Abstract

CRISPR–Cas9-based genetic screens have successfully identified cell type–dependent liabilities in cancer, including acute myeloid leukemia (AML), a devastating hematologic malignancy with poor overall survival. Because most of these screens have been performed in vitro using established cell lines, evaluating the physiologic relevance of these targets is critical. We have established a CRISPR screening approach using orthotopic xenograft models to validate and prioritize AML-enriched dependencies in vivo, including in CRISPR-competent AML patient-derived xenograft (PDX) models tractable for genome editing. Our integrated pipeline has revealed several targets with translational value, including SLC5A3 as a metabolic vulnerability for AML addicted to exogenous myo-inositol and MARCH5 as a critical guardian to prevent apoptosis in AML. MARCH5 repression enhanced the efficacy of BCL2 inhibitors such as venetoclax, further highlighting the clinical potential of targeting MARCH5 in AML. Our study provides a valuable strategy for discovery and prioritization of new candidate AML therapeutic targets.

Significance: There is an unmet need to improve the clinical outcome of AML. We developed an integrated in vivo screening approach to prioritize and validate AML dependencies with high translational potential. We identified SLC5A3 as a metabolic vulnerability and MARCH5 as a critical apoptosis regulator in AML, both of which represent novel therapeutic opportunities.

Introduction

Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by the accumulation of abnormal myeloblasts. Despite the efficacy of chemotherapy and stem cell transplantation for some patients, cure rates for AML remain between 35% and 40% overall and less than 15% for older adults (1). Continued efforts are needed to identify new therapeutic strategies for these patients.

The successful adaptation of CRISPR–Cas9 approaches for genetic screens has become a powerful tool for the unbiased discovery of essential genes in mammalian cells (2, 3). First-generation, large-scale functional genomic screens to identify the critical genes involved in cancer cell maintenance have been completed, such as the Broad Institute's and Sanger Center's Cancer Dependency Maps (DepMap; https://depmap.org/; refs. 4, 5). These efforts have revealed hundreds of potential genetic vulnerabilities in AML cells in vitro. How to distinguish candidates with the highest translational potential, however, remains a challenge. Therefore, a secondary functional validation approach is necessary to prioritize those gene targets for therapeutic targeting to guide the development of new antileukemia treatments.

These large-scale genetic screens were primarily performed in vitro. Thus, one important consideration in prioritizing candidate genes is to evaluate their essentiality in a proper in vivo microenvironment because the niche may influence the physiologic behavior of cancer cells. Human AML orthotopic disease modeling is highly physiologically relevant, as AML cells will engraft in the bone marrow microenvironment in the mouse. In vivo CRISPR screening has been performed to identify tumor growth modulators in several genetically engineered mouse models of hematologic malignancies (6–8). However, the feasibility of such an application in human AML orthotopic xenograft models has not been demonstrated. Therefore, we optimized a protocol for CRISPR screening in orthotopic xenograft models to enable the systematic evaluation of the physiologic relevance of top AML dependencies emerging from genome-scale CRISPR–Cas9 in vitro screens.

Established AML cell lines are amenable to genome-scale screens; however, they cannot fully recapitulate all pathophysiologic aspects of the disease. Target validation directly in primary patient samples is desirable yet not readily accessible. Instead, the AML patient-derived xenograft (PDX) has emerged as a valuable preclinical model that largely reflects the molecular and phenotypic characteristics of the primary disease (9, 10). To interrogate the translational relevance of the targets identified from established cell lines, we developed AML PDX models amenable to genome editing. By combining in vivo screening and CRISPR-competent PDX models, we devised an integrated pipeline to prioritize AML dependencies and investigated the top novel targets emerging from this approach.

ResultsIn Vivo CRISPR Screens Using Xenograft Models of Human AML

To identify AML-enriched dependency genes, we explored the DepMap Avana CRISPR–Cas9 screen dataset and selected the genes that AML cells are more dependent on for growth compared with the other cancer types included in the screen. This gene set was intersected with additional AML in vitro screen datasets, including the combined Broad Institute and Novartis short hairpin RNA (shRNA) screens and a focused in vitro CRISPR screen in AML cell lines (11, 12). These 200 top-ranked AML-enriched gene dependencies were involved in various biological pathways, such as chromatin and transcriptional regulation, metabolism, and mitochondria organization (Supplementary Fig. S1A and S1B). To distinguish the on-target from off-target antiproliferative effects caused by CRISPR-mediated DNA cutting in amplified regions (13, 14), we designed three targeting single-guide RNAs (sgRNA) and three intronic control sgRNAs for each gene. With 120 additional negative control sgRNAs, a focused library with 1,320 sgRNAs was constructed (Fig. 1A).

