Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Oct 15.
Published in final edited form as: Blood. 2026 Sep 17;148(12):1559–1571. doi: 10.1182/blood.2026033819

PKMYT1 is a Targetable Vulnerability in del(17p) High-Risk Multiple Myeloma

Anaïs Schavgoulidze 1,2,3,*, Jian Cui 1,*, Jessica Encinas Mayoral 1,*, Vanessa Favasuli 1,4, Srikanth Talluri 1, Sabrina Maheo 2,3, Chloé Cerutti 2,3, Masood Shammas 1, Daniel Primo 5, Carmen Vicente 6, Marta Larrayoz 6, José A Martínez-Climent 6, Kenneth C Anderson 1, Anil Aktas-Samur 1, Mehmet Kemal Samur 7, Hervé Avet-Loiseau 2,3, Jill Corre 2,3, Nikhil C Munshi 1,8,#, Mariateresa Fulciniti 1,#
PMCID: PMC13598913  NIHMSID: NIHMS2207133  PMID: 42237635

Abstract

Deletion of 17p is among the most adverse cytogenetic abnormalities in multiple myeloma (MM). By integrating RNA-seq data from patient MM cells with genetic dependency data from MM cell lines, we identified the protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) kinase, a member of the Wee family, as a potential therapeutic target in MM cells harboring del(17p). Genetic suppression or pharmacological inhibition of PKMYT1 activity with the selective inhibitor RP-6306 triggered accumulation of DNA damage, micronucleus formation and mitotic catastrophe, resulting in preferential cell death in del(17p) MM cells while largely sparing del(17p)-negative MM cells and healthy cells. RP-6306 also reduced tumor burden and extended survival in vivo in both xenograft and TP53-deficient syngeneic models. Collectively, our findings nominate PKMYT1 as an actionable target and support PKMYT1 inhibition as a biomarker-driven therapeutic strategy for patients with del(17p)/TP53-deficient MM.

Keywords: Multiple myeloma, Del(17p), Synthetic lethality, PKMYT1, DNA damage

Introduction

Multiple myeloma (MM) is a malignant disorder characterized by the clonal proliferation of plasma cells in the bone marrow that remains largely incurable. While treatment advances have greatly extended the overall survival of most patients, those carrying the high-risk genetic abnormality del(17p) (about 10% of newly diagnosed patients) have not benefited as much from these advancements (1), even in the era of quadruplet therapies (2–4). In contrast to solid tumors, where both TP53 alleles are often knocked out, most del(17p) patients still retain one functional copy of TP53, yet these patients still have a bad prognosis (5). In fact, accumulating evidence confirms the independent prognostic impact of del(17p) on both OS and PFS (6). Despite the need for therapies to specifically treat this patient subset, few studies in MM and other malignancies have attempted to target specific vulnerabilities engendered by the loss of 17p. Some studies have found that heterozygous deletion of TP53 often encompasses a neighboring essential gene, POLR2A, which renders cancer cells susceptible to further inhibition of POLR2A (7–9). In vitro, a BCMA antibody conjugated with an inhibitor of POLR2A has superior activity in del(17p) myeloma cells (9).

To find more such vulnerabilities, we performed a comprehensive RNA-seq analysis to identify global gene expression changes in patients with del(17p), compared with control MM and normal plasma cells (PCs). We identified the protein kinase membrane-associated tyrosine/threonine 1 (PKMYT1, also known as MYT1) as upregulated in del(17p) MM cells and a predictor of poor outcome. Moreover, by interrogating genetic dependency data from MM cell lines, we observed an enhanced genetic dependency on PKMYT1 in del(17p) MM cells. PKMYT1 is a member of the WEE family of serine/threonine kinases, which regulates the G2/M phase transition by inhibiting CDK1 activity, ensuring genome integrity (10–12). In contrast to WEE1, PKMYT1 is not essential for cell cycle progression. However, its overexpression or hyperactivation is linked to poor prognosis in several cancers, and its inhibition has shown synthetic lethal activity with CCNE1 amplification (13). RP-6306, also known as Lunresertib, is a first-in-class, oral small molecule inhibitor of PKMYT1. It has been identified as a strong synthetic lethal partner to multiple genomic alterations, including CCNE1 amplification and FBXW7 and PPP2R1A loss of function, which are key genetic drivers in a broad range of cancers.

Here, we show that treatment with the PKMYT1 inhibitor RP-6306 specifically targeted del(17p) MM cell lines and primary cells from del(17p) patients, leading to irreparable DNA damage and cell death both in vitro and in vivo. This study may provide the rationale for developing personalized medicine in del(17p) MM.

Methods

MM patients’ RNA-seq datasets.

Bone marrow samples were collected from 192 newly diagnosed multiple myeloma (NDMM) patients (Toulouse dataset), plus 319 NDMM patients and 16 healthy donors (IFM2009 dataset)(32). All patients signed an informed consent form, allowing research on their samples, and the study was accepted by the Toulouse Ethics committee. The independent patient RNA-seq dataset contains 507 newly diagnosed MM patient samples and 21 normal plasma cells. Patients in the Toulouse dataset were randomly selected. A collaboration request for the dataset can be submitted via myelomaresearch.net.

Cell lines.

All myeloma cell lines were cultured in RPMI-1640 (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco, Life Technologies, Carlsbad, CA, United States), 1% penicillin/streptomycin, and 1% L-glutamine, with 2.5 ng/mL of IL-6 for INA6, ANBL6 and XG1 cells. Primary MM cells and bone marrow mononuclear cells were isolated using Ficoll-Hypaque density gradient sedimentation and CD138 microbead separation from BM aspirates of MM patients following informed consent and institutional review board (Dana-Farber Cancer Institute) approval. The murine primary MM cell lines MM5080 and MM7027 have been previously described (Larrayoz et al., Nat. Med. 2023). These cells harbor heterozygous deletions of the Trp53 gene. As a control, a Trp53 wild-type murine primary MM cell line was used. Cells were maintained in appropriate culture medium supplemented with 10% fetal bovine serum, penicillin G (100 U/mL), and streptomycin (0.1 mg/mL), and cultured at 37 °C in a humidified atmosphere containing 5% CO2. Cultures were routinely tested and confirmed to be mycoplasma-free.

