PO.IM02.04 · 免疫学

通过G4X在细胞水平进行空间转录组学检测结直肠癌复发中微环境STAT1表达的浆细胞

Cell-level spatial transcriptomics detection of microenvironmental STAT1-expressing plasma cells in colorectal cancer recurrence by G4X

海报缩略图:通过G4X在细胞水平进行空间转录组学检测结直肠癌复发中微环境STAT1表达的浆细胞
编号 4235 展板 3 时间 4/21 09:00–12:00 区域 Section 6 主讲 Minh-Khang Le, PhD
分会场 Adaptive Immunity in Cancer
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作者与单位 Authors & Affiliations

I-Chuang Liao1, Minh-Khang Le1, Vivek Pujara1, Haley Jun1, Devanshi Pratiher1, Jane C. Figueiredo2, Nathalie Nguyen1, Chintda Santiskulvong1, Angie Laguna1, Yi Zhang1, Keluo Yao1, Joshua Jay Levy1

1Cedars-Sinai Medical Center, Los Angeles, CA,2Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA

摘要 Abstract

中文摘要
引言:STAT1介导干扰素信号传导和免疫反应,在癌症中发挥多种作用。在结直肠癌(CRC)中,浆细胞是关键的免疫介质,这些细胞中的STAT1表达可能反映其功能状态。STAT1表达较低的浆细胞与减弱的抗肿瘤免疫反应相关,从而促进肿瘤逃逸。理解这一关系可能揭示CRC的新型预后生物标志物和治疗靶点。 材料与方法:三十例CRC样本接受了G4X空间分析,每例均手动选择4.5 × 4.5 mm²的捕获区域,针对关键组织学特征,最终获得了针对360基因面板、涵盖3,088,252个细胞的空间基因表达谱。组织切片按照G4X空间转录组学方案处理,实现亚细胞分辨率的RNA检测。由一位合作病理学家进行人工病理注释,识别包括癌、异型增生/原位癌、良性上皮、交界区、肌层和间质在内的区域。癌相关细胞由两个标准定义:(1)位于注释的癌区域内的空间定位,以及(2)缺乏经典上皮和恶性上皮标志物(CEACAM5、EPCAM、MUC12)。差异表达分析确定了相关的细胞标志物,并对表达这些标志物的细胞类型比例进行了量化。细胞水平回归模型纳入了患者随机截距和临床协变量(年龄、性别、微卫星状态、T和N分期),随后对所有30例样本进行样本水平单变量分析,以评估STAT1阳性细胞簇与临床复发之间的关联。 结果:差异表达分析确定STAT1为癌相关细胞中上调最显著的基因(log2倍数变化 = 2.11;校正后p < 0.001)。对STAT1阳性癌相关细胞进行聚类,揭示了10个不同的细胞簇,包括5种癌相关成纤维细胞亚型、肿瘤相关巨噬细胞、混合T细胞、2种上皮样细胞和浆细胞。回归分析一致表明STAT1阳性浆细胞的比例与结直肠癌复发之间存在负相关(OR = 0.19;95% CI = 0.03-1.13;p = 0.068)。 结论:结直肠癌微环境中表达STAT1的浆细胞与复发风险显著相关。这些发现支持在更大的研究队列中通过免疫组织化学进行进一步验证,展示了利用空间转录组学洞见来指导常规病理学以进行患者分层的前景。
查看英文原文 English abstract
Introduction: STAT1 mediates interferon signaling and immune responses with diverse roles in cancer. In colorectal cancer (CRC), plasma cells are critical immune mediators, and STAT1 expression in these cells may reflect their functional status. Lower STAT1-expressing plasma cells have been linked to a weakened anti-tumor immune response that facilitates tumor escape. Understanding this relationship could reveal novel prognostic biomarkers and therapeutic targets for CRC. Materials and Methods: Thirty CRC samples underwent G4X spatial profiling, each with a manually selected 4.5 × 4.5 mm² capture area targeting key histological features, resulting in spatial gene expression profiles for a 360-gene panel for 3,088,252 cells. Tissue sections were processed following the G4X spatial transcriptomics protocol enabling RNA detection at subcellular resolution. Manual pathology annotation was performed by a collaborating pathologist, identifying regions including cancer, dysplasia/cancer in situ, benign epithelium, interface, muscularis, and stroma. Cancer-associated cells were defined by two criteria: (1) spatial localization within annotated cancer regions, and (2) absence of canonical epithelial and malignant epithelial markers (CEACAM5, EPCAM, MUC12). Differential expression analyses identified associated cell markers, and the proportions of cell types expressing these markers were quantified. Cell-level regression models incorporated random patient intercepts and clinical covariates (age, gender, microsatellite status, T and N stage), followed by sample-level univariate analyses across all 30 samples to assess associations between STAT1-positive cell clusters and clinical recurrence. Results: Differential expression analysis identified STAT1 as the top upregulated gene in cancer-associated cells (log2 fold-change = 2.11; adjusted p < 0.001). Clustering of STAT1-positive cancer-associated cells revealed 10 distinct cell clusters, including 5 cancer-associated fibroblast subtypes, tumor-associated macrophages, mixed T cells, 2 epithelial-like cells, and plasma cells. Regression analyses consistently indicated a negative association between proportion of STAT1-positive plasma cells and colorectal cancer recurrence (OR = 0.19; 95% CI = 0.03-1.13; p = 0.068). Conclusion: STAT1-expressing plasma cells in the colorectal cancer microenvironment are significantly associated with recurrence risk. These findings support further validation efforts through immunohistochemistry in a larger study cohort, demonstrating the promise of leveraging spatial transcriptomic insights to inform routine pathology for patient stratification.
利益披露 Disclosure
I. Liao, None.. M. Le, None.. V. Pujara, None.. H. Jun, None.. D. Pratiher, None.. C. Santiskulvong, None.. A. Laguna, None.. Y. Zhang, None.

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