JOURNAL ARTICLE

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

  • Published In: Briefings in Bioinformatics, 2023, v. 24, n. 5. P. N.PAG 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Yan; Zhang, Shao-Wu; Xie, Ming-Yu; Zhang, Tong 3 of 3

Abstract

This article presents PhenoDriver, an interpretable computational framework designed to identify personalized cancer driver genes and elucidate their oncogenic mechanisms by linking driver genes to clinical phenotypic alterations. Applied to a cohort of 988 breast cancer patients from The Cancer Genome Atlas, PhenoDriver demonstrated superior performance compared to existing methods in detecting both recurrent and rare driver genes at individual and cohort levels. The framework distinguishes tumor suppressor genes (TSGs) from oncogenes (OGs) by incorporating regulatory edge properties in a signal transduction network and constructs regulatory subnetworks to explain how driver genes such as TP53, MAP3K1, and HTT influence cancer-related pathways. Additionally, PhenoDriver identified an unreported breast cancer subtype with poor prognosis characterized by co-driving effects of TP53 and PIK3CA, highlighting its potential to inform precision medicine and targeted therapies.

Additional Information

  • Source:Briefings in Bioinformatics. 2023/09, Vol. 24, Issue 5, pN.PAG
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1467-5463
  • DOI:10.1093/bib/bbad291
  • Accession Number:172331656
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