This retrospective study was approved by the institutional review board of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine (approval numbers: XHEC-C-2021-145-1, XHEC-C-2023-020-1). All patients had provided written informed consent at the time of hospital admission for the use of their clinical data. De-identified data were used for analysis. The study was conducted in accordance with institutional guidelines and the principles of the Declaration of Helsinki. Patients who underwent radical or partial nephrectomy for renal tumors between January 2012 and December 2018 were retrospectively reviewed across seven tertiary medical centers in China, coordinated by the Department of Urology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. Inclusion criteria were: (1) histologically confirmed ccRCC; (2) availability of formalin-fixed paraffin-embedded (FFPE) tissue suitable for TMA construction; and (3) complete clinical follow-up data including OS. Exclusion criteria were: (1) non-clear cell histology (n = 105 cores); (2) insufficient tissue quality for mIF staining or quality control failure (n = 95 cores); and (3) incomplete clinical or pathological records. After applying these criteria, a total of 834 patients (1,633 TMA cores across 18 batches) were included in the final analysis. All samples were obtained prior to the administration of any systemic therapy. We utilized two independent cohorts for model development and clinical validation.

To systematically decode the complex spatial heterogeneity of the tumor microenvironment (TME), we developed PhenoSSP, an end-to-end deep learning framework designed for high-precision single-cell phenotyping and spatial analysis [Figure 1A]. The workflow begins by processing raw 7-channel mIF images (containing DAPI, PanCK, CD3, CD4, CD8, PD-1, and FOXP3). Through cell segmentation, the tissue is discretized into individual cell-centered patches, preserving the local morphological context of each cell.

A critical challenge in TME analysis is the inherent class imbalance, where tumor and stromal cells vastly outnumber specific immune subsets. To mitigate this, we engineered a Hierarchical Classification Strategy [Figure 1B]. This cascading architecture operates in two stages:1. Stage 1 (Coarse Classifier): Functions as a high-level filter, rapidly distinguishing cells into three major lineages: Epithelial (Tumor), Immune, and Other/Stromal. This step effectively reduces the search space and suppresses false positives from abundant non-immune cells.2. Stage 2 (Immune Expert Classifier): Cells identified as “Immune” are routed to a specialized fine-grained classifier. This expert model focuses exclusively on resolving subtle phenotypic differences to determine specific subtypes (e.g., distinguishing CD3⁺CD4⁺CD8^- helper T cells from CD4⁺FOXP3⁺ Tregs).

The backbone of our framework utilizes a ViT-S/16 architecture optimized for multi-channel spatial data [Figure 1C]. Unlike standard RGB models, our architecture incorporates a specialized Marker Embedding layer alongside traditional Patch and Positioning Embeddings. This design allows the model to explicitly encode channel-specific information before processing it through the Transformer Encoder. Finally, a dedicated MLP Classifier Head projects the aggregated 384-dimensional [CLS] token into class probabilities, ensuring robust feature extraction even from noisy inputs.

We benchmarked PhenoSSP against a comprehensive suite of baselines, ranging from traditional machine learning algorithms (SVM, random forest) to non-hierarchical deep learning models (Flat CNN, Flat ViT).

The first stage of our hierarchical pipeline focuses on high-confidence lineage separation. As shown in the confusion matrix [Figure 2A], PhenoSSP achieved near-perfect classification for Epithelial cells (99.4% Recall) and robust identification of Immune cells (94.0% Recall). The per-class metrics [Figure 2B] further corroborate this stability, where the model maintained a high F1-score of 78.4% for the heterogeneous “Immune” category, successfully minimizing the leakage of immune cells into the “Other” class.

In the context of overall performance, the circular benchmark plot [Figure 2C] highlights the substantial advantage of our approach. PhenoSSP (green) achieved a Balanced Accuracy of 71.0% and an F1-Macro of 70.7%. This represents a significant performance margin over the Flat CNN (62.5% Bal Acc) and Flat ViT (59.5% Bal Acc) architectures. Notably, traditional methods like SVM and Gradient Boosting trailed by over 30%, underscoring that simple intensity features are insufficient for resolving the complex heterogeneity of the TME.

