Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Gregory C Howard; Jing Wang; Kristie Lindsey Rose; Purvi Patel; Tina Tsui; Andrea C Florian; Shelly L Lorey; Brian C Grieb; Brianna N Smith; Macey J Slota; Elizabeth M Reynolds; Soumita Goswami; Michael R Savona; Taekyu Lee; Stephen W Fesik; Qi Liu; William P Tansey

doi:10.1101/2023.07.26.550648

This is a preprint.

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[Preprint]. 2023 Jul 29:2023.07.26.550648. [Version 1] doi: 10.1101/2023.07.26.550648

Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Gregory C Howard ^a, Jing Wang ^b,^c, Kristie Lindsey Rose ^d,^e, Purvi Patel ^e, Tina Tsui ^e, Andrea C Florian ^a,^f, Shelly L Lorey ^a, Brian C Grieb ^a,^g, Brianna N Smith ^g, Macey J Slota ^a,^h, Elizabeth M Reynolds ^a, Soumita Goswami ^a, Michael R Savona ^g, Taekyu Lee ^d, Stephen W Fesik ^d,^h,ⁱ, Qi Liu ^b,^c, William P Tansey ^a,^d,^*

^aDepartment of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

^bDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA

^cCenter for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA

^dDepartment of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

^eMass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

^fCurrent address: Department of Biology, Belmont University, Nashville, TN 37212, USA

^gDepartment of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA

^hDepartment of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Current address: Department of Urology, University of California San Francisco, San Francisco CA 94143, USA

ⁱDepartment of Chemistry, Vanderbilt University, Nashville, TN 37232, USA

Corresponding author: Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232. Phone: 615-322-1993. william.p.tansey@vanderbilt.edu

Roles

Gregory C Howard: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing–Original Draft, Writing–Review & Editing, Visualization

Jing Wang: Software, Formal Analysis, Data Curation, Writing–Review & Editing, Visualization

Kristie Lindsey Rose: Methodology, Software, Formal Analysis, Investigation, Data Curation, Writing–Review & Editing

Purvi Patel: Investigation, Writing–Review & Editing

Tina Tsui: Software, Formal Analysis, Data Curation, Writing–Review & Editing

Andrea C Florian: Methodology, Investigation, Writing–Review & Editing

Shelly L Lorey: Investigation, Writing–Review & Editing

Brian C Grieb: Investigation, Writing–Review & Editing

Brianna N Smith: Investigation, Writing–Review & Editing

Macey J Slota: Investigation, Writing–Review & Editing

Elizabeth M Reynolds: Investigation

Soumita Goswami: Investigation, Writing–Review & Editing

Michael R Savona: Writing–Review & Editing

Taekyu Lee: Writing–Review & Editing, Visualization

Stephen W Fesik: Writing–Review & Editing, Funding Acquisition

Qi Liu: Software, Formal Analysis, Data Curation, Writing–Review & Editing

William P Tansey: Conceptualization, Writing–Original Draft, Supervision, Funding Acquisition

PMCID: PMC10402127 PMID: 37546802

Summary

The chromatin-associated protein WDR5 is a promising target for cancer drug discovery, with most efforts blocking an arginine-binding cavity on the protein called the “WIN” site that tethers WDR5 to chromatin. WIN site inhibitors (WINi) are active against multiple cancer cell types in vitro, the most notable of which are those derived from MLL-rearranged (MLLr) leukemias. Peptidomimetic WINi were originally proposed to inhibit MLLr cells via dysregulation of genes connected to hematopoetic stem cell expansion. Our discovery and interrogation of small molecule WIN site inhibitors, however, revealed that they act in MLLr cell lines to suppress ribosome protein gene (RPG) transcription, induce nucleolar stress, and activate p53. Because there is no precedent for an anti-cancer strategy that specifically targets RPG expression, we took an integrated multi-omics approach to further interrogate the mechanism of action of WINi in MLLr cancer cells. We show that WINi induce depletion of the stock of ribosomes, accompanied by a broad translational choke, induction of a DNA damage response, and changes in alternative mRNA splicing that inactivate the p53 antagonist MDM4. We also show that WINi are synergistic with agents including venetoclax and BET-bromodomain inhibitors. Together, these studies reinforce the concept that WINi are a novel type of ribosome-directed anti-cancer therapy and provide a resource to support their clinical implementation in MLLr leukemias and other malignancies.

Introduction

WDR5 is a highly-conserved protein that moonlights in a variety of functions in the nucleus. It rose to prominence as a component of epigenetic writer complexes that deposit histone H3 lysine 4 (H3K4) methylation (Guarnaccia & Tansey, 2018), but was subsequently found to act outside these complexes to facilitate the integrity of the mitotic spindle (Ali et al, 2017), bookmark genes for reactivation after mitosis (Oh et al, 2020), and promote transcription of a subset of ribosomal protein genes [(RPGs); (Bryan et al, 2020)] via recruitment of the oncoprotein transcription factor MYC to chromatin (Thomas et al, 2019). WDR5 is also frequently overexpressed in cancer, where its overexpression correlates with aggressive disease and poor clinical outcomes (Guarnaccia & Tansey, 2018). Accordingly, WDR5 is an auspicious target for inhibition in a range of malignancies including MLL-rearranged (MLLr) leukemias (Aho et al, 2019a; Cao et al, 2014), MYC-driven cancers (Aho et al, 2019b), C/EBPα-mutant leukemias (Grebien et al, 2015), p53 gain-of-function cancers (Zhu et al, 2015), neuroblastomas (Bryan et al., 2020), rhabdoid tumors (Florian et al, 2022), and metastatic breast cancers (Cai et al, 2022).

Although WDR5 PROTACs have been described (Li et al, 2022; Yu et al, 2021), safety concerns over destroying a pan-essential protein such as WDR5 (Siladi et al, 2022) means that most drug discovery efforts have focused on small molecule inhibition of key binding sites on the protein. Some initiatives target a hydrophobic cleft on WDR5 known as the “WDR5-binding motif” (WBM) site (Chacon Simon et al, 2020; Macdonald et al, 2019) that contacts MYC (Thomas et al, 2015). But the majority of efforts target the “WDR5-interaction” (WIN) site of WDR5 (Guarnaccia & Tansey, 2018)—an arginine binding cavity that tethers WDR5 to chromatin (Aho et al., 2019a) and makes contact with partner proteins carrying an arginine-containing “WIN” motif (Guarnaccia et al, 2021). Multiple WIN site inhibitors (WINi) have been described (Aho et al., 2019a; Bolshan et al, 2013; Cao et al., 2014; Chen et al, 2021a; Chen et al, 2021b; Grebien et al., 2015; Karatas et al, 2017; Karatas et al, 2013; Li et al, 2016; Senisterra et al, 2013; Tian et al, 2020; Wang et al, 2018), including those that are orally bioavailable and have antitumor activity in vivo (Chen et al., 2021b; Teuscher et al, 2023). Given the intense interest in developing WINi for cancer therapy, and the rapid pace of improvement in these molecules, it is likely that WIN site inhibitors will be ready for clinical vetting in the near future.

That said, controversy remains regarding the mechanism of action of WIN site inhibitors, even in the context of MLLr leukemias, where there is strong empirical support for their utility. MLL-rearranged leukemias are defined by translocation of one copy of MLL1—a gene that encodes one of six MLL/SET proteins that are the catalytic subunits of the histone methyltransferase (HMT) complexes responsible for H3K4 methylation (Guarnaccia & Tansey, 2018). The near universal retention of a pristine copy of MLL1 in these cancers led to the idea that MLLr leukemias depend on wild-type MLL1 to support the activity of oncogenic MLL1-fusion oncoproteins (Thiel et al, 2010)—a function in turn that depends on insertion of a low affinity WIN motif within MLL1 into the WIN site of WDR5 (Alicea-Velazquez et al, 2016). Consistent with this notion, early peptidomimetic WINi are active against MLLr leukemia cells in vitro and are reported to suppress levels of H3K4 methylation at canonical MLL1-fusion target genes such as the HOXA loci, causing cellular inhibition through a combination of differentiation and apoptosis (Cao et al., 2014). Subsequently, however, wild-type MLL1 was shown to be dispensable for transformation by MLL-fusion oncoproteins (Chen et al, 2017), and our analysis of picomolar small molecule WINi revealed that they act in MLLr cells without inducing significant changes in the expression of HOXA genes or levels of H3K4 methylation (Aho et al., 2019a). Instead, WIN site inhibitors displace WDR5 from chromatin and directly suppress the transcription of ~50 genes, the vast majority of which are connected to protein synthesis, including half the cohort of RPGs. We also found that WINi provoke nucleolar stress and induce p53-dependent cell death. Based on our findings, we proposed that WIN site inhibitors kill MLLr cells via depletion of part of the ribosome inventory that induces apoptosis via a ribosome biogenesis stress response.

The concept of ribosome-directed cancer therapies is not new (Laham-Karam et al, 2020; Temaj et al, 2022). Besides mTOR and translational inhibitors, one of the most prevalent strategies in this realm is inhibition of ribosomal RNA (rRNA) production or processing, which is a feature of both existing chemotherapies such as platinum-containing compounds (Bruno et al, 2017), as well as newer targeted RNA polymerase I inhibitors (Drygin et al, 2011; Peltonen et al, 2014). Although these agents exert their anti-cancer effects through multiple mechanisms (Laham-Karam et al., 2020), they are generally thought to disrupt the stoichiometry of RNA and protein components of the ribosome, leading to an excess of ribosomal proteins that inactivate MDM2 to induce p53-dependent cancer cell death. The paradigm we developed for WINi is modeled after that of rRNA inhibitors, although it is important to note that a significant point of divergence from rRNA poisons is that in this model WINi induce p53 not by promoting ribosomal protein accumulation, but by causing a selective imbalance in the ribosome subunit inventory. How such an imbalance could lead to p53 induction, as well as other consequences it may have on MLLr cellular processes, remains unknown.

Fortifying understanding of the mechanism of action of WINi in MLLr cancer cells is key to their clinical implementation. At present, there is no precedent for the mechanism we propose, no understanding of the impact of selective ribosome subunit depletion on translation or other tumor-relevant processes, and no expectations for how resistance to WINi could emerge or how their anti-tumor actions could be made more effective. To ameliorate these deficiencies, we took an integrated multiomics approach, combining transcriptional and translational profiling with genome-wide CRISPR screens to probe WINi action in MLLr cells. Our studies show that although the transcriptional effects of WINi on ribosome subunit expression are confined to those RPGs directly regulated by WDR5, effects at the protein level are not, and WIN site inhibition leads to diminution of the entire stock of cytosolic ribosomes. Ribosome subunit attrition is accompanied by a broad translational choke, induction of a DNA damage response, and activation of p53—driven largely via RPL22-dependent alternative splicing of the p53 antagonist MDM4. We also show that WINi are synergistic with approved and targeted agents including venetoclax and BET-bromodomain inhibitors. Collectively, these findings solidify a novel mechanism of action for WIN site inhibitors in MLLr cells and highlight a path for their optimal clinical implementation.

Results

Impact of WINi on the transcriptome of MLLr cancer cells

Our model for the action of WINi in MLLr leukemia cells is based on analysis of two early generation compounds (Aho et al., 2019a): C3 (K_d = 1.3 nM) and C6 (K_d = 100 μM). Subsequently (Tian et al., 2020), we discovered more potent molecules such as C16 (K_d < 20 μM) that have not been extensively profiled. To ask if improvements in the potency of WINi have resulted in divergent activities, we first compared C6 with C16 (Figure 1A). Both molecules bind the WIN site of WDR5 (Figure 1B and Figure 1—figure supplement 1A), but differ in affinity due to a bicyclic dihydroisoquinolinone core that locks C16 into a favorable binding conformation (Figure 1C). Consistent with its higher affinity, C16 is ~20 times more potent than C6 in inhibiting MV4;11 (MLL–AF4) and MOLM13 (MLL–AF9) leukemia lines (Figure 1—figure supplement 1B–C): In MV4;11 cells, for example, the GI₅₀ for C6 is 1 μM, compared to 46 nM for C16. These differences in potency are reflected at the level of RPG suppression. Using a target engagement assay (Florian et al., 2022) that measures transcript levels from seven RPGs—five (RPS14, RPS24, RPL26, RPL32, and RPL35) that are always bound by WDR5 and two (RPS11 and RPS14) that are never bound—we observe that maximal suppression of RPG transcripts occurs at ~2 μM for C6 and ~100 nM for C16 in MV4;11 (Figure 1D) and MOLM13 (Figure 1—figure supplement 1D) cells. To functionally compare these two inhibitors of different potencies, we used these RPG-normalized doses in all our subsequent studies.