Figure 1.

In vivo CRISPR screens prioritize genetic dependencies in human AML. A, Schematic of the library design. B, Schematic of in vitro and in vivo CRISPR screening approach. C, Scatter plots showing the correlation of relative abundance of sgRNAs in bone marrow (BM) versus spleen (SPL). Data points representing negative control sgRNAs (black solid circles) are indicated. D, Venn diagram showing the number of in vivo hits scoring in bone marrow of each AML model. E, Scatter plots showing the in vitro and in vivo depletion scores of SLC5A3 and MARCH5 at a gene level in three AML models. Data points representing the median value of each intronic sgRNA set are indicated (black hollow circles). Scores of SLC5A3 and MARCH5 (blue and red solid circles) and their intronic controls (blue and red hollow circles) are highlighted.

To better evaluate the therapeutic potential of candidate genes, we sought to investigate their in vivo essentiality not only in established AML cell lines but also in PDX models, argued to be the most faithful to primary human disease (11). CRISPR-mediated genetic studies in PDXs have been challenging due to the poor transduction efficiency and limited growth in vitro. To develop PDX models that are tractable for CRISPR editing, we screened a cohort of PDX samples and identified those transducible and suitable for short-term in vitro culture (Supplementary Table S1). These PDX cells were transduced with lentivirus coexpressing Cas9 and a fluorescent protein (GFP or mCherry). Cas9-expressing PDX cells were purified based on fluorescence and expanded via serial transplantation into immunodeficient NSGS (NOD scid gamma SGM3) mice (Supplementary Fig. S2A and S2B). Cas9 activity was assessed using a fluorescent protein (mAmetrine)–linked sgRNA targeting CD33. More than 80% of PDX cells receiving the sgRNA became CD33 negative, indicating that a high Cas9 activity can be achieved in these models (Supplementary Fig. S2C).

Next, to ensure sufficient library representation in vivo, we optimized the screening conditions through barcoding experiments using a library of 3,152 barcodes. Five to 10 million barcoded MV4-11 cells were injected via tail vein into NSGS mice. Sublethal irradiation was necessary for improved barcode representation in bone marrow and reduced mouse-to-mouse variation (Supplementary Fig. S2D and S2E). Although the barcode distribution was skewed in individual mice, even with irradiation, a complete and balanced library representation could be recovered by combining readouts from multiple mice (Supplementary Fig. S2F and S2G).

With all conditions optimized, we then performed parallel in vivo and in vitro screens using Cas9-expressing MV4-11 and U937 cell lines, as well as PDX16-01(CALM–AF10 fusion, NF1, PHF6, and TP53 mutations; Fig. 1B). AML cells were transduced with the screen library in duplicate and selected for 2 days with puromycin when an aliquot of cells was collected as the input reference. We then injected 10 million cells per mouse by tail vein into four to five irradiated mice per replicate and in parallel cultured an aliquot of cells from each replicate in vitro. In vitro cultures were harvested 2 (for MV4-11) or 3 (for U937 and PDX16-01) weeks later, and the bone marrow and spleens were collected when the mice displayed signs of overt disease, such as hindlimb paralysis and dyspnea, with high leukemic engraftment (Supplementary Fig. S3A and S3B). There was strong replicate reproducibility for both the in vitro and in vivo results, although the data generated from U937 displayed a smaller dynamic range likely due to weaker Cas9 activity (Supplementary Fig. S3C). The average relative abundance of each sgRNA in the output compared with the input samples was determined. Both the abundance and depletion of individual sgRNA in the bone marrow versus the spleen were strongly correlated (Fig. 1C; Supplementary Fig. S3D); therefore, we focused on the bone marrow data for the downstream analysis.

