Tumor draining lymph nodes connected to cold triple-negative breast cancers are characterized by Th2-associated microenvironment

Guo, Weihua; Tan, Jiayi; Wang, Lei; Egelston, Colt A.; Simons, Diana L.; Ochoa, Aaron; Lim, Min Hui; Wang, Lu; Solomon, Shawn; Waisman, James; Wei, Christina H.; Hoffmann, Caroline; Song, Joo; Schmolze, Daniel; Lee, Peter P.

doi:10.1038/s41467-024-52577-y

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Published: 04 October 2024

Tumor draining lymph nodes connected to cold triple-negative breast cancers are characterized by Th2-associated microenvironment

Nature Communications volume 15, Article number: 8592 (2024) Cite this article

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Abstract

Tumor draining lymph nodes (TDLN) represent a key component of the tumor-immunity cycle. There are few studies describing how TDLNs impact lymphocyte infiltration into tumors. Here we directly compare tumor-free TDLNs draining “cold” and “hot” human triple negative breast cancers (TDLN_Cold and TDLN_Hot). Using machine-learning-based self-correlation analysis of immune gene expression, we find unbalanced intranodal regulations within TDLN_Cold. Two gene pairs (TBX21/GATA3-CXCR1) with opposite correlations suggest preferential priming of T helper 2 (Th2) cells by mature dendritic cells (DC) within TDLN_Cold. This is validated by multiplex immunofluorescent staining, identifying more mature-DC-Th2 spatial clusters within TDLN_Cold versus TDLN_Hot. Associated with this Th2 priming preference, more IL4 producing mast cells (MC) are found within sinus regions of TDLN_Cold. Downstream, Th2-associated fibrotic TME is found in paired cold tumors with increased Th2/T-helper-1-cell (Th1) ratio, upregulated fibrosis growth factors, and stromal enrichment of cancer associated fibroblasts. These findings are further confirmed in a validation cohort and public genomic data. Our results reveal a potential role of IL4⁺ MCs within TDLNs, associated with Th2 polarization and reduced immune infiltration into tumors.

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Introduction

Tumor infiltrating lymphocytes (TIL) are now widely associated with prognosis and response to immune checkpoint inhibitors (ICIs)¹. Why some tumors have high levels of TILs (“hot tumors”), but others have low TILs (“cold tumors”) remains a key open question in oncology. Tremendous efforts have been made to identify hurdles of lymphocyte infiltrations into tumor beds in context of the cancer-immunity cycle (CIC), which has been considered as a framework of how the immune system surveys and kills cancer cells². Multiple mechanisms within the tumor microenvironment (TME) that may lead to paucity of TILs have been found, including lack of tumor antigens, ineffective antigen presentation, defective T cell activation, and fibrotic stromal regions^3,4,5. A number of therapeutic approaches are under development to improve TIL levels within tumors and to trigger the CIC^5,6. However, TIL levels of most tumors remain insufficient to support long-term responses to ICI treatments due to an incomplete understanding of key operating mechanisms within the CIC.

Tumor draining lymph nodes (TDLN) are the key sites of encounter between systemic immune surveillance and tumor associated antigens (TAA)⁷ within the CIC. To initiate effective anti-tumor adaptive immunity, TAAs bound with major histocompatibility complex (MHC) molecules on dendritic cells (DC) are presented to naïve T lymphocytes within paracortex of TDLNs, where stromal structures enable DCs to efficiently interact with TAA-specific naïve T lymphocytes⁸. Co-stimulatory molecules on DCs are also required to interact with receptors on T cells for successful priming⁹. In situ cytokine profiles dictate the effector functions of primed T cells, such as T helper 1 (Th1) cells vs. T helper 2 (Th2) cells¹⁰. With such important functions in driving anti-tumor immunity, TDLNs were shown to be integral to effective ICI response in recent animal studies¹¹. However, underlying mechanisms of how TDLNs impact lymphocyte infiltration into TME in humans remain poorly understood.

To investigate the impact of TDLNs on lymphocyte infiltration in human TMEs, we focus on paired human tumor-free TDLN samples from TNBC patients with hot vs. cold tumors. Within TDLN_Cold, preferred Th2-cell priming by mature DCs has been found by correlation analysis of gene expression and spatial clustering analysis of multiplex immunofluorescent (mIF) staining on mature DCs and Th2 cells. Correspondingly, within paired cold tumors, we observe increased Th2/Th1 ratios and more fibrotic microenvironments compared to hot tumors. Equally important, we found enrichment of IL4⁺ mast cells (MC) within sinus regions of TDLN_Cold in both discovery and validation cohorts (n = 43). Our findings not only unravel potential mechanisms of how TDLNs impact lymphocyte infiltration in TME, but also provide a potential target (i.e., IL4⁺ MCs within TDLNs) to increase lymphocytes infiltration into tumors. In context of ICI treatment, combination treatment with MC inhibitors could be helpful to facilitate ICI responses by enhancing lymphocyte infiltration.

Results

Similar static immune profiles between TDLN_Cold and TDLN_Hot

This study utilized human triple-negative breast cancer (TNBC) tumors with paired TDLNs. Since tumor invasion can substantially impact immune properties of TDLNs¹², we focused on cases with available tumor-free TDLNs. Stromal TIL percentage of each tumor was scored by a pathologist from H&E-stained slides¹³. Tumors were graded as cold (<10% stromal TIL), hot (>60% stromal TIL), or intermediate (10-60% stromal TIL). We initially collected and analyzed 7 cold tumor-TDLN_Cold pairs and 8 hot tumor-TDLN_Hot pairs (Fig. 1, Table S1) as a Discovery set. FFPE samples were subjected to NanoString® analyses (Data file S1, Fig. S1). By using the NanoString® Pan-Cancer immune panel, we quantified the expression of 730 immune genes of these 15 tumor-free TDLNs and paired tumors. Initial analyses of the gene expression between TDLN_Cold vs. TDLN_Hot found no significantly differentially expressed genes (adjusted p-value < 0.10 and absolute log2 fold change > 1.0; Fig. S2A), suggesting that TDLN_Cold and TDLN_Hot had similar overall immune transcriptomic profiles. As the major difference between cold vs. hot tumors is their T cell infiltration levels, we examined genes related to T cell activation (Fig. S2C) and T cell proliferation (Fig. S2D) based on the panel annotation. Somewhat surprisingly, no significant changes in expression profiles were found between TDLN_Cold and TDLN_Hot for T cell activation (Fig. S2E) and T cell proliferation (Fig. S2F). Further analysis of 14 immune cell scores (Data file S2) provided by NanoString also showed no significantly different immune cell types between TDLN_Cold vs. TDLN_Hot (Fig. S2B, Data file S3). Since TDLN is the major site for antigen presentation by DCs, we also estimated the cell abundances of major DC subtypes (i.e., cDC1, cDC2, and CCR7⁺ mature DC) based on a set of signatures from a single-cell RNA-sequencing dataset¹⁴ (Data file S2). Again, no abundance differences were found in all three DC subtypes (Fig. S2G).