We performed RNA-sequencing (RNA-Seq) on MV4;11 cells treated for 48 hours with DMSO, C6, or C16 (Figure 1—figure supplement 1E and Figure 1—source data 1). Both compounds elicit thousands of gene expression changes (Figure 1E), a majority of which are less than two-fold in magnitude (Figure 1—figure supplement 1F). We had previously performed RNA-Seq on MV4;11 cells treated with 2 μM C6 and observed just ~75 induced and ~460 reduced transcripts (Aho et al., 2019a). In this earlier work, however, increased variance among replicates made it difficult for as many small gene expression changes to reach statistical significance as in the current study (Figure 1—figure supplement 1G). Comparing the new RNA-Seq datasets, we observe similar effects of C6 and C16 on the MV4;11 cell transcriptome (Figure 1F), with more than 80% of the transcripts altered by C16 altered in the same direction by C6 (Figure 1G). In both cases, suppressed genes are enriched in those bound by WDR5 in MV4;11 cells (Figure 1H); although a majority of transcriptional changes occur at loci bereft of detectable WDR5 binding (Figure 1F). For both compounds, expression of genes connected to protein synthesis, the cell cycle, DNA replication, mTORC signaling, and MYC are reduced, while expression of those connected to chemokine signaling, apoptosis and p53 are induced (Figure 1I and Figure 1—source data 2). Indeed, ~90 “consensus” p53 target genes (Fischer, 2017) are induced by C6/C16 (Figure 1—figure supplement 2A–B), including the tumor suppressor ZMAT3 [Figure 1F; (Bieging-Rolett et al, 2020)]. Finally, we note that for both compounds, the transcriptional effects on RPG expression are almost entirely confined to those RPGs bound by WDR5 (Figure 1J). The conspicuous exception to this trend is RPL22L1—a paralog of RPL22—mRNA levels which are strongly reduced by C6/C16 (Figure 1F and Figure 1J). This feature of the response is not confined to MLLr cells, as RPL22L1 expression is also decreased by WINi in sensitive rhabdoid tumor cell lines (Florian et al., 2022), but not in the insensitive K562 leukemia line (Bryan et al., 2020) (Figure 1—figure supplement 2C). Despite not being a direct WDR5 target gene, therefore, RPL22L1 expression is recurringly suppressed by WINi in responsive cancer cell lines.

Together, these data reveal that improvements in the potency of WINi have not resulted in substantive changes in their impact on the transcriptome of MLLr cells, and reinforce the concept that inhibition of select RPG expression—and induction of a p53-related transcriptional program—defines the response of the transcriptome to WIN site blockade in this setting.

Impact of WINi on the translatome of MLLr cancer cells

Treatment of MV4;11 cells with 2 μM C6 results in decreased translation, as measured by bulk labeling of nascent polypeptide chains with O-propargyl-puromycin (OPP) (Aho et al., 2019a). To determine more precisely the effects of WIN site inhibition on translational processes, we performed ribosome profiling [Ribo-Seq; (McGlincy & Ingolia, 2017)] in parallel with the RNA-Seq analyses described above. By sequencing ribosome protected fragments (RPF) in treated and control cells, and normalizing to transcript levels from RNA-Seq, we calculated the translation efficiency (TE) of each transcript and used this to determine how C6/C16 influence translation, independent of effects on mRNA abundance.

In these experiments, RPF have the characteristic length of ribosome-protected mRNA fragments (28–32 nucleotides; Figure 2—figure supplement 1A), are enriched in coding sequences (Figure 2—figure supplement 1B), and map to the expected reading frame Figure 2—figure supplement 1C), all of which indicate successful profiling. In contrast to RNA-Seq, where we see equal numbers of transcript increases and decreases in response to C6/C16, the overwhelming translational effect of these compounds is inhibitory (Figure 2A). Of the ~10,000 transcripts profiled, between ~4,500 (C16) and ~5,900 (C6) transcripts show decreased TE, compared to less than 10 transcripts with increased translation (Figure 2B and Figure 2—source data 1). As we observed in the RNA-Seq, changes in TE are generally less than two-fold (Figure 2A) and there is extensive overlap between the two inhibitors, with ~90% of the transcripts decreased in TE by C16 also decreased by C6 (Figure 2C). In general, C6/C16 reduce translation of mRNAs in a manner independent of basal translation efficiencies (Figure 2D), although if we bin transcripts according to basal TE we observe that the number of highly-translated transcripts (fourth quartile) impacted by C6/C16 is greater than for those transcripts with lower basal TE (Figure 2E). Within these quartiles, however, the magnitude of reduction in TE is equivalent (Figure 2—figure supplement 1D). Interestingly, mRNAs carrying better matches to the 5’TOP motif—which links translation to mTORC1 signaling (Philippe et al, 2020)—show less decrease in TE compared to those with poorer matches (Figure 2—figure supplement 1E), suggesting that mTORC1-regulated mRNAs may be spared from the full translational effects of WIN site inhibition.

Interrogating specific changes in TE induced by C6/C16, we see that the biological categories of transcripts with decreased TE echo many of those observed with decreased mRNA levels, but include more genes in each category. For example, manually curating each list for transcripts encoding the ~60 validated substrates of the protein arginine methyltransferase PRMT5 (Radzisheuskaya et al, 2019) reveals that 42 are translationally suppressed by C6/C16, compared to just 10 that are suppressed at the mRNA level (Figure 2—figure supplement 1F). Probing for Hallmark categories in the Human Molecular Signatures Database [MSigDB; (Liberzon et al, 2015)] uncovers the extent of this phenomenon (Figure 2—source data 2), with categories linked to MYC, E2F, mTORC1 signaling, and the G2M checkpoint (Figure 2F) all represented by more genes in the ribosome profiling than in the RNA-Seq experiments. Within these categories, a majority of genes suppressed by C6/C16 at the mRNA level are also further suppressed translationally (Figure 2—figure supplement 1G). This is not a general trend in the response, however, as fewer than half of the total transcripts with reduced mRNA abundance experience this additional translational inhibition (Figure 2—figure supplement 1H). The finding that TE changes induced by C6/C16 extend the biological characteristics of changes in mRNA abundance may indicate a role for impaired translation in contributing to at least some of the mRNA level differences triggered by WINi. Indeed, comparing transcripts with decreased TE but no mRNA decrease with transcripts with decreased TE and mRNA levels reveals the latter are enriched in so-called “optimal codons” (Wu et al, 2019) that normally promote mRNA stability but are are linked to mRNA instability when translation is inhibited (Figure 2—figure supplement 1I).

Implicit in the previous discussion, a majority of the translational decreases triggered by C6/C16 occur at transcripts for which there are no significant changes in mRNA abundance (Figure 2—figure supplement 1H). Expectedly, genes with decreased TE but no mRNA level changes are enriched in several of the major Hallmark categories described above (Figure 2—source data 3). But we also observe enrichment in genes connected to the proteasome, spliceosome, mRNA surveillance, and translation (Figure 2G). The latter category includes subunits of the mitochondrial ribosome (Figure 2—figure supplement 2A), translation and ribosome biogenesis factors, and an expanded cohort of transcripts from RPGs (Figure 2H). Indeed, compared to mRNA levels, where C6/C16-induced changes are confined (with the exception of RPL22L1) to a decrease in expression of WDR5-bound RPGs, translational effects are not, and some of the most pronounced TE changes occur at non-WDR5 ribosomal protein target genes. Thus, beyond what we have been able to infer from previous studies, WINi causes a widespread reduction in the ability of MLLr cells to efficiently translate mRNAs connected to almost every aspect of protein synthesis and homeostasis.

Impact of WINi on the ribosome inventory of MLLr cancer cells

Based on the finding that WDR5 controls expression of half the RPGs, we speculated that WINi induce a ribosome subunit imbalance that leads to induction of p53 (Aho et al., 2019a). It is also possible, however, that quality control mechanisms deplete the entire inventory of ribosomal proteins during prolonged WIN site blockade. To distinguish between these possibilities, we tracked changes in ribosomal protein levels at two timepoints: 24 hours, when there is no overt cellular response to WIN site inhibition, and 72 hours, when cell proliferation begins to be inhibited (Aho et al., 2019a). The abundance of ribosomal proteins allows for the use of label-free quantitative mass spectrometry [LFQMS; (Cox et al, 2014)] in whole cell lysates to feasibly track ribosome protein levels, while also providing insight into other changes in protein levels promoted by WIN site inhibitor.

In this analysis, we tracked ~3,200 proteins at each timepoint (Figure 3A and Figure 3—source data 1), ~850 of which are significantly altered by C16 treatment. Consistent with the subtle effects of WINi on mRNA abundance and TE, most differences in protein levels triggered by C16 are less than two-fold in magnitude (Figure 3B and 3C). At 24 hours, ~90% of the proteins that change in response to C16 score as increased, whereas by 72 hours this number drops to ~60% (Figure 3A). Most instances of increased protein levels are transient—fewer than one third of these proteins are still induced at day three (Figure 3D)—whereas a majority of the proteins decreased at day one are also decreased at day three. Enrichment analysis (Figure 3—figure supplement 1A and Figure 3—source data 2) reveals that proteins induced at 24 hours are modestly enriched in those connected to exocytosis and leukocyte activation, as well as mTORC1 signaling and MYC. By 72 hours, we see induction of proteins linked to glycolysis and fatty acid metabolism, as well as apoptosis. Additionally, manual curation reveals that the number of induced p53 target proteins increases over time: 13 are induced at 24 hours, compared to 24 at the three day point (Figure 3E). Commensurate with the onset of a functional response to WINi, therefore, is a modest expansion in the apparent impact of p53 on the proteome, as well as the emergence of apoptotic response indicators.

Not surprisingly, proteins that are reduced in abundance at 24 hours are significantly enriched in those linked to the ribosome (Figure 3—figure supplement 1B). This enrichment becomes stronger at 72 hours. We also observe, at 72 hours, suppression of proteins linked to MYC and E2F targets, as well as mTORC1 signaling. In terms of ribosome components, this analysis reveals a progressive decline in the ribosomal protein inventory. Going from 24 to 72 hours, there is an increase in the number of impacted ribosomal subunits as well as in the magnitude of their suppression (Figure 3F), and eventually almost all ribosomal subunits are in deficit, regardless of whether or not they are encoded by a WDR5-bound gene (Figure 3G). Interestingly, RPL22L1 is the most strongly suppressed protein at 72 hours (Figure 3C), with its levels reduced by an order of magnitude after three days of C16 treatment. In sum, this analysis reveals that changes in ribosomal protein levels predicted from our transcriptomic studies manifest in reduced expression of ribosome components. Contrary to our earlier idea that WINi promote ribosome subunit imbalance, however, these data support a simpler model in which these inhibitors ultimately induce attrition of the majority of ribosomal proteins.

A loss of function screen for modulators of the response to WINi

Next, we conducted a loss of function screen to identify genes that modulate the response of MLLr cells to WINi (Figure 4A). First, we carried out a screen using the GeCKOv.2 sgRNA library (Joung et al, 2017), which targets ~19,000 genes with six sgRNAs each, as well as ~1,200 miRNAs (four sgRNAs each). After transducing the library into MV4;11 cells expressing Cas9, we treated for two weeks with 2 μM C6, during which time rapidly growing cells emerged within the transduced population (Figure 4—figure supplement 1A). We harvested genomic DNA, performed next-generation sequencing, and compared sgRNA representation before and after C6 treatment. We inventoried genes with significant enrichment/depletion in corresponding sgRNAs in the treated population, removed pan-essential genes (Tsherniak et al, 2017), and created a custom library in which protein-coding “hits” are targeted by four different sgRNAs (Doench et al, 2016). The smaller library was screened against C6 or C16 in Cas9-expressing MV4;11 cells. This two-tiered approach allowed us to efficiently screen two different WIN site inhibitors and to identify high-confidence hits that are validated with up to 10 distinct sgRNAs.

Although the first tier did not discriminate between genes that modulate fitness and those that modulate WINi response, several interesting observations emerged. Guide RNAs corresponding to ~70 genes were enriched and ~675 were depleted (Figure 4B), most of the latter of which are pan-essential (Figure 4—source data 1). Satisfyingly, TP53 is the most highly enriched gene in the screen (Figure 4B and Figure 4—figure supplement 1B). CDKN2A also scored as highly enriched in the initial screen (Figure 4B)—specifically those sgRNAs targeting p14^ARF(Figure 4—figure supplement 1C–1D), an inhibitor of the p53 ubiquitin ligase MDM2 (Sherr, 2001). Further support for the importance of p53 is evidenced by network analysis (Chang & Xia, 2023) of the 27 miRNAs flagged as enriched (Figure 4—source data 1), which display connections to p53 (Figure 4—figure supplement 1E). Finally, we note that the second most highly-enriched gene in the first tier encodes a ribosomal protein: RPL22 (Figure 4B and Figure 4—figure supplement 1F). Because of the strong enrichment of sgRNAs against RPL22 and TP53, we removed both genes from the second tier screen.

The second tier screen (Figure 4—figure supplement 1G and Figure 4—source data 2) revealed that the response of MV4;11 cells to C6 and C16 is very similar, both in terms of the enriched/depleted genes and their rankings (Figure 4C). A majority of genes that modulate the response to C6 similarly modulate the response to C16 (Figure 4D). For most of the depleted genes that appear specific to one WINi, similar depletion is observed with the other WIN site inhibitor, but is generally just over the FDR cutoff (Figure 4—figure supplement 1H). But for C6-specific enriched genes we see that most have high FDR values in the C16 samples, arguing that the earlier generation compound has expanded, off-target, activities. Gene ontology (GO) enrichment analysis of the 57 common genes emerging from the screen revealed enrichment in four overlapping categories connected to p53 signaling, apoptosis, the DNA damage response, and histone modifications (Figure 4E and Figure 4—figure supplement 1I). The representation of genes connected to p53 and to apoptosis reinforces the importance of p53-mediated cell death to the response of MLLr cells to WINi. We observe, for example, that loss of function of the p53 antagonist and ubiquitin ligase MUL1 (Jung et al, 2011), increases sensitivity to C6/C16, whereas loss of canonical p53 effectors NOXA, BAX, and ZMAT3 is associated with a decrease in response. The DNA damage response (DDR) category overlaps with that of p53 but is nonetheless distinct and includes genes encoding the ATM and CHK2 kinases (Blackford & Jackson, 2017) and the FOXM1 transcription factor that activates DDR gene expression networks (Zona et al, 2014). This category also includes two depleted genes, encoding FBXL5—which antagonizes ATM signaling (Chen et al, 2014)—and DYRK1A—a kinase involved in the DDR (Laham et al, 2021), DREAM complex activation (Litovchick et al, 2011), and RPG transcription (Di Vona et al, 2015). Consistent with representation of DDR genes in hits from the screen, C16 induces the DNA damage marker ɣ-H2AX in MV4;11 cells (Figure 4—figure supplement 1J and Figure 4—source data 3), implying that DNA damage, and the ability to mount a DDR, are part of the mechanism of action of WINi in this setting.

A majority of genes in the histone modification category, when disrupted, blunt the response to both WINi (Figure 4E). These genes include those encoding the H3K27 demethylase KMD6A (Lan et al, 2007), the MLL/SET protein KMT2D [MLL2; (Shinsky et al, 2015)], and ARID4A—a component of the mSin3/HDAC1 co-repressor complex (Lai et al, 2001). The most conspicuous sensitizing gene in this group is BRD3, a member of the BET family of proteins that includes BRD2 and BRD4 (Eischer et al, 2023). Interestingly, although BRD4 was not included in the second screen tier as it is pan essential, BRD2 was not included because it was not significantly enriched/depleted in the first tier (Figure 4—source data 1), revealing that the actions of BRD3 in modulating response to WINi are not shared with all family members. Further supporting the importance of BRD3 to the response, we note that SPOP, which targets BET family proteins for proteasomal destruction (Janouskova et al, 2017), is one of the most significantly enriched hits from the screen (Figure 4E).

Collectively, these findings demonstrate functional involvement of the ribosomal protein RPL22 in the response to WINi, confirm the importance of the p53 network to robust inhibition of MLLr cell growth by these agents, and reveal a role for DNA damage in the response to WIN site inhibitor. These findings also identify a number of candidate predictive biomarkers of response that can be further interrogated for their value as patient-selection criteria.

Identification of agents that synergize with WINi in MLLr cells

Given the ways in which resistance to WINi can arise (Figure 4), the most efficacious application of these agents will likely be in combination with other therapies. We therefore asked whether C16 synergizes with 11 approved or targeted agents. Several of the agents were chosen based on the results of our screen. Harmine—an inhibitor of the DYRK1A kinase (Gockler et al, 2009)—and the BET family inhibitor mivebresib (McDaniel et al, 2017) each target the product of sensitizing genes, whereas venetoclax inhibits BCL-2 (Souers et al, 2013)—an inhibitor of BAX, which scored as a resistance gene. We also tested agents connected to the DDR [etoposide, olaparib, and the ATR inhibitor VE821; (Charrier et al, 2011)], protein synthesis and homeostasis (alvespimycin and rapamycin), and p53 (nutlin-3a). Due the the enrichment of PRMT5 substrates in our translational profiling (Figure 2—figure supplement 1F), we queried the PRMT5 inhibitor pemrametostat (Chan-Penebre et al, 2015). And because DOT1L inhibitors suppress not only classic MLL fusion target genes (Bernt & Armstrong, 2011; Daigle et al, 2011) but also RPGs (Lenard et al, 2020), we tested for synergy with the DOT1L inhibitor pinometostat (Daigle et al., 2011). We treated MV4;11 cells with a dose matrix spanning 49 unique dose combinations and quantified synergy δ-scores using the Zero Interaction Potency (ZIP) model (Yadav et al, 2015) (Figure 5A–B, Figure 5—figure supplement 1A and 1B, and Figure 5—source data 1).

Figure 5. — (A) Peak synergy (> 0) and antagonism (< 0) ZIP Delta (δ) scores from synergy assays in which MV4;11 cells were treated for three days with 49 unique dose combinations of C16 and the indicated compound of interest (n = 4). See Figure 5—source data 1 for numerical ZIP Delta analysis output. (B) Heatmaps of MV4;11 cell growth inhibition at each dose of C16 and the indicated six compounds. The remaining five combinations tested are shown in Figure 5—figure supplement 1. (C) As in (A) but for MOLM13 cells. See Figure 5—source data 1 for numerical ZIP Delta analysis output. (D) As in (B) but for MOLM13 cells. The remaining five combinations tested are shown in Figure 5—figure supplement 2. (E) Number of genes with significantly (FDR < 0.05) altered transcript levels following treatment of MV4;11 cells with C16 (100 nM), mivebresib (Mibv; 2.5 nM), or the combination for 48 hours, as determined by RNA-Seq (n = 3). See Figure 5—source data 2 for complete output of RNA-Seq analysis. (F) UpSet plot, showing the overlap of genes suppressed (left) or induced (right) in response to C16, mivebresib, or the combination. (G) UpSet plot, showing the breakdown of Reactome “Translation” pathway genes suppressed in response to C16, mivebresib, or the combination. (H) Enrichment of Reactome Pathways in genes with increased transcripts following treatment of MV4;11 cells with C16, mivebresib, or the combination. See Figure 5—source data 3 for complete output of enrichment analyses.

In MV4;11 cells, we observe synergy with mivebresib, pemrametostat, pinometostat, etoposide, harmine, and venetoclax. Within this group are three agents selected based on sensitizing targets from the CRISPR screen, providing additional support for the role of DRYK1A, BCL-2/BAX, and BRD3 in the responsiveness to WINi. Of the three agents connected to the DDR, only etoposide displays significant synergy. Agents that target protein synthesis and homeostasis yield mixed results—we observe potent antagonism with the mTOR inhibitor (Raught et al, 2001) rapamycin (peak δ-score −16), while the HSP90 inhibitor alvespimycin (Schnur et al, 1995) is either antagonistic or synergistic, depending on dose (Figure 5A). Finally, we note that—of the agents displaying synergy—four are particularly strong (peak δ-scores >10) and observed at agent doses consistent with on-target activity (Figure 5—figure supplement 1B), suggesting that mivebresib, pemrametostat, pinometostat, and venetoclax should be prioritized for in vivo testing. Focusing on these agents is further justified by our finding that all four are synergistic with C16 in MOLM13 cells (Figure 5C–D and Figure 5—figure supplement 2A and 2B).

To understand how combination with another agent impacts the response to WINi, we profiled MV4;11 cells treated for forty-eight hours with C16 and mivebresib, either as single agents or in combination, at concentrations that yield peak synergy between them (100 nM C16 and 2.5 nM mivebresib). By RNA-Seq (Figure 5—source data 2), it is clear that the functional synergy between C16 and mivebresib is apparent at the transcript level, with more than 6,200 gene expression changes in the combination treatment, compared to less than 1,800 for C16 and 2,700 for mivebresib (Figure 5E). Notable are the very distinct transcriptional profiles induced by each agent alone, with fewer than 200 shared gene expression changes in each direction (Figure 5F). The impact on RPG expression of both agents is additive (Figure 5—figure supplement 3A), but in general we find that the combination of C16 and mivebresib dysregulates similar categories of genes for each agent alone, but with substantially more genes in each category (Figure 5—source data 3). This is clear for genes linked to translation (Figure 5G), to p53 (Figure 5—figure supplement 3B), and to the induction of apoptosis (Figure 5H). Thus, although further investigation is needed, this analysis is consistent with the idea that synergy between C16 and mivebresib results from alterations in the expression of distinct but complementary sets of genes that ultimately conspire to augment induction of p53.

WINi inactivate MDM4 in an RPL22-dependent manner

Despite the importance of p53 in the response to WINi, WIN site inhibitors cause only a slight increase, if any, in p53 levels [Figure 6—figure supplement 1A, Figure 6—source data 1 and (Aho et al., 2019a)]. Interestingly, inactivation or loss of RPL22 in cancer is associated with increased expression of RPL22L1 and inclusion of exon 6 in MDM4 (Ghandi et al, 2019), an event that promotes MDM4 expression by preventing formation of a “short” MDM4 mRNA isoform (MDM4s) that is destroyed by nonsense-mediated decay (Rallapalli et al, 1999). MDM4 is intriguing because it can suppress p53 without altering its stability (Francoz et al, 2006). It is also intriguing because skipping of exon 6 in the MDM4 mRNA is stimulated by ZMAT3 (Bieging-Rolett et al., 2020; Muys et al, 2021) and antagonized by RPL22L1 (Larionova et al, 2022)—two genes that are oppositely regulated by WINi. We therefore asked if WINi induce changes in the levels of mRNA splice isoforms and if this includes MDM4.

