The intersection of genetic and chemical genomic screens identifies GSK-3α as a target in human acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults. Long-term survival of patients with AML has changed little over the past decade, necessitating the identification and validation of new AML targets. Integration of genomic approaches with small-molecule and genetically based high-throughput screening holds the promise of improved discovery of candidate targets for cancer therapy. Here, we identified a role for glycogen synthase kinase 3α (GSK-3α) in AML by performing 2 independent small-molecule library screens and an shRNA screen for perturbations that induced a differentiation expression signature in AML cells. GSK-3 is a serine-threonine kinase involved in diverse cellular processes, including differentiation, signal transduction, cell cycle regulation, and proliferation. We demonstrated that specific loss of GSK-3α induced differentiation in AML by multiple measurements, including induction of gene expression signatures, morphological changes, and cell surface markers consistent with myeloid maturation. GSK-3α–specific suppression also led to impaired growth and proliferation in vitro, induction of apoptosis, loss of colony formation in methylcellulose, and anti-AML activity in vivo. Although the role of GSK-3β has been well studied in cancer development, these studies support a role for GSK-3α in AML.

Cure rates for patients with acute myeloid leukemia (AML) have changed little over the past decade. Only a minority of patients are long-term survivors, and existing high-dose chemotherapy regimens and/or stem cell transplantation have significant short- and long-term morbidity (1–3). This seemingly slow progress in improving outcomes for patients with AML is in sharp contrast to the tempo of laboratory-based discovery. New genomic approaches have dramatically altered the pace of identifying candidate molecular lesions in human disease, including AML (4, 5). The challenge now is to functionally validate emerging targets and to harness this knowledge toward therapeutic benefit.

To this end, we have extended genomic approaches to small-molecule and genetically based high-throughput screening. In particular, we have focused on the identification and validation of new AML differentiation targets. AML is characterized by both defects in proliferation and differentiation, with current therapy primarily focused on the proliferation defect (6). The success of all-trans retinoic acid (ATRA) differentiation therapy in the treatment of patients with the AML subtype acute promyelocytic leukemia (APL) supports the notion of differentiation therapy for AML more broadly (1, 7). Efforts to identify new differentiation agents have been hampered by lack of knowledge of a drug­gable target and the inherent challenges of traditional cell-based phenotypic screening. In order to address these limitations, our laboratory developed a gene expression–based high-throughput screening (GE-HTS) approach, in which gene expression signatures serve as surrogates for different biological states, in this case AML versus mature myeloid cells (8–10). Using the intersection of chemical biology and high-throughput genetic screening, we sought to identify AML differentiation targets by measuring the induction of a complex gene expression signature indicative of myeloid maturation. Glycogen synthase kinase 3α (GSK-3α) emerged as a top candidate.

GSK-3 is a multifunctional serine threonine kinase involved in diverse cellular processes, including differentiation, signal transduction, cell cycle regulation, and proliferation, with an emerging role in human malignancy (11, 12). GSK-3β has been reported as a candidate target in leukemia and other malignancies (13–18). In this manuscript, we report a role for GSK-3α in human AML.

Intersection of chemical and genetic screens identifies loss of GSK-3α as a modulator of differentiation. We conducted 2 independent small-molecule library screens using a gene expression signature as a read-out for myeloid differentiation. Signature genes were measured with the previously described GE-HTS approach, using ligation-mediated amplification (LMA) and a fluorescent bead-based detection system (8–10). 3,517 compounds from the Spectrum/Prestwick Bioactives library and Harvard Stem Cells Bioactives library were screened in triplicate in the AML cell line HL-60. We independently screened a collection of 84 kinase inhibitors in 2 AML cell lines, HL-60 and U937, again measuring a complex myeloid differentiation signature. Multiple pan-GSK-3 inhibitors scored in both screens (Figure 1). Independently, a genetic screen was conducted of a kinome-focused sublibrary of the RNAi Consortium short hairpin RNA (shRNA) library, composed of 5,036 shRNAs that target 1,000 genes. This lentivirally delivered library was screened in HL-60 cells in the primary screen and then in both HL-60 and U937 cells in the secondary screen, with the myeloid differentiation signature assayed. GSK-3 also emerged as a top candidate, with shRNAs specific for GSK-3α scoring highly (Figure 1).

