Towards an interpretable deep learning model of cancer
Journal article, 2025

Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.

Author

Avlant Nilsson

Karolinska University Hospital

Chalmers, Life Sciences, Systems and Synthetic Biology

Massachusetts Institute of Technology (MIT)

Nikolaos Meimetis

Massachusetts Institute of Technology (MIT)

Douglas A. Lauffenburger

Massachusetts Institute of Technology (MIT)

npj Precision Oncology

2397-768X (eISSN)

Vol. 9 1 46

Deep Learning the Immune System

Swedish Research Council (VR) (2019-06349), 2020-01-01 -- 2023-12-31.

Subject Categories (SSIF 2025)

Bioinformatics and Computational Biology

Cancer and Oncology

DOI

10.1038/s41698-025-00822-y

PubMed

39948231

More information

Latest update

3/19/2025