Why do pathway methods work better than they should?
Open accessPublished version of a preprint
Abstract
Pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. These methods often infer pathway activity based on the expression of genes belonging to a given pathway, even though the proteins ultimately determine the activity of a given pathway. Furthermore, the association between gene expression levels and protein activities is not well‐characterized. Here, we posit that pathway‐based methods are effective not because of the correlation between expression and activity of members of a given pathway, but because pathway gene sets overlap with the genes regulated by transcription factors (TFs). Thus, pathway‐based methods do not inform about the activity of the pathway of interest but rather reflect changes in TF activities.
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COSMOS has been developed to model functional states of proteins (e.g., activities) which are more informative than protein abundance.
“To characterize such links and integrate the different data modalities, we used COSMOS (Dugourd et al, 2021), that has been developed to model functional states of proteins (e.g., activities) which are more informative than protein abundance as shown before (Szalai and Saez-Rodriguez, 2020; Dugourd and Saez-Rodriguez, 2019).”
From Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition (2024) · cites this paper as Szalai and Saez-Rodriguez, 2020
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
mRNAs, proteins and the emerging principles of gene expression control
Buccitelli, C., & Selbach, M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0258-4 · Cited by 1,243 · see citing works
ChEA3: transcription factor enrichment analysis by orthogonal omics integration
Keenan, A. B., Torre, D., Lachmann, A., Leong, A. K., Wojciechowicz, M. L., Utti, V., Jagodnik, K. M., Kropiwnicki, E., Wang, Z., & Ma’ayan, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz446 · Cited by 1,136 · see citing works
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323 · see citing works
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Douglass, E. F., Allaway, R. J., Szalai, B., Wang, W., Tian, T., Fernández-Torras, A., Realubit, R., Karan, C., Zheng, S., Pessia, A., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100492 · Cited by 68 · see citing works
Self-citationNetwork integration of thermal proteome profiling with multi-omics data decodes PARP inhibition
Burtscher, M. L., Gade, S., Garrido-Rodriguez, M., Rutkowska, A., Werner, T., Eberl, H. C., Petretich, M., Knopf, N., Zirngibl, K., Grandi, P., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00025-w · Cited by 7 · see citing works
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- Authors
- Szalai, B., & Saez‐Rodriguez, J.
- Affiliations
- Semmelweis University (HU)
- Institute of Computational Biomedicine Faculty of Medicine Heidelberg University Germany
- Corresponding author
- Szalai, Bence
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2020-12
- Volume
- 594
- Issue
- 24
- Pages
- 4189-4200
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- OpenAlex
- W3109313148
- Cited by
- 34
- References
- 4
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 8 · 2023: 4 · 2022: 7 · 2021: 8 · 2020: 1
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- FEBS Letters (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Magyar Tudományos Akadémia (460044)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- pathway analysis, cancer gene expression, pathway activity inference, transcription factor regulation, dysregulated pathways
- MeSH terms
- Humans, Neoplasms, Transcription Factors, Signal Transduction, Regulon, Gene Regulatory Networks
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