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Identifying cancer pathway dysregulations using differential causal effects.

ABSTRACT: 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. 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 data sets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx dataset. Finally, in an exploratory analysis on breast cancer data from TCGA, we recover known and discover new genes involved in breast cancer progression. 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 data are available at Bioinformatics online.

SUBMITTER: Jablonski KP

PROVIDER: S-EPMC8896597 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Identifying cancer pathway dysregulations using differential causal effects.

Jablonski Kim Philipp KP Pirkl Martin M Ćevid Domagoj D Bühlmann Peter P Beerenwinkel Niko N

Bioinformatics (Oxford, England) 20220301 6

<h4>Motivation</h4>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.<h4>Results</h4>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 canc ...[more]

PMID: 34927666

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Identifying cancer pathway dysregulations using differential causal effects.

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Identifying cancer pathway dysregulations using differential causal effects.

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