We calculated a normalized depletion score for each sgRNA (see Supplementary Methods and Supplementary Table S2). The median value of each set of three sgRNAs was used to represent the score of the corresponding gene. Using the intronic guide population as a null distribution, we defined hits for each model (Supplementary Table S3). In vitro and in vivo hits were generally well correlated. However, a modest number of targets did not score well in vivo, with a few targets displaying in vitro versus in vivo discrepancies in multiple models (Supplementary Fig. S3E and S3F). These results underscore the importance of an in vivo validation strategy for refining the hits emerging from a primary in vitro screen. Notably, many genes were confirmed as hits in PDX16-01 in vivo and overlapped with those validating in the MV4-11 and U937 models (Fig. 1D), lending support to the relevance of using AML cell lines for dependency identification. Gene Ontology analysis showed an enrichment of metabolism- and mitochondria-associated pathways in all three models (Supplementary Fig. S4A and S4B), consistent with recent findings that AML cells rely on unique metabolic and mitochondrial properties for survival (15, 16). In addition, several hematopoietic lineage–related transcription factors appeared to be strong in vivo dependencies (Supplementary Fig. S4C), corroborating recent studies targeting transcriptional vulnerabilities in AML (17, 18). Further supporting the validity of our screen, the transcription factors KMT2A (also called MLL, mixed lineage leukemia gene) and ZFP64 specifically scored in MV4-11, which is driven by an MLL fusion oncogene. ZFP64 is required to sustain expression of this fusion (Supplementary Fig. S4C; ref. 19). Altogether, our screen provided an informative list of AML targets with high physiologic relevance (Supplementary Table S3).

Next, we focused on targets that previously had not been described as AML dependencies and ranked highly as in vivo hits in all three models in the bone marrow: the sodium/myo-inositol cotransporter SLC5A3 and the mitochondria-localized RING-type ubiquitin E3 ligase MARCH5 (Fig. 1E; Supplementary Fig. S5A; refs. 20, 21). SLC5A3 and MARCH5 also displayed strong depletion scores in the data from spleen in the MV4-11 and PDX16-01 models and a modest depletion score in the U937 model (Supplementary Fig. S5B). We re-mined the latest edition of the DepMap CRISPR screening datasets, which continue to be expanded, and confirmed that SLC5A3 and MARCH5 were indeed strong dependencies in most AML cell lines, displaying a more essential role in AML compared with other cancer types (Supplementary Fig. S5C; Supplementary Table S4). We therefore selected these two targets for further validation as novel therapeutic opportunities for AML.

SLC5A3 Is Required for Sustaining AML Growth

SLC5A3 belongs to the solute carrier family, and among all five solute carrier family members included in our screen library, SLC5A3 was the top scoring (Supplementary Fig. S6A). We validated this dependency in several AML cells lines and PDX models using two independent SLC5A3-targeting sgRNAs. SLC5A3 depletion suppressed the growth of AML cells as demonstrated by an in vitro competition assay (Fig. 2A and B). As a validated SLC5A3 antibody was unavailable, we confirmed the genomic editing of the SLC5A3 locus by Sanger sequencing and the Inference of CRISPR Edits (ICE) analysis, with a high editing efficiency achieved (Supplementary Fig. S6B–S6D). The on-target effect was further supported by using a CRISPR-resistant SLC5A3 cDNA to rescue the growth defect (Fig. 2C). The cellular alterations after SLC5A3 deletion were examined. Interestingly, SLC5A3 inhibition disturbed cell-cycle distribution in a cell context–dependent manner, including reduced S phase and increased sub-G1, G1/G0, or G2 phase (Supplementary Fig. S6E). SLC5A3 depletion did not promote obvious differentiation, as assessed by CD11b staining, except for PDX17-14 (MLL–AF10 fusion), which displayed an upregulation of CD11b expression associated with morphology changes consistent with differentiation (Supplementary Fig. S6F and S6G). By contrast, an upregulation of Annexin V and cleaved caspase 3 levels was consistently observed in all AML models tested, indicating that apoptotic cell death is a common consequence of SLC5A3 disruption (Fig. 2D and E).

Figure 2.