Dysfunctional immune coordination revealed by intranodal self-analysis in TDLN_Cold

Given the lack of significant differences in cell type abundance or static gene expression between TDLN_Cold vs. TDLN_Hot, we hypothesized that differences may lie in cell-cell interactions. To address this, we investigated immune coordination within TDLNs draining cold vs. hot tumors. We developed an intranodal self-correlation analysis approach, a computational method combining correlation analysis with unsupervised clustering algorithms (pseudocodes are available in Supplementary Methods). We first calculated Pearson’s correlation coefficient (PCC) of the expression values for each pair of genes in TDLN_Cold vs. TDLN_Hot. Next, we applied two unsupervised clustering algorithms (i.e., hierarchical clustering and k-means clustering) with the same parameters to group gene pairs based on the correlation similarities for TDLN_Cold and TDLN_Hot separately. Since gene-to-gene correlations can reflect gene regulatory relationships, and organized gene regulatory relationships are necessary to perform normal cellular functions^15,16, we proposed that the clustered pattern of gene-to-gene correlations can represent intranodal gene regulation.

We first implemented the hierarchical clustering algorithm on heatmaps with gene correlation matrices for TDLN_Cold vs. TDLN_Hot (Fig. 2A, left upper panel and right lower panel). To directly address the difference in clustered patterns between TDLN_Cold and TDLN_Hot, we aligned the clustered orders of TDLN_Cold and TDLN_Hot into each other (Fig. 2A, right upper panel and left lower panel). This combined analysis revealed that gene-to-gene correlations of TDLN_Hot were clustered in a more organized manner compared to TDLN_Cold.

**Fig. 2: Intranodal self-correlation analysis reveals dysfunctional transcriptome-based immune regulation in tumor draining lymph nodes (TDLN_Cold) with cold tumors.**

To quantify this observation, we implemented k-means clustering for the PCCs of TDLN_Cold and TDLN_Hot separately, then calculated Silhouette scores for 2 to 12 clusters (Fig. 2B, Supplementary Methods). Significantly lower Silhouette scores were observed in TDLN_Cold (p = 6.0 × 10⁻⁴), indicating that the PCC similarities within a k-means cluster of TDLN_Cold was lower than TDLN_Hot, and the differences crossing k-means clusters in TDLN_Cold is less distinguishable compared to TDLN_Hot. In summary, poorly organized gene-to-gene correlation in TDLN_Cold indicated that its immune coordination was dysfunctional compared to TDLN_Hot, which might lead to the “cold” immunophenotype in its paired tumor. In addition, such dysfunctional intranodal immune coordination might impair the robustness of the TDLN response to extra-nodal stimulation and tumor antigen.

Beyond clustering patterns between TDLN_Cold and TDLN_Hot, we also compared the specific PCCs of each gene pair. To avoid spurious strong correlations driven by a single outlier and other data points with low variance, we implemented the leave-one-out comparison (LOOC) for each gene pair inspired by the leave-one-out cross validation in machine learning (detail methods are described in Methods as well as Supplementary Methods). Among the most differentially correlated gene pairs based on statistics from LOOC (Fig. S3), we identified that correlations of TBX21- and GATA3-CXCR1 pairs were both significantly different between TDLN_Cold and TDLN_Hot (p_TBX21-CXCR1 < 1.0 × 10⁻⁴ [Fig. 2C], p_GATA3-CXCR1 = 1.7 × 10⁻³ [Fig. 2D])—but in opposite directions. TBX21-CXCR1 correlation was positive (r_Cold = 0.846, p_Cold = 0.02) in TDLN_Cold, but negative (r_Hot = −0.878, p_Hot = 4.1 × 10⁻³) in TDLN_Hot (Fig. 2C). GATA3-CXCR1 showed the opposite pattern (r_Cold = −0.557, p_Cold = 0.19; r_Hot = 0.901, p_Hot = 2.2 × 10⁻³; Fig. 2D). DCs have been described to downregulate CXCR1 during maturation within the T cell zone of TDLNs¹⁷. TBX21 (T-bet) and GATA3 are well-recognized markers of Th1 and Th2 cells, respectively¹⁰. We hypothesized that intranodal correlations of TBX21- vs. GATA3-CXCR1 can reflect differential naïve CD4⁺ T cell polarization by mature DCs in TDLN_Cold vs. TDLN_Hot. Within these contexts, our data suggested that in TDLN_Cold, GATA3 expression would be upregulated during the downregulation of CXCR1 in DC maturation and priming. Therefore, we further hypothesized that mature DCs in TDLN_Cold would preferably prime naïve CD4⁺ T cells into Th2 cells.

Preferred Th2 cell polarization within TDLN_Cold

To support the hypothesis of preferred Th2 polarization by mature DCs in TDLN_Cold, we implemented multiplex immunofluorescence (mIF) staining of TDLN_Cold and TDLN_Hot samples (Fig. S4, Fig. 3A and Fig. 3B, Data file S1). We used DC-LAMP3 as a mature DC marker¹⁸ and T-bet/GATA3 as Th1/Th2 cell markers, combined with CD3 and CD4 T cell markers. Mature DC, Th1, and Th2 cells were defined by CD3^-DC-LAMP3⁺ cell, CD3⁺CD4⁺T-bet⁺ cell, and CD3⁺CD4⁺GATA3⁺ cell, respectively (Table S2, Fig. S5). First, we quantified the abundances of each cell type within TDLNs in two ways, cell densities (cells/mm², Fig. S6A) and relative cell abundances (cells/total cells, Fig. S6B). Consistent with our analysis of NanoString cell type scores, no difference in densities or relative abundances of any cell type quantified by this mIF panel were observed. This indicated that abundances of mature DC, Th1, Th2, or CD3⁺CD4^- cells within TDLNs were not associated with lymphocyte infiltration into their paired tumors (Fig. S6A, B). Besides the overall abundances normalized to areas and total cell numbers, we further quantified the Th2 percentage relative to total CD4⁺ T cells (CD3⁺CD4⁺ cells), and the ratio of Th2 to Th1 cell numbers. We found that both Th2% to total CD4⁺ T cells and Th2/Th1 ratios were significantly higher in TDLN_Cold compared to TDLN_Hot (p = 6.0 × 10⁻⁴ and p = 6.0 × 10⁻⁴). This finding supports the preferred Th2 polarization in CD4⁺ T cells within TDLN_Cold (Fig. 3C, D).