Figure 1

Three expression-based screens identify GSK-3α as a target of AML differentiation. We performed 3 independent GE-HTS differentiation screens: (a) a bioactive small-molecule library screen in HL-60 cells, (b) a kinome-focused shRNA library screen in U937 and HL-60 cells, and (c) a kinase inhibitor-focused small-molecule library screen in U937 and HL-60 cells. Perturbations were scored by consensus classification with 5 algorithms: summed score, weighted summed score, naive Bayes, K-nearest neighbor, and support vector machine. A compound or an shRNA was considered a hit if it was classified as differentiated by all 5 methods. For the bioactive screen, each compound was screened at 1 dose. 3,232 compounds did not score across any of the 5 scoring metrics. The number of compounds scoring is indicated above each histogram bar. In the shRNA screen, the fraction of hairpins that scored for each gene is depicted. For the kinase inhibitor small-molecule screen, compounds were pinned at multiple concentrations. The number of chemical wells scoring across the number of indicated scoring algorithms is depicted. Four GSK-3 inhibitors scored across all 5 methods. The number of doses that scored is indicated in parentheses. GSK-3b i I, GSK-3b Inhibitor I; GSK-3b i VI, GSK-3b Inhibitor VI.

Chemical inhibition of GSK-3 induces differentiation in AML cell lines. Compounds identified across the chemical screens included the pan-GSK-3 inhibitors: indirubin-3′-monoxime, kenpaullone, Ro 31-8220, staurosporine, GSK-3b Inhibitor I, and GSK-3b Inhibitor VI. Neither cytosine arabinoside nor daunorubicin, 2 cytotoxics currently used in AML therapy, scored in the primary screen, suggesting that the GE-HTS differentiation assay is not merely a measurement of toxicity. We next confirmed that these small molecules induce the differentiation signature by GE-HTS in a dose-dependant manner (Supplemental Figure 1; supplemental material available online with this article; doi: 10.1172/JCI46465DS1). In order to further validate the results of this small-molecule library screen, we tested 2 commonly used pan-GSK-3 inhibitors not in the original screen: a tool compound SB216763 (an ATP competitive inhibitor of GSK-3) and lithium chloride (LiCl) (a direct inhibitor that indirectly enhances N-terminal phosphorylation of the inhibitory phosphorylation sites). For subsequent experiments, we chose to focus on these 2 pan-GSK-3 inhibitors: SB216763, because of its frequent use in other studies, and LiCl, because of its clinical relevance. Although the compounds inactivate GSK-3 by different mechanisms, neither is isoform specific, and both the GSK-3α and GSK-3β isoforms are inhibited. We evaluated 6 AML cell lines after 3 days of treatment with SB216763 and LiCl and observed induction of the differentiation signature in a dose-dependant manner across all cell lines (Figure 2A). In addition, we evaluated morphologic changes in U937, MOLM-14, THP-1, and HL-60 cells. We observed changes consistent with macrophage-like differentiation, with a decreased nuclear-to-cytoplasmic ratio, condensation of the nucleus, and cytoplasmic ruffling (Figure 2B and Table 1). There was also evidence of myeloid maturation, as measured by an increase in expression of the surface marker CD11b (Figure 2C). We noted that these AML cell lines represent both mixed-lineage leukemia–rearranged (MLL-rearranged) and MLL wild-type disease. As in the recent report by Wang et al., we also failed to see specificity of response in a viability-based assay for MLL-rearranged human AML (Supplemental Figure 2 and ref. 13). These results indicate that pan-GSK-3 inhibition induces myeloid maturation in AML cells in vitro, independent of the presence of the MLL rearrangement.

Figure 2

Pan-GSK-3 inhibition induces AML differentiation in cell lines in vitro. (A) AML cell lines were treated with SB216763 versus DMSO and LiCl versus NaCl for 3 days. The differentiation score (summed score), as determined using the GE-HTS assay, is depicted. 1 μM ATRA was used as the positive control. Red indicates signature induction. (B) May-Grunwald Giemsa staining of AML cell lines after 3 days of LiCl treatment demonstrates cellular differentiation compared with that in NaCl-treated controls. Images were acquired by light microscopy under oil with an Olympus BX41 microscope and Q-capture software (original magnification, ×1,000). (C) Histograms depicting an increase in CD11b staining by flow cytometry with 20 mM LiCl versus 20 mM NaCl after 5 days of treatment.

