食管癌亚型的单细胞比较分析揭示了肿瘤微环境的差异,解释了不同的免疫治疗反应。

IF 24.9 1区 医学 Q1 ONCOLOGY
Seungbyn Baek, Junha Cha, Min Hee Hong, Gamin Kim, Yoon Woo Koh, Dahee Kim, Wonrak Son, Chan-Young Ock, Seungeun Lee, Martin Hemberg, Seong Yong Park, Hye Ryun Kim, Insuk Lee
{"title":"食管癌亚型的单细胞比较分析揭示了肿瘤微环境的差异,解释了不同的免疫治疗反应。","authors":"Seungbyn Baek,&nbsp;Junha Cha,&nbsp;Min Hee Hong,&nbsp;Gamin Kim,&nbsp;Yoon Woo Koh,&nbsp;Dahee Kim,&nbsp;Wonrak Son,&nbsp;Chan-Young Ock,&nbsp;Seungeun Lee,&nbsp;Martin Hemberg,&nbsp;Seong Yong Park,&nbsp;Hye Ryun Kim,&nbsp;Insuk Lee","doi":"10.1002/cac2.70046","DOIUrl":null,"url":null,"abstract":"<p>Esophageal cancer comprises 2 anatomically shared but histologically different subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Previous bulk-level genomic and clinical studies have shown that ESCC shares molecular features with head and neck squamous cell carcinoma (HNSCC) [<span>1</span>] and is generally more responsive to immune checkpoint blockade (ICB) therapies than EAC [<span>2</span>], which is similar to gastric adenocarcinoma (GAC) [<span>1</span>]. Recent clinical trials have further demonstrated clinical benefits from various ICB therapies, including combination approaches, for ESCC [<span>3</span>].</p><p>To further expand the comparison at single-cell resolution of tumor microenvironment (TME), we conducted single-cell transcriptomic analysis on tumors from 35 patients representing 4 cancer types located near the esophagus: ESCC, EAC, HNSCC, and GAC (Supplementary Materials and Methods). By integrating newly generated single-cell datasets with published datasets (Supplementary Table S1) [<span>4, 5</span>], we analyzed more than 200,000 cells within TME (Supplementary Figure S1, Supplementary Figure S2A-C, Supplementary Table S2). This high-resolution approach allowed the dissection of cellular heterogeneity of malignant cells and various immune components within the TME (Figure 1A).</p><p>Differentially expressed gene (DEG) analysis of malignant cells revealed a clear separation based on epithelial cell origin (Supplementary Figure S2D). For detailed cancer cell states, we generated 14 malignant metaprograms (MPs) using non-negative matrix factorization (Supplementary Table S3). Histology-specific MPs, representing squamous or glandular differentiation, dominated the expression landscape, while additional MPs distinguished tumor types based on cell cycle dynamics, endocrine-like features, and activation of Aldo-keto reductase family 1 (AKR1) family genes, thereby providing insights into diverse and shared oncogenic processes (Figure 1B). Furthermore, we generated MPs from immune and stromal compartments and calculated correlations among them (Supplementary Figure S2E, Supplementary Table S3). We identified clusters of coordinated MPs, including an “immune activating” cluster characterized by interferon signaling and activation of adaptive immunity, predominantly enriched in HNSCC and ESCC. In contrast, heat-shock protein (HSP) MP, negatively correlated with the immune activating cluster, was more common in EAC and GAC, suggesting potentially immunosuppressive TMEs.</p><p>To better understand the immune compartments of the TME, we conducted in-depth analyses of each major immune cell type using subclustering approaches. We first focused on CD8<sup>+</sup> T cells for their roles in anti-tumor immunity. We identified several key subtypes including naive/memory, effector, stress-response (HSP high), and exhausted populations (Supplementary Figure S3A-B, Supplementary Table S4). For exhausted populations, we evaluated whether each tumor type exhibited varying degrees of exhaustion and tumor reactivity. Indeed, both HNSCC and ESCC displayed higher tumor reactivity and exhaustion (Figure 1C, Supplementary Table S5). Higher levels of tumor infiltrating T cells for ESCC compared to EAC were validated with artificial intelligence (AI)-guided analysis of hematoxylin and eosin (H&amp;E) slide (Figure 1D, Supplementary Figure S3C-D, Supplementary Table S6). Another key subset identified was a T cell population with high HSP expression, recently identified as a stress-responsive population and a poor indicator of ICB responses [<span>6</span>]. The trajectory analyses identified distinct exhaustion and HSP trajectories for these populations (Figure 1E-F, Supplementary Figure S3E-F). For exhaustion trajectory, there were increasingly more populations at the late stage for HNSCC and ESCC but opposite for EAC and GAC (Figure 1G), suggesting inadequate activation of tumor-reactive populations for adenocarcinomas.</p><p>To identify key genes contributing to the differences in CD8<sup>+</sup> T cell populations, we performed DEG analysis comparing cells with high and low exhaustion and neoantigen-reactive scores (Supplementary Figure S3G, Supplementary Table S7). Among the top DEGs, we confirmed that C-X-C Motif Chemokine Ligand 13 (<i>CXCL13</i>) was highly enriched in effector and exhausted T cell populations, with GAC samples exhibiting very low percentages of <i>CXCL13</i>-expressing cells compared to all other samples (Supplementary Figure S3H). Furthermore, effector and exhausted CD8<sup>+</sup> T cells lacking <i>CXCL13</i> expression showed low signature scores for both exhaustion and neo-antigen reactivity, comparable to other non-effector CD8<sup>+</sup> T cells (Supplementary Figure S3I). Pathway analysis revealed that CXCL13<sup>+</sup> CD8<sup>+</sup> T cells are associated with increased T cell infiltration and activation through interferon-γ signaling, costimulation by CD28 family, and antigen processing pathways (Figure 1H).