The TP53 Apoptotic Network Is a Primary Mediator of Resistance to BCL2 Inhibition in AML Cells

Resisting cell death is a hallmark of cancer as well as an essential feature of acquired drug resistance (1–3). To maintain their growth and survival, cancer cells often modulate cell death mechanisms by overexpression of antiapoptotic BCL2 family members, including BCL2, BCL2L1, and MCL1. This overexpression aims to neutralize BH3 domains of the apoptotic activators BID, BIM, and PUMA, thus preventing liberation of the proapoptotic proteins BAX and BAK from inhibitory interactions with antiapoptotic BCL2 family proteins (4–6). Elevated levels of MCL1 protein are commonly observed in cancer and are associated with poor survival in myeloid hematologic malignancies (7–10). Aberrant prosurvival regulation can also be achieved by inactivation of p53 function, which controls many aspects of apoptosis (11, 12). These processes alter the balance of proapoptotic and antiapoptotic proteins, permitting cancer cells, otherwise primed for apoptosis, to survive and resist therapies.

The elucidation of BH3 protein family interactions involved in promoting or inhibiting apoptosis has enabled the development of small-molecule inhibitors targeting specific antiapoptotic family members leading to release of the proapoptotic proteins BAX and BAK, their oligomerization and mitochondrial outer membrane permeabilization, and ultimately execution of the irreversible apoptotic cascade (13–17). The latest generation of BH3 mimetics, venetoclax, selectively targets BCL2, thus overcoming the deficiencies and side effects of its predecessors, navitoclax and ABT-737, which targeted BCL2 and BCL2L1. Selective MCL1 inhibitors have also been developed and are in clinical development (18–20). Venetoclax has FDA approval for use in patients with chronic lymphocytic leukemia (CLL), and its efficacy has been evaluated for other hematologic malignancies; it has been recently approved in combination with a hypomethylating agent for acute myeloid leukemia (AML; refs. 21–25). Intrinsic or acquired resistance is a central concern with single-agent targeted therapies, with many instances now documenting favorable initial responses that give way to disease relapse resulting from loss of drug sensitivities (26).

For venetoclax, both acquired and intrinsic resistance is anticipated from initial clinical studies using venetoclax monotherapy on patients with relapsed/refractory AML, which have shown modest efficacy due to either intrinsic or acquired drug resistance (24). The efficacy of venetoclax in patients with AML is dependent on BCL2 expression levels, and resistance to venetoclax may result from overexpression of the antiapoptotic proteins MCL1 or BCL2L1 (21, 24). Similar mechanisms have been observed in lymphoma cell lines, as several studies have shown resistance to venetoclax developing primarily through alterations in levels of other antiapoptotic proteins (27, 28). AML cell lines rendered resistant to venetoclax through prolonged exposure develop a dependency on MCL1 or BCL2L1 (19, 29). In AML cells, the dependency on MCL1 for survival can be overcome by MAPK/GSK3 signaling via activation of p53, which when coupled with BCL2 inhibition enhances the antileukemic efficacy of venetoclax (30).

Drug combinations offer a strategy to overcome limitations in the durability and overall effectiveness of targeted agents resulting from intrinsic mechanisms of resistance. Understanding these mechanisms is essential for identifying drug targets that will form the basis for new combinatorial therapeutic strategies to circumvent drug resistance. To identify essential target genes and pathways contributing to venetoclax resistance in AML, we used a genome-wide CRISPR/Cas9 screen on an AML patient–derived cell line, MOLM-13. Our findings identify TP53, BAX, and PMAIP1 as key genes whose inactivation establishes venetoclax resistance, centering on regulation of the mitochondrial apoptotic network as a mechanism of controlling drug sensitivity. Cell lines with TP53 or BAX knockouts (KO) show perturbations in metabolic profile, energy production, and mitochondrial homeostasis. In addition, TP53 KO cells acquired sensitivity to TRK inhibitors, suggesting a new dependency on this survival pathway, indicating changes in transcriptional activity in the venetoclax-resistant setting may offer drug combinations to overcome resistance.

To understand possible mechanisms of venetoclax resistance in the setting of AML, we screened an AML cell line, MOLM-13, which harbors the most common genetic lesion observed in AML (FLT3-ITD), for loss-of-function genes conferring venetoclax resistance. We initially generated Cas9-expressing MOLM-13 cells as described (ref. 31; Fig. 1A) and confirmed Cas9 functionality (Fig. 1B). Systematic genetic perturbations on a genome-wide scale were introduced by infecting Cas9-expressing cells with lentiviruses carrying a CRISPR library consisting of an average of 5 single-guide RNAs (sgRNA) per gene for 18,010 genes, hereafter referred to as Y. Kosuke (31). After transduction, we further selected cells with puromycin for 5 days to ensure stable guide integration, and then subjected cells to treatment with either 1 μmol/L venetoclax or vehicle alone (DMSO) for 14 days while collecting DNA at various time points (Fig. 1C). By day 7 of drug treatment, venetoclax reduced cell viability by approximately 50% relative to 75% reduction without drug exposure. PCR-amplified libraries of barcodes representing unique sgRNA sequences obtained from DNA extracted from either control (DMSO) or drug-treated cells were subjected to deep sequencing and analyzed using a MAGeCK pipeline analysis (ref. 32; Fig. 1D; Supplementary Tables S1 and S2). Plotting the average log fold change for sgRNA counts per gene versus cumulative P values generated by robust rank aggregation analyses revealed a high enrichment of p53 and BAX in venetoclax-resistant populations as well as a significant enrichment for several other genes involved in the apoptosis pathway or mitochondrial homeostasis, such as PMAIP1 (NOXA), TFDP1, and BAK1 (BAK; Fig. 1E). These findings are concordant with results of a similar screen, described in a companion paper by Chen and colleagues in this issue. To confirm our results, we performed a parallel screen in MOLM-13 cells using a distinct genome-wide library, Brunello (33), which also identified TP53, BAX, and PMAIP1 among the top-enriched sgRNAs (Fig. 1F; Supplementary Tables S1 and S2). The distribution of concordance, defined as a statistically significant increase or decrease (P < 0.05) in the same direction among all sgRNAs for a given gene, showed higher average percent concordance in the Y. Kosuke library screen compared with the Brunello library screen (0.77 ± 0.07, n = 916 vs. 0.62 ± 0.004, n = 2310, unpaired two-tailed t test, P < 0.0001); therefore, we focused on the Y. Kosuke screen results for pathway analyses. Ranking genes with statistically significant enriched sgRNAs (P < 0.05) based on concordance as described above and a ≥3 log fold change compared with controls identified 234 genes (Supplementary Table S3). Functional enrichment analyses of these 234 candidates indicated an enrichment in genes involved in mitochondrial processes, such as apoptosis transcriptional regulation (TP53 and TFDP1; refs. 13 and 34) and apoptosis sensitization [PMAIP1 (NOXA); ref. 35], genes required for mitochondrial membrane pore formation (BAX, BAK, SLC25A6, and TMEM14A; refs. 13, 34), and additional genes involved in apoptosis (proapoptotic BNIP3L; ref. 36; Supplementary Table S4). The top-enriched sgRNAs showed a high degree of concordance in fold change relative to controls (Fig. 1G; Supplementary Tables S1 and S2) and had a baseline of more than 100 normalized counts in control samples, precluding artificial inflation of fold changes in resistant cells (Fig. 1H).

Our study used two independent sgRNA guide libraries, which provided a high degree of confidence with respect to the top hits identified. Analysis of genome-wide CRISPR screen knockouts is challenged by off-targeting, sgRNA guide efficiency, and other factors that can lead to library-specific artifacts and striking differences between libraries (31, 33). To prioritize candidates for validation, we developed a tier structure that incorporates three key factors: evidence (determined by the number of sgRNA guide hits per gene), concordance (indicated by the agreement across the set of guides for a given gene), and discovery (based on expanding effect size threshold) to rank sgRNA hits and enable a progression to pathway analysis for lower-scoring hits (Supplementary Methods). Using this prioritization scheme, the tier 1 hits (n = 149) revealed significant biological identity with the TP53 Regulation of cytochrome C release pathway (Reactome; corrected P < 0.001), which is concordant with our initial analysis.

To validate the screen hits, we designed several individual sgRNAs to knock out TP53, BAX, PMAIP1, TFDP1, and several other top candidate genes along with nontargeting controls. Analyses of drug sensitivity at 14 days after transduction of MOLM-13 cells with individual sgRNAs revealed a loss of venetoclax sensitivity (Fig. 2A). The top candidates, including TP53 and BAX, were also validated by single-guide inactivation in an additional cell line, MV4-11 (Fig. 2B and C), with many IC₅₀ values significantly exceeding initial drug concentrations used for the sgRNA screen. Analyses of protein levels for the top candidates, BAX, TP53, and PMAIP1, demonstrated significant loss of protein upon sgRNA inactivation (Fig. 2D; Supplementary Fig. S1A and S1B). Although BAX is reported to be a p53 transcriptional target (reviewed in ref. 37), its levels remained unchanged when TP53 was inactivated, indicating that other transcriptional factors may regulate BAX levels in these cells (38). Levels of other p53 target gene products such as PMAIP1, PUMA, and BAK1 were decreased in TP53 KO cells (Supplementary Fig. S1A and S1C). At the same time, levels of antiapoptotic proteins BCL2 and MCL1 were reduced in all four tested TP53 knockout lines, inversely correlating with increased BCLXL expression (Fig. 2D; Supplementary Fig. S1C). Analysis of protein levels revealed increases in the ratios of BCLXL to BCL2 in TP53 KO cells (Supplementary Fig. S1D). Venetoclax directly binds BCL2; therefore, a decrease in BCL2 expression in TP53 KO cells may contribute to the loss of sensitivity. The inverse correlation of low BCL2/high BCLXL expression with null TP53 status is similarly observed in pediatric acute lymphoblastic leukemia (39). In a large AML patient cohort (Beat AML; ref. 40), BCL2 expression positively correlated with TP53 expression, whereas both BCLXL and MCL1 had inverse correlations (Fig. 2E). Increased levels of BCLXL in TP53 KO cells correlate with sensitivity to a BCL2/BCLXL inhibitor (AZD-4320; Fig. 2F). Because both MOLM-13 and MV4-11 cell lines carry an FLT-ITD mutation, we introduced loss-of-function mutations in TP53, BAX, and PMAIP1 into OCI-AML2, an FLT3 wild-type (WT) cell line, and observed similarly diminished sensitivities to venetoclax (Supplementary Fig. S1E and S1F).

