PO.CL01.13 · 临床研究
利用基于注意力的建模对非裔美国结直肠癌患者中MMR-p与MMR-d肿瘤-免疫相互作用的空间表征
Spatial characterization of tumor-immune interactions in MMR-p and MMR-d among African American colorectal cancer patients using attention-based modeling
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：结直肠癌（CRC）对非裔美国人群的影响尤为严重，其发病率和死亡率均高于非西班牙裔白人患者。免疫浸润在不同CRC表型之间差异很大，尤其是在微卫星不稳定（MSI）与微卫星稳定（MSS）肿瘤之间。近期的单细胞研究显示，MSI肿瘤表现出更强的抗肿瘤免疫，免疫细胞与恶性细胞类型之间存在协调变化，凸显了通过空间分析理解肿瘤-免疫相互作用的必要性。
目的：利用先进的计算和机器学习方法研究非裔美国患者MSI和MSS肿瘤的空间细胞组织，以表征免疫标志物特征。方法：使用10X Genomics Xenium 5k进行空间分辨的单细胞转录组分析，该技术可原位测量多达5,000个基因。由于此类数据需要复杂的处理，我们采用了机器学习创新方法——包括自监督学习——以及稳健的伪影校正工具，以提取具有生物学意义的信号。我们引入了一个包含四个CRC样本（2个MSI，2个MSS）的试点数据集，并展示了使用精选标志物集对空间CRC数据进行预处理和注释的分析流程。
结果：空间分析揭示了不同表型之间独特的免疫-肿瘤生态。MSS肿瘤显示T/NK细胞和B细胞群体耗竭，而MSI肿瘤则表现出更高的免疫细胞丰度和强大的细胞毒性CD8⁺ T细胞活性，以CXCL13、GZMA、GZMB和GZMK表达升高为标志。MSI样本还显示出由表达CXCL10的促炎髓系细胞支持的活化T细胞程序，与免疫热、治疗应答的微环境相一致。相比之下，MSS肿瘤以成纤维细胞为主导并发生代谢重编程，特征为TGFB1、FAP、COL1A1、LDHA和SLC2A1高表达，反映出T细胞浸润极少的免疫冷状态。为分析细胞通讯，我们应用了AMICI——一种可解释的基于注意力的模型，可根据细胞的空间邻域预测其基因表达。AMICI揭示了表型特异性的相互作用模式、邻域长度尺度以及介导免疫-基质-肿瘤信号传导的关键基因。
结论：这项试点研究建立了一个整合空间与计算的框架，用于剖析非裔美国患者CRC中MSI-MSS的差异。未来的工作将扩展基于AMICI的分析，以进一步解析邻域驱动的表型以及塑造免疫浸润和治疗应答的细胞-细胞通讯网络。
查看英文原文 English abstract
Background: Colorectal cancer (CRC) disproportionately affects African American individuals, who experience higher incidence and mortality than non-Hispanic White patients. Immune infiltration varies widely across CRC phenotypes, particularly between microsatellite instability (MSI) and microsatellite stable (MSS) tumors. Recent single-cell studies show that MSI tumors demonstrate stronger anti-tumor immunity, with coordinated variation between immune and malignant cell types, highlighting the need for spatial analyses to understand tumor-immune interactions.
Aim: To investigate spatial cellular organization in MSI and MSS tumors from African American patients using advanced computational and machine learning approaches to characterize immunomarker profiles. Methods: Spatially resolved single-cell transcriptomic profiling was performed using 10X Genomics Xenium 5k, which measures up to 5,000 genes in situ. Because such data require sophisticated processing, we employed machine learning innovations-including self-supervised learning-and robust artifact-correction tools to derive biologically meaningful signals. We introduce a pilot dataset of four CRC samples (2 MSI, 2 MSS) and present an analysis pipeline for preprocessing and annotating spatial CRC data using curated marker sets.
Results: Spatial profiling revealed distinct immune-tumor ecologies across phenotypes. MSS tumors showed depleted T/NK and B-cell populations, whereas MSI tumors exhibited higher immune-cell abundance and strong cytotoxic CD8⁺ T-cell activity marked by elevated CXCL13, GZMA, GZMB, and GZMK expression. MSI samples also demonstrated activated T-cell programs supported by pro-inflammatory myeloid cells expressing CXCL10, consistent with an immune-hot, therapy-responsive microenvironment. In contrast, MSS tumors were fibroblast-driven and metabolically reprogrammed, characterized by high TGFB1, FAP, COL1A1, LDHA, and SLC2A1 expression, reflecting an immune-cold state with minimal T-cell infiltration. To analyze cellular communication, we applied AMICI-an interpretable attention-based model predicting a cell's gene expression from its spatial neighbors. AMICI revealed phenotype-specific interaction patterns, neighborhood length scales, and key genes mediating immune-stromal-tumor signaling.
Conclusion: This pilot study establishes an integrated spatial and computational framework for dissecting MSI-MSS differences in CRC among African American patients. Future work will expand AMICI-based analyses to further resolve neighborhood-driven phenotypes and cell-cell communication networks that shape immune infiltration and therapeutic response.
利益披露 Disclosure
H. Brim, None..
K. Desai, None..
S. Dixi, None..
J. Hong, None..
N. Fernandez, None..
S. Sim, None..
S. Farhi, None..
R. Zafar, None..
E. Azizi, None..
H. Ashktorab, None.