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The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC), which is the most lethal gynecologic malignancy, is not well understood. Here, we perform comprehensive multi-platform omics analyses, including integrated analysis, and immune monitoring on primary and metastatic sites from highly clinically annotated HGSC samples based on a laparoscopic triage algorithm from patients who underwent complete gross resection (R0) or received neoadjuvant chemotherapy (NACT) with excellent or poor response. We identify significant distinct molecular abnormalities and cellular changes and immune cell repertoire alterations between the groups, including a higher rate of NF1 copy number loss, and reduced chromothripsis-like patterns, higher levels of strong-binding neoantigens, and a higher number of infiltrated T cells in the R0 versus the NACT groups., Competing Interests: Declaration of Interests A.A.J. consults with Roche/Genentech, Aravive, and Almac Group; has research funding from AstraZeneca, Pfizer, Bristol-Myers Squibb, Immatics, Iovance Biotherapeutics; honoraria from Gerson Lehrman Group; and travel support from AstraZeneca and MedImmune. K.R., A.D., and D.C. are employees of Akoya Biosciences. N.D.F. consults with Tesaro. S.N.W. has clinical research grants from AstraZeneca, ArQule, Bayer, Clovis Oncology, Cotinga Pharmaceuticals, NCCN, Novartis, Roche/Genentech, and Tesaro and consults with AstraZeneca, Circulogene, Clovis Oncology, Merck, Novartis, Pfizer, Roche/Genentech, Takeda, and Tesaro. R.L.C. has clinical research grants from AstraZeneca, Merck, Clovis Oncology, Genmab, Roche/Genentech, Janssen, V Foundation, and Gateway for Cancer Research and consults with AstraZeneca, Merck, Tesaro, Medivation, Clovis Oncology, Genmab, GamaMabs, Agenus, Regeneron, OncoQuest, OncoSec, Roche/Genentech, and Janssen. G.B.M. consults with AstraZeneca, ImmunoMet, Ionis, Nuevolution, PDX Phamaceuticals, SignalChem, Symphogen, and Tarveda Therapeutics; has stock options with Catena Pharmaceuticals, ImmunoMet, SignalChem, Spindletop Captial, and Tarveda Therapeutics; sponsored research from AstraZeneca, ImmunoMet, Pfizer, NanoString, and Tesaro and travel support from Chrysallis BioTherapeutics; and has licensed technology to NanoString and Myriad Genetics. Y.C.’s spouse owns stock in Celsion. T.P.C. consults with Thermo Fisher Scientific and has research funding from AbbVie. G.L.M. consults with Merck, Kiyatek, Renovia, and Tesaro and has research funding from Merck. A.K.S. consults with Merck and Kiyatec, has research funding from M-Trap, and is a shareholder of Bio-Path Holdings. All other authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

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 with 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. Cancer Res; 77(11); 2903-13. ©2017 AACR ., (©2017 American Association for Cancer Research.)