To assess whether the top candidates from the screen have a direct relevance to gene perturbation in patients with AML, we analyzed the correlation of venetoclax drug sensitivity with gene-expression levels and mutational status using the Beat AML patient sample dataset (40). Our analyses indicated that 16 patient samples harboring deleterious TP53 mutations showed a trend toward reduced venetoclax sensitivity (measured as increases in AUC in a nonlinear modeling of drug response curves) compared with 282 AML patient samples with WT TP53 (Mann–Whitneyt test, P < 0.04; Fig. 3A). Furthermore, separation of TP53 expression levels into high and low quartiles across the total TP53 WT cohort showed high quartile samples were more sensitive to venetoclax than low quartile samples (Fig. 3B). Regardless of TP53 mutational status, both TP53 and BAX expression levels were inversely correlated with venetoclax sensitivity (high AUC), underscoring the relevance of our CRISPR-derived loss-of-function candidates to patient-level responses ex vivo (Fig. 3C). As expected, BCL2 levels showed an inverse correlation with venetoclax sensitivity, whereas BCLXL expression directly correlated with venetoclax sensitivity (Fig. 3C).

To characterize venetoclax sensitivity in MOLM-13 cells with KOs for TP53, BAX, or PMAIP1, we evaluated an early marker of apoptosis by measuring the percentage of Annexin V–positive cells in response to venetoclax treatment (100 nmol/L) (41). Cells with KOs for TP53, BAX, or PMAIP1 showed a significant decrease in the number of cells undergoing apoptosis (Fig. 4A and B). This was accompanied by sustained phosphorylation of MAPK1/3 (ERK1/2) during venetoclax treatment and increase in total levels of AKT and MAPK1/3 in cells with inactivation of TP53 and BAX (Fig. 4C).

The top hits from our CRISPR screen pointed toward perturbations in mitochondrial regulators and effectors of the apoptotic program. p53, BAX, PMAIP1, TFDP1, and BAK function in a proapoptotic manner: p53 acts a sensor of cellular stress and transcriptional regulator of several proapoptotic genes; TFDP1 activates and translocates PMAIP1 into the mitochondria; PMAIP1 promotes activation of the apoptotic program; and BAX/BAK function as its effectors. In this manner, we observed that inactivation of TP53 led to alterations in the expression of antiapoptotic BCL2 family members and many TP53 transcriptional targets that affect mitochondrial homeostasis and cellular metabolism (Fig. 2D; Supplementary Fig. S1). TP53 inactivation also altered response to a variety of stress stimuli and rendered cells resistant to many other pharmacologic agents. These changes prompted us to investigate the impact of overall response to mitochondrial stress in cells briefly exposed to venetoclax (Fig. 5A–D). Cells with inactivation of p53 and BAX were less likely to undergo mitophagy in response to the mitochondrial uncoupler carboyl cyanide m-chlorophenyl hydrazine (CCCP), regardless of venetoclax treatment, with more pronounced protection observed in TP53 mutants, suggesting overall changes in mitochondrial homeostasis (Fig. 5A and B). Additionally, TP53- and BAX-mutant cells were less likely to lose mitochondrial membrane potential in response to CCCP alone, with venetoclax potentiating the CCCP uncoupling action (Fig. 5C and D).

Venetoclax inhibits BCL2 by mimicking the BH3 binding of apoptotic sensitizers/activators. Both venetoclax and chemical inhibition of BCL2 influence energy production by decreasing oxidative phosphorylation in the mitochondria of leukemia stem cells (42) and can affect various metabolic activities in the cells, including amino acid catabolism (43). In addition, we observed reduced levels of PUMA in TP53 KO cells (Supplementary Fig. S1C), recently identified as a p53-induced metabolic switch (44). Because p53 regulates many genes whose products determine the balance of reactive oxygen species (ROS) production in response to cellular stress (45–47), we evaluated whether oxidative phosphorylation was perturbed in cells inactivated for TP53 by measuring rates of oxygen consumption and electron transport across the mitochondrial membrane. Both outputs were enhanced in mutant cells (Fig. 5E and F), accompanied by a moderate increase in cellular ROS (Fig. 5G), as oxidative phosphorylation is a major cellular source of ROS production (48). Interestingly, both TP53 and BAX KO cells were still susceptible to cell death by elesclomol, a drug that induces cellular death through an increase in cellular oxidation (49), suggesting that apoptosis can still be triggered in venetoclax-resistant cells through disruption of mitochondrial homeostasis (Fig. 5H). These results suggest an inhibition of a general mitochondrial stress response coupled with increased cellular respiration.