To investigate the source of this performance gain, we conducted a qualitative analysis on “hard examples” characterized by conflicting spectral signals. As illustrated in Figure 2D, we examined a representative cell (Sample A-2/cell_7403) that posed a classic “Intensity Trap”. This cell exhibited an anomalously high PanCK intensity (223.45), likely due to signal spillover or nonspecific staining, which exceeds the typical threshold for tumor cells. While a traditional intensity-based gating strategy would erroneously classify this as “Epithelial”, PhenoSSP correctly identified it as “Immune” (Ground Truth: Immune cell) with high confidence (> 0.7). This confirms that our hierarchical framework effectively transcends simple intensity thresholds by leveraging learned spatial and morphological contexts to resolve signal ambiguity.

To ensure the clinical reliability of the model, we conducted a comprehensive evaluation combining qualitative “Visual Turing Tests” with quantitative benchmarking against established baselines.

First, we assessed the model’s ability to reconstruct spatial phenotypes in dense tissue environments. As illustrated in Figure 3A, PhenoSSP generated phenotype maps that exhibited high morphological and spatial concordance with expert ground truth. Notably, the model successfully delineated the boundary between the tumor nest (Epithelial, yellow) and the stromal compartment, while accurately resolving spatially intermingled immune subsets such as cytotoxic T cells (green) and helper T cells (magenta), even in crowded regions where raw markers overlap.

Quantitatively, we evaluated the model’s performance on fine-grained immune subtyping using F1-scores. The radar chart in Figure 3B visualizes the holistic performance advantage of our hierarchical approach. PhenoSSP (green line) consistently envelops the performance polygons of both Flat ViT (grey) and Flat CNN (red) baselines, demonstrating superior sensitivity and precision across all five immune subtypes.

This performance advantage is further detailed in Figure 3C, where PhenoSSP achieved high F1-scores for distinct T cell populations, reaching 78.2% for CD3⁺CD4⁺CD8^- cells and 78.4% for CD3⁺CD4^-CD8⁺ cells. Even for the most challenging phenotype, CD4⁺FOXP3⁺ Tregs, which are often obscured by low signal-to-noise ratios, the model maintained a robust F1-score of 57.1%, significantly outperforming baseline fluctuations.

Finally, we performed a rigorous methodological comparison against traditional machine learning algorithms (SVM, random forest, logistic regression) and flat deep learning models [Figure 3D]. While traditional methods struggled to identify complex phenotypes (e.g., failing to detect CD3⁺CD4^-CD8⁺ cells with near-zero F1-scores), PhenoSSP demonstrated substantial resilience. It not only surpassed traditional ML methods by a wide margin but also outperformed the Flat ViT architecture by approximately 20%-40% in F1-scores across key subtypes. This confirms that the hierarchical incorporation of spatial constraints is critical for resolving phenotypic ambiguity that flat architectures fail to capture.

A key advantage of PhenoSSP is its ability to learn robust, interpretable representations from noisy inputs. To evaluate this, we visualized the high-dimensional feature space using t-distributed stochastic neighbor embedding (t-SNE) to compare the raw pixel intensity data against the deep features extracted by PhenoSSP [Figure 4A].

As shown in the visualization, the raw intensity data resulted in a diffuse, highly overlapping distribution with a low Silhouette score of 0.060, confirming that pixel intensity alone is insufficient to distinguish complex cell phenotypes in the presence of background noise. In stark contrast, the PhenoSSP latent space demonstrated a nearly 10-fold improvement in cluster separability, achieving a Silhouette score of 0.568. Notably, the Immune population (green), which was previously entangled with the Epithelial (blue) and Other (orange) classes, emerged as a distinct, tightly grouped cluster in the PhenoSSP space. This quantitative and qualitative improvement indicates that the model successfully disentangles semantic biological identity from low-level background artifacts.