Table 1

Differentiation in LiCl-treated AML cell lines

GSK-3 inhibition differentiates primary patient blasts in vitro. Because small-molecule treatment of cell lines does not always recapitulate the effects of treatment of primary patient cells, we extended testing of pan-GSK-3 inhibitors to primary patient AML blasts. In Figure 3, we demonstrate the induction of the differentiation score by GE-HTS as well as the corresponding morphologic changes after 3 days of LiCl treatment across multiple primary samples in vitro (Figure 3 and Supplemental Table 1). There was a response to at least 1 dose of LiCl in 7 out of 10 samples and a dose response in 4 out of 7 samples. Additionally, 3 out of 10 samples responded with a dose response to SB216763 (Supplemental Figure 3A). A 200-count differential for a subset of the patients is listed in Supplemental Figure 3B.

Figure 3

Pan-GSK-3 inhibition induces AML differentiation in primary patient blasts in vitro. (A) Histograms depicting the differentiation score (summed score) for 7 primary patient samples treated in vitro for 3 days with LiCl. Error bars represent the mean ± SD of 5 replicates. *P < 0.05, **P < 0.01, ***P < 0.001 by 1-way ANOVA using Tukey’s multiple comparison test. (B) May-Grunwald Giemsa staining of primary patient blasts at day 3 after treatment with LiCl demonstrates monocyte/macrophage-like cellular differentiation compared with that in the NaCl-treated controls. Images were acquired by light microscopy under oil with an Olympus BX41 microscope and Q-capture software (original magnification, ×1,000). No cytospins were available for patient 2.

Loss of GSK-3α leads to differentiation of AML cells in vitro. Currently available small-molecule inhibitors of GSK-3 target both the α and β isoforms. In order to dissect whether simultaneous inhibition of both isoforms was necessary for the signature induction in human AML cells in vitro, we used an shRNA screen targeting the kinome. In both our primary and secondary shRNA screens, GSK-3α emerged as one of the top hits in 2 AML cell lines, HL-60 and U937 (Figure 1). We used a consensus classification system, with 5 scoring metrics to identify candidate shRNAs (9). In order to be considered a hit, the shRNA needed to score across all 5 scoring metrics. Two out of five shRNAs against GSK-3α scored in HL-60 cells and 4 out of 5 shRNAs against GSK-3α scored in U937 cells in the secondary screen. shRNAs against the GSK-3β isoform did not score highly in the primary screen and thus were not evaluated in the secondary screen. However, given the high degree of homology between the GSK-3α and GSK-3β isoforms, we confirmed that the shRNAs in the library had specificity for their respective isoforms and that the relative degree of knockdown was similar (Supplemental Figure 4, A and B, and Supplemental Table 2). In order to confirm the results of the screen, we cross-validated these findings in 2 additional AML cell lines, MOLM-14 and THP-1, each of which expresses an MLL rearrangement. Loss of GSK-3α across all 4 AML cell lines induced differentiation, as measured by changes in gene expression and alteration of cellular morphology (Figure 4, A and B). Genome-wide expression profiling was then performed, comparing the effects of shRNAs targeting GSK-3α or GSK-3β with the effects of a scrambled control shRNA (shControl). Two shRNAs for each isoform were evaluated in 4 AML cell lines (HL-60, MOLM-14, THP-1, and U937). GSK-3α suppression was enriched for gene sets associated with monocyte differentiation, which were generated from a comprehensive gene expression data set profiling cord blood samples sorted for various stages of hematopoietic differentiation (Figure 4C and ref. 19). GSK-3β suppression, in contrast, was not enriched for the same gene sets. The effects of loss of GSK-3α on differentiation were further characterized in U937 and MOLM-14 cells; GSK-3α–specific shRNAs induced expression of the myeloid maturation marker CD11b (Figure 4D).

