Ashley Shanna L, Xia Meng, Murray Susan, O'Dwyer David N, Grant Ethan, White Eric S, Flaherty Kevin R, Martinez Fernando J, Moore Bethany B
Graduate Program in Immunology, University of Michigan, Ann Arbor, MI, United States of America.
Biostatistics Department, University of Michigan School of Public Health, Ann Arbor, MI, United States of America.
PLoS One. 2016 Aug 4;11(8):e0159878. doi: 10.1371/journal.pone.0159878. eCollection 2016.
Biomarkers in easily accessible compartments like peripheral blood that can predict disease progression in idiopathic pulmonary fibrosis (IPF) would be clinically useful regarding clinical trial participation or treatment decisions for patients. In this study, we used unbiased proteomics to identify relevant disease progression biomarkers in IPF.
Plasma from IPF patients was measured using an 1129 analyte slow off-rate modified aptamer (SOMAmer) array, and patient outcomes were followed over the next 80 weeks. Receiver operating characteristic (ROC) curves evaluated sensitivity and specificity for levels of each biomarker and estimated area under the curve (AUC) when prognostic biomarker thresholds were used to predict disease progression. Both logistic and Cox regression models advised biomarker selection for a composite disease progression index; index biomarkers were weighted via expected progression-free days lost during follow-up with a biomarker on the unfavorable side of the threshold.
A six-analyte index, scaled 0 to 11, composed of markers of immune function, proteolysis and angiogenesis [high levels of ficolin-2 (FCN2), cathepsin-S (Cath-S), legumain (LGMN) and soluble vascular endothelial growth factor receptor 2 (VEGFsR2), but low levels of inducible T cell costimulator (ICOS) or trypsin 3 (TRY3)] predicted better progression-free survival in IPF with a ROC AUC of 0.91. An index score ≥ 3 (group ≥ 2) was strongly associated with IPF progression after adjustment for age, gender, smoking status, immunomodulation, forced vital capacity % predicted and diffusing capacity for carbon monoxide % predicted (HR 16.8, 95% CI 2.2-126.7, P = 0.006).
This index, derived from the largest proteomic analysis of IPF plasma samples to date, could be useful for clinical decision making in IPF, and the identified analytes suggest biological processes that may promote disease progression.
在诸如外周血等易于获取的样本中,能够预测特发性肺纤维化(IPF)疾病进展的生物标志物,对于患者参与临床试验或治疗决策具有临床实用价值。在本研究中,我们运用非靶向蛋白质组学方法来鉴定IPF中与疾病进展相关的生物标志物。
使用包含1129种分析物的慢解离速率修饰适配体(SOMAmer)阵列检测IPF患者的血浆,并在接下来的80周内跟踪患者的预后情况。采用受试者工作特征(ROC)曲线评估每种生物标志物水平的敏感性和特异性,并在使用预后生物标志物阈值预测疾病进展时估计曲线下面积(AUC)。通过逻辑回归和Cox回归模型为综合疾病进展指数选择生物标志物;通过随访期间在阈值不利一侧的生物标志物所损失的预期无进展天数对指数生物标志物进行加权。
一个由免疫功能、蛋白水解和血管生成标志物组成的六分析物指数(范围为0至11)[纤维胶凝蛋白-2(FCN2)、组织蛋白酶S(Cath-S)、天冬酰胺内肽酶(LGMN)和可溶性血管内皮生长因子受体2(VEGFsR2)水平较高,但诱导性T细胞共刺激分子(ICOS)或胰蛋白酶3(TRY3)水平较低]预测IPF患者有更好的无进展生存期,ROC曲线下面积为0.91。在调整年龄、性别、吸烟状况、免疫调节、预计用力肺活量百分比和预计一氧化碳弥散量百分比后,指数评分≥3(分组≥2)与IPF进展密切相关(风险比16.8,95%置信区间2.2 - 126.7,P = 0.006)。
该指数源自迄今为止对IPF血浆样本进行的最大规模蛋白质组学分析,可能有助于IPF的临床决策制定,并且所鉴定的分析物提示了可能促进疾病进展的生物学过程。