Department of Breast Disease, Henan Breast Cancer Center, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
Breax Laboratory, PCAB Research Center of Breath and Metabolism, Beijing, 100071, China.
Anal Chim Acta. 2024 Sep 1;1320:342883. doi: 10.1016/j.aca.2024.342883. Epub 2024 Jul 10.
In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC).
A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring.
On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC.
Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs.
在这项研究中，呼气检测被认为是一种有前途的方法，可用于检测和监测乳腺癌（BC）。
使用高压光子电离时间飞行质谱（HPPI-TOFMS）平台检测呼吸样本中的挥发性有机化合物（VOC）。然后，构建基于 VOC 的机器学习（ML）模型，用于诊断 BC 及其进展监测。最终，共有 1981 名女性有可用的呼气样本纳入研究，其中 937 名（47.3%）被诊断为 BC。VOC 面板用于 ML 模型构建，以用于 BC 检测和进展监测。
在盲测队列中，该基于 VOC 的模型成功区分了有和无 BC 的患者，其敏感性、特异性和接受者操作特征曲线（ROC）下面积（AUC）值分别为 85.9%、90.4%和 0.946。当区分无淋巴结转移（LNM）或肿瘤-淋巴结-转移（TNM）分期 0/I/II 或 III/IV 疾病的患者时，相应的 AUC 值分别为 0.840 和 0.708。虽然开发的基于 VOC 的模型在尝试根据病理模式（导管原位癌（DCIS）与浸润性 BC（IBC））或分子亚型（管腔型 vs 人表皮生长因子受体 2（HER2+）型 vs 三阴性 BC（TNBC））区分患者时表现不佳。
总体而言，基于 HPPI-TOFMS 的呼吸组学方法可能对 BC 的检测和进展监测具有价值。需要进一步研究来探索所鉴定 VOC 的基本机制。
