Quantifying heterocellular communication and spatial intratumoral heterogeneity from high dimensional spatial proteomics data

肿瘤微环境(TME)由恶性和非恶性细胞组成，每种细胞都对 肿瘤内空间异质性(ITH)和异细胞通讯改变了肿瘤的组成和 TME的架构。高度的ITH与转移进展和治疗相关 回应。以前对空间ITH的研究一直是有限的，因为在 生物标志物的细胞分辨率、空间背景和维度。最近出现了从多到多的爆炸性增长 成像方式(例如，荧光成像、质谱仪成像)使得量子fi阳离子大于7 和多达&gt；100个生物标记物，通过使用迭代的顺序多路复用2到3个生物标记物成像 标签-图像-染料失活循环。这种新类型数据的产生既提供了独特的机会 和挑战。目前还没有最先进的方法来利用空间数据的复杂性进行推断 具有高维生物标志物的肿瘤生物学。在这个项目中，我们将探索一个空间复杂性 TME在基于HxIF的结直肠癌空间蛋白质组学数据中的应用 (51个生物标记物DAPI，356个患者样本)以阐明异质细胞通讯网络 通过细胞表型、微域提取和网络生物学推断促进空间ITH 算法。我们将演示我们的算法对超出CRC的癌症类型的适用性 多重免疫荧光乳腺癌组织样本 在目标1中，我们将继续开发细胞表型的无监督学习算法 异质性(LEAPH)用于识别特化的、稀有的和过渡细胞群体。应用初步结果 在HxIF CRC数据上的LEAPH揭示了与CRC文献一致的细胞异质性模式 (干细胞分化、免疫逃避、巨噬细胞进化)。我们将结合机器学习- 基于LEAPH的方法来测量每个表型的空间分布模式，并将它们与 CRC进展(例如，重现)。在目标2中，我们将更详细地量化空间ITH，方法是 基于结果数据的差异表达的成对或成组的空间关系(例如，重复与 5年内无复发)，以揭示有预后潜力的表型域、微域。我们 预期与配对或分组空间交互作用相比，预后能力会有所改善 基于单一表型的目标1的空间ITH特征。在目标3中，我们将剖析微域- 使用因果推理网络模型的特定异质细胞通信动力学。我们希望能确定 诱发恶性表型的紧急信令网络，例如来自CRC共识的已知特征 分子亚型。本项目中构建的算法将通过以下方式实现和传播 肿瘤异质性研究交互可视化环境(Thrive)，这是一个开源工具，用于 协助癌症研究人员进行交互式假设测试，并指导治疗策略的设计。

The tumor microenvironment (TME) is composed of malignant and non-malignant cells, each contributing to spatial intratumoral heterogeneity (ITH) and heterocellular communication altering the composition and architecture of the TME. A high degree of ITH is correlated to metastatic progression and therapeutic response. Previous studies investigating spatial ITH have been limited due to a steep trade-off between cellular resolution, spatial context, and dimensionality of biomarkers. A recent explosion of multi to hyperplexed imaging modalities (e.g., fluorescence imaging, mass-spec imaging) enable the quantification of greater than 7 and up to > 100 biomarkers through sequentially multiplexed imaging of 2 to 3 biomarkers using iterative cycles of label-image-dye inactivation. The generation of this new type of data poses both unique opportunities and challenges. There are no state-of-the-art methods for harnessing the complexity of spatial data to infer tumor biology with a high dimensionality of biomarkers. In this project, we will probe the spatial complexity of a TME in hyperplexed immunofluorescence (HxIF) based spatial proteomics colorectal carcinoma (CRC) data (51 biomarkers + DAPI, 356 patient samples) to elucidate the heterocellular communication networks promoting spatial ITH through cellular phenotyping, microdomain extraction, and network biology inference algorithms. We will demonstrate the applicability of our algorithms to cancer types beyond CRC with multiplexed immunofluorescence breast cancer tissue samples In Aim 1, we will continue to develop unsupervised learning algorithm for cellular phenotypic heterogeneity (LEAPH) to identify specialized, rare, and transitional cell populations. Initial results applying LEAPH on the HxIF CRC data have revealed cellular heterogeneity patterns consistent with CRC literature (STEM cell differentiation, immune evasion, macrophage evolution). We will incorporate machine learning- based methods into LEAPH to measure spatial distribution patterns of each phenotype and correlate them with CRC progression (e.g., recurrence). In Aim 2, we will quantify spatial ITH in greater detail by identifying differentially expressed pair- or group-wise spatial relationships based on outcome data (e.g., recurrence vs no-recurrence within 5 years) to reveal phenotypic domains, microdomains, with prognostic potential. We expect improvement of prognostic power with pair- or group-wise spatial interactions in comparison to the single-phenotype based spatial ITH characterization of Aim 1. In Aim 3, we will dissect the microdomain- specific heterocellular communication dynamics with causal inference network models. We expect to identify emergent signaling networks conferring malignant phenotypes, such as known features from CRC consensus molecular subtypes. The algorithms constructed in this project will be implemented and disseminated through the Tumor Heterogeneity Research Interactive Visualization Environment (THRIVE), an open source tool to assist cancer researchers in interactive hypotheses testing and guiding the design of therapeutic strategies.