Validation of a blood protein signature for non-small cell lung cancer
Validation of a blood protein signature for non-small cell lung cancer
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DOI：
10.1186/1559-0275-11-32
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发表时间：
2014-01-01
影响因子：
3.8
通讯作者：
Ostroff, Rachel M.
中科院分区：
文献类型：
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作者：
Mehan, Michael R.;Williams, Stephen A.;Ostroff, Rachel M.
背景：CT筛查肺癌在降低死亡率方面是有效的，但也有值得关注的领域，包括4%的阳性预测值和间歇性癌症的发展。可以处理这些限制的血液测试将是有用的，但由于血液采集的差异可能导致不同人群之间的重复性较差，此类测试的发展受到了阻碍。结果：利用SOMAscan技术生成了基于血液的蛋白质组图谱，该技术测量了1033种蛋白质。首先，用样本映射向量(SMV)评估分析前的可变性，SMV是一组蛋白质，检测与样本收集相关的蛋白质水平的混杂因素。为了发现肺癌生物标志物，选择了一组收集良好的血清样本，不受分析前变异性的影响。在SMV得分较高的样本子集中测试样本收集变化对这些候选标记的影响，以便可以使用最健壮的标记来创建疾病分类器。发现样本集(n=363)来自对94例非小细胞肺癌(NSCLC)患者和269名长期吸烟者和良性肺结节对照组的多中心研究。分析结果显示，所有病例(腺癌68%，鳞癌32%)的AUC为0.85，尤其是鳞癌的AUC为0.93。该小组是通过在两个独立队列中进行盲法预测来验证的(第一次验证中n=138，第二次验证中n=135)。在解除第二个队列的盲目之前，该模型被重新校准为小组格式。AUC总体为0.81和0.77，仅鳞状细胞肿瘤为0.89和0.87。对15%的疾病患病率的估计阴性预测值为93%，对肺鳞状肿瘤的阴性预测值为99%。分类器中的蛋白质在破坏细胞外基质、代谢动态平衡和炎症方面发挥作用。结论：选择耐受样本处理变异的生物标记物可以产生稳健的肺癌生物标记物，这些标记物在独立验证中表现一致。它们形成了检测肺癌的敏感标志，特别是鳞状细胞组织学。这种无创性检查可以提高CT筛查的阳性预测值，有可能避免对非恶性肺结节的侵袭性评估。
Background: CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.Results: Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.Conclusions: Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.