Multimodal Cell-Free DNA Embeddings are Informative for Early Cancer Detection

Felix Jackson

Keywords: epigenetics, multimodal, embeddings, cell-free DNA, cancer, early detection, genetics, transformer, deep learning

TL;DR: A classifier of cell-free DNA that natively incorporates multimodal information, so outperforms previous cell-free DNA models, and generalises well on liver cancer cohorts

Abstract: Cell-free DNA is a promising biomarker for early cancer detection, as it circulates in the blood and can be extracted non-invasively. However, methods of analysing the genetic and epigenetic patterns present in cell-free DNA are outdated, and fail to fully capture the wealth of biological information contained within these molecules. We present a Transformer based deep learning model that combines the three distinct modalities contained within cell-free DNA: epigenetic information in the form of DNA methylation patterns, genetic sequence, and cell-free DNA fragment length. After training on publicly available data, we demonstrate our model can accurately distinguish liver cancer patients using cell-free DNA samples alone. We demonstrate model generalisability by accurate classification of liver cancer patients from entirely distinct patient cohorts. Finally, we show that the vector embeddings of cell-free DNA learnt by this multimodal deep-learning model are biologically informative, and may help shed light on the origins and aetiology of this elusive bio-molecule.