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Lombardo, Elia; Hess, Julia; Kurz, Christopher; Riboldi, Marco; Marschner, Sebastian; Baumeister, Philipp; Lauber, Kirsten; Pflugradt, Ulrike; Walch, Axel; Canis, Martin; Klauschen, Frederick; Zitzelsberger, Horst; Belka, Claus; Landry, Guillaume and Unger, Kristian (2022): DeepClassPathway: Molecular pathway aware classification using explainable deep learning. In: European Journal of Cancer, Vol. 176: pp. 41-49

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Abstract

Objective: HPV-associated head and neck cancer is correlated with favorable prog-nosis;however, its underlying biology is not fully understood. We propose an explainable con-volutional neural network (CNN) classifier, DeepClassPathway, that predicts HPV-status and allows patient-specific identification of molecular pathways driving classifier decisions.Methods: The CNN was trained to classify HPV-status on transcriptome data from 264 (13% HPV-positive) and tested on 85 (25% HPV-positive) head and neck squamous carcinoma pa-tients after transformation into 2D-treemaps representing molecular pathways. Grad-CAM saliency was used to quantify pathways contribution to individual CNN decisions. Model sta-bility was assessed by shuffling pathways within 2D-images.Results: The classification performance of the CNN-ensembles achieved ROC-AUC/PR-AUC of 0.96/0.90 for all treemap variants. Quantification of the averaged pathway saliency heat -maps consistently identified KRAS, spermatogenesis, bile acid metabolism, and inflammation signaling pathways as the four most informative for classifying HPV-positive patients and MYC targets, epithelial-mesenchymal transition, and protein secretion pathways for HPV-negative patients.Conclusion: We have developed and applied an explainable CNN classification approach to transcriptome data from an oncology cohort with typical sample size that allows classification while accounting for the importance of molecular pathways in individual-level decisions. 2022 Elsevier Ltd. All rights reserved.

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