Lung cancer remains the leading cause of cancer related deaths worldwide, reportedly contributing to 1.8 million of the 10.0 million mortalities documented in the year 2020. Although advancements have been made in therapeutics and diagnostic methods, formulation of effective treatments and development of drug resistance continues to be a challenge. These challenges arise from our lack of understanding of intricate signaling pathways, such as the Growth Factor Receptor Network (GFRN), which contributes to complex lung tumor heterogeneity allowing for drug resistance development. In this study, gene expression signatures of six GFRN oncogenes overexpressed in human mammary epithelial cells (HMECs) were generated to interrogate this pathway’s downstream crosstalk, beyond initial mutation status. Utilization of this method may reveal novel phenotypic patterns that could be used to improve targeted therapies for lung cancer. Thus, using computational analysis tools, gene expression signatures were generated of BAD (BAD), HER2 (ERBB2), IGF1R (IGF1R), RAF (RAF1), and KRAS (G12V), using the Bioconductor package, Adaptive Signature Selection and InteGratioN (ASSIGN). Gene lists of various lengths were generated ranging from 5 to 500 genes produced in 25 gene increments. Pathway activation estimates were predicted in 541 lung adenocarcinoma (LUAD) tumors acquired from The Cancer Genome Atlas (TCGA). Each gene signature underwent validation using proteomics data from The Cancer Proteome Atlas (TCPA) and gene expression. Following thorough analysis, optimal gene signatures were determined for the genes BAD, HER2, IGF1R, RAF, and KRAS. In all, the optimized GFRN pathway-specific gene signatures were able to distinguish upregulated pathway activity within TCGA patient tumor samples. With the use of drug response data, novel phenotypic patterns may be revealed identifying drug targets to improve individualized drug targeted therapy for lung cancer