Reagents.

RP-6306 (lunresertib, PKMYT1 inhibitor), RO-3306 (CDK1 inhibitor), MG132, melphalan and bendamustine were purchased from Selleck (USA). Compounds were dissolved in dimethyl sulfoxide (DMSO).

Generation of CRISPR/Cas9-KO cell line.

Cells were spin-infected with an all-in-one lentiviral vector delivering both single-guide RNA and Cas9 nuclease, purchased from Horizon Discovery (Lafayette, CO, USA). The all-in-one CRISPR-PKMYT1 KO lentiviral sgRNAs include clone ID: VSGHSOMG_30423180, VSGHSOMG_30380280, VSGHSOMG_30470430, cat. no. GSGH12181-15EG9088 and non-targeting control lentiviral sgRNAs include clone ID: SVC19050802, cat. no. GSGC12058. Positive cells were sorted by flow based on their GFP expression.

Cell viability.

Cell viability was measured by CellTiter-Glo Luminescent Cell Viability Assay (CTG; Promega, G7572), and luminescence was measured using the SpectraMax M Series (Molecular Devices). IC50 values for each HMCL were determined using nonlinear regression analysis in GraphPad Prism software.

Colony formation assay.

500 H929 or KMS11 cells were resuspended in 2 mL of Human Methylcellulose Complete Media Without EPO (R&D Systems, HSC004) and seeded in a 24-well plate. Vehicle or 0.5 μM RP-6306 was added to the final volume of 2 mL. The experiment was run in triplicate. After 14 days, wells were stained with 300 μL of 1 mg/mL Nitro Blue Tetrazolium chloride (Millipore Sigma, N6876), and 24 hours later, colonies were counted using the GelCount colony counter (Oxford Optronix).

BrdU, Apoptosis and DNA damage staining.

Cells were seeded in T-25 flasks and treated with RP-6306 for 24 hours. BrdU was added to the cultures for 1 hour at the end of the treatment (at a final concentration of 10 μM). Cells were collected and processed with the “Apoptosis, DNA Damage and Cell Proliferation Kit” (cat. no. 562253, BD Biosciences, USA), following the manufacturer’s protocol. Samples were acquired using a Fortessa flow cytometer (BD Biosciences), and the data were analyzed with FlowJo v10.10.0 software.

Annexin V and PI staining.

Cells were seeded in T-25 flasks and collected after 96 hours of treatment with RP-6306. Cells were resuspended in 1X Annexin-V binding buffer (BD Biosciences, 556454) and stained with 2 μL of Annexin-V FITC (BioLegend, 640906) for 15 minutes at RT. Then, 1 μL of PI (BD Biosciences, 51-6621E) was added to each condition. Samples were acquired using a Fortessa flow cytometer (BD Biosciences), and the data were analyzed with FlowJo v10.10.0 software.

Micronucleus assay.

The cells were cultured in the presence or absence of RP-6306 and were evaluated for micronuclei, a marker of genomic instability (37,38), using the Micronucleus Assay MicroFlow Kit (Litron Laboratories, New York, USA).

Humanized MM model in SCID mice.

All mouse experiments were approved by and conducted according to the relevant regulatory standards of the Institutional Animal Care and Use Committee at the Dana-Farber Cancer Institute. Six-week-old male NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice were purchased from The Jackson Laboratory (#005557). Mice were maintained in pathogen-free conditions, housed in ventilated cages and fed ad libitum. 5 × 106 KMS11 cells were subcutaneously injected into the mice. Upon detection of measurable tumors (approximately 200 mm3 tumor size), mice were randomly assigned to the different experimental groups: (i) vehicle, (ii) low-dose RP-6306 (5 mg/kg), and (iii) high-dose RP-6306 (10 mg/kg). While undergoing treatment, mice were routinely weighed. The tumor measurements were carried out using a caliper, and tumor volumes (mm3) were calculated as follows: (length × width2)/2. The endpoint was established at a tumor volume higher than 2,000 mm3, and mice were then euthanized with CO2. Tumors were removed, dissociated using gentleMACS Dissociator (Miltenyi Biotec, 130-093-235) and incubated with trypsin-EDTA (0.05%; Gibco 25300054) for 10 minutes at 37°C under rotation. Tumor cells were then filtered and pelleted. OS and progression-free survival (PFS) curves were estimated using the Kaplan-Meier method and were compared using the log-rank test.

MM5080-derived syngeneic model.

Syngeneic transplants were established by injection of 5 × 106 MM5080 cells resuspended in DPBS into the tail vein of 8–10-week-old C57BL/6 mice of both sexes. Mice (n = 8) were randomly assigned to three treatment groups: (1) vehicle, (2) RP-6306 at 5 mg/kg, or (3) RP-6306 at 10 mg/kg. Treatment was initiated on day +3 post-injection. RP-6306 was prepared in 10% DMSO and 90% (20% SBE-β-CD in saline) and administered orally twice daily, five days per week, for three weeks. Therapeutic responses were evaluated using Kaplan-Meier survival analysis and compared using the log-rank test. All in vivo experiments were approved by the University of Navarra Ethics Committee for Animal Experimentation.

Statistical analysis.

The significance of differences between experimental variables was analyzed using the statistical test specified in each legend. Analyses were performed using either R version 4.3.2 or GraphPad Prism software version 10.

Results

PKMYT1 is a functionally and clinically relevant target in del(17p) MM cells.

To investigate therapeutically actionable vulnerabilities in del(17p) MM, we integrated genome-scale dependency screens in MM cell lines with gene expression profiles in MM cells from patients. We first analyzed RNA-sequencing data from the CD138+ MM cells of 98 newly diagnosed multiple myeloma (NDMM) patients harboring del(17p) and 94 non-del(17p)/TP53–wild-type patients (Supplemental Table 1). Relative to standard risk MM, del(17p) cases exhibited extensive differential expression, with 445 genes downregulated and 439 upregulated (Figure 1A); notably, 17% of the downregulated genes (75/445) reside on 17p. Integrating these results with two additional MM cohorts (MMRF-CoMMpass and an internal dataset) yielded 50 genes recurrently dysregulated in del(17p) MM (Figure 1B). STRING analysis indicated that a large proportion of these genes are involved in cell-cycle regulation and cell division, with a subset enriched for the G2/M phase transition (Figure 1C and Supplemental Figure S1A).

