Daily Anna, Ravishankar Prashanth, Wang Wanyi, Krone Ryan, Harms Steve, Klimberg V Suzanne
Namida Lab Inc., Fayetteville, AR, USA.
Elite Research, LLC, Irving, TX, USA.
Biomark Res. 2022 Oct 25;10(1):76. doi: 10.1186/s40364-022-00420-1.
There is a growing body of evidence to support tears as a non-traditional biological fluid in clinical laboratory testing. In addition to the simplicity of tear fluid processing, the ability to access key cancer biomarkers in high concentrations quickly and inexpensively is significantly enhanced. Tear fluid is a dynamic environment rich in both proteomic and genomic information, making it an ideal medium for exploring the potential for biological testing modalities.
All protocols involving human subjects were reviewed and approved by the University of Arkansas IRB committee (13-11-289) prior to sample collection. Study enrollment was open to women ages 18 and over from October 30, 2017-June 19, 2019 at The Breast Center, Fayetteville, AR and Bentonville, AR. Convenience sampling was used and samples were age/sex matched, with enrollment open to individuals at any point of the breast health continuum of care. Tear samples were collected using the Schirmer strip method from 847 women. Concentration of selected tear proteins were evaluated using standard sandwich ELISA techniques and the resulting data, combined with demographic and clinical covariates, was analyzed using logistic regression analysis to build a model for classification of samples.
Logistic regression analysis produced three models, which were then evaluated on cases and controls at two diagnostic thresholds and resulted in sensitivity ranging from 52 to 90% and specificity from 31 to 79%. Sensitivity and specificity variation is dependent on the model being evaluated as well as the selected diagnostic threshold providing avenues for assay optimization.
The work presented here builds on previous studies focused on biomarker identification in tear samples. Here we show successful early classification of samples using two proteins and minimal clinical covariates.
越来越多的证据支持眼泪作为临床实验室检测中的一种非传统生物流体。除了眼泪流体处理的简便性外，快速且低成本地获取高浓度关键癌症生物标志物的能力也显著增强。眼泪流体是一个富含蛋白质组和基因组信息的动态环境，使其成为探索生物检测方式潜力的理想介质。
在采集样本之前，所有涉及人类受试者的方案均经过阿肯色大学机构审查委员会（13 - 11 - 289）的审查和批准。2017年10月30日至2019年6月19日，研究招募对象为阿肯色州费耶特维尔市和本顿维尔市乳腺中心18岁及以上的女性。采用便利抽样，样本进行了年龄/性别匹配，招募对象为处于乳腺健康连续护理任何阶段的个体。使用Schirmer试纸法从847名女性中采集眼泪样本。使用标准夹心ELISA技术评估所选眼泪蛋白质的浓度，并将所得数据与人口统计学和临床协变量相结合，使用逻辑回归分析来构建样本分类模型。
逻辑回归分析产生了三个模型，然后在两个诊断阈值下对病例和对照进行评估，灵敏度范围为52%至90%，特异性范围为31%至79%。灵敏度和特异性的变化取决于所评估的模型以及所选的诊断阈值，这为检测优化提供了途径。
本文所呈现的工作建立在先前专注于眼泪样本中生物标志物识别的研究基础之上。在这里，我们展示了使用两种蛋白质和最少的临床协变量成功实现样本的早期分类。
