Croner Lisa J, Dillon Roslyn, Kao Athit, Kairs Stefanie N, Benz Ryan, Christensen Ib J, Nielsen Hans J, Blume John E, Wilcox Bruce
Applied Proteomics, Inc, 3545 John Hopkins Court, Suite 150, San Diego, CA 92121 USA.
Department of Surgical Gastroenterology 360, Hvidovre Hospital, University of Copenhagen, 2650 Hvidovre, Denmark.
Clin Proteomics. 2017 Jul 25;14:28. doi: 10.1186/s12014-017-9163-z. eCollection 2017.
The aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format-electrochemiluminescence immunoassays-to quantify protein concentrations. The aim was achieved by building and validating a CRC classifier using concentration measures from a large sample set representing a true intent-to-test (ITT) symptomatic population.
4435 patient samples were drawn from the sample set. Samples were collected at seven hospitals across Denmark between 2010 and 2012 from subjects with symptoms of colorectal neoplasia. Colonoscopies revealed the presence or absence of CRC. 27 blood plasma proteins were selected as candidate biomarkers based on previous studies. Multiplexed electrochemiluminescence assays were used to measure the concentrations of these 27 proteins in all 4435 samples. 3066 patients were randomly assigned to the Discovery set, in which machine learning was used to build candidate classifiers. Some classifiers were refined by allowing up to a 25% indeterminate score range. The classifier with the best Discovery set performance was successfully validated in the separate Validation set, consisting of 1336 samples.
The final classifier was a logistic regression using ten predictors: eight proteins (A1AG, CEA, CO9, DPPIV, MIF, PKM2, SAA, TFRC), age, and gender. In validation, the indeterminate rate of the new panel was 23.2%, sensitivity/specificity was 0.80/0.83, PPV was 36.5%, and NPV was 97.1%.
The validated classifier serves as the basis of a new blood-based CRC test for symptomatic patients. The improved performance, resulting from robust concentration measures across a large sample set mirroring the ITT population, renders the new test the best available for this population. Results from a test using this classifier can help assess symptomatic patients' CRC risk, increase their colonoscopy compliance, and manage next steps in their care.
目的是通过开发一种新的结直肠癌(CRC)分类器并将其用于新的检测方案,对现有的针对高危症状患者的基于血液的CRC检测进行改进。新检测采用了一种稳健的检测形式——电化学发光免疫分析——来定量蛋白质浓度。通过使用来自代表真实检测意向(ITT)症状人群的大样本集的浓度测量值构建并验证CRC分类器,实现了这一目标。
从样本集中抽取4435份患者样本。2010年至2012年期间,在丹麦的七家医院从患有结直肠肿瘤症状的受试者中收集样本。结肠镜检查揭示了CRC的存在与否。基于先前的研究,选择了27种血浆蛋白作为候选生物标志物。采用多重电化学发光分析来测量所有4435份样本中这27种蛋白质的浓度。3066名患者被随机分配到发现集,在该集中使用机器学习构建候选分类器。通过允许高达25%的不确定评分范围对一些分类器进行了优化。在由1336份样本组成的单独验证集中成功验证了发现集性能最佳的分类器。
最终的分类器是一种逻辑回归模型,使用了十个预测因子:八种蛋白质(A1AG、CEA、CO9、DPPIV、MIF、PKM2、SAA、TFRC)、年龄和性别。在验证中,新检测组的不确定率为23.2%;灵敏度/特异性为0.80/0.83,阳性预测值为36.5%,阴性预测值为97.1%。
经过验证的分类器是一种针对症状患者的新的基于血液的CRC检测的基础。通过在反映ITT人群的大样本集中进行稳健的浓度测量而实现的性能提升,使新检测成为该人群可用的最佳检测方法。使用该分类器进行检测的结果有助于评估症状患者的CRC风险,提高他们的结肠镜检查依从性,并管理其后续护理步骤。