PO.TB10.04 · 肿瘤生物学
通过空间微域和网络生物学解码肿瘤微环境异质性以预测免疫治疗结局
Decoding tumor microenvironment heterogeneity through spatial microdomains and network biology to predict immunotherapy outcomes
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摘要 Abstract
中文摘要
背景:空间生态位通常通过各种细胞类型的共现或共定位来量化,是健康和疾病中组织组织化的常用衡量指标。然而,这些空间指标如何量化组织异质性、这种异质性如何与组织内的功能组织化相关联、以及功能组织化如何影响患者结局,三者之间尚缺乏清晰的联系。
方法:SpaceIQ™ 多组学分析平台从互信息角度对空间细胞-细胞通讯进行正式分析,以解决组织异质性问题。该方法可将组织虚拟解剖为不同的肿瘤微环境(TME)程序。然后,通过基于组成比例对这些空间 TME 程序进行聚类,揭示患者间的异质性,这一过程独立于患者结局。至关重要的是,这些空间 TME 程序作为高效的"微域",为细胞间相互作用和空间调节的"网络生物学"提供关键的局部背景,展现出对患者结局的强大预测能力。
结果:我们分析了一项公开可用的基于 51-plex 免疫荧光的空间蛋白质组学数据(CODEX 平台),数据来自经检查点治疗的皮肤 T 细胞淋巴瘤患者,采用基于微域和网络生物学的空间分析。我们的发现表明:(i)单独的检查点表达是患者反应的不良预测因子;(ii)与多标记表型相比,不同细胞类型之间的空间相互作用适度提高了预测准确性;(iii)在预测反应方面,微域显著优于非空间方法和基于检查点表达的方法。
结论:与非空间或基于检查点表达的方法相比,利用微域和网络生物学的空间分析显著提高了预测准确性。这一进展有助于改善生物标志物驱动的患者选择和靶向治疗优化。
查看英文原文 English abstract
Background: Spatial niches, often quantified through the co-occurrence or colocalization of various cell types, are a common measure of tissue organization in health and disease. However, a clear link is missing between how these spatial measures quantify tissue heterogeneity, how that heterogeneity relates to functional organization within the tissue, and how the functional organization impacts patient outcomes.
Methods: The SpaceIQ™ multi-omics analysis platform addresses tissue heterogeneity through a formal analysis of spatial cell-cell communication from a mutual information perspective. This methodology allows for the virtual dissection of tissue into distinct tumor microenvironment (TME) programs. Inter-patient heterogeneity is then revealed by clustering these spatial TME programs based on their compositional fractions, a process independent of patient outcomes. Crucially, these spatial TME programs serve as highly effective "microdomains" that provide critical local context for cell-to-cell interactions and spatially modulated "network biology," demonstrating strong predictive power for patient outcomes.
Results: We analyzed a publicly available 51-plex immunofluorescence based spatial proteomics data (CODEX platform) from checkpoint-treated cutaneous T-cell lymphoma patients, using spatial analysis based on microdomains and network biology. Our findings demonstrate that: (i) checkpoint expressions alone are poor predictors of patient response; (ii) spatial interactions between different cell types moderately improve prediction accuracy compared to multi-marker phenotypes; and (iii) microdomains significantly outperform non-spatial methods and checkpoint expression-based approaches in predicting response.
Conclusions: Spatial analysis leveraging microdomains and network biology significantly enhance prediction accuracy compared to non-spatial or checkpoint expression-based methods. This advancement enables improved biomarker-driven patient selection and targeted therapy optimization.
利益披露 Disclosure
A. Tosun, None..
R. Yan, None..
B. Falkenstein, None..
F. Pullara, None..
S. Chennubhotla, None.