**Fig. 3: Differential spatial distributions between tumor draining lymph nodes (TDLN) with cold and hot tumors.**

To investigate the potential mechanism for preferred Th2 polarization, we quantified the spatial relationships between mature DCs and Th1/Th2 cells. We implemented a spatial clustering algorithm, density-based spatial clustering of applications (DBSCAN), a widely used spatial clustering algorithms in biological studies^19,20,21, on mature DC, Th1, and Th2 cells (more details described in the Methods section). Based on their intra-cluster cell compositions, we defined Th1-dominated clusters as clusters which contained both mature DC and Th1 cells, with Th1 higher than Th2 cell numbers. In contrast, Th2-dominated clusters have mature DCs with higher Th2 than Th1 cells. Cluster compositions of each slide are provided in Data file S4. We calculated the percentages of Th1- and Th2-dominated clusters relative to total cluster numbers for each TDLN. We then compared the cluster percentages between TDLN_Cold vs. TDLN_Hot for each Th-dominated cluster type. These showed that Th2-dominated clusters were enriched in TDLN_Cold compared to TDLN_Hot (p = 0.05, Fig. 3F), while percentage of Th1-dominated clusters were comparable (p = 0.53, Fig. 3E). In sum, Th2 dominated clusters were enriched within TDLN_Cold, indicating preferential Th2 cell polarization compared to Th1 cells (Fig. 3G).

IL4 from MCs drives preferred Th2 polarization in TDLN_Cold

To gain further insight into what may be associated with the preferential mature-DC-Th2 polarization within TDLN_Cold, we examined IL4 expression, an essential cytokine in Th2 polarization^22,23,24. Upregulation of IL4 was observed in TDLN_Cold compared to TDLN_Hot (Fig. 4A, p = 0.02 with outlier exclusion; Fig. S2A, p = 0.04, log2 fold change = −0.739 without outlier exclusion and p-value adjustment). Since Th2 cell abundances were similar between TDLN_Cold and TDLN_Hot, we hypothesized that the upregulation of IL4 could be derived from other cell types. To investigate the IL4 resources, we examined the correlation coefficients and slopes between IL4 expression and 14 cell type scores from NanoString® nCounter® Pan-Cancer immune profiling panel (Fig. S7A) and found that the correlation between MC score and IL4 expression is the strongest with slope = 0.75 and r = 0.56 (Fig. S7B, p = 0.03) among all the 14 cell types.

**Fig. 4: IL4 from mast cells (MC) skews naïve CD4⁺ T cell polarization to T helper 2 (Th2) cells in tumor draining lymph nodes (TDLN) with cold tumors.**

Expression of multiple MC markers and the MC score were significantly upregulated in TDLN_Cold (Fig. 4B, p_TPSAB1 = 2.3 × 10⁻³ without the outlier; Fig. S8A–D, p_KIT = 0.23, p_CMA1 = 0.09, p_TPSAB1 = 0.02, p_IL3RA = 0.40 with outliers and without p-value adjustment) based on our NanoString data and corresponding MC signatures. Due to the low abundance of MCs within lymph nodes, this limits the accuracy of gene deconvolution methods^25,26,27. Bulk transcriptomic data with strict statistical adjustments could underestimate any modest enrichment of MCs within TDLN_Cold based on differential gene expression analysis and comparisons of estimated immune cell abundances. As such, to examine MCs more accurately within TDLN_Cold compared to TDLN_Hot, we quantified MC densities by performing immunohistochemical (IHC) staining for CD117⁺ cells (Fig. 4C and Fig. 4E, Fig. S9A, Data file S1). We found that MC densities in TDLN_Cold were significantly higher than TDLN_Hot (Fig. 4G, p = 2.3 × 10⁻³, Fig. S10A). To further validate this finding, we collected another cohort with 10 TDLNs_Cold- and 10 TDLNs_Hot-tumor pairs (Table S3) as an independent validation cohort. The same definition of cold/hot tumors was applied to this independent validation cohort with the same pathologist. We similarly performed CD117 IHC staining and quantified MC densities (Fig. 4D, F, Fig. S9B). These results confirmed that MC densities within TDLNs can accurately reflect T cell infiltration of paired tumors (Fig. 4H, AUC = 0.80, p-value = 0.01, Fig. S10B, C, Data file S1).

To confirm the IL4 expression on MCs, we co-stained IL4 and CD117 on 30 ROIs (n_Cold = 15, n_Hot = 15, 1 mm × 0.62 mm) from 22 TDLN slides (n_Cold = 11 and n_Hot = 11), including samples from both discovery and validation cohorts (Data file S1, S5). We identified IL4⁺ MCs (IL4⁺CD117⁺ cells) within both TDLN_Cold and TDLN_Hot (Fig. 4I, J, Data file S5, Fig. S11, S12). The cell densities of IL4⁺ MCs were significantly higher in TDLN_Cold compared to TDLN_Hot (Fig. 4K, p = 0.01).

