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.
Highlights
Knowledge on intracellular signal transduction pathways governing cancer cell behavior and controling cell division is rapidly increasing
Major assumptions The key assumption in the Bayesian pathway model we present is that functional activity of a signaling pathway is determined by activity of its respective transcription complex, while the latter can be inferred from mRNA expression data of its transcriptional target genes
Initial validation of the Wnt pathway model Two instances of the Wnt model were created, a first one for initial proof of concept using cell line data for calibration, and a second one using a larger calibration dataset with patient data, with the advantage of better reflecting the variation encountered across patient samples
Summary
Knowledge on intracellular signal transduction pathways governing cancer cell behavior and controling cell division is rapidly increasing This development has elicited a paradigm shift toward development of a whole new category of "targeted drugs," aiming to target the aberrant signaling pathway, which drives tumor growth in the individual patient with cancer [1]. Currently available tests often lack predictive value with respect to targeted therapy response. These tests demonstrate (over-)expression of key proteins in signaling pathways of interest, e.g., estrogen receptor (ER) or HER2 in breast cancer, or identify DNA mutations (e.g., in the PIK3CA gene) or structural changes (like HER2 coding gene amplification) in genes encoding
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