor sample, we initially normalized the expression counts using the SCTransform function in Seurat. During normalization, we regressed out the percentage of mitochondria genes as well as cell cycle effects, including S phase and G2/M phase signature scores. Subsequently, we extracted center-scaled data for the 2000 most highly variable genes (HVGs). Negative values in the expression matrix were replaced with zero to comply with the non-negativity requirement of NMF. We then used the nmf function in the NMF R package (version 0.25) to dissect the top 10 ranked co-expressed gene modules in each tumor sample. For each identified gene module, we selected the top 50 genes with the highest weights. These genes were used to define a specific intra-tumor expression program. In total, we identified 490 expression programs across 49 tumor samples. The pairwise Jaccard index for all programs was calculated as follows:
Jaccard index = (programA ∩ programB) / (programA ∪ programB) (1)
where programA and programB denote two distinct intratumor expression programs.

Finally, only expression programs with a Jaccard index > 0.1 were retained, resulting in 306 non-redundant expression programs. These programs were then grouped into metaprograms via hierarchical clustering with the “ward.D2” method. The Jaccard index served as the similarity metric for this clustering analysis. For each metaprogram, genes shared by at least five expression programs were defined as meta-signatures. This process resulted in the identification of nine metaprograms, including: cell cycle, neuronal-like, chemokine regulation, proximal tubule, EMT I and II, MHC-II, stress response, and an undefined metaprogram. The undefined metaprogram was excluded from further analysis. The gene signatures of the eight remaining metaprograms are listed in Supplementary Dataset 13.

Definition of predictive index and therapeutic index
To overcome the constraints imposed by limited sample sizes, we applied the Predictive Index (Pi) and the Therapeutic Index (Ti) to integrate scRNA-seq data with continuous measurements of tumor size changes. The Pi was designed to evaluate whether the baseline proportion of immune cell populations correlates with treatment-induced changes in tumor size. These tumor size changes were quantified as the relative differences in tumor dimensions between post-treatment and pre-treatment samples. Mathematically, the Pi is defined as follows:
Pi = (-slope / |slope|) * R^2 (2)
where the slope and R2 values were derived from a linear regression model, denoted as lm(y1 ~ x1). This model was employed to estimate the correlation between the baseline cellular proportions (x1) of the respective immune cell clusters and the tumor size changes (y1) observed in patients undergoing different treatment regimens. A positive Pi indicates that a higher baseline level of the corresponding immune cell cluster is associated with a more favorable response to TKI and/or ICI therapy. Conversely, a negative Pi suggests that a higher baseline level of the relevant immune cell cluster is linked to a poorer response to TKI and/or ICI therapy.

The Ti, in contrast, was used to assess whether dynamic changes in immune cell proportions over the course of treatment correlate with tumor size alterations. Specifically, immune dynamics were calculated as the relative change in cell-type proportions between pre-treatment and post-treatment samples. The Ti is defined as follows:
Ti = (-slope / |slope|) * R^2 (3)
where the slope and R2 values were derived from a linear regression model, denoted as lm(y2_x2). This model was employed to estimate the correlation between the changes in cellular proportions (x2) of the corresponding immune cell cluster and the alterations in tumor size (y2) observed in patients receiving different treatment regimens. A positive Ti indicates that an increased level of cellular proportion for the relevant immune cell cluster is associated with a more favorable clinical response. Conversely, a negative Ti suggests that an increased level of cellular proportion for this cell cluster is linked to a less favorable clinical response. Overall, the methodologies adopted in this study were analogous to those utilized in a previously published triple-negative breast cancer study9.

Tissue enrichment analysis of clusters
To quantify the enrichment of cell types across distinct tissue groups—namely PreR, PostR, PreNR, and PostNR—we conducted a systematic comparison of the “Observed” and “Expected” cell counts within each cell cluster. This comparison was enabled by calculating the ratio of observed to expected cell counts (R ), following the formula below57:
o/e
R = Observed / Expected (4)
o/e
The expected number of cells for each cell type within a given tissue group was determined using a Chi-squared test based on overall cell distributions. A cell cluster was considered enriched in a specific tissue group if R > 1, indicating a higher-than-expected representation of that cluster in the group.
o/e

Pseudo-bulk differential expression analysis
DEGs between different conditions derived from scRNA-seq were identified using a pseudobulk analysis strategy. In brief, for each sample-cell type pairing, pseudobulk count data were generated using the AggregateExpression function from Seurat, applied to the raw count matrix. Subsequently, these pseudobulk count matrices were imported into the DESeq2 R package (version 1.40.2) to calculate DEGs. A negative binomial generalized linear model was used, without incorporating covariates. DEGs were computed for each PostNR group in comparison to the PostR. Only genes that were significantly differentially expressed, defined by a P < 0.05 and a log fold change (log FC) > 1, were considered. This selection criterion focused solely on upregulated genes.

Gene set enrichment analysis (GSEA)
Overrepresentation analysis of gene sets was performed using the “fora” function within the fgsea R package (version 1.20.0).

The fgsea R package was used to assess differential enrichments of gene sets between two different groups of cell subsets. For the comparison of a specific cell type across two conditions, we utilized the FindMarkers function in Seurat to the log FC values for all genes. These log FC values served as the ranking metric for downstream GSEA.

The Hallmark58, KEGG59, Reactome60, and Gene Ontology (GO) gene sets61 were obtained from the msigdbr R package (version 7.4.1) and the Molecular Signatures Database (MSigDB) website (http://software.broadinstitute.org/gsea/msigdb). In addition to these well-established gene sets, we curated custom gene sets from previously published studies. Specifically, RCC-related meta-program signatures were obtained from Li et al.27, a pan-cancer neutrophil signature from Wu et al.14, and aPD-1 responder signature Sade- Feldman M et al.62.

Gene signature identifying and scoring
We applied the FindAllMarkers function in Seurat to identify the specific genes for each cell subset. This analysis was conducted based on the MAST method56, with sample identity incorporated as a latent variable to account for potential sample-specific effects. To select signature genes, we chose the top 50 cell-specific marker genes based on their average expression levels.

Based on the scRNA-seq data, we computed multiple gene signature scores. For each gene signature, individual cell scores were assigned using the AddModuleScore_UCell function from the UCell R package (version 1.3.1). The function was applied with its default settings. For bulk RNA-seq or pseudobulk data, we evaluated the signature score of a gene set in a sample based on the normalized gene expression. This was achieved using the single sample gene set enrichment analysis (ssGSEA)63 method implemented in the GSVA R package (version 1.44.5).

For the correlation analysis, we employed the cor function from the stats R package (version 4.1.1). All correlation coefficients (Rho) and their corresponding p-values were determined through Pearson correlation analysis.

Trajectory analysis
Monocle2 (version 2.22.0) enabled us to elucidate the complex progression of neutrophils toward the ICI plus TKI resistant phenotype64. By employing the differentialGeneTest function, we identified genes with a q-value < 0.05. These genes were then used to order the cells in pseudotime analysis. Once the cell trajectories were established, we applied the Branched Expression Analysis Modeling (BEAM) approach to identify genes that exhibited differential expression between the trajectory branches65. For genes that showed significant branch-dependence (adjusted P < 0.05), we visualized their expression patterns across pseudotime using the plot_genes_branched_heatmap function.

We also analyzed the lineage developmental trajectory of neutrophils using the Monocle3 R package (version 1.0.0)66. The count matrix, which included gene expression profiles from neutrophils, along with the UMAP embeddings obtained during sub-clustering, were incorporated into a CellDataSet object. Subsequently, we partitioned the cells using the cluster_cells function. Based on this clustering, we inferred a trajectory graph using the learn_graph function. The pseudotime for each individual cell was then calculated by applying the order_cells function. Specifically, the neutrophil subtype with the highest MMP9+ neutrophil signature was designated as the root state for pseudotime calculation. The trajectory analysis of CD8+ T cells was conducted following the procedures described above.

RNA velocity analysis
To investigate the potential for cell transition among CD8+ T cell subtypes, we integrated velocyto (version 0.17.15)67 and Dynamo (version 1.4.0)68 to calculate and visualize the ratio of spliced to unspliced transcripts in each CD8+ T cell subtype. This approach enabled us to infer their respective maturation states. In detail, we initiated the process by generating Loom files for all samples from their corresponding BAM files. This step was carried out using velocyto with the GENCODE GRCh38 genome annotation file. Subsequently, we followed the standard workflow in Dynamo to compute RNA velocity values for each gene in every cell.

Cell-cell interactions analyses
The CellChat R package (version 1.6.1)69, containing the ligand–receptor interaction databases for human, was used to establish the intercellular communication networks among different cell clusters. Initially, we used the CellChatDB.human function within CellChat to assess the primary signaling inputs and outputs across all cell clusters. For subsequent analyses, we selected receptors and ligands that were expressed in more than 10 cells within a candidate cell cluster. The interactions between distinct cell clusters via putative ligand–receptor pairs were visualized using the ggplot2 R package (version 3.5.1 ).

NicheNet analysis, facilitated by the nichenetr R package (version 1.1.1), was used to detect potential ligands originating from SAA+ tumor cells that contribute to the distinctive phenotype displayed by Neu_06_VEGFA. We utilized the Seurat-based implementation of NicheNet for this analysis. A gene was considered as expressed if it had non-zero expression in at least 10% of the cells within a specific cell type. The calculation to determine the top ligand-receptor pairs was executed for each cell type using the nichenet_seuratobj_cluster_de function. In this function, SAA+ tumor cells were designated as the sender, Neu_05_MMP9 as the receiver_reference, and Neu_06_VEGFA as the receiver_effected. Genes that were differentially expressed between Neu_05_MMP9 and Neu_06_VEGFA (with a log FC > 0.4 and an adjusted P < 0.05) were regarded as the gene set of interest.

Public single-cell RNAseq data analysis
We downloaded the raw UMI count matrices and associated cell annotations from scRNA-seq datasets derived from three patient cohorts: NSCLC patients treated with anti–PD-1 in combination with chemotherapy (GSE243013), HCC patients treated with anti–PD-1 in combination with lenvatinib (GSE235863), and CRC patients treated with anti–PD-1 (GSE236581). Subsequently, the raw UMI counts were normalized and filtered using Seurat, following the procedures described above.

The dataset (raw counts matrix) from Bi et al.38, which is associated with RCC patients treated with anti–PD-1 in combination with TKIs, was downloaded from the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP1288/tumor-and-immune-reprogramming-during-immunotherapy-in-advanced-renal-cell-carcinoma#study-summary). The dataset (raw counts matrix) from Krishna et al.12, related to RCC patients treated with anti–PD-1 in combination with anti-CTLA-4, was downloaded from https://trace.ncbi.nlm.nih.gov/Traces/index.html?view=analysis&acc=SRZ190804. The dataset (raw counts matrix) from Hu et al.70, which is associated with NSCLC patients treated with anti–PD-1 in combination with chemotherapy, was downloaded from the GEO portal (GSE207422). We utilized the raw counts matrix to create a Seurat object. This object was then normalized using Seurat’s SCTransform function71. After that, it was integrated and re-clustered according to Seurat’s RPCA, FindIntegrationAnchors, and IntegrateData methods following the description provided above.

To validate the transcriptomic states identified in our dataset, we performed label transfer of neutrophils using Seurat. We used the scRNA-seq dataset of neutrophils from our previously annotated dataset as the reference, and the scRNA-seq dataset of neutrophils from the Hu et al. dataset as the query. In brief, we computed transfer anchors between the reference and our query dataset using the FindTransferAnchors function. This function is based on shared gene expression and mutual nearest neighbors (MNNs) in a shared low-dimensional space. Next, we employed these anchors to assign cell type labels and project the reference dimensional reduction onto our dataset using the MapQuery function.

The dataset from Stone et al.33 (GSE232696), related to the Saa-/- mouse model, was also included. Subsequently, it was imported into R using the CreateSeuratObject function in Seurat. In brief, the scRNA-seq data was normalized using the SCTransform function. After that, it was integrated and re-clustered according to Seurat’s RPCA, FindIntegrationAnchors, and IntegrateData methods following the description provided above. These cell clusters were then annotated based on known cell-lineage-specific markers.