</p><p>CD4<sup>+</sup> T cells and B cells also play key roles in immunotherapy-related activities, particularly through the formation of tertiary lymphoid structure (TLS) [<span>7</span>]. Among their subtypes (Supplementary Figure S4A-D, Supplementary Tables S8-S9), we focused on CD4<sup>+</sup> follicular helper T (Tfh) cells and germinal center B (GCB) cells. Our analysis revealed that ESCC, and to a lesser extent HNSCC, harbored higher proportions of Tfh cells with elevated <i>CXCL13</i> expression (Supplementary Figure S4E), a chemokine known to promote early TLS maturation by attracting B cells [<span>8</span>]. We observed a positive correlation among these populations and an enrichment of GCBs and TLS signatures in ESCC (Figure 1I, Supplementary Figure S4F, Supplementary Table S5). Moreover, cell-cell interaction analyses confirmed that the crosstalk among TLS components including Tfh cells, GCBs, dendritic cells (DCs), and fibroblasts was stronger in HNSCC and ESCC than in EAC and GAC, supporting the notion that effective TLS formation contributes to superior immunotherapeutic responses (Figure 1J). These interactions were validated with a higher TLS density for ESCC quantified by AI-powered H&amp;E slide analyzer (Figure 1K, Supplementary Figure S3D).</p><p>Among identified cell subtypes in the myeloid compartment (Supplementary Figure S4G-H, Supplementary Table S10), we focused on tumor-associated macrophages (TAMs) because they play roles in both immune activation and suppression. While canonical polarization markers failed to distinguish TAM states (Supplementary Figure S4I, Supplementary Table S5), DEG analysis revealed that Macrophage receptor with collagenous structure (<i>MARCO</i>) was enriched in EAC and GAC, while <i>CXCL9</i> and <i>CXLC10</i> were enriched in HNSCC and ESCC (Figure 1L, Supplementary Table S11). These markers appear mutually exclusive, with only 0.4% of TAMs co-expressing both (Figure 1M). Pathway analysis and gene signature scores further indicated that CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs were enriched with interferon-γ pathways, a positive indicator of ICB response, while MARCO<sup>+</sup> TAMs were enriched in hypoxia pathways (Figure 1N-O). Recent studies linked hypoxia to resistance to ICB by interfering with other immune populations [<span>9</span>]. Negative correlations among the gene signature scores further validated exclusivity of these populations (Figure 1P).</p><p>Two cell types exhibited the most noticeable patterns: CD8<sup>+</sup> T cells and TAMs. CXCL13<sup>+</sup> CD8<sup>+</sup> T cells and CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs displayed high levels of cellular interactions (Figure 1Q), consistent with a coordinated, interferon-γ-driven response that may underlie enhanced sensitivity to ICB therapies. In HNSCC and ESCC patients, these populations were significantly co-abundant (Figure 1R). By contrast, HSP-high CD8⁺ T cells and MARCO⁺ TAMs—associated with hypoxic and stress-related signaling—likely contribute to the formation of “cold” tumors that are less responsive to immunotherapy, as reflected by their higher interactions (Supplementary Figure S4J). Notably, GAC patients showed significant co-abundance of these 2 subtypes (Supplementary Figure S4K).</p><p>To validate the involvement of these subtypes in immunotherapy responses, we used bulk RNA sequencing (RNA-seq) datasets from 8 cohorts across diverse cancer types (Supplementary Table S12). With gene signatures extracted from these populations (Supplementary Table S13), we calculated co-enrichment scores (co-score) for CXCL13<sup>+</sup>CD8<sup>+</sup> T cells and CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs. These co-scores showed significant differences between responders and non-responders in 6 cohorts (Figure 1S) and were highly predictive for responders (Area under the receiver operating characteristic curve [AUROC] &gt; 0.7) in 7 cohorts (Figure 1T). We also calculated differences in 2 signature scores (diff-score) for CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs and MARCO<sup>+</sup> TAMs. The diff-scores showed significant differences between responders and non-responders for 6 cohorts (Supplementary Figure S4L) and were predictive for responders in these cohorts (Supplementary Figure S4M). These results suggest that cellular signatures distinguishing esophageal cancer subtypes can predict ICB responses across diverse cancer types.