Abstract
Many driver pathways for cancer cell proliferation have been reported. Driver pathway activation is often evaluated based on a single hotspot mutation such as EGFR L858R. However, because of complex intratumoral networks, the impact of a driver pathway cannot be predicted based on only a single gene mutation. Here, we developed a novel diagnostic system named the “EGFR impact score” which is based on multiplex mRNA expression profiles, which can predict the impact of the EGFR pathway in lung cancer cells and the effect of EGFR-tyrosine kinase inhibitors on malignancy. The EGFR impact score indicated robust predictive power for the prognosis of early-stage lung cancer because this score can evaluate the impact of the EGFR pathway on the tumor and genomic instability. Additionally, the molecular features of the poor prognostic group resembled those of biomarkers associated with immune checkpoint inhibitors. The EGFR impact score is a novel prognostic and therapeutic indicator for lung adenocarcinoma.
Highlights
Many driver pathways for cancer cell proliferation have been reported
We developed a novel diagnostic system to predict the EGFR network status comprehensively based on gene expression profiles, which we named the EGFR impact score
The result that www.nature.com/scientificreports the EGFR impact score can distinguish hotspot mutations from VUSs in the EGFR gene means the strategy can potentially predict the functional abnormality of VUSs that have been unreported as pathogenic mutations
Summary
Many driver pathways for cancer cell proliferation have been reported. Driver pathway activation is often evaluated based on a single hotspot mutation such as EGFR L858R. The presence of tumors that respond to TKIs despite not carrying EGFR structural mutations has been reported[10,11] These results indicated the existence of EGFR pathway activation in the absence of EGFR structural mutations. This study first aimed to develop a predictive model of responsiveness to TKI based on mRNA expression profiles It is controversial whether EGFR mutation is a prognostic factor in early-stage lung adenocarcinoma[12,13,14,15,16]. These expression profiles do not influence treatment decision-making Considering these situations, the second purpose of this study was to clarify whether EGFR network expression profiles can be used to predict the prognosis of early-stage lung adenocarcinoma. To develop new treatment strategies for the poor prognostic group identified using our new system, we clarified the molecular biological features of lung cancer using multi-omics data
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