Figure 1. Integrated transcriptomic and functional analyses identify PKMYT1 as a target in del(17p) MM.

Figure 1.

Open in a new tab

(A) Gene expression profile showing differentially expressed genes between monoallelic del(17p) (n = 98) and non-del(17p) MM (n = 94). Vertical dashed lines are at log2(fold change) of 0.5 and −0.5, and the horizontal dashed line represents the threshold for −log10(adjusted p-value) of 0.05. (B) Dysregulated genes across the Toulouse and CoMMpass cohorts, plus an independent internal cohort (IIC) with 507 newly diagnosed MM patient samples. 50 genes were commonly dysregulated: 42 upregulated (p = 9.99e-05) and 8 downregulated genes (p = 0.0005). The significance of the number of overlapping genes was determined by a bootstrap analysis.

(C) STRING analysis showing the existence of a highly interconnected functional protein network among the 50 commonly dysregulated genes in del(17p) versus non-del(17p). MIR22HG (lncRNA) was excluded by STRING.

(D) Mean dependency scores between del(17p) (n = 4) and non-del(17p) (n = 7) MM cell lines based on DepMap Chronos dependency score (22Q4+Score database). The 22 genes with a more significant dependency in del(17p) MM cell lines are highlighted in red. These genes were selected as follows: genes with a significant statistical difference between del(17p) and non-del(17p) cell lines, using a Mann-Whitney test; AND genes with a mean dependency score below −0.5 in del(17p) cell lines; AND genes with a difference between the mean dependency in non-del(17p) and the mean dependency in del(17p) below 0.

The Venn diagram shows the overlap between the 50 commonly dysregulated genes in del(17p) newly diagnosed MM patients and the 22 genes with a significant preferential dependency in del(17p) MM cell lines; only PKMYT1 was found to be a common gene.

(E) PKMYT1 Chronos scores stratified by 17p status in MM cell lines (double-hit defined as concurrent del(17p) and TP53 mutation). Gene dependency data (Chronos scores) were obtained from the Cancer Dependency Map (DepMap) Broad Institute project (DepMap Public Release 23Q4; https://depmap.org/portal/). Non-del(17p) MM, n = 5; del(17p), n = 4; TP53-mutated, n = 5; double-hit, n = 5. Data was analyzed by the global Kruskal-Wallis test for global comparison (P=0.044) and the Mann-Whitney test for pairwise comparisons.

(F) PKMYT1 TPM expression in plasma cells among healthy subjects (normal plasma cells) and newly diagnosed MM patients stratified by their del(17p) status (PC = plasma cells) in the Toulouse dataset. Patients with double-hit (n=62) were also included in this analysis. Data were analyzed by the global Kruskal-Wallis test for global comparison and the Mann-Whitney test for pairwise comparisons.

(G) Volcano plot showing significantly differentially expressed genes (DEGs) between cells with and without del(17p). Each point refers to a DEG. The blue and orange dots represent the downregulated DEGs (n = 872) and upregulated DEGs (n = 884) in the del(17p) cell group, respectively. The grey dots represent the non-significant DEGs (n = 6,412). PKMYT1 is represented by the black dot. The vertical lines represent a log2(fold change) threshold of 1.5 and −1.5. The horizontal line represents the significance threshold for a −log10(false discovery rate of 5%).

(H) Kaplan-Meier overall survival of NDMM patients from the MMRF-CoMMpass dataset according to PKMYT1 TPM expression (high: top quartile; with the rest in the low group). Kaplan-Meier survival curves were compared by log-rank test.

By leveraging data from genome-wide Cas9-based CRISPR KO screens39, we identified 22 genes with a greater impact on MM cell lines with del(17p) compared to MM cell lines without del(17p) (Figure 1D). Combining these data with the transcriptomic profile of MM cells from patients with del(17p) MM, we identified the PKMYT1 kinase as the only dysregulated gene with a greater dependency in del(17p) MM cell lines compared to those without del(17p) (mean dependency score, del(17p): −1.30 vs WT: −0.89, P = 0.01) (Figures 1D, E and Supplemental Figure S1B). We further confirmed a trend toward higher dependency in del(17p) cells in two additional genome-wide CRISPR-KO datasets in MM cell lines (Supplemental Fig S1C).

In patients, PKMYT1 expression increased from normal plasma cells to MM cells without del(17p) and was significantly upregulated in del(17p) and double-hit MM cells (P < 0.0001) (Figure 1F). Using single-cell transcriptomics on patients with subclonal del(17p), we confirmed within the same patient that PKMYT1 was overexpressed in tumor cells harboring del(17p) compared to cells without del(17p) (Figure 1G, Supplemental Fig S1D), and it was within the top 30 upregulated genes in the 5 patients examined. Finally, high expression of PKMYT1 was associated with poor overall survival (HR = 3.1, P < 0.0001) in the CoMMpass dataset (Figure 1H).

Collectively, these functional genomics and patient transcriptomic data identify PKMYT1 as a del(17p)-associated, clinically relevant dependency in MM and nominate it as a promising therapeutic target for this high-risk subgroup.

PKMYT1 is a genetic vulnerability in del(17p) MM cells.

PKMYT1 expression was found to be significantly higher both at the RNA and protein levels in del(17p) MM cell lines compared to non-del(17p) MM cell lines (Supplemental Fig S2A–C). CRISPR-Cas9-mediated genetic depletion reduced PKMYT1 expression (Figure 2A, Supplemental Fig S2D), leading to a time-dependent decrease in cell viability in two MM cell lines with del(17p); however, the same PKMYT1-targeting sgRNAs did not have a similar effect in the non-del(17p) MM cell line H929. The inhibition of cell growth was confirmed by flow cytometry analysis, where expression of PKMYT1-targeting sgRNAs resulted in depletion of the sgRNA+/GFP+ cell population over time (Figure 2B–C, Supplemental Fig S2E).

Figure 2. Genetic depletion of PKMYT1 represses del(17p) MM cell viability.

Figure 2.