</p><p>In summary, comprehensive single-cell analysis demonstrated that the distinct factors within the TME could explain the differential responses to ICB therapies between esophageal cancer subtypes. ESCC maintains a favorable TME characterized by interferon-γ-dominated, immune-activating populations while relative depletion of these favorable populations and higher prevalence of hypoxia-related populations in EAC could be linked to immune suppression. A recent single-cell study with neoadjuvant chemo-immunotherapy-treated ESCC patients similarly identified associations of interferon-γ-related genes to favorable treatment responses [<span>10</span>]. These contrasting TMEs not only provide insight into the mechanistic basis for the varied efficacy of immunotherapy but also suggest that tailoring treatment strategies to modulate specific immune subsets or to stratify patients based on the immune subsets could enhance clinical outcomes. This study lays the groundwork for developing predictive biomarkers and targeted interventions that consider unique immune landscapes associated with different tumor histology. However, due to the limited sample sizes and functional validations, future studies with larger cohorts and additional experimental validations are needed to further validate these results and to reduce possible confounders. Furthermore, future studies should explore additional factors influencing the TME, such as genetic mutations or environmental conditions, to provide further insight into tumor-immune interactions.</p><p>Seong Yong Park, Hye Ryun Kim, and Insuk Lee conceived the study. Seungbyn Baek performed single-cell transcriptome data analysis under the supervision of Insuk Lee. Junha Cha assisted bioinformatic analysis. Seong Yong Park and Hye Ryun Kim organized clinical samples and data collections. Min Hee Hong, Yoon Woo Koh, and Dahee Kim contributed to clinical sample collection. Gamin Kim contributed sample preparation. Wonrak Son, Chan-Young Ock, and Seungeun Lee performed AI-guided analysis of H&amp;E slide. Martin Hemberg provided advice on single-cell data analysis. Insuk Lee and Hye Ryun Kim contributed to the financial and administrative support for this study. Seungbyn Baek, Seong Yong Park, Hye Ryun Kim, and Insuk Lee wrote the manuscript.</p><p>The authors declare that they have no conflicts of interest.</p><p>This research was supported by the Bio &amp; Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science and ICT (RS-2021-NR059674, RS-2022-CC125144, 2022M3A9F3016364, 2022R1A2C1092062, and RS-2025-00553825), Brain Korea 21 (BK21) FOUR program, and the Technology Innovation Program (20022947; funded by the Ministry of Trade Industry &amp; Energy; MOTIE, Korea), Yonsei Fellow Program funded by Youn Jae Lee.</p><p>This study was approved by the Institutional Review Board of Yonsei University Severance Hospital with IRB No 4-2016-0678. Written informed consent was obtained prior to enrollment and sample collection at Yonsei University Severance Hospital. The research conformed to the principles of the Helsinki Declaration.</p>","PeriodicalId":9495,"journal":{"name":"Cancer Communications","volume":"45 9","pages":"1194-1199"},"PeriodicalIF":24.9000,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cac2.70046","citationCount":"0","resultStr":"{\"title\":\"Comparative single-cell analysis of esophageal cancer subtypes reveals tumor microenvironment distinctions explaining varied immunotherapy responses\",\"authors\":\"Seungbyn Baek,&nbsp;Junha Cha,&nbsp;Min Hee Hong,&nbsp;Gamin Kim,&nbsp;Yoon Woo Koh,&nbsp;Dahee Kim,&nbsp;Wonrak Son,&nbsp;Chan-Young Ock,&nbsp;Seungeun Lee,&nbsp;Martin Hemberg,&nbsp;Seong Yong Park,&nbsp;Hye Ryun Kim,&nbsp;Insuk Lee\",\"doi\":\"10.1002/cac2.70046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Esophageal cancer comprises 2 anatomically shared but histologically different subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Previous bulk-level genomic and clinical studies have shown that ESCC shares molecular features with head and neck squamous cell carcinoma (HNSCC) [<span>1</span>] and is generally more responsive to immune checkpoint blockade (ICB) therapies than EAC [<span>2</span>], which is similar to gastric adenocarcinoma (GAC) [<span>1</span>]. Recent clinical trials have further demonstrated clinical benefits from various ICB therapies, including combination approaches, for ESCC [<span>3</span>].</p><p>To further expand the comparison at single-cell resolution of tumor microenvironment (TME), we conducted single-cell transcriptomic analysis on tumors from 35 patients representing 4 cancer types located near the esophagus: ESCC, EAC, HNSCC, and GAC (Supplementary Materials and Methods). By integrating newly generated single-cell datasets with published datasets (Supplementary Table S1) [<span>4, 5</span>], we analyzed more than 200,000 cells within TME (Supplementary Figure S1, Supplementary Figure S2A-C, Supplementary Table S2). This high-resolution approach allowed the dissection of cellular heterogeneity of malignant cells and various immune components within the TME (Figure 1A).</p><p>Differentially expressed gene (DEG) analysis of malignant cells revealed a clear separation based on epithelial cell origin (Supplementary Figure S2D). For detailed cancer cell states, we generated 14 malignant metaprograms (MPs) using non-negative matrix factorization (Supplementary Table S3). Histology-specific MPs, representing squamous or glandular differentiation, dominated the expression landscape, while additional MPs distinguished tumor types based on cell cycle dynamics, endocrine-like features, and activation of Aldo-keto reductase family 1 (AKR1) family genes, thereby providing insights into diverse and shared oncogenic processes (Figure 1B). Furthermore, we generated MPs from immune and stromal compartments and calculated correlations among them (Supplementary Figure S2E, Supplementary Table S3). We identified clusters of coordinated MPs, including an “immune activating” cluster characterized by interferon signaling and activation of adaptive immunity, predominantly enriched in HNSCC and ESCC. In contrast, heat-shock protein (HSP) MP, negatively correlated with the immune activating cluster, was more common in EAC and GAC, suggesting potentially immunosuppressive TMEs.</p><p>To better understand the immune compartments of the TME, we conducted in-depth analyses of each major immune cell type using subclustering approaches. We first focused on CD8<sup>+</sup> T cells for their roles in anti-tumor immunity. We identified several key subtypes including naive/memory, effector, stress-response (HSP high), and exhausted populations (Supplementary Figure S3A-B, Supplementary Table S4). For exhausted populations, we evaluated whether each tumor type exhibited varying degrees of exhaustion and tumor reactivity. Indeed, both HNSCC and ESCC displayed higher tumor reactivity and exhaustion (Figure 1C, Supplementary Table S5). Higher levels of tumor infiltrating T cells for ESCC compared to EAC were validated with artificial intelligence (AI)-guided analysis of hematoxylin and eosin (H&amp;E) slide (Figure 1D, Supplementary Figure S3C-D, Supplementary Table S6). Another key subset identified was a T cell population with high HSP expression, recently identified as a stress-responsive population and a poor indicator of ICB responses [<span>6</span>]. The trajectory analyses identified distinct exhaustion and HSP trajectories for these populations (Figure 1E-F, Supplementary Figure S3E-F). For exhaustion trajectory, there were increasingly more populations at the late stage for HNSCC and ESCC but opposite for EAC and GAC (Figure 1G), suggesting inadequate activation of tumor-reactive populations for adenocarcinomas.</p><p>To identify key genes contributing to the differences in CD8<sup>+</sup> T cell populations, we performed DEG analysis comparing cells with high and low exhaustion and neoantigen-reactive scores (Supplementary Figure S3G, Supplementary Table S7). Among the top DEGs, we confirmed that C-X-C Motif Chemokine Ligand 13 (<i>CXCL13</i>) was highly enriched in effector and exhausted T cell populations, with GAC samples exhibiting very low percentages of <i>CXCL13</i>-expressing cells compared to all other samples (Supplementary Figure S3H). Furthermore, effector and exhausted CD8<sup>+</sup> T cells lacking <i>CXCL13</i> expression showed low signature scores for both exhaustion and neo-antigen reactivity, comparable to other non-effector CD8<sup>+</sup> T cells (Supplementary Figure S3I). Pathway analysis revealed that CXCL13<sup>+</sup> CD8<sup>+</sup> T cells are associated with increased T cell infiltration and activation through interferon-γ signaling, costimulation by CD28 family, and antigen processing pathways (Figure 1H).</p><p>CD4<sup>+</sup> T cells and B cells also play key roles in immunotherapy-related activities, particularly through the formation of tertiary lymphoid structure (TLS) [<span>7</span>]. Among their subtypes (Supplementary Figure S4A-D, Supplementary Tables S8-S9), we focused on CD4<sup>+</sup> follicular helper T (Tfh) cells and germinal center B (GCB) cells. Our analysis revealed that ESCC, and to a lesser extent HNSCC, harbored higher proportions of Tfh cells with elevated <i>CXCL13</i> expression (Supplementary Figure S4E), a chemokine known to promote early TLS maturation by attracting B cells [<span>8</span>]. We observed a positive correlation among these populations and an enrichment of GCBs and TLS signatures in ESCC (Figure 1I, Supplementary Figure S4F, Supplementary Table S5). Moreover, cell-cell interaction analyses confirmed that the crosstalk among TLS components including Tfh cells, GCBs, dendritic cells (DCs), and fibroblasts was stronger in HNSCC and ESCC than in EAC and GAC, supporting the notion that effective TLS formation contributes to superior immunotherapeutic responses (Figure 1J). These interactions were validated with a higher TLS density for ESCC quantified by AI-powered H&amp;E slide analyzer (Figure 1K, Supplementary Figure S3D).</p><p>Among identified cell subtypes in the myeloid compartment (Supplementary Figure S4G-H, Supplementary Table S10), we focused on tumor-associated macrophages (TAMs) because they play roles in both immune activation and suppression. While canonical polarization markers failed to distinguish TAM states (Supplementary Figure S4I, Supplementary Table S5), DEG analysis revealed that Macrophage receptor with collagenous structure (<i>MARCO</i>) was enriched in EAC and GAC, while <i>CXCL9</i> and <i>CXLC10</i> were enriched in HNSCC and ESCC (Figure 1L, Supplementary Table S11). These markers appear mutually exclusive, with only 0.4% of TAMs co-expressing both (Figure 1M). Pathway analysis and gene signature scores further indicated that CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs were enriched with interferon-γ pathways, a positive indicator of ICB response, while MARCO<sup>+</sup> TAMs were enriched in hypoxia pathways (Figure 1N-O). Recent studies linked hypoxia to resistance to ICB by interfering with other immune populations [<span>9</span>]. Negative correlations among the gene signature scores further validated exclusivity of these populations (Figure 1P).