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. Author manuscript; available in PMC: 2025 Jul 1.
Published in final edited form as: Br J Haematol. 2024 Jun 12;205(1):207–219. doi: 10.1111/bjh.19563

Upregulation of HOXA3 by Isoform-Specific Wilms Tumor 1 Drives Chemotherapy Resistance in Acute Myeloid Leukemia

Basil Allen 1,#, Lindsey Savoy 1,#, Peter Ryabinin 2,#, Daniel Bottomly 2,#, Reid Chen 1,#, Bonnie Goff 1, Anthony Wang 1, Shannon K McWheeny 3, Haijiao Zhang 1,*
PMCID: PMC11448753  NIHMSID: NIHMS1995764  PMID: 38867543

Summary

Upregulation of the Wilms' Tumor 1 (WT1) gene is common in acute myeloid leukemia and is associated with poor prognosis. WT1 generates 12 primary transcripts through different translation initiation sites and alternative splicing. The short WT1 transcripts express abundantly in primary leukemia samples. We observed that overexpression of short WT1 transcripts lacking exon 5 with and without the KTS motif (sWT1+/− and sWT1−/−) led to reduced cell growth. However, only sWT1+/− overexpression resulted in decreased CD71 expression, G1 arrest, and cytarabine resistance. Primary AML patient cells with low CD71 expression exhibit relative resistance to cytarabine, suggesting that CD71 may serve as a potential biomarker for chemotherapy. RNAseq differential expressed gene expression analysis identified two transcription factors, HOXA3 and GATA2, that are specifically upregulated in sWT1+/− cells, whereas CDKN1A is upregulated in sWT1−/− cells. Overexpression of either HOXA3 or GATA2 reproduced the effects of sWT1+/−, including decreased cell growth, G1 arrest, reduced CD71 expression, and cytarabine resistance. HOXA3 expression correlates with chemotherapy response and overall survival in NPM1 mutation-negative leukemia specimens. Overexpression of HOXA3 leads to drug resistance against a broad spectrum of chemotherapeutic agents. Our results suggest that WT1 regulates cell proliferation and drug sensitivity in an isoform-specific manner.

Keywords: WT1, HOXA3, Chemotherapy resistance, biomarkers

Classification: Biological Sciences, Cell Biology

INTRODUCTION

WT1 is located at chromosome 11p13 and encodes a transcription factor, which could either repress or stimulate promoter activity of various genes depending on promoter architecture and cell type.1-3 This protein is comprised of a proline-glutamine-rich N terminus and four carboxyl-terminal zinc fingers. The complexity of the protein is highlighted by the generation of alternative isoforms that translate into varying capacities for DNA binding.4-6 Different translation initiation sites produce three major protein isoforms: the long-length WT1 transcript (lWT1), the middle-length canonical WT1 (mWT1), and the short-length WT1 transcript (sWT1) (Figure 1).1,7-9 Alternative splicing results in four major WT1 isoforms: the first leading to inclusion/exclusion of exon 5 (17AA+/17AA-) and the second to inclusion/exclusion of three amino acids, lysine, threonine, and serine (KTS) at the end of exon 9 between zinc-fingers 3 and 4, namely KTS+/KTS-.

Figure 1.

Figure 1.

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The schematic diagram illustrates the alternative start codons and splicing sites of the 12 major WT1 transcripts.

WT1 was originally identified as a tumor suppressor, and the loss or mutation of this gene has been associated with the development of Wilms’ tumor, a childhood kidney cancer.10-16 Recent research has revealed that WT1 is also involved in the regulation of cell survival, proliferation, and differentiation, and may act as an oncogene.10,17-21 Notably, the expression of WT1 is upregulated in 90% of AML samples and the majority of AML cell lines.22-28 Higher levels of WT1 expression have been linked to increased blast cell counts and a greater likelihood of progression from myelodysplastic syndrome (MDS) to AML.22,29,30 AML patients who fail to reduce WT1 transcript levels to undetectable levels after chemotherapy have a higher risk of relapse.22,24,25,29,31 Consequently, WT1 mRNA expression is now considered a poor prognostic factor and an indicator of minimal residual disease (MRD) in MDS and AML.32,33 Additionally, the upregulation of WT1 has been associated with chemotherapy resistance in solid tumors such as breast and non-small cell lung cancer.34-36 However, the specific transcripts and underlying mechanisms involved in this process require further investigation.

This study aims to characterize the expression patterns of WT1 transcripts in AML and elucidate their potential role in chemoresistance.

METHODS

Patient Samples

Samples were obtained with written, informed consent from all patients according to the Declaration of Helsinki and approved Institutional Review Boards (IRB) protocol (Oregon Health & Science University (OHSU) IRB no. 4422). Mononuclear cells (MNCs) were isolated by Ficoll gradient centrifugation from freshly obtained BM aspirates or PB draws. The samples were stained for CD45 (BioLegend #304038 and #982306) and CD71 (BioLegend #334110) and sorted via flow cytometry. Available samples were used from both sexes and all age-ranged adult patients with myeloid malignancies.