Furthermore, to understand the morphological basis of these decisions, we generated pixel-level saliency maps by computing the gradient of the predicted class score with respect to the input image [Figure 4B]. This analysis reveals that the model automatically learns to focus on biologically valid subcellular structures without explicit segmentation supervision. Specifically, for CD8⁺ T cells, the model’s attention is strictly confined to the perinuclear membrane region, forming a distinct ring-like pattern that precisely co-localizes with the CD8 marker while explicitly avoiding the nuclear region (DAPI). Similarly, for FOXP3⁺ Tregs, attention is concentrated on the nuclear area. This confirms that PhenoSSP decisions are driven by specific, biologically relevant signal localization rather than background noise or confounding factors.

To decipher the spatial architecture of immune evasion, we first quantified the global immune landscape. Consistent with the “hot” tumor phenotype of RCC, tumor tissues exhibited significantly higher CD8⁺ T cell infiltration density compared to adjacent normal tissues (Figure 5A, P < 0.001). However, survival analysis exposed a critical limitation in current stratification methods: simple CD8⁺ T cell density failed to serve as a statistically significant predictor of OS (Figure 5B, P = 0.057). While high infiltration showed a marginal trend towards better outcomes, the lack of robust significance suggests that effector abundance alone does not guarantee antitumor efficacy, implying that the functional state of these T cells may be compromised by local microenvironmental factors.

We hypothesized that the specific spatial arrangement of suppressor cells drives this dysfunction. By calculating a density-normalized Interaction Score, we found that Tregs in non-survivors were specifically enriched within a 30 μm radius of CD8⁺ T cells (Figure 5C, P = 0.032). Notably, this spatial attraction diminished at larger scales (50-100 μm, Supplementary Figure 1A), strongly pointing towards a proximity-dependent or short-range paracrine suppression mechanism.

Visualization of representative spatial maps confirmed this topological distinction: survivors [Figure 5D, left] displayed a spatially dispersed topology characterized by the complete absence of Tregs in the vicinity of CD8⁺ T cells (Score = 0.00). In sharp contrast, non-survivors [Figure 5D, right] exhibited a dense “suppressive niche”, where CD8⁺ T cells were physically encircled by Tregs (Score = 9.60), creating a barrier to effective antitumor immunity.

The clinical utility of this spatial feature was validated via Kaplan-Meier analysis. Patients with a high Spatial Interaction Score (indicating the presence of tight suppressive niches) had significantly shorter OS compared to those with spatially separated immune profiles (Log-rank test, P = 0.012, Figure 5E).

To verify that this metric captures true topological complexity rather than simple cell crowding, we performed rigorous correlation analyses. The Interaction Score showed only a weak negative correlation with overall cell density (Spearman R = -0.37, Supplementary Figure 1B), confirming it as an independent spatial biomarker. Furthermore, we compared our method against a naïve “nearest neighbor distance” metric. As shown in Supplementary Figure 1C, simple physical distance failed to distinguish between survivors and non-survivors (P = 0.4744), likely because it is confounded by global cell density variation. This validates the necessity of PhenoSSP’s density-normalized scoring algorithm.

Among conventional immune metrics, the CD8⁺/Treg ratio remained prognostic (Supplementary Figure 2A, P = 0.0280), whereas the absolute density of Tregs alone had no prognostic value (Supplementary Figure 2B, P = 0.4672). Collectively, these results demonstrate that clinical outcomes in RCC are determined not by the quantity of suppressor cells, but by their spatial positioning relative to cytotoxic effectors.

A continuous-scale analysis across 12 radii (10-150 μm) was then performed to evaluate whether the 30 μm radius represents a data-driven inflection point rather than an arbitrary threshold [Supplementary Figure 3]. The prognostic significance of the Interaction Score exhibited a clear radius-dependent pattern: statistical significance emerged at 20 μm [hazard ratio (HR) = 1.82, P = 0.029], peaked sharply at 35 μm (HR = 2.38, P = 0.002), and progressively attenuated at larger radii (HR = 1.56, P = 0.098 at 150 μm). This 20-35 μm significance window corresponds precisely to the biologically established range of juxtacrine signaling, including CTLA-4-mediated trans-endocytosis of co-stimulatory ligands and short-range IL-2 diffusion gradients. These results confirm that the 30 μm radius used in this study falls within an empirically validated optimal range, rather than representing an arbitrarily selected threshold.

