Deep Ovarian Cancer Metabolomics

项目总结 卵巢癌(OC)是美国女性癌症相关死亡的第五大原因， 最致命的妇科疾病。除了缺乏高度特异的生物标志物外，还缺乏症状 通常只有25%的OC病例在FIGO I期被诊断出来。 癌(HGSC)是OC最常见的形式，但也存在三种更罕见的组织亚型- 子宫内膜样变，透明细胞，粘液样。一个有效的早期诊断筛查策略将特别是 优势，因为5年OC存活率可高达90%。不幸的是，蛋白质生物标记物 因为CA-125没有足够的阳性预测价值，从临床角度来看是有用的。我们 假设有关早期HGSC和其他卵巢癌的有用信息可以在 血清代谢体。我们在人类和OC模型中的初步研究，如双基因敲除Dester-Pten 我们团队成员最近开发的鼠标，在这方面表现出了巨大的希望-平均灵敏度和 在储存的人血清样本中，早期检测的特异性分别达到97.8%和99.0%，甚至更高 在小鼠体内达到100%。这些结果促使我们对代谢组进行了更深入的研究 随着时间的推移，在较大的血清样本组中，与早期卵巢癌相关的变化。我们会 在小鼠身上进行代谢组学实验，并将未识别的人血清样本以高得多的 超高效液-质联用多种模式的数据融合覆盖比以前 光谱分析(UPLC-MS)和核磁共振(核磁共振)，以及以途径为中心的数据分析。 我们还建议用深度覆盖的组织块来补充血清水平的代谢组学实验 结合使用基质辅助激光在2-D和3-D中进行光谱成像(MSI) 解吸/电离(MALDI)和解吸电喷雾电离(DESI)具有互补性 电离机制。此外，我们建议偏离通常使用的暂定方法 仅用准确的质量识别光谱特征，并实现“深层代谢物注释” 这一方法既使用了“融合”的高分辨率技术(高场轨道MS、MS/MS、二维核磁共振)，又使用了 基于行波和漂移管离子碰撞截面预测的新技术 移动性--MS

PROJECT SUMMARY Ovarian cancer (OC) is the 5th leading cause of cancer-related deaths for U.S. women and the deadliest gynecological disease. Lack of symptoms in addition to the deficiency of highly specific biomarkers for detection typically result in only 25% of OC cases being diagnosed at FIGO stage I. High-grade serous carcinoma (HGSC) is the most prevalent form of OC, but three rarer histological subtypes also exist— endometrioid, clear cell, and mucinous. An effective screening strategy for early diagnosis would be particularly advantageous since 5-year OC survival rates can be as high as 90%. Unfortunately, protein biomarkers such as CA-125 do not have sufficient positive predictive value to be useful from a clinical perspective. We hypothesize that useful information regarding early stage HGSC and other ovarian cancers can be found in the serum metabolome. Our pilot studies in both humans and OC models, such as the double-knockout Dicer-Pten mouse recently developed by our team members, show great promise in this regard— average sensitivity and specificity for early detection have reached 97.8% and 99.0% in banked human serum samples, and up to100% in mice. These results have prompted us to perform a much deeper investigation of metabolome alterations associated with early stage ovarian cancers in larger serum sample sets, and over time. We will perform metabolomics experiments in mice and banked de-identified human serum samples with much higher coverage than before by “data fusing” various modes of ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR), coupled with pathway-centric data analysis. We also propose supplementing serum-level metabolomics experiments with deep-coverage tissue mass spectrometry imaging (MSI) in both 2-D and 3-D, using a combination of matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), which have complementary ionization mechanisms. Furthermore, we propose to depart from the commonly used approach of tentatively identifying spectral features by only using accurate masses, and implement a “deep metabolite annotation” approach that uses both “fused” high-resolution techniques (high field Orbitrap MS, MS/MS, 2-D NMR) and a new technology based on collisional cross section predictions for both travelling wave and drift tube ion mobility-MS.