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Discrimination of Head and Neck Squamous Cell Carcinoma Patients and Healthy Adults by 10-Color Flow Cytometry: Development of a Score Based on Leukocyte Subsets

机译:十色流式细胞术区分头颈部鳞状细胞癌患者和健康成年人:基于白细胞亚群的评分发展

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摘要

Background: Leukocytes in peripheral blood (PB) are prognostic biomarkers in head and neck squamous cell carcinoma cancer patients (HNSCC-CPs), but differences between HNSCC-CPs and healthy adults (HAs) are insufficiently described. Methods: 10-color flow cytometry (FCM) was used for in-depth immunophenotyping of PB samples of 963 HAs and 101 therapy-naïve HNSCC-CPs. Absolute (AbsCC) and relative cell counts (RelCC) of leukocyte subsets were determined. A training cohort (TC) of 43 HNSCC-CPs and 43 HAs, propensity score (PS)-matched according to age, sex, alcohol, and smoking, was used to develop a score consecutively approved in a validation cohort (VC). Results: Differences in AbsCC were detected in leukocyte subsets (p < 0.001), but had low power in discriminating HNSCC-CPs and HAs. Consequently, RelCC of nine leukocyte subsets in the TC were used to calculate 36 ratios; receiver operating characteristic (ROC) curves defined optimum cut-off values. Binary classified data were combined in a score based on four ratios: monocytes-to-granulocytes (MGR), classical monocytes-to-monocytes (clMMR), monocytes-to-lymphocytes (MLR), and monocytes-to-T-lymphocytes (MTLR); ≥3 points accurately discriminate HNSCC-CPs and HAs in the PS-matched TC (p = 2.97 × 10−17), the VC (p = 4.404 × 10−178), and both combined (p = 7.74 × 10−199). Conclusions: RelCC of leukocyte subsets in PB of HNSCC-CPs differ significantly from those of HAs. A score based on MGR, clMMR, MLR, and MTLR allows for accurate discrimination.
机译:背景:外周血(PB)中的白细胞是头颈部鳞状细胞癌患者(HNSCC-CP)的预后生物标志物,但对HNSCC-CP和健康成年人(HAs)之间的差异描述不足。方法:采用10色流式细胞术(FCM)对963例HA和101例未接受过HNSCC-CP的PB样品进行深入免疫表型分析。确定白细胞亚群的绝对(AbsCC)和相对细胞计数(RelCC)。使用43个HNSCC-CP和43个HA的训练队列(TC),根据年龄,性别,饮酒和吸烟对倾向得分(PS)进行匹配,以制定在验证队列(VC)中连续批准的得分。结果:在白细胞亚群中检测到AbsCC的差异(p <0.001),但在区分HNSCC-CP和HA方面的功效较低。因此,TC中9个白细胞亚群的RelCC被用来计算36个比率。接收器工作特性(ROC)曲线定义了最佳截止值。二进制分类数据以基于四个比率的分数进行组合:单核细胞与粒细胞(MGR),经典单核细胞与单核细胞(clMMR),单核细胞与淋巴细胞(MLR)和单核细胞与T淋巴细胞( MTLR); ≥3分可准确区分PS匹配TC中的HNSCC-CP和HA(p = 2.97×10 −17 ),VC(p = 4.404×10 −178 ),然后将两者合并(p = 7.74×10 −199 )。结论:HNSCC-CPs PB的白细胞亚群的RelCC与HAs显着不同。基于MGR,clMMR,MLR和MTLR的评分可实现准确的区分。

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