bioRxivSystems BiologyPreprint
Oncogenic PIK3CA corrupts growth factor signaling specificity
Ralitsa Radostinova MadseniD, Alix Le MaroisiD, Oliwia N MrukiD, Margaritis VoliotisiD, S. Yin, Jahangir SufiiD, Xiao QiniD, Salome Jingchen ZhaoiD, Julia GórczyńskaiD, D. Morelli, L. Davidson, Erik A. SahaiiD, Viktor I. KorolchukiD, Christopher J. TapeiD, Bart VanhaesebroeckiD
Abstract
Pathological activation of the PI3K/AKT pathway is among the most frequent defects in human cancer and is also the cause of rare overgrowth disorders. Yet, there is currently no systematic understanding of the quantitative flow of information within PI3K/AKT signaling and how it is perturbed by disease-causing mutations. Here, we develop scalable, single-cell approaches for systematic analyses of signal processing within the PI3K pathway, enabling precise calculations of its information transfer for different growth factors. Using genetically-engineered human cell models with allele dose-dependent expression of PIK3CAH1047R, we show that this oncogene is not a simple, constitutive pathway activator but a context-dependent modulator of extracellular signal transfer. PIK3CAH1047Rreduces information transmission downstream of IGF1 while selectively enhancing EGF-induced signaling and transcriptional responses. This leads to a gross reduction in signaling specificity, akin to "blurred" signal perception. The associated increase in signaling heterogeneity promotes phenotypic diversity in a human cervical cancer cell line model and in human induced pluripotent stem cells. Collectively, these findings and the accompanying methodological advances lay the foundations for a systematic mapping of the quantitative mechanisms of PI3K/AKT-dependent signal processing and phenotypic control in health and disease. One-sentence summarySingle-cell signaling and information theoretic analyses reveal that oncogenic PI3K/AKT activation leads to a gross reduction in signaling specificity, context-dependent EGF response amplification as well as increased phenotypic heterogeneity.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 2
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