Unlike the Warburg effect where rapidly proliferating cancer cells in glucose-rich conditions switch to less efficient aerobic glycolysis using the pentose phosphate shunt (48), increased oxygen consumption in TP53 KO cells suggests increased energy production via anaerobic glycolysis in venetoclax-resistant cells. Accordingly, we performed a global metabolomics analysis of MOLM-13 cells with inactivation of TP53, BAX, and nontargeting controls. Unexpectedly, we observed a significant decrease in major glycolysis intermediates, such as D-glucose and D-glucose-6-phosphate; however, levels of pyruvate, the final metabolite entering into the tricarboxylic acid (TCA) cycle, remained unperturbed (Fig. 6A and B; Supplementary Fig. S2; Supplementary Table S5). We also observed a decrease in levels of 6-phospho-D-gluconate, but not in subsequent intermediates in the pentose phosphate pathway (Supplementary Fig. S2; Supplementary Table S5). Additionally, a decrease in amino acid levels and urea cycle intermediates highlighted the most significantly affected pathways (Fig. 6A–D) in both BAX- and TP53-mutant cells in comparison with control cells. These reductions were accompanied by increases in nucleotide synthesis, suggesting the utilization of more glucose and amino acids for the purpose of nucleotide synthesis and cellular proliferation (Fig. 6C and D; Supplementary Table S5). We also performed parallel metabolomic studies on TP53-mutant and WT patient samples (Supplementary Fig. S3A–S3C; Supplementary Table S5) and found concordance for decreases in glucose and pyruvate levels, amino acids, and urea cycle intermediates (Supplementary Fig. S3A, 3B, 3D, and 3E), offering validation of the altered metabolic states observed in the cell line models (Supplementary Fig. S3). These results, together with the diminished sensitivity to venetoclax and perturbations in mitochondrial responses, illustrate the significance of our screen hits as depicted in Fig. 6E. From the transcriptional machinery initiated by p53 in response to cellular stress to various sensitizers such as PMAIP1 and TFDP1 to downstream mitochondrial pore effectors such as BAX, BAK, SLC25A6, and TMEM14A, our hits demonstrate the central involvement of mitochondria in conferring venetoclax sensitivity. Resistance may result from alterations in any of these components, for example, TP53 mutation affecting BCL2 and BCL2L1 expression (Fig. 6F) or BAX loss of function affecting mitochondrial outer membrane pore formation (Fig. 6G).

To evaluate whether the mitochondrial and metabolic changes led to alterations in additional signaling pathways, we evaluated drug sensitivities in MOLM-13 and MV4-11 cells with TP53 and BAX KOs relative to parental and nontargeting control cells using a panel of small-molecule inhibitors that target a variety of distinct signaling pathways activated in cancers. We observed many commonly altered drug responses among venetoclax-resistant cell lines, including a core of drug responses that were common to both modified MOLM-13 and MV4-11 cells (Fig. 7A; Supplementary Table S6). In addition to loss of venetoclax sensitivities, tyrosine kinase inhibitor sensitivities to the AKT/PI3K pathway (GDC-0941, PI-103), as well as YM-155, an inhibitor of survivin, were decreased (Fig. 7A; Supplementary Table S6). Although the BAX KO showed a similar sensitivity to the MCL1 inhibitor AZD5991 relative to control cells, AZD5991 sensitivity was diminished in TP53 KO MOLM-13 cells, indicating that these cells may not depend on MCL1 for survival. The MCL1 dependency further decreased in prolonged TP53 KO cultures, possibly due to compensatory mechanisms of survival. Except for venetoclax, few, if any, inhibitors showed concordant changes between BAX-inactivated MV4-11 and MOLM-13 cells (Supplementary Table S6). Additional changes for cells with TP53 inactivation included the loss of sensitivity to all six tested FLT3 inhibitors (crenolanib, sorafenib, sunitinib, quizartinib, midostaurin, and gilteritinib; Fig. 7B; Supplementary Fig. S4A). Cells with TP53 KO showed sustained gain of sensitivity to several NTRK/ALK/ROS1 inhibitors, namely, entrectinib (50), crizotinib (51, 52), GW-2580 (53), and AZD1480 (ref. 54; Fig. 7A, C and D; Supplementary Fig. S4B; Supplementary Table S6). Neurotropin receptors (NTRK1/2/3, also known as TRKA/B/C) are well documented to play a role in neuronal development. NTRK receptors bind neurotrophin ligands and are single transmembrane catalytic receptors with intracellular tyrosine kinase activity. NTRK receptors have recently gained attention for their oncogenic role in a variety of cancer types, occurring as a component of fusion genes (55–58). NTRK receptor activation is coupled to RAS/MAPK, PI3K, or PLCγ signaling pathways to promote survival and proliferation (58, 59). Although some TRK inhibitors have additional targets beyond NTRKs, such as ALK and ROS1, additional testing with a more selective TRK inhibitor, larotrectinib, also revealed a profound gain of sensitivity, indicating new dependences on the NTRK pathway for cell survival (Fig. 7C and D). To test whether NTRK transcripts were upregulated in TP53 knockout cells, we evaluated mRNA expression of NTRK1, NTRK2, and NTRK3 in MOLM-13 and MV4-11 cells with and without TP53 deletion. Although NTRK1 and NTRK2 were expressed at lower levels in KO cells, NTRK3 RNA was highly expressed in TP53 KO cells, indicating a transcriptional component of NTRK3 upregulation (Fig. 7E). NTRK1 was expressed at a lower level, albeit higher, in control cells, explaining sensitivity to entrectinib, whereas in TP53 KO cells the mRNA expression switches to NTRK3. Analyses of TRK protein levels revealed increases in overall and phosphorylated levels of TRK proteins in the p53 loss-of-function setting, and we observed significant decreases in phosphorylation of TRK and downstream MAPK signaling in response to treatment with entrectinib in TP53 KO MOLM-13 cells, but not in control or parental cells (Fig. 7F). Treatment of TP53 KO cells with entrectinib resulted in G₁ cell-cycle blockade (Supplementary Fig. S5A and S5B), and treatment of these cells with entrectinib and venetoclax together was effective in both parental and TP53 KO cell lines but not synergistic relative to each single agent (Supplementary Fig. S5C). Analysis of cell lysates obtained from AML patient samples revealed a marked increase in TRK protein with detectable levels of phosphorylated pan-TRK and TRKC in TP53-mutant versus undetectable levels in WT cases (Fig. 7G). Ex vivo analysis of leukemic blasts from these patients with AML showed increased sensitivity to entrectinib in those samples harboring TP53 mutations (Supplementary Fig. S6A). A preliminary patient-derived xenograft study using two independent TP53-mutant samples showed a response to entrectinib treatment as detected by reduced spleen weight and disease burden relative to vehicle control (Supplementary Fig. S6B).

