Temporal Molecular and Biological Assessment of an Erlotinib-Resistant Lung Adenocarcinoma Model Reveals Markers of Tumor Progression and Treatment Response

Abstract

Lung cancer patients with activating mutations in the epidermal growth factor receptor (EGFR) kinase who are treated long-term with tyrosine kinase inhibitors (TKIs) often develop secondary mutations in EGFR associated with resistance. Mice engineered to develop lung adenocarcinomas driven by the human EGFR T790M resistance mutation are similarly resistant to the EGFR TKI erlotinib. By tumor volume endpoint analysis, these mouse tumors respond to BIBW 2992 (an irreversible EGFR/HER2 TKI) and rapamycin combination therapy. In order to correlate EGFR-driven changes in the lung with response to drug treatment, we performed an integrative analysis of global transcriptome and metabolite profiling compared to quantitative imaging and histopathology at several time points during tumor progression and treatment. Responses to single drug treatments were temporary, while combination therapy elicited a sustained response. During tumor development, metabolomic signatures indicated a shift to high anabolic activity and suppression of anti-tumor programs with 11 metabolites consistently present in both lung tissue and blood. Combination drug treatment reversed many of the molecular changes found in tumored lung. Data integration linking cancer signaling networks with metabolic activity identified key pathways such as glutamine and glutathione metabolism that signified response to single or dual treatments. Results from combination drug treatment suggest that metabolic transcriptional control through C-MYC and SREBP, as well as ELK1, NRF1 and NRF2, depends on both EGFR and mTORC1 signaling. Our findings establish the importance of kinetic therapeutic studies in preclinical assessment and provide in vivo evidence that TKI-mediated antiproliferative effects also manifest in specific metabolic regulation.

β-Catenin Contributes to Lung Tumor Development Induced by EGFR Mutations

“…2E). Our observations are supported by previous observations that β-catenin-TCF/LEF target genes such as cyclin D1 and c-Myc are upregulated in NSCLC harboring EGFR-T790M (23, 40). Inhibition of serine/threonine phosphorylation by GSK3β is a well-recognized mechanism of β-catenin stabilization (8).…”

Mitogen-activated protein kinase-activated protein kinase 2 mediates resistance to hydrogen peroxide-induced oxidative stress in human hepatobiliary cancer cells

“…Interestingly, EGFR has been also shown to regulate the antioxidant transcriptional factor Nrf2 by increasing its activity in lung cancer cells. In these cells, EGFR/Nrf2 favors cell proliferation, tumor progression and resistance to chemotherapies [50][51][52]. Our data showed that MK2 is involved in EGFR activation and regulates Nrf2 in a positive way.…”

Activation of the PD-1 Pathway Contributes to Immune Escape in EGFR-Driven Lung Tumors

“…Microarray expression profiling of the mouse T790M/L858R (TL) tumors as compared to controls revealed increased levels of Pd-1 , Pd-l1 ( Cd274 ), Ctla-4 , Il-6 , Tgf-β1 , and granulin ( Grn ) along with ligands for the EGFR (EGFR mutant vs WT for the gene set shown p=3×10 −20 ) (Fig 1a). Analysis of microarray data from previously reported datasets showed no significant differences in Pd-l1 and Pd-l2 expression among tumors derived from multiple models of EGFR-driven lung adenocarcinoma (L858R, L858R/T790M (TL) and Exon 19 deletion/T790M (TD)) (17, 18), indicating that EGFR-driven tumors of a variety of EGFR mutations display elevated Pd-l1 and Pd-l2 expression as compared to normal lung (data not shown). We next confirmed the expression of PD-L1 on tumor (CD45 − humanEGFR + ) and associated hematopoietic cells by flow cytometry and immunohistochemistry in EGFR-driven mouse lung adenocarcinomas (IHC) (Fig 1b, Supplementary Fig 1).…”

“…Expression data from wild type and EGFR transgenic mice were obtained from a previous study (18) and converted into log2 values. Pearson correlation coefficient p values were calculated by comparing expression values for each transcript over all samples to either PD-L1 or PD-1 expression.…”