Beyond spatial architecture, we next asked whether the Interaction Score provides independent prognostic information after accounting for established clinicopathological variables. Using PFS as the endpoint and a biologically motivated threshold (Score = 1.0), the high Interaction Score group (n = 152) exhibited significantly shorter PFS than the low group (n = 449; log-rank P = 0.008; Supplementary Figure 4A). In univariate Cox regression, the high group had a 2.22-fold increased risk of progression (HR = 2.22, 95%CI: 1.30-3.80, P = 0.004). After adjusting for age, sex, and Fuhrman grade, the Interaction Score remained an independent predictor of disease progression (HR = 1.87, 95%CI: 1.09-3.23, P = 0.024; Supplementary Figure 4B), confirming that the spatial metric captures prognostic information not accounted for by conventional clinical parameters.

We also examined whether CD8⁺ T cells within the suppressive niche exhibit elevated exhaustion markers by comparing PD-1 MFI inside vs. outside the 30 μm boundary. At the patient level, niche-resident CD8⁺ T cells exhibited statistically higher PD-1 expression (paired Wilcoxon signed-rank test, N = 845, P < 0.0001, Cohen’s d = 0.24; Supplementary Figure 5), with 489/845 (57.9%) patients showing elevated PD-1 inside the niche. However, the modest effect size (median difference of 2.3%) suggests that PD-1 upregulation alone does not fully account for the functional suppression within the niche, consistent with the hypothesis that the proximity-dependent suppressive mechanism operates primarily through PD-1-independent pathways such as CTLA-4-mediated trans-endocytosis and local IL-2 deprivation.

A key concern is whether FOXP3⁺ cells in the ccRCC microenvironment represent true Tregs or transiently activated conventional CD4⁺ T cells. We addressed this by analyzing an independent pan-cancer CD4⁺ T cell scRNA-seq atlas^[27]. From this atlas, we extracted 723 CD4⁺ T cells from the ccRCC subset, of which 154 were FOXP3⁺ and 569 were FOXP3^-. FOXP3⁺ cells clustered in a spatially coherent region on UMAP that co-expressed canonical Treg signature genes, including IL2RA (CD25), CTLA4, IKZF2 (Helios), TNFRSF18 (GITR), TIGIT, and ENTPD1 (CD39) [Supplementary Figure 6A]. Dot plot analysis suggest that FOXP3⁺ cells exhibited high detection rates for all seven canonical Treg markers (76%-100%), whereas FOXP3^- T cells showed negligible expression (0%-19%; Supplementary Figure 6B). Quantitative comparison further confirmed that CTLA4 (90.3% vs. 18.5% non-zero) and IL2RA (76.0% vs. 12.8% non-zero) expression were markedly elevated in FOXP3⁺ cells [Supplementary Figure 6C]. These results demonstrate that FOXP3 expression in the ccRCC TME is consistently associated with a comprehensive Treg transcriptomic program, supporting the validity of the CD4⁺FOXP3⁺ definition used in our mIF-based spatial analysis.

The functional relevance of the suppressive niche was further validated using an orthogonal technology platform. We analyzed a publicly available Xenium spatial transcriptomics dataset of a Stage III ccRCC specimen (10× Genomics, Xenium In Situ Gene and Protein Expression data for FFPE Human RCC; 465,534 cells, 405 genes; available at https://www.10xgenomics.com/datasets). Among 15,302 CD8⁺ T cells, 7,133 (46.6%) were located within 30 μm of a Treg - a proportion significantly exceeding the random expectation of 35.8% (permutation test, Z = 14.62, P < 0.001; Supplementary Figure 7A), confirming non-random spatial association.