SLC5A3 is essential for AML growth. A and B, AML cell lines (A) and PDX models (B) were transduced with nontargeting sgRNA (sgNT) and SLC5A3 sgRNA (sgSLC-1 and sgSLC-2) vectors that coexpress GFP. Cell growth was evaluated in an in vitro competition proliferation assay as measured by the change in percentage of GFP+. C, Competitive growth of MV4-11 cells transduced with empty vector (Ctrl) or CRISPR-resistant SLC5A3 cDNA upon endogenous SLC5A3 knockout. For A–C, results represent mean + SD, n = 2. D and E, Flow cytometry analysis of Annexin V (D) and immunoblot analysis of full-length (fl) and cleaved (c) caspase 3 (E) in AML cells expressing the indicated sgRNAs at day 12 after transduction. For D, results represent mean + SD, n = 3. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001, determined by unpaired two-sided t test. F, Schematic of evaluating SLC5A3 dependency in vivo using PDX cells. NSGS mice were transplanted with PDX16-01 cells expressing doxycycline (Dox)-inducible sgNT or sgSLC-1. Doxycycline-containing food was delivered from day 7 posttransplantation. G, Human CD45 flow cytometry analysis to evaluate leukemia burden in bone marrow aspiration samples at 2 weeks after starting doxycycline-containing diet. Mean and SD were plotted; P value was calculated by unpaired two-sided t test. H, Survival curves of mice from G. The P value was calculated by log-rank test.

We next asked whether disruption of SLC5A3 posttransplantation can repress AML progression in vivo. PDX16-01 cells were transduced with a doxycycline-inducible CRISPR vector coexpressing GFP (22). Purified GFP+ cells were transplanted into NSGS mice, and the nontargeting control or SLC5A3-targeting sgRNA was induced 1 week later by serving a doxycycline-containing diet (Fig. 2F). Compared with the control group, mice receiving SLC5A3-knockout cells displayed significantly reduced leukemic burden as evaluated by bone marrow aspiration, as well as prolonged survival (Fig. 2G and H). Notably, a subset of the leukemia cells from the SLC5A3-knockout group was GFP negative at the time of disease progression, which was not observed in the control group (Supplementary Fig. S6H and S6I). In accordance, ICE analysis showed that only a minor population of these cells retained SLC5A3 locus editing (Supplementary Fig. S6J). Therefore, it is likely that AML cells escaping from SLC5A3 deletion outgrew and contributed to leukemia progression, emphasizing the essential role of SLC5A3 for AML progression in vivo.

SLC5A3 Transports Myo-inositol to Support AML Cell Proliferation

Myo-inositol and its derivatives are involved in several cellular processes. Because SLC5A3 is one of the major myo-inositol transporters, we investigated whether the growth defect caused by SLC5A3 inactivation resulted from the myo-inositol deficiency. A previous study in Slc5a3-knockout mice has shown that myo-inositol provided at supraphysiologic concentrations can bypass SLC5A3 to enter the cells, possibly via other low-affinity transporters (23). Standard culture medium contains around 0.3 mmol/L myo-inositol, similar to the level detected in human serum (24). Strikingly, with the addition of supplementary myo-inositol in the culture medium, the proliferation of SLC5A3-knockout cells was completely rescued (Fig. 3A). Of note, extra myo-inositol did not promote the growth of parental AML cells (Supplementary Fig. S7A). In accordance, depletion of the basal myo-inositol from the culture medium largely impeded the growth of parental AML cells, causing similar phenotypes as SLC5A3 deletion, with cell context–dependent alterations of cell cycle and induction of apoptotic cell death (Fig. 3B; Supplementary Fig. S7B and S7C). Together, these data reveal that myo-inositol is a critical metabolite for AML.

Figure 3.

Myo-inositol imported via SLC5A3 sustains AML. A, Competitive growth was evaluated for SLC5A3-knockout MV4-11 and U937 cells in regular culture medium, which contains ∼0.3 mmol/L myo-inositol (MI), or culture medium supplemented with extra MI at the indicated concentrations. sgNT, nontargeting sgRNA; sgSLC-1 and sgSLC-2, SLC5A3 sgRNA vectors. B, Cumulative cell growth of AML cells in myo-inositol–depleted medium with (MI+) or without (MI–) 0.3 mmol/L myo-inositol reconstituted. Results represent mean ± SD, n = 3. **, P < 0.01; ****, P < 0.0001, determined by unpaired two-sided t test. C, Scatter plots showing the linear correlation between CERES dependency scores of SLC5A3 and the expression level of SLC5A3 or ISYNA1 across AML cell lines (n = 26) or other cancer cell lines (n = 904) in the DepMap dataset. Each dot represents a cell line; the shaded area represents the 95% confidence level interval for the linear model. TPM, transcripts per kilobase million. D, Immunoblot analysis of ISYNA1 protein levels in AML cells. The M07e cell line was used as a control with high ISYNA1 expression. E, Immunoblot confirming the overexpression of ISYNA1 in the indicated AML cells. F, Competitive growth of AML cells with or without ISYNA1 overexpression upon SLC5A3 knockout. G, Immunoblot analysis of ISYNA1 in M07e cells transduced with sgNT or ISYNA1 sgRNAs. H, Competitive growth of cells in G was evaluated upon SLC5A3 deletion. For A, F, and H, results represent mean + SD, n = 2.

Because a subset of AML cell lines was not dependent on SLC5A3 based on the DepMap dataset (Supplementary Fig. S5C), we explored the potential biomarkers associated with SLC5A3 essentiality in AML. SLC5A3 was ubiquitously expressed in AML cell lines, and its expression was not correlated with its dependency. Intriguingly, however, the low expression of inositol-3-phosphate synthase 1 (ISYNA1) predicted a strong SLC5A3 dependency in AML cell lines (Fig. 3C). In addition to importing myo-inositol from the extracellular fluid, cells can also synthesize myo-inositol de novo from glucose 6-phosphate, and ISYNA1 encodes the rate-limiting enzyme in this myo-inositol biosynthesis pathway (25). Thus, we postulated that SLC5A3 becomes essential in AML cells with insufficient myo-inositol biosynthesis capacity. We confirmed the low expression of the ISYNA1 protein in AML cells sensitive to SLC5A3 deletion, and importantly, overexpression of ISYNA1 can completely relieve the SLC5A3 dependency (Fig. 3D–F). Moreover, knockout of ISYNA1 in the ISYNA1-high cell line M07e exacerbated the growth defect associated with SLC5A3 depletion (Fig. 3G and H). Altogether, these results strongly demonstrate that SLC5A3 is required for maintaining sufficient myo-inositol levels to support AML proliferation.

MARCH5 Loss Represses AML Cell Growth In Vitro and In Vivo

We next sought to validate the dependency of AML cells on MARCH5. Inactivating MARCH5 via either doxycycline-inducible CRISPR or shRNA systems induced a severe growth defect in various AML cell lines and PDX models (Fig. 4A and B; Supplementary Fig. S8A and S8B). The growth defect could be reversed by a CRISPR-resistant cDNA encoding wild-type MARCH5, proving the on-target effect. By contrast, MARCH5 mutations (H43W and C68S) that disrupt its RING domain and thus ubiquitinase function ablated the rescuing ability (26, 27), indicating the requirement for the catalytic function of MARCH5 in AML (Fig. 4C; Supplementary Fig. S8C and S8D). In addition, for MARCH5 validation, we deployed a dTAG system, which uses a hetero-bifunctional small molecule that binds the FKBP12F36V-fused target protein (i.e., MARCH5) and an E3 ligase complex (i.e., VHL), bringing the two in close proximity and leading to the ubiquitination and proteasome-mediated degradation of the target protein (Supplementary Fig. S8E; ref. 28). We were able to establish the MARCH5 dTAG degradation system in both AML cell line and PDX models in which we deleted endogenous MARCH5 by CRISPR and expressed exogenous FKBP12F36V-hemagglutinin (HA)–tagged MARCH5 protein at a physiologic level comparable to the endogenous one (Supplementary Fig. S8F). dTAG-MARCH5 cells displayed a similar basal proliferation rate and apoptosis compared with control cells expressing Cas9 only (Supplementary Fig. S8F–S8H). Similar to CRISPR deletion of MARCH5, MARCH5 degradation with the dTAG molecule dTAGV-1 markedly impaired cell growth (Fig. 4D and E).

Figure 4.