Statistics and reproducibility

All experiments were performed several times with biological and technical replicates. All statistical analyses were performed using GraphPad Prism Version 9.3.1. The data were expressed as the mean ± standard error of the mean (MED). Statistical significance was determined using two-tailed Student’s t-tests (Mann-Whitney test), one-way ANOVAs, Wilcoxon matched-pairs signed rank tests, and Pearson Correlation Coefficients tests as indicated. No statistical method was used to predetermine sample size and no data were excluded from the analyses. All experiments were reproducible.

Additional materials and methods are detailed in the Supplemental File.

RESULTS

WT1 transcript and exon expression in AML samples

Although WT1 overexpression is common in most AML samples, the distribution and abundance of its different transcripts and isoforms have not been well characterized. Using RNAseq data from over 561 AML patient samples in the Beat AML cohort37,38, we mapped the abundance and distribution of six available GRCh37/hg37 annotated WT1 transcripts (Table S1).37,38 The data showed that the sWT1+/− and sWT1−/+ transcripts are the most abundant, accounting for roughly 50% of the total WT1 transcripts (Figure 2A). With an r-value greater than 0.5, all WT1 transcripts are significantly correlated with each other and the total WT1 expression, such as sWT1+/− with sWT1−/− or sWT1−/+ (Figure 2B-C). However, there are two exceptions: lWT1+/+ with lWT1+/− (r=0.24) and sWT1+/+ (r=0.29), which exhibit weaker correlations. We conducted an exon-level expression analysis of WT1, which revealed significant co-expression of all 24 annotated exons (Figure 2D).

Figure 2.

Figure 2.

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(A) The graph depicts mean ± SEM of WT1 transcript expression levels in the Beat AML cohort. The comparison between l+/+ vs. l−/− and l+/− vs. s+/+ does not yield significant differences. However, all other pairwise comparisons demonstrate significant distinctions, with a p-value of <0.0001. (B) The graph depicts a positive correlation between sWT1+/− and sWT1+/+ or sWT1−/+, as indicated by Pearson Correlation tests, from the Beat AML cohort (n=560). (C) Graph depicts the correlation between specific WT1 transcripts within the Beat AML cohort. (D) The graph depicts the correlation between annotated WT1 exons within the BeatAML cohort. The correspondence between WT1 exons and Ensembl IDs is provided in the supplementary file. (E) The graph depicts 95% confidence interval and mean log fold change of different WT1 transcript expression in leukemia samples with and without common mutations from the Beat AML cohort. (F) The graph depicts 95% confidence interval and mean log fold change of WT1 transcript expression in different cytogenetic, FAB, and clinical outcome subgroups (comparing “yes” vs. “no” for indicated parameters). (G) The graph depicts the distribution of p-values for differential exon expression in WT1 with and without common mutations and separates the distributions by the sign of the log fold change (effect size) when applicable. (H) The graph illustrates a strong consistency in Spearman correlation (r) values between WT1 and all other genes in the TCGA and Beat AML cohorts. (I) Graphs depict positive correlations between WT1 and GATA2/RUNX1 as indicated by Pearson Correlation tests, from the Beat AML (n=493) and TCGA cohort (n=165). (J) Graphs depict mutual exclusive mutation patterns of WT1, RUNX1, and GATA2 mutations in AML/MDS cohorts.

AML samples carrying mutations in FLT3, NPM1, IDH2, and WT1 have higher total WT1 expression, while those with RUNX1, ASXL1, and CEBPA mutations have lower WT1 expression (Table S2 and Figure 2E). AML samples with PML-RARA and CBFB-MYH11 fusions exhibit elevated WT1 expression, whereas those with MLL fusions exhibit reduced expression (Figure 2F). Among AML FAB subtypes, M1 and M3 are characterized by higher WT1 expression, while M5 shows reduced expression (Figure 2F). AMLs with adverse or intermediate prognoses as defined by ELN2017 are characterized by reduced WT1 transcript expression (Figure 2F). A trend of increased WT1 transcript expression was observed in relapsed disease samples compared to de novo samples (Figure 2F). Consistent with our findings for WT1 transcript expression, AML samples with NPM1 and/or FLT3-ITD mutations and those with M3 subtypes exhibited high expression of all WT1 exons (Table S3), while samples with mutations in CEBPA, ASXL1, GATA2, and TP53, as well as those with M5 and MLL translocations showed low expression (Figure 2G).

We further performed a gene coexpression analysis using the Beat AML and TCGA AML datasets, which showed high concordance (Figure 2 and Table S4). We observed that WT1 expression is positively correlated with the RUNX1 transcription network, TP53-mediated transcription, and RNA polymerase I activity (Table S5). Notably, RUNX1 and GATA2 were significantly coexpressed with WT1 (Figure 2I). Moreover, mutations in WT1, GATA2, and RUNX1 exhibit mutual exclusivity (Figure 2J), suggesting a potential impact of these genes on a shared pathway or a synthetically lethal interaction.

sWT1+/− expression induces cytarabine resistance

Expression of the sWT1 transcripts is abundant in AML, the function of which has not yet been well characterized. Therefore, we aim to investigate the function of sWT1, specifically focusing on its impact on chemoresistance. We focused on cytarabine, since it has been a major drug for AML treatment for more than three decades.

