PO.PR01.03 · 预防研究
应用于RADIOHEAD队列的nELISA高通量蛋白质图谱分析：来自接受检查点抑制剂治疗患者的最大规模血浆蛋白质组学研究的洞见
nELISA high-throughput protein profiling applied to the RADIOHEAD cohort: Insights from the largest plasma proteomics study of patients receiving checkpoint inhibitor therapy
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摘要 Abstract
中文摘要
背景 蛋白质组学在癌症免疫治疗中具有巨大前景，人们正在早期疾病识别、患者筛选及不良事件预测方面付出大量努力。尽管潜力巨大，但现有用于分析循环蛋白质的工具成本高、通量低，使得此类研究进展缓慢、代价高昂，限制了其广泛应用。因此，该领域的蛋白质组学研究一直受限于数十到数百的样本量，制约了发现关键生物标志物的效力。
方法 Nomic平台是一种高度多重化的免疫测定技术，可在每台仪器每日1536个样本中实现对数百种蛋白质的图谱分析，且成本显著降低。该方法通过将抗体对置于彩色编码微粒表面来微型化夹心免疫测定，随后可通过高通量流式细胞术进行分析。RADIOHEAD是一项前瞻性研究，纳入来自社区肿瘤诊所的1070例既往未接受免疫治疗的泛肿瘤患者，这些患者接受标准治疗的免疫检查点抑制剂（ICI）治疗方案。在ICI治疗前后以及irAE发生后采集了纵向样本。
结果 我们此前报道了利用nELISA蛋白质图谱分析平台对RADIOHEAD队列3000个样本中的600种循环蛋白质进行定量，结果识别出超过200种与ICI应答相关的蛋白质以及超过150种与irAE发生相关的蛋白质。在此，我们进一步剖析该数据集以捕获治疗特异性生物标志物。具体而言，对临床数据的分析识别出影响ICI应答的因素，包括年龄、吸烟、化疗、放疗、全身性皮质类固醇、阿片类药物等。我们将展示与这些因素各自相关的生物标志物及其对ICI应答的影响。
结论 将这些纵向样本的nELISA蛋白质图谱分析与相关人口统计学元数据及临床结局相配对，为识别临床上可操作的机制以指导ICI治疗策略提供了机会。在此，我们重点介绍与患者结局相关的生物标志物和蛋白质特征，以揭示更多洞见并进一步加速癌症免疫治疗领域的研究。
查看英文原文 English abstract
Background Proteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting their wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in the 10s and 100s, restricting the power to discover key biomarkers.
Methods The Nomic platform is a highly multiplexed immunoassay technology that enables the profiling of hundreds of proteins across 1536 samples per instrument daily, at significantly reduced costs. The method miniaturizes sandwich immunoassays by placing antibody pairs on the surface of color-coded microparticles, which can then be analyzed via high-throughput flow cytometry. RADIOHEAD is a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics. Longitudinal samples were collected pre- and post-ICI, as well as following irAEs.
Results We previously reported leveraging the nELISA protein profiling platform to quantify 600 circulating proteins across 3000 samples from the RADIOHEAD cohort, resulting in the identification of greater than 200 proteins associated with response to ICI and greater than150 proteins associated with the development of irAEs. Here, we further dissect the dataset to capture treatment-specific biomarkers. Specifically, analysis of clinical data identified factors impacting response to ICI, including age, smoking, chemotherapy, radiotherapy, systemic corticosteroids, opioids, etc. We will present biomarkers associated with each of these factors, and their impact on response to ICI.
Conclusions Pairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms to guide ICI therapeutic approaches. Here, we highlight biomarkers and protein signatures related to patient outcomes, to reveal additional insights and further accelerate research in the field of cancer immunotherapy.
利益披露 Disclosure
A. R. Johnson, None.
J. Eberlein,
Nomic Bio Employment.
G. Ongo, None..
M. Dagher, None.