Deep Ovarian Cancer Metabolomics

项目总结

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.