The genomic landscape of sensitivity to arsenic trioxide uncovered by genome-wide CRISPR-Cas9 screening

Abstract

Introduction: Arsenic trioxide (ATO) is a promising anticancer drug for hematological malignancy. Given the dramatic efficacy of acute promyelocytic leukemia (APL), ATO has been utilized in other types of cancers, including solid tumors. Unfortunately, the results were not comparable with the effects on APL, and the resistance mechanism has not been clarified yet. This study intends to identify relevant genes and pathways affecting ATO drug sensitivity through genome-wide CRISPR-Cas9 knockdown screening to provide a panoramic view for further study of ATO targets and improved clinical outcomes.

Methods: A genome-wide CRISPR-Cas9 knockdown screening system was constructed for ATO screening. The screening results were processed with MAGeCK, and the results were subjected to pathway enrichment analysis using WebGestalt and KOBAS. We also performed protein-protein interaction (PPI) network analysis using String and Cytoscape, followed by expression profiling and survival curve analysis of critical genes. Virtual screening was used to recognize drugs that may interact with the hub gene.

Results: We applied enrichment analysis and identified vital ATO-related pathways such as metabolism, chemokines and cytokines production and signaling, and immune system responses. In addition, we identified KEAP1 as the top gene relating to ATO resistance. We found that KEAP1 expression was higher in the pan-cancer, including ALL, than in normal tissue. Patients with acute myeloid leukemia (AML) with higher KEAP1 expression had worse overall survival (OS). A virtual screen showed that etoposide and eltrombopag could bind to KEAP1 and potentially interact with ATO.

Discussion: ATO is a multi-target anticancer drug, and the key pathways regulating its sensitivity include oxidative stress, metabolism, chemokines and cytokines, and the immune system. KEAP1 is the most critical gene regulating ATO drug sensitivity, which is related to AML prognosis and may bind to some clinical drugs leading to an interaction with ATO. These integrated results provided new insights into the pharmacological mechanism of ATO and potentiate for further applications in cancer treatments.

Keywords: CRISPR-Cas9 screening; KEAP1; arsenic trioxide; leukemia; virtual screening.

Figure 1

Genome-wide CRISPR-Cas9 screening identified key modulators attributing to ATO sensitivity. (A) The schematic outline of the genome-wide CRISPR-Cas9 screening. (B) The volcano plot of genes significantly enriched (red dots) or depleted (green dots) in ATO treatment groups compared with vehicle. The cutoff threshold was |log₂ fold change| ≥1 and P < 0.05. (C) The positively selected genes in ATO treatment groups compared with vehicle. (D) The negatively selected genes in ATO treatment groups compared with vehicle.

Figure 2

The enrichment analysis of depleted and enriched genes was identified by screening. (A) The bar charts of Gene Ontology (GO) enriched terms. The enrichment analysis was performed and plotted by WebGestalt. The top most relevant GO terms with an adjusted P < 0.05 were shown in the charts. (B) Pathway enrichment analysis. The enrichment analysis was performed by KOBAS. REACTOME, PANTHER, and KEGG-enriched terms were presented separately. The color demonstrated the Q-value of each pathway, and the size of the dots demonstrated the count of genes related to the pathway.

Figure 3

Protein–protein interaction (PPI) network among depleted or enriched genes. (A) The hub genes of the PPI. The PPI network was generated by the STRING database with default settings and was built by Cytoscape. The cluster of hub genes within the red frame was calculated using MCODE in Cytoscape with the default setting. (B) The top enriched REACTOME pathways of the hub genes. The enrichment analysis was performed and visualized by Cytoscape using the plugin Cluego and CluePedia. The sector graphs indicated the portion of related genes compared with all the enriched pathway genes.

Figure 4

KEAP1 was a vital target of ATO with clinical significance in pan-cancer. (A) The Venn diagram of overlapping genes between two studies plotted by ImageGP. (B) The read counts of sgRNAs targeting KEAP1 in screening. (C) The correlation analysis of KEAP1 gene expression and sensitivity to ATO. Gene expression showed the log₂ (FPKM + 1) values. The correlation analyses were performed using Pearson correlation. (D) The box plot derived from KEAP1 expressions data for acute leukemias of ambiguous lineage (ALAL) compared with normal samples from non-cancerous pediatric tissues. The plot was generated by TNMplot. Significant differences were tested by the Mann-Whitney U test. (E) The Kaplan–Meier survival plot comparing overall survival (OS) with high and low KEAP1 expression in AML patients generated using GEPIA2. Significance was tested by log-rank test. (F) The pan-cancer gene expression profiles of KEAP1 generated by TNMplot. Significant differences (P < 0.05) by the Mann-Whitney U test were marked with red*. (G) The docking poses of two drugs. The yellow dot lines indicate hydrogen bonds. The blue sticks represent residues.