Open in a new tab

(A) KMS20, JJN3 and H929 MM cell lines were infected with either GFP-labeled non-targeting (NT) control or all-in-one CRISPR-PKMYT1 KO lentiviral sgRNAs and FACS-sorted 72 hours after infection. Western blot analysis was performed for evaluation of PKMYT1 expression in the H929, JJN3 and KMS20 cell lines after infection with control or PKMYT1 sgRNAs. GAPDH was used as a loading control, and the PKMYT1/GAPDH ratio is shown (upper panel). Cell proliferation was evaluated over time by Cell-Titer Glo (CTG) assay in non-del(17p) (H929) and del(17p) (JJN3, KMS20) MM cell lines following CRISPR-mediated PKMYT1 knockout. Data show the percentage of cell growth compared to non-targeting control cells at each given time point. Data represents mean +/− SD from three independent experiments performed with three technical replicates. Statistical significance on Day 4 was determined by one-way ANOVA with Dunnett’s multiple comparisons test (bottom panel).

(B-C) Del(17p)-positive JJN3 cells were infected with either non-targeting (NT) control or all-in-one CRISPR-PKMYT1 lentiviral sgRNAs (3 replicates). Three days post-infection, cells were seeded and evaluated over time by flow cytometry. Shown is the fraction of live, sgRNA-expressing (GFP+) cells over time, compared with day 0. Representative dot plots are shown in (B), while mean +/− SD of three independent experiments performed in triplicate is shown in (C). Statistical significance on Day 11 was determined by one-way ANOVA with Dunnett’s multiple comparisons test.

PKMYT1 is critical in maintaining the S phase and G2/M-phase DNA damage checkpoints by negatively regulating CDK1 through Thr14 and Tyr15 phosphorylation. Concordantly, we detected decreased CDK1 phosphorylation at T14 after PKMYT1 genetic depletion (Supplemental Fig S2F). Among the G2 checkpoints, only the loss of PKMYT1 function was significantly more effective in del(17p) versus non-del(17p) MM cell lines (Supplemental Fig S2G). Altogether, these data suggest that del(17p) may confer vulnerability to the inhibition of PKMYT1.

Pharmacological inhibition of PKMYT1 with RP-6306 impairs del(17p) MM cell growth.

To evaluate the therapeutic potential of PKMYT1 inhibition in del(17p) MM cells, we utilized RP-6306 (lunresertib), a recently developed selective inhibitor of PKMYT1’s catalytic activity (13), currently in phase I/II clinical trials for the treatment of advanced gynecologic and digestive tumors (https://clinicaltrials.gov/NCT05601440, NCT05147272, NCT05605509, NCT04855656, NCT05147350, NCT06107868). Treating a panel of MM cell lines, including non-del(17p), monoallelic del(17p), TP53-mutated and double-hit MM cell lines, with RP-6306 caused a time- and dose-dependent decrease in cell viability in MM cells harboring del(17p) and/or TP53 mutation (Figure 3A, Supplemental Fig S3A), with a significantly lower IC50 in this subgroup compared to non-del(17p) MM cell lines (Figure 3B). MM1S represents an exception to the del(17p)-associated trend, as this del(17p)–/TP53–wild-type line remains sensitive to RP-6306, suggesting that additional factors contribute to PKMYT1 inhibitor sensitivity in MM. Notably, MM1S exhibits high basal CCNE1 expression, which may underlie this response. Indeed, in our MM cell line panel, we observed a trend between RP-6306 response and CCNE1 expression, although not significant (Supplemental Fig S3B).

Figure 3. Pharmacological inhibition of PKMYT1 impairs viability and induces apoptosis in del(17p) and p53-altered HMCLs.

Figure 3.

Open in a new tab

(A) Dose-response curves in a panel of 14 HMCLs (gray = wild-type, blue = del(17p), red = TP53 mutation, purple = del(17p)+TP53 mutation). Cell viability was assessed by CTG assay after 96 hours of treatment and compared to untreated cells. Each cell line was evaluated in at least three independent experiments performed in triplicate. Mean +/− SD of all independent experiments is shown.

(B) RP-6306 IC50 was calculated using GraphPad Prism. Differences between the two groups of cell lines were evaluated with a two-tailed Mann-Whitney test.

(C) TMT-based phosphoproteomics of del(17p)-positive KMS11 cells treated with 0.5 μM RP-6306 for 6 h versus untreated control. 15,465 phosphorylation sites were identified, with significantly altered sites highlighted in the plot. A fold change >1.2 was considered upregulated, while a fold change <0.8 was defined as downregulated (vertical black dotted lines). Statistical significance was determined at p < 0.05 (horizontal black dotted line).

(D) Kinase–substrate enrichment analysis (KSEA) of TMT-based phosphoproteomics data, showing significantly enriched kinases (p < 0.05). Positive scores (orange) indicate increased kinase activity, whereas negative scores (blue) indicate decreased activity.

(E) (Top) Western blot of total CDK1 and phospho-T14 CDK1 in non-del(17p) (MM1S, H929) and del(17p) cell lines (JJN3, KMS11, ANBL6) after a 4 h exposure to vehicle (−) or 0.5 μM RP-6306 (+). (Bottom) Relative quantification of the pCDK1/CDK1 ratio, normalized to GAPDH between non-del(17p) (gray) and del(17p) (blue) MM cell lines. The points inside each bar graph indicate the number of independent experiments performed for each cell line. The error bars represent mean +/− SD.

(F) Representative dot plots of Annexin V and PI staining in non-del(17p) MM cells (H929) and del(17p) cells (JJN3) after 96-hour treatment with vehicle or 0.5 μM RP-6306, analyzed by flow cytometry.

(G) Percentage of Annexin V-positive cells in non-del(17p) MM cells (KMS27, MM1S and H929) and del(17p) cells (KMS11, JJN3, KMS20, ANBL6 and RPMI8226) after a 96-hour treatment with vehicle or 0.5 μM RP-6306, analyzed by flow cytometry. Two-way ANOVA (Šídák’s multiple comparisons test) was used. The error bars represent mean +/− SD.

(H) Quantification of the number of H929 or KMS11 colonies after 14 days with vehicle or 0.5 μM RP-6306 in methylcellulose semi-solid medium. Graph shows mean +/− SD of three replicates per cell line, analyzed by 2-way ANOVA (Šídák’s multiple comparisons test).