Another important question was whether IL4⁺ MCs need to be close to mature DC and T cells to skew the Th2 polarization. Considering the potential of IL4 to diffuse across different regions within TDLNs, we hypothesized that IL4 producing MCs may not need to be directly adjacent to T cells or DCs to influence their biology. Based on the qualitative observation of sinus enrichment of MCs from our H-DAB staining (Fig. S9A, B), we hypothesized that IL4⁺ MCs would also accumulate within the sinus region and IL4 could diffuse into surrounding regions to skew Th2 cell polarization. Guided by a clinical pathologist expert in lymphoma and lymph node structure research, we stratified the ROIs into B cell zones, T cell zones, and sinus regions (Fig. 4I, J, Fig. S12) based on CD3 staining. We found that densities of MCs were higher in the sinus (p < 1.0 × 10⁻⁴) and T cell zones (p < 1.0 × 10⁻⁴) than B cell zones (Fig. S10D). Comparing sinus and T cell zones, there was a trend (p = 0.07, 2-sided Wilcoxon matched-pairs signed rank test) towards more MCs within sinuses (Fig. S10D). Densities of IL4⁺ MCs were also higher within sinuses (p < 1.0 × 10⁻⁴) and T cell zones (p = 8.0 × 10⁻⁴) than B cell zones (Fig. 4M), while sinuses were also slightly enriched for IL4⁺ MCs (p = 0.08, 2-sided Wilcoxon matched-pairs signed rank test) compared to T cell zones. These spatial patterns indicate that IL4⁺ MCs within sinuses and T cell zones would associate with Th2 polarization in direct and indirect manners. Lastly, we compared the densities of IL4⁺ MCs between TDLN_Cold and TDLN_Hot within each TDLN region (Fig. 4N). TDLN_Cold has more IL4⁺ MCs than TDLN_Hot within both T cell zones (p = 4.2 × 10⁻³) and sinuses (p = 0.03).

In addition to densities of IL4⁺ MCs, we also calculated the percentages of IL4⁺ MCs in relation to total MCs. Higher percentages of IL4⁺ MCs in TDLN_Cold were observed as compared to TDLN_Hot when considering entire TDLNs (Fig. 4L, p = 6.4 × 10⁻³). Within sinuses, but not T cell zones, percentages of IL4⁺ MCs were significantly higher in TDLN_Cold compared to TDLN_Hot (p_Sinus = 9.3 × 10⁻³, p_{T cell zone} = 0.41, Fig. 4O).

TDLN-driven type 2 immunity in tumor microenvironment

To complete the cancer-immunity cycle, we sought to connect our findings within TDLNs with the immune profiles of their paired tumors. Two NanoString® panels (i.e., nCounter® Pan-Cancer Immune Profiling panel and Cancer Pathway panel) representing more than 1000 immuno-oncological genes were used to investigate the TME of these primary tumors. Starting from differential gene expression analysis, we observed upregulation of type 2 immunity-related genes (Fig. S13A, C) in cold primary tumors compared to hot. Specifically, PDGFC and VEGFA, two growth factors secreted by tissue-repairing macrophages (TsRpM), were upregulated in cold tumors based on the Immune Profiling panel (Fig. S13B). Pro-fibrosis genes, including INHBB, LAMA5, PPARGC1A, and COL11A1 were upregulated in cold tumors based on the Cancer Pathway panel (Fig. S13D). Similarly, we implemented self-correlation analysis for gene expressions from the Immune Profiling panel of cold vs. hot primary tumors (same as what was done for TDLNs). We found that the clustering pattern of self-correlation matrix was less organized in cold tumors compared to hot ones, indicating that immune coordination was dysfunctional in cold tumors as well (Fig. S13E). In addition to the gene expression profiles, we estimated immune cell abundances from NanoString® annotation and corresponding nCounter® analysis (Data file S2). All immune cell types, except for MCs, were lower in cold tumors as compared to hot tumors (Fig. S13F).

We looked further into the relative abundances and gene expression differences between cold vs. hot tumors. First, we calculated the Th2/Th1 ratios by dividing the Th2 cell type score to the Th1 cell type score from NanoString® nSolver analyzer v4.0 for each sample. We found that Th2/Th1 ratios were significantly negatively correlated with PTPRC (CD45) and CD3D expression in TNBC tumors (Fig. 5A, B), which supported the Th2/Th1 axis being a key feature of immune infiltration. As predicted from our TDLN results, we found significantly higher Th2/Th1 ratios in cold paired primary tumors as compared to hot (Fig. 5E, p = 6.0 × 10⁻⁴). To further validate in a larger cohort, we utilized the METABRIC database with all available TNBC primary tumor samples and corresponding transcriptomic data. METABRIC-TNBC tumor samples were stratified into cold vs. hot samples based on PTPRC expression (bottom/top 35 percentile), respectively²⁸. The abundances of Th2 and Th1 cells were estimated by MCP-Counter method using the same signatures from NanoString®. Th2/Th1 ratios were calculated by dividing the estimated abundances. Similar negative correlations between Th2/Th1 ratios and PTPRC/CD3D (Fig. 5H/I) were found in METABRIC-TNBC samples as from our in-house samples. METABRIC-TNBC samples also showed Th2/Th1 ratios in cold tumors were significantly higher than in hot tumors (Fig. 5L, p < 1.0 × 10⁻⁴).

**Fig. 5: Impact of preferred T helper 2 (Th2) cell polarization to microenvironment of primary tumors (PT).**

Increased Th2/Th1 ratios within cold tumors mirrored the preferential Th2 cell polarization in paired TDLN_Cold. We further hypothesized that relative Th2 cell enrichment is associated with type 2 immune response limiting immune infiltration into cold tumors. Specifically, relative Th2 cell enrichment could increase IL4 production which would activate TsRpMs^29,30,31. TsRpM could then secrete multiple growth factors (e.g., platelet-derived growth factors, PDGFs) to stimulate cancer associated fibroblasts^32,33,34,35 (CAFs), which could limit lymphocyte infiltration by creating a fibrotic TME^36,37,38. In support of this hypothesis, we examined the relative expression of molecules mentioned above using both in-house and METABRIC-TNBC samples. We found that the relative expression of IL4 to T cell marker CD3D was significantly higher in cold tumors (Fig. 5F, p_NanoString = 1.2 × 10⁻³; Fig. 5M, p_{METABRIC-TNBC} < 1.0×10⁻⁴). Relative expression of PDGFC to macrophage marker CD68 was significantly higher in cold tumors (Fig. 5G, p_NanoString = 3.0 × 10⁻⁴; 5 N, p_{METABRIC-TNBC} < 1.0 × 10⁻⁴). We also investigated the correlations between Th2, TsRpM, and CAF cell type scores (Data file S2) using the MCP-counter method³⁹. These showed that Th2, TsRpM, and CAF were significantly positively correlated with each other in both in-house and METABRIC datasets (Fig. 5C, D, J, K). These strong correlations across key components in type 2 immunity further support the hypothesis that type 2 immune response modulates immune infiltration into tumors.