To assess the influence of sWT1 transcripts on cytarabine sensitivity, we utilized both a doxycycline-regulated expression system and a constitutive EF1α promoter-driven expression system for overexpressing sWT1 transcripts (Figure 3A-B). We subsequently performed a competitive drug assay, where we monitored the percentage changes in WT1 transcript-overexpressing cells (identified by a fluorescent marker, DsRed or GFP) when exposed to different doses of cytarabine, with the results normalized to transduction efficiency of the untreated cells.

Figure 3.

Figure 3.

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(A) MOLM13 cells were infected with lentiviruses encoding doxycycline (DOX)-inducible WT1 transcripts and an empty control with an out of frame green fluorescent protein (GFP) fluorescence marker. The competitive drug graphs depict the normalized percentage of transduced cells (normalized to no drug treatment, drug induced change%) treated with dose gradients of cytarabine in the presence or absence of Dox for 48 hours measured by flow cytometry. (B) MOLM14, OCIAML2, and OCIAML3 cells were infected with lentiviruses encoding doxycycline (Dox)-inducible sWT1+/− transcripts with a green fluorescent protein (GFP). The competitive drug graphs depict the normalized percentages of transduced cells (normalized to no drug treatment, Drug induced change%) treated with dose gradients of cytarabine in the presence or absence of Dox for 48 hours measured by flow cytometry (C) MOLM13 cells were infected with lentiviruses encoding Dox- inducible sWT1 transcript with a GFP outframe fluorescence marker. The competitive drug graphs depict the normalized percentage of transduced cells (normalized to no drug treatment, drug induced change%) treated with dose gradients of cytarabine in the presence for 2 days measured by flow cytometry. (D) Similar competitive drug assays were performed on MOLM14, OCIAML2, and OCIAML3 leukemia cells transduced with Dox- inducible sWT1+/− transcript. (E) MOLM13, MOLM14, OCIAML2, and OCIAML3 cells were infected with lentiviruses encoding constitutive sWT1+/− transcript and an empty control with a dsred outframe fluorescence marker. The competitive drug graphs depict the normalized percentage of transduced cells (normalized to no drug treatment, drug induced change%) treated with dose gradients of cytarabine in the presence for 2 days measured by flow cytometry. (F) The representative flow contour plots demonstrated increased transduced sWT1+/− (Dsred%) when treated with increased doses of cytarabine for 2 days. (G) The graph depicts the mRNA expression level of WT1 in the presence and absence of Dox measured by reverse transcription of extracted RNA. The housekeeping gene GAPDH was used as an internal loading control. (H) OCIAML2 and MOLM13 cells were infected with lentiviruses encoding a Dox- inducible shRNA targeting WT1 exon 7. Drug curves depict mean ± SEM of viabilities of OCIAML2 and MOLM13 cells transduced with shRNA targeting WT1 exon 7 treated with dose gradients of cytarabine in the presence or absence of Dox. Viability was assessed via MTS assay and normalized to no drug treatment controls.

We found that sWT1+/−, but not other sWT1 transcripts, induced cytarabine resistance in a dose-dependent manner in all tested four cell lines (MOLM13, MOLM14, OCIAML2, and OCIAML3, Figure 3C-F) in both the Dox- inducible and constitutive expressing lentiviral vector systems. Interestingly, sWT1+/− overexpression specifically promoted cytarabine resistance without affecting sensitivity to daunorubicin or venetoclax. Consistent with these findings, silencing WT1 exon 7 (shared by all sWT1 isoforms) using shRNAs specifically sensitized cells to cytarabine while leaving their response to daunorubicin and venetoclax unchanged (Figure 3G-H).

sWT1+/− overexpression inhibits S phase entry

Cytarabine is a DNA nucleoside analog that exerts its cytotoxic effects by disrupting normal DNA synthesis through direct incorporation into growing DNA strands. It is known to be active against rapidly dividing tumor cells, particularly those in the S phase.39 We, therefore, conducted a competitive growth assay to assess the impact of WT1 transcripts on cell proliferation. Consistent with previous studies that have demonstrated growth inhibition by the WT1 C-terminal DNA binding domain without the KTS motif, our results indicated that sWT1+/− and sWT1−/− inhibited cell growth in various AML cell lines (MOLM13, MOLM14, GDM, OCIAML1, OCIAML2, and OCIAML3), whereas sWT1+/+ and sWT1−/+ with the KTS domain did not (Figure 4A-B). To better understand why sWT1+/− and sWT1−/− both induce cell growth inhibition, but only sWT1+/− induces cytarabine resistance, we performed cell cycle analyses. Our results demonstrated that sWT1+/− expression decreased the S-phase population, suggesting a role for WT1 in regulating the G1/S checkpoint and impeding cell entry into the S phase (Figure 4C-D). Interestingly, sWT1−/− led to a significant accumulation of cells in the G2/M phase, indicating a G2/M arrest (Figure 4C-D). Previous studies have suggested a functional interaction between WT1 and TP53 and have shown that the tumor suppressor function of WT1 is dependent on wild-type TP53.40-42 To investigate whether TP53 influences the growth inhibition effect of sWT1+/−, we conducted a competitive growth assay in TP53 knockout (Figure 4E-F) and TP53 mutated (Figure 4G) cells and found that the growth inhibition effect of sWT1+/− was independent of TP53 function. Our findings were further confirmed by demonstrating the growth inhibitory effect of sWT1+/− on mouse hematopoietic stem and progenitor cells (HSPCs) in two distinct culture media (Figure 4H).

