Profiles of Basal and Stimulated Receptor Signaling Networks Predict Drug Response in Breast Cancer Lines

Abstract

Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. This analysis identified key proteins that could serve as biomarkers of drug sensitivity. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt; whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

Receptor Level Mechanisms Are Required for Epidermal Growth Factor (EGF)-stimulated Extracellular Signal-regulated Kinase (ERK) Activity Pulses

“…EGF-stimulated ERK activity, measured by immunoblot for phosphorylated ERK (pERK), is typically reported to peak within 10 -30 min following EGF stimulation and then to decay rapidly; in some cases, a second, sustained period of attenuated ERK activity follows (47,48). A notable difference between this stereotypical profile and the single-cell data presented here is the lack of a pronounced decline following the initial increase.…”

“…The observed pERK signal in this phase varies between published studies using population level measurements, with some reporting a rapid down-regulation of pERK in MCF10A cells stimulated with saturating EGF (48,51) and others reporting a more sustained response (52,53). We attribute these differences to variation in the frequency at which EGFR is restimulated, either by the continuous presence of EGF or by autocrine EGF ligand signaling.…”

Modeling Signaling Networks to Advance New Cancer Therapies

“…In the absence of good prior knowledge, reverse-engineering approaches can also be used to infer network structure from the data alone (66), although these often require larger amounts of data. Alternatively, predictive models can still be produced using non-mechanistic models (67,68).…”

The opportunities of mining historical and collective data in drug discovery

“…Although still in its infancy in comparison to other biological signatures and hampered by technical challenges such as the reproducibility of phosphorylation sites [67,68], the utility of phosphoprotein profiles as biomarkers for drug response predictions has been reported by a number of groups. Q4 Niepel et al have used phosphosignaling profiles (focusing on receptor tyrosine kinases after cell stimulation with physiologically relevant ligands) to build a partial-least-squares regression model to predict the response of breast cancer cell lines to 23 therapeutics [69]. In the future, a great impact on the use of phosphoprotein profiles as small molecule descriptors is to be anticipated by the release of a phosphoproteomics dataset by the Broad Institute and Library of Integrated Network-Based Cellular Signatures (LINCS) initiative that will allow the comparison of perturbagens through their phosphosignaling across multiple cell lines.…”