(I) Time- and dose-dependent effects of RP-6306 on primary MM cells from two patients, assessed using CellTiter-Glo. Patient 1 harbored del(17p) with 60% clonality; Patient 2 was double-hit with del(17p) and TP53 c.396G>C (p.K132N) (97% VAF). Cell viability was measured relative to untreated controls and is presented as mean +/− SD of triplicate.

(J-K) ExviTech automated flow cytometry platform (Vivia biotech, Madrid, Spain) was used to evaluate the activity of RP-6306 against primary myeloma cells in BMMCs from 4 MM patients after 120-hour treatment with different RP-6306 concentrations. Cell survival is also shown to demonstrate the absence of toxicity of the RP-6306 inhibitor against healthy CD138-negative population.

Short exposure to RP-6306 (6 hours) significantly affected the phosphorylation status of numerous sites in the del(17p) KMS11 MM cell line, including reduced phosphorylation at threonine 14 (T14) and tyrosine 15 (Y15) on CDK1, consistent with PKMYT1’s role as a kinase (Figure 3C, Table S2, Supplemental Fig S3C). Kinase-substrate enrichment analysis (KSEA) was performed to identify kinases whose activity is altered by treatment with RP-6306. This analysis confirmed CDK1 kinase activity as the most positively enriched, while PKMYT1 kinase activity, as expected, was significantly inhibited (Figure 3D). This effect was also observed 48 hours post-treatment, while only a limited number of proteins were affected (Supplemental Fig S3D, S3E).

Reduced phosphorylation at threonine 14 (T14) and tyrosine 15 (Y15) on CDK1 was further investigated by Western blot analysis and observed in both non-del(17p) and del(17p) cells (Figure 3E). However, RP-6306-induced CDK1 hyperactivation was only lethal to del(17p) MM cell lines (Figure 3A, 3B, Supplemental Fig S3A), and adding the CDK1 inhibitor RO-3306 partially reversed the cell growth inhibition following RP-6306 treatment (Supplemental Fig S3F), suggesting the importance of CDK1 hyperactivation in the growth defect caused by PKMYT1 inhibition in del(17p) cells.

We further confirmed that initial growth inhibition was followed by irreversible apoptotic cell death in del(17p) cells (Figures 3F, 3G), as shown by time-course washout studies (Supplemental Fig S3G). Additionally, treatment with RP-6306 significantly reduced the clonogenic potential of del(17p) MM cells, while non-del(17p) MM cells remained unaffected (Figure 3H, Supplemental Fig S3H). Finally, we confirmed the impact of PKMYT1 inhibition ex vivo in primary MM cells from patients with and without del(17p) and/or TP53 mutation. Evaluation of RP-6306 treatment in CD138+ cells from a patient with a secondary plasma cell leukemia (60% del(17p) clonality) and CD138+ cells from a patient progressing after 6 cycles of DARA-RVd induction (del(17p) and TP53 mutation) confirmed a dose- and time-dependent activity of RP-6306 against primary MM cells (Figure 3I). Moreover, treatment of bone marrow mononuclear cells (BMMNCs) from bone marrow aspirates of 4 MM patients showed a greater effect on primary MM cells harboring a TP53 mutation or double-hit (Fig. 3J), compared with non-del(17p) MM cells (Fig. 3K). The IC50 values determined in these bulk primary cultures are likely to be influenced by the relative abundance of del(17p) cells within each individual sample. Importantly, no significant toxicity was observed in CD138-negative cell populations (Figure 3J-K), further supporting its selective anti-myeloma effect. These data suggest a rationale for targeting PKMYT1 with RP-6306 in patients harboring del(17p) and/or TP53 mutation.

RP-6306 induces DNA damage and replication stress in del(17p) MM cells.

CDK1 controls DNA replication and the G2/M phase transition (14,15), and its overactivation induces replication stress through aberrant firing of replication origins (16,17). Therefore, we tracked cell cycle progression and DNA replication after PKMYT1 inhibition using BrdU pulse labeling and flow cytometry, along with γH2AX and PARP staining to detect DNA damage and apoptotic cell death. Short-term treatment with RP-6306 caused the accumulation of γH2AX-positive cells in del(17p) cell lines, and the effect was more pronounced in the G2/M+ population (Figures 4A–B, Supplemental Fig S4A–B). The induction of DNA damage was not associated with a large increase in PARP cleavage, suggesting it preceded the induction of apoptosis that was observed at a later timepoint (Figure 3F). We confirmed by western blot analysis that PKMYT1 inhibition by RP-6306 tended to increase the levels of γH2AX and replication stress markers (phospho-CHK2 and phospho-RPA32) in del(17p) MM cell lines but not in 17p-intact MM cells (Figures 4C–D). Interestingly, in the MM1S (del(17p)–/TP53–wild-type) sensitive line, RP-6306 induced a delayed DNA damage response, with minimal changes after short exposure, as shown in Figure 3E, but clear induction after 24 h of treatment (data not shown). PKMYT1 KO produced comparable effects to RP-6306, with del(17p) PKMYT1 KO cells experiencing DNA damage and replication stress (Supplemental Fig S4C).

Figure 4. Abrogation of PKMYT1 activity increases DNA damage and replication stress in del(17p) MM cells.

Figure 4.

Open in a new tab

(A) (left panel) Representative flow cytometry staining of intracellular γH2AX and cleaved PARP in G2+ ANBL6 (del(17p)+TP53mutation) cells after 24-hour treatment with vehicle or 0.5 μM RP-6306. (right panel) Bar plot with mean +/− SD of 4 independent experiments, assessed by two-tailed paired t-tests.

(B) Percentage of γH2AX-positive cells within G2+ non-del(17p) cells (H929, KMS27, and MM1S), del(17p) or double-hit cells (KMS20, KMS11, and ANBL6) after 24-hour treatment with and without RP-6306. Each cell line was tested in three independent experiments and then grouped based on the presence/absence of del(17p). Sidak’s multiple comparisons test was used to calculate the p value. Error bars are mean +/− SD.

(C) Representative western blot analysis of three independent experiments of whole-cell lysates from control and treated (0.5 μM RP-6306, 24 hours) del(17p) MM cell lines (JJN3, KMS20, KMS11, ANBL6) and non-del(17p) MM cells (H929, MOLP8, KMS21, MM1S) using the indicated antibodies. GAPDH was used as a loading control. (Right) Densitometric analysis: The ratio of phospho-H2AX/GAPDH was calculated with ImageJ and represented as fold change from untreated cells. 4 cell lines for each group (non-del(17p) and del(17p)) are shown, analyzed by a one-tailed t-test. Error bars are mean +/− SD.

(D) Representative western blot analysis of three independent experiments of whole-cell lysates from control and treated (0.5 μM RP-6306, 24 hours) del(17p) MM cell lines (JJN3, KMS20, KMS11, ANBL6) and non-del(17p) MM cells (H929, MOLP8, KMS21, MM1S) using the indicated antibodies. GAPDH was used as a loading control. (Below) Densitometric analysis: The ratios of phosphorylated/total forms of CHK2 and RPA32 were analyzed with ImageJ software and represented as fold change from untreated cells. 4 cell lines for each group (non-del(17p) and del(17p)) are shown, analyzed by a one-tailed t-test.

(E) Representative plots of micronuclei quantification by flow cytometry showing dose-dependent accumulation of micronuclei after a 24 h treatment with RP-6306 in the ANBL6 cell line (del(17p)+TP53mutation) but not in the non-del(17p) MM cell line (H929). Micronuclei: pink population, nuclei: green population. (F) Micronuclei quantification (error bars = mean +/− SD) after PKMYT1 inhibition for non-del(17p) (2 cell lines) and del(17p) (3 cell lines) MM cell lines, analyzed by two-way ANOVA with Sidak’s test.

(G-H) (G) Representative images of mitotic KMS11 (del(17p)) cells after 24h-treatment with 1 μM RP-6306 or control. (H) Quantification of normal and defective mitotic cells in KMS27 and KMS11 MM cell lines (3 replicates). Error bars = mean +/− SD.

To further support the presence of heightened DNA damage in del(17p) cells following treatment, we observed a marked and statistically significant increase in micronucleus formation—an established indicator of replication stress and genomic instability (Figures 4E–F). Finally, PKMYT1 inhibition led to the formation of supernumerary centrosomes and a disorganized spindle and induced chromosome pulverization (a marker of mitotic catastrophe) in del(17p) cells but not in MM cells with intact 17p (Figures 4G–H, Supplemental Fig S4D). Consistent with this heightened mitotic vulnerability and genomic instability, PKMYT1 inhibition also increased the sensitivity of del(17p) cells to DNA-damaging agents (Supplemental Fig S4E).

RP-6306 selectively targets TP53-deficient MM cells, reducing tumor growth and prolonging survival in xenograft and syngeneic models of MM.

To assess the effectiveness of RP-6306 against del(17p) cells in vivo, NSG mice were inoculated with KMS11 cells. Upon reaching a tumor volume of approximately 100 mm3, we randomly assigned the mice to three treatment groups: (i) vehicle, (ii) low-dose RP-6306 (5 mg/kg), and (iii) higher-dose RP-6306 (10 mg/kg) twice daily for 5 days/week for three weeks. Treatment with RP-6306 at low and higher doses significantly reduced tumor volume compared to vehicle-treated mice (Figure 5A), and prolonged survival (Figure 5B). Monitoring mouse body weights and blood counts demonstrated that the treatment with RP-6306 did not cause toxicity in vivo (Figure 5C, Supplementary Fig S5A).

Figure 5. RP-6306 significantly reduces tumor volume and prolongs survival in mice.

Figure 5.

Open in a new tab

(A) Sub-lethally irradiated SCID mice were injected subcutaneously with KMS11 cells. Mice were randomized and treated with either RP-6306 or vehicle at first detection of tumor (tumor volume approximately 200 mm3). Mice received either 5 or 10 mg/kg RP-6306 for 5 consecutive days/week for 3 weeks. Tumor volume was measured in 2 perpendicular dimensions by caliper twice a week. Baseline values were not significantly different among groups. Analyzed by a mixed-effects model. *: <0.05; **: <0.01; ***: <0.001; ****: <0.0001. Error bars = mean +/− SD. (B) Survival was evaluated from the first day of treatment until death. Survival curves (Kaplan-Meier) were analyzed by the log-rank test.

(C) Quantification of white blood cell count (WBC), platelet (PLT), and hemoglobin (Hb) levels in mice treated with vehicle (n=6), 5 mg/kg (n=5), or 10 mg/kg (n=12) of RP-6306. Analyzed by unpaired t-tests. Error bars = mean +/− SD.

(D) Western blot analysis was performed on lysates from tumors excised from representative mice and probed with the indicated antibodies.

(E–F-G) Protein band intensities were quantified using ImageJ. (E) γH2AX/GAPDH ratio was calculated for each tumor. The mean γH2AX/GAPDH ratio for each group is shown, expressed relative to untreated cells. (F-G) Signals were first normalized to GAPDH, and data are presented as the mean ratio of phosphorylated to total protein for each group. Statistical analysis was performed using unpaired t test with Welch’s correction. Error bars represent mean +/− SD.

(H) Primary murine MM cells were seeded in black 96-well plates and treated with increasing concentrations of RP-6306 for 48 or 72 h. Cell viability was assessed using the Deep Blue Cell Viability Kit and measured with a Varioskan Flash fluorometer using SkanIt software v2.4.3. Treatments were performed at a density of 0.25 × 106 cells/mL, and all experiments were conducted in triplicate. Error bars represent mean +/− SD.

(I) Transplantation of MM5080 cells carrying Trp53 loss was conducted in immunocompetent C57BL/6 mice by IV injection. Mice (n = 5/group) were randomly assigned to three treatment groups: (1) vehicle, (2) RP-6306 at 5 mg/kg, or (3) RP-6306 at 10 mg/kg. Treatment was initiated on day +3 post-injection. RP-6306 was administered orally twice daily, five days per week, for three weeks. Therapeutic responses were evaluated using a log-rank test.

(J) Bone marrow infiltration in the MM5080 transplant model was assessed at endpoint by flow cytometric quantification of GFP-positive MM5080 cells in the bone marrow. Values are shown as the percentage of MM5080 cells normalized to the mean of the vehicle-treated group. Error bars represent mean +/− SD. P-values determined by Kruskal-Wallis test.

