The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Pathway-based personalized analysis of cancer
Open accessAbstract
We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm’s performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.
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Cites
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“CIN tumors (85% of colon cancers) exhibit abnormal numbers of chromosomes, deletions and ampli fi cations of smaller genomic regions, and translocations, and tend to have p53 mutations (29, 30).”
BackgroundIntegrins in cancer: biological implications and therapeutic opportunities
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Oncogenic pathway signatures in human cancers as a guide to targeted therapies
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Cited by
Perturbation-response genes reveal signaling footprints in cancer gene expression
(2017) · doi:10.1038/s41467-017-02391-6 · Cited by 1,108
Principles and methods of integrative genomic analyses in cancer
Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K. M., Frigessi, A., & Børresen-Dale, A.-L. (2014) · Nature Reviews Cancer · doi:10.1038/nrc3721 · Cited by 371
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“Recent studies have demonstrated the advantage of pathway-based analysis in assessing tumor biology [34, 35].”
Evaluation and comparison of multi-omics data integration methods for cancer subtyping
Duan, R., Gao, L., Gao, Y., Hu, Y., Xu, H., Huang, M., Song, K., Wang, H., Dong, Y., Jiang, C., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009224 · Cited by 122
Metadata
- Authors
- Drier, Y., Sheffer, M., & Domany, E.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2013-04
- Volume
- 110
- Issue
- 16
- Pages
- 6388-6393
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2011417152
- Cited by
- 337
- References
- 40
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- green
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