RNA-Seq data (Figure 1) were interrogated for alternative splicing events (Shen et al, 2014). At an FDR < 0.05 and a threshold of ≥ 5% change in exon inclusion (Δψ), C6 and C16 each result in changes in ~1,000 differentially-spliced mRNAs (Figure 6—figure supplement 1B), ~250 of which are shared between the two inhibitors (Figure 6A). Many of these changes reflect events with low read counts or at minor splice sites (Figure 6—source data 2), representative examples of which are presented in Figure 6–figure supplement 1C–D. That said, WINi clearly promote accumulation of MDM4 transcripts in which exon 6 is skipped (Figure 6B). We also observe splicing changes at RPL22L1 itself (Figure 6–figure supplement 1E), where WINi leads to the depletion of transcripts in which exon 2 is spliced to a distal 3’ acceptor site in exon 3. This splicing event encodes the RPL22L1a isoform that is incorporated into ribosomes (Larionova et al., 2022). Splicing to the proximal 3’ acceptor site, which generates a non-ribosomal RPL22L1b isoform that modulates splicing, is insensitive to WINi. We confirmed the impact of C16 on MDM4 and RPL22L1 splice isoforms by semi-quantitative RT-PCR (Figure 6—figure supplement 1F and Figure 6—source data 3) and quantitative RT-PCR (Figure 6—figure supplement 1G). Based on these observations, we conclude that treatment of MLLr cells with WINi promotes the selective loss of transcripts encoding RPL22L1a and MDM4.

The association of RPL22 loss with increased expression of RPL22L1 and inclusion of exon 6 in MDM4 (Ghandi et al., 2019) prompted us to ask how RPL22 contributes to the response of MLLr cells to WINi. Knockout (KO) of RPL22 (Figure 6—figure supplement 2A and Figure 6—source data 4) decreases the sensitivity of MV4;11 and MOLM13 cells to C16 by three- to five-fold compared to non-targeted (NT) control cells (Figure 6C and Figure 6—figure supplement 2B), as well as attenuating the modest induction of p53 protein observed in the MV4;11 line (Figure 6D and Figure 6—source data 5). The response of relatively insensitive (p53-null) K562 cells, in contrast, is unaffected by RPL22 disruption (Figure 6C). RNA-Seq analysis (Figure 6—figure supplement 2C–D and Figure 6—source data 6) reveals that disruption of RPL22 does not impact the effect of WINi on WDR5-bound RPGs (Figure 6—figure supplement 2E), but it does block the effects of C16 on expression of RPL22L1 and ZMAT3 (Figure 6—figure supplement 2D), as well as tempering its ability to suppress genes connected to the cell cycle, mTORC1 signaling, and MYC (Figure 6—figure supplement 2F and Figure 6—source data 7). Notably, RPL22 loss also impairs induction of genes involved in p53 signaling (Figure 6E and Figure 6—figure supplement 2G). We also observe that mitochondrial RPGs are induced by WINi uniquely in RPL22-null cells (Figure 6—figure supplement 2H). We conclude that RPL22 is needed for a majority of the characteristic responses of MLLr cells to WINi, including activation of p53.

Finally, we asked if RPL22 knockout alters patterns of alternative splicing induced by WINi. Thousands of differences were detected in splice isoforms between the various pairwise comparisons (Figure 6—source data 8). In general, RPL22KO cells show fewer C16-induced changes in alternative splicing patterns than NT cells (Figure 6—figure supplement 3A). As we observed above, a majority of the changes reflect events with low read counts or at minor splice sites, with two notable exceptions: MDM4 and RPL22L1. In the absence of WINi, disruption of RPL22 promotes exon 6 retention in MDM4 (Figure 6—figure supplement 3B) and induces expression of the mRNA splice isoform encoding RPL22La (Figure 6—figure supplement 3C). In the presence of C16, disruption of RPL22 mitigates skipping of exon 6 in MDM4 (Figure 6F) and largely blocks suppression of the RPL22L1a-encoding mRNA isoform (Figure 6—figure supplement 3C). Importantly, these changes in mRNA isoforms manifest at the protein level (Figure 6G and Figure 6—source data 9) as we observe that both MDM4 and RPL22L1 protein expression is suppressed by C16, that RPL22L1 is induced by RPL22 disruption, and that loss of RPL22 blocks a majority of the ability of C16 to reduce expression of the MDM4 and RPL22L1 proteins. We also note that loss of RPL22 blocks induction of the p53 target, p21. Taken together, these data demonstrate that RPL22 loss can cause the induction of RPL22L1 and inclusion of exon 6 in MDM4 observed in cancer (Ghandi et al., 2019), and reveal that splicing-driven suppression of MDM4 is a major mechanism through which WINi activate p53 in MLLr cells.

Discussion

Here, we describe an integrated multiomic approach to characterize the mechanism of action of WDR5 WIN site inhibitors in MLL-rearranged cancer cells. By combining transcriptional, translational, and proteomic profiling with genome-wide loss of function screens, we demonstrate the broad impact of WINi on the ribosomal protein complement and translational capacity of MLLr cells, reveal the importance of multiple arms of the p53 response pathway in cellular inhibition, and uncover a role for alternative splicing of MDM4 in activating p53 in this setting. Collectively, these data cast WIN site inhibitors as a novel ribosome-directed anti-cancer therapy and provide insight into patient selection criteria, mechanisms of resistance, and strategies to improve WINi efficacy in the clinic.

The primary targets of WINi, we propose, are the cohort of ~40 RPGs that are bound by WDR5 in all cell types examined (Aho et al., 2019a; Bryan et al., 2020; Florian et al., 2022). These RPGs are rapidly transcriptionally suppressed in response to WIN site blockade (Aho et al., 2019a; Bryan et al., 2020; Florian et al., 2022) or WDR5 degradation (Siladi et al., 2022), and show sustained suppression at the mRNA level. These same RPGs are suppressed by disruption of the MYC–WDR5 interaction (Thomas et al., 2019), implying that the function of WDR5 at these genes is to recruit MYC, a prominent target of MLL-fusion oncoproteins (Ahmadi et al, 2021). Previously, we posited that suppression of half the RPGs would lead to a ribosomal subunit imbalance (Aho et al., 2019a), but our analysis shows that WINi deplete the entire ribosomal inventory. Part of this depletion is driven by decreased translation of ribosomal mRNAs, although it is also possible that ubiquitin-mediated quality control mechanisms or ribophagy (Zhao et al, 2022) degrade ribosomal proteins under these conditions.

The effects of WINi on ribosomal protein levels are extensive in terms of the number of impacted subunits, but not the magnitude of change, which caps at around a 30% decrease by 72 hours. This cap could be set by the maximal contribution of MYC–WDR5 to the expression of target RPGs, which is two-fold as determined by genetic disruption of the MYC–WDR5 interaction (Thomas et al., 2019), degradation of WDR5 (Siladi et al., 2022), or WIN site blockade. Although modest, it should be noted that a 30% decrease in ribosomal protein levels corresponds to a loss of up to three million ribosomes per cell (Shore & Albert, 2022), and in that light it is not surprising that WINi result in reduced translational efficiencies of about half of all actively-translated mRNAs. Unlike perturbations such as ER stress (Advani & Ivanov, 2019), WINi does not promote preferential translation of stress-responsive mRNAs, although there is some specificity in terms of the translational consequences. Messenger RNAs carrying 5’TOP motifs, for example, are spared from the full translational impact of C6/C16, and we see distinct biological clustering of mRNAs with decreased translation efficiency. Whether these patterns are intrinsic to WINi, or a general response of MV4;11 cells to translational stress, remains to be determined. Overall, we conclude that WINi do not trigger extensive translational reprogramming, but rather act to induce a widespread yet restrained translational choke.

Downstream of ribosomal protein attrition, we observe several responses to WINi that are shared with rRNA inhibitors. Induction of DNA damage, as measured by accumulation of ɣ-H2A.X, is also seen with the RNA polymerase I inhibitor CX-5461 (El Hassouni et al, 2019; Quin et al, 2016), and this has been proposed to be an important and exploitable attribute in terms of its future clinical application (Sanij et al, 2020). The fact that not all rRNA inhibitors induce DNA damage [e.g., (Peltonen et al., 2014)], as well as the action of CX-5461 as a G-quadruplex (G4) stabilizer (Xu et al, 2017), has cast doubt on the contribution of rRNA inhibition versus G4 stabilization to the DNA damage component of CX-5461 activity. But the demonstration that C6/C16 induce γ-H2A.X and depend on components of the DDR for full activity, together with a growing appreciation of the susceptibility of rDNA to damage and of the nucleolus as a hub for the DDR (Xuan et al, 2021)—suggests that DNA damage and the response to it may be an inherent part of the mechanism of action of ribosome-directed anti-cancer strategies.

Activation of p53, and p53-dependent cellular inhibition, is another point of convergence of WDR5 and rRNA inhibitors. Not only do we detect activation of p53 target genes in response to WIN site blockade, but we observe synergistic activation of p53 target genes with the BET bromodomain inhibitor mivebresib and suppression of p53 targets upon deletion of RPL22; both of which correlate with cellular sensitivity. We also recover multiple components of the p53 signaling pathway as “resistance” genes in our CRISPR screens, including p53 itself and the splicing factor ZMAT3. Inhibition of rRNA synthesis is thought to activate p53 by generating excess ribosomal proteins that bind to and inactivate MDM2 (Pfister, 2019). It is likely that inhibition of MDM2 contributes to p53 activation in response to C6/C16, as we see a modest increase in p53 levels in MV4;11 cells exposed to WINi, and find that loss of the MDM2 inhibitor p14^ARF renders MV4;11 cells less sensitive to C6/C16. Here, however, we also find that there is a second route of p53 activation at work, in which WINi promote accumulation of alternatively spliced isoforms of MDM4 mRNA in which exon 6 is skipped, driving down MDM4 protein levels. Because MDM4 inhibits p53 via proteolysis-independent mechanisms (Francoz et al., 2006), these findings explain how WIN site inhibitors can induce a robust p53 target gene signature in the absence of frank induction of p53 protein. They also point to a dominant role of the MDM4–p53 axis in mediating the response of MLLr cells to WIN site blockade. Whether rRNA inhibition triggers p53 activation by a similar mechanism has, to our knowledge, yet to be reported.

In considering the mechanism through which WINi inactivate MDM4, it is possible that the balance of RPL22L1 and ZMAT3, which are oppositely regulated in response to WIN site inhibition, governs the extent of MDM4 exon 6 inclusion. ZMAT3 is induced in response to p53 activation and promotes skipping of exon 6 in MDM4 (Bieging-Rolett et al., 2020). RPL22L1, in contrast, which is potently suppressed by WIN site inhibitor, promotes exon 6 inclusion (Larionova et al., 2022). Any process that tips the balance in favor of ZMAT3, therefore, would be expected to inhibit MDM4, activate p53, and initiate a feed-forward mechanism that drives ZMAT3 expression and fortifies p53 induction. Induction of ZMAT3 alone should be sufficient to trigger this circuit, but the conspicuous suppression of RPL22L1 by WIN site inhibitor suggests that this may also contribute to the response. Paradoxically, the isoform of RPL22L1 that is suppressed by WIN site blockade, RPL22L1a, is linked in glioblastoma cells to ribosome function, not splicing (Larionova et al., 2022), while the splicing-relevant RPL22L1b isoform is resistant to WINi. One possibility is that, despite its recurrence and prominence, suppression of RPL22L1a by C6/C16 does not contribute to MDM4 suppression, and induction of ZMAT3 is the critical driving factor. Alternatively, RPL22L1a may indeed act to control splicing in MLLr cells, in contrast to what has been reported in glioblastoma. Further investigation is required.

The RPL22–RPL22L1–MDM4 nexus we encountered has been inferred by genome-wide studies of the Cancer Cell Line Encyclopedia [CCLE; (Ghandi et al., 2019)], and our work here demonstrates that loss of RPL22 causes induction of RPL22L1 and promotes inclusion of exon 6 in MDM4. We have no evidence that RPL22 itself plays a role in the response to WINi in otherwise unperturbed MLLr cells. Rather, we suggest that its recovery as a resistance gene in our CRISPR screen is tied to its ability to suppress RPL22L1 expression (O’Leary et al, 2013), and the ectopic effect of its deletion on preventing WINi-induced RPL22L1a decline. That said, RPL22 status is likely to be highly relevant in terms of patient selection criteria. RPL22 is frequently inactivated or deleted in primary cancer samples (Ghandi et al., 2019; Goudarzi & Lindstrom, 2016; Kandoth et al, 2013), as well as 7% of lines in the CCLE (Cao et al, 2017). Unlike other RPGs, mutation or deletion of RPL22 is not associated with mutational inactivation of p53, and indeed there is a strong tendency for wild-type p53 to be retained in RPL22 mutant/deletion lines (Cao et al., 2017; Ghandi et al., 2019). In practical terms, therefore, cancers that retain wild-type p53 but otherwise are mutated/deleted for RPL22, or overexpress RPL22L1, would not be expected to robustly respond to WIN site inhibitor.