Comprehensive gene expression profiling revealed a striking functional dichotomy between niche-resident and niche-external CD8⁺ T cells [Supplementary Figure 7B]. CD8⁺ T cells inside the niche exhibited significantly elevated expression of naive/central memory markers (IL7R/CD127, Cohen’s d = 0.51; SELL/CD62L, d = 0.47; CD28, d = 0.29) along with CTLA4 (d = 0.10), while showing significantly reduced expression of cytotoxic effector molecules (NKG7, d = -0.27; GZMA, d = -0.18; PRF1, d = -0.14; Supplementary Figure 7C and D), terminal exhaustion markers (HAVCR2/TIM-3, d = -0.28; LAG3, d = -0.19; PDCD1/PD-1, d = -0.09), and the proliferation marker MKI67 (d = -0.10). This transcriptomic profile suggests that CD8⁺ T cells within the Treg niche are maintained in an early, non-activated state rather than having undergone effector differentiation followed by suppression. The concurrent elevation of CD28 (the target of CTLA-4-mediated trans-endocytosis) and CTLA4 within the niche provides direct molecular evidence supporting the CTLA-4-dependent co-stimulation blockade mechanism proposed in this study.

Finally, we assessed cohort-level generalizability using an independent TCGA-KIRC dataset (N = 480). Immune cell fractions were estimated from bulk RNA-seq data using CIBERSORT (TIMER3.0) , and a Treg/CD8⁺ ratio was computed as a bulk-level proxy. Patients with a high ratio exhibited significantly shorter OS (log-rank P < 0.001; HR = 1.85, 95%CI: 1.37-2.52; Supplementary Figure 8). While this bulk-level proxy cannot capture the spatial proximity quantified by the Interaction Score, the directional consistency across a large, independent multi-institutional cohort provides additional support for the prognostic relevance of Treg-CD8⁺ immune balance in ccRCC.

The prognostic failure of simple CD8⁺ T cell density in our cohort highlights the unique immunological landscape of ccRCC, which fundamentally differs from other “hot” tumors like melanoma or non-small cell lung cancer (NSCLC), where spatial heterogeneity of immune infiltration typically correlates with active antitumor responses^[28]. While high infiltration in lung cancer often signifies an active antitumor response, ccRCC is characterized by a state of “immunogenic yet immunosuppressed” equilibrium^[29,30]. Importantly, this paradoxical uncoupling of infiltration and cytotoxicity is not isolated to RCC; similar phenomena have been reported in specific subtypes of prostate cancer, ER⁺ breast cancer, and uveal melanoma^[2,31]. In this context, the majority of infiltrating CD8⁺ T cells are not functional effectors. As illuminated by recent pan-cancer studies, these environments are often dominated by “bystander” CD8⁺ T cells (which recognize viral rather than tumor antigens)^[32] and cells trapped in a state of terminal exhaustion (e.g., CD39⁺, TIM-3⁺ phenotype)^[33-35].

This distinct biology explains why global metrics like tumor mutational burden (TMB), which is effective in high-mutation tumors, often fails to predict outcomes in ccRCC, a cancer type known for its relatively low mutational load but high immune infiltration^[36]. Our findings suggest that in ccRCC, the clinical outcome is not determined by the quantity of neoantigens (TMB) or the quantity of T cells (Density), but rather by the spatial quality of the immune microenvironment. By identifying the 30 μm suppressive niche, PhenoSSP essentially distinguishes between “spatially unconstrained cells (consistent with a bystander phenotype)” and “suppressed effectors” (tumor-reactive cells actively restrained by Treg proximity).

Crucially, the identification of this 30 μm interaction radius is biologically significant. This scale corresponds precisely to the range of juxtacrine signaling and short-range diffusion (e.g., CTLA-4 mediated trans-endocytosis^[10]) and short-range cytokine gradients (e.g., local IL-2 deprivation or TGF-β signaling^[11]). This suggests that in high-risk patients, Tregs are not merely present but are actively recruited to the immediate vicinity of effector T cells to exert potent suppression via juxtacrine signaling. Thus, the “CD8⁺ paradox” in ccRCC is not a failure of immune recruitment, but a failure of spatial positioning, where effector cells are physically constrained within proximity-dependent suppressive niches^[9,37].