To further support a more fibrotic TME in cold tumors, we re-analyzed another public transcriptomic dataset (GSE88715). In this previous study, the authors collected 38 TNBC primary tumors and stratified these tumors into four groups (i.e., immune desert [ID], marginal restricted [MR], stromal restricted [SR] and fully inflamed [FI]) based on the immune infiltration levels and locations from clinical pathologists³⁶. They micro-dissected the samples into epithelium and stroma regions and quantified their gene expression via microarrays for each region. To align their groups with our cold/hot definitions based on stromal TIL percentages (i.e., TIL% > 60% as hot, while TIL% <10% as cold), we considered ID and MR as the cold tumors (n = 16), and SR and FI as the hot tumors (n = 22). First, we compared the gene expression profiles between cold and hot tumors within stroma (Fig. S14A) and epithelium (Fig. S14B) regions. Within the stroma regions (Fig. S14C), cold tumors upregulated tissue repairing and fibrosis related genes (i.e., FGF1, COL10A1, MMP11, COL5A2), while cytotoxicity and CD8⁺ T cell markers were upregulated in hot tumors (i.e., CD8A, CD8B, GZMB, CXCL13). In contrast, the expression profiles of epithelium regions were similar between cold and hot tumors (Fig. S14B). From cell abundances estimated by MCP-Counter, T cells and CD8⁺ T cells were significantly enriched in stroma regions of hot tumors (Fig. 5O, P, p_adj = 1.7 × 10⁻³, Fig. S14D), while CAF was the only significantly upregulated cell type in stroma of cold tumors (p_adj = 2.2 × 10⁻³, Fig. 5Q, R, Fig. S14D). These results suggest higher stromal CAFs in cold tumors compared to hot tumors, leading to a more fibrotic TME in cold tumors. We further compared T cell scores between epithelium and stroma regions within cold primary tumors and found a significantly lower T cell score in epithelium vs. stroma regions (Fig. S14E). Associated with this T cell stasis within stroma, more CAFs were found in stroma (Fig. S14F) compared to epithelium regions.

Discussion

To gain insights into tumor extrinsic factors that regulate immune infiltration into tumors, we investigated the impact of TDLNs by implementing a machine-learning-inspired intranodal self-correlation analysis of gene expression data, with further spatial analysis of mIF staining. Our results provide valuable insights into how anti-tumor immunity may unfold over time systemically (Fig. S15). Our initial results showed that immune cell abundances and static gene expressions were not different between TDLN_Cold and TDLN_Hot. Rather, correlations between immune components and their clustering patterns were significantly different between TDLN_Cold and TDLN_Hot. Following initial findings from our intranodal self-correlation analysis, we further implemented mIF staining to investigate CD4⁺ T cell polarization in TDLNs. In line with results from self-correlation analysis, spatial analysis shows enrichment of mature-DC-Th2 clusters in TDLN_Cold, suggesting that mature DCs within TDLN_Cold may preferentially polarize naive CD4⁺ T cells to Th2 cells. We also found that Th2 cell polarization preference was associated with increased IL4 production by MCs in TDLN_Cold. Downstream, preferred Th2 polarization within TDLN_Cold was associated with higher Th2 compared to Th1 cells in paired cold tumors. This Th2 enrichment was found to be linked to a type 2 immune response within cold tumors, along with more activating tissue repair macrophages and cancer-associated fibroblasts.

Low abundance of MCs within lymph nodes limited the ability of gene deconvolution approaches from bulk transcriptomic data with strict statistical adjustments to uncover potential MC enrichment and IL4 upregulation from comparative analysis^25,26,27. Going beyond bulk transcriptomic analysis, we implemented mIF and IHC staining to accurately quantify MCs along with IL4 expression in our initial cohort and an additional independent validation cohort. We found consistent enrichment of MCs and IL4 expression on MCs within the sinus regions of TDLN_Cold.

Correlation analysis has been applied to transcriptomic data since early 2000s^40,41,42. In cancer immunology, it has been successfully used to unravel various biological insights, e.g., identification of potential therapeutic targets⁴³, biomarkers⁴⁴, dysregulated pathways, and key regulatory changes⁴⁵. In our study, we combined two distinct features of correlation analysis into our intranodal self-correlation analysis. Focusing initially on clustering patterns, we discovered dysfunctional immune coordination in TDLN_Cold compared to TDLN_Hot. Inspired by leave-one-out cross validation, an evaluation method used in machine learning, we further identified specific gene pairs (i.e., TBX21- vs. GATA3-CXCR1) representing coordinated cellular regulations. Importantly, both major findings derived from self-correlation analysis of transcriptomic data were validated by spatial analysis of mIF staining, highlighting the capacity of correlation analysis to discover cellular dysregulations.

Our findings consistently revealed that even though there are no overall cell abundance differences (except for MCs) between TDLN_Cold vs TDLN_Hot, there are gene correlation, relative Th2/Th1 ratio or percentage of Th2 cells to total CD4⁺ T cells and spatial relationship differences that are biologically consistent. Several papers have shown that spatial organization, rather than numerical differences, of immune cells within TDLNs^46,47 or tumors⁴⁸ correlate with clinical outcomes. In this study, preferential Th2 polarization in TDLN_Cold vs. TDLN_Hot is suggested by increased relative Th2 cell abundances (to Th1 or to total CD4⁺ T cells) and different spatial distributions of mature-DC-Th cells, rather than overall Th cell abundances. These findings suggest that preferential Th2 polarization could only impact the relative abundances of Th2 cells to Th1 and total CD4⁺ T cells, but not the overall abundances between TDLN_Cold vs. TDLN_Hot. Potential mechanism of the similar overall Th cell abundances is the rapid and similar egress rates of Th cells such that these cells do not accumulate within TDLNs⁴⁹, supported by our similar expression levels of Th2 egress related genes.