Figure 4.

Figure 4.

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(A) MOLM13, MOLM14, OCIAMl2, OCIAML3, and GDM cell lines were infected with constitutive lentivirus encoding WT1 transcripts or an empty control with a red fluorescent protein (Dsred). The competitive growth graphs depict the percentage changes of transduced cells (Dsred+%) over 11 days in culture measured by flow cytometry. (B) MOLM13, MOLM14, OCIAMl2, OCIAML3, and GDM cell lines were infected with Dox inducible lentivirus encoding sWT1+/−. The dot plots depict the percentage changes of transduced cells (GFP%) at day 6 in the presence or absence of Dox. Significance was determined by a Wilcoxon matched-pairs signed rank test (*p < 0.05). (C) Representative cell cycle histograms for OCIAML3 cells expressing sWT1+/− or sWT1−/− (Dsred+) or non-transduced control cells (Dsred−). (D) The bar graphs show the percentages of cells in different cell cycle stages, calculated by the FlowJo Watson Pragmatic Algorithm from three cell lines (MOLM13, OCIAML2, and OCIAML3). Statistical analyses were determined by paired two-tailed t tests. (E) The immunoblot image shows p53 protein levels in cells expressing either a sgRNA targeting p53 (sgTP53) or a non-specific control sgRNA (sgNT). Vinculin and Actin served as loading controls. (F) OCIAML2 and OCIAML3 cells transduced with sgRNAs targeting TP53 were subsequently transduced with sWT1+/− DsRed plasmid following puromycin selection. Competitive growth curves show the percentage changes in the sWT1+/− expressing population (DsRed+) over time. (G) GDM and MOLM14 cells were treated with increasing doses of the MDM2 inhibitor idasanutlin for one month, generating idasanutlin-resistant cell lines that were found to harbor TP53 mutations through exome sequence analysis (paper in revision). These cells were subsequently transduced with sWT1+/− DsRed plasmid. Competitive growth curves depict percentage changes of sWT1+/− expressing cells (Dsred+%) on these idasanutlin-resistant TP53 mutant cell lines over time. (H) Bar graphs show a significant reduction in colony numbers of mouse hematopoietic stem and progenitor cells (HSPCs) expressing sWT1+/− compared to empty vector controls, both in M3434 pancytokine and M3435 myeloid-enriched methylcellulose media. Statistical analyses were determined by two-tailed t tests.

CD71 is a potential biomarker for cytarabine resistance

Considering the persisting challenge of detecting WT1-specific transcripts in patient samples, our goal was to identify cell surface markers capable of distinguishing slowly dividing cells exhibiting high sWT1+/− expression and S phase blockage and conferring chemotherapy resistance. Previous studies have demonstrated that CD71 is an indicator of actively dividing cells. We performed cell cycle analysis on CD71 high, medium, and dim expression cells. Strikingly, CD71 expression directly affects cell cycle progression: cells expressing higher levels of CD71 displayed a marked increase in S and G2/M phases, whereas CD71 dim cells primarily stagnated in G1 (Figure 5A). Therefore, we compared CD71 expression in cells overexpressing sWT1 versus control cells. Intriguingly, We noted a notable decrease in CD71 expression within AML cell lines that overexpressed sWT1+/−, but not in those overexpressing other WT1 transcripts, including sWT1−/−, which also hinders cell growth. This observation implies that CD71 could serve as an S-phase-specific marker, given that sWT1+/− uniquely impedes cells from entering the S-phase, while sWT1−/− induces G2/M phase arrest. Consequently, primary patient cells with low CD71 expression may exhibit resistance to cytarabine treatment, as this drug primarily targets cells during the S-phase. (Figure 5B-C).

Figure 5.

Figure 5.

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(A) Representative flow cytometry DAPI cell cycle histograms (bottom panel) in different CD71 expression categories (top panel). (B) Representative FACS dot plots of OCIAML2 cells transduced with the indicated overexpression vector (Dsred+) and stained for CD71. (C) Mean Fluorescence Intensity (MFI) of CD71 on sWT1 transcript expressing and non-transduced cells. Significance was determined by paired two-tailed t tests. (D) Schematic illustrating the experimental workflow of evaluating cytarabine sensitivity in CD71high and CD71dim expressing leukemia samples-populations for cytarabine treatment. Cells were treated with cytarabine for 24-48 hours and quantified by MTS viability assay. (E) The graph depicts higher mean ± SEM of cell viabilities of CD71dim cells compared to CD71high patient cells treated with cytarabine for 48 hours determined by MTS assays. (F) Representative FACS histograms depict decreased CD71 expression in both CD71high and CD71dim cells treated with cytarabine. (G) The graph depicts the mean (from four technical replicates) MTS absorbance of both CD71high and CD71dim leukemia cells culture for 48 hours in basal Stemspan medium. Statistical significances were assessed using two-tailed tests and expressed as **p < 0.01. (H) Graphs depict decreased CD71 expression in both CD71high and CD71dim cells treated with cytarabine from three donors. (I) The bar graphs depict real time PCR ∆∆Ct values of WT1, HOXA3, and GATA2 in sorted CD71− cells compared to CD71+ cells from 4 different AML samples. HPRT was used as the reference control. Statistical significance was determined using two-tailed Student’s t-tests (Mann-Whitney test).

To further validate CD71 as a biomarker for cytarabine sensitivity, we analyzed the cytarabine sensitivity of primary leukemic patient samples based on CD71 expression. Blast cells were isolated by CD45 staining and subsequently sorted into CD71low and CD71high populations. These cell populations were then subjected to cytarabine treatment separately (Figure 5D). As predicted, CD71low primary cells exhibited relative resistance to cytarabine compared to CD71high cells from the same patient sample (Figure 5E). Even within the sorted CD71 expressing populations, higher cytarabine concentrations further enriched for cells expressing lower CD71, as indicated by the decrease in CD71 mean fluorescence intensity (MFI, Figure 5F-H). Moreover, CD71− cells display markedly higher expression of WT1, HOXA3, and GATA2 compared to CD71+ cells (Figure 5I).

HOXA3 and GATA2 are downstream targets of and phenocopy sWT1+/−

To further investigate the mechanisms underlying the growth and drug response patterns in sWT1+/− overexpressing cells, we performed RNAseq analysis (Figure 6A). We first confirmed that WT1 was significantly upregulated in all four transcripts, as compared to the empty vector (Table S6). We then conducted DEG analyses comparing cells overexpressing sWT1+/− with empty vector, sWT+/+, sWT1−/+, or sWT. We focused on upregulated genes, as previous cytarabine CRISPR knockout screening only identified DCK and SLC29A as cytarabine resistance drivers43-45. Among the top 2000 upregulated genes with an expression >2 (log cpm), a total of 340 genes were found to be shared across all four comparisons (Table S7-8). The most significantly enriched pathways were related to cell cycle and mitosis, DNA repair, and developmental biology (Table S9). The majority of cell cycle- and DNA repair-related genes, such as CDK1, CCNA2, BLM, and XRCC2, have been characterized to promote cell cycle progression and cell proliferation 46-49 This is in contrast to the observed growth inhibition in sWT1+/− overexpressing cells. Therefore, these genes were not considered for further analysis. We then focus on embryogenesis-related genes due to their function as checkpoints to negatively regulate cell growth and maintain the balance between cell proliferation and differentiation (Table S10).

Figure 6.

Figure 6.

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(A-B) The Venn graph depicts the number of differentially expressed genes between cells expressing sWT1+/− (A) or sWT1−/− (B) and cells expressing other sWT1 transcripts and an empty control vector. (B) RNAseq heatmap indicating differentially expressed genes between sWT1+/− and other transcripts. The average expression of the indicated genes in primary leukemia samples from the BeatAML cohort is also displayed. (C) The competitive growth assay plot demonstrates a growth disadvantage of HOXA3 expressing cells (Dsred+) determined by flow cytometry. (D) Representative flow cytometry plots showing decreased CD71 expression in OCIAML3 and MOLM13 cells transduced expressing HOXA3 (Dsred+). (E) The competitive drug graphs depict increased resistance of HOXA3 expressing cells indicated by increased percentages of Dsred+ cells treated with dose gradients of cytarabine in the presence of doxycycline for 48 hours measured by flow cytometry. (F) The competitive growth assay plot shows a growth disadvantage of cells expressing GATA2. (G) Representative flow cytometry plot showing decreased CD71 expression in OCIAML3 and MOLM13 cells transduced with a GATA2 overexpression vector. (H) The competitive drug graphs depict the percentages of GATA2 expression cells (MOLM13, OCIAML3) treated with dose gradients of cytarabine and venetoclax for 48 hours measured by flow cytometry. (I) The representative immunoblot images showing increased p21 (encoded by CDKN1A) in Dox inducible sWT1−/− overexpressing cells in the presence of Dox for 3 days. (J) RT-qPCR data showing 2^-ΔΔCt changes of HOXA3, GATA2, and WT1 in sWT1+/−, HOXA3, GATA2, or HOXA3 overexpressing cells normalized to HPRT and empty vector control cells. (K) Representative cell cycle histograms depict percentages of cells in different cell cycle stages in HOXA3 or GATA2 overexpressing (Dsred+) or control (Dsred−) cells. The experiments were performed from 2-3 cell lines (OCIAML3, MOLM13, and MOLM14). (L) The graph demonstrates low expression of HOXA3 in CMK and K562 cells compared to other leukemia cell lines. Relative mRNA expression was normalized to HPRT control. (M) The competitive drug curves depict percentage changes of HOXA3, sWT1+/− and GATA2 overexpressing (DsRed+) K562 and CMK cells treated with a gradient dose of cytarabine.

We further analyzed DEG analyses comparing cells overexpressing sWT1−/− with empty vector, sWT+/+, sWT1−/+, or sWT+/− (Table S11). Among the top 2000 upregulated genes with an expression >2 (log cpm), a total of 1002 genes were found to be shared across all four comparisons (Figure 6B and Table S12). Surprisingly, the most significantly enriched pathways are related to the immune system, cytokines, and interleukins (Table S13). Genes that are involved in the negative regulation of cell growth include FOXO/TP53-mediated transcription of cell cycle genes, the SMAD/TGFB pathway, and the extrinsic apoptosis pathway, such as CDNK1A (p21), RB1, ZNF385A, etc. (Table S14). These data further highlight the distinct function of SWT1+/− and sWT1−/−.

Among the 23 embryogenesis-related genes that expressed >0 log CPM in AML samples from the beat AML cohort, we are particularly interested in HOXA3 and GATA2, as these genes or the associated gene cluster are known to play essential roles in hematopoiesis and upregulated at the highest level in sWT1+/−. Moreover, GATA2 expression positively correlates with WT1 expression (Figure 2I), and GATA2 and WT1 mutations mutually exclude each other (Figure 2J), indicating a potential shared functionality.

We then overexpressed HOXA3 or GATA2 in different AML cell lines. Strikingly, competitive growth and drug assays demonstrated that cells overexpressing HOXA3 or GATA2 exhibited impaired growth, downregulation of CD71, G1 arrest, and resistance to cytarabine, similar to sWT1+/− cells (Figure 6C-I). We also validate that sWT1−/− overexpression induces p21 expression using immunoblot (Figure 6J).

Since ectopic expression of sWT1+/−, HOXA3, and GATA2 displayed similar phenotypes, we aimed to determine the direction of regulation between them. We quantified the expression of WT1, HOXA3, and GATA2 in cells overexpressing each gene using RT-qPCR. Our results showed that WT1 upregulated both HOXA3 and GATA2, and HOXA3 induced GATA2 expression, whereas neither GATA2 nor HOXA3 affected WT1 expression (Figure 6K). These data indicate that WT1 is likely upstream of HOXA3 and GATA2, and HOXA3 is upstream of GATA2.

The high degree of splicing and sequence homology restrict the applicability of CRISPR-based approaches for specific transcript knockout, limiting further genetic validation studies. Alternatively, we overexpressed sWT1, GATA2, and HOXA3 in various leukemia cell lines that exhibited varying expression levels of GATA2 and HOXA3 to investigate whether GATA2 and HOXA3 are required for the effect mediated by sWT1. Our results revealed that HOXA3 consistently reduced CD71 expression in all four tested AML cell lines, as well as in K562 and CMK, two MPN-transformed AML cell lines. GATA2 and sWT1 decreased CD71 expression in all four AML cell lines, but not for CMK and K562 (Supplementary Figure 1). We performed RT-PCR to assess the expression levels of WT1, HOXA3, and GATA2 in these cell lines and observed that K562 and CMK cells had significantly lower HOXA3 expression compared to the other AML cells (Figure 6L). Furthermore, only overexpressing HOXA3, but not sWT1+/− or GATA2 confers K562 and CMK cells cytarabine resistance (Figure 6M). These findings suggest that the observed phenotypes are highly context-dependent and a baseline level of HOXA3 expression is required.

HOXA3 predicts chemotherapy sensitivity of AML patients

To gain further insight, we explored the expression patterns of HOXA3 and GATA2 across various leukemia subtypes and investigated their potential associations with patient outcomes (Table S15). Similar to WT1, both HOXA3 and GATA2 expression are elevated in AML samples carrying NPM1 and FLT3-ITD mutations (Figure 7A). In particular, HOXA3 is significantly upregulated in NPM1 mutant samples, highlighting its specific association with this mutation (Figure 7B). We further performed HOXA3 and GATA2 expression patterns in NPM1 mutation negative samples (Figure 7C and Table S16). Consistent with the coexpression of WT1 and GATA2, FAB subtype MLL fusions, M5 samples, and ELN adverse groups exhibit a trend of lower GATA2 expression, whereas M1 exhibits higher GATA2 expression. Interestingly, GATA2 mutations are associated with a higher expression of GATA2, but lower WT1 expression. GATA2 is known to be subjected to self-regulation. These data indicate that WT1 may regulate GATA2 expression or both genes are under the same transcriptional regulation. GATA2 self-regulation is downstream of WT1 regulation. No other correlation between GATA2 expression and AML outcomes is observed.