Figure 4

Loss of GSK-3α in AML induces differentiation in vitro. (A) Isoform-specific GSK-3–directed shRNAs or a control shRNA were introduced into 4 AML cell lines, and the differentiation score (summed score) was determined using the GE-HTS assay. Red indicates induction of the differentiation score. (B) Morphology was evaluated 7 days after infection with May-Grunwald Giemsa staining of cytospin preparations. Images were acquired by light microscopy under oil with an Olympus BX41 microscope and Q-capture software (original magnification, ×1,000). (C) Four AML cell lines were profiled on Affymetrix expression arrays with shRNAs directed against GSK-3α or GSK-3β in comparison to a scrambled control shRNA (shCTRL), and GSEA was performed. Enrichment plots are shown for GSK-3α and GSK-3β knockdown in gene sets associated with monocyte maturation. FDR, false discovery rate; Monocyte up, upregulated in monocytes; Monocyte down, downregulated in monocytes. (D) Loss of GSK-3α leads to an increase in CD11b expression by flow cytometry 6 days after infection.

Loss of GSK-3α decreases AML cell growth and proliferation, induces apoptosis in vitro, and attenuates colony formation in methylcellulose. When cells differentiate they often have decreased proliferation, undergo cell cycle arrest, and ultimately undergo apoptosis. Suppression of GSK-3α with 4 shRNAs inhibited proliferation, as measured by decreased BrdU incorporation, with a corresponding decrease in cell number over a time course compared with a control shRNA (Figure 5, A and B). Multiple GSK-3α–specific shRNAs also induced a G₁ cell cycle arrest and apoptosis and significantly impaired colony formation (Figure 5, C and D, and Figure 6A). Ectopic expression of a GSK3A cDNA immune to the effects of the shRNA rescued this phenotypic alteration on colony formation, suggesting that the effects of the shRNA were on-target for GSK-3α (Figure 6B). A decrease in colony formation with GSK-3β–targeting shRNAs was also observed (Figure 6A).

Figure 5

Phenotypic alterations associated with GSK-3α loss in MOLM-14 and U937 cells. (A) Cell number, as measured by trypan blue exclusion. Shown are the mean ± SD of 3 replicates. *P < 0.001 by 2-way repeated-measures ANOVA, with a Bonferroni post-hoc test comparing each shRNA to the shControl. The effects of GSK-3α suppression on (B) BrdU incorporation, (C) cell cycle, and (D) apoptosis are depicted. In B, the mean ± SD of 3 biological replicates is shown on the left, and a representative experiment is shown on the right. *P < 0.001 by 1-way ANOVA with Tukey’s multiple comparison test. In C and D, representative experiments of 2 biological replicates are shown. In D, numbers represent percentages of cells in each quadrant.

Figure 6

Isoform-specific loss of GSK-3 impairs colony formation in methylcellulose. (A) AML cell lines were infected with isoform-specific GSK-3–directed shRNAs or a control shRNA and plated in methylcellulose. The graph illustrates the relative number of colonies as a ratio to that in shControl. Data represent mean ± SD of 2 biological replicates. *P < 0.01, ^#P < 0.001 by 1-way ANOVA using Tukey’s multiple comparison test. (B) In MOLM-14 cells, overexpression of a GSK-3α cDNA immune to the effects of the 3′ UTR–directed hairpin shGSK3A_6 rescues the colony formation phenotype caused by GSK-3α knockdown. Data represent mean ± SD of 2 biological replicates. Significance was calculated by Student’s t test. The immunoblot demonstrates GSK-3α overexpression and loss of the endogenous protein with shGSK3A_6.

In vivo testing of GSK-3 inhibition in AML. In vitro studies provide valuable preclinical information, but they do not fully recapitulate response in the bone marrow microenvironment. One challenge in the direct application of pan-GSK-3 inhibitors, such as LiCl, to AML therapy is that these molecules induce stabilization of β-catenin (Supplemental Figure 4, C and D), which has been reported to promote AML self-renewal (20). Indeed, a recent study by Yeung et al. demonstrated the importance of concurrent knockdown of β-catenin with pan-GSK-3 inhibitor treatment in a mouse model of AML in order to effectively target AML-initiating cells (21). We observed that cells grown short-term in 384-well format, as in the GE-HTS screens, were not dependent on β-catenin, whereas cells grown in a more physiologically relevant ex vivo assay (i.e., methylcellulose) were dependent (Supplemental Figure 5). Because isoform-specific loss of GSK-3 does not promote stabilization of β-catenin, an alternative strategy would be isoform-selective inhibition (22).

We first confirmed that loss of GSK-3α did not stabilize β-catenin in AML cells in vitro (Supplemental Figure 4A). In addition, loss of GSK-3α did not activate TCF, a downstream effector of nuclear β-catenin, nor did it enrich for gene sets correlated with WNT or CTNNB1 in genome-wide expression analysis by Gene Set Enrichment Analysis (GSEA) (Supplemental Figure 6).

We next extended testing of GSK-3 isoform-specific inhibition to a human AML orthotopic xenograft model in which U937 cells were labeled with luciferase (U937-LucNeo) and propagated in NOD-SCID IL2Rγ^null (NSG) xenografts. In our first study, loss of GSK-3α (shGSK3A_5) impaired progression of this U937 orthotopic xenograft, as measured in vivo by both bioluminescence and spleen weight (Figure 7, A and B). Using bioluminescence as a surrogate marker for disease burden, we next evaluated the effects of 2 additional shRNAs (shGSK3A_8 and shGSK3A_9) targeting the GSK-3α isoform. Both demonstrated statistically significant anti-AML activity (Figure 7C). In an independent experiment looking at the end point of survival, animals inoculated with U937-LucNeo cells expressing a fourth GSK-3α–directed shRNA (shGSK3A_6) had a survival advantage (Figure 7D). Thus, inhibition of GSK-3α impaired engraftment and impacted survival in vivo. In these studies, 1 out of 4 GSK-3β–directed shRNAs (shGSK3B_2) tested demonstrated statistically significant impairment of AML in vivo (Figure 7D and Supplemental Figure 7).

Figure 7

GSK-3α loss has anti-AML activity in vivo. (A) U937-LucNeo cells infected with pLKO.1 vectors against GSK-3α (shGSK3A_5) and lacZ (shControl_2) were injected on day 0. Bioluminescence was quantified as a measure of disease burden. Data are represented as mean ± SEM of 6 mice per cohort. ***P < 0.001 by 2-way repeated-measures ANOVA, with a Bonferroni post-hoc test comparing shGSK3A_5 with shControl_2. (B) Spleen weights were measured on day 20 after injection. There were 5 mice in the shControl_2 cohort and 6 mice in the shGSK3A_5 cohort. Each symbol represents an individual mouse, and horizontal bars represent mean values. **P < 0.01 by Student’s t test. (C) U937-LucNeo cells infected with pLKO.1 vectors against GSK-3α (shGSK3A_8 and shGSK3A_9) and lacZ (shControl_2) were injected on day 0. Bioluminescence was quantified as a measure of disease burden. Data are represented as mean ± SEM of 6 mice per cohort. **P < 0.01, ***P < 0.001 by 2-way repeated-measures ANOVA, with a Bonferroni post-hoc test comparing each shRNA to shControl_2. (D) Survival is shown for mice engrafted with U937-LucNeo cells infected with pLKO.1 vectors against GSK-3α (shGSK3A_6), GSK-3β (shGSK3B_1), and lacZ (shControl_2). Mice were engrafted on day 0. There were 6 mice in each cohort, with statistical significance calculated by log-rank test. *P < 0.05, shGSK3A_6 survival curve relative to shControl_2 survival curve.

AML remains the most common form of acute leukemia in adults and the second most common form in children. Although advances have been made in predicting outcome based upon molecular features, there has been little progress in the last decade in altering cure rates. One exception is the successful treatment of patients with APL, defined by the PML-RARα rearrangement, with ATRA differentiation therapy. To this end, we sought to identify alternative candidate targets for AML differentiation. We leveraged new genomic approaches for cancer discovery with the integration of signature-based small-molecule library screening and high-throughput shRNA screening. Multiple pan-GSK-3 inhibitors scored in 2 chemical screens and were confirmed to both induce differentiation and to alter cell viability in AML. We next turned to the shRNA screen to further detail the role of GSK-3. In this screen, shRNAs specifically directed against GSK-3α scored highly.

