Cosma Georgina, McArdle Stéphanie E, Reeder Stephen, Foulds Gemma A, Hood Simon, Khan Masood, Pockley A Graham
School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
Front Immunol. 2017 Dec 18;8:1771. doi: 10.3389/fimmu.2017.01771. eCollection 2017.
Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features (8452728 (8 Effector Memory cells); 4452728 (4 Terminally Differentiated Effector Memory Cells re-expressing CD45RA); 319 (B cells); 35684 (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.
在缺乏基于活检的确切证据的情况下，确定前列腺特异性抗原（PSA）水平低于20 ng/ml的无症状个体是否患有前列腺癌，仍然是临床医生面临的重大挑战，他们必须决定这些PSA值低的个体是否患有前列腺癌。在此，我们提出了一种先进的计算数据提取方法，该方法可以根据使用多参数流式细胞术生成的外周血免疫细胞谱，识别PSA水平<20 ng/ml的男性中前列腺癌的存在。对通过多参数流式细胞术分析进行前列腺癌常规检测（包括组织活检）的个体所产生的外周血中关键白细胞群体的存在和患病率相关的免疫表型数据集进行统计分析，未能确定白细胞群体谱与良性疾病（无前列腺癌）或前列腺癌的存在之间的显著关系。相比之下，一种遗传算法计算方法从一组20个特征中识别出了5个流式细胞术特征的子集（8452728（8个效应记忆细胞）；4452728（4个重新表达CD45RA的终末分化效应记忆细胞）；319（B细胞）；35684（NKT细胞）），这些特征有可能区分良性疾病和前列腺癌。这些特征被用于使用k近邻分类算法构建前列腺癌预测模型。所提出的模型将流式细胞术特征集作为输入，其性能优于将PSA值作为输入的预测模型。具体而言，基于流式细胞术的模型的准确率为83.33%，曲线下面积（AUC）为83.40%，最佳ROC点为假阳性率（FPR）=16.13%，真阳性率（TPR）=82.93%，而基于PSA的模型的准确率为77.78%，AUC为76.95%，最佳ROC点为FPR=29.03%，TPR=82.93%。将PSA和流式细胞术预测指标相结合，准确率为79.17%，AUC为78.17%，最佳ROC点为FPR=29.03%，TPR=85.37%。结果表明了基于计算智能的方法在询问免疫表型数据集方面的价值，并且与单独使用PSA检测相比，将外周血表型分析与PSA水平相结合可提高诊断准确性。这些研究还表明，癌症的存在反映在外周血免疫表型谱的变化中，这可以通过对复杂的流式细胞术数据集进行计算分析和解释来识别。