Mimicking our in vitro results, in vivo treatment with RP-6306 increased the expression of γ-H2AX and phospho-CHK2 in tumor cells retrieved from mice (Figures 5D–F, Supplemental Fig S5B–D), with a trend toward lower phospho-CDK1 (Figure 5G). Notably, tumor cells harvested at endpoint remained sensitive to RP-6306 when tested ex vivo (Supplemental Fig S5E).

Furthermore, the efficacy of PKMYT1 inhibition against TP53-altered MM cells was tested in murine MM cell lines established from primary myelomas developed in genetically engineered mice (19). RP-6306 selectively reduced the viability of two Trp53-deficient murine MM cell lines, MM5080 and MM7027, in a dose- and time-dependent manner, while a Trp53 wild-type primary MM cell line was barely affected under identical conditions (Figure 5H). Consistent with these in vitro findings, RP-6306 treatment markedly prolonged the survival of immunocompetent C57BL/6 mice transplanted with MM5080 cells carrying Trp53 loss. Thus, mice receiving RP-6306 at 5 mg/kg or 10 mg/kg twice daily showed a dose-dependent improvement in overall survival compared with vehicle-treated controls (Figure 5I). Bone marrow infiltration was assessed at endpoint by flow cytometric quantification of GFP-positive MM5080 cells, revealing a reduced frequency of tumor cells in the bone marrow of treated mice compared with controls (Figure 5J). These data indicate that RP-6306 exerts potent antitumor activity against Trp53-deficient MM cells in both in vitro and in vivo settings.

Discussion

In MM, del(17p) is a high-risk cytogenetic abnormality strongly associated with poor clinical outcome, underscoring its significance as a prognostic marker and a therapeutic challenge (6,20,21). Here, we aimed to uncover potential vulnerabilities imparted by 17p loss and, in turn, identify genetic and pharmacological dependencies that could be exploited to target essential survival pathways activated by 17p deletion. While loss of TP53 located on 17p has been strongly associated with disruptions in the G1/S checkpoint, impaired DNA damage response, and defective repair mechanisms, ultimately driving genomic instability and tumor progression (22,23), the available data suggests the role of additional genes, perturbed by del(17p), in driving the functional phenotype of these MM cells through various regulatory and compensatory pathways. In this study, we sought to identify genes that might drive these compensatory mechanisms, providing a functional advantage to del(17p) MM cells.

We identified PKMYT1, a G2/M checkpoint regulator, as a novel vulnerability in MM cells with del(17p) and/or TP53 mutations. PKMYT1 is a serine/threonine kinase and a member of the Wee1 family of proteins with a significant role in the G2/M checkpoint of the cell cycle. It phosphorylates CDK1 and prevents early entry of the cell into mitosis by maintaining CDK1-cyclin B1 in an inactive state. The early mitotic entry triggered by PKMYT1 inhibition, coupled with CDK1 hyperactivation, has been shown to be particularly damaging to certain cancer cells, as it exacerbates replication stress and synergizes with DNA damage, underscoring the importance of PKMYT1 as a vulnerability in cancer cells experiencing high replication stress (13,24,25). Our findings further extend the therapeutic relevance of PKMYT1 inhibition to MM, particularly in the context of 17p deletion and TP53 dysfunction. Interestingly, prior studies in pancreatic ductal adenocarcinoma have shown that abrogation of the G2 checkpoint via PKMYT1 inhibition is synthetically lethal in p53-deficient tumor cells (26), aligning with our observations in MM.

PKMYT1 inhibitors, along with inhibitors of the related Wee kinase family member WEE1 (12,27), have demonstrated preclinical efficacy as monotherapy in cancers characterized by high replication stress, such as in models with cyclin E hyperactivation (13). While WEE1 is recognized as an essential gene, PKMYT1 stands out as a potentially more selective therapeutic target, as its inhibition does not appear to significantly impact long-term cell growth in normal cells (28). WEE1 inhibitors have been previously explored in MM (29), but their clinical application has been hindered by pleiotropic effects and associated toxicities (24). Additionally, PKMYT1 has been identified as a candidate predictive biomarker for acquired resistance to WEE1 inhibitors (24), and dual WEE1 and PKMYT1 inhibition has been proposed as a strategy to enhance tumor cell selectivity while reducing the required doses of each inhibitor, thereby minimizing toxicity (30). Our study provides a fresh perspective by identifying PKMYT1 as a promising and potentially more targeted therapeutic vulnerability in MM cells with 17p loss, paving the way for innovative treatment strategies tailored to the high-risk patient population.

CCNE1 amplification and mutations in FBXW7 or PPP2R1A have been associated with RP-6306 sensitivity in other cancer cell context. In our panel of MM cell lines, we observed a trend between RP-6306 response and cyclin E expression, although not significant, as well as a trend toward increased CCNE1 expression in del(17p) patients (data not shown). However, due to the lack of matched genomic annotations for the specific models tested, we could not evaluate whether co-occurring alterations (e.g., FBXW7 or PPP2R1A mutations) contribute to the increased sensitivity observed in TP53-altered cells. Future studies using genomically annotated cohorts and/or isogenic models will be required to determine whether RP-6306 sensitivity is driven by TP53 alterations alone or in combination with additional genetic features.

We, however, believe that PKMYT1 is not inherently “irrelevant” in TP53–wild-type patients. Instead, we propose that PKMYT1 inhibition is most effective in cells with high baseline replication stress that rely on PKMYT1 to restrain CDK1 and prevent premature mitotic entry. In this context, TP53-altered cells may be more sensitive because loss of p53 weakens the G1/S checkpoint, increasing reliance on the G2/M checkpoint. As a result, PKMYT1 inhibition may have a larger therapeutic window in TP53-altered disease, while TP53–wild-type cells may be less sensitive or require combination strategies.

By activating CDK1, PKMYT1 inhibition drives cells into premature mitosis with under-replicated and damaged DNA, ultimately resulting in mitotic catastrophe. In parallel experiments, we observed that co-treatment with the CDK1 inhibitor RO-3306 only partially rescued RP-6306-induced cell death in del(17p) MM cells. This indicates a potential role of other kinases in driving the observed phenotype. These intermediates may be responsible for both perturbation of PKMYT1 activity in del(17p) MM and the selective sensitivity of del(17p) MM cells to RP-6306. Further investigations will be necessary to uncover these potential contributors and fully understand the underlying mechanisms.