The spatial biomarkers identified by PhenoSSP carry potential therapeutic implications for overcoming cancer drug resistance. The finding that a pre-existing suppressive niche, defined by the physical enrichment of Tregs within 30 μm of CD8⁺ T cells, predicts poor outcomes provides a mechanistic rationale for patient-specific treatment selection. We propose three specific and testable strategies for disrupting the suppressive niche, each grounded in the spatial mechanisms revealed by our data.

First, CTLA-4 blockade may serve as a targeted approach for Treg depletion within the niche. Our data demonstrate that the suppressive niche operates at a 30 μm scale, precisely matching the functional range of CTLA-4-mediated trans-endocytosis of co-stimulatory ligands^[10]. Anti-CTLA-4 antibodies such as ipilimumab deplete intratumoral Tregs via FcγR-dependent antibody-dependent cellular cytotoxicity^[38], which would physically dismantle these suppressive architectures. This rationale is consistent with the demonstrated superiority of nivolumab plus ipilimumab over sunitinib in intermediate- and poor-risk ccRCC in CheckMate-214^[39], and with the observation by Murakami et al. that Foxp3⁺PD-1⁺ Tregs form spatial niches with CD8⁺ T cells that become more immunosuppressive under PD-1 blockade alone^[37]. Our Interaction Score could thus serve as a companion biomarker to prospectively identify patients most likely to benefit from ipilimumab-containing regimens. A testable prediction arising from this analysis is that patients with an Interaction Score above the median will derive significantly greater PFS benefit from nivolumab-ipilimumab combination vs. PD-1 monotherapy, compared to patients with low Interaction Scores.

Second, next-generation Treg-selective depletion agents offer additional opportunities for niche disruption. Fc-engineered anti-CTLA-4 antibodies (e.g., botensilimab) and anti-CCR8 antibodies are designed to selectively deplete intratumoral Tregs while minimizing peripheral autoimmunity^[38,40]. Our spatial framework provides a quantitative readout to evaluate whether these agents successfully reduce local Treg density within the 30 μm niche of CD8⁺ T cells. The corresponding testable prediction is that effective niche disruption, measured as a post-treatment Interaction Score below 1, will correlate with CD8⁺ T cell functional reactivation and objective tumor response.

Third, cytokine-based niche remodeling represents a complementary strategy. The 30 μm radius corresponds to the diffusion range of IL-2, a cytokine locally consumed by Tregs to suppress CD8⁺ T cell function^[11]. IL-2 variants engineered for preferential CD8⁺ T cell binding, such as bempegaldesleukin (NKTR-214), could specifically counteract this proximity-dependent suppression by restoring local IL-2 signaling within the niche^[41]. The testable prediction is that IL-2 variant therapy combined with PD-1 blockade will preferentially benefit patients with high Interaction Scores by rescuing cytokine-deprived CD8⁺ T cells within suppressive niches.

Importantly, our data also suggest that not all patients require niche disruption. Tumors with high CD8⁺ infiltration but low Treg proximity (low Interaction Score) may harbor spatially unconstrained, potentially functional effector cells that could respond to PD-1 monotherapy or tyrosine kinase inhibitor (TKI) regimens targeting angiogenesis. This aligns with genomic studies showing that PBRM1 mutations are associated with angiogenesis-driven but less immunosuppressive environments, whereas BAP1 mutations often drive an inflamed but highly suppressive phenotype^[42,43]. The Interaction Score may therefore enable a spatial stratification framework: high-score patients directed toward Treg-targeting combinations, and low-score patients toward PD-1 monotherapy or anti-angiogenic strategies.

The identification of a proximity-dependent suppressive niche provides a candidate spatial framework for understanding why a substantial proportion of ccRCC patients may exhibit primary resistance to PD-1/PD-L1 monotherapy despite harboring an immunologically “hot” phenotype^[6]. We propose that the 30 μm suppressive niche represents a candidate structural unit of immune resistance that may operate through mechanisms orthogonal to the PD-1/PD-L1 axis.

