Genome-wide CRISPR/Cas9 screen identifies etoposide response modulators associated with clinical outcomes in pediatric AML

Abstract

Etoposide is used to treat a wide range of malignant cancers, including acute myeloid leukemia (AML) in children. Despite the use of intensive chemotherapeutic regimens containing etoposide, a significant proportion of pediatric patients with AML become resistant to treatment and relapse, leading to poor survival. This poses a pressing clinical challenge to identify mechanisms underlying drug resistance to enable effective pharmacologic targeting. We performed a genome-wide CRISPR/Cas9 synthetic-lethal screening to identify functional modulators of etoposide response in leukemic cell line and integrated results from CRISPR-screen with gene expression and clinical outcomes in pediatric patients with AML treated with etoposide-containing regimen. Our results confirmed the involvement of well-characterized genes, including TOP2A and ABCC1, as well as identified novel genes such as RAD54L2, PRKDC, and ZNF451 that have potential to be novel drug targets. This study demonstrates the ability for leveraging CRISPR/Cas9 screening in conjunction with clinically relevant endpoints to make meaningful discoveries for the identification of prognostic biomarkers and novel therapeutic targets to overcome treatment resistance.

Graphical abstract

Figure 1.

Summary of significant genes hit from etoposide CRISPR screen. (A) CRISPR/Cas9 loss-of-function screening overall schema. (B) Significant genes were identified by CRISPR screen at day 4 (early), day 12 (intermediate), and day 18 (late) in response to etoposide exposures, with EtoSGs highlighted in blue and EtoRGs highlighted in red (FDR < 0.1). (C) Volcano plots of gene hits with log₂(FC) values between the 3 exposures times of etoposide. (D) Rank plots of the top 10 EtoRGs and EtoSGs with their respective etoposide scores across 3 time points, with a higher negative score indicating increased etoposide resistance and a higher positive score indicating increased etoposide sensitivity. EtoRGs, etoposide resistance genes; EtoSGs, etoposide sensitive genes.

Figure 2.

Integration of significant genes hit across 3 time points of etoposide exposures. (A) Venn diagram of significant genes identified by CRISPR screening at day 4 (purple), day 12 (green), and day 18 (orange), with 8 EtoSGs (blue) and 3 EtoRGs (red), were common at all time points. (B) Eleven EtoSGs/EtoRGs at all time points of etoposide exposure. (C) Eight EtoSGs/EtoRGs at day 4 and day 12 of etoposide exposure. (D) Three EtoSGs/EtoRGs at day 4 and day 18 of drug exposure. (E) Twenty-eight EtoSGs/EtoRGs at day 12 and day 18 of etoposide exposure. (F) Twenty-five significant genes with etoposide time dependence score derived from MAGeCK-MLE, where fitting time linearity of etoposide score with time points of day 0 (time 0), day 4 (time 1), day 12 (time 2), and day 18 (time 3), where high etoposide time dependence score indicating genes exhibit consistent etoposide response changes. All significant genes were identified at FDR < 0.1. (G) Spearman rank correlation between significant EtoRGs genes with only negative etoposide score (resistance score) and their corresponding essential score at each time point. The etoposide score was determined by comparing etoposide to vehicle control at each time point, whereas the essential score was determined by comparing vehicle control at each time point to day 0, with “ns” indicating P > .05.

Figure 3.

Functional pathway enrichment and PPI of the candidate genes. (A) Functional pathway enrichment with GO, KEGG, and Reactome comparing across all time points of significant EtoRGs (12 genes for day 4, 25 genes for day 12, 19 genes for day 18) and EtoSGs (160 genes for day 4, 69 genes for day 12, 72 genes for day 18). (B) STRING PPI network of 50 genes with at least 2 significant time points (nodes represent proteins, and edges connecting nodes represent interaction with concealing singletons). (C) The IPA canonical pathway hierarchical clustering of 50 significant candidate genes over 3 time points reveals shared canonical pathways (FDR < 0.05).

Figure 4.

Clinical relevance of etoposide CRISPR screen and etoposide pharmacology genes. (A) Schematic showing anticipated relationship between significant hits from CRISPR screen and association with clinical outcome in AML. (B) Flowchart and summary results of association analysis between diagnostic leukemic cell gene expression levels of EtoRGs (n = 10 genes) and EtoSGs (n = 40 genes) with 4 clinical outcome endpoints (EFS, OS, RR, and MRD1) in pediatric patients with AML from St Jude AML02 trial. Twenty-six genes significantly associated with at least 1 clinical outcome (P < .05 in Cox proportional hazard, fine method of Gray, and logistic regression) are listed; of these, 19 showed significant association with consistent direction. (C) Etoposide pharmacology pathway (PharmGKB.org) highlighting roles of ABCC1 and TOP2A. (D) Kaplan-Meier survival curves showing high ABCC1 expression and low TOP2A expression associated with poor OS and EFS (∗, ∗∗, ∗∗∗ indicate significant terms at the P < .05, P < .01, and P < .001 statistical levels, respectively). Gene expression association analysis was performed with (referred as Risk adj) or without adjusting (referred as Unadj) for initial risk–group assignment.

Figure 5.

Association of etoposide genes from CRISPR screen with outcome in pediatric AML. (A) High expression of EtoRGs: PRKDC and RAD54L2 showed unfavorable EFS, OS, and RR in pediatric AML. (B) High expression of EtoSGs: TKT with favorable EFS, OS, and RR; RPE with better EFS and OS; CSK1B with favorable OS; and SKP2 with lower RR in pediatric patients with AML treated in AML02 trial. Corresponding STRING networks showed interactions with respective genes with other genes. (∗, ∗∗, ∗∗∗ indicate significant terms at the P < .05, P < .01, and P < .001 statistical levels, respectively). Gene expression association analysis was performed with (referred as Risk adj) or without adjusting (referred as Unadj) for the initial risk group assignment.

Figure 5.

Association of etoposide genes from CRISPR screen with outcome in pediatric AML. (A) High expression of EtoRGs: PRKDC and RAD54L2 showed unfavorable EFS, OS, and RR in pediatric AML. (B) High expression of EtoSGs: TKT with favorable EFS, OS, and RR; RPE with better EFS and OS; CSK1B with favorable OS; and SKP2 with lower RR in pediatric patients with AML treated in AML02 trial. Corresponding STRING networks showed interactions with respective genes with other genes. (∗, ∗∗, ∗∗∗ indicate significant terms at the P < .05, P < .01, and P < .001 statistical levels, respectively). Gene expression association analysis was performed with (referred as Risk adj) or without adjusting (referred as Unadj) for the initial risk group assignment.

Figure 6.

Impact of siRNA-mediated knockdown of TOP2A, RAD54L2, PRKDC, and ZNF451 on etoposide sensitivity. (A and C) The bar plots depict the effect of siRNA-mediated knockdown on etoposide sensitivity in relative cell viability (%) for Kasumi-1 (A) and KG-1 (C) cells. (B and D) Confirmations of siRNA knockdown are shown in relative messenger RNA expression (%) by quantitative PCR for Kasumi-1 (B) and KG-1(D) cells. Data are presented as mean ± standard deviation. Relative cell viability bar plots are representative with 3 technical replicates for each sample, whereas quantitative PCR experiments included 2 technical replicates for each sample. (∗, ∗∗, ∗∗∗, ∗∗∗∗ indicate significant terms at the P < .05, P < .01, P < .001, and P < .0001 statistical levels, respectively).