Although p53 is important for the action of WINi in MLLr cells, there are likely other stress response mechanisms that mediate cellular inhibition by these agents. A number of p53-independent nucleolar stress responses have been identified [e.g., (Boglev et al, 2013; Jayaraman et al, 2017; Pfister et al, 2015)], including those involving DNA damage response pathways that delay S-phase progression and induce a G2 cell cycle arrest (Sanij et al., 2020). Conversely, we might also expect cells to be able to mount protective responses to WIN site blockade. In this regard, it is curious that four resistance genes identified in our CRISPR screen—UBA6, BIRC6, KCMF1, and UBR4—encode members of a newly-identified BIRC6 ubiquitin-ligase complex (Cervia et al, 2023), the function of which is to prevent aberrant activation of the integrated stress response (ISR). The ISR is a central regulator of protein homeostasis (Costa-Mattioli & Walter, 2020) that drives protective translational reprogramming in response to multiple cellular stresses. There is no indication that the ISR is activated by WINi; indeed the master regulator of ISR, ATF4, is suppressed by C6/C16 at the mRNA and translational levels. But the finding that loss of all four members of the BIRC6 complex blunts the response to WINi implies that ISR activation can be a mechanism through which cells evade the full impact of these agents.

As with most monotherapies, future single agent WINi treatment paradigms are likely to encounter resistance, either by activation of protective responses such as those proposed above, or by mutations in one or more of the resistance genes recovered in our CRISPR screen. Identification of agents that can be used in combination with WINi to increase cancer cell inhibition is thus crucial. Our relatively limited synergy screening identified a number of combinations that should be prioritized for in vivo testing. We found that WINi act synergistically with the BCL-2 inhibitor venetoclax (Souers et al., 2013). This is mechanistically rationalized by our recovery of BAX as a resistance gene, and is noteworthy because venetoclax is an approved therapy for several blood-borne cancers. We also identified notable synergies with experimental agents targeting BET bromodomain family members, DOT1L, and PRMT5. The combination with mivebresib is rationalized based on identification of BRD3 as a sensitizing gene, and likely results from the ability of C16 and mivebresib to inhibit distinct sets of genes connected to translation, the impact of which is to enhance p53 induction. BET bromodomain inhibitors have struggled in clinical trials due to dose-limiting toxicities (Shorstova et al, 2021), but their combination with WINi could form the basis of a more effective therapy with less side effects. Moreover, given the mechanism the underlying synergy between C16 and BET inhibitors, we would expect this combination to be effective in other wild-type p53 cancer settings where WINi are active, such as neuroblastoma (Bryan et al., 2020) and rhabdoid tumors (Florian et al., 2022). Expanded synergy screening is needed to identify and understand the full spectrum of combination approaches that could be used to ultimately enhance and extend the clinical utility of WIN site inhibitors.

Materials and Methods

Key resources

All key resources are provided in Supplementary File 1—Key Resources Table.

Materials availability

Plasmids and cell lines generated in this study are available upon request from the corresponding author (william.p.tansey@vanderbilt.edu).

Data availability

Ribo-Seq, RNA-Seq, and CRISPR screen data are deposited at Gene Expression Omnibus (GEO) with the accession number GSE206931. Quantitative proteomics data are deposited at the ProteomeXchange Consortium via the PRIDE partner repository with identifier PXD035129.

Cell lines

MV4;11 (RRID: CVCL_0064), MOLM13 (RRID: CVCL_2119), and K562 (RRID: CVCL_0004) cell lines and their derivatives were cultured in RPMI-1640 media with 10% FBS, 10 U/mL penicillin, and 10 μg/mL streptomycin at 37°C and 5% CO₂. HEK293T (RRID: CVCL_1926) cells were cultured in DMEM media with 10% FBS, 10 U/mL penicillin, and 10 μg/mL streptomycin at 37°C and 5% CO₂. MV4;11 and MOLM13 cell lines are male. K562 and HEK293T cell lines are female. Cell lines were split every 2 to 4 days and suspension cells maintained between 1x10⁵ and 1x10⁶ cells/mL. All cell lines tested negative for mycoplasma.

Generation of RPL22-null cell lines

MV4;11, MOLM13, and K562 control (NT) and RPL22 knockout (KO) cell lines were generated by CRISPR using the multi-guide Synthego Gene Knockout System. Briefly, ribonucleoprotein (RNP) complexes containing Cas9-2NLS (Synthego) and either non-targeting (NT) control sgRNA#1 (Synthego) or RPL22 sgRNAs (Synthego Gene Knockout Kit v2 – human – RPL22) were formed by incubating 90 pmol sgRNA and 10 pmol Cas9-2NLS in Buffer R (Component of Neon Transfection System Kit; Thermo Scientific) at room temperature for 10 minutes. MV4;11, MOLM13, or K562 cells were electroporated (2 x 10⁵ cells per reaction) with RNP complexes using the Neon Transfection System (Thermo Fisher Scientific) with the following parameters using Buffer R in 10 μL reactions: MV4;11 cells: 1175 V Pulse, 40 ms Pulse Width, 1 Pulse; MOLM13 cells: 1075 V Pulse, 30 ms Pulse Width, 2 Pulses; K562 cells: 1450 V Pulse, 10 ms Pulse Width, 3 Pulses. Cells recovered undisturbed in media absent of antibiotics for 48 hours before expansion and screening for loss of RPL22 expression by western blot analysis.

Multiplex gene expression assays

Cells were treated with 0.1% DMSO or varying concentrations of C6 or C16 for 24 hours. A custom QuantiGene Plex panel (ThermoFisher Scientific) was used in conjunction with the QuantiGene Sample Processing Kit for cultured cells (ThermoFisher Scientific), and QuantiGene Plex Assay kit (ThermoFisher Scientific) to quantify transcripts following the manufacturer’s instructions. Probe regions and accession numbers are as follows: RPS24 (NM_001026, region 5-334), RPL35 (NM_007209, region 2-430), RPL26 (NM_000987, region 37-445), RPS14 (NM_005617, region 61-552), RPL32 (NM_000994, region 95-677), RPS11 (NM_001015, region 139-634), RPL14 (NM_003973, region 108-530), and GAPDH (NM_002046, region 2-407). The Average Net Mean Fluorescence Intensity was read on a Luminex FLEXMAP 3D System (Invitrogen). Signals from RPGs were normalized internally to those from GAPDH, and then to the DMSO control. Dose response curves from the mean of biological replicates were calculated with the R package drc (Ritz et al, 2015).

Western blot analysis

Cells were collected by centrifugation and washed once with ice-cold PBS. Cells were lysed in either RIPA buffer (50 mM Tris, pH 8.0; 150 mM NaCl; 5 mM EDTA; 1.0% NP-40; 0.5% Sodium Deoxycholate; 0.1% SDS) or Triton-X buffer (50 mM Tris, pH 8.0; 150 mM NaCl; 5 mM EDTA; 1% Triton X-100), each supplemented with protease and phosphatase inhibitors (2X cOmplete, EDTA-free, Protease Inhibitor Cocktail [Roche]; 1X PhosSTOP Phosphatase Inhibitor [Roche]; 100 μg/mL Pefabloc SC [Roche]), while incubating on ice for 10 minutes. Chromatin was sheared by brief sonication at 25% on ice, insoluble material cleared by centrifugation, and protein quantified by Pierce BCA Protein Assay (Thermo Scientific). Protein samples were diluted to equal concentrations in lysis buffer and boiled for 5 minutes in 1X Laemmli Sample Buffer. Samples were run on 4-20% TGX Precast Polyacrylamide Gels (Bio-Rad) or hand-cast single percentage polyacrylamide gels, wet transferred to Amersham Protran Western Blotting Nitrocellulose Membrane (Cytiva) for one hour at 100V in Towbin Buffer (25 mM Tris; 192 mM glycine; 10% methanol), and blocked in 5% milk in TBS-T before incubation overnight with one of the following primary antibodies: anti-p53 (Santa Cruz Biotechnology, Cat# sc-126), anti-RPL22 (Santa Cruz Biotechnology, Cat# sc136413), anti-RPL22L1 (Thermo Fisher Scientific, Cat# PA5-63266), anti-MDM4 (Sigma-Aldrich, Cat# M0445), anti-p21 (Cell Signaling Technology, Cat# 2947), anti-H2A.X (Cell Signaling Technology, Cat# 2595), anti-phospho-H2A.X (S139) (Cell Signaling Technology, Cat# 9718), anti-α-actinin (Cell Signaling Technology, Cat# 12413), or anti-GAPDH (Cell Signaling Technology, Cat# 8884). Membranes were washed three times with TBS-T and, if required, incubated with anti-Mouse-HRP secondary antibody (Jackson ImmunoResearch Laboratories, Inc., Cat# 115-035-174) or anti-Rabbit-HRP (Cell Signaling Technology, Cat# 7074) for one hour. Blots were developed with Clarity ECL Western Blotting Substrate (Bio-Rad) and imaged on a ChemiDoc Imaging System (Bio-Rad).