MCs play important roles in immune activation⁵⁰, wound healing⁵¹, allergy⁵², and tumor immunology⁵³. Tumor infiltrating mast cells (TIMC) have found to associate with prognosis in multiple cancer types, including breast cancer⁵⁴, pancreatic cancer, ovarian cancer, and liver cancer⁵⁵. In prostate adenocarcinoma models, treatment with MC degranulation inhibitor (cromolyn) inhibited tumor growth⁵⁶. However, the immuno-oncological functions of MCs in either TME or systemic immunity are still poorly understood^57,58. Both in vivo experiments with a MC-deficient mouse breast cancer model and in vitro co-culture experiments demonstrated that MCs promoted tumor growth by promoting estrogen receptor activities in breast cancer cells⁵⁹. In our study, we found that IL4⁺ MCs are associated with preferred Th2 polarization in TDLNs by enriching within the sinus and T cell zones of TDLNs associated with cold TNBC tumors. Similarly, preferred Th2 cell polarization in TDLNs was also identified by De Monte et al.⁶⁰ in pancreatic ductal adenocarcinomas, in which the TME has limited lymphocyte infiltration and enrichment of Th2 cells⁴⁴. In their mouse model, basophils within TDLNs were found as the primary source of IL4 production, and their recruitment from blood partially relied upon the chemokine CCL7/MCP3 released from “alternatively activated” monocytes⁶⁰. Other examples of Th1 vs. Th2 priming preference driving divergent downstream immune responses were shown in two recent elegant animal studies. In an animal model of contact hypersensitivity induced by the hapten fluorescein isothiocyanate, injection of diphtheria toxin increased the population of CD11b⁺ type 2 conventional DCs (cDC2) in skin-draining lymph nodes, which significantly increased the Th2-dependent skin inflammation⁶¹. In addition, a recent animal study of lung cancer showed that IFNγ within mediastinal lymph nodes (mLNs) skewed regulatory T (Treg) cells to become Th1-like effector Treg cells which suppressed cDC1s and led to mLN-specific failure of cytotoxic T cell activation⁶². Our findings suggest a potential therapeutic opportunity in modulating MCs within TDLNs to promote T cell infiltration into tumors and enhance immunotherapies. Consistent with this view, a recent study using humanized melanoma mouse model demonstrated that anti-PD1 treatment combined with sunitinib could deplete MCs and achieve complete regression of tumors, while anti-PD1 treatment only could not⁶³.

Downstream to TDLNs, we also found an increased Th2/Th1 ratio within cold tumors, which is consistent with previous studies^44,64. Major cytokines secreted by Th2 cells, IL-4, and IL-13, could stimulate tumor-associated macrophages (TAM) to promote tissue-repairing functions and create an anti-inflammatory immune contexture^65,66,67,68. Specifically, tissue-repairing TAMs could secrete IL-10, TGF-β, VEGFs, PDGFs, MMPs, and osteopontins⁶⁹ to induce tissue fibrosis through fibroblasts. In our findings, upregulation of VEGFA and PDGFC were associated with limited immune infiltration. Additionally, we found strong correlations between Th2 cell, TsRpM, and CAF, which are consistent with the Th2-TsRpM-CAF axis limiting immune infiltration³⁶. In a recent study with three different breast cancer mouse models, inhibition of Th2 function with suplatast tosilate significantly improved infiltration of CD8⁺ T cells and reduced M2-like macrophages with reduction of IL4 production⁷⁰. By combining the ICB treatments, inhibition of Th2 function improved ICB efficacy without increasing potential adverse effects⁷⁰. Based on this Th2-TsRpM-CAF axis, various potential therapeutic targets are being developed and some drugs have been shown to be effective in combination with ICI treatments⁶⁸.

Due to inherent limitations of research from human patient samples, our findings are observational and correlative by nature. However, our conclusions are derived from multiple different experimental techniques (including intra-nodal self-correlation analysis of transcriptomic data, cell abundance analysis, and spatial clustering analysis of histological staining), and further validated with an independent patient cohort with total 43 TDLN-tumor pairs. It is impossible to directly manipulate TDLNs or tumors in live patients. While there are inbred mouse models with cold vs. hot tumors, these are of different tumor types with different mutational burden. Our findings are on a single tumor type, triple negative breast cancer, with different levels of immune infiltration. These valuable human findings provide a direct and more precise representation of human biology than animal studies.

In sum, our results highlight the important roles of TDLNs in regulating T cell infiltration into tumors, and the value of paired TDLN-tumor studies to identify key mechanisms within the entire CIC. These findings provide translationally valuable insights (i.e., association between TDLN immune profile and type 2 immunity within tumors) and potential therapeutic strategies (i.e., modulate IL4⁺ MCs within TDLNs) to enhance lymphocyte infiltration into tumors.

Methods

Study design

Specimens for both discovery and validation cohorts were collected and biobanked under IRB 07047 at City of Hope (Note S1). Secondary research use of specimens in this study was approved under IRB 12195 and IRB 11273 at City of Hope. Patient characteristics of discovery and validation cohorts are summarized in Table S1 and Table S3. TDLNs in both discovery and validation cohorts were sentinel lymph nodes without any metastasis. TDLNs were neoadjuvant treatment-free and collected at diagnosis with paired primary tumors. The classification of tumor samples as TNBC was performed by clinical pathologists. The stratification of “cold” (n_Discovery = 7, n_Validation = 14) and “hot” (n_Discovery = 8, n_Validation = 14) tumors was performed by clinical pathologists examining the stromal TIL percentages in H&E-stained slides (<10% and >60%), respectively¹³.

Quantification of gene expression and cell type score through NanoString for TDLNs and paired tumors

RNA was extracted from 10 µm thick slices of unbaked formalin fixed paraffin embedded (FFPE) tissue using Qiagen miRNeasy FFPE kits. RNA transcripts related to immuno-oncology were detected using NanoString® PanCancer Immune Panel with nCounter® technology (NanoString Technologies) for both TDLN and primary tumors. RNA transcripts related to oncology were detected using NanoString® PanCancer Cancer Pathway Panel. The regions for RNA extraction were selected by a clinical pathologist with valid TDLN regions (Fig S1, Data file S1). RNA concentration was assessed with the Nanodrop spectrophotometer ND-1000 and Qubit 3.0 Fluorometer (Thermo Fischer Scientific). RNA fragmentation and quality control were further determined by 2100 Bioanalyzer (Agilent). Total RNA was hybridized overnight at 65 °C for 14 to 18 h as per manufacturers’ recommendations. Post-hybridization, probe-target mixture was purified by nCounter Prep Station and then quantified with nCounter Digital Analyzer. Quality control and normalization of data were performed with nSolver Analysis Software version 4.0 and nCounter Advanced Analysis Software version 2.0.115, in which the measured gene expression values were normalized to the geometric mean of 40 housekeeping genes. The cell type scores were calculated as the mean of the log2 expression levels for all the probes included in the final calculation for that specific cell type (Data file S2).

