PO.CL01.14 · 临床研究
从超高多重空间数据中衍生高保真、低多重的临床特征以预测免疫治疗应答
Deriving high-fidelity, low-plex clinical signatures from ultra-high-plex spatial data for immunotherapy response prediction
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摘要 Abstract
中文摘要
背景：超高多重多重免疫荧光（mIF）成像可实现对肿瘤微环境（TME）的详细表征，然而将这些丰富的数据集转化为临床可部署的低多重生物标志物，仍是精准免疫肿瘤学的一大障碍。现有的预测模型严重依赖高维特征或宽泛的表型panel，限制了可扩展性、可解释性和常规病理整合。需要一个系统性框架，将空间解析的分子信息压缩为最少的、临床级多重的、却又高信息量的、能够预测免疫治疗应答的特征。
方法：我们的SpaceIQ™平台是一款多组学分析工具，将空间蛋白质组学数据与优化的特征选择算法相整合，以提炼分子特征。我们采用无偏细胞分型和微域发现，利用逐点互信息（PMI）分析，识别这些无偏细胞类型之间空间相互作用的差异表达网络。该网络中的每一个相互作用（无论是成对的还是更高阶的团），都代表一个能够预测患者应答的潜在空间预后模型。我们方法的一个关键组成部分，是利用低多重panel识别针对给定无偏细胞类型富集的亚群阈值细胞群。差异团的最终预后模型将空间邻近性评分与这些低多重标志物强度相结合。
结果：对接受检查点治疗的皮肤T细胞淋巴瘤患者试验标本的超高多重（≥51个标志物）空间数据分析表明，肿瘤-免疫和免疫-免疫相互作用呈现为具有6-8个标志物最小特征的微域，且具有高预测准确性（AUC = 0.87，95% CI（0.865-0.881））。
结论：SpaceIQ平台能够从超高多重mIF数据集中提取紧凑、临床实用的生物标志物panel，而不损失预测能力。通过将空间微域生物学与稀疏特征衍生相联系，该框架支持精准免疫治疗生物标志物的可扩展部署，并增强临床实践中的患者选择策略。
查看英文原文 English abstract
Background: Ultra-high-plex multiplex immunofluorescence (mIF) imaging enables detailed characterization of the tumor microenvironment (TME), yet translating these rich datasets into clinically deployable, low-plex biomarkers remains a major barrier for precision immuno-oncology. Existing predictive models rely heavily on high-dimensional features or broad phenotypic panels, limiting scalability, interpretability, and routine pathology integration. A systematic framework is needed to compress spatially resolved molecular information into minimal, clinical-plex, yet highly informative signatures capable of predicting immunotherapy response.
Methods: Our SpaceIQ™ platform is a multi-omic analysis tool that integrates spatial proteomics data with optimized feature selection algorithms to distill molecular signatures. We employ unbiased cell typing and microdomain discovery to identify a differentially expressed network of spatial interactions between these unbiased cell types, utilizing pointwise mutual information (PMI) analysis. Each interaction (either pairwise or higher-order cliques) within this network represents a potential spatial prognostic model capable of predicting patient response. A key component of our approach is the identification of a subset threshold cell population that is enriched for a given unbiased cell type using a low-plex panel. The final prognostic model for a differential clique combines a spatial proximity score with these low-plex marker intensities.
Results: Analysis of ultra-high-plex (=51 markers) spatial data from trial specimens of checkpoint-treated cutaneous T-cell lymphoma patients demonstrated that tumor-immune and immune-immune interactions emerge as microdomains with minimal signatures of 6-8 markers and high prediction accuracies (AUC = 0.87, 95% CI (0.865-0.881)).
Conclusions: The SpaceIQ platform enables the extraction of compact, clinically practical biomarker panels from ultra-high-plex mIF datasets without sacrificing predictive power. By linking spatial microdomain biology to sparse signature derivation, this framework supports scalable deployment of precision immunotherapy biomarkers and enhances patient-selection strategies in clinical practice.
利益披露 Disclosure
R. Yan, None..
B. Falkenstein, None..
A. Tosun, None..
F. Pullara, None..
S. Chennubhotla, None.