Within this radius, Tregs exert potent suppression through at least three proximity-dependent mechanisms that PD-1 blockade alone cannot overcome: (i) CTLA-4-mediated trans-endocytosis of CD80/CD86 from antigen-presenting cells, depriving CD8⁺ T cells of essential co-stimulatory signals^[10]; (ii) local IL-2 consumption, creating a cytokine-deprived microenvironment that impairs CD8⁺ T cell survival and proliferation even after checkpoint release^[11]; and (iii) short-range TGF-β signaling, which drives the terminal exhaustion program in neighboring CD8⁺ T cells^[44]. Critically, all three mechanisms are contact-dependent or short-range paracrine effects, explaining why the suppressive signal is restricted to the 30 μm radius and diminishes at larger scales (50-100 μm, Supplementary Figure 1A).

Consistent with this interpretation, our post hoc analysis of PD-1 expression within the niche revealed only a modest elevation at the protein level (Cohen’s d = 0.24, Supplementary Figure 5), indicating that the proximity-dependent suppression is not primarily mediated through the PD-1/PD-L1 axis. This finding provides a mechanistic explanation for why PD-1 blockade alone is insufficient to overcome the suppressive niche, and strengthens the rationale for targeting PD-1-independent mechanisms, particularly CTLA-4-mediated trans-endocytosis and local IL-2 deprivation, in patients harboring these spatial architectures.

The Xenium spatial transcriptomic analysis further refined our mechanistic understanding: CD8⁺ T cells within the niche retained a naive/memory-like transcriptomic signature (high IL7R, SELL, CD28; low GZMA, PRF1) rather than an effector-to-exhausted trajectory [Supplementary Figure 7B]. This suggests that Treg proximity does not merely suppress pre-existing effector function but actively prevents the initiation of the cytotoxic program, consistent with a model in which CTLA-4-mediated depletion of co-stimulatory ligands intercepts T cell priming at its earliest stage.

This spatial perspective reframes the “CD8⁺ paradox” as a potential drug resistance phenotype rather than merely a prognostic curiosity. In patients with high Interaction Scores, the pre-existing spatial architecture of suppression may constitute a structural barrier that limits the efficacy of PD-1 blockade: even when PD-1/PD-L1 engagement is disrupted, CD8⁺ T cells remain functionally constrained by the surrounding Treg niche. This is consistent with clinical observations that ccRCC patients with inflamed but immunosuppressive microenvironments often exhibit primary resistance to ICB monotherapy^[5], and with the finding by Murakami et al. that Foxp3⁺PD-1⁺ Treg niches become more immunosuppressive under PD-1 blockade^[37]. Our data are thus consistent with the concept that the TME may architecturally pre-establish conditions unfavorable for immunotherapy efficacy prior to treatment initiation, although direct validation in ICB-treated cohorts remains essential.

A critical distinction must be made between prognostic and predictive biomarkers. Our entire cohort (N = 834) comprises exclusively treatment-naïve patients; therefore, the Spatial Interaction Score is established here as a prognostic biomarker associated with OS, not a validated predictive biomarker for response to ICB. While the biological rationale linking the suppressive niche to ICB resistance is compelling, direct demonstration of predictive utility requires validation in ICB-treated cohorts. Specifically, retrospective analysis of pre-treatment biopsies from CheckMate-214 or KEYNOTE-426 trial participants would provide an ideal testing ground. All discussion of immunotherapy resistance in this manuscript should therefore be interpreted as hypothesis-generating rather than as established clinical evidence.

Second, our definition of Tregs relies on CD4⁺FOXP3⁺ co-expression. Although FOXP3 is the canonical Treg marker, transient FOXP3 expression can occur in activated conventional CD4⁺ T cells^[45]. To address this concern, we performed orthogonal validation using an independent pan-cancer CD4⁺ T cell scRNA-seq atlas^[27], demonstrating that FOXP3⁺ cells in the ccRCC microenvironment co-express a comprehensive panel of canonical Treg markers (IL2RA, CTLA4, IKZF2, TNFRSF18, TIGIT, ENTPD1), confirming their identity as true Tregs [Supplementary Figure 6]. Nevertheless, the inclusion of additional markers such as Helios and CD25 in future mIF panels would enable more precise Treg functional subtyping.

