Selection of personalized patient therapy through the use of knowledge-based computational models that identify tumor-driving signal transduction pathways

Wim Verhaegh, Henk van Ooijen, Márcia A Inda, Pantelis Hatzis, Rogier Versteeg, Marcel Smid, John Martens, John Foekens, Paul van de Wiel, Hans Clevers, Anja van de Stolpe

Research output: Contribution to journal/periodicalArticleScientificpeer-review

Abstract

Increasing knowledge about signal transduction pathways as drivers of cancer growth has elicited the development of "targeted drugs," which inhibit aberrant signaling pathways. They require a companion diagnostic test that identifies the tumor-driving pathway; however, currently available tests like estrogen receptor (ER) protein expression for hormonal treatment of breast cancer do not reliably predict therapy response, at least in part because they do not adequately assess functional pathway activity. We describe a novel approach to predict signaling pathway activity based on knowledge-based Bayesian computational models, which interpret quantitative transcriptome data as the functional output of an active signaling pathway, by using expression levels of transcriptional target genes. Following calibration on only a small number of cell lines or cohorts of patient data, they provide a reliable assessment of signaling pathway activity in tumors of different tissue origin. As proof of principle, models for the canonical Wnt and ER pathways are presented, including initial clinical validation on independent datasets from various cancer types.

Original languageEnglish
Pages (from-to)2936-45
Number of pages10
JournalCancer Research
Volume74
Issue number11
DOIs
Publication statusPublished - 01 Jun 2014

Keywords

  • Antineoplastic Agents
  • Bayes Theorem
  • Computer Simulation
  • Humans
  • Individualized Medicine
  • Models, Biological
  • Models, Statistical
  • Neoplasms
  • Signal Transduction
  • Transcriptome

Fingerprint

Dive into the research topics of 'Selection of personalized patient therapy through the use of knowledge-based computational models that identify tumor-driving signal transduction pathways'. Together they form a unique fingerprint.

Cite this