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Article

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

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
*
Author to whom correspondence should be addressed.
Authors contributed equally to this work.
Academic Editors: Fabio Pagni and Galimberti Stefania
Cancers 2021, 13(7), 1512; https://doi.org/10.3390/cancers13071512
Received: 22 February 2021 / Revised: 12 March 2021 / Accepted: 23 March 2021 / Published: 25 March 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 might be utilized for emerging clinical challenges to ultimately improve patient outcomes.
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. View Full-Text
Keywords: ovarian cancer; molecular subtypes; diagnostic classifier; MALDI-IMS ovarian cancer; molecular subtypes; diagnostic classifier; MALDI-IMS
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MDPI and ACS Style

Kassuhn, W.; Klein, O.; Darb-Esfahani, S.; Lammert, H.; Handzik, S.; Taube, E.T.; Schmitt, W.D.; Keunecke, C.; Horst, D.; Dreher, F.; George, J.; Bowtell, D.D.; Dorigo, O.; Hummel, M.; Sehouli, J.; Blüthgen, N.; Kulbe, H.; Braicu, E.I. Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging. Cancers 2021, 13, 1512. https://doi.org/10.3390/cancers13071512

AMA Style

Kassuhn W, Klein O, Darb-Esfahani S, Lammert H, Handzik S, Taube ET, Schmitt WD, Keunecke C, Horst D, Dreher F, George J, Bowtell DD, Dorigo O, Hummel M, Sehouli J, Blüthgen N, Kulbe H, Braicu EI. Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging. Cancers. 2021; 13(7):1512. https://doi.org/10.3390/cancers13071512

Chicago/Turabian Style

Kassuhn, Wanja, 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. 2021. "Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging" Cancers 13, no. 7: 1512. https://doi.org/10.3390/cancers13071512

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