Together, these data have identified a mechanism of venetoclax resistance in AML, namely, the p53 apoptosis machinery, and demonstrated an approach to identify and validate candidate genes involved in drug resistance. In addition, our data suggest a dependency on new survival pathways (e.g., NTRK) in TP53-mutant cells and patients. Cells resistant to venetoclax exhibited perturbations in mitochondrial homeostasis, were abnormally protective in responses to various mitochondrial stressors, and exhibited significant metabolic differences, indicative of high proliferation rates, energy production, and DNA synthesis. These metabolic differences were echoed in AML patient samples with TP53 mutations.

Venetoclax therapy for CLL has produced durable responses in a majority of patients (79%), with complete response in 20% of patients (60). Venetoclax has shown limited success in AML clinical trials as a single agent due to drug resistance in the majority of patients (24). Both of these clinical results are mirrored in venetoclax sensitivity observed ex vivo in patient samples assayed in the Beat AML cohort (40), where we observed greater activity of venetoclax on CLL (141 patients) than on AML (289 patients) specimens, with a median IC₅₀ for AML significantly higher than that for CLL (1.4 μmol/L vs. 0.1 μmol/L, respectively; Mann–Whitney P < 0.001). The efficacy of venetoclax in newly diagnosed patients with AML, when administered in combination with azacytidine, has been more robust, although the TP53-mutant subset exhibited the worst response rate of any subset of patients reported in the study (25, 61), and this combination, as well as other venetoclax combinations, is likely to see broader use in AML and in CLL, prompting a need to better understand possible mechanisms of resistance. Our findings identify genes affecting control and execution of apoptosis in mitochondria (TP53, BAX, and PMAIP1); their inactivation individually establishes venetoclax resistance in AML cell lines. These results correlate with a recent study reporting activation of p53 through inhibition of MDM2 overcomes venetoclax resistance in AML cell lines (30). The mechanism of venetoclax action is concentrated on the mitochondria, a center of energy production through cellular respiration as well as initiation of apoptosis; its target, BCL2, functions in both cell survival and the oxidative phosphorylation process. In many ways, homeostasis of mitochondria and cell survival in apoptosis are tightly linked. Attesting to this point, venetoclax-resistant cells show both: likely resistance through changes in expression of prosurvival and proapoptotic proteins and perturbation in mitochondrial stress sensitivities.

Loss of p53 affected drug sensitivities to a wide range of small molecules, including FLT3 inhibitors. Many tyrosine kinase inhibitors have multiple targets, making it difficult to dissect sensitivity change unless a group of related inhibitors is affected. For example, a previous study of patients with relapsed/refractory AML with newly acquired crenolanib resistance identified TP53 mutations associated with resistance (62). Our results expand on that finding and include loss of sensitivity changes for many FLT3 inhibitors, an observation that is likely due to disruption of the normal apoptotic response in TP53-mutant cells. Additionally, we observed a new dependency on the NTRK pathway, which was confirmed by a similar increase in sensitivity to several inhibitors, either affecting NTRK/ALK/ROS1 signaling such as entrectinib, or the NTRK-selective inhibitor larotrectinib. TP53 mutations in AML are typically rare (<8% of all cases); however, a dysfunctional WT TP53 is much more common and requires functional assessment of TP53. In general, TP53 mutations confer poor prognosis and reduced sensitivity to a variety of small molecules, and frequently associate with relapsed/refractory AML cases (62).

