Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology

• Verfasst von: Gustav, Marco [VerfasserIn]
• Verfasst von: Reitsam, Nic Gabriel [VerfasserIn]
• Verfasst von: Carrero, Zunamys I. [VerfasserIn]
• Verfasst von: Loeffler, Chiara M. L. [VerfasserIn]
• Verfasst von: van Treeck, Marko [VerfasserIn]
• Verfasst von: Yuan, Tanwei [VerfasserIn]
• Verfasst von: West, Nicholas P. [VerfasserIn]
• Verfasst von: Quirke, Philip [VerfasserIn]
• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Verfasst von: Brenner, Hermann [VerfasserIn]
• Verfasst von: Favre, Loëtitia [VerfasserIn]
• Verfasst von: Märkl, Bruno [VerfasserIn]
• Verfasst von: Stenzinger, Albrecht [VerfasserIn]
• Verfasst von: Brobeil, Alexander [VerfasserIn]
• Verfasst von: Hoffmeister, Michael [VerfasserIn]
• Verfasst von: Calderaro, Julien [VerfasserIn]
• Verfasst von: Pujals, Anaïs [VerfasserIn]
• Verfasst von: Kather, Jakob Nikolas [VerfasserIn]
• Titel: Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology
• Verf.angabe: Marco Gustav, Nic Gabriel Reitsam, Zunamys I. Carrero, Chiara M.L. Loeffler, Marko van Treeck, Tanwei Yuan, Nicholas P. West, Philip Quirke, Titus J. Brinker, Hermann Brenner, Loëtitia Favre, Bruno Märkl, Albrecht Stenzinger, Alexander Brobeil, Michael Hoffmeister, Julien Calderaro, Anaïs Pujals & Jakob Nikolas Kather
• E-Jahr: 2024
• Jahr: 23 May 2024
• Umfang: 11 S.
• Illustrationen: Illustrationen, Diagramme
• Fussnoten: Gesehen am 25.11.2024
• Titel Quelle: Enthalten in: npj precision oncology
• Ort Quelle: [London] : Springer Nature, 2017
• Jahr Quelle: 2024
• Band/Heft Quelle: 8(2024), Seite 1-11
• ISSN Quelle: 2397-768X
• DOI: doi:10.1038/s41698-024-00592-z
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Diagnostic markers
• Sach-SW: Mathematics and computing
• Sach-SW: Pathology
• Sach-SW: Tumour biomarkers
• K10plus-PPN: 190942479X

Abstract

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as “positive” by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.