Ribo-Seq

Ribo-seq was performed as previously described with some modifications (McGlincy & Ingolia, 2017). MV4;11 cells treated for 48 hours with either 0.1% DMSO, 2 μM C6, or 100 nM C16 were washed with ice-cold PBS and lysed in 400 μL Lysis Buffer (20 mM Tris, pH 7.4; 150 mM NaCl; 15 mM MgCl₂; 1 mM DTT; 100 μg/mL cycloheximide; 1% Triton X-100; 25 U/mL Turbo DNase I) by incubation on ice for 10 minutes followed by homogenization by syringe. Lysates were cleared by centrifugation at 4°C, and RNA quantified by Qubit RNA HS Assay (Invitrogen) following manufacturer’s instructions. 30 μg RNA was diluted in 200 μL Polysome Buffer (20 mM Tris, pH 7.4; 150 mM NaCl; 5 mM MgCl₂; 1 mM DTT; 100 μg/mL cycloheximide) and incubated with 15 U RNase I (Lucigen) for 45 minutes while rotating at room temperature. RNA digestion was quenched with 10 μL SuPERaseIn RNase Inhibitor (Invitrogen) and samples transferred to 13 mm x 51 mm ultracentrifuge tubes (Beckman-Coulter), underlaid with 900 μL 1 M sucrose in polysome buffer supplemented with 20 U/mL SUPERaseIn RNase Inhibitor, and centrifuged at 540,628 x g one hour at 4°C. Ribosome pellets were suspended in TRIzol Reagent (Invitrogen), and RNA extracted from ribosome pellets by Direct-zol RNA MiniPrep Kit (Zymo Research). RNA and carrier glycogen were precipitated by adding 1.5 volumes 100% isopropanol supplemented with 0.12 M NaOAc, pH 5.5, followed by incubation on dry ice for 30 minutes and centrifugation at 16,800 x g for 30 minutes at 4°C. RNA was resuspended in 5 μL 10 mM Tris, pH 8.0, and 1X Denaturing Sample Loading Buffer (98% Formamide; 10 mM EDTA; 300 μg/mL bromophenol blue) and subjected to electrophoresis on 15% polyacrylamide TBE-Urea gels (Invitrogen). Gels were stained briefly with 1X SYBR Gold (Invitrogen), 17-34 nucleotide fragments excised, and RNA fragments extracted by mechanical disruption, suspension in 500 μL RNA Gel Extraction Buffer (300 mM NaOAc, pH 5.5; 1 mM EDTA; 0.25% SDS), freezing on dry ice for 30 minutes, and rotating overnight at room temperature. Polyacrylamide was removed by centrifugation through Costar Spin-X columns (Corning) and RNA precipitated with isopropanol as described above. RNA was dephosphorylated by incubation with 5 U T4 Polynucleotide Kinase in 1X T4 PNK Buffer (New England BioLabs) supplemented with SUPERaseIn RNase Inhibitor for one hour at 37°C and ligated to barcoded linkers (NI-810: /5Phos/NNNNNATCGTAGATCGGAAGAGCACACGTCTGAA/3ddC/; NI-811: /5Phos/NNNNNAGCTAAGATCGGAAGAGCACACGTCTGAA/3ddC/; NI-812: /5Phos/NNNNNCGTAAAGATCGGAAGAGCACACGTCTGAA/3ddC/) pre-adenylated with 100 U of T4 RNA Ligase 2, truncated K227Q, in 1X T4 RNA Ligase Buffer (New England BioLabs) supplemented with 35% w/v PEG-8000 and incubated at 37°C for three hours. Ligation was verified by electrophoresis, samples combined, and linker-ligated RNA precipitated with isopropanol as described above. Ribosomal RNA was depleted from samples using the RiboCop rRNA Depletion Kit (Lexogen) and RNA precipitated with isopropanol as described above. Linker-ligated RNA was reverse transcribed with 200 U SuperScript III in 1X First Strand Buffer (Invitrogen), dNTPs, DTT, 10 U SUPERaseIn RNase Inhibitor and primer NI-802 (/5Phos/NNAGATCGGAAGAGCGTCGTGTAGGGAAAGAG/iSp18/GTGACTGGAGTTCAGACGTGTGCTC). RNA template was hydrolyzed for 15 minutes at 98°C in the presence of 0.1M NaOH and cDNA precipitated with isopropanol as described previously. Reverse-transcribed DNA was subjected to electrophoresis on 15% polyacrylamide Novex TBE-Urea gels (Invitrogen) and the 105-nucleotide reverse-transcription product excised from polyacrylamide as described above, except with DNA Gel Extraction Buffer (300 mM NaCl; 10 mM Tris, pH 8.0; 1 mM EDTA). cDNA was circularized with 100 U CircLigase ssDNA Ligase (Lucigen) in the presence of 1X CircLigase Buffer, ATP, and MnCl₂ at 60°C for one hour followed by 80°C for 10 minutes. Circularized cDNA was quantified by qPCR, amplified using Phusion Polymerase (New England BioLabs) with unique dual-indexed primers (UDI0050_i5: AATGATACGGCGACCACCGAGATC-TACACGCTCCGACACACTCTTTCCCTACACGACGCT CTTCCGATCT; UDI0050_i7: CAAGCAGAAGACGGCATACGAGATTAGAGC-GCGTGACTGGAGTTCAGACGTGT; UDI0051_i5: AATGATACGGCGACCACCGAGAT-CTACACATACCAAGACACTCTTTCCCTACACGACGCT; UDI0051_i7: CAAGCAGAAGACGGCATACGAGATAACCTGTTGTGACTGGAGTTCAGACGTGT; UDI0052_i5: AATGATACGGCGACCACCGAGATCTACACGCGTTGGAACACTCTTTCCCTACAC-GACGCT; UDI0052_i7: CAAGCAGAAGACGGCATACGAGATGGTTCACCGTG-ACTGGAGTTCAGACGTGT), amplicons subjected to electrophoresis on 8% polyacrylamide Novex gels (Invitrogen), and products >160 bp excised as described. Libraries were submitted to VANTAGE (Vanderbilt Technologies for Advanced Genomics) for sequencing on a NovaSeq 6000.

Ribo-Seq data analysis

Adapters were trimmed from reads using cutadapt (Martin, 2011), and UMIs removed from reads and attached to read IDs using UMI-tools (Smith et al, 2017). Reads were demultiplexed using sabre and aligned against ribosomal RNA using bowtie2 (Langmead & Salzberg, 2012). Reads not mapping to rRNA were mapped to the hg19 transcriptome using STAR (Dobin et al, 2013) and deduplicated using UMI-tools (Smith et al., 2017). Count tables for reads mapping to central ORFs were generated using the coverage command from bedtools (Quinlan & Hall, 2010). After batch removal, Ribo-Seq read counts were normalized to mRNA read counts using Xtail (Xiao et al, 2016) to calculate translation efficiencies and statistics. FDR values were calculated using the Cochran-Mantel-Haenszel test (CMH). Genes with significantly altered translation efficiencies were those with FDR < 0.05 and absolute log2FC > 0.25. Identification of optimal ribosome protected fragment (RPF) P-site offsets, RPF triplet periodicity, and RPF localization to CDS and UTR regions was performed with the R package riboWaltz (Lauria et al, 2018).

RNA-Seq

For RNA-Seq performed in parallel with Ribo-Seq, RNA was isolated by Direct-zol RNA MiniPrep Kit (Zymo Research) from 100 μL of cell lysates after homogenization by syringe and clearing by centrifugation. For combination WINi/BETi treatment, MV4;11 cells were treated for 48 hours with either 0.2% DMSO, 100 nM C16, 2.5 nM mivebresib, or combined 100 nM C16 and 2.5 nM mivebresib, and RNA isolated by Direct-zol RNA MiniPrep Kit (Zymo Research) with on-column DNAse-treatment. For RNA-Seq in MV4;11 NT and RPL22 KO cells, cultures were treated for 48 hours with either 0.1% DMSO or 100 nM C16 before RNA isolation as described above for WINi/BETi RNA-Seq. For all RNA-Seq experiments, RNA was submitted to the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core facility for library preparation with rRNA-depletion using standard Illumina protocols and sequencing on an Illumina NovaSeq 6000.

RNA-Seq data analysis

Adapters were trimmed from RNA-Seq reads using cutadapt (Martin, 2011) and reads aligned to the hg19 genome using STAR (Dobin et al., 2013). Gene expression was quantified using featureCounts (Liao et al, 2014) and differential analysis performed using DESeq2 (Love et al, 2014) which calculates p-values through the Wald test and adjusts p-values by the Benjamini-Hochberg procedure to calculate FDR. Changes in levels of alternative splicing events were quantified using rMATS which calculates changes in exon inclusion levels (Δψ), and p-values through a likelihood-ratio test. Genes with significantly altered transcript levels are those with FDR < 0.05. Significant changes in alternative splicing events are those with FDR < 0.05 and Δψ > 5%.

Generation of Cas9-expressing MV4;11 cells

To generate Cas9 expression lentivirus, HEK293T cells were transfected with the viral transfer plasmid lentiCas9-Blast (Sanjana et al, 2014) (gift from Feng Zhang; Addgene plasmid # 52962), the viral packaging plasmid psPAX2 (gift from Didier Trono; Addgene plasmid # 12260), and the viral envelope plasmid pMD2.G (gift from Didier Trono; Addgene plasmid # 12259) using Lipofectamine 3000 Transfection Reagent (Invitrogen). After 48 hours, virus-containing media was collected and used to transduce MV4;11 cells by spinfection (2 hours; 1000 x g; room temperature; 8 μg/mL hexadimethrine bromide). Following spinfection, virus-containing media was replaced with fresh media and cells allowed to recover for 48 hours before selection with 10 μg/mL blasticidin (Research Products International). A clonal MV4;11 Cas9 cell line was established by serial dilution of the population and screening for retention of WIN site inhibitor sensitivity.

Tier 1 CRISPR screen

Tier 1 CRISPR screens were performed essentially as described (Joung et al., 2017). Briefly, the Human GeCKOv2 CRISPR Knockout Pooled Library (A+B) in the lentiGuide-Puro vector backbone (gift from Feng Zhang; Addgene Plasmid # 1000000048) was amplified and purified as directed by Addgene. Lentiviral particles were generated by transfecting HEK293T cells with the GeCKOv2 CRISPR Knockout Pooled Plasmid Library, psPAX2 (gift from Didier Trono; Addgene plasmid # 12260), and pMD2.G (gift from Didier Trono; Addgene plasmid # 12259) using Lipofectamine 3000 Transfection Reagent (Invitrogen). After 48 hours, viral media was collected, aliquoted, and stored at −80°C. In duplicate, clonal Cas9-expressing MV4;11 cells were transduced by spinfection (2 hours; 1000 x g; room temperature; 8 μg/mL hexadimethrine bromide) with a volume of virus-containing media sufficient to infect 30 percent of cells and at a scale to generate >200 transduced cells per sgRNA in the library. Cells recovered in fresh media overnight, were split 1:2, and selected with 1 μg/mL puromycin for 48 hours to generate the MV4;11 Cas9 + GeCKOv2 population.

MV4;11 Cas9 and MV4;11 Cas9 + GeCKOv2 cells were treated with either 0.1% DMSO or 2 μM C6, replenished every three days with fresh media and C6, and counted daily by trypan blue exclusion. DMSO-treated populations were grown until cultures reached > 8 x 10⁵ cells/mL to verify C6-treatment efficacy. C6-treated MV4;11 Cas9 + GeCKOv2 populations were maintained below 8 x 10⁵ cells/mL and grown until a resistant population emerged relative to C6-treated MV4;11 Cas9 cells. Genomic DNA was isolated from MV4;11 Cas9 + GeCKOv2 cells collected before and following sustained C6-treatment using the Quick-gDNA MidiPrep Kit (Zymo Research) per manufacturer’s directions. Sequencing libraries were generated by amplifying sgRNA sequences from genomic DNA using barcoded Illumina-compatible adapter-containing primers and NEBNext High-Fidelity 2x PCR Master Mix (New England BioLabs). PCR products were pooled and purified with a ZymoSpin V column with Reservoir (Zymo Research). Libraries were sequenced on an Illumina NextSeq 500 in the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core facility.

Cloning targeted sgRNA library for second tier screen

The Tier 2 sgRNA plasmid library was generated as previously described with some modifications (Joung et al., 2017). Briefly, sgRNA sequences against a curated collection of genes and 200 non-targeting control sgRNA sequences were extracted from the Brunello sgRNA Library (Doench et al., 2016). For genes of interest not included in the Brunello library, four sgRNAs targeting each gene were designed with the CHOPCHOP sgRNA design tool (Labun et al, 2019). sgRNA sequences were appended with 5’ and 3’ flanking sequences and synthesized as an Oligo Pool (Figure 4 — source data 2; Twist Bioscience) followed by PCR amplification using NEBNext HiFidelity 2X Master Mix (New England BioLabs) with Fwd Primer (GTAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACC) and KO Rev Primer (ACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC). PCR amplicons were subjected to agarose gel size selection using the NucleoSpin Gel and PCR Clean-up Kit (Macherey-Nagel). Amplicons were cloned into BsmBIv2-digested (New England BioLabs) lentiGuide-PURO plasmid (gift from Feng Zhang; Addgene plasmid # 52963) via Gibson Assembly (New England BioLabs). Gibson Assembly products were precipitated with isopropanol and electroporated into Endura ElectroCompetent E. coli (Lexogen). Amplified plasmids were isolated from E. coli using the Nucleobond Xtra Maxi EF Kit (Macherey-Nagel) and adequate representation of sgRNAs in the library was verified by next-generation sequencing and analysis with the python script count_spacers.py (Joung et al., 2017).

Tier 2 CRISPR screen

Tier 2 sgRNA Library lentiviral particles were generated and MV4;11 Cas9 cells transduced as described above for the Tier 1 screen at a scale to achieve >500 cells per sgRNA in the library. MV4;11 Cas9 + Targeted sgRNA Library populations were treated with either 0.1% DMSO, 2 μM C6, or 100 nM C16 for 15 days. Cultures were maintained below 8 x 10⁵ cells/mL and cultures replenished every three days with media and fresh DMSO, C6, or C16. Genomic DNA was isolated, and Illumina-compatible next-generation sequencing libraries generated as described above for the Tier 1 screen. Libraries were sequenced on an Illumina NovaSeq 6000 in the Vanderbilt Technologies for Advanced Genomics (VANTAGE) core facility.

CRISPR Screen data analysis

Adapters were trimmed from reads using cutadapt (Martin, 2011). Generation of sgRNA count tables and determination of significant gene-level alterations in sgRNA representation were performed using MAGeCK (Li et al, 2014) which utilizes a negative binomial model to determine p-values of sgRNA changes and ranks sgRNAs by significance. Gene-level alterations and p-values were calculated from the ranked list of sgRNAs using the modified robust ranking aggregation (α-RRA) algorithm and FDR values calculated by the Benjamini-Hochberg procedure. Tier 1 screen analysis compared populations before and after C6 treatment. Tier 2 screen analysis compared DMSO-treated populations to C6- or C16-treated populations. Significantly enriched or depleted genes were those with FDR < 0.05.

Cell viability assays

Opaque 384-well plates were seeded with 250 cells per well in 25 μL media supplemented with either 0.1% DMSO or a 3-fold dilution series of C6 or C16, all in technical quadruplicate wells. Cells were grown for 72 hours before equilibrating to room temperature and addition of 12.5 μL CellTiter-Glo Luminescent Cell Viability Assay reagent (Promega). At room temperature and protected from light, plates were rocked for 5 minutes, incubated for 20 minutes, and luminescence measured on a GloMax Explorer Multimode Microplate Reader (Promega). To calculate relative cell viability, mean fluorescence from quadruplicate treatment wells was divided by mean fluorescence from quadruplicate DMSO wells. Dose response curves, GI₅₀ concentrations, and standard error values were calculated from at least three biological replicates with the R package drc (Ritz et al., 2015).

Synergy assays

Opaque 384-well plates were seeded with 250 cells per well in 25 μL media supplemented with either 0.2% DMSO, a three-fold dilution series of either C16 or Compound 2, or a combination of three-fold dilutions of both C16 and Compound 2 covering a 7x7 dose matrix, all in quadruplicate wells. Compound 2 consisted of either nutlin-3a (Cayman Chemical Company), rapamycin (MedChem Express), pinometostat (Cayman Chemical Company), harmine (Sigma-Aldrich), mivebresib (Cayman Chemical Company), venetoclax (Cayman Chemical Company), etoposide (Cayman Chemical Company), olaparib (Cayman Chemical Company), VE-821 (Cayman Chemical Company), pemrametostat (Selleck Chemicals), or alvespimycin (Cayman Chemical Company). Following 72 hours, plates were equilibrated to room-temperature and 12.5 μL CellTiter-Glo Cell Viability Assay (Promega) reagent added to each well. While protected from light, plates were rocked for five minutes, incubated for 20 minutes, and luminescence measured on a GloMax Explorer Multimode Microplate Reader (Promega).

Synergy assay data analysis

Technical replicate wells were averaged and resulting means used to calculate relative cell viability by dividing drug treatment by DMSO treatment. Mean δ-scores and standard deviations were calculated from three biological replicates via SynergyFinder Plus (Zheng et al, 2022) using the Zero Interaction Potency (ZIP) model (Yadav et al., 2015). ZIP δ-scores represent the percent of growth inhibition beyond that expected if the agents do not potentiate one another. δ-scores greater than zero are synergistic, δ-scores of zero are additive, and δ-scores less than zero are antagonistic. Statistical significances of peak synergistic and antagonistic δ-scores were calculated by one-sample t-tests using the tsum.test function from the R package PASWR. Significant synergy and antagonism δ-scores were those with p < 0.05.

Quantitative proteomics

In quadruplicate, MV4;11 cells were treated with 0.1% DMSO or 250 nM C16 for either 24 or 72 hours. Cells were collected by centrifugation and washed three times with ice-cold 1X PBS before lysis on ice in SDS Lysis Buffer (5% SDS; 50 mM Ammonium Bicarbonate). Chromatin was sheared by brief sonication at 25% on ice and insoluble material cleared by centrifugation. Soluble proteins were quantified by Pierce BCA Protein Assay (Thermo Scientific).

Protein samples for LC-MS/MS analyses were prepared by S-Trap (ProtiFi) digestion. Protein samples (50 μg) were reduced with DTT (Millipore Sigma) at a final concentration of 20 mM at 95°C for 10 minutes and alkylated with iodoacetamide (Millipore Sigma) at a final concentration of 40 mM at RT for 30 minutes in the dark. Aqueous phosphoric acid (Fisher Scientific) was added to the samples at a final concentration of 1.2% followed by 90% methanol containing 100 mM TEAB at 6.6 times the volume of the sample. The samples were loaded on the S-Trap micro columns and centrifuged at 4000 x g until all the volume was passed through the column. The columns were washed four times with 150 μL 90% methanol containing 100 mM TEAB, pH 7.1. Proteins were digested with trypsin gold (Promega) at 1:50 enzyme to protein ratio in 50 mM TEAB, pH 8.0, for one hour at 47°C. Peptides were eluted by serial addition of 40 μL each of 50 mM TEAB, 0.2% formic acid, and 35 μL of 0.2% formic acid in 50% acetonitrile. Eluted peptides were dried in a speed-vac concentrator, resuspended in aqueous 0.1% formic acid, and analyzed by LC-coupled tandem mass spectrometry (LC-MS/MS).

An analytical column (360 μm O.D. x 100 μm I.D.) was packed with 25 cm of C18 reverse phase material (Jupiter, 3 μm beads, 300Å; Phenomenex) directly into a laser-pulled emitter tip. Peptides were loaded on the reverse phase column using a Dionex Ultimate 3000 nanoLC and autosampler. The mobile phase solvents consisted of 0.1% formic acid, 99.9% water (solvent A) and 0.1% formic acid, 99.9% acetonitrile (solvent B). Peptides were gradient-eluted at a flow rate of 350 nL/min, using a 120-minute gradient. The gradient consisted of the following: 1-100 min, 2-38% B; 100-108 min, 38-90% B; 108-110 min, 90% B; 110-111 min, 90-2% B; 111-120 min (column re-equilibration), 2% B. Upon gradient elution, peptides were analyzed using a data-dependent method on an Orbitrap Exploris 480 mass spectrometer (Thermo Scientific), equipped with a nanoelectrospray ionization source. The instrument method consisted of MS1 using an MS AGC target value of 3x10⁶, followed by up to 15 MS/MS scans of the most abundant ions detected in the preceding MS scan. The intensity threshold for triggering data-dependent scans was set to 1x10⁴, the MS2 AGC target was set to 1x10⁵, dynamic exclusion was set to 20s, and HCD collision energy was set to 30 nce.

Quantitative proteomics data analysis

For identification of peptides, LC-MS/MS data were searched with Maxquant, version 2.0.1.0 (Cox & Mann, 2008). MS/MS spectra were searched with the Andromeda search engine (Cox et al, 2011) against a human database created from the UniprotKB protein database (2021) and the default Maxquant contaminants. Default parameters were used for Maxquant, with the addition of selecting LFQ and match between runs as a global parameter. Maxquant parameters included first and main search mass tolerances of 20 ppm and 4.5 ppm, respectively. Variable modifications included methionine oxidation and N-terminal acetylation, and carbamidomethyl cysteine was selected as a fixed modification. A maximum of two missed cleavages was allowed. The false discovery rate (FDR) was set to 0.01 for peptide and protein identifications. Label-free quantitative (LFQ) analysis of identified proteins was performed with the MSstats R package (Choi et al, 2014), version 4.0.1, using default parameters which include the following: equalize medians for the normalization method, log2 transformation, Tukey’s median polish as the summary method, and model-based imputation. Protein fold changes were considered as significant with adjusted p-values ≤ 0.05.

RNA Isolation and cDNA synthesis

Cell pellets were suspended in TRIzol Reagent (Invitrogen), rotated for 15 minutes at room temperature, and insoluble cellular debris pelleted by centrifugation. The soluble fraction was mixed with equal volume of 100% ethanol and RNA was isolated using the Direct-zol RNA Miniprep Kit (Zymo Research) according to manufacturer’s instructions, including on-column DNA digestion. Complementary DNA (cDNA) was synthesized in 20 μL cDNA reactions containing 1 μg RNA, Random Hexamers (Invitrogen), and SuperScript III Reverse Transcriptase (Invitrogen) per manufacturer’s instructions. Final cDNA products were diluted 5-fold with nuclease-free water before use in semi-quantitative RT-PCR or quantitative RT-PCR.

Semi-Quantitative RT-PCR

PCR reactions were performed with 2 μL cDNA template using primers amplifying splicing variants of RPL22L1 (RPL22L1_RTPCR_F: ATGGCGCCGCAGAAAGAC; RPL22L1_RTPCR_R: CTAGTCCTCCGACTCTGATT) or MDM4 (MDM4_RTPCR_F: GAAAGACCCAAGCCCTCTCT; MDM4_RT_PCR_F: GCAGTGTGGGGATATCGTCT), or within GAPDH (GAPDH_RTPCR_F: TCACCAGGGCTGCTTTTAAC; GAPDH_RTPCR_R: ATCGCCCCACTTGATTTTGG) using Taq DNA Polymerase (New England BioLabs) with primer-specific annealing temperatures and cycle numbers (RPL22L1: 50°C, 30 cycles; MDM4: 54°C, 33 cycles; GAPDH: 52°C, 27 cycles). PCR Products were electrophoretically separated on 2% agarose gels in TBE buffer, gels incubated 30 minutes in TBE buffer containing 1X SYBR Safe DNA Stain (Invitrogen) with agitation, and imaged on a ChemiDoc Imaging System (Bio-Rad).

Quantitative RT-PCR

qPCR reactions containing 1X KAPA SYBR Fast qPCR Master Mix (Roche), transcript-specific primers, and 2 μL cDNA template were performed in technical duplicate wells on a C1000 Touch Thermal Cycler (Bio-Rad) with a CFX96 Touch Real-Time PCR Detection System (Bio-Rad). Primer pairs targeted total RPL22L1 (RPL22L1ab_qPCR_F: TCGAGTGGTTGCATCTGACA; RPL22L1ab_qPCR_R: TCCTCCGACTCTGATTCATCT), RPL22L1a (RPL22L1a_qPCR_F: CGCCGCAGAAAGACAGGAA; RPL22L1a_qPCR_R: CTCCCGTAGAAATTGCTCAAAAT), RPL22L1b (RPL22L1b_qPCR_F: CGCAGAAAGACAGGAAGCC; RPL22L1b_qPCR_R: TGCAAAACTAGGGAAGAGAACC), MDM4 exon 5 - 6 junction (MDM4_Jnct_5_6_qPCR_F: AGAATCTTGTCACTTTAGCCACT; MDM4_Jnct_5_6_qPCR_R: CGAGAGTCTGAGCAGCATCT), MDM4 exon 6-7 junction (MDM4_Jnct_6_7_qPCR_F: TCAAGACCAACTGAAGCAAAGT; MDM4_Jnct_6_7_qPCR_R: TAGGCAGTGTGGGGATATCG), MDM4 exon 4 ( MDM4_Ex_4_qPCR_F: AGCAACTTTATGATCAGCAGGAG; MDM4_Ex_4_qPCR_R: GACGTCCCAGTAGTTCTCCC, MDM4 exon 7 (MDM4_Ex_7_qPCR_F: AGAGGAAAGTTCCACTTCCAGA; MDM4_Ex_7_qPCR_R: ATGCTCTGAGGTAGGCAGTG), or GAPDH (GAPDH_qPCR_F: AAGGTGAAGGTCGGAGTCAAC; GAPDH_qPCR_R: GTTGAGGTCAATGAAGGGGTC). Ct values for each well were determined by the BioRad CFX Manager Software v3.1 using the regression model, and mean Ct values from technical replicate wells used for subsequent calculations. Relative isoform levels were calculated via the 2(^−ΔΔCt) algorithm by internally normalizing isoform-specific Ct values to GAPDH Ct values, then relative to DMSO-treatment.

