title: Integrative Systemic and Local Metabolomics with Impact on Survival in High-Grade Serous Ovarian Cancer.
abstract: Purpose:Cancer metabolism is characterized by alterations including aerobic glycolysis, oxidative phosphorylation, and need of fuels and building blocks.Experimental Design:Targeted metabolomics of preoperative and follow-up sera, ascites, and tumor tissues, RNA sequencing of isolated tumor cells, local and systemic chemokine, and local immune cell infiltration data from up to 65 high-grade serous ovarian cancer patients and 62 healthy controls were correlated to overall survival and integrated in a Systems Medicine manner.Results:Forty-three mainly (poly)unsaturated glycerophospholipids and four essential amino acids (citrulline) were significantly reduced in patients with short compared with long survival and healthy controls. The glycerophospholipid fingerprint is identical to the fingerprint from isolated (very) low-density lipoproteins (vLDL), indicating that the source of glycerophospholipids consumed by tumors is (v)LDL. A glycerophospholipid-score (HR, 0.46;P= 0.007) and a 100-gene signature (HR, 0.65;P= 0.004) confirmed the independent impact on survival in training (n= 65) and validation (n= 165) cohorts. High concentrations of LDLs and glycerophospholipids were independently predictors for favorable survival. Patients with low glycerophospholipids presented with more systemic inflammation (C-reactive protein and fibrinogen negatively and albumin positively correlated) but less adaptive immune cell tumor infiltration (lower tumor and immune cell PD-L1 expression), less oxygenic respiration and increased triglyceride biosynthesis in tumor cells, and lower histone expressions, correlating with higher numbers of expressed genes and more transcriptional noise, a putative neo-pluripotent tumor cell phenotype.Conclusions:Low serum phospholipids and essential amino acids are correlated with worse outcome in ovarian cancer, accompanied by a specific tumor cell phenotype.Clin Cancer Res; 23(8); 2081-92. ©2016 AACR.
authors: Department of Obstetrics and Gynecology, Comprehensive Cancer Center (CCC), Medical University of Vienna, Vienna, Austria.
authors: Anna
authors: A
authors: Bachmayr-Heyda
authors: Department of Obstetrics and Gynecology, Comprehensive Cancer Center (CCC), Medical University of Vienna, Vienna, Austria.
authors: Stefanie
authors: S
authors: Aust
authors: Department of Obstetrics and Gynecology, Comprehensive Cancer Center (CCC), Medical University of Vienna, Vienna, Austria.
authors: Katharina
authors: K
authors: Auer
authors: Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
authors: Samuel M
authors: SM
authors: Meier
authors: Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria.
authors: Klaus G
authors: KG
authors: Schmetterer
authors: Department of Pathology, Medical University of Vienna, Vienna, Austria.
authors: Sabine
authors: S
authors: Dekan
authors: Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
authors: Christopher
authors: C
authors: Gerner
authors: Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria. dietmar.pils@univie.ac.at.
authors: Dietmar
authors: D
authors: Pils
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000032:analysis*; D018284:Cystadenocarcinoma, Serous / Q000175:diagnosis / Q000378:metabolism* / Q000401:mortality; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D060787:Neoplasm Grading; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000378:metabolism* / Q000401:mortality; D011379:Prognosis; D016016:Proportional Hazards Models; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor