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

Abstract

Cytarabine, daunorubicin, and etoposide (ADE) have been the standard backbone of induction chemotherapy regimen for patients with pediatric acute myeloid leukemia (pAML) for >5 decades. However, chemoresistance is still a major concern, and a significant proportion of pAML becomes resistant to ADE treatment and relapse, leading to poor survival. Therefore, there is a considerable need to identify mechanisms mediating drug resistance for overcoming chemoresistance. Herein, we performed synthetic lethal CRISPR/Cas9 screens using the ADE components to identify response markers. We further integrated significant markers in 3 independent pAML clinical cohorts treated with only an ADE regimen to identify drug response biomarkers with prognostic significance. We were able to identify several mediators that represent clinically and biologically significant marker genes for ADE treatment, such as BCL2, CLIP2, and VAV3, which are resistant markers to ADE, with high expression associated with poor outcomes in pAML treated with ADE, and GRPEL1, HCFC1, and TAF10, which are sensitive markers to ADE, with high expression showing beneficial outcomes. Notably, BCL2, CLIP2, and VAV3 knockdowns in their expression in AML cell lines sensitized the cells more to the ADE components, suggesting that these modulators should be further studied as potential therapeutic targets to overcome chemoresistance.

Graphical abstract

Figure 1.

Overall significant gene markers from ADE CRISPR screens. (A) CRISPR/Cas9 loss-of-function screening overall schema. (B) Significant marker genes were identified by CRISPR screens in response to AraC, Dauno, or Etop exposures, with sensitive marker genes for each drug highlighted in blue and resistant genes highlighted in red (SD cutoff, 2), whereas nonsignificant marker genes or inert marker genes highlighted in gray. (C) Top 25 sensitive and bottom 25 resistant markers for each drug screen CRISPR results. Dauno, daunorubicin; Etop, etoposide; I, inert markers for each drug; R represents resistant markers for each drug; S, sensitive markers for each drug. Diagram was created with BioRender.com.

Figure 2.

Biological pathway enrichment analysis with GSEA for each drug and ORA comparison among all drugs. (A) GSEA dot plot with Reactome biological pathway database for the whole gene set of CRISPR screens with decreasing ranking in diff-β scores for each drug. (B) Bar plots showed several significant genes that met each drug's 2 SD cutoff for resistant or sensitive genes. (C) ORA dot plot showed CRISPR significant gene comparing across 3 drugs; significant pathway gene sets have FDR <0.1; P value represents adjusted P value with FDR. FDR, false discovery rate; GPCR, G protein-coupled receptors; GTP, guanosine triphosphate; PpS, Peters plus syndrome; TSR, thrombospondin type 1 repeat; TPBS, temtamy preaxial brachydactyly syndrome; tRNA, transfer RNAs.

Figure 3.

Clinical integration of significant CRISPR marker genes across ADE drugs in pAML cohorts treated with only ADE regimen. (A) All significant CRISPR signature marker genes for each drug with 3748 unique genes were further investigated in 3 independent pAML cohorts treated with ADE to determine the association between diagnostic gene expression and clinical outcomes (EFS, OS, and MRD1). (B) Each gene was evaluated to determine whether sensitive markers from CRISPR results with high expression led to beneficial (HR/OR <1) or resistant markers from CRISPR results with high expression led to detrimental outcomes (HR/OR >1) in all 3 clinical cohorts. Collectively, these marker genes were deemed to be biologically important genes with prognostic potential. (C) Significant markers genes with CRISPR screens and at least 1 significant clinical outcome in pAML cohorts treated with ADE either with or without risk adjustment. Significant outcomes were defined as P value < .05 in HR or OR.

Figure 4.

Heat map results of significant CRISPR screen markers and their association analysis from 3 independent pAML cohorts. CRISPR column showed significant markers of the gene with either a R, I, or S feature for each drug’s CRISPR screen. TARGET-0531, TARGET1031, and AML02 column showed association analysis of EFS, OS, and MRD1 with (RISK.adj) and without risk adjustment (unadj). HR was estimated for EFS and OS using the Cox proportional model, and OR was calculated for MRD1 using logistic regression. Genes with OR/HR >1 were defined as detrimental, and OR/HR <1 were defined as beneficial outcomes. Resistant features and detrimental outcomes are in red; sensitive features and beneficial outcomes are in blue; and inert features are in gray. ∗P ≤ .05; ∗∗P ≤ .01; ∗∗∗P ≤ .001. I, inert; R, resistant; S, sensitive.

Figure 5.

BCL2 and CLIP2 expression association analysis across 3 pediatric patients with AML treated with ADE-containing regimens. High expression of BCL2 and CLIP2 showed unfavorable outcomes in at least one of the end points (EFS, OS, and MRD1) in AML02, TARGET.0531, and TARGET.1031. ∗P < .05; ∗∗P < .01; ∗∗∗P < .001.

Figure 6.

BCL2 and CLIP2 expression in pediatric healthy vs pAML bone marrow. Diagnostic gene expression of healthy and pAML from the whole TARGET data set was evaluated, regardless of treatment protocols. All patients with AML were subdivided into low, standard, and high-risk groups. The Wilcoxon test was used to compare each AML risk group with healthy individuals, and logistic regression with the risk additive model was used to estimate the OR of gene expression, which increases with risk. BM, bone marrow; ns, not significant.

Figure 7.

siRNA-mediated BCL2, CLIP2, and VAV3 knockdown increased ADE components’ sensitivity. (A) Results of MOLM-13 cell line, with bar plot (left) showing the effect of siRNA-mediated knockdown for each gene in relative messenger RNA expression compared with NTC. The viability inhibition curves for each ADE component (top); an siRNA-mediated knockdown confirmation quantitative polymerase chain reaction (qPCR) plot (left) for each gene interest compared with NTC, and IC50 values were calculated for each cell condition per drug plot. Bar plots (bottom) showing specific drug concentrations with cell viability (%) of siRNA in the gene of interest compared with NTC for each ADE component. (B) Results of ML-2. Data are presented as mean ± SD. Cell viability and qPCR experiments included 3 technical replicates for each sample condition. ∗P < .05; ∗∗P < .01; ∗∗∗P < .001; ∗∗∗∗P < .0001. Dauno, daunorubicin; Etop, etoposide.