MARCH5 inhibition suppresses AML cell growth. A, MV4-11 cells were transduced with doxycycline (Dox)-inducible nontargeting sgRNA (sgNT) and MARCH5 sgRNA (sgM5-1 and sgM5-2) vectors that coexpress GFP. Immunoblot analysis of MARCH5 was performed on day 6 after doxycycline treatment (top). Cell growth was evaluated in a competition proliferation assay (bottom). B, Competitive growth of Cas9-PDX cells transduced with GFP-linked sgNT or sgM5-1. C, Immunoblot analysis of MV4-11 cells expressing an empty vector (Ctrl), CRISPR-resistant MARCH5 wild-type (WT), or ligase-defective mutant (H43W or C68S) cDNA (top). Competitive growth of these cells was evaluated upon endogenous MARCH5 knockout (bottom). D, Immunoblot analysis of FKBP-HA-MARCH5 with HA antibody in dTAG-MARCH5 AML cells treated with 500 nmol/L dTAGV-1 for 24 hours (NB4), 4 hours (PDX16-01), or 2 hours (PDX17-14). E, Competitive growth of dTAG-MARCH5 AML cells treated with DMSO (Ctrl) or 500 nmol/L dTAGV-1. For A–C and E, results represent mean + SD, n = 2. F, Schematic of in vivo competition assay with PDX cells. Mouse bone marrow cells were collected for evaluating end GFP+ percentage. G, Representative flow cytometry analysis of input and end GFP percentage of sgNT- or sgM5-1–expressing cells. H, Relative abundance of PDX cells expressing sgNT or sgM5-1, as calculated by normalizing end GFP percentage to input GFP percentage. The P value was calculated by unpaired two-tailed t test, n = 4. I, NSGS mice were transplanted with MV4-11 cells expressing doxycycline-inducible sgNT or sgM5-2. Doxycycline-containing food was served from day 4 posttransplantation. Representative bioluminescence images are shown on the indicated day posttransplantation. J, Quantification of serial bioluminescence imaging. The data were normalized to the baseline readout on day 3. n = 5; results represent mean ± SD. The P value was calculated using unpaired two-tailed t test with measurements on day 21. K, Survival curves of mice used in J. The P value was calculated by log-rank test.

MARCH5 dependency was further confirmed using an in vivo competition assay in a third PDX model (PDX68555 with an MLL–AF9 fusion and a FLT3 mutation). PDX cells expressing a GFP-linked MARCH5 sgRNA were depleted in NSGS mice, as evidenced by a dramatic reduction of the GFP+ fraction in engrafted cells. In contrast, the PDX cells expressing a nontargeting sgRNA were maintained (Fig. 4F–H). To confirm that the in vivo growth disadvantage of MARCH5-depleted cells is not caused by homing defects, we used MV4-11 cells expressing luciferase and doxycycline-inducible CRISPR directed against MARCH5. Doxycycline-mediated deletion of MARCH5 posttransplantation led to a marked attenuation of AML progression in NSGS mice as monitored by bioluminescence imaging, which translated to prolonged survival (Fig. 4I–K). We examined the MARCH5 sgRNA-expressing cells collected from the leukemic mice and found that the MARCH5 expression was partially restored as compared with the cells with MARCH5 knockout induced in vitro, supporting that loss of MARCH5 is incompatible with AML maintenance (Supplementary Fig. S8I). Collectively, these results demonstrate that targeting MARCH5 can suppress the progression of AML cells both in vitro and in vivo.

Differential MARCH5 Dependency in Healthy Human Hematopoietic Stem and Progenitor Cells Compared with AML Blasts