Spatial transcriptomic (ST)
Sample preparation and library construction
RNA was extracted from formalin-fixed, paraffin-embedded tumor (FFPE) tissue blocks using the RNeasy FFPE Kit (Qiagen, #73504), and RNA quality was assessed by calculating the DV200 value. Tissue sections passing quality control (DV200 > 30%) were used for ST analysis according to the Visium CytAssist Spatial Gene Expression protocol (10x Genomics, CG000495, CG000518, CG000520). Five-micrometer tissue sections were mounted on Visium-compatible glass slides and processed through dewaxing, staining, and cross-linking with a human whole-transcriptome probe set. After target–probe hybridization, the slides were processed on the Visium CytAssist instrument for permeabilization and RNase treatment. The hybridized probes were then ligated to spatially barcoded oligonucleotides. Library preparation was performed using the Visium CytAssist Spatial Gene Expression for FFPE Human Transcriptome Probe Kit (10x Genomics, #100044). The libraries were sequenced on an Illumina NovaSeq X Plus at Annoroad Gene Technology (Beijing, China).

Spatial transcriptomics data analysis
The raw sequencing reads from spatial ST underwent quality checking and were mapped to the human transcriptome reference GRCh38-2020-A using SpaceRanger (version 3.0.1). The gene-spot matrices, generated subsequent to ST data processing, were analyzed using the Seurat R package. We filtered out Visium spots with fewer than 200 total counts or 500 detected genes, as well as spots with >25% mitochondrial gene content. The filtered count matrices underwent normalization via the SCTransform function. Dimensionality reduction and clustering operations were carried out using the RunPCA, FindNeighbors, and FindClusters functions in Seurat. Specifically, these functions were applied at a resolution of 0.4, incorporating the first 30 principal components (PCs), with the aim of identifying spatial niches. The sample information is presented in Supplementary Dataset 1 and 2.

Celltype deconvolution and tumor core annotation
The Redeconve R package (version 1.1.0)72 was employed to perform deconvolution of ST spots into distinct cell types by applying its deconvoluting function. Our scRNA-seq dataset was used as a reference, while the ST data functioned as the query dataset. Subsequently, the cell-type deconvolution data were added to the metadata of the Seurat object for downstream analysis.

The genome-wide copy numbers at the spot level were inferred from the transcriptomic data using the CopyKAT R package with the following specified parameters: “ngene.chr = 5, win.size = 25, KS.cut = 0.15, genome = hg20”. After that, the identified diploid and aneuploid states were extracted from CopyKAT and integrated into the metadata of the Seurat object, enabling subsequent downstream analysis.

The activities of Hallmark gene sets specific to a spatial niche were quantified using the “ssGSEA” approach, leveraging the aggregated gene expression profile. Subsequently, spatial niches within the tissue section were grouped through agglomerative clustering employing the complete linkage method, which relied on the Euclidean distance derived from the gene-set activities.

A spatial niche was classified as a “tumor core” if it met all of the following conditions: (1) cell type deconvolution by Redeconve estimated a tumor cell proportion greater than 25%; (2) CopyKAT estimated an aneuploid vs. diploid probability exceeding 20%; and (3) elevated pathway activity in DNA repair and MYC TARGETS V1/V2 signatures. In addition, all “tumor core” regions were pathologically confirmed as RCC by a board-certified pathologist.

Cell type co-localization analysis
The spatial co-localization index between cell types of interest was computed using a previously described analytical pipeline73. Cell type co-localization analysis was performed as previously described with minor adjustments73,74. To assess the spatial co-localization between VEGFA⁺ neutrophils and CD8⁺ T cell subsets, we leveraged cell type deconvolution results generated by the spotiphy algorithm (version 0.3.1)75 from ST slides. For each cell type of interest, we computed a weighted two-dimensional kernel density estimate across the tissue section using the wkde2d function in the Nebulosa R package (version 1.16.0), with spot-level deconvolved proportions as weights. This yielded continuous spatial density maps reflecting the local enrichment of each cell population. We then quantified the similarity between the spatial distributions of any two cell types using the Kullback–Leibler (KL) divergence, calculated via the KL function in the philentropy R package. Lower KL divergence values indicate greater spatial overlap. Subsequently, we generated a null distribution of KL divergence values by permuting cell type assignments across ST spots. Specifically, we randomly shuffled the deconvolution matrix 1,000 times while preserving the 80% of spots and cell type proportions per spot. For each permutation, we recomputed the density maps and KL divergence for the same cell type pair.

Bulk RNA-seq and survival analysis
Survival analyses were conducted across multiple patient cohorts that underwent bulk RNA-seq and were subjected to diverse treatments with varying endpoints. These cohorts included the TCGA-KIRC cohort, IMmotion15076, the JAVELIN Renal 101 trial77, Checkmate78, the study by Zhang et al.79, IMvigor21080 and the study by Liu et al.81. Subsequently, the signature scores and the expression levels of SAA genes (calculated as the mean value of SAA1 and SAA2) were dichotomized according to the optimal cut-off point determined by the surv_cutpoint function from the survminer R package (version 0.5.0). KM survival curves were generated to visualize survival differences, and two-sided log-rank tests were used to determine statistical significance. A P < 0.05 was considered statistically significant.

Furthermore, univariate Cox proportional hazards regression analysis was carried out using the coxph function from the survival R package (version 3.7.0), and the results were visualized in forest plots using the forestmodel R package (version 0.6.2).

Antibodies, reagents, key commercial kit and software
Detailed information on the antibodies, key reagents, cell lines, animal strains, software and algorithms used in this study is provided in Supplementary Dataset 19.

Isolation and culture of neutrophils
Peripheral blood samples were obtained from treatment-naïve RCC patients. Neutrophils were isolated using magnetic bead–based separation according to the manufacturer’s instructions (CD15 MicroBeads, human; Miltenyi Biotec; #130-046-601), yielding >99% CD15+ purity as assessed by flow cytometry. Purified neutrophils were cultured in complete RPMI-1640 medium supplemented with 10% FBS and stimulated with recombinant human SAA1 protein (Absin, #abs05051) for 12 h. After stimulation, neutrophils were collected by centrifugation and subsequently used for flow cytometric analysis and RNA extraction.

Flow cytometry
After stimulation, neutrophils were harvested, washed with PBS, and stained for surface markers. Cells were incubated with PE anti-human CD15 (SSEA-1; BioLegend; #301906) and Alexa Fluor® 647 anti-human CD66a/c/e (BioLegend, #342318) according to the manufacturer’s instructions. After staining, cells were washed and resuspended in staining buffer for acquisition. Data were acquired using an Attune NxT Flow Cytometer (Thermo Fisher Scientific) and analyzed with FlowJo software (v10.8.1).

Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from neutrophils using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, #RC112-01). First-strand cDNA was synthesized using the HiScript IV RT SuperMix (Vazyme, #R423-01) according to the manufacturer’s instructions. Quantitative real-time PCR was performed using SYBR FAST PCR Master Mix (Vazyme, #Q712-02) on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad). Relative mRNA expression was calculated using the ΔCt method, with PPIA as the internal control. PCR primers were designed and synthesized by Biomed (China), and sequences for PPIA and CEACAM1 are listed in Supplementary Dataset 19.

SAA blockade in vivo
For the orthotopic RCC model, Renca-Luc cells (HyCyte, #TCM-C784L; 1×10⁶ cells in 50 μL Matrigel; LABLEAD, #MG6248) were implanted into the right kidney of 5-week-old male BALB/c mice (GemPharmatech, #N000020). Seven days post-implantation, mice were randomized into four groups (n = 5 per group):
(1) Vehicle control: i.p. injection of IgG (#A2123, Selleck; 100 μg/mouse, every 3 days, total 3 doses) and daily i.g. administration of 1% CMC-Na (Selleck, #S6703).
(2) SAA blockade: i.p. injection of anti-SAA1/2 antibody (R&D Systems, #AF2948; 5 μg/mouse, twice weekly, total 2 doses) plus daily i.g. administration of 1% CMC-Na.
(3) Combination group: i.p. injection of anti–PD-1 antibody (Selleck, #A2122; 100 μg/mouse, every 3 days, total 3 doses) plus Axitinib (Selleck, #S1005; 30 mg/kg/day, i.g., total 7 doses).
(4) Triple combination therapy: anti-SAA1/2 antibody, anti–PD-1 antibody, and Axitinib were administered following the same dosing schedules as in groups (2) and (3).

Mice were housed under a 12 h light/12 h dark cycle at a controlled temperature (22–25 °C) and humidity (50–60%). On day 16 post-implantation, mice were euthanized via CO asphyxiation. Body weight and kidney weight were measured and recorded.

CEACAM1–TIM-3 blockade in vivo
In the CEACAM1–TIM-3 blockade model, Renca-Luc cells (5×105 suspended in 50 μl of Matrigel; LABLEAD, #MG6248) were orthotopically injected into the right kidney of 5-week-old male BALB/c-hPD-1 humanized mice (#T002726, GemPharmatech, China). After seven days, mice were randomly allocated to four groups (n = 5 per group):
(1) Vehicle control: i.p. injection of IgG (Selleck, #A2052; 100 μg/mouse, every 3 days, total 3 doses).
(2) CEACAM1–TIM-3 blockade: i.p. administration of MPL-T7 (MCE, #HY-163028; 20 mg/kg, every 2 days, total 3 doses).
(3) PD-1 blockade: i.p. injection of Toripalimab (TopAlliance, #JS001; 100 μg/mouse, every 3 days, total 3 doses).
(4) Combination therapy: both ML-T7 and Toripalimab were administered using the same regimens as in groups (2) and (3).

Mice were housed under a 12 h light/12 h dark cycle at a controlled temperature (22–25 °C) and humidity (50–60%). On day 16 post-implantation, mice were euthanized via CO2 asphyxiation. Body weight and kidney weight were measured and recorded.

Mouse single-cell preparation and 10x Genomics GEM-X sequencing
Tumor specimens were harvested from mice treated in the in vivo CEACAM1–TIM-3 blockade experiment. For each sample, tumors from two mice were pooled. Tumor tissues were enzymatically digested to obtain single-cell suspensions, which were sequentially filtered through 70-μm and 30-μm strainers (Miltenyi Biotec, #130-098-458 and #130-098-462). Red blood cells were lysed using red blood cell lysis (Miltenyi Biotec, #130-094-183). Cell viability was assessed using Countstar® Rigel, and Dead Cell Removal Kit (Miltenyi Biotec, #130-090-101) was used when necessary to remove dead cells. Cells were resuspended in PBS with 0.04% BSA at a final concentration of 700–1200 cells/μL.

Single-cell libraries were prepared using the Chromium Single Cell 3’ Reagent Kit v4 (10x Genomics, #1000691) and Chromium Chip v4 (10x Genomics, #1000690) following the manufacturer’s protocol provided by Novogene. RNA was barcoded on the Chromium X system. Subsequent steps, including reverse transcription, cDNA amplification, and library construction, were performed using the Chromium Library Construction Kit v4 (10x Genomics, #1000694). Final libraries were sequenced on an Illumina NovaSeq X Plus platform (PE150) at Novogene (Beijing).

Mouse single cell RNAseq analysis
Sc-RNAseq data were analyzed using our previously described bioinformatics pipeline, as detailed in82, with minor modifications tailored to this study. Briefly, data pre-processing and quality control were carried out separately for each library. Raw sequencing reads were pre-processed using cellranger (version 8.0.1). First, raw bcl files were de-multiplexed with mkfastq, and then gene expression matrices were generated using cellranger “count” with the mouse GRCm39-2024-A reference genome. Low-quality cells were excluded if they did not meet all the following criteria: number of expressed genes (nFeature_RNA) > 100, < 6,000, and percentage of mitochondrial gene reads < 25%. Doublets were removed using the scDblFinder R package with default settings (dbr = NULL; for 10x data, it is generally safe to leave the dbr parameter empty, and it will be automatically estimated). After filtering, 124,559 cells remained, with a median of 2,187 genes detected per cell.

