Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.

ABSTRACT:

Background

Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.

Methods

We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.

Results

We trained MuAt models on 2587 whole cancer genomes (24 tumour types

SUBMITTER: Sanjaya P 

PROVIDER: S-EPMC10326961 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature