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利用数字空间分析技术对接受过黑色素瘤免疫治疗的患者进行高多重预测标志物的发现。

High-Plex Predictive Marker Discovery for Melanoma Immunotherapy-Treated Patients Using Digital Spatial Profiling.

机构信息

Department of Pathology, Yale School of Medicine, New Haven, Connecticut.

NanoString Technologies, Seattle, Washington.

出版信息

Clin Cancer Res. 2019 Sep 15;25(18):5503-5512. doi: 10.1158/1078-0432.CCR-19-0104. Epub 2019 Jun 12.

Abstract

PURPOSE

Protein expression in formalin-fixed, paraffin-embedded tissue is routinely measured by IHC or quantitative fluorescence (QIF) on a handful of markers on a single section. Digital spatial profiling (DSP) allows spatially informed simultaneous assessment of multiple biomarkers. Here we demonstrate the DSP technology using a 44-plex antibody cocktail to find protein expression that could potentially be used to predict response to immune therapy in melanoma. The NanoString GeoMx DSP technology is compared with automated QIF (AQUA) for immune marker compartment-specific measurement and prognostic value in non-small cell lung cancer (NSCLC). Then we use this tool to search for novel predictive markers in a cohort of 60 patients with immunotherapy-treated melanoma on a tissue microarray using a 44-plex immune marker panel measured in three compartments (macrophage, leukocyte, and melanocyte) generating 132 quantitative variables.

RESULTS

The spatially informed variable assessment by DSP validates by both regression and variable prognostication compared with QIF for stromal CD3, CD4, CD8, CD20, and PD-L1 in NSCLC. From the 132 variables, 11 and 15 immune markers were associated with prolonged progression-free survival (PFS) and overall survival (OS). Notably, we find PD-L1 expression in CD68-positive cells (macrophages) and not in tumor cells was a predictive marker for PFS, OS, and response.

CONCLUSIONS

DSP technology shows high concordance with QIF and validates based on both regression and outcome assessment. Using the high-plex capacity, we found a series of expression patterns associated with outcome, including that the expression of PD-L1 in macrophages is associated with response.

摘要

目的

在福尔马林固定、石蜡包埋组织中,蛋白质表达通常通过免疫组化(IHC)或少数几种标志物的定量荧光(QIF)在一个切片上进行测量。数字空间分析(DSP)允许同时对多个生物标志物进行空间信息丰富的评估。在这里,我们使用 44 重抗体鸡尾酒展示了 DSP 技术,以发现可能用于预测黑色素瘤对免疫治疗反应的蛋白质表达。NanoString GeoMx DSP 技术与自动化 QIF(AQUA)在非小细胞肺癌(NSCLC)中进行免疫标志物特定部位测量和预后价值进行了比较。然后,我们使用该工具在一个由 60 名接受免疫治疗的黑色素瘤患者组成的组织微阵列中,使用三个部位(巨噬细胞、白细胞和黑素细胞)测量的 44 重免疫标志物面板寻找新的预测标志物,生成 132 个定量变量。

结果

与 QIF 相比,DSP 的空间信息变量评估通过回归和变量预测验证了 NSCLC 中基质 CD3、CD4、CD8、CD20 和 PD-L1 的准确性。在 132 个变量中,有 11 个和 15 个免疫标志物与无进展生存期(PFS)和总生存期(OS)延长相关。值得注意的是,我们发现 CD68 阳性细胞(巨噬细胞)中 PD-L1 的表达而不是肿瘤细胞中的表达是 PFS、OS 和反应的预测标志物。

结论

DSP 技术与 QIF 具有高度一致性,并基于回归和结果评估进行验证。使用高多重容量,我们发现了一系列与结果相关的表达模式,包括 PD-L1 在巨噬细胞中的表达与反应相关。

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Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma.
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