Molecular Biology of the CellCancer BiologyPeer-reviewed journal article
Epithelial-mesenchymal cell state heterogeneity predetermines differential phospho-signaling responses to epidermal growth factor stimulation
Felix V KohaneiD, Chantelle Johnstone, Daniel NeumanniD, Ihuan GunawaniD, Tim Huang, Fatemeh VafaeeiD, Christine Louise ChafferiD, John George LockiD
Abstract
Understanding why isogenic cancer cells respond differently to equivalent oncogenic stimuli is vital for optimizing anticancer therapies. Emerging evidence suggests that pre-existing differences in cell state may modulate signaling responses to new stimuli, but the interplay of specific cell states and signals remains unclear. We investigated whether epithelial-mesenchymal (E/M) state, a major axis of cancer cell heterogeneity, influences signaling responses to epidermal growth factor (EGF), a critical oncogenic stimulus in non-small cell lung cancer (NSCLC). We imaged >64,000 A549 NSCLC cells labeled for DNA, F-actin and alternate signaling markers (p-AKT-S473, p-AKT-T308, p-ERK or p-S6) after acute stimulation. Quantitative single-cell morphological and spatial profiling defined a stimulus-invariant E/M state landscape over which EGF signaling responses were compared. This revealed state-dependent differences in signal-activation magnitudes, dynamics and subcellular routing. AKT responses exhibited phosphosite- and compartment-specific dynamics across states, with epithelial cells showing strong, transient membrane-localized S473 and higher internalized T308, whereas mesenchymal cells displayed weaker but sustained nuclear and ruffle-localized S473. Regression-based computational multiplexing concurrently inferred all signaling responses per cell, mapping state-dependent divergence in multi-molecular signaling trajectories. E/M state thus pre-determines distinctive spatiotemporal profiles of EGF-induced signaling, with implications for signaling functions and anti-signaling therapy responses across E/M state-diverse tumors.
- organism: human
- assay: spatial transcriptomics
- research focus: cell signaling & trafficking
- approach: statistical model
- Citations
- 1
- FWCI
- 0.41
- Field percentile
- top 33%
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