Predicting clonal somatic mutations from tumour spatial transcriptomics data using a graph convolutional neural network
Year: 2026
Type: preprint
Abstract: Spatial transcriptomics (ST) has revolutionized our understanding of tumour biology but inherently lacks information on the upstream somatic driver mutations. We developed a spatially-aware graph convolutional neural network approach (SPOT-Mut) that infers mutated (sub)clones directly from ST data. SPOT-Mut was trained on virtual ST slides with clones simulated from a large collection of existing RNA and matched DNA sequencing data. SPOT-Mut was first evaluated for predicting mutations in TP53 , the most frequently mutated gene in human cancer. Precision and recall values exceeded 95% in most analysed cancer types and the model was validated on ST and matched DNA sequencing data from serial slices obtained from cutaneous SCC samples. SPOT-Mut was then trained on 62 frequently mutated driver genes, resulting in highly performing somatic mutation prediction models for 40 driver genes, including KRAS , KEAP1 , CTNNB1 and APC . With the increasing availability of ST data and upcoming atlases, SPOT-Mut potentiates the localization of (sub)clonal alterations in TP53 and other common driver mutations.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7167821478