Explainable AI model reveals informative mutational signatures for cancer-type classification

Abstract

Background/Objectives: The prediction of cancer types is primarily reliant on driver genes and their specific mutations. The advancement in novel omics technologies has led to the acquisition of additional genetic data. When integrated with artificial intelligence models, there is considerable potential for this to enhance the accuracy of cancer diagnosis. As mutational signatures can provide insights into repair mechanism malfunctions, they also have the potential for more accurate cancer diagnosis. Methods: First, we compared unsupervised and supervised machine learning approaches to predict cancer types. We employed deep and artificial neural network architectures with an explainable component like layerwise relevance propagation to extract the most relevant features for the cancer-type prediction. Ten-fold cross-validation and an extensive grid search were used to optimize the neural network architecture using driver gene mutations, mutational signatures and topological mutation information as input. The PCAWG dataset was used as input to discriminate between 17 primary sites and 24 cancer types. Results: Overall, our approach showed that the most relevant mutation information to discriminate between cancer types is increased by >10% using the whole genome or intergenic and intronic genome regions instead of exome information. Furthermore, the most relevant features for most cancer types, except for two, are in the mutational signatures and not the topological mutation information. Conclusions: Informative mutational signatures outperformed the prediction of cancer types in comparison to driver gene mutations and added a new layer of diagnostic information. As the degree of information within the mutational signatures is not solely based on the frequency of occurrence, it is even possible to separate cancer types from the same primary site by the different relevant mutations. Furthermore, the comparison of informative mutational signatures allowed the cancer-type assignment of specific impaired repair mechanisms.

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Titel	Explainable AI model reveals informative mutational signatures for cancer-type classification
Medien	Cancers (Basel)
Verlag	MDPI AG
Heft	11
Band	17
Verfasser	Jonas Wagner, Jan Oldenburg, Neetika Nath, Prof. Dr. Stefan Simm
Seiten	1731
Veröffentlichungsdatum	01.05.2025
Zitation	Wagner, Jonas; Oldenburg, Jan; Nath, Neetika; Simm, Stefan (2025): Explainable AI model reveals informative mutational signatures for cancer-type classification. Cancers (Basel) 17 (11), 1731.