利用呼吸中的挥发性生物标志物预测乳腺癌风险。

Prediction of breast cancer risk with volatile biomarkers in breath.

机构信息

Breath Research Laboratory, Menssana Research Inc, 211 Warren St, Newark, NJ, 07103, USA.

Department of Medicine, New York Medical College, Valhalla, NY, USA.

出版信息

Breast Cancer Res Treat. 2018 Jul;170(2):343-350. doi: 10.1007/s10549-018-4764-4. Epub 2018 Mar 23.

DOI:10.1007/s10549-018-4764-4

PMID:29569019

Abstract

BACKGROUND

Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk.

METHODS

We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV.

RESULTS

Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%.

CONCLUSION

Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

摘要

背景

人体呼吸中含有挥发性有机化合物（VOC），这些化合物是乳腺癌的生物标志物。我们研究了呼吸 VOC 生物标志物作为乳腺癌风险指标的阳性和阴性预测值（PPV 和 NPV）。

方法

我们采用超净呼吸采集气球从 54 名经活检证实患有乳腺癌的女性和 124 名无癌症对照者中采集呼吸样本。采用气相色谱（GC）结合质谱（GC-MS）或表面声波检测（GC-SAW）分析呼吸 VOC。色谱图随机分配到训练集或验证集。蒙特卡罗分析确定了训练集中乳腺癌的显著呼吸 VOC 生物标志物，并将这些生物标志物纳入多元算法中以预测验证集中的疾病。在未拆分的数据集中，预测算法生成的判别函数（DF）值随敏感性、特异性、PPV 和 NPV 而变化。

结果

使用 GC-MS，测试准确性为 90%（未拆分数据集中接收者操作特征曲线下面积），交叉验证准确性为 77%。使用 GC-SAW，测试准确性为 86%，交叉验证准确性为 74%。两种检测方法的准确率均为 90%（未拆分数据集中接收者操作特征曲线下面积），交叉验证准确率为 77%。两种检测方法均为 86%，交叉验证准确率为 74%。使用两种检测方法，DF 值较低与乳腺癌风险较低相关（NPV>99.9%）。DF 值较高与乳腺癌风险较高和 PPV 上升至 100%相关。