The cleaned datasets were then normalized using Seurat with SCTransform, following a Gamma-Poisson Generalized Linear Model. The data were scaled while regressing out the cell cycle scores and the percentage of expressed mitochondrial genes. To identify inter-sample anchors for sample integration, the FindIntegrationAnchors function was employed, using the top 3,000 highly variable genes identified by the SelectIntegrationFeatures function across all samples. Subsequently, the IntegrateData function was used to generate a combined and batch-corrected expression matrix. PCA was performed using RunPCA with the top 50 principal PCs. Dimensionality reduction was carried out using RunUMAP with the top 50 dimensions. Nearest-neighbor analysis was conducted using FindNeighbors with the top 50 dimensions and a k.param value set to 50. Clustering was performed using FindClusters with a resolution of 0.6 to obtain an initial clustering result. Globally distinguishing marker genes for each cell cluster were identified using Seurat’s FindAllMarkers function with the MAST method (Supplementary Dataset 18). Cell clusters were then preliminarily classified by manually inspecting marker genes. For more precise cell type classification, we followed the “sub-clustering of major cell types” step described above.

Enzyme-Linked Immunosorbent Assay (ELISA) assay
SAA levels in pretreatment plasma samples from patients were quantified using a Human SAA ELISA kit (Jonlnbio, #JL10489) following the manufacturer’s protocol. The assay was performed in a sandwich ELISA format. Briefly, samples and standards were added to 96-well plates pre-coated with a capture antibody specific for human SAA. After incubation and washing, a biotin-conjugated detection antibody was added, followed by streptavidin–HRP. Color development was achieved using TMB substrate, and the reaction was stopped with an acidic stop solution. Absorbance was measured at 450 nm using a microplate reader, and SAA concentrations were calculated from a standard curve.

Multiplex immunohistochemistry (mIHC) and analysis
FFPE tissues were sectioned at 5 μm, deparaffinized in xylene and rehydrated through a graded ethanol series then washed with distilled water. Antigen retrieval was performed by microwaving in boiling EDTA buffer for 15 minutes. Blocking was carried out using Antibody Diluent/Block (Alpha X Bio). mIHC was performed using a 6-plex, 7-color staining panel. The following primary antibodies were used and incubated for 1 hour at 37°C:
Panel 1: CD66b (Abcam, #ab197678, 1:100), SAA (Abcam, #ab207445, 1:1000), pan-CK (ZSGB-BIO, #ZM0069, 1:200), and CD8 (ZSGB-BIO, #ZA0508, 1:100).
Panel 2: VEGFA (Proteintech, #66828-1-Ig, 1:1000), CD66a (ABclonal, #A26413PM, 1:8000), and CD66b (Abcam, #ab197678, 1:100).
Panel 3: VEGFA (ABclonal, #A17877, 1:2000), CD66a (ABclonal, #A26413PM, 1:8000), Ly6G (Servicebio, #GB11229, 1:600), and CD8 (Abcam, #ab21734, 1:2000).
Slides were then incubated with Alpha X Polymer HRP Ms+Rb (Alpha X Bio) for 10 minutes at 37°C and developed using the Alpha X 7-Color IHC Kit (Alpha X Bio, #AXT37100041), with fluorophores assigned to each target antibody. Between staining cycles, antibody stripping was achieved by heat-mediated epitope retrieval. Nuclei were counterstained with DAPI. Whole-slide images were acquired using Axioscan 7 (ZEISS, v3.3) and analyzed using HALO software (Indica Labs, v3.6).

