Deep Learning Models of Cancer Mechanisms
Abstract
Cancers are heterogeneous in terms of mutation profile, cell type of origin, and micro-environment, and finding effective treatments for each individual cancer is the aim of precision cancer medicine. This is challenging since cellular processes are highly interconnected and complex. We develop deep learning models of the molecular networks of cells, to identify systems-level mechanisms in cancer.
Deep learning models excel at finding complex relations in large quantities of data but are not always easy to interpret. To address this, we constrain the models, so that their predictions align with physical interactions between bio molecules. The models cover metabolism, signal-transduction and gene regulation and are trained on high throughput datasets such as transcriptomics, metabolomics, and proteomics from different cell types, interventions, and conditions.
Using our models, we aim to find new drug targets and biomarkers, predict resistance mechanisms, and improve the understanding cell-cell interactions in the tumor microenvironment. Our long-term goal is to enable computer aided design of cancer medicine.
For this work the LEMBAS framework [1] and our extension to cover cellular metabolism will be used. LEMBAS is a deep learning framework for modeling cellular signaling that has proven useful in predicting transcription in response to ligand stimulation and drugs, and for generating biologically interpretable predictions.
[1] Nilsson, A., Peters, J.M., Meimetis, N. et al. Artificial neural networks enable genome-scale simulations of intracellular signaling. Nat Commun 13, 3069 (2022). https://doi.org/10.1038/s41467-022-30684-y
