TY  - JOUR
AU  - Ing, Alex
AU  - Andrades, Alvaro
AU  - Cosenza, Marco Raffaele
AU  - Korbel, Jan
TI  - Integrating multimodal cancer data using deep latent variable path modelling.
JO  - Nature machine intelligence
VL  - 7
IS  - 7
SN  - 2522-5839
CY  - [London]
PB  - Springer Nature Publishing
M1  - DKFZ-2025-01550
SP  - 1053 - 1075
PY  - 2025
N1  - #LA:B480#
AB  - Cancers are commonly characterized by a complex pathology encompassing genetic, microscopic and macroscopic features, which can be probed individually using imaging and omics technologies. Integrating these data to obtain a full understanding of pathology remains challenging. We introduce a method called deep latent variable path modelling, which combines the representational power of deep learning with the capacity of path modelling to identify relationships between interacting elements in a complex system. To evaluate the capabilities of deep latent variable path modelling, we initially trained a model to map dependencies between single-nucleotide variant, methylation profiles, microRNA sequencing, RNA sequencing and histological data using breast cancer data from The Cancer Genome Atlas. This method exhibited superior performance in mapping associations between data types compared with classical path modelling. We additionally performed successful applications of the model to stratify single-cell data, identify synthetic lethal interactions using CRISPR-Cas9 screens derived from cell lines and detect histologic-transcriptional associations using spatial transcriptomic data. Results from each of these data types can then be understood with reference to the same holistic model of illness.
KW  - Breast cancer (Other)
KW  - Computer science (Other)
KW  - Data integration (Other)
KW  - Machine learning (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40709098
C2  - pmc:PMC12283373
DO  - DOI:10.1038/s42256-025-01052-4
UR  - https://inrepo02.dkfz.de/record/303199
ER  -