</p><p>Two cell types exhibited the most noticeable patterns: CD8<sup>+</sup> T cells and TAMs. CXCL13<sup>+</sup> CD8<sup>+</sup> T cells and CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs displayed high levels of cellular interactions (Figure 1Q), consistent with a coordinated, interferon-γ-driven response that may underlie enhanced sensitivity to ICB therapies. In HNSCC and ESCC patients, these populations were significantly co-abundant (Figure 1R). By contrast, HSP-high CD8⁺ T cells and MARCO⁺ TAMs—associated with hypoxic and stress-related signaling—likely contribute to the formation of “cold” tumors that are less responsive to immunotherapy, as reflected by their higher interactions (Supplementary Figure S4J). Notably, GAC patients showed significant co-abundance of these 2 subtypes (Supplementary Figure S4K).</p><p>To validate the involvement of these subtypes in immunotherapy responses, we used bulk RNA sequencing (RNA-seq) datasets from 8 cohorts across diverse cancer types (Supplementary Table S12). With gene signatures extracted from these populations (Supplementary Table S13), we calculated co-enrichment scores (co-score) for CXCL13<sup>+</sup>CD8<sup>+</sup> T cells and CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs. These co-scores showed significant differences between responders and non-responders in 6 cohorts (Figure 1S) and were highly predictive for responders (Area under the receiver operating characteristic curve [AUROC] &gt; 0.7) in 7 cohorts (Figure 1T). We also calculated differences in 2 signature scores (diff-score) for CXCL9<sup>+</sup>CXCL10<sup>+</sup> TAMs and MARCO<sup>+</sup> TAMs. The diff-scores showed significant differences between responders and non-responders for 6 cohorts (Supplementary Figure S4L) and were predictive for responders in these cohorts (Supplementary Figure S4M). These results suggest that cellular signatures distinguishing esophageal cancer subtypes can predict ICB responses across diverse cancer types.</p><p>In summary, comprehensive single-cell analysis demonstrated that the distinct factors within the TME could explain the differential responses to ICB therapies between esophageal cancer subtypes. ESCC maintains a favorable TME characterized by interferon-γ-dominated, immune-activating populations while relative depletion of these favorable populations and higher prevalence of hypoxia-related populations in EAC could be linked to immune suppression. A recent single-cell study with neoadjuvant chemo-immunotherapy-treated ESCC patients similarly identified associations of interferon-γ-related genes to favorable treatment responses [<span>10</span>]. These contrasting TMEs not only provide insight into the mechanistic basis for the varied efficacy of immunotherapy but also suggest that tailoring treatment strategies to modulate specific immune subsets or to stratify patients based on the immune subsets could enhance clinical outcomes. This study lays the groundwork for developing predictive biomarkers and targeted interventions that consider unique immune landscapes associated with different tumor histology. However, due to the limited sample sizes and functional validations, future studies with larger cohorts and additional experimental validations are needed to further validate these results and to reduce possible confounders. Furthermore, future studies should explore additional factors influencing the TME, such as genetic mutations or environmental conditions, to provide further insight into tumor-immune interactions.</p><p>Seong Yong Park, Hye Ryun Kim, and Insuk Lee conceived the study. Seungbyn Baek performed single-cell transcriptome data analysis under the supervision of Insuk Lee. Junha Cha assisted bioinformatic analysis. Seong Yong Park and Hye Ryun Kim organized clinical samples and data collections. Min Hee Hong, Yoon Woo Koh, and Dahee Kim contributed to clinical sample collection. Gamin Kim contributed sample preparation. Wonrak Son, Chan-Young Ock, and Seungeun Lee performed AI-guided analysis of H&amp;E slide. Martin Hemberg provided advice on single-cell data analysis. Insuk Lee and Hye Ryun Kim contributed to the financial and administrative support for this study. Seungbyn Baek, Seong Yong Park, Hye Ryun Kim, and Insuk Lee wrote the manuscript.</p><p>The authors declare that they have no conflicts of interest.</p><p>This research was supported by the Bio &amp; Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science and ICT (RS-2021-NR059674, RS-2022-CC125144, 2022M3A9F3016364, 2022R1A2C1092062, and RS-2025-00553825), Brain Korea 21 (BK21) FOUR program, and the Technology Innovation Program (20022947; funded by the Ministry of Trade Industry &amp; Energy; MOTIE, Korea), Yonsei Fellow Program funded by Youn Jae Lee.</p><p>This study was approved by the Institutional Review Board of Yonsei University Severance Hospital with IRB No 4-2016-0678. Written informed consent was obtained prior to enrollment and sample collection at Yonsei University Severance Hospital. The research conformed to the principles of the Helsinki Declaration.</p>\",\"PeriodicalId\":9495,\"journal\":{\"name\":\"Cancer Communications\",\"volume\":\"45 9\",\"pages\":\"1194-1199\"},\"PeriodicalIF\":24.9000,\"publicationDate\":\"2025-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cac2.70046\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Communications\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cac2.70046\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Communications","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cac2.70046","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
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摘要

食管癌包括两种解剖学上相同但组织学上不同的亚型:食管鳞状细胞癌(ESCC)和食管腺癌(EAC)。先前的大量基因组和临床研究表明,ESCC与头颈部鳞状细胞癌(HNSCC)[1]具有相同的分子特征,并且通常对免疫检查点阻断(ICB)治疗的反应比EAC[2]更灵敏,这与胃腺癌(GAC)[1]相似。最近的临床试验进一步证明了各种ICB疗法(包括联合疗法)对ESCC bb0的临床益处。为了进一步扩大肿瘤微环境(TME)单细胞分辨率的比较,我们对食管附近ESCC、EAC、HNSCC和GAC 4种癌症类型的35例患者的肿瘤进行了单细胞转录组学分析(Supplementary Materials and Methods)。通过整合新生成的单细胞数据集和已发布的数据集(补充表S1)[4,5],我们分析了TME内超过20万个细胞(补充图S1,补充图S2A-C,补充表S2)。这种高分辨率的方法可以解剖恶性细胞的细胞异质性和TME内的各种免疫成分(图1A)。恶性细胞的差异表达基因(DEG)分析显示基于上皮细胞来源的明显分离(补充图S2D)。对于详细的癌细胞状态,我们使用非负矩阵分解生成了14个恶性元程序(MPs)(补充表S3)。组织学特异性MPs,代表鳞状或腺体分化,在表达图谱中占主导地位,而其他MPs根据细胞周期动力学、内分泌样特征和Aldo-keto还原酶家族1 (AKR1)家族基因的激活来区分肿瘤类型,从而提供了对多种共享的致癌过程的见解(图1B)。此外,我们从免疫室和间质室生成MPs,并计算它们之间的相关性(补充图S2E,补充表S3)。我们确定了协同MPs簇,包括一个以干扰素信号和适应性免疫激活为特征的“免疫激活”簇,主要富集于HNSCC和ESCC。相比之下,热休克蛋白(HSP) MP与免疫激活簇呈负相关,在EAC和GAC中更为常见,表明TMEs可能具有免疫抑制作用。为了更好地了解TME的免疫区室,我们使用亚聚类方法对每种主要免疫细胞类型进行了深入分析。我们首先关注CD8+ T细胞在抗肿瘤免疫中的作用。我们确定了几个关键亚型,包括幼稚/记忆、效应、应激反应(HSP高)和疲惫人群(补充图S3A-B,补充表S4)。对于衰竭人群,我们评估了每种肿瘤类型是否表现出不同程度的衰竭和肿瘤反应性。事实上,HNSCC和ESCC都表现出更高的肿瘤反应性和衰竭(图1C,补充表S5)。与EAC相比,ESCC的肿瘤浸润T细胞水平更高,通过人工智能(AI)指导的苏木精和伊红(H&amp;E)切片分析得到验证(图1D,补充图S3C-D,补充表S6)。鉴定出的另一个关键亚群是高HSP表达的T细胞群,最近被鉴定为应激反应性群体,并且是ICB反应的不良指标[6]。轨迹分析确定了这些种群中不同的衰竭和热sp轨迹(图1E-F,补充图S3E-F)。对于衰竭轨迹,HNSCC和ESCC在晚期有越来越多的人群,而EAC和GAC则相反(图1G),这表明腺癌的肿瘤反应性群体激活不足。为了确定导致CD8+ T细胞群差异的关键基因,我们进行了DEG分析,比较了高和低衰竭和新抗原反应评分的细胞(补充图S3G,补充表S7)。在最高的deg中,我们证实C-X-C Motif趋化因子配体13 (CXCL13)在效应和耗尽的T细胞群中高度富集,与所有其他样品相比,GAC样品中表达CXCL13的细胞百分比非常低(补充图S3H)。此外,与其他非效应CD8+ T细胞相比,缺乏CXCL13表达的效应CD8+ T细胞和耗竭的CD8+ T细胞在耗竭和新抗原反应性方面都表现出较低的特征评分(Supplementary Figure s311)。通路分析显示,CXCL13+ CD8+ T细胞通过干扰素-γ信号、CD28家族共刺激和抗原加工途径与T细胞浸润和活化增加相关(图1H)。 这些对比的TMEs不仅为免疫治疗不同疗效的机制基础提供了见解,而且还表明,调整治疗策略以调节特定免疫亚群或根据免疫亚群对患者进行分层可以提高临床结果。该研究为开发预测性生物标志物和考虑与不同肿瘤组织学相关的独特免疫景观的靶向干预奠定了基础。然而,由于样本量和功能验证有限,未来的研究需要更大的队列和额外的实验验证来进一步验证这些结果并减少可能的混杂因素。此外,未来的研究应探索影响TME的其他因素,如基因突变或环境条件,以进一步了解肿瘤-免疫相互作用。Seong Yong Park, Hye Ryun Kim和Insuk Lee构思了这项研究。Seungbyn Baek在Insuk Lee的指导下进行了单细胞转录组数据分析。Junha Cha协助生物信息学分析。Park Seong Yong和Hye Ryun Kim组织了临床样本和数据收集。Min Hee Hong, Yoon Woo Koh和Dahee Kim为临床样本收集做出了贡献。Gamin Kim贡献了样品制备。Wonrak Son、Chan-Young Ock和Seungeun Lee对H&amp;E玻片进行了人工智能引导分析。Martin Hemberg就单细胞数据分析提供了建议。Insuk Lee和Hye Ryun Kim为这项研究提供了财政和行政支持。白承彬、朴成勇、金惠润、李仁淑等人撰写了手稿。作者声明他们没有利益冲突。本研究得到了由科学和信息通信技术部资助的国家研究基金生物和医疗技术发展计划(RS-2021-NR059674, RS-2022-CC125144, 2022M3A9F3016364, 2022R1A2C1092062和RS-2025-00553825), Brain Korea 21 (BK21) FOUR计划,技术创新计划(20022947,由韩国贸易工业和能源部资助),Yonsei Fellow计划由Youn Jae Lee资助。本研究已获得延世大学Severance医院机构审查委员会批准,IRB号4-2016-0678。在延世大学Severance医院登记和采集样本前获得书面知情同意。这项研究符合《赫尔辛基宣言》的原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparative single-cell analysis of esophageal cancer subtypes reveals tumor microenvironment distinctions explaining varied immunotherapy responses