Our future studies will focus on assessing NTRK inhibitor sensitivity in TP53-mutant AML patient sample xenografts. The detection of the TRK receptors on immature leukemic cell lines demonstrated that NTRK1 expression and activation are not restricted to the nervous system, but are also an important element in early hematopoiesis (63). TRK receptors, which promote the proliferation and survival of erythroblasts, dendritic cells, lymphocytes, monocytes, and macrophages (64), have also been detected in human bone marrow cells (65). TRK expression occurs in myeloid leukemia cell lines and in primary leukemic cells from patients with AML (66). Subsequent studies have shown that activation of TRK receptors in hematologic malignancies could result from chromosomal rearrangements (67), point mutations (68), truncations (69), and transcriptional changes (70). Although the prevalence of NTRK-mediated cancers is <1%, fusions involving NTRK3 were observed in various hematologic malignancies including AML (67). The efficacy of TRK inhibitors has been demonstrated in NTRK fusion–positive human leukemia cell lines and patient-derived xenograft studies, highlighting the potential clinical utility of these inhibitors for a subset of patients with leukemia (71, 72).

In our study, sensitivity to TRK inhibitors correlated with an increase in expression of NTRK3 and a concomitant decrease in NTRK1 in TP53 KO cells at the RNA and protein levels, indicating a transcriptional regulation. We also observed increased TRK protein levels in TP53-mutant patient samples. Among genes that regulate NTRK expression, RUNX family proteins bind NTRK loci (73) and have regulatory roles in both hematopoietic development and cancer (74). RUNX1 (AML1), a frequently mutated gene in AML, is often disrupted by translocation (75). RUNX1 isoforms can either enhance or repress gene expression in AML cell lines (76) with isoform RUNX1A having a repressor function due to the absence of its C-terminal transactivation domain. AML1 expression is decreased in both TP53-mutant patient samples and TP53 KO cell lines (Supplementary Fig. S6C and S6D), suggesting a potential mechanism whereby the interplay between various RUNX isoforms may regulate NTRK expression levels. In mice, p53 binds to the distal Runx1 promoter, leading to AML1 upregulation and lymphoma development (77). Future studies will examine whether NTRK loci occupancy with various RUNX1 isoforms leads to upregulation of specific NTRKs in TP53-mutant cells.

Our screen identified genes whose inactivation confers venetoclax resistance. The most significant hits have a similar mode of action in apoptosis, namely, a strong proapoptotic activity. These included p53, a transcription factor with both activating and repressing functions controlling many aspects of cellular responses, including genotoxic stress pertinent to apoptotic signaling, and several p53 transcriptional targets, BAX, BAK1/BAK, and PMAIP1 (37, 78, 79). We found changes in the gene expression of several p53-regulated genes altered in our TP53 KO MOLM-13 cells, including proapoptotic PMAIP1, BAK1, and PUMA, without notable changes to BAX expression. BAX and BAK are critical for both mitochondrial outer membrane permeabilization (MOMP) in apoptosis and mitochondrial permeability transition pore (MPTP) formation. PMAIP1 senses apoptotic signaling and inhibits MCL1. TFDP1, together with E2F1, activates and translocates PMAIP1 into mitochondria (80), and SLC25A6 (ANT3), a nucleotide ADP/ATP translocase that is involved in formation of MPTP, is important for depolarization in mitochondrial recycling (refs. 81–83; Fig. 6E). Importantly, venetoclax induced membrane depolarization in control cells (Fig. 5) and showed synergy with CCCP in mutant cells, suggesting that it is part of its mechanism of action. In fact, we found that mitochondria in venetoclax-resistant cells show a different response to mitochondrial stress, and both TP53- and BAX-mutant cells had decreased mitophagy and exhibited membrane depolarization protection.

In TP53 KO cells, we found changes beyond direct p53 target genes. These included downregulation of BCL2 and MCL1 and significant upregulation of BCLXL, without notable changes to BAX expression. With the exception of MCL1, the upregulation of BCLXL and downregulation of BCL2 correlate well with expression levels of p53 in a large AML cohort (40) as well as in pediatric acute lymphoblastic leukemia (39). These alterations in expression of proapoptotic proteins may reflect compensatory changes. TP53 KO cells were sensitive to the BCL2/BCLXL inhibitor (AZD-4320); however, BAX KO cells were 10-fold less sensitive. In contrast, TP53 KO, but not BAX KO, cells were less sensitive to a selective MCL1 inhibitor (AZD-5991). These differences may reflect upregulation of BCLXL and a decrease in MCL1 levels in TP53 KO cells but not in BAX KO cells. With regard to BAX KO insensitivity to venetoclax, although both BAX and BAK can result in MOMP, our data may suggest functional differences between BCL2-driven and MCL1-driven BAX and BAK inhibition. The mechanism may involve localization of BAX (cytosolic) and BAK (mitochondrial) prior to the oligomerization processes required for MOMP.

