Identifying cancer pathway dysregulations using differential causal effects.

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.