Figure 7.

Figure 7.

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(A), (C) The graphs depict 95% confidence interval and mean log fold changes of HOXA3 and GATA2 mRNA expression in leukemia samples with and without indicated mutations (Left), cytogenetic events, specific FAB subtypes, and clinical outcome groups (right) from the Beat AML cohort all samples (A) or only NPM1 WT samples (C). (B) The graphs depict increased HOXA3 expression (MED) in the Beat AML and TCGA AML samples in NPM1 mutation vs. NPM1 WT samples determined by two-tailed t tests, ****p<0.0001. (D) The bar graph compares HOXA3 mRNA expression in Beat AML patient samples that responded (CR+PR) or showed resistance to chemotherapies determined by a Mann-Whitney test. (E) The bar graph compares HOXA3 mRNA expression in de novo or relapsed patient samples from the Beat AML cohort determined by a Mann-Whitney test. (F) The graph depicts a negative correlation between HOXA3 expression and patient OS in the Beat AML cohort determined by a Spearman correlation coefficient test, ***p<0.001. (G) The Kaplan-Meier survival curves depict shorter DFS and OS time in patients with above compared to below HOXA3 expression from the TCGA cohort, determined by Log-rank (Mantel-Cox) tests. (H) The graph illustrates the percentage of viable cells (OCIAML2, MOLM14, and OCIAML3) overexpressing HOXA3 treated with a dose gradient of Platinum, Cisplatin, and Gemcitabine.

Interestingly, similar to WT1 transcripts, HOXA3 is uniquely upregulated in AML samples, but not other hematological malignancies, including CML, MDS, CNL, CLL, and ALL (except for t(11q23)ALL)37,50-52 (Figure 7D and Supplementary Figure 2). Samples with FLT3-ITD, NPM1, BCOR, and DNMT3A mutations showed increased HOXA3 expression, while those with CEBPA, GATA2, and NRAS mutations showed decreased expression (Figure 7A-C). Notably, AML samples with good prognosis fusion markers: RUNX1-RUNX1T1, CBFB-MYC, and FAB M3 exhibit reduced HOXA3 expression, whereas those with M1, MLL fusions, ELN adverse and intermediate samples exhibit elevated expression. Strikingly, HOXA3 expression is significantly higher in chemoresistant samples compared to chemo responders in the Beat AML samples (Figure 7E). There is a negative correlation between HOXA3 expression with survival time in the Beat AML cohort (Figure 7F). Similarly, in the TCGA cohort, a high HOXA3 expression predicts a shorter overall survival (OS) and disease-free survival (DFS) (Figure 7G). These data suggest that HOXA3 could serve as a potential biomarker for both prognosis and chemo response in NPM1 WT samples.

Given the notable correlation between HOXA3 and cytarabine resistance, we further investigated whether HOXA3 also induces drug resistance to other chemotherapeutic agents. Strikingly, HOXA3 overexpression markedly induced resistance to other tested chemotherapies; including gemcitabine, cisplatin, and platinum (Figure 7H), hinting at its potential role in fueling chemoresistance across a spectrum of solid tumors. We performed RNAseq analysis on HOXA3 overexpression MOLM13 and OCIAML3 cells. In consistency with the transcription repression function of HOXA3, we observed 561 vs. 45 genes that are downregulated at ≤−1 or upregulated at ≥1 levels (Table S17-21). The downregulated genes are associated with immune function and mature myeloid differentiation. The upregulated genes are associated with metabolomics. In consistency with Figure 6J, HOXA3 upregulates GATA2 expression, suggesting GATA2 is downstream of HOXA3.

DISCUSSION

The WT1 gene undergoes alternative initiation and splicing, generating at least 12 major transcripts. WT1 exon and transcript expression analysis showed that while the majority of AML samples upregulate WT1, there is significant heterogeneity. Patients with MLL fusions, RUNX1/ASXL1/GATA2 mutations, and FAB M5 samples demonstrated reduced WT1 expression, whereas samples carrying FLT3 and NPM1 mutations or FAB M3 patients expressed higher levels of WT1. WT1 mRNA expression is increasingly being used for prognosis and MRD tracking, while antibodies and CAR-T cells targeting the WT1 protein are currently undergoing active preclinical and clinical investigation as potential treatments for AML. Based on our data, it appears that WT1 may not be an ideal marker for MRD or a viable treatment target for AML patients with MLL and RUNX1 fusions as well as those with FAB M5 subtypes due to the observed low expression levels.