Intranodal self-correlation analysis for gene expression

The log2 transformed normalized gene expression was used in self-correlation analysis. The Pearson correlation coefficient (PCC) for each gene pair was calculated within “cold” and “hot” cohorts, separately. The hierarchical clustering for gene-to-gene correlation matrices of “cold” and “hot” cohorts was implemented separately by the hierarchical clustering algorithm in ComplexHeatmap (v2.10.0) R package with its default settings. The clustering orders of the “cold” or “hot” cohort were mapped to the other cohort (i.e., “hot” and “cold” cohort) respectively to directly compare the different clustering results. To further quantify the differences of cluster patterns, the K-means clustering algorithm (Python sklearn.cluster.KMeans function, v1.1.1) was implemented to both correlation matrices and average silhouette scores were calculated from 2 to 12 clusters by Python function sklearn.metrics.silhouette_score (v1.1.1), respectively. The statistical comparisons for specific gene pairs were conducted by cocor R package v1.1.3 (i.e., Fisher’s z test) with two independent groups settings⁷¹. To avoid the potential high correlation driven by single outlier, a customized comparison approach (i.e., leave-one-out comparison, LOOC) inspired by leave-one-out cross validation was implemented with R scripts. Generally, for each gene pair in each cohort, a set of PCCs was calculated by iteratively leaving a sample out of the PCC calculation until each sample was left for one time. Then the means of LOO PCC sets between TDLN_Cold and TDLN_Hot was compared by multiple t test with FDR p-value adjustment implemented in R language (Supplementary Methods).

Multiplex immunofluorescence staining

Formalin-fixed paraffin-embedded (FFPE) tumor-negative TDLNs were used to perform immunofluorescent staining and spatial analysis. FFPE tissue samples were sectioned at a thickness of 3μm, baked, and placed on positively charged glass slides. They were deparaffinized with xylene and rehydrated with decreasing gradient concentrations (100%, 95%, 70%, and 0%) of ethanol, respectively. Antigen retrieval was performed with AR9 buffer (pH9) (Akoya Biosciences) or AR6 buffer (pH6) (Akoya Biosciences) using a microwave oven. Slides were washed with MilliQ water and then with TBS (Biocare Medical) with 0.1% Tween-20 (TBST) followed by blocking with Antibody Diluent Background Reducing (S3022, Agilent Dako). For mature-DC-Th cell panel, the following primary antibodies were incubated for 1 h at room temperature: anti-human CD3 (polyclonal; DAKO; 1:1000); anti-human CD4 (clone EP204; Cell Marque; 1:200); anti-human GATA3 (clone L50-823; Biocare Medical; 1:1000); anti-human T-bet (clone 4B10; Abcam; 1:500); anti-human DC-LAMP (clone 1010E1.01; Novus; 1:1000). For IL4-MC panel, the following primary antibodies were incubated for 1 h at room temperature: anti-human CD3 (polyclonal Rb A0452; DAKO; 1:1000); anti-human IL-4 (clone C1 Mu ab239508; Abcam; 1:500); anti-human CD117 (clone YR145 Rb ab32363; Abcam; 1:2000). The slides were then incubated with Mach 2 Rabbit or Mouse HRP-Polymer (Biocare Medical), and then immunofloursecently labeled using Opal Fluorophores (Akoya Biosciences). To continue with the next primary antibody staining, the next epitope was exposed using microwave treatment as described above. DAPI (Akoya Biosciences; 1:100) was stained after the final marker and the slides were mounted using ProLong Gold Antifade Reagent (P36930, Thermo Fisher). Slides were imaged using the Vectra 3 automated quantitative pathology imaging system (Akoya Biosciences).

For mature-DC-Th cell panel, multispectral images were unmixed and multi-component tifs were exported using inForm Software (Akoya Biosciences) and tif images were stitched for each patient using a customized QuPath (v0.2.0-m9) script⁷². The cell segmentation was implemented by “Cell detection” function in QuPath considering DAPI expression. The cell phenotyping was implemented by thresholding approach for each marker and further defined by the marker status (Table S2). The marker expression was visualized by heatmaps and density plots to examine the phenotyping.

For IL4-MC panel, the same unmix and export procedure as mature-DC-Th cell panel was implemented. ROIs with 1 mm wide and 0.62 mm height were selected randomly from the QuPath (v0.4.4) stitched whole TDLN slides. The representative areas of B cell zones, T cell zones, and sinuses in each ROI were annotated by clinic pathologists based on CD3 staining. A QuPath pixel classifier was trained for each ROI to split the ROI into B cell zone, T cell zone, and sinus subregions. The cell segmentation was implemented by “Cell detection” function in QuPath considering DAPI expression. The cell phenotyping was implemented by thresholding approach for each marker and further defined by the marker status. The marker expression was visualized by heatmaps and density plots to examine the phenotyping.

Spatial clustering for mature DC, Th1 and Th2 cells

The measurement table exported from QuPath included the x–y coordinates of each cell on a 2D space and the cell phenotypes (i.e., mature DC, Th1, and Th2 cells). For the spatial clustering of mature DC, Th1, and Th2 cells, DBSCAN was implemented with Python function (sklearn.cluster.DBSCAN)⁷³. The default minimum number of core cells (minPts = 5) was used for all the slides. The maximum distance between two cells for one to be considered as in the neighborhood of the other (epsilon) was determined by selecting the elbow points on the k-distance graphs generated by Python function (sklearn.neighbors.NearestNeighbors) with default settings for each slide. The elbow point selection was implemented by Python function (kneed.KneeLocator) (Data S4). For all the other parameters in DBSCAN, the default settings were used. The detailed cluster assignment of each cell in each TDLN was also provided in Data S4. The cluster annotation was determined by the cell composition of each cluster. Specifically, the mature-DC-Th1 dominated clusters must include mature DC and Th1 cells and if Th2 cells were included as well, more Th1 cells should be in these clusters compared to Th2 cells and vice versa to mature-DC-Th2 dominated clusters. The Python scripts used here are available in the GitHub repository with full details.