GSK-3 is a constitutively active serine threonine kinase that inhibits the Wnt and hedgehog pathways while activating NF-κB. GSK-3 has been reported to play a role in normal cellular homeostasis, including hematopoiesis (23, 24), as well as in a diversity of human diseases, such as Alzheimer disease (25), non-insulin-dependent diabetes mellitus (26), and bipolar disorder (27). More recently, and somewhat surprisingly, GSK-3 has emerged as a target in hematological malignancies. For example, GSK-3β missplicing, leading to an increase in β-catenin and enhanced serial replating, was reported in the granulocyte-macrophage progenitors in blast crisis chronic myelogenous leukemia (14). Chemical inhibition of GSK-3 was also reported to induce apoptosis in chronic lymphocytic leukemia and in multiple myeloma (15, 28, 29). Moreover, GSK-3 was initially reported as a target in MLL-rearranged acute leukemia and then more broadly in hematopoietic cells transformed by HOX genes by promoting the conditional association of CREB and its coactivators CBP and TORC with MEIS1 (13, 17). Interestingly, in our genome-wide expression studies, GSK-3α–specific loss was anticorrelated with gene sets generated by the coexpression of HOXA9 and MEIS1 (Supplemental Figure 8). Whether GSK-3α specifically promotes CREB association warrants further study. Our genome-wide expression studies also point to the downregulation of a MYC program as another potential mechanism linking the loss of GSK-3α with induction of AML differentiation. Multiple MYC gene expression signatures are downregulated with loss of GSK-3α by GSEA (Supplemental Figure 9). Several studies implicate downregulation of MYC as a mechanism to promote AML differentiation, including a recent study by Zuber et al. reporting that the loss of MYC expression by small-molecule inhibition of the bromodomain-containing 4 protein (BRD4) leads to AML differentiation (30).

While these results point to GSK-3 as a target in leukemia, there are some concerns about targeting GSK-3 in AML with available pan-GSK-3 small-molecule inhibitors. In its active state, GSK-3 inhibits the Wnt pathway by phosphorylating β-catenin, marking it for proteasomal degradation. When GSK-3 is pan-inhibited, β-catenin is stabilized, enters the nucleus, and initiates its transcriptional programs (11, 22, 31, 32). Recently, Wang et al. demonstrated that reactivation of β-catenin signaling is required for the transformation of progenitor cells by certain oncogenes, including HOXA9, MEIS1, and MLL (20). Thus, stabilization of β-catenin with pan-GSK-3 inhibition may promote self-renewal in some AML progenitor cells. Interestingly, the pan-GSK-3 inhibitor lithium was used in the 1970–1980s in clinical trials in an attempt to decrease the time of neutropenia, based on the observation that lithium increased neutrophil counts in patients being treated for psychiatric disease (33–36). In a clinical trial, patients with AML treated with cytosine arabinoside and daunorubicin were randomized to receive lithium versus a placebo. The incidence of complete remission was actually significantly lower in patients receiving lithium (75% versus 49%; P = 0.012) (37). Because the doses of lithium needed to achieve maximal effect in vitro are supratherapeutic, it is possible that more potent agents would have greater activity in vivo. The potential leukemogenic effect of increasing β-catenin, however, is still a concern, even with more potent pan-GSK-3 inhibitors. In a recent report, Yeung et al. demonstrate the dependency of the MLL leukemic stem cell (LSC) compartment on β-catenin (21). They also demonstrate that the MLL LSCs are resistant to the effects of GSK-3 pan-inhibition, supporting the inadequacy of lithium as a single-agent therapy in some molecular subtypes of AML. The LSCs become sensitized to lithium in the presence of β-catenin knockdown, suggesting that pan-GSK-3 inhibition in combination with inhibition of Wnt signaling is a potential therapeutic strategy in AML (21).

An alternative, more parsimonious approach to translating our findings to the clinic might be to develop a GSK-3 isoform-specific inhibitor. Although 90% homologous in their kinase domains, the α and β isoforms of GSK-3 are not functionally redundant (11, 12, 32). For example, GSK-3α knockout mice show signs of metabolic and neuronal developmental abnormalities (38, 39), while the GSK-3β knockout is lethal late in gestation due to hepatic apoptosis, a phenotype similar to that seen in the NF-κB knockout (40, 41). However, both isoforms of GSK-3 need to be inhibited for β-catenin stabilization. Doble et al. have demonstrated through the generation of an allelic series of mouse embryonic stem cell (ESC) lines with 0 to 4 functional GSK-3 alleles that specific loss of the GSK-3α or GSK-3β alleles did not lead to an increase in β-catenin. It was only when 3 out of the 4 GSK-3 alleles were lost that a rise in β-catenin levels was observed. GSK-3α/β double-knockout ESCs displayed hyperactivated Wnt/β-catenin signaling, and their ability to differentiate was impaired (22). Moreover, as demonstrated in this report, genetic loss of either the α or β isoform in human AML does not lead to β-catenin stabilization, whereas pan-inhibition does (Supplemental Figures 4 and 6).

