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; Elena I Braicu

doi:10.3390/cancers13071512

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Cancers (Basel). 2021 Mar 25;13(7):1512. doi: 10.3390/cancers13071512.

Authors

Wanja Kassuhn^{1

2}, Oliver Klein³, Silvia Darb-Esfahani^{4

5}, Hedwig Lammert⁴, Sylwia Handzik^{3

4}, Eliane T Taube⁴, Wolfgang D Schmitt⁴, Carlotta Keunecke^{1

2}, David Horst⁴, Felix Dreher⁶, Joshy George⁷, David D Bowtell⁸, Oliver Dorigo⁹, Michael Hummel⁴, Jalid Sehouli^{1

2}, Nils Blüthgen^{4

10}, Hagen Kulbe^{1

2}, Elena I Braicu^{1

2}

Affiliations

¹ Tumorbank Ovarian Cancer Network, ENGOT biobank, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany.
² Department of Gynecology, European Competence Center for Ovarian Cancer, Charité-Universitätsmedizi Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Virchow Klinikum, 13353 Berlin, Germany.
³ BIH Center for Regenerative Therapies BCRT, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany.
⁴ Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany.
⁵ Institute of Pathology Berlin-Spandau and Berlin-Buch, 13589 Berlin, Germany.
⁶ Alacris Theranostics GmbH, 12489 Berlin, Germany.
⁷ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
⁸ Sir Peter MacCallum Department of Oncology, The University of Melbourne, 3010 Parkville, Victoria, Australia.
⁹ Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Stanford Women's Cance Center, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹⁰ IRI Life Sciences, Humboldt University, 10115 Berlin, Germany.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

Keywords: MALDI-IMS; diagnostic classifier; molecular subtypes; ovarian cancer.