Quantification and statistical analyses
For all experiments, unless otherwise noted in the figure legends, the value of ‘n’ denotes biologically independent samples. This includes independent experiments for in vitro assays, individual mice for animal studies, and individual patients for clinical analyses. Statistical analyses were conducted using R (version 4.1.1) and Python (version 3.8.12) to process sequencing data and analyze clinical variables. The repertoire of statistical analyses utilized in this study included Wilcoxon rank-sum test, Student’s t test, Chi-square test, and the P values derived from GSEA enrichment. Log-rank tests were conducted to assess differences in survival outcomes. In the context of CellChat analysis, P values were computed via a one-sided permutation test to evaluate the significance of cell-cell communication patterns. The statistical tools and threshold for each analysis are explicitly described with the Results or detailed in the Figure Legends or Methods sections. P < 0.05 was considered statistically significant. In terms of statistical significance, multiple levels were defined: p values less than 0.05, 0.01, 0.001, and 0.0001 were considered statistically significant, denoted by *, **, ***, and ****, respectively. In all box plots, the center line in the boxplots indicates the median, the lower and upper hinges correspond to the first and third quartiles, and the whiskers extend at most 1.5 times the interquartile range past the upper and lower quartiles. In all bar graphs and dot plots, data are presented as mean ± standard error of the mean (SEM).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study introduces noteworthy novel findings with relevance to cancer immunotherapy, particularly RCC therapy, aligning with the interest and impact standards of Nature Communications:\nNovel Mechanistic Insight: This is the first study to use single-cell analysis to identify an RCC resistance mechanism involving the SAA–CEACAM1–TIM-3 axis. While prior studies hinted at individual components, this research novelly connects them, revealing how tumor-derived SAA creates an immunosuppressive environment via specific neutrophils, impacting T cell immunity. This provides new insights beyond common factors like T cell exhaustion, highlighting tumor–myeloid interactions as a key resistance node.\nClinical Relevance: Despite the revolution in RCC treatment with TKI plus ICI combinations, some patients do not respond or relapse. This study addresses a key clinical question by identifying SAA as a potential predictive biomarker (elevated in non-responders’ blood pre-treatment) and suggesting therapeutic targets (SAA and the CEACAM1–TIM-3 pathway). The preclinical evidence for combining a CEACAM1–TIM-3 blocker with PD-1 blockade supports potential new RCC therapies, meeting urgent clinical needs.\nGenerality: Although focused on RCC, the study draws parallels to other cancers, noting similar neutrophil-mediated resistance in NSCLC, HCC, and CRC datasets, and referencing CEACAM1’s role in melanoma and lung cancer. This suggests broader applicability in solid tumor immunotherapy, with the novel concept of targeting tumor-secreted acute-phase proteins like SAA to enhance immunotherapy.\nDepth and Breadth: The study’s comprehensive approach, from basic discovery to therapeutic proof-of-concept, is a strength. It goes beyond descriptive single-cell analysis to test hypotheses, demonstrating a complete narrative from problem identification to mechanism discovery and solution proposal, significantly enhancing its importance.\nIn summary, the manuscript merits publication in Nature Communications after revision, given its substantial contributions to understanding immunotherapy resistance and proposing actionable strategies. The authors should emphasize in the cover letter the study’s advancements beyond prior work, particularly the dual role of SAA.\nI recommend Minor Revision for this manuscript. The study is robust overall, but a few targeted refinements are needed to maximize its clarity and impact prior to acceptance. The required revisions are primarily clarifications and minor additional analyses; no fundamental flaws are present. Addressing the points below will ensure the manuscript achieves its full potential for a high-impact venue.\nMajor points:\n1. Please add a clear description of how “responders” and “non-responders” were defined (e.g., RECIST criteria, complete/partial response vs progressive disease, etc.) for the PLAGH patient cohort. Also clarify how the public scRNA-seq datasets were integrated in terms of response – for instance, did the GSE264586 dataset have annotated responders vs non-responders, or were those samples only used for general pre/post analysis? A concise summary in the Methods or Results of patient clinical characteristics (perhaps referring to a Supplementary Table) would be valuable. This will help readers understand the clinical context of the samples and ensure that the comparisons (PreR vs PreNR, etc.) are on solid footing.\n2. Since you combined three different scRNA-seq datasets, please briefly describe how batch effects were handled. For example, was a Seurat integration or Harmony method used to correct batch differences? The concern is that technical differences could influence clustering or gene expression. If not already in Methods, include a statement of how integration was done and whether any analyses were done cohort-by-cohort to validate that key findings (e.g., neutrophil subset enrichment, SAA upregulation) are consistent across cohorts. This would reassure readers that the conclusions are not artifacts of merging datasets.\n3. In the TIM-3 blockade experiment, you found an increase in macrophages and changes in their inflammasome activity. This is intriguing. Consider commenting on the role of macrophages in the TIM-3/PD-1 combination therapy. Does this suggest TIM-3 blockade works partly by reprogramming macrophages (in addition to reinvigorating T cells)? A sentence or two in the discussion interpreting the macrophage enrichment (perhaps referencing the NLRP3 findings) would be insightful. It would tie back to the earlier observation that macrophages (like neutrophils) were enriched in non-responders, implying that the combination therapy also tackles the myeloid suppressors.\n4. The limitations section in the discussion is thoughtful and appreciated. You might consider adding one more point regarding the sample size and patient heterogeneity. For instance, 34 patients is a good number for single-cell work, but still modest for clinical generalization – you could note that validation in larger independent cohorts (especially for the SAA biomarker aspect) would be valuable. This is somewhat implied already by mentioning “further validation for clinical translation”. Emphasizing it will pre-empt any concern that the study might be underpowered on the clinical side.\n5. In the concluding part of the discussion or the abstract, ensure that the broader implications are clearly stated. For example, one might explicitly state: “These findings not only elucidate a mechanism of resistance in RCC but also suggest that measuring SAA levels and co-targeting tumor–neutrophil interactions (e.g., via SAA or CEACAM1/TIM-3 inhibitors) could be a strategy to improve immunotherapy outcomes.” This drives home the significance. Currently, the abstract’s final sentence does capture this, and the last lines of discussion do as well – just make sure they are framed as strongly as possible without overstating (you have done well to use “promising therapeutic target” and “mechanistic insights” phrasing).\nMinor points:\n1. Ensure that all figure panels are properly labeled and referenced in the text. There are instances where multiple panels are cited in one sentence (e.g., Fig. 4b,c or Extended Data Fig. 5d,e). In the figure legends, make sure each panel is explained. This avoids any ambiguity, especially for readers who may jump to figures.\n2. If not already done, add scale bars and units to any microscopy images and clearly label axes on graphs (including stating the statistical test and n in figure legends). For example, in Fig. 4e,f where you mention CD8+ T cell and neutrophil infiltration, the figure should have clear quantitative scales (cells per area or % area, etc.).\n3. Consider merging or simplifying figure panels if possible. The number of panels is high; for instance, if Fig. 3 has panels a–j, is there any way to condense some (perhaps combine panels that show related data, such as merging the two panels of SAA+ cell quantification pre vs post into one panel with dual grouping)? Use your judgment based on the actual data – the goal is to make the figures visually clear. Reviewers and readers can otherwise get lost matching subpanels to descriptions.\n4. Double-check consistency of terminology (e.g., use “combination therapy” vs “TKI+ICI” uniformly; use either “non-responder” or “resistant patient” consistently unless there is a subtle difference in meaning).\n5. Address any remaining typos or grammatical issues. One example to fix: “(5) EMT II (MP6; SAA1, SAA2 and CXCL14)” where two items are labeled (5) – likely a numbering typo in the Results; please correct numbering of the enumerated modules (MP5 and MP6 were both labeled “(5)” in the text excerpt). These small details will improve the professionalism of the final text.\nBy addressing these suggestions, the manuscript will be further strengthened in clarity and completeness. No fundamental experimental revisions are demanded beyond what the authors have already accomplished, aside from an optional added validation of SAA’s effect which would be a bonus. Primarily, the revision should focus on clarifying methodology, slightly extending mechanistic discussion, and polishing presentation.\nThis paper is a comprehensive analysis of TME in RCC, especially after immunotherapy, it is a timely study with broad appeal to the NC readership.", "reviewer_2": "Gu et al. present a well-executed study that combines single-cell and spatial transcriptomic analyses to dissect the immune-tumor interactions underlying therapeutic resistance in advanced aRCC. The integration of multi-omics data with clinical response information and experimental validation is impressive, and the work provides mechanistic insights with relatively clear translational relevance. The identification of the SAA-CEACAM1-TIM-3 axis as a driver of immunosuppressive TME and therapy resistance may open new avenues for therapeutic intervention.\nMajor Comments:\n1. The manuscript identifies the SAA-CEACAM1-TIM-3 axis as a potential mechanistic pathway contributing to resistance to TKI plus ICI therapy. However, much of this mechanistic link remains inferred from transcriptomic correlations and ligand-receptor analyses. Although the authors demonstrate that SAA inhibition enhances therapeutic efficacy in a mouse model, direct functional validation of the key molecular players (e.g., SAA1/2, CEACAM1, and TIM-3) would greatly strengthen the mechanistic claims, even just in cell-based systems. For example, knockdown or overexpression studies in RCC cell lines, neutrophil co-culture assays, or cytokine signaling blockade experiments could directly test the causality of these interactions. At minimum, perturbation experiments in vitro would reinforce the conclusion that this axis is mechanistically responsible for the observed phenotypes rather than merely correlative.\n2. The manuscript reports several mechanistic findings involving SAA+ tumor cells, VEGFA+ neutrophils, Tregs, CEACAM1-TIM-3 signaling, and angiogenesis. While each of these findings is biologically meaningful, presenting them all in equal detail makes the narrative somewhat diffuse. The authors may consider prioritizing one central mechanism, such as the SAA-CEACAM1-TIM-3 immunoregulatory loop, as the main storyline, while condensing or relocating secondary mechanisms (e.g., Treg involvement, VEGFA signaling). This refinement would sharpen the mechanistic focus and help readers appreciate the central conceptual advance.\n3. The spatial transcriptomic analyses provide valuable contextual information, but the spatial localization of the SAA+ tumor regions relative to VEGFA+ neutrophils and CD8+ T cells could be presented more quantitatively. Strengthening the link between spatial proximity and inferred signaling axes, for instance, through co-localization metrics or neighborhood enrichment analyses, would provide stronger evidence for spatially coordinated immunoregulation. This integration would also guide selection of targetable microenvironmental niches for future therapeutic exploration.\n4. To further enhance the translational potential of the study, it would be beneficial to discuss strategies for dual targeting of SAA and CEACAM1, given their compensatory relationship. A clear statement on how these preclinical findings may inform patient stratification or biomarker development in RCC would also strengthen clinical relevance.", "reviewer_3": "This study was conducted using clinical trial specimens together with public datasets and focuses on the core challenge of resistance to combined immune-targeted therapy in renal cell carcinoma (RCC). Through single-cell sequencing and humanized mouse models, the authors propose a novel mechanism whereby tumor cell–neutrophil interactions can mediate the resistance of immunotherapy combined with targeted therapy in RCC.\nFit with Nature Communications: Nature Communications values mechanistic discoveries that bridge fundamental biological understanding with clinical translation. This study exemplifies that mission by beginning with clinically validated phenomena, applying state-of-the-art single-cell technologies to interrogate the biological “black box,” and completing the translational exploration preliminarily through further validation in humanized mouse models. The conceptual design—spanning from clinical observation to mechanistic exploration and translational confirmation—closely aligns with the journal’s aim to publish conceptually innovative and broadly relevant biomedical research.\nNovel Mechanistic Point: The study reveals that VEGFA⁺ neutrophils (Neu_06) are enriched in non-responders and specifically interact with SAA⁺ tumor cells. This interaction promotes T-cell exhaustion through the CEACAM1–TIM3 ligand–receptor axis, thereby establishing a new mechanistic framework linking metabolic reprogramming in myeloid cells to adaptive immune dysfunction. Such a finding provides an original and well-supported explanation for resistance to immunotherapy combined with targeted therapy in RCC.\nTranslational Value: Using RCC samples from patients with distinct efficacy and some public datasets, the study identifies actionable pathways driving resistance. The demonstration that inhibition of SAA in Balb/c mice or pharmacologic blockade of CEACAM1–TIM3 signaling in humanized mouse models can both enhance treatment efficacy, which confers strong translational value. These results not only support biomarker-based patient stratification but also propose combination therapeutic strategies with the potential to improve outcomes in patients who are super-resistant to current immunotherapies—a population with critical unmet clinical needs.\nI recommend minor Revision for this manuscript. The study is robust overall, but a few targeted refinements are needed to maximize its clarity and impact prior to acceptance. The required revisions are primarily clarifications and minor additional analyses; no fundamental flaws are present. Addressing the points below will ensure the manuscript achieves its full potential for a high-impact venue.