To further expand therapeutic possibilities, we investigated combination strategies and observed synergistic activity when PKMYT1 inhibition was combined with DNA-damaging agents. These data suggest that in del(17p) cells, which often lack functional TP53 and therefore have impaired DNA damage responses, PKMYT1 inhibition could further compromise the cells’ ability to manage genomic stress. As a result, when exposed to DNA-damaging agents, these cells accumulate unrepaired lesions more rapidly, leading to enhanced cytotoxicity. These results underscore the potential of leveraging genomic vulnerabilities in MM cells through combination approaches.

Additionally, studies in other cancers have demonstrated that RP-6306 treatment can induce interferon responses and promote T-cell infiltration (31), suggesting that PKMYT1 inhibition may enhance antitumor immune responses and increase susceptibility to immunotherapy. These findings highlight the need for further studies to explore the role of RP-6306 in modulating immune responses within the MM tumor microenvironment, as well as the potential for integrating PKMYT1 inhibition into combination strategies that include immunotherapy.

In summary, we highlight PKMYT1 as a novel and selective vulnerability specifically in del(17p) MM and its importance as a therapeutic target, offering a promising option for patients with limited treatment outcomes. In the era of risk-adapted treatment and biomarker-driven therapeutic interventions, our finding that PKMYT1 is a specific vulnerability in del(17p) patients has the potential to be a personalized medicine approach for this high-risk entity.

Supplementary Material

1
2
3
4

Key points:

  • PKMYT1 is highly expressed in del(17p) MM, and its inhibition represents a vulnerability.

  • PKMYT1 inhibitor triggers DNA damage and mitotic catastrophe, leading to apoptosis in del(17p) MM cells.

Acknowledgments

We acknowledge gratefully Céline Mazzotti for single-cell data generation (IUCT-Oncopole, Toulouse, France); Luka Pavageau for RNA-seq processing (IUCT-Oncopole, Toulouse, France); Christina Usher (DFCI, Boston, USA) for comments on the manuscript; Tianyi Liu and Ludovica Ganino for their contributions to this work during their internship.

Funding

This study was primarily supported by NIH/NCI P01 (CA155258-10) to MF and NCM; a Department of Veterans Affairs I01 (BX001584) to NCM; Paula and Rodger Riney Foundation Grant to NCM and KCA. CV, ML and JAM-C were supported by Spanish Ministry of Health ISCIII-FIS grants PI25/01640 and CIBERONC-CB16/12/00489. JC1 was partially supported by the National Natural Science Foundation (grant 824B2004). AS was partially supported by a grant from the French foundation for research into myeloma and monoclonal gammopathies (FFRMG).

Author disclosures

AS reports honoraria from Janssen, Sanofi, Amgen and Menarini Stemline, outside the submitted work. JC2 reports honoraria from Janssen, Sanofi, Adaptive and Amgen, outside the submitted work. CV, ML, and JAM-C report research funding from MIMO Biosciences, not related to this work. JAM-C is a founder and holds stock options of MIMO Biosciences. MKS reports personal fees from K36, AbbVie, and the Neuberg Center for Genomic Medicine outside the submitted work. KCA reports personal fees from Janssen, AstraZeneca, Pfizer, Genetech-Roche, C4 Therapeutics, Dynamic Cell Therapeutics, OncoPep, NextRNA, Predicta, Starton, and Window outside the submitted work. NCM reports personal fees from Bristol Myers Squibb, Celgene, Jannsen, Pfizer, Regeneron, IASO, GSK, AbbVie, Legend, Novartis, Sebia, and DCT and other support from C4 Therapeutics outside the submitted work. No disclosures were reported by the other authors.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data Sharing Statement.

The de-identified and annotated processed data elements can be requested from the corresponding author or https://www.myelomaresearch.net/faq.

References

  • 1.Corre J, Perrot A, Hulin C, Caillot D, Stoppa AM, Facon T, et al. Improved survival in multiple myeloma during the 2005-2009 and 2010-2014 periods. Leukemia. 2021. Dec;35(12):3600–3. [DOI] [PubMed] [Google Scholar]
  • 2.Costa LJ, Chhabra S, Medvedova E, Dholaria BR, Schmidt TM, Godby KN, et al. Daratumumab, Carfilzomib, Lenalidomide, and Dexamethasone With Minimal Residual Disease Response-Adapted Therapy in Newly Diagnosed Multiple Myeloma. J Clin Oncol. 2022. Sep;40(25):2901–12. [DOI] [PubMed] [Google Scholar]
  • 3.Hari P, Pasquini MC, Stadtmauer EA, Fraser R, Fei M, Devine SM, et al. Long-term follow-up of BMT CTN 0702 (STaMINA) of postautologous hematopoietic cell transplantation (autoHCT) strategies in the upfront treatment of multiple myeloma (MM). J Clin Oncol. 2020. May 20;38(15_suppl):8506–8506. [Google Scholar]
  • 4.Touzeau C, Perrot A, Hulin C, Manier S, Macro M, Chretien ML, et al. Daratumumab, carfilzomib, lenalidomide, and dexamethasone with tandem transplant for high-risk newly diagnosed myeloma. Blood. 2024. May 16;143(20):2029–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Corre J, Perrot A, Caillot D, Belhadj Merzoug K, Hulin C, Leleu X, et al. Del17p without TP53 mutation confers poor prognosis in intensively treated newly diagnosed multiple myeloma patients. Blood [Internet]. 2020. Oct 20 [cited 2020 Dec 24];(blood.2020008346). Available from: 10.1182/blood.2020008346 [DOI] [PMC free article] [PubMed]
  • 6.Avet-Loiseau H, Davies FE, Samur MK, Corre J, D’Agostino M, Kaiser MF, et al. International Myeloma Society/International Myeloma Working Group Consensus Recommendations on the Definition of High-Risk Multiple Myeloma. J Clin Oncol Off J Am Soc Clin Oncol. 2025. Jun 9;JCO2401893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Li