Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma

Abstract

Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments. Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach. Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling. Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma.

Keywords: TKI resistance; immunotherapy; lung adenocarcinoma; master regulator; relapse.

FIGURE 1

Pairwise comparison of published prognostic gene signatures for LUAD. All signatures were taken from a review study (Tang et al., 2017) and the original references were listed in Supplementary Table S1. Jaccard index was used to quantify the level of overlap between two signatures and shown as a heatmap (A). The number of genes in each signature was shown as a bar plot (B).

FIGURE 2

A workflow to identify master regulators driving prognosis in patients with LUAD. (A) Comparison of master regulators predicted with two well-established LUAD signatures, one was the best-scoring signature benchmarked in a previous review study (Xie signature) (Xie et al., 2011), the other is embryonic stem cells signature (ESC) (Ben-Porath et al., 2008). Both signatures were tested across seven LUAD gene expression datasets and master regulators were identified with a p-value cutoff of 0.01 (Section 2). The results were summarized to show how many times a master regulator was identified and represented as a 2D histogram. (B) Comparison of master regulators predicted with random signatures. (C) Evaluation of robustness. The Jacard index shows the similarity of master regulators predicted from subsampling of LUAD signatures (100 times), as well as that from random background.

FIGURE 3

Master regulators driving prognosis in early-stage LUAD. (A) A Venn diagram of three prognostic signatures for early-stage LUAD. Signature names as well as the number of genes in each signature were shown. (B) The Venn diagram of activated and repressed master regulators, predicted from three prognostic signatures for early-stage LUAD. (C) The lists of shared master regulators that are predicted from all three signatures. (D) Expression of shared master regulators is associated with poor survival in patients with stage I LUAD in dataset GSE14814. Hazard ratio (HR) and log-rank test p-value are also shown.

FIGURE 4

Master regulators driving EGFR-TKI resistance. (A) A new EGR-TKI resistance signature. EGFR-TKI resistant samples were compared to parental cell lines and the signature was defined as the genes that were significantly upregulated in two independent datasets. (B) Enrichment of EMT pathway in EGFR-TKI resistance signature. (C) Comparison of master regulators predicted with EGFR-TKI signature and EMT pathway signature. Both signatures were tested across seven LUAD gene expression datasets and master regulators were identified with a p-value cutoff of 0.01 (Section 2). The results were summarized to show how many times a master regulator was identified and represented as a 2D histogram. (D) Pathways enriched in top-ranking master regulators predicted using TKI resistance signature or EMT pathway signature. (E) Selected master regulators grouped by enriched pathways. To avoid redundancy, a gene in multiple pathways was only assigned to the one with higher significance.

FIGURE 5

Master regulators that are associated with enhanced responses in immune checkpoint blockade-based immunotherapy. (A) Comparison of master regulators predicted from T cell infiltration and T cell dysfunction signatures. The same set of master regulators that were identified in at least six datasets were shown. (B) Pathways enriched in top-ranking master regulators. The top three most enriched pathways were shown. (C) Selected master regulators grouped by enriched pathways.

FIGURE 6

Master regulators that are associated with poor responses in ICB-based immunotherapy. (A) Comparison of master regulators predicted from T cell infiltration and T cell dysfunction signatures. The master regulators predicted in at least six datasets with T cell dysfunction signature were shown. (B) Pathways enriched in top-ranking master regulators. (C) Selected master regulators grouped by enriched pathways.