five

Oncogenic PIK3CA corrupts growth factor signalling specificity

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE251956
下载链接
链接失效反馈
官方服务:
资源简介:
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, unlike the related oncogenic RAS/MAPK pathway, there is currently no systematic understanding of the quantitative flow of information within PI3K/AKT signalling and how it is perturbed by disease-causing mutations. Here, we develop scalable, single-cell approaches for systematic studies 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. PIK3CAH1047R reduces information transfer downstream of IGF1 while selectively enhancing EGF-induced signalling and transcriptional responses. This leads to a gross reduction in signalling specificity, akin to “blurred” signal perception. The associated increase in signalling heterogeneity increases phenotypic diversity in a cervical cancer cell 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. Total RNA sequencing of CRISPR/Cas9 HeLa clones either expressing wildtype PIK3CA or one or two copies of PIK3CA-H1047R alongside a C-terminal frameshift variant on the remaining alleles. One clone, RM12, has a C-terminal frameshift on all three PIK3CA alleles and therefore no catalytically active PIK3CA protein.
创建时间:
2024-12-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作