Spatially resolved, single cell biomarkers of B cell lymphoma

项目摘要/摘要 弥漫性大B细胞淋巴瘤(DLBCL)是最常见的非霍奇金淋巴瘤(NHL)。DLBCL 肿瘤是高度异质性的，分子图谱显示了多达六种与预后相关的不同类型。 然而，这些分类主要是基于恶性细胞的特征。非恶性 肿瘤免疫微环境(TME)中的细胞可以是肿瘤生长的强大调节器，并且可以 针对癌症治疗，但包括TME元素的生物标记物尚未开发出来。我们 假设下一代淋巴瘤免疫靶向疗法的成功开发 将需要空间分辨的、高度多元化的单细胞生物标志物的TME成分和 结构。在本研究中，我们建议对超过2000例患者进行成像质量细胞分析(IMC)。 侵袭性B细胞淋巴瘤的目标如下：特定目标1：验证空间衍生蛋白生物标记物 DLBCL的预后(n-830名患者)。具体目标2：分析组织学不同的单细胞拓扑结构 侵袭性B细胞淋巴瘤(n=1380)：在所有侵袭性B细胞中识别共享的基于TME的生物标志物 淋巴瘤。具体目标3：确定哪些TME元素调节化疗耐药和中介反应 通过体外模型系统进行淋巴瘤的免疫治疗。在前两个目标中，我们将应用我们的小说 丰度加权分数(M-SCORE)，专门为IMC分析开发，用于验证候选生物标记物 (如Pd-L1+/CCR4+/TIM-3+)，并确定新的生物标志物。我们还将应用我们的单元格空间 免疫细胞组织区域(RICO)聚类算法识别肿瘤免疫空间簇 这与临床结果有关。空间分辨率、单单元、高度多路复用的数字图像 这里提出的分析将对美国约8万例新的NHL病例产生临床影响。特定目标1将验证 并以病理学家熟悉的图像格式发现蛋白质生物标记物，从而实现无缝临床 通过，特殊目标2将完善应用于挑战淋巴瘤的空间系统生物学方法 类型和特定目标3将允许对预后和预测性生物标志物进行临床前验证。

Project Summary/Abstract Diffuse Large B-Cell Lymphoma (DLBCL) is the most common form of Non-Hodgkin Lymphoma (NHL). DLBCL tumors are highly heterogeneous and molecular profiling has revealed up to six distinct prognostically relevant sub-groups, however these classifications are primarily based on profiles of the malignant cells. Non-malignant cells in the tumor immune microenvironment (TME) can be powerful modulators of tumor growth and can be targeted for cancer therapy, yet biomarkers that include TME elements have not been developed. We hypothesize that successful development of next generation immune targeting therapies for lymphoma will require spatially resolved, highly multiplexed single cell based biomarkers of TME composition and structure. In this study we propose to perform imaging mass cytometry analysis (IMC) on over 2000 cases of aggressive B cell lymphoma with the following aims: Specific Aim 1: Validate spatially-derived protein biomarkers of DLBCL outcomes (n-830 patients). Specific Aim 2: Analyze the single cell topology of histologically diverse aggressive B cell lymphomas (n=1380) to identify shared TME based biomarkers across all aggressive B cell lymphoma. Specific Aim 3: Determine which TME elements modulate chemoresistance and mediate response to immune therapies in lymphoma through in vitro model systems. In the first two Aims, we will apply our novel abundance weighted score (M-score), developed specifically for IMC analysis, to validate candidate biomarkers (such as PD-L1+/CCR4+/TIM-3+) and identify new biomarkers. We will also apply our single cell spatial clustering algorithm, Regions of Immune Cell Organization (RICO), to identify tumor-immune spatial clusters that are associated with clinical outcome. The spatial resolved, single cell, highly multiplexed, digital image analysis proposed here will have clinical impact for the ~80,000 new US cases of NHL. Specific Aim 1 will validate and discover protein biomarkers in a image format familiar to pathologists allowing for seamless clinical adoption, Specific Aim 2 will refine methods for spatial systems biology applied to challenging lymphoma types, and Specific Aim 3 will allow pre-clinical validation of prognostic and predictive biomarkers.