Comparative single-cell analysis of esophageal cancer subtypes reveals tumor microenvironment distinctions explaining varied immunotherapy responses

Esophageal cancer comprises 2 anatomically shared but histologically different subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Previous bulk-level genomic and clinical studies have shown that ESCC shares molecular features with head and neck squamous cell carcinoma (HNSCC) [1] and is generally more responsive to immune checkpoint blockade (ICB) therapies than EAC [2], which is similar to gastric adenocarcinoma (GAC) [1]. Recent clinical trials have further demonstrated clinical benefits from various ICB therapies, including combination approaches, for ESCC [3].

To further expand the comparison at single-cell resolution of tumor microenvironment (TME), we conducted single-cell transcriptomic analysis on tumors from 35 patients representing 4 cancer types located near the esophagus: ESCC, EAC, HNSCC, and GAC (Supplementary Materials and Methods). By integrating newly generated single-cell datasets with published datasets (Supplementary Table S1) [4, 5], we analyzed more than 200,000 cells within TME (Supplementary Figure S1, Supplementary Figure S2A-C, Supplementary Table S2). This high-resolution approach allowed the dissection of cellular heterogeneity of malignant cells and various immune components within the TME (Figure 1A).

Differentially expressed gene (DEG) analysis of malignant cells revealed a clear separation based on epithelial cell origin (Supplementary Figure S2D). For detailed cancer cell states, we generated 14 malignant metaprograms (MPs) using non-negative matrix factorization (Supplementary Table S3). Histology-specific MPs, representing squamous or glandular differentiation, dominated the expression landscape, while additional MPs distinguished tumor types based on cell cycle dynamics, endocrine-like features, and activation of Aldo-keto reductase family 1 (AKR1) family genes, thereby providing insights into diverse and shared oncogenic processes (Figure 1B). Furthermore, we generated MPs from immune and stromal compartments and calculated correlations among them (Supplementary Figure S2E, Supplementary Table S3). We identified clusters of coordinated MPs, including an “immune activating” cluster characterized by interferon signaling and activation of adaptive immunity, predominantly enriched in HNSCC and ESCC. In contrast, heat-shock protein (HSP) MP, negatively correlated with the immune activating cluster, was more common in EAC and GAC, suggesting potentially immunosuppressive TMEs.

To better understand the immune compartments of the TME, we conducted in-depth analyses of each major immune cell type using subclustering approaches. We first focused on CD8+ T cells for their roles in anti-tumor immunity. We identified several key subtypes including naive/memory, effector, stress-response (HSP high), and exhausted populations (Supplementary Figure S3A-B, Supplementary Table S4). For exhausted populations, we evaluated whether each tumor type exhibited varying degrees of exhaustion and tumor reactivity. Indeed, both HNSCC and ESCC displayed higher tumor reactivity and exhaustion (Figure 1C, Supplementary Table S5). Higher levels of tumor infiltrating T cells for ESCC compared to EAC were validated with artificial intelligence (AI)-guided analysis of hematoxylin and eosin (H&E) slide (Figure 1D, Supplementary Figure S3C-D, Supplementary Table S6). Another key subset identified was a T cell population with high HSP expression, recently identified as a stress-responsive population and a poor indicator of ICB responses [6]. The trajectory analyses identified distinct exhaustion and HSP trajectories for these populations (Figure 1E-F, Supplementary Figure S3E-F). For exhaustion trajectory, there were increasingly more populations at the late stage for HNSCC and ESCC but opposite for EAC and GAC (Figure 1G), suggesting inadequate activation of tumor-reactive populations for adenocarcinomas.

To identify key genes contributing to the differences in CD8+ T cell populations, we performed DEG analysis comparing cells with high and low exhaustion and neoantigen-reactive scores (Supplementary Figure S3G, Supplementary Table S7). Among the top DEGs, we confirmed that C-X-C Motif Chemokine Ligand 13 (CXCL13) was highly enriched in effector and exhausted T cell populations, with GAC samples exhibiting very low percentages of CXCL13-expressing cells compared to all other samples (Supplementary Figure S3H). Furthermore, effector and exhausted CD8+ T cells lacking CXCL13 expression showed low signature scores for both exhaustion and neo-antigen reactivity, comparable to other non-effector CD8+ T cells (Supplementary Figure S3I). Pathway analysis revealed that CXCL13+ CD8+ T cells are associated with increased T cell infiltration and activation through interferon-γ signaling, costimulation by CD28 family, and antigen processing pathways (Figure 1H).

CD4+ T cells and B cells also play key roles in immunotherapy-related activities, particularly through the formation of tertiary lymphoid structure (TLS) [7]. Among their subtypes (Supplementary Figure S4A-D, Supplementary Tables S8-S9), we focused on CD4+ follicular helper T (Tfh) cells and germinal center B (GCB) cells. Our analysis revealed that ESCC, and to a lesser extent HNSCC, harbored higher proportions of Tfh cells with elevated CXCL13 expression (Supplementary Figure S4E), a chemokine known to promote early TLS maturation by attracting B cells [8]. We observed a positive correlation among these populations and an enrichment of GCBs and TLS signatures in ESCC (Figure 1I, Supplementary Figure S4F, Supplementary Table S5). Moreover, cell-cell interaction analyses confirmed that the crosstalk among TLS components including Tfh cells, GCBs, dendritic cells (DCs), and fibroblasts was stronger in HNSCC and ESCC than in EAC and GAC, supporting the notion that effective TLS formation contributes to superior immunotherapeutic responses (Figure 1J). These interactions were validated with a higher TLS density for ESCC quantified by AI-powered H&E slide analyzer (Figure 1K, Supplementary Figure S3D).