Quantification and statistical analysis

The n for each experiment, representing biological replicates, can be found in the figure legends. The statistical test and threshold for statistical significance for each experiment can be found below.

Structure alignment

Images of C6 and C16 bound to the WIN-Site of WDR5 and overlaid structures in WDR5-binding conformations were generated with PyMOL using published X-ray crystal structures [C6, PDB: 6E23 (Aho et al., 2019a); C16, PDB: 6UCS (Tian et al., 2020)].

GSEA and ORA

Gene set enrichment analyses and over-representation analyses were performed with the R package fgsea (Korotkevich et al, 2021) using the Molecular Signatures Database v7.4 (Liberzon et al., 2015; Liberzon et al, 2011; Subramanian et al, 2005). Significantly enriched or depleted gene sets were those with FDR < 0.05.

Supplementary Material

Supplement 1

Figure 1—source data 1. Output of RNA-Seq analysis of MV4;11 cells treated with C6/C16.

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Supplement 2

Figure 1—source data 2. GSEA Hallmark and Over Representation Analysis (ORA) Hallmark enrichment analysis of differentially expressed genes in RNA-Seq.

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Supplement 3

Figure 2—source data 1. Output of Ribo-Seq analysis of MV4;11 cells treated with C6/C16.

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Supplement 4

Figure 2—source data 2. Hallmark, Reactome, and KEGG enrichment analysis of differentially translated genes in Ribo-Seq.

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Supplement 5

Figure 2—source data 3. Enrichment analysis of differentially-translated genes, broken down by mRNA level change direction.

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Supplement 6

Figure 3—source data 1. Output of LFQMS analysis of MV4;11 cells treated with C16.

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Supplement 7

Figure 3—source data 2. Enrichment analysis of proteins altered in abundance by 24 or 72 hours of C16 treatment.

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Supplement 8

Figure 4—source data 1. Output of the Tier 1 screen.

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Supplement 9

Figure 4—source data 2. Output of the Tier 2 screen.

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Supplement 10

Figure 4—source data 3. Raw unprocessed gel images corresponding to Figure 4—figure supplement 1J.

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Supplement 11

Figure 5—source data 1. Peak synergy and antagonism scores for MV4:11 and MOLM13 cells treated with C16 in combination with 11 agents.

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Supplement 12

Figure 5—source data 2. Output of RNA-Seq analysis of MV4;11 cells treated with C16, mivebresib, or both.

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Supplement 13

Figure 5—source data 3. Enrichment analysis of differentially expressed genes in RNA-Seq of MV4;11 cells treated with C16, mivebresib, or both.

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Supplement 14

Figure 6—source data 1. Raw unprocessed gel images corresponding to Figure 6—figure supplement 1A.

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Supplement 15

Figure 6—source data 2. Output of rMATS analysis of MV4;11 cells treated with C6/C16.

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Supplement 16

Figure 6—source data 3. Raw unprocessed gel images corresponding to Figure 6—figure supplement 1F.

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Supplement 17

Figure 6—source data 4. Raw unprocessed gel images corresponding to Figure 6—figure supplement 2A.

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Supplement 18

Figure 6—source data 5. Raw unprocessed gel images corresponding to Figure 6D.

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Supplement 19

Figure 6—source data 6. Output of RNA-Seq analysis of NT and RPL22KO MV4;11 cells treated with C16.

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Supplement 20

Figure 6—source data 7. GSEA Hallmark and GOBP enrichment analysis of differentially expressed genes in RNA-Seq of NT and RPL22KO MV4;11 cells treated with C16.

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Supplement 21

Figure 6—source data 8. Output of rMATS analysis of NT and RPL22KO MV4;11 cells treated with C16.

media-21.xlsx^{(5.7MB, xlsx)}

Supplement 22

Figure 6—source data 9. Raw unprocessed gel images corresponding to Figure 6G.

media-22.pdf^{(892.3KB, pdf)}

Supplement 23

Supplementary File 1—Key Resources Table.

media-23.xlsx^{(17.6KB, xlsx)}

Supplement 24

NIHPP2023.07.26.550648v1-supplement-24.pdf^{(8.2MB, pdf)}

Acknowledgements

For reagents we thank D. Trono and F. Zhang. For assistance we thank Lu Chen, David Cortez, Rachel Green, Matthew Hall, Ian Macara, Kavi Mehta, Bill Moore, Jonathan Shrimp, and Jamie Wangen. The VANTAGE Shared Resource is supported by the CTSA Grant (RR024975), the Vanderbilt Ingram Cancer Center (CA068485), the Vanderbilt Vision Center (EY008126), and NIH/NCRR (RR030956). Core services for QuantiGene assays performed through Vanderbilt University Medical Center’s Digestive Disease Research Center were supported by NIH grant DK058404. We acknowledge support of the Vanderbilt Proteomics Core in the Mass Spectrometry Research Center, supported in part by the Vanderbilt Ingram Cancer Center. This work was supported by awards from the NIH/NCI—under Chemical Biology Consortium Contract No. HHSN261200800001E (SWF and WPT), and CA200709 (WPT)—as well as grants from the Robert J. Kleberg, Jr., and Helen C. Kleberg Foundation (WPT and SWF). BCG was supported by the Brock Family Fellowship, the NCI (CA217834/CA268703), and an American Society for Clinical Oncology Young Investigator’s Award.

Footnotes

Declarations of Interest

Fesik, S. W., Stauffer, S. R., Salovich, J. M., Tansey, W. P., Wang, F., Phan, J., Olejniczak, E. T., inventors. WDR5 inhibitors and modulators. United States Patent US 10,501,466. 10 December 2019.

Fesik, S. W., Stauffer, S. R., Tansey, W. P., Olejniczak, E. T., Phan, J., Wang, F., Jeon, K., Gogliotti, R. D., inventors. WDR5 inhibitors and modulators. United States Patent US 10,160,763. 25 December 2018.

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Associated Data

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

Supplementary Materials

Supplement 1

Figure 1—source data 1. Output of RNA-Seq analysis of MV4;11 cells treated with C6/C16.

media-1.xlsx^{(1.3MB, xlsx)}

Supplement 2

Figure 1—source data 2. GSEA Hallmark and Over Representation Analysis (ORA) Hallmark enrichment analysis of differentially expressed genes in RNA-Seq.

media-2.xlsx^{(29.3KB, xlsx)}

Supplement 3

Figure 2—source data 1. Output of Ribo-Seq analysis of MV4;11 cells treated with C6/C16.

media-3.xlsx^{(844.7KB, xlsx)}

Supplement 4

Figure 2—source data 2. Hallmark, Reactome, and KEGG enrichment analysis of differentially translated genes in Ribo-Seq.

media-4.xlsx^{(76.6KB, xlsx)}

Supplement 5

Figure 2—source data 3. Enrichment analysis of differentially-translated genes, broken down by mRNA level change direction.

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Supplement 6

Figure 3—source data 1. Output of LFQMS analysis of MV4;11 cells treated with C16.

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Supplement 7

Figure 3—source data 2. Enrichment analysis of proteins altered in abundance by 24 or 72 hours of C16 treatment.

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Supplement 8

Figure 4—source data 1. Output of the Tier 1 screen.

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Supplement 9

Figure 4—source data 2. Output of the Tier 2 screen.

media-9.xlsx^{(96.2KB, xlsx)}

Supplement 10

Figure 4—source data 3. Raw unprocessed gel images corresponding to Figure 4—figure supplement 1J.

media-10.pdf^{(267.1KB, pdf)}

Supplement 11

Figure 5—source data 1. Peak synergy and antagonism scores for MV4:11 and MOLM13 cells treated with C16 in combination with 11 agents.

media-11.xlsx^{(11.8KB, xlsx)}

Supplement 12

Figure 5—source data 2. Output of RNA-Seq analysis of MV4;11 cells treated with C16, mivebresib, or both.

media-12.xlsx^{(2.1MB, xlsx)}

Supplement 13

Figure 5—source data 3. Enrichment analysis of differentially expressed genes in RNA-Seq of MV4;11 cells treated with C16, mivebresib, or both.

media-13.xlsx^{(46.3KB, xlsx)}

Supplement 14

Figure 6—source data 1. Raw unprocessed gel images corresponding to Figure 6—figure supplement 1A.

media-14.pdf^{(209.7KB, pdf)}

Supplement 15

Figure 6—source data 2. Output of rMATS analysis of MV4;11 cells treated with C6/C16.

media-15.xlsx^{(54KB, xlsx)}

Supplement 16

Figure 6—source data 3. Raw unprocessed gel images corresponding to Figure 6—figure supplement 1F.

media-16.pdf^{(441.8KB, pdf)}

Supplement 17

Figure 6—source data 4. Raw unprocessed gel images corresponding to Figure 6—figure supplement 2A.

media-17.pdf^{(230.9KB, pdf)}

Supplement 18

Figure 6—source data 5. Raw unprocessed gel images corresponding to Figure 6D.

media-18.pdf^{(150.8KB, pdf)}

Supplement 19

Figure 6—source data 6. Output of RNA-Seq analysis of NT and RPL22KO MV4;11 cells treated with C16.

media-19.xlsx^{(3.1MB, xlsx)}

Supplement 20

Figure 6—source data 7. GSEA Hallmark and GOBP enrichment analysis of differentially expressed genes in RNA-Seq of NT and RPL22KO MV4;11 cells treated with C16.

media-20.xlsx^{(772KB, xlsx)}

Supplement 21

Figure 6—source data 8. Output of rMATS analysis of NT and RPL22KO MV4;11 cells treated with C16.

media-21.xlsx^{(5.7MB, xlsx)}

Supplement 22

Figure 6—source data 9. Raw unprocessed gel images corresponding to Figure 6G.

media-22.pdf^{(892.3KB, pdf)}

Supplement 23

Supplementary File 1—Key Resources Table.

media-23.xlsx^{(17.6KB, xlsx)}

Supplement 24

NIHPP2023.07.26.550648v1-supplement-24.pdf^{(8.2MB, pdf)}

Data Availability Statement

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PERMALINK

This is a preprint.

Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Gregory C Howard

Jing Wang

Kristie Lindsey Rose

Purvi Patel

Tina Tsui

Andrea C Florian

Shelly L Lorey

Brian C Grieb

Brianna N Smith

Macey J Slota

Elizabeth M Reynolds

Soumita Goswami

Michael R Savona

Taekyu Lee

Stephen W Fesik

Qi Liu

William P Tansey

Roles

Summary

Introduction

Results

Impact of WINi on the transcriptome of MLLr cancer cells

Figure 1. Impact of WINi on the transcriptome of MLLr cancer cells.

Impact of WINi on the translatome of MLLr cancer cells

Figure 2. Impact of WINi on the translatome of MLLr cancer cells.

Impact of WINi on the ribosome inventory of MLLr cancer cells

Figure 3. Impact of WINi on the ribosome inventory of MLLr cancer cells.

A loss of function screen for modulators of the response to WINi

Figure 4. A loss of function screen for modulators of the response to WINi.

Identification of agents that synergize with WINi in MLLr cells

Figure 5. Identification of agents that synergize with WINi in MLLr cells.

WINi inactivate MDM4 in an RPL22-dependent manner

Figure 6. WINi inactivate MDM4 in an RPL22-dependent manner.

Discussion

Materials and Methods

Key resources

Materials availability

Data availability

Cell lines

Generation of RPL22-null cell lines

Multiplex gene expression assays

Western blot analysis

Ribo-Seq

Ribo-Seq data analysis

RNA-Seq

RNA-Seq data analysis

Generation of Cas9-expressing MV4;11 cells

Tier 1 CRISPR screen

Cloning targeted sgRNA library for second tier screen

Tier 2 CRISPR screen

CRISPR Screen data analysis

Cell viability assays

Synergy assays

Synergy assay data analysis

Quantitative proteomics

Quantitative proteomics data analysis

RNA Isolation and cDNA synthesis

Semi-Quantitative RT-PCR

Quantitative RT-PCR

Quantification and statistical analysis

Structure alignment

GSEA and ORA

Supplementary Material

Acknowledgements

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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