We next attempted to determine whether MARCH5 is required by healthy human CD34+ hematopoietic stem and progenitor cells (HSPC). Because of the low efficiency of lentiviral transduction of Cas9 into CD34+ HSPCs, we used the nucleofection of Cas9–sgRNA ribonucleoprotein complexes (RNP) to enable genome editing in these cells and evaluated their progenitor activity via colony formation assays (Fig. 5A). We first validated this approach using AML cells. RNPs containing MARCH5 sgRNA were introduced into NB4 and AML PDX cells; RNPs with sgRNA targeting a gene desert region in chromosome 2 (sgCHR2) or the common essential gene RPA3 were also included as negative and positive controls, respectively. A high genome editing efficiency was achieved for all RNPs (Supplementary Fig. S9A). As expected, MARCH5 deletion dramatically impaired the colony-forming capacity of AML cells, causing a more than 80% reduction of colony number and largely decreasing colony size (Fig. 5B; Supplementary Fig. S9B). We then evaluated CD34+ HSPCs derived from either bone marrow or umbilical cord blood and obtained comparable genome editing efficiency (Supplementary Fig. S9C). In contrast to AML cells, sgMARCH5-nucleofected CD34+ cells displayed only a 10% to 30% and 30% to 50% reduction of colony number in the erythroid and myeloid lineages, respectively (Fig. 5C). Colonies of the myeloid lineage were relatively more affected, with a more frequent appearance of smaller and/or less compacted colonies (Supplementary Fig. S9D). Nonetheless, these results highlight that healthy human HSPCs are less dependent on MARCH5 compared with AML cells, supporting the potential therapeutic window for MARCH5-targeted treatment.

Figure 5.

Human CD34+ HSPCs are less dependent on MARCH5 for colony formation compared with AML cells. A, Schematic of evaluating the essentiality of MARCH5 in colony formation of human HSPCs via nucleofection. B and C, Colony formation assays of AML cells (B) or CD34+ HSPCs (C) nucleofected with the indicated RNPs. CD34+ cells were derived from bone marrow (BM) or umbilical cord blood (UCB) from three independent donors. Results represent mean + SD, n = 3. *, P < 0.05; **, P < 0.01; ***, P < 0.001, determined by unpaired two-sided t test. sgM5-1 and sgM5-2, MARCH5 sgRNA RNPs.

MARCH5 Prevents Apoptosis in AML

MARCH5 inactivation in AML cells resulted in a slight reduction of S phase and increase in sub-G1 and G1/G0 phases (Supplementary Fig. S10A and S10B). Induction of the sub-G1 phase suggested the occurrence of cell death. Indeed, apoptosis was consistently observed and strongly induced in some models, as indicated by upregulated cleaved caspase 3 and Annexin V (Fig. 6A and B; Supplementary Fig. S10C). In PDX17-14, which was highly sensitive to MARCH5 inhibition, 2-hour treatment with dTAGV-1 was sufficient to prime cells for apoptosis, as indicated by BH3 profiling (Supplementary Fig. S10D; ref. 29). Importantly, knockout of the mitochondrial apoptosis effectors BAX or BAK1 reversed the apoptosis induction and growth defect of MARCH5-null cells (Fig. 6C; Supplementary Fig. S10E–S10G). AML cell lines displayed differential reliance on BAX and BAK1 for the execution of MARCH5 depletion–mediated apoptosis, and in some models, such as NB4 and PDX17-14, double knockout of BAX and BAK1 was required to rescue MARCH5 inactivation (Fig. 6D; Supplementary Fig. S10H and S10I). These findings demonstrate that apoptosis induction is an essential cellular mechanism accounting for the inhibitory effect of MARCH5 depletion in AML.

Figure 6.

Inhibition of MARCH5 activates the mitochondrial apoptosis pathway. A and B, Immunoblot analysis of full-length (fl) and cleaved (c) caspase 3 (A) and flow cytometry analysis of Annexin V (B) in MV4-11 cells transduced with the indicated sgRNAs at day 10 after doxycycline (Dox) treatment. sgNT, nontargeting sgRNA; sgM5-1 and sgM5-2, MARCH5 sgRNA vectors. C, Competitive growth was evaluated for the control or BAX/BAK1-knockout MV4-11 cells, which were generated with three independent sgRNAs each, upon MARCH5 deletion. D, Competitive growth assay for control, BAX-, BAK1-, and double (DKO)–knockout NB4 cells with MARCH5 depletion. E, Scatter plot showing the Pearson correlations between CERES dependency scores of MARCH5 and each other gene across AML cell lines or all cancer cell lines in the DepMap CRISPR screen dataset. Each dot represents a gene. F, Scatter plot showing the linear correlation between CERES dependency scores of MARCH5 and MCL1 across AML cell lines or other cancer cell lines in the DepMap dataset. Each dot represents a cell line; the shaded area represents the 95% confidence level interval for the linear model. G and H, Competitive growth of AML cells expressing a control vector or antiapoptotic BCL2 proteins with MARCH5 knockout. MCL1 displayed distinct rescue eff