\nMajor Concerns:\n1. Role and differentiation trajectory of neutrophils\nNeutrophils are a critical component of the tumor microenvironment. In Figure 1c, the predictive index indicates that baseline neutrophil abundance shows the strongest negative correlation with response to ICI+TKI in RCC, thereby justifying the authors’ focus on neutrophil subclusters. The study further suggests that differentiation from Neu_01 to Neu_02 mediates therapeutic response, while differentiation from Neu_01 to Neu_06 contributes to resistance. In Figure 2e, Neu_01 shows the most negative correlation with efficacy in the predictive index, yet its dynamic change in the treatment index is most positively correlated with therapeutic response. Given that Neu_01 can differentiate into both Neu_02 and Neu_06, could the authors clarify whether there is a significant preference in Neu_01 differentiation during the course of immunotherapy combined with targeted therapy in RCC? In addition, the treatment index highlights Neu_03 more prominently than Neu_06—why, then, was subsequent analysis focused primarily on Neu_06? The authors are encouraged to clarify these points.\n2. Selection of CEACAM1 and LGALS3 as key markers of Neu_06\nThe authors state that CEACAM1 and LGALS3 are the two most characteristic markers of Neu_06. However, in Figure 2b, the expression level of CEACAM1 does not appear to be the highest compared with other molecules, such as LDHA or HK2, which are strongly associated with anaerobic glycolysis. What is the rationale for prioritizing CEACAM1 in the downstream analyses? Regarding LGALS3, the authors note in line 150 that this molecule is broadly expressed in the tumor microenvironment, yet it is not commonly reported in neutrophils. How do the authors interpret the unusually high expression of LGALS3 within the Neu_06 subset, given that this gene is not typically a canonical neutrophil marker? Clarification of this apparent discrepancy would be helpful.\n3. Interpretation of CEACAM1 expression differences across datasets\nIn GSE243013 and GSE235863, CEACAM1 expression differs between response groups, but these datasets consist of post-treatment specimens. Are these differences a consequence of therapy (i.e., an adaptive or compensatory phenomenon), or do they represent pre-existing molecular determinants that drive poor response? Would pre-treatment specimens offer a more accurate reflection of the true driver mechanisms underlying resistance? The authors are encouraged to clarify their interpretation of these findings.\n4. Mechanistic interpretation of CEACAM1 and TIM-3 co-regulation\nIn Figure 4i, the authors report that CEACAM1 expression in neutrophils increases compensatorily following Axitinib plus anti–PD-1 treatment, suggesting a link to therapeutic resistance. Given that CEACAM1 and TIM-3 function as paired receptor–ligand partners, the results imply that combined blockade of TIM-3 could potentially broaden the responder population. However, numerous clinical trials combining PD-1 and TIM-3 inhibitors have yielded disappointing outcomes. Could the authors reconcile this apparent contradiction between the preclinical observations and the clinical reality? A more nuanced discussion of context-dependent mechanisms would enhance the translational clarity of this section.\nMinor Concerns:\n1. Line 193 – numbering error: In line 193, the numbering within “(5) EMT II (MP6; SAA1, SAA2 and CXCL14), (6) MHC-II …” appears incorrect. Please revise the numbering to maintain sequential consistency.\n2. Figure 3d – definition of MP6: In Figure 3d, the boundary box highlighting MP6 (EMT-II) may not fully encompass all relevant regions, and there seems to be an undefined area between MP6 and MP7. The authors are advised to recheck and clarify the demarcation between these two modules to ensure accuracy.\n3. Figure 5a – potential inconsistency in CD8⁺ T-cell proportions: In Figure 5a, the representative immunofluorescence images appear to show a higher proportion of CD8⁺ T cells in the PT_Pre_NR group compared with PT_Post_R group, which seems slightly inconsistent with the quantitative data presented in the statistical plot. Please verify this point and ensure consistency between the visual and quantitative results.\n4. Lines 309–316 – clarification of mechanistic reasoning: In lines 309–316, the authors conclude that post-treatment resistance in RCC is attributed to the increased proportion of PD-1⁻TIM-3⁺CD8⁺ T cells in the Post_NR group, implying that TIM-3 plays a pivotal role in mediating resistance to ICI + TKI therapy. However, this conclusion logically depends on the premise that PD-1⁺ T cells are significantly reduced after therapy. Although this reduction is observable in Figure 5c, the manuscript would benefit from an explicit textual description emphasizing this key observation to make the mechanistic reasoning more coherent and convincing.\n5. Line 744 – correction of the formatting errors: The placement of the underline for the link is incorrect. Please correct the placement of the underline for the link.\n6. Figure 6 – suggested addition for completeness: As the central conclusion of the study revolves around the interaction among tumor cells, neutrophils, and T cells in driving therapeutic resistance, it is recommended that the authors include in Figure 6 a comparative analysis of mNeu_Ldha and mPd1–Tim3⁺Cd8⁺ T-cell populations across different treatment groups following ML-T7 therapy. This addition would further reinforce the integrative mechanistic framework proposed by the study.\nBy addressing these suggestions, the manuscript will be further strengthened in clarity and completeness. Overall, this is an elegant, well-structured, and timely study that is suitable for publication in Nature Communications after minor revisions to improve clarity, figure presentation, and some text details."}, "author_response": "This document provides a detailed point-to-point response to all reviewer comments.\nIn the main text and the methods, minor text changes are not tracked, but all major\ntext changes are highlighted in blue.\nReviewer #1:\nThe study introduces noteworthy novel findings with relevance to cancer\nimmunotherapy, particularly RCC therapy, aligning with the interest and impact\nstandards of Nature Communications:\nNovel Mechanistic Insight: This is the first study to use single-cell analysis to identify\nan RCC resistance mechanism involving the SAA–CEACAM1–TIM-3 axis. While\nprior studies hinted at individual components, this research novelly connects them,\nrevealing how tumor-derived SAA creates an immunosuppressive environment via\nspecific neutrophils, impacting T cell immunity. This provides new insights beyond\ncommon factors like T cell exhaustion, highlighting tumor–myeloid interactions as a\nkey resistance node.\nClinical Relevance: Despite the revolution in RCC treatment with TKI plus ICI\ncombinations, some patients do not respond or relapse. This study addresses a key\nclinical question by identifying SAA as a potential predictive biomarker (elevated in\nnon-responders’ blood pre-treatment) and suggesting therapeutic targets (SAA and the\nCEACAM1–TIM-3 pathway). The preclinical evidence for combining a CEACAM1–\nTIM-3 blocker with PD-1 blockade supports potential new RCC therapies, meeting\nurgent clinical needs.\nGenerality: Although focused on RCC, the study draws parallels to other cancers,\nnoting similar neutrophil-mediated resistance in NSCLC, HCC, and CRC datasets,\nand referencing CEACAM1’s role in melanoma and lung cancer. This suggests\nbroader applicability in solid tumor immunotherapy, with the novel concept of\ntargeting tumor-secreted acute-phase proteins like SAA to enhance immunotherapy.\nDepth and Breadth: The study’s comprehensive approach, from basic discovery to\ntherapeutic proof-of-concept, is a strength. It goes beyond descriptive single-cell\nanalysis to test hypotheses, demonstrating a complete narrative from problem\nidentification to mechanism discovery and solution proposal, significantly enhancing\nits importance.\nIn summary, the manuscript merits publication in Nature Communications after\nrevision, given its substantial contributions to understanding immunotherapy\nresistance and proposing actionable strategies. The authors should emphasize in the\ncover letter the study’s advancements beyond prior work, particularly the dual role of\nSAA.\nI recommend Minor Revision for this manuscript. The study is robust overall, but a\nfew targeted refinements are needed to maximize its clarity and impact prior to\nacceptance. The required revisions are primarily clarifications and minor additional\nanalyses; no fundamental flaws are present. Addressing the points below will ensure\nthe manuscript achieves its full potential for a high-impact venue.\nResponse: We thank the reviewer for the positive assessment of our work and for\nrecommending Minor Revision. We appreciate the constructive suggestions, which\nhave helped us further clarify and strengthen the manuscript. In the revision, we have\naddressed all points raised, including clearer definitions of clinical\nresponders/non-responders, additional information on dataset integration and\nbatch-effect handling, an expanded discussion on macrophage involvement, and a\nmore explicit statement of study limitations and broader implications. We have also\ncorrected figure labeling, terminology consistency, and minor textual issues. A\ndetailed point-by-point response is provided below.\nMajor points:\n1. Please add a clear description of how “responders” and “non-responders” were\ndefined (e.g., RECIST criteria, complete/partial response vs progressive disease, etc.)\nfor the PLAGH patient cohort. Also clarify how the public scRNA-seq datasets were\nintegrated in terms of response – for instance, did the GSE264586 dataset have\nannotated responders vs non-responders, or were those samples only used for general\npre/post analysis? A concise summary in the Methods or Results of patient clinical\ncharacteristics (perhaps referring to a Supplementary Table) would be valuable. This\nwill help readers understand the clinical context of the samples and ensure that the\ncomparisons (PreR vs PreNR, etc.) are on solid footing.\nResponse: We thank the reviewer for this important point. In the revised manuscript,\nwe have added a clear description of how responders (CR/PR) and non-responders\n(PD/SD) were defined according to RECIST v1.1 criteria for the PLAGH cohort\n(Methods, Line 481-485, Supplementary Table 1). Concerning the integration of\npublic datasets, to enhance the robustness of our findings, we integrated two public\nscRNA-seq datasets (HRA0047111 and GSE2645862). In their original publications,\nthese datasets already provided clear annotations for sample categories and treatment\nresponse information. The sequencing data from all samples across these datasets\nwere integrated and processed collectively for subsequent bioinformatic analysis.\n2. Since you combined three different scRNA-seq datasets, please briefly describe\nhow batch effects were handled. For example, was a Seurat integration or Harmony\nmethod used to correct batch differences? The concern is that technical differences\ncould influence clustering or gene expression. If not already in Methods, include a\nstatement of how integration was done and whether any analyses were done\ncohort-by-cohort to validate that key findings (e.g., neutrophil subset enrichment,\nSAA upregulation) are consistent across cohorts. This would reassure readers that the\nconclusions are not artifacts of merging datasets.\nResponse: We the reviewer for raising this important question. Among the three\ndatasets, the PLAGH cohort was processed for tissue dissociation, single-cell capture,\nand library construction using the SeekOne® Single Cell 3’ Transcriptome platform\nkit tissue dissociation, single-cell capture, and library preparation, whereas the\nHRA004711 and GSE264586 datasets were generated on the 10x Genomics platform.\nAs stated in the Methods section (Line 525-542), we integrated these three\nindependent scRNA-seq datasets using the reciprocal PCA (RPCA) workflow in\nSeurat to correct for batch effects and generate an integrated and unbatched dataset.\nTherefore, it was necessary to evaluate the effectiveness of data integration.\nTo evaluate the effectiveness of this integration, we first visualized cell types on\nUMAP plots colored by dataset of origin and observed a consistent distribution of cell\ntypes across different technologies and batches (Response Fig. 1a). In addition, we\ncalculated the average expression levels of marker genes for each cell type in each\ndataset individually, and the overall expression profiles were found to be highly\nconcordant (Response Fig. 1b). Collectively, these results demonstrate that despite the\ntechnical and batch differences among the three datasets, the data integration achieved\nfavorable outcomes with no significant batch effects after the above-described\npreprocessing steps.\nResponse Fig. 1 The scRNA-seq landscape of aRCC tissues. (a) UMAP of each data\nsubset. (b) Heatmaps showing canonical cell-type marker expression patterns in\nPLAGH cohort, HRA004711 and GSE264586 datasets. Each column represents\naverage expression after cells are grouped by cell-type annotations (now\nSupplementary Figs. 1a, b in the revised manuscript).\n3. In the TIM-3 blockade experiment, you found an increase in macrophages and\nchanges in their inflammasome activity. This is intriguing. Consider commenting on\nthe role of macrophages in the TIM-3/PD-1 combination therapy. Does this suggest\nTIM-3 blockade works partly by reprogramming macrophages (in addition to\nreinvigorating T cells)? A sentence or two in the discussion interpreting the\nmacrophage enrichment (perhaps referencing the NLRP3 findings) would be\ninsightful. It would tie back to the earlier observation that macrophages (like\nneutrophils) were enriched in non-responders, implying that the combination therapy\nalso tackles the myeloid suppressors.\nResponse: We thank the reviewer for highlighting this interesting observation. In\nresponse to Reviewer #3’s comment regarding our rationale for selecting CEACAM1\nas the key neutrophil marker for subsequent research, we performed gene expression\nprofiling and visualization of CEACAM1, HAVCR2, and LGALS3 across major cell\ntypes. Our results revealed that HAVCR2 (the gene encoding TIM3) is predominantly\nexpressed in monocytes, macrophages, dendritic cells, mast cells, T cells, and NK\ncells, with the highest expression observed in macrophages (Response Fig. 5). This\nexpression pattern partially explains why anti-PD-1 combined with anti-TIM3 therapy\nexerts direct effects on macrophages, leading to significant functional alterations and\nconsequent anti-tumor activity.\nTo further elaborate on this point, we have added a concise interpretation of the\nmacrophage-related findings in the revised Discussion section (Line 437-450). This\naddition strengthens the conceptual link between lymphoid and myeloid compartment\nremodeling in overcoming resistance to checkpoint blockade.\n4. The limitations section in the discussion is thoughtful and appreciated. You might\nconsider adding one more point regarding the sample size and patient heterogeneity.