Although much of the literature has focused on the GSK-3β isoform, we focused on GSK-3α because it scored highly in our shRNA AML differentiation screen. Inhibition of GSK-3α with multiple shRNAs induced differentiation, as measured by induction of the GE-HTS differentiation signature, expression of myeloid surface markers, genome-wide expression changes consistent with differentiation, and induction of morphological characteristics of monocyte/macrophage-like maturation in vitro. There was also impaired cell growth and proliferation, induction of cell cycle arrest and apoptosis, and loss of colony formation in methylcellulose. Moreover, 4 GSK-3α–specific shRNAs led to a decrease in disease burden and/or prolonged survival in an in vivo model. While we hypothesize that the AML cells undergo differentiation and then ultimately arrest, as is described in APL with ATRA therapy (42), another possibility is that there is a heterogeneous response to GSK-3α inhibition, with one population undergoing differentiation and another undergoing apoptosis. Interestingly, recent studies reported a surprising role for GSK-3α inhibition in cardiomyocyte differentiation (43) and a role for GSK-3α expression in protection against apoptosis with ischemic injury to the heart (44).

In treating patients with cancer, we are in transition from an era dominated by the use of cytotoxic chemotherapy to an era with the hope of “targeted” therapy. Our results support the further refinement of potential drug targets to the granular level of isoform specificity. Indeed, with the recognition of the nonoverlapping roles of phosphoinositide 3-kinase (PI3K) isoforms in malignancy, the development of PI3K isoform-specific inhibitors is already well underway (45). To our knowledge, no GSK-3α–specific small-molecule inhibitors have been described and a reported GSK-3β–selective molecule is not yet commercially available (46). Given the high degree of homology of the kinase domains of the α and β isoforms, one potential strategy would be to develop an allosteric small-molecule inhibitor of GSK-3α or GSK-3β. While our studies demonstrate a more potent effect with shRNA directed against GSK-3α compared with that directed against GSK-3β, there are several possible explanations for this observation. Though our immunoblot quantification suggests near equivalent relative protein knockdown, such measurements are notoriously imprecise. Thus, subtle differences in knockdown could contribute to a difference in the observed phenotype, or the full phenotypic effect may require complete loss of GSK-3β. Moreover, the slower kinetics of loss of GSK-3 with shRNA may alter the phenotype observed compared with that of a small-molecule inhibitor, and the loss of the protein may induce a phenotype different from loss of its enzymatic activity. Indeed, it will be important to test both GSK-3α– and GSK-3β–selective small-molecule inhibitors as these molecules are characterized.

In summary, these studies suggest a role for GSK-3α in AML differentiation and support a potential role for GSK-3α–directed targeted therapy. Moreover, they illustrate the strength of integrating multiple orthogonal screens toward cancer target identification.

View Supplemental data

We thank Serena Silver, Jen Grenier, and David Root for RNAi screening guidance and the Chemical Biology Platform at the Broad Institute. We thank all of the patients who contributed invaluable primary samples. We thank Alexandre Puissant, Annie Carlton, and Jacob Berchuck for their technical advice and assistance. This work was supported by the National Cancer Institute (R01 CA140292) (to K. Stegmaier), the American Cancer Society (to K. Stegmaier), an ASH Fellow Scholar Award in Clinical/Translational Research (to K. Stegmaier), Howard Hughes Medical Institute (to K. Stegmaier and L.S. Li), a Smith Family New Investigator Award supported by the Richard Allan Barry Fund at the Boston Foundation (to K. Stegmaier), CancerCare Manitoba (to V. Banerji), CancerCare Manitoba Foundation (to V. Banerji), the University of Manitoba (to V. Banerji), and the Terry Fox Foundation through an award from the National Cancer Institute of Canada (to V. Banerji).

Address correspondence to: Kimberly Stegmaier, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, Massachusetts 02215, USA. Phone: 617.632.4438; Fax: 617.632.4850; E-mail: kimberly_stegmaier@dfci.harvard.edu.

Conflict of interest: The authors have declared that no conflict of interest exists.

Reference information: J Clin Invest. 2012;122(3):935–947. doi:10.1172/JCI46465.

Versha Banerji’s present address is: Manitoba Institute of Cell Biology and CancerCare Manitoba, Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.

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