H-DAB staining

FFPE tissue samples were sectioned at a thickness of 5μm, baked, and placed on positively charged glass slides. Slides were loaded on a Ventana Discovery Ultra (Ventana Medical Systems, Roche Diagnostics) automated IHC staining machine for deparaffinization, rehydration, endogenous peroxidase activity inhibition, and heat inactivation antigen retrieval (pH 8.5). Anti-human CD117, c-kit (Clone: YR145; Cell Marque; 1:50) rabbit antibody was incubated followed by DISCOVERY anti-Rabbit HQ and DISCOVERY anti-HQ-HRP. The stains were then visualized with DISCOVERY ChromoMap DAB and counterstained with hematoxylin (Ventana) and sealed with coverslips. The whole slide images were acquired with NanoZoomer S360 Digital Slide Scanner (Hamamatsu) and viewed by NDP.view image viewer software.

The whole slide of each sample was analyzed by QuPath v0.3.2⁷². The cell segmentation waw implemented by “Cell detection” function in QuPath considering hematoxylin OD intensity. CD117⁺ cells (i.e., MCs) were identified by “Training object classifier” function with manually selected CD117⁺ cells and random-forest algorithm.

Statistics and reproducibility

Graphs and statistics were performed using Graphpad Prism 9 (v9.4), R (v4.1.3), and Python (v3.9.13) as described. Statistics described were generated using multiple 2-sided Mann–Whitney tests with Holm–Sidak p-value adjustment, Kruskal–Wallis test with Dunn’s multiple comparison, paired 2-sided Student’s t test, 2-sided Fisher’s z test, or 2-sided Mann–Whitney tests. No statistical method was used to predetermine the sample size. All outliers were identified and removed by Grubbs’ method (alpha = 0.05) or ROUT method with Q = 1% in Graphpad Prism 9. The investigators were blinded to allocation during experiments and outcome assessment. Calculated p values are displayed as “ns”, p > 0.05; *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001; ****, p ≤ 0.0001. Experimental specific detailed statistic methods are described in corresponding figure legends.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The NanoString data (pan-cancer pathway panel) generated in this study have been deposited in the GEO database under accession code GSE272453. The NanoString data (pan-cancer immune profiling panel) generated in this study have been deposited in the GEO database under accession code GSE272455 (Pan-cancer immune profiling panel). The raw data of mIF staining in this study are provided in the Supplementary Datasets on FigShare [https://doi.org/10.6084/m9.figshare.25157960]. The detailed spatial clustering data from mIF staining in this study are provided in the Supplementary Datasets on FigShare [https://doi.org/10.6084/m9.figshare.25157924]. The processed data used in the figures/Supplementary Figs. is provided in Source Data on FigShare [https://doi.org/10.6084/m9.figshare.26384302]. Source data are provided with this paper.

Code availability

All scripts used in this study are available in GitHub [https://github.com/weihuaguo/tdln_cold_hot_tnbc] and Zenodo [https://doi.org/10.5281/zenodo.13126674].

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Acknowledgements

The authors would like to thank Michele Kirschenbaum for obtaining patient consent, procuring tissue samples, and obtaining clinical information. We thank Dr. Russell Rockne and Dr. Andrei Rodin for suggestions on algorithm improvement. We thank Dr. Kim Margolin for valuable translational suggestions. We thank Dr. Haiqing Li for critical review of this manuscript. We appreciate the funding resource of Stand Up To Cancer and Breast Cancer Research Foundation grant SU2C-BCRF 2015-001 (PPL).

Author information

Lei Wang
Present address: International Cancer Center, Shenzhen University Medical School, 518060, Shenzhen, Guangdong, China
Min Hui Lim
Present address: Genomics Core, Cleveland Clinic, Cleveland, OH, 44106, USA
Christina H. Wei
Present address: Pathology Laboratory Administration, Los Angeles General Medical Center, Los Angeles, CA, 90033, USA
Caroline Hoffmann
Present address: Owkin, Inc., New York, NY, 10003, USA

Authors and Affiliations

Department of Immuno-Oncology, Beckman Research Institute, City of Hope, Duarte, CA, USA
Weihua Guo, Jiayi Tan, Lei Wang, Colt A. Egelston, Diana L. Simons, Min Hui Lim, Shawn Solomon, Caroline Hoffmann & Peter P. Lee
Irell & Manella Graduate School of Biological Sciences, City of Hope Comprehensive Cancer Center, Duarte, CA, 91010, USA
Jiayi Tan
Department of Surgery, City of Hope Comprehensive Cancer Center, Duarte, CA, 91010, USA
Aaron Ochoa
Mork Family Department of Chemical Engineering & Material Science, University of Southern California, Los Angeles, CA, 90089, USA
Lu Wang
Department of Medical Oncology, City of Hope, Duarte, CA, 91010, USA
James Waisman
Department of Pathology, City of Hope, Duarte, CA, 91010, USA
Christina H. Wei, Joo Song & Daniel Schmolze

Authors

Weihua Guo
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Jiayi Tan
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Peter P. Lee
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Contributions

W.G. and P.P.L. conceptualized experiments; W.G., J.T., Lei W., C.A.E., D.L.S., A.O., M.L., S.S. and J.S. conducted experiments; W.G., Lei W. and Lu W. analyzed experimental data; W.G. and P.P.L. wrote the original manuscript; W.G., J.T., Lei W., Lu W., C.A.E., D.L.S., M.L., S.S., J.W., C.W., C.H., D.S. and P.P.L. revised and finalized the manuscript.

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Correspondence to Peter P. Lee.

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C.H. serves as advisor and has received honoraria from Nanobiotix and Owkin. All other authors declare that they have no competing interests.

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Nature Communications thanks Jo Van Ginderachter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Guo, W., Tan, J., Wang, L. et al. Tumor draining lymph nodes connected to cold triple-negative breast cancers are characterized by Th2-associated microenvironment. Nat Commun 15, 8592 (2024). https://doi.org/10.1038/s41467-024-52577-y

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Received: 30 March 2023
Accepted: 10 September 2024
Published: 04 October 2024
DOI: https://doi.org/10.1038/s41467-024-52577-y

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