\nFor instance, 34 patients is a good number for single-cell work, but still modest for\nclinical generalization – you could note that validation in larger independent cohorts\n(especially for the SAA biomarker aspect) would be valuable. This is somewhat\nimplied already by mentioning “further validation for clinical translation”.\nEmphasizing it will pre-empt any concern that the study might be underpowered on\nthe clinical side.\nResponse: We appreciate the suggestion. Although 34 patients represent a relatively\nrobust sample size for single-cell studies, we agree that larger independent cohorts\nwould strengthen generalizability, especially for evaluating circulating SAA as a\npredictive biomarker. We added this limitation and emphasized the need for future\nvalidation in expanded prospective cohorts (Discussion, Line 451-456S).\n5. In the concluding part of the discussion or the abstract, ensure that the broader\nimplications are clearly stated. For example, one might explicitly state: “These\nfindings not only elucidate a mechanism of resistance in RCC but also suggest that\nmeasuring SAA levels and co-targeting tumor–neutrophil interactions (e.g., via SAA\nor CEACAM1/TIM-3 inhibitors) could be a strategy to improve immunotherapy\noutcomes.” This drives home the significance. Currently, the abstract’s final sentence\ndoes capture this, and the last lines of discussion do as well – just make sure they are\nframed as strongly as possible without overstating (you have done well to use\n“promising therapeutic target” and “mechanistic insights” phrasing).\nResponse: We revised the Abstract and the last paragraph of the Discussion to\nhighlight that our findings support measuring circulating SAA and co-targeting the\ntumor–neutrophil axis, emphasizing translational relevance without overstatement.\nMinor points:\n1. Ensure that all figure panels are properly labeled and referenced in the text. There\nare instances where multiple panels are cited in one sentence (e.g., Fig. 4b, c or\nSupplementary Fig. 5d, e). In the figure legends, make sure each panel is explained.\nThis avoids any ambiguity, especially for readers who may jump to figures.\nResponse: We thank the reviewer for the careful reading. All figure panels have been\nreviewed to ensure complete labeling and one-to-one correspondence with text\nreferences. Figure legends have also been expanded for clarity.\n2. If not already done, add scale bars and units to any microscopy images and clearly\nlabel axes on graphs (including stating the statistical test and n in figure legends). For\nexample, in Fig. 4e, f where you mention CD8+ T cell and neutrophil infiltration, the\nfigure should have clear quantitative scales (cells per area or % area, etc.).\nResponse: We have added scale bars and units to all microscopy images, ensured\nclear axis labels for all graphs, and specified statistical tests and sample sizes in the\nfigure legends.\n3. Consider merging or simplifying figure panels if possible. The number of panels is\nhigh; for instance, if Fig. 3 has panels a–j, is there any way to condense some\n(perhaps combine panels that show related data, such as merging the two panels of\nSAA+ cell quantification pre vs post into one panel with dual grouping)? Use your\njudgment based on the actual data – the goal is to make the figures visually clear.\nReviewers and readers can otherwise get lost matching subpanels to descriptions.\nResponse: We reviewed all multi-panel figures and merged several panels to simplify\nvisual presentation without loss of information. The revised figures are more concise\nand easier to navigate.\n4. Double-check consistency of terminology (e.g., use “combination therapy” vs\n“TKI+ICI” uniformly; use either “non-responder” or “resistant patient” consistently\nunless there is a subtle difference in meaning).\nResponse: We have standardized terminology throughout the manuscript, consistently\nusing “responder/non-responder\" to describe clinical outcomes and “combination\ntherapy” when referring to treatment modality.\n5. Address any remaining typos or grammatical issues. One example to fix: “(5) EMT\nII (MP6; SAA1, SAA2 and CXCL14)” where two items are labeled (5) – likely a\nnumbering typo in the Results; please correct numbering of the enumerated modules\n(MP5 and MP6 were both labeled “(5)” in the text excerpt). These small details will\nimprove the professionalism of the final text.\nResponse: We thank the reviewer for noting the typographical errors. All identified\nminor issues, including the duplicated numbering in the module list (MP5/MP6), have\nbeen corrected. A full proofreading pass was also conducted to ensure accuracy and\nprofessionalism.\nBy addressing these suggestions, the manuscript will be further strengthened in clarity\nand completeness. No fundamental experimental revisions are demanded beyond what\nthe authors have already accomplished, aside from an optional added validation of\nSAA’s effect which would be a bonus. Primarily, the revision should focus on\nclarifying methodology, slightly extending mechanistic discussion, and polishing\npresentation. This paper is a comprehensive analysis of TME in RCC, especially after\nimmunotherapy, it is a timely study with broad appeal to the NC readership.\nResponse: We thank the reviewer for the positive evaluation. As suggested, we have\nclarified key methodological steps, improved the mechanistic descriptions, and\npolished the presentation. In addition, we added an in vitro validation showing that\nSAA-stimulated neutrophils directly increase CEACAM1 expression (Response Fig.\n2), which also addresses the related comment in Reviewer #2. These revisions further\nstrengthen the clarity and completeness of the manuscript.\nReviewer #2 :\nGu et al. present a well-executed study that combines single-cell and spatial\ntranscriptomic analyses to dissect the immune-tumor interactions underlying\ntherapeutic resistance in advanced aRCC. The integration of multi-omics data with\nclinical response information and experimental validation is impressive, and the work\nprovides mechanistic insights with relatively clear translational relevance. The\nidentification of the SAA-CEACAM1-TIM-3 axis as a driver of immunosuppressive\nTME and therapy resistance may open new avenues for therapeutic intervention.\nResponse: We thank the reviewer for the encouraging comments and recognition of\nour multi-omics integration and mechanistic findings. We appreciate the positive\nassessment of the SAA–CEACAM1–TIM-3 axis and its translational relevance. We\nhave revised the manuscript to further clarify key methodological details and improve\nthe presentation. We also added new in-vitro data supporting the direct effect of\nSAA-stimulated neutrophils on CEACAM1 upregulation, which strengthens the\nproposed mechanism. We are grateful for the reviewer’s constructive feedback.\nMajor Comments:\n1. The manuscript identifies the SAA-CEACAM1-TIM-3 axis as a potential\nmechanistic pathway contributing to resistance to TKI plus ICI therapy. However,\nmuch of this mechanistic link remains inferred from transcriptomic correlations and\nligand-receptor analyses. Although the authors demonstrate that SAA inhibition\nenhances therapeutic efficacy in a mouse model, direct functional validation of the\nkey molecular players (e.g., SAA1/2, CEACAM1, and TIM-3) would greatly\nstrengthen the mechanistic claims, even just in cell-based systems. For example,\nknockdown or overexpression studies in RCC cell lines, neutrophil co-culture assays,\nor cytokine signaling blockade experiments could directly test the causality of these\ninteractions. At minimum, perturbation experiments in vitro would reinforce the\nconclusion that this axis is mechanistically responsible for the observed phenotypes\nrather than merely correlative.\nResponse: We thank the reviewer for this insightful suggestion. In the revised\nmanuscript, we added a direct in vitro functional perturbation experiment\ndemonstrating that SAA stimulation upregulates CEACAM1 expression in neutrophils\n(Response Fig. 2), providing functional evidence for the proposed SAA–CEACAM1\naxis. In these experiments, neutrophils were freshly isolated from peripheral blood of\ntreatment-naïve RCC patients using CD15 magnetic bead–based separation (>99%\npurity) and subsequently stimulated with recombinant human SAA protein.\nTo extend these findings in vivo, SAA inhibition reduced neutrophil infiltration\nand enhanced the efficacy of PD-1 blockade in an RCC mouse model (Fig. 4d, g).\nImportantly, mIHC analysis of clinical samples further validated these observations,\nrevealing a significantly higher proportion of CEACAM1+ neutrophils in\nnon-responders compared with responders (Fig. 2j), supporting the clinical relevance\nof CEACAM1 upregulation in therapeutic resistance.\nMoreover, the CEACAM1–TIM-3 interaction has been well established in prior\nstudies³⁻⁴, providing additional mechanistic support. Collectively, these\ncomplementary in vitro, in vivo, and patient-derived data strengthen the causal link\nbetween the SAA–CEACAM1–TIM-3 axis and the establishment of an\nimmunosuppressive TME, ultimately driving resistance to TKI plus ICI therapy.\nRegarding the reviewer’s suggestion for genetic perturbation experiments, we\nnote that the short lifespan and limited transfectability of primary neutrophils make\ndirect SAA knockdown technically challenging. Therefore, we employed recombinant\nSAA protein to model its biological effects, an approach that is physiologically\nrelevant given that SAA functions as a secreted acute-phase protein.\nResponse Fig. 2 SAA stimulation upregulates CEACAM1 expression in RCC\npatients’ neutrophils. (a) Representative flow cytometry histograms showing surface\nCD66a/c/e expression in human neutrophils following stimulation with increasing\nconcentrations of recombinant human SAA (0, 250, 500, and 1,000 ng/mL) for 12\nhours (now Fig. 4a in the revised manuscript). (b) Quantification of relative\nCEACAM1 expression levels assessed by flow cytometry (left) and qRT-PCR (right)\nunder the indicated SAA concentrations. Each dot represents an independent sample\n(n = 8). Data are presented as mean ± SEM. P values were calculated using an\nunpaired two-sided t-test (now Fig. 4b in the revised manuscript). (c) Flow\ncytometric gating strategy demonstrates efficient enrichment of CD15⁺ neutrophils\n(>99% purity) following magnetic separation. Sequential gating excluded debris and\ndoublets prior to assessment of CD15 and CD66a/c/e expression (now\nSupplementary Fig. 6b in the revised manuscript).\n2. The manuscript reports several mechanistic findings involving SAA+ tumor cells,\nVEGFA+ neutrophils, Tregs, CEACAM1-TIM-3 signaling, and angiogenesis. While\neach of these findings is biologically meaningful, presenting them all in equal detail\nmakes the narrative somewhat diffuse. The authors may consider prioritizing one\ncentral mechanism, such as the SAA-CEACAM1-TIM-3 immunoregulatory loop, as\nthe main storyline, while condensing or relocating secondary mechanisms (e.g., Treg\ninvolvement, VEGFA signaling). This refinement would sharpen the mechanistic\nfocus and help readers appreciate the central conceptual advance.\nResponse: We thank the reviewer for this valuable suggestion, which has\nsignificantly helped refine the clarity of our mechanistic narrative. In the revised\nmanuscript, we have restructured the narrative to center on the SAA–CEACAM1–\nTIM-3 immunoregulatory loop as the primary mechanistic axis driving\nimmunotherapy resistance in aRCC. To sharpen the mechanistic focus, content\ndescribing secondary mechanisms including Treg infiltration, VEGFA+ neutrophil\nmediated angiogenesis has been streamlined and concisely integrated into the main\nResults section (Line 242-251, 300-302) . Through this revision, we aim to provide a\nmore focused and coherent narrative that enables readers to readily grasp the central\nmechanistic insight of our study.\n3. The spatial transcriptomic analyses provide valuable contextual information, but\nthe spatial localization of the SAA+ tumor regions relative to VEGFA+ neutrophils\nand CD8+ T cells could be presented more quantitatively. Strengthening the link\nbetween spatial proximity and inferred signaling axes, for instance, through\nco-localization metrics or neighborhood enrichment analyses, would provide stronger\nevidence for spatially coordinated immunoregulation. This integration would also\nguide selection of targetable microenvironmental niches for future therapeutic\nexploration.\nResponse: We thank the reviewer for giving us suggestions on how to improve our\nwork. We agree that a more quantitative spatial analysis would strengthen the link\nbetween tumor-derived SAA and its immunomodulatory effects on VEGFA+\nneutrophils and CD8+ T cells. We have carefully addressed this concern\nthrough supplementary quantitative spatial analyzes, and the detailed revisions are\nsummarized below:\n1. Spatial localization characteristics between SAA+ tumor regions\nand VEGFA+ neutrophils\nTo clarify the spatial relationship between SAA+ tumor cells and VEGFA+ neutrophils,\nwe stratified the TME into two distinct compartments including tumor core and tumor\nmargin. We observed distinct spatial distributions of SAA+ tumor cells between\nresponders and non-responders based on the deconvolution results generated by the\nspotiphy algorithm. Notably, in non-responders, SAA+ tumor cells were predominantly\nlocalized within the tumor core, with only a minor subset scattered in the tumor\nmargin (Response Fig. 3a). In contrast, VEGFA+ neutrophils are significantly enriched\nin the tumor margin (Response Fig. 3b), a spatial domain that overlaps with the\ndistribution of these scattered SAA⁺ tumor cells (Response Fig. 3b).\nGiven that SAA is a secreted protein, we hypothesize that SAA produced by\ntumor cells within the tumor core diffuses to the tumor margin and exerts paracrine\neffects on VEGFA+ neutrophils. Supporting this model, the mIHC showed that both\nVEGFA+ neutrophils (Fig. 2d) and SAA+ tumor cells (Fig. 3h) are significantly more\nabundant in non-responders compared to responders after treatment, suggesting a\npotential SAA–neutrophil axis in non-responders. Critically, our in vitro experiments\ndemonstrated that recombinant SAA significantly upregulates CEACAM1 expression\nin RCC patients’s neutrophils (Response Fig. 2), indicating that tumor-derived SAA\ncan modulate neutrophil function independently of direct cell–cell contact.\nUnexpectedly, the presence of scattered SAA+ tumor cells in the tumor margin\nprompted us to further explore their potential interaction with VEGFA+ neutrophils,\nparticularly in non-responder patients. To test this hypothesis, we computed a\nco-localization index between SAA+ tumor cells and VEGFA+ neutrophils specifically\nwithin the tumor margin, following a previously described analytical pipeline3.\nNotably, our data demonstrated that non-responders (STpost3 and STpost4) exhibit\nsignificantly higher co-localization indices compared to responders (STpost1 and\nSTpost2). This finding supports the potential interaction between SAA⁺ malignant\ncells and VEGFA+ neutrophils in the tumor margin of non-responders. Comparative\nanalysis further revealed that patient STpost4 exhibited the highest co-localization\nindex among all patients (Response Fig. 3c). Importantly, clinical follow-up data\nshowed that STpost4 subsequently experienced tumor recurrence, suggesting that this\nspatial co-localization may not only be associated with resistance to ICI plus TKI\ncombination therapy but also with disease progression (Supplementary Table 1).\nResponse Fig. 3 Spatial distribution of SAA+ tumor cells and VEGFA+ neutrophils.