Among identified cell subtypes in the myeloid compartment (Supplementary Figure S4G-H, Supplementary Table S10), we focused on tumor-associated macrophages (TAMs) because they play roles in both immune activation and suppression. While canonical polarization markers failed to distinguish TAM states (Supplementary Figure S4I, Supplementary Table S5), DEG analysis revealed that Macrophage receptor with collagenous structure (MARCO) was enriched in EAC and GAC, while CXCL9 and CXLC10 were enriched in HNSCC and ESCC (Figure 1L, Supplementary Table S11). These markers appear mutually exclusive, with only 0.4% of TAMs co-expressing both (Figure 1M). Pathway analysis and gene signature scores further indicated that CXCL9+CXCL10+ TAMs were enriched with interferon-γ pathways, a positive indicator of ICB response, while MARCO+ TAMs were enriched in hypoxia pathways (Figure 1N-O). Recent studies linked hypoxia to resistance to ICB by interfering with other immune populations [9]. Negative correlations among the gene signature scores further validated exclusivity of these populations (Figure 1P).

Two cell types exhibited the most noticeable patterns: CD8+ T cells and TAMs. CXCL13+ CD8+ T cells and CXCL9+CXCL10+ TAMs displayed high levels of cellular interactions (Figure 1Q), consistent with a coordinated, interferon-γ-driven response that may underlie enhanced sensitivity to ICB therapies. In HNSCC and ESCC patients, these populations were significantly co-abundant (Figure 1R). By contrast, HSP-high CD8⁺ T cells and MARCO⁺ TAMs—associated with hypoxic and stress-related signaling—likely contribute to the formation of “cold” tumors that are less responsive to immunotherapy, as reflected by their higher interactions (Supplementary Figure S4J). Notably, GAC patients showed significant co-abundance of these 2 subtypes (Supplementary Figure S4K).

To validate the involvement of these subtypes in immunotherapy responses, we used bulk RNA sequencing (RNA-seq) datasets from 8 cohorts across diverse cancer types (Supplementary Table S12). With gene signatures extracted from these populations (Supplementary Table S13), we calculated co-enrichment scores (co-score) for CXCL13+CD8+ T cells and CXCL9+CXCL10+ TAMs. These co-scores showed significant differences between responders and non-responders in 6 cohorts (Figure 1S) and were highly predictive for responders (Area under the receiver operating characteristic curve [AUROC] > 0.7) in 7 cohorts (Figure 1T). We also calculated differences in 2 signature scores (diff-score) for CXCL9+CXCL10+ TAMs and MARCO+ TAMs. The diff-scores showed significant differences between responders and non-responders for 6 cohorts (Supplementary Figure S4L) and were predictive for responders in these cohorts (Supplementary Figure S4M). These results suggest that cellular signatures distinguishing esophageal cancer subtypes can predict ICB responses across diverse cancer types.

In summary, comprehensive single-cell analysis demonstrated that the distinct factors within the TME could explain the differential responses to ICB therapies between esophageal cancer subtypes. ESCC maintains a favorable TME characterized by interferon-γ-dominated, immune-activating populations while relative depletion of these favorable populations and higher prevalence of hypoxia-related populations in EAC could be linked to immune suppression. A recent single-cell study with neoadjuvant chemo-immunotherapy-treated ESCC patients similarly identified associations of interferon-γ-related genes to favorable treatment responses [10]. These contrasting TMEs not only provide insight into the mechanistic basis for the varied efficacy of immunotherapy but also suggest that tailoring treatment strategies to modulate specific immune subsets or to stratify patients based on the immune subsets could enhance clinical outcomes. This study lays the groundwork for developing predictive biomarkers and targeted interventions that consider unique immune landscapes associated with different tumor histology. However, due to the limited sample sizes and functional validations, future studies with larger cohorts and additional experimental validations are needed to further validate these results and to reduce possible confounders. Furthermore, future studies should explore additional factors influencing the TME, such as genetic mutations or environmental conditions, to provide further insight into tumor-immune interactions.

Seong Yong Park, Hye Ryun Kim, and Insuk Lee conceived the study. Seungbyn Baek performed single-cell transcriptome data analysis under the supervision of Insuk Lee. Junha Cha assisted bioinformatic analysis. Seong Yong Park and Hye Ryun Kim organized clinical samples and data collections. Min Hee Hong, Yoon Woo Koh, and Dahee Kim contributed to clinical sample collection. Gamin Kim contributed sample preparation. Wonrak Son, Chan-Young Ock, and Seungeun Lee performed AI-guided analysis of H&E slide. Martin Hemberg provided advice on single-cell data analysis. Insuk Lee and Hye Ryun Kim contributed to the financial and administrative support for this study. Seungbyn Baek, Seong Yong Park, Hye Ryun Kim, and Insuk Lee wrote the manuscript.

The authors declare that they have no conflicts of interest.

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science and ICT (RS-2021-NR059674, RS-2022-CC125144, 2022M3A9F3016364, 2022R1A2C1092062, and RS-2025-00553825), Brain Korea 21 (BK21) FOUR program, and the Technology Innovation Program (20022947; funded by the Ministry of Trade Industry & Energy; MOTIE, Korea), Yonsei Fellow Program funded by Youn Jae Lee.

This study was approved by the Institutional Review Board of Yonsei University Severance Hospital with IRB No 4-2016-0678. Written informed consent was obtained prior to enrollment and sample collection at Yonsei University Severance Hospital. The research conformed to the principles of the Helsinki Declaration.

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来源期刊
Cancer Communications
Cancer Communications Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
25.50
自引率
4.30%
发文量
153
审稿时长
4 weeks
期刊介绍: Cancer Communications is an open access, peer-reviewed online journal that encompasses basic, clinical, and translational cancer research. The journal welcomes submissions concerning clinical trials, epidemiology, molecular and cellular biology, and genetics.
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