Metabolic Markers and Statistical Prediction of Serous Ovarian Cancer Aggressiveness by Ambient Ionization Mass Spectrometry Imaging.
Metabolic Markers and Statistical Prediction of Serous Ovarian Cancer Aggressiveness by Ambient Ionization Mass Spectrometry Imaging.
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DOI：
10.1158/0008-5472.can-16-3044
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发表时间：
2017-06-01
期刊：
影响因子：
11.2
通讯作者：
Eberlin LS
中科院分区：
文献类型：
--
作者：
Sans M;Gharpure K;Tibshirani R;Zhang J;Liang L;Liu J;Young JH;Dood RL;Sood AK;Eberlin LS
卵巢高级别浆液性癌（HGSC）是妇科恶性肿瘤中死亡率最高的一种，发展迅速，侵袭性强。不同的是，浆液性交界性卵巢肿瘤（BOT）可以进展为低级别浆液性癌，并具有相对惰性的临床行为。HGSC和BOT之间潜在的生物学差异需要准确的诊断方法和定制的治疗方案，并且侵袭性的分子标记物的鉴定可以提供有价值的生化见解并改善疾病管理。在这里，我们使用解吸电喷雾电离（DESI）质谱（MS）的图像和化学表征的代谢概况HGSC，BOT，和正常卵巢组织样本。EST-MS成像能够清晰地显示浆液性BOT中细小的乳头状分支，并能够表征肿瘤异质性的空间特征，例如HGSC中的相邻坏死和间质。确定了癌症侵袭性的预测标志物，包括各种游离脂肪酸、代谢物和复合脂质，如神经酰胺、甘油磷酸甘油、心磷脂和甘油磷酸胆碱。从78个不同组织样本中以正离子和负离子模式采集的总共89，826个单个像素构建的分类模型，与正常组织相比，能够诊断和预测HGSC和所有肿瘤样本，总体一致性分别为96.4%和96.2%。HGSC和BOT的歧视，实现了93.0%的整体准确性。有趣的是，我们的分类模型允许识别三个BOT样本，这些样本呈现出可能与低度恶性癌发展相关的不寻常组织学特征。我们的研究结果表明，作为一个强大的方法，快速浆液性卵巢癌诊断的基础上改变代谢签名。
Ovarian high-grade serous carcinoma (HGSC) results in the highest mortality among gynecological cancers, developing rapidly and aggressively. Dissimilarly, serous borderline ovarian tumors (BOT) can progress into low-grade serous carcinomas and have relatively indolent clinical behavior. The underlying biological differences between HGSC and BOT call for accurate diagnostic methodologies and tailored treatment options, and identification of molecular markers of aggressiveness could provide valuable biochemical insights and improve disease management. Here we used desorption electrospray ionization (DESI) mass spectrometry (MS) to image and chemically characterize the metabolic profiles of HGSC, BOT, and normal ovarian tissue samples. DESI-MS imaging enabled clear visualization of fine papillary branches in serous BOT and allowed for characterization of spatial features of tumor heterogeneity such as adjacent necrosis and stroma in HGSC. Predictive markers of cancer aggressiveness were identified, including various free fatty acids, metabolites, and complex lipids such as ceramides, glycerophosphoglycerols, cardiolipins, and glycerophosphocholines. Classification models built from a total of 89,826 individual pixels, acquired in positive and negative ion modes from 78 different tissue samples, enabled diagnosis and prediction of HGSC and all tumor samples in comparison to normal tissues, with overall agreements of 96.4% and 96.2%, respectively. HGSC and BOT discrimination was achieved with an overall accuracy of 93.0%. Interestingly, our classification model allowed identification of three BOT samples presenting unusual histologic features that could be associated with the development of low-grade carcinomas. Our results suggest DESI-MS as a powerful approach for rapid serous ovarian cancer diagnosis based on altered metabolic signatures.