Third, our post hoc analysis of PD-1 expression within the niche revealed only a modest protein-level elevation (Cohen’s d = 0.24; Supplementary Figure 5), and the Xenium spatial transcriptomic analysis further showed that PDCD1 transcript levels were paradoxically lower inside the niche (Cohen’s d = -0.09; Supplementary Figure 7). This apparent discrepancy between protein and transcript levels is consistent with the known post-transcriptional regulation of PD-1: PD-1 protein has a long surface half-life and accumulates on the membrane even after transcriptional downregulation, whereas PDCD1 mRNA reflects the current transcriptional state of the cell^[46]. The naive/memory-like transcriptomic profile of niche-resident CD8⁺ T cells (low PDCD1 transcription) coupled with modest protein-level retention is therefore biologically coherent and does not represent a true contradiction. While these findings support our conclusion that the suppressive mechanism is PD-1-independent, they also highlight that PD-1 was not incorporated into the Interaction Score itself. Future iterations of PhenoSSP that integrate PD-1 status into the scoring framework may enable a more granular characterization of effector cell functional states and help distinguish exhausted from bystander CD8⁺ T cells within the niche.

Fourth, our primary spatial analysis is derived from a cohort processed on a single mIF platform. To partially address this, we performed cross-platform validation using an independent Xenium spatial transcriptomics dataset (10× Genomics; 465,534 cells), which confirmed the non-random Treg-CD8⁺ spatial enrichment (permutation test, Z = 14.62, P < 0.001) and revealed a coherent transcriptomic signature of niche-resident CD8⁺ T cells [Supplementary Figure 7]. Nevertheless, multi-center mIF-based validation remains the foremost priority for establishing clinical generalizability. The large scale of our cohort (834 patients, 1,633 cores, 18 batches) and the cross-platform biological consistency provide a solid foundation for prospective clinical translation.

Fifth, while the saliency maps and feature space visualizations presented in this study provide evidence that PhenoSSP attends to biologically relevant subcellular structures, the internal feature attribution mechanisms of the Transformer architecture is not fully transparent. Integrating more advanced explainability methods into PhenoSSP is a direction for future work.

Looking forward, integrating PhenoSSP with spatial transcriptomics would allow direct linkage of the suppressive niche to exhaustion gene signatures (e.g., ENTPD1, HAVCR2) and genomic drivers (BAP1, PBRM1)^[42,43], enabling construction of a multi-modal “Spatial-Functional Atlas” of immune evasion. Extensions to graph-based decoding of tumor evolution^[47] and biomechanics-driven 3D architecture inference^[48] could further enrich the spatial understanding of immune evasion and its potential implications for drug resistance.

In conclusion, PhenoSSP shifts the analytical focus from cell density metrics to spatial architecture characterization in the TME. By enabling the precise, AI-driven identification of rare immune subsets in spatial proteomics data, our framework revealed that the spatial enrichment of Tregs within a 30 μm radius of CD8⁺ T cells, rather than effector abundance, is a key determinant of clinical outcome in ccRCC. We hypothesize that this “suppressive proximity” signature may reflect a candidate spatial mechanism of primary resistance to immune checkpoint monotherapy: the pre-existing niche architecture functionally constrains CD8⁺ T cells through PD-1-independent mechanisms, providing a potential spatial explanation for why a subset of immunologically “hot” tumors fails to respond to PD-1 blockade. Based on these findings, the Interaction Score represents a candidate prognostic biomarker that may help identify patients who could benefit from Treg-targeting combination strategies, particularly anti-CTLA-4 therapy, to disrupt suppressive niches and restore antitumor immunity. Prospective validation in ICB-treated cohorts will be essential to establish the predictive utility of this spatial biomarker for overcoming cancer drug resistance.