Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model

Ovarian cancer is a deadly disease
killing more than any other
gynecologic cancer. Nonspecific symptoms, combined with a lack of
early detection methods, contribute to late diagnosis and low five-year
survival rates. High-grade serous carcinoma (HGSC) is the most common
and deadliest subtype that results in 90% of ovarian cancer deaths.
To investigate metabolic patterns for early detection of this deadly
ovarian cancer, Dicer-Pten double
knockout (DKO) mice that phenocopy many of the features of metastatic
HGSC observed in women were studied. Using ultraperformance liquid
chromatography–mass spectrometry (UPLC–MS), serum samples
from 14 early-stage tumor (ET) DKO mice and 11 controls were analyzed
in depth to screen for metabolic signatures capable of differentiating
early-stage HGSC from controls. Iterative multivariate classification
selected 18 metabolites that, when considered as a panel, yielded
100% accuracy, sensitivity, and specificity for classification. Altered
metabolic pathways reflected in that panel included those of fatty
acids, bile acids, glycerophospholipids, peptides, and some dietary
phytochemicals. These alterations revealed impacts to cellular energy
storage and membrane stability, as well as changes in defenses against
oxidative stress, shedding new light on the metabolic alterations
associated with early ovarian cancer stages.