Metabolic profiling of our mutant cells further indicated changes in energy utilization. Perturbations in the metabolism of cancer cells have been long recognized; for example, some cancer cells rely on glycolysis and pentose phosphate pathways versus cellular respiration, known as the Warburg effect (48). This phenomenon is directed toward utilization of carbon sources for nucleotides, fatty acid and protein synthesis required for cellular proliferation. Although MOLM-13 cells are leukemia-derived with a cancer metabolism, inactivation of p53 and BAX further exacerbated metabolic changes: Increased cellular proliferation led to a diversion of resources into production of nucleotides, fatty acids, and proteins required for membrane production. Curiously, this was accompanied by increased cellular respiration/increase in ROS production relative to parental cells, suggesting that mitochondria are overcompensating for the glycolysis intermediates shunting into the pentose phosphate pathway. Similar metabolic changes, accompanied by increased oxidative phosphorylation as a result of p53 inactivation, were previously observed in other tissues (84). It is possible that p53 inactivation also has a transcriptional impact on the observed metabolic changes, because it is known to negatively control glycolysis through the induction of TIGAR expression (45), which degrades fructose-2,6-bisphosphate and opposes the Warburg effect. Similarly, the TP53–PUMA axis has been implicated in metabolic switching toward suppressing oxidative phosphorylation and glycolysis correlating with decreased levels of PUMA in TP53 KO cells and the observed metabolic changes (44). Several other changes in metabolic profile, namely increases or decreases in several amino acids (variable between TP53- and BAX-mutant cells) and urea cycle intermediates (common to both TP53- and BAX-mutant cells), suggest the presence of other catabolic shunts to provide carbon sources for amino acids necessary for new cell production. Metabolic perturbations similar to those observed in TP53 KO cells were found in our primary AML patient samples. It has been observed that treatment of leukemic stem cells (LSC) from newly diagnosed patients with AML with venetoclax and a DNMT inhibitor (DNMTi) leads to reduced amino acid levels, inhibition of oxidative phosphorylation, and cell death (43). Furthermore, LSCs obtained from patients with AML are resistant to venetoclax and a DNMTi due to their ability to compensate for amino acid loss by increasing fatty-acid metabolism (43), an effect we also observed in TP53 KO cells and in AML patient samples. Given that DNMTi incorporate into DNA and inhibit DNA methylation, it may be that the increased nucleotide synthesis observed in TP53 KO cells elevates competition between nucleotides and chemical nucleotide analogues, thereby rendering DNMT inhibitors less effective.

Identification of a significant collection of apoptosis and mitochondria homeostasis controlling genes in our genome-wide screen for venetoclax resistance validated our top hit findings. However, although they provide mechanistic insight, some validated hits do not correlate with available patient sample data. For example, we did not find a correlation between low PMAIP1 expression and low venetoclax sensitivity in our AML patient cohort, nor were mutations in PMAIP1 associated with loss of venetoclax sensitivity, underlying the importance of leveraging an independent patient data resource to the screen. Although some hits identified in the screen are not represented as either mutations or low expressers in the Beat AML dataset, collectively they identify the larger network of genes mechanistically responsible for venetoclax sensitivity. Although we focused on the loss of function of genes conferring venetoclax resistance, our screen predicts that gain-of-function mutations in prosurvival genes in the apoptotic cascade will also result in resistance to venetoclax. Notably, a companion study in this issue by Chen and colleagues, where the focus of investigation was on a discovery of venetoclax-sensitizing genes, revealed genes in pertinent mitochondrial functions. Further analysis of the venetoclax-sensitizing genes in this companion study uncovered impacts on mitochondrial function and metabolic pathways that were opposite to the affects observed with venetoclax-resistance genes here. Collectively, these data suggest that mitochondrial function and specific metabolic pathways are essential in governing both sensitization and resistance to venetoclax.

Recent clinical trial success with the combination of venetoclax and a DNMTi (25) identified lower response rates in patients with TP53 mutations than in the overall cohort (47% vs. ∼70% overall), aligning with our findings here. Indeed, patients in the TP53 mutations subset exhibited the worst response rate of any subset of patients reported in the study with all other molecular/clinical groups in the study exhibiting 60% to 91% complete response/complete response with incomplete blood count recovery (CR/CRi) rate (including the poor cytogenetic risk group). Further, the TP53-mutant patient group had the lowest odds ratio for CR/CRi response and was the only variable that significantly correlated with worse response. A longer study may elucidate whether those TP53TP53-mutant patients receiving the combination treatment will relapse at a higher rate, possibly with a clone that has higher allele frequency TP53 mutation.

A complete Methods section is provided in Supplementary Material.

Human MOLM-13 cells were obtained from the Sanger Institute Cancer Cell Line Panel. Human MV4-11, OCI-AML2, and THP-1 were obtained from ATCC. Authentication was performed on all cell lines used in this study at the Oregon Health and Science University (OHSU) DNA Services Core facility. All cell lines were tested for Mycoplasma on a monthly schedule.

Loss-of-function screens were performed using two human genome-wide sgRNA libraries—Y. Kosuke library (31) and Brunello library (33)—both purchased form Addgene (#73179 and #67989, respectively).