WT1 upregulation has previously been linked to chemoresistance. Our study demonstrates that WT1 expression influences cytarabine sensitivity in an isoform-specific manner. Notably, both the exon 5 inclusion (sWT1+/−) and exclusion (sWT1−/−) transcripts without the KTS domain reduce cell growth. However, only sWT1+/− triggers cell cycle arrest at the G1 phase and confers cytarabine resistance. This finding suggests that WT1 isoforms regulate cell cycle progression via distinct mechanisms. Mechanistically, we discovered that HOXA3 and GATA2 act as distinct downstream targets of sWT1+/−, exhibiting phenotypes similar to those observed with sWT1+/− overexpression. The observed phenotypes are dependent on HOXA3 expression, as evidenced by the lack of reduced CD71 expression or cytarabine resistance in chronic leukemia cell lines with minimal HOXA3 expression upon overexpression of either sWT1+/− or GATA2. On the contrary, in sWT1−/−, the RNAseq data reveals the upregulation of crucial tumor suppressor genes like CDKN1A, RB1, and TGFB pathway genes. This upregulation potentially contributes to the inhibition of cell growth mediated by sWT1−/−, as the elevated expression of these genes have been shown to induce G2/M arrest, mirroring the cell cycle perturbation observed in sWT1−/−.

Furthermore, our findings reveal that CD71 low expression is a unique characteristic of sWT1+/− expressing cells and/or cells not entering the S phase of the cell cycle. This suggests CD71 could serve as a marker to identify a subset of non-dividing leukemia cell populations potentially resistant to chemotherapy.

We showed that HOXA3 exhibits exclusive expression in AML samples. While this study identifies WT1 as a regulator of HOXA3 gene expression, the high association of HOXA3 with the HOXA cluster genes suggests that additional regulatory mechanisms are likely involved. Previous research has revealed that the MLL and MEIS oncoproteins function as upstream constitutive activators of the HOX-dependent pathway, enhancing HOXA gene expression and promoting myeloid transformation.53-55 Studies have identified NPM1 as an essential upstream regulator of leukemia stem cell (LSC) epigenetic marker genes, primarily regulating the HOXA gene family including HOXA5, HOXA6, HOXA7, HOXA9, and HOXA10.56-58 Mutations in NPM1 lead to the abnormal cytoplasmic localization of the mutant protein (NPM1c), maintaining HOX/MEIS1 expression to support the leukemic state in NPM1-mutant acute myeloid leukemia (AML).58-60 BCOR is implicated in the regulation of HOXA genes, and mutations in BCOR result in increased HOXA expression.61,62 Intriguingly, BCOR deletion has been associated with cytarabine resistance.61,63,64 In line with these data, HOXA3 is upregulated in MLL fusion and NPM1 mutant cases. In addition to NPM1 mutant cases, HOXA3 expression levels can predict prognosis and chemotherapy response. Therefore, HOXA3 may serve as a superior alternative biomarker to WT1 for prognosis and chemotherapy responses. Additionally, the emergence of recently developed menin inhibitors presents a new opportunity for overcoming HOXA3-mediated chemoresistance.65-68

In summary, our study demonstrated that sWT1+/− decreases cell proliferation by inducing G1 arrest, downregulating CD71 expression, and conferring cytarabine resistance. Moreover, low CD71-expressing AML cells are relatively resistant to cytarabine treatment and therefore could be used as a biomarker for chemosensitivity. Additionally, the upregulation of HOXA3 and GATA2 mirrors the same phenotypes observed with sWT1+/− upregulation. Notably, high HOXA3 expression levels are associated with adverse disease, short survival, and chemotherapy resistance in NPM1 WT AML samples. These findings underscore the need for further preclinical and clinical investigations to validate the utility of HOXA3 as a biomarker and to explore its potential therapeutic implications.

Supplementary Material

Supinfo1
Supinfo3

ACKNOWLEDGMENTS

We thank Kara Johnson for her invaluable support in general lab management. We thank our patients for the generous donation of their tissue samples. We express our appreciation to the dedicated patient sample processors for their diligent work in processing the patient samples. We acknowledge OHSU Massively Parallel Sequencing Shared Resource (MPSSR) and the flow cytometry Shared Resource for their technical assistance, exceptional support and expertise, and valuable guidance throughout the study.

FUNDING INFORMATION

H.Z. is additionally supported by a National Cancer Institute R00 (5K99CA237630/ 5 R00 CA237630-05) grant, the Oregon Medical Research Foundation New Investigator Award, and the Translational Oncology Program Pilot Award (TOP-2023-002).

Footnotes

CONFLICT OF INTEREST STATEMENT

No competing interests to disclose.

Competing Interest Statement: No competing interests to disclose.

DATA AVAILABILITY STATEMENT

Gene expression data were obtained from the Beat AML public Vizome interface [www.vizome.org] and expressed as log2 transformed normalized RPKM. The RNA-seq data were deposited to the Gene Expression Omnibus (GEO) under accession number GSE239734. The source data associated with each figure are provided as a supplementary table. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supinfo1
NIHMS1995764-supplement-Supinfo1.zip (18.9MB, zip)
Supinfo3
NIHMS1995764-supplement-Supinfo3.docx (546.5KB, docx)

Data Availability Statement

Gene expression data were obtained from the Beat AML public Vizome interface [www.vizome.org] and expressed as log2 transformed normalized RPKM. The RNA-seq data were deposited to the Gene Expression Omnibus (GEO) under accession number GSE239734. The source data associated with each figure are provided as a supplementary table. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.

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