\n(a-b) Boxplots showing the proportion of SAA+ tumor cells (a) and VEGFA+\nneutrophils (b) across distinct tissue regions (tumor core vs. tumor margin) in four\npatients. (c) Boxplots comparing the co-localization index between SAA+ tumor cells\nand VEGFA+ neutrophils in the tumor margin across four patients. P values were\ncalculated using unpaired two-sided t-test. In all boxplots, the center line in the\nboxplots indicates the median, the lower and upper hinges correspond to the first and\nthird quartiles, and the whiskers extend at most 1.5 times the interquartile range past\nthe upper and lower quartiles (Now Supplementary Fig. 5g-i in the revised\nmanuscript).\n2. Quantitative analysis of spatial distance between CD8+ T cells\nand VEGFA+ neutrophils\nTo further strengthen the characterization of the spatial regulatory network in TME,\nwe performed quantitative cell-cell distance measurement analysis focusing on\nCD8+ T cells subsets and VEGFA+ neutrophils. To quantitatively assess the spatial\nrelationships between these cell subsets, we applied Kullback-Leibler (KL)\ndivergence analysis4,5. Density plots consistently revealed that VEGFA+ neutrophils\nexhibited greater spatial similarity and pronounced co-localization with three CD8+ T\ncells subsets (PD-1-TIM-3+, PD-1+TIM-3+, PD-1-TIM-3-), which was reflected by\nlower KL divergence values (Response Fig. 4a-d). Notably, among all non-responder\npatients (STpost3 and STpost4), the KL divergence values between VEGFA+\nneutrophils and the three aforementioned CD8+ T cell subsets were below 2\n(Response Fig. 4a-b). In contrast, higher KL divergence values were observed\nbetween these cell populations in another responder sample (STpost2) (Response Fig.\n4c). For STpost1, due to the extremely low proportion of VEGFA+ neutrophils\n(Response Fig. 3b) obtained via deconvolution, co-localization analysis between\nVEGFA+ neutrophils and CD8+ T cells was not performed for this sample. We have\nprovided more details in the Methods session “Cell type co-localization analysis” in\nthe revised manuscript.\nResponse Fig. 4 Spatial co-localization between VEGFA+ neutrophils and CD8+ T cell\nsubsets. (a-c) Spatial mapping of cell types onto spatial transcriptomics (ST) slides\nfrom three post-treatment samples STpost3 (a), STpost4 (b), and STpost2 (c) using\nthe spotiphy deconvolution method. Color intensity represents estimated relative\nabundance for all cell types (top left) and individual cell type (top right). The density\nplots display the KL divergence of the observed co-localization pattern relative to a\nnull distribution derived from spatial permutation (bottom). (d) Boxplots comparing\nthe co-localization index between VEGFA+ neutrophils and CD8+ T cell subsets\nindicated in the tumor margin across three patients. P values were calculated using\ntwo-sided Student’s t-test. In all boxplots, the center line in the boxplots indicates the\nmedian, the lower and upper hinges correspond to the first and third quartiles, and the\nwhiskers extend at most 1.5 times the interquartile range past the upper and lower\nquartiles (Now Fig.5e, Supplementary Fig. 10a, b in the revised manuscript)..\n4. To further enhance the translational potential of the study, it would be beneficial to\ndiscuss strategies for dual targeting of SAA and CEACAM1, given their\ncompensatory relationship. A clear statement on how these preclinical findings may\ninform patient stratification or biomarker development in RCC would also strengthen\nclinical relevance.\nResponse: We thank the reviewer for highlighting the translational potential of\ntargeting SAA and CEACAM1, a point also raised by Reviewer #1. In response, we\nhave expanded the Discussion (Line 403-450) to explicitly address the translational\nimplications of these findings, including therapeutic strategies, patient stratification,\nand biomarker development.\nAs now discussed, although SAA and CEACAM1 exhibit a compensatory\nrelationship, their druggability differs substantially. SAA, as a secreted acute-phase\nprotein, is less tractable as a direct therapeutic target, whereas CEACAM1 is a\nmembrane-bound receptor with higher target specificity. Consequently, simultaneous\ndual targeting may be challenging in practice. Nevertheless, SAA remains a promising\npredictive biomarker for immunotherapy response (Fig. 3j) and multiple independent\nstudies, including recent evidence in hepatocellular carcinoma6,7. We also\nacknowledge that the current sample size for SAA-based analyzes is limited, and that\nvalidation in larger, independent cohorts will be required to firmly establish its\npredictive value and clinical applicability.\nFrom a therapeutic perspective, our findings suggest a potential stratified\napproach: SAA may serve as a biomarker to identify patients at risk of primary\nresistance, while CEACAM1 represents a more actionable target for patients with\nimmune-refractory disease. In this context, re-challenge strategies targeting\nCEACAM1 are conceptually supported by existing phase II clinical data on CM248.\nAlthough CEACAM1-directed antibodies are not yet clinically available in China, we\nare currently pursuing translational development through collaboration with a\ndomestic pharmaceutical partner (Junshi Biosciences Co., Ltd.) to advance\nCEACAM1-targeted therapeutic strategies. Additional preclinical validation studies\nare ongoing, and detailed data cannot be disclosed at this stage due to confidentiality\nconsiderations.\nReviewer #3:\nThis study was conducted using clinical trial specimens together with public datasets\nand focuses on the core challenge of resistance to combined immune-targeted therapy\nin renal cell carcinoma (RCC). Through single-cell sequencing and humanized mouse\nmodels, the authors propose a novel mechanism whereby tumor cell–neutrophil\ninteractions can mediate the resistance of immunotherapy combined with targeted\ntherapy in RCC.\nFit with Nature Communications: Nature Communications values mechanistic\ndiscoveries that bridge fundamental biological understanding with clinical translation.\nThis study exemplifies that mission by beginning with clinically validated phenomena,\napplying state-of-the-art single-cell technologies to interrogate the biological “black\nbox,” and completing the translational exploration preliminarily through further\nvalidation in humanized mouse models. The conceptual design—spanning from\nclinical observation to mechanistic exploration and translational\nconfirmation—closely aligns with the journal’s aim to publish conceptually\ninnovative and broadly relevant biomedical research.\nNovel Mechanistic Point: The study reveals that VEGFA⁺ neutrophils (Neu_06) are\nenriched in non-responders and specifically interact with SAA⁺ tumor cells. This\ninteraction promotes T-cell exhaustion through the CEACAM1–TIM3 ligand–\nreceptor axis, thereby establishing a new mechanistic framework linking metabolic\nreprogramming in myeloid cells to adaptive immune dysfunction. Such a finding\nprovides an original and well-supported explanation for resistance to immunotherapy\ncombined with targeted therapy in RCC.\nTranslational Value: Using RCC samples from patients with distinct efficacy and\nsome public datasets, the study identifies actionable pathways driving resistance. The\ndemonstration that inhibition of SAA in Balb/c mice or pharmacologic blockade of\nCEACAM1–TIM3 signaling in humanized mouse models can both enhance treatment\nefficacy, which confers strong translational value. These results not only support\nbiomarker-based patient stratification but also propose combination therapeutic\nstrategies with the potential to improve outcomes in patients who are super-resistant\nto current immunotherapies—a population with critical unmet clinical needs.\nI recommend Minor Revision for this manuscript. The study is robust overall, but a\nfew targeted refinements are needed to maximize its clarity and impact prior to\nacceptance. The required revisions are primarily clarifications and minor additional\nanalyses; no fundamental flaws are present. Addressing the points below will ensure\nthe manuscript achieves its full potential for a high-impact venue.\nResponse: We thank the reviewer for the thorough and encouraging assessment of our\nstudy and for recommending Minor Revisions. We appreciate the recognition of our\nintegrated approach combining clinical samples, public datasets, single-cell analyzes,\nand humanized mouse models to uncover the SAA–CEACAM1–TIM-3–mediated\nmechanism of immunotherapy resistance in RCC. In response, we have clarified\nmethodological details, emphasized the central mechanistic storyline, slightly\nextended the mechanistic discussion, and polished the presentation. We believe these\nrevisions further strengthen the clarity, rigor, and overall impact of the manuscript for\nthe Nature Communications readership.\nMajor Concerns:\n1. Role and differentiation trajectory of neutrophils\nNeutrophils are a critical component of the tumor microenvironment. In Figure 1c, the\npredictive index indicates that baseline neutrophil abundance shows the strongest\nnegative correlation with response to ICI+TKI in RCC, thereby justifying the authors’\nfocus on neutrophil subclusters. The study further suggests that differentiation from\nNeu_01 to Neu_02 mediates therapeutic response, while differentiation from Neu_01\nto Neu_06 contributes to resistance. In Figure 2e, Neu_01 shows the most negative\ncorrelation with efficacy in the predictive index, yet its dynamic change in the\ntreatment index is most positively correlated with therapeutic response. Given that\nNeu_01 can differentiate into both Neu_02 and Neu_06, could the authors clarify\nwhether there is a significant preference in Neu_01 differentiation during the course\nof immunotherapy combined with targeted therapy in RCC? In addition, the treatment\nindex highlights Neu_03 more prominently than Neu_06—why, then, was subsequent\nanalysis focused primarily on Neu_06? The authors are encouraged to clarify these\npoints.\nResponse: We sincerely thank the reviewer for the careful review and the insightful\ncomments. The predictive index (Pi) and treatment index (Ti) analyzes provided\ncomplementary evidence supporting the functional relevance of neutrophil\nheterogeneity in determining response to ICI plus TKI therapy in RCC (Results,\nLines 142-145). Specifically, the Pi analysis identified the Neu_01_S100A8 subset as\nthe neutrophil population most strongly associated with primary resistance,\nunderscoring its role as a critical upstream node within the neutrophil compartment.\nIntegrating Ti analysis with trajectory inference further revealed that therapeutic\nresponse is not dictated by Neu_01 abundance per se, but rather by its differentiation\nfate during treatment. In this context, an increase in Neu_06_VEGFA—arising from\nNeu_01 along the resistance-associated trajectory—emerged as a key mediator of\ntreatment resistance, whereas alternative differentiation toward Neu_02 was\nassociated with favorable response (Fig. 2e). Collectively, these results indicate that\nNeu_01 functions as a plastic progenitor-like state, with skewed differentiation\ntoward Neu_06 representing a dominant mechanism underlying resistance to ICI plus\nTKI therapy in RCC.\n2. Selection of CEACAM1 and LGALS3 as key markers of Neu_06\nThe authors state that CEACAM1 and LGALS3 are the two most characteristic\nmarkers of Neu_06. However, in Figure 2b, the expression level of CEACAM1 does\nnot appear to be the highest compared with other molecules, such as LDHA or HK2,\nwhich are strongly associated with anaerobic glycolysis. What is the rationale for\nprioritizing CEACAM1 in the downstream analyses? Regarding LGALS3, the authors\nnote in line 150 that this molecule is broadly expressed in the tumor\nmicroenvironment, yet it is not commonly reported in neutrophils. How do the authors\ninterpret the unusually high expression of LGALS3 within the Neu_06 subset, given\nthat this gene is not typically a canonical neutrophil marker? Clarification of this\napparent discrepancy would be helpful.\nResponse: We thank the reviewer for these important comments. The selection of\nCEACAM1 and LGALS3 as characteristic markers of the Neu_06 subset was not based\nsolely on expression magnitude, but on a combination of subset specificity and\nfunctional relevance to therapeutic resistance. Although glycolysis-associated genes\nsuch as LDHA and HK2 are highly expressed in Neu_06, these molecules reflect a\ngeneral metabolic activation state shared across multiple cell types and lack\nspecificity for immune regulation. In contrast, CEACAM1 is preferentially expressed\nin neutrophils and stromal cells and functions as a key immunomodulatory molecule\n(Response Fig. 5). Its interaction with TIM-3 has been shown to promote T cell\ndysfunction and immunosuppressive remodeling of the tumor microenvironment,\ndirectly aligning with the resistance mechanisms investigated in this study. This\nfunctional relevance motivated our prioritization of CEACAM1 for downstream\nanalyzes. With respect to LGALS3, we agree that it is broadly expressed in the tumor\nmicroenvironment and is not a canonical neutrophil marker (Response Fig. 5).\nHowever, LGALS3 can be induced in neutrophils under inflammatory or stress\nconditions9. We interpret the elevated LGALS3 expression in Neu_06 as an acquired,\ncontext-dependent phenotype associated with therapy-induced remodeling of the\nTME, rather than a constitutive neutrophil lineage marker. We have revised the\nmanuscript to clarify these points and to better contextualize the biological\nsignificance of CEACAM1 and LGALS3 in Neu_06 (Results, Lines 155-161) .\nResponse Fig. 5 Dot plot showing expression levels of marker genes (CEACAM1,\nLGALS3 and HAVCR2) across major cell types. Dot size indicates the fraction of\nexpressing cells in each cluster, colored by normalized expression levels (now\nSupplementary Fig. 3h in the revised manuscript).\n3. Interpretation of CEACAM1 expression differences across datasets\nIn GSE243013 and GSE235863, CEACAM1 expression differs between response\ngroups, but these datasets consist of post-treatment specimens. Are these differences a\nconsequence of therapy (i.e., an adaptive or compensatory phenomenon), or do they\nrepresent pre-existing molecular determinants that drive poor response? Would\npre-treatment specimens offer a more accurate reflection of the true driver\nmechanisms underlying resistance? The authors are encouraged to clarify their\ninterpretation of these findings.\nResponse: We appreciate the reviewer’s insightful question regarding the\ninterpretation of CEACAM1 expression differences in the post-treatment datasets\nGSE243013 and GSE235863. In GSE243013, we observed a higher proportion of\nCEACAM1⁺ neutrophils in the post-treatment postNR group compared to responders\npostR. Similarly, in the bulk RNA-seq dataset GSE235863, CEACAM1 gene\nexpression was significantly elevated in postNR samples. These consistent findings\nacross two independent cohorts suggest that sustained or induced CEACAM1\nexpression is associated with poor therapeutic response.