Pipeline for executing analyses of CRISPR library sequences was performed using MAGeCK analyses (32).

Small-molecule inhibitors, purchased from LC Laboratories Inc. and Selleck Chemicals, were reconstituted in DMSO and stored at −80°C.

Animal studies were conducted with approval from the OHSU Institutional Animal Care and Use Committee. Patient samples were injected into cohorts of NSGS mice and allowed to engraft until the peripheral blood showed approximately 1% human CD33 chimerisms.

Two million MOLM-13 and MV4-11 cells were flash-frozen as cell pellet and metabolomics analyses were performed via ultra-high-pressure liquid chromatography–mass spectrometry (UHPLC-MS; Vanquish and Q Exactive, Thermo Fisher) as previously reported (85).

Cells were loaded in complete medium with 100 nmol/L Mitophagy Dye (Mitophagy Detection Kit, Dojindo Molecular Technologies). The data were analyzed post acquisition using FlowJo software (Tree Star). Ten thousand cells were acquired using channels 405-2-A (lyso Dye), 561-3-A (Mitophagy Dye), and 640-1-A (MitoTracker Dye).

Mitochondrial membrane potential was assessed using tetramethylrhodamine methyl ester bound to cationic carbocyanine (JC-1, Dojingo Molecular Technology). Cells were treated with 100 nmol/L venetoclax or DMSO and with or without CCCP for 2 hours prior to the addition of the dye and flow cytometry.

Raw data files for CRISPR screens have been deposited at the Gene Expression Omnibus and can be found under the accession number GSE130414.

A.V. Danilov has received commercial research grants from Gilead Sciences, Verastem, Takeda, AstraZeneca, and Verastem Oncology, and is a consultant/advisory board member for AbbVie, AstraZeneca, Genentech, Verastem Oncology, Seattle Genetics, TG Therapeutics, Curis, Celgene, Teva Oncology, and Gilead Sciences. B.J. Druker serves on the board of directors of Amgen, Burroughs Wellcome Fund, and CureOne; reports receiving other commercial research support from Novartis, Bristol-Myers Squibb, and Pfizer (institutional funding—PI or co-investigator on clinical trials funded via contract with OHSU); has ownership interest (including stock, patents, etc.) in Amgen, Blueprint Medicines, MolecularMD (inactive—acquired by ICON Laboratories), GRAIL, Patent 6958335 (exclusively licensed to Novartis), Henry Stewart Talks, Merck via Dana-Farber Cancer Institute (royalty payments); is a consultant/advisory board member for Aileron Therapeutics, ALLCRON, Third Coast Therapeutics, Vivid Biosciences, Monojul (inactive), Baxalta (inactive), CTI Biopharma (inactive), Aptose, Beta Cat, Blueprint Medicines, Celgene, Cepheid, GRAIL (former), Gilead (former), and Patient True Talk; and is an uncompensated joint steering committee member for Beat AML LLC. J.W. Tyner has received commercial research grants from Agios, Aptose, Array, AstraZeneca, Constellation, Genentech, Gilead, Incyte, Janssen, Seattle Genetics, Syros, and Takeda; has received honoraria from the speakers bureaus of Therapeutic Advances in Childhood Leukemia and Hermedicus - Acute Leukemia Forum; has ownership interest in Vivid Biosciences; and is a consultant/advisory board member of Vivid Biosciences. No potential conflicts of interest were disclosed by the other authors.

Conception and design: T. Nechiporuk, S.E. Kurtz, C.E. Tognon, J.W. Tyner

Development of methodology: T. Nechiporuk, T. Liu, A. D'Alessandro, S.K. Joshi

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): T. Nechiporuk, S.E. Kurtz, T. Liu, C.L. Jones, A. D'Alessandro, A. d'Almeida, S.K. Joshi, A.V. Danilov, B.H. Chang

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): T. Nechiporuk, S.E. Kurtz, O. Nikolova, T. Liu, A. D'Alessandro, R. Culp-Hill, S.K. Joshi, M. Rosenberg, A.V. Danilov, J.W. Tyner

Writing, review, and/or revision of the manuscript: T. Nechiporuk, S.E. Kurtz, O. Nikolova, T. Liu, R. Culp-Hill, S.K. Joshi, C.E. Tognon, B.J. Druker, B.H. Chang, J.W. Tyner

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): T. Nechiporuk, R. Culp-Hill, J.W. Tyner

Study supervision: A. D'Alessandro, B.J. Druker, J.W. Tyner

Other (development of prioritization framework to assist in rigor and reproducibility): S.K. McWeeney

We thank Wesley Horton and Kevin Watanabe-Smith for analytical support and the OHSU Massively Parallel Sequencing Shared Resource and Flow Cytometry Core for technical support. This study was also supported by grants from the NCI (1U01CA217862, 1U54CA224019, and 3P30CA069533-18S5). J.W. Tyner received grants from the V Foundation for Cancer Research, the Gabrielle's Angel Foundation for Cancer Research, and the NCI (1R01CA183947). S.K. Joshi is supported by the ARCS Scholar Foundation and the NCI (1F30CA239335-10).

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