Deep Ovarian Cancer Metabolomics

项目摘要 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。除了缺乏高度特异性的生物标志物外，缺乏症状 通常只有25%的OC病例在FIGO I期被诊断。高级别浆液性 癌（HGSC）是OC最常见的形式，但也存在三种罕见的组织学亚型- 类胶质、透明细胞和粘液性。一个有效的早期诊断筛查策略将特别是 这是有利的，因为5年OC存活率可高达90%。不幸的是，蛋白质生物标志物， 因为CA-125不具有从临床角度有用的足够的阳性预测值。我们 假设关于早期HGSC和其他卵巢癌有用信息可以在 血清代谢组我们在人类和OC模型中的初步研究，例如双敲除Dicer-Pten 我们的团队成员最近开发的鼠标在这方面表现出很大的希望-平均灵敏度和 对库存人血清样品的早期检测特异性分别达到97.8%和99.0%， 在小鼠中达到100%。这些结果促使我们对代谢组学进行更深入的研究 在更大的血清样本集中，随着时间的推移，与早期卵巢癌相关的变化。我们将 在小鼠中进行代谢组学实验， 通过“数据融合”多种模式的超高效液相色谱-质谱联用， 质谱（UPLC-MS）和核磁共振（NMR），结合以路径为中心的数据分析。 我们还建议补充血清水平的代谢组学实验与深覆盖的组织质量 光谱成像（MSI）在2-D和3-D，使用矩阵辅助激光的组合 解吸/电离（MALDI）和解吸电喷雾电离（DESI），它们具有互补性 电离机制此外，我们建议不采用通常采用的暂定 仅使用准确的质量来识别光谱特征，并实施“深层代谢物注释” 该方法使用“融合”高分辨率技术（高场Orbitrap MS，MS/MS，2-D NMR）和 基于行波和漂移管离子碰撞截面预测的新技术 流动性-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.