\nHowever, we fully acknowledge the reviewer’s important point because both\ndatasets consist of post-treatment specimens, it remains unclear whether elevated\nCEACAM1 reflects a pre-existing resistance determinant or an adaptive/compensatory\nresponse to therapy. Although we observed a trend toward higher abundance of\nCEACAM1+ neutrophils in PreR compared to PreNR (Fig. 2i), the limited sample size\nof pre-treatment cohorts prevents us from drawing definitive conclusions regarding\nthis difference. We agree that pre-treatment samples would be ideal for identifying\ntrue driver mechanisms of resistance, and this limitation is now explicitly\nacknowledged in the revised manuscript (Discussion, lines 451-456). Future studies\nintegrating longitudinal pre- and post-treatment samples will be essential to resolve\nthe causal direction of CEACAM1 upregulation.\nFinally, we sincerely apologize for the confusion caused by unclear figure\nlabeling in the original submission. We have carefully revised the relevant figure\nannotations (now Fig. 2i, 2k in the revised manuscript) to ensure accurate and\nunambiguous presentation of the post-treatment group comparisons. We hope these\nclarifications improve the interpretability of our findings.\n4. Mechanistic interpretation of CEACAM1 and TIM-3 co-regulation\nIn Figure 4i, the authors report that CEACAM1 expression in neutrophils increases\ncompensatorily following Axitinib plus anti–PD-1 treatment, suggesting a link to\ntherapeutic resistance. Given that CEACAM1 and TIM-3 function as paired receptor–\nligand partners, the results imply that combined blockade of TIM-3 could potentially\nbroaden the responder population. However, numerous clinical trials combining PD-1\nand TIM-3 inhibitors have yielded disappointing outcomes. Could the authors\nreconcile this apparent contradiction between the preclinical observations and the\nclinical reality? A more nuanced discussion of context-dependent mechanisms would\nenhance the translational clarity of this section.\nResponse: We thank the reviewer for this important and insightful comment. We\nagree that the apparent discrepancy between our preclinical findings and the modest\nefficacy of PD-1/TIM-3 combination therapies in clinical trials likely reflects strong\ncontext dependency.\nFirst, our data derive from patients who had developed resistance to PD-1–based\ntherapy. In this setting, we observed marked enrichment of CEACAM1+ neutrophils\nand dominant TIM-3–mediated T cell dysfunction, suggesting that the CEACAM1–\nTIM-3 axis becomes a key immunosuppressive pathway specifically in the post–PD-1\nresistance context. Although we noted a trend toward higher CEACAM1+ neutrophil\nabundance in pre-treatment responders (PreR) compared to non-responders (PreNR),\nthe limited size of pre-treatment cohorts precludes definitive conclusions about\nbaseline differences. Importantly, this implies that targeting CEACAM1–TIM-3 may\nbe most relevant for re-challenge or salvage strategies in PD-1–refractory disease,\nrather than as a first-line intervention. In contrast, most completed PD-1/TIM-3\nclinical trials enrolled treatment-naïve patients, where high response rates to standard\nimmune checkpoint inhibitors (ICIs) may mask any incremental benefit of dual\nblockade while increasing toxicity.\nSecond, TIM-3 signaling is modulated by multiple ligands including galectin-9,\nHMGB1, phosphatidylserine, and CEACAM1 and operates within a network of\noverlapping immunosuppressive circuits. Likewise, CEACAM1 engages other\nreceptors beyond TIM-3. Without stratification for neutrophil abundance, CEACAM1\nexpression, or myeloid-dominant immunosuppression, TIM-3 blockade alone may be\ninsufficient to overcome resistance in unselected patient populations.\nAccordingly, we have revised the Discussion to clarify that our findings support\na context-specific role for the CEACAM1–TIM-3 axis, providing a mechanistic\nrationale for biomarker-guided re-challenge strategies in PD-1–refractory renal cell\ncarcinoma (RCC), rather than advocating its use as a universal first-line combination\n(Discussion, Line 426-436). We believe this refined interpretation reconciles our\nmechanistic observations with current clinical trial outcomes and underscores the\nneed for patient stratification based on myeloid features and CEACAM1 expression in\nfuture studies.\nMinor Concerns:\n1. Line 193 – numbering error: In line 193, the numbering within “(5) EMT II (MP6;\nSAA1, SAA2 and CXCL14), (6) MHC-II …” appears incorrect. Please revise the\nnumbering to maintain sequential consistency.\nResponse: We thank the reviewer for pointing out this numbering error. We have\ncorrected the sequence in the revised manuscript.\n2. Figure 3d – definition of MP6: In Figure 3d, the boundary box highlighting MP6\n(EMT-II) may not fully encompass all relevant regions, and there seems to be an\nundefined area between MP6 and MP7. The authors are advised to recheck and clarify\nthe demarcation between these two modules to ensure accuracy.\nResponse: We thank the reviewer for this suggestion. We have rechecked the\nboundaries of MP6 and MP7 in Figure 3d and clarified the demarcation between the\ntwo modules in both the figure and the legend to ensure accurate representation.\nBriefly, the metaprograms (MPs) in our study were derived using Non-negative\nMatrix Factorization (NMF) applied to single-cell transcriptomic data, followed by\nmodule assignment based on gene set enrichment and inter-sample consistency\n(Methods session). The Jaccard index is widely used to evaluate the similarity\nbetween gene sets or biological modules, and its calculation is based on the\nintersection and union of elements. During clustering, modules are formed by\naggregating gene sets that exhibit high Jaccard similarity.\nHowever, we noticed that the gene sets corresponding to MP6 (EMT-II) and\nMP7 show relatively low Jaccard similarity with each other. At the same time, a small\nsubset of gene signatures displays intermediate similarity to both modules, which\nleads to the formation of an undefined area between them (Fig. 3d and Supplementary\nFig. 4a-4b). Importantly, this undefined area reflects the biological complexity of\nTME remodeling rather than a technical artifact in module definition. We interpret\nthis phenomenon as a manifestation of the intricate TME dynamics induced by\ncombined PD-1 blockade and tyrosine kinase inhibition, which aligns with the\nwell-established heterogeneity and plasticity of aRCC10.\n3. Figure 5a – potential inconsistency in CD8⁺ T-cell proportions: In Figure 5a, the\nrepresentative immunofluorescence images appear to show a higher proportion of\nCD8⁺ T cells in the PT_Pre_NR group compared with PT_Post_R group, which\nseems slightly inconsistent with the quantitative data presented in the statistical plot.\nPlease verify this point and ensure consistency between the visual and quantitative\nresults.\nResponse: We thank the reviewer for this observation. We have updated Fig. 5a to\nensure consistency between the representative immunofluorescence images and the\nquantitative analysis.\n4. Lines 309–316 – clarification of mechanistic reasoning: In lines 309–316, the\nauthors conclude that post-treatment resistance in RCC is attributed to the increased\nproportion of PD-1⁻TIM-3⁺CD8⁺ T cells in the Post_NR group, implying that TIM-3\nplays a pivotal role in mediating resistance to ICI + TKI therapy. However, this\nconclusion logically depends on the premise that PD-1⁺ T cells are significantly\nreduced after therapy. Although this reduction is observable in Figure 5c, the\nmanuscript would benefit from an explicit textual description emphasizing this key\nobservation to make the mechanistic reasoning more coherent and convincing.\nResponse: We thank the reviewer for this helpful comment. Our conclusion that\nPD-1⁻TIM-3⁺ CD8⁺ T cells contribute to resistance to ICI plus TKI therapy is based\non the observation that PD-1⁺ exhausted CD8⁺ T cells are markedly reduced after\ntreatment, particularly in non-responders. Although this reduction was evident in Fig.\n5c, we agree that it was not sufficiently emphasized in the original text. Accordingly,\nwe have revised the Results section (Lines 312–317) to explicitly describe the\npost-treatment decrease in PD-1⁺ CD8⁺ T cells and the concomitant enrichment of\nPD-1⁻TIM-3⁺ CD8⁺ T cells, thereby strengthening the mechanistic rationale for\nimplicating TIM-3 in acquired resistance.\n5. Line 744 – correction of the formatting errors: The placement of the underline for\nthe link is incorrect. Please correct the placement of the underline for the link.\nResponse: We thank the reviewer for noticing this formatting issue. The underline for\nthe link in line 744 has been corrected in the revised manuscript.\n6. Figure 6 – suggested addition for completeness: As the central conclusion of the\nstudy revolves around the interaction among tumor cells, neutrophils, and T cells in\ndriving therapeutic resistance, it is recommended that the authors include in Figure 6 a\ncomparative analysis of mNeu_Ldha and mPd1–Tim3⁺Cd8⁺ T-cell populations across\ndifferent treatment groups following ML-T7 therapy. This addition would further\nreinforce the integrative mechanistic framework proposed by the study.\nResponse: We thank the reviewer for this valuable suggestion. In response, we\nperformed a comparative analysis of Ceacam1+ neutrophils, Pd1+Tim3+ CD8+ T cells,\nand Pd1-Tim3+ CD8+ T cells across distinct treatment groups, including control, anti–\nPD-1 monotherapy, CEACAM1–TIM-3 blockade, and combination therapy.\nFirst, combination therapy markedly reduced the proportion of Ceacam1+\nneutrophils compared to all other groups (Response Fig. 6). In contrast, for Pd1-Tim3+\nCD8+ T cells, the combination therapy increased their proportion compared to the\ncontrol group (Response Fig. 6).\nSecond, the proportion of Pd1+Tim3+ CD8+ T cells was elevated in all treatment\ngroups relative to the control group (Response Fig. 6). This finding suggests that\nwhile combination therapy effectively targets myeloid-mediated immunosuppression,\nit may not fully reverse or prevent the accumulation of highly exhausted CD8+ T cells\nco-expressing both checkpoints. This incomplete reversal could be attributed to\npersistent antigen exposure or compensatory feedback mechanisms within the tumor\nmicroenvironment. Notably, compared with anti-PD-1 monotherapy, combination\ntherapy further downregulated the expression of multiple inhibitory immune\ncheckpoints including Pdcd1, Lag3, Havcr2, and Ctla4 on CD8+ T cells, indicating a\npartial reversal of T cell exhaustion (Fig. 6h).\nThese findings highlight the dual-layered nature of the tumor immune response,\nwhereby effective modulation of the myeloid compartment does not automatically\ntranslate into complete restoration of T cell functionality. We have now integrated\nthese results into Response Fig. 6 and expanded the accompanying text to discuss this\nnuanced interplay (Lines 354-369). We believe this addition enriches the integrative\nmechanistic framework of our study rather than simplifying it, by illustrating the\ndistinct dynamics of myeloid and lymphoid compartments under therapeutic pressure.\nResponse Fig. 6 Lollipop plots showing the proportion of Ceacam1+ neutrophils,\nPd1+Tim3+, and Pd1-Tim3+ CD8+ T cells in tumor samples from mice treated with\ncontrol, Toripalimab, ML-T7, or Toripalimab + ML-T7 as indicated (n = 2 per group;\ntotal n = 8) (now Supplementary Fig. 10d in the revised manuscript).\nBy addressing these suggestions, the manuscript will be further strengthened in clarity\nand completeness. Overall, this is an elegant, well-structured, and timely study that is\nsuitable for publication in Nature Communications after minor revisions to improve\nclarity, figure presentation, and some text details.\nResponse: We thank the reviewer for the positive and encouraging comments. We\nhave carefully addressed all specific suggestions, clarified the methodology, refined\nthe figures, and polished the text. We believe these revisions further improve the\nclarity, completeness, and overall presentation of the manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am pleased with the revisions made to the manuscript. The author has thoughtfully addressed all my previous comments and suggestions, demonstrating both diligence and a strong commitment to improving the paper. The revisions have significantly strengthened the clarity of the methodology, deepened the interpretation of the results, and enhanced the overall coherence of the narrative. I believe it meets the standards of Nature Communications for publication and will be a valuable contribution to the field of RCC TME research. I have no further requests for revision and recommend acceptance in its current form.", "reviewer_2": "In this revision, Gu et al. addressed most of the issues raised by the reviewers. The article now focuses on the SAA-CEACAM1-TIM-3 immunoregulatory loop and uses both clinical data and humanized mouse models to uncover the mechanisms. The story is now very complete and is ready for publication after correcting some minor issues:\nMinor issues:\n1. For plots in Response Fig. 3c, Response Rig. 4d, please adjust the y-axis limits to match the data range; the current maximum is much higher than the actual values.\n2. In line 120, please consider leaving a blank between “S100A12+” and reference “14” to improve readability.\n3. In line 283, 306, 441, 444, and 450, it should be a noun “analysis” not a verb “analyzes”.\n4. In line 442, it should be “heterogeneities”.", "reviewer_3": "All my concerns have been resolved in this revision. I have no further comments.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank the reviewer for the positive and encouraging comments. We are pleased that the revisions were found to have substantially improved the clarity, depth, and overall coherence of the manuscript. We appreciate the reviewer’s careful evaluation and recommendation for acceptance.\nResponse: We thank the reviewer for the constructive comments and are pleased that the revised manuscript is considered complete and suitable for publication. We have carefully addressed all the minor issues raised, as detailed below.\nMinor issues:\n1. For plots in Response Fig. 3c, Response Rig. 4d, please adjust the y-axis limits to match the data range; the current maximum is much higher than the actual values.\nResponse: We agree with the reviewer’s suggestion. The y-axis limits in Response Fig. 3c and Response Fig. 4d have been adjusted to more accurately reflect the actual data range, improving visual clarity and interpretability.\n2. In line 120, please consider leaving a blank between “S100A12+” and reference “14” to improve readability.\nResponse: Thank you for pointing this out. A space has been added between “S100A12+” and reference “14” to improve readability.\n3. In line 283, 306, 441, 444, and 450, it should be a noun “analysis” not a verb “analyzes”.\nResponse: We agree with the reviewer. The term “analyzes” has been corrected to the noun form “analysis” at all indicated locations. In addition, we have carefully checked the entire manuscript to ensure consistent and correct usage of this term throughout the text.\n4. In line 442, it should be “heterogeneities”.\nResponse: We respectfully note that “heterogeneity” is generally treated as an uncountable noun in scientific writing. To maintain grammatical correctness and consistency with standard usage in the literature, we have retained the term “heterogeneity” rather than “heterogeneities” to ensure clarity.\nResponse: We thank the reviewer for confirming that all concerns have been fully addressed. We appreciate the time and effort devoted to the evaluation of our manuscript.\nResponse: We thank the reviewer for their contribution and for participating in the Nature Communications peer review training initiative. We appreciate the thoughtful assessment provided."}]

Decision letter