Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging
Wanja Kassuhn, Oliver Klein, Silvia Darb-Esfahani, Hedwig Lammert, Sylwia Handzik, Eliane T. Taube, Wolfgang D. Schmitt, Carlotta Keunecke, David Horst, Felix Dreher, Joshy George, David D. Bowtell, Oliver Dorigo, Michael Hummel, Jalid Sehouli, Nils Blüthgen, Hagen Kulbe, and Elena I. Braicu
Cancers, 2021
High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes mightbe utilized for emerging clinical challenges to ultimately improve patient outcomes.