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Updated: Oct 9, 2025
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Published on: October 3, 2025
Identifying cancer pathway dysregulations using differential causal effects
Kim Philipp Jablonski1,2, Martin Pirkl1,2, Domagoj Ćevid3
1Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
Motivation:
Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer.
Results:
We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype-Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression.
Availability And Implementation:
The method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
A new computational method, Differential Causal Effects (dce), identifies dysregulated signaling pathways in cancer by accounting for confounding factors. This approach improves accuracy in detecting true biological signals for cancer research.
Area of Science:
- Computational biology
- Systems biology
- Genomics
Background:
- Cellular signaling pathways regulate cell behavior.
- Dysregulation of these pathways, often due to abnormal gene and protein expression from mutations, is a hallmark of diseases like cancer.
Purpose of the Study:
- To introduce a novel computational approach, Differential Causal Effects (dce), for analyzing signaling pathway dysregulation in cancer.
- To improve the detection of true biological signals by accounting for confounding factors in gene expression data.
Main Methods:
- Utilized a statistical framework of causality to compare normal and cancerous cells.
- Developed the dce method to detect dysregulated edges in signaling pathways.
- Extended dce to handle unobserved dense confounding, including batch effects and cell cycle states.
Main Results:
- dce outperforms competing methods on synthetic datasets and CRISPR knockout screens.
- Validated latent confounding adjustment properties on a Genotype-Tissue Expression (GTEx) dataset.
- Identified known and discovered novel genes implicated in breast cancer progression using The Cancer Genome Atlas (TCGA) data.
Conclusions:
- The dce method provides a robust approach for identifying dysregulated signaling pathways in cancer.
- Accurate identification of pathway dysregulation can aid in understanding cancer progression and discovering therapeutic targets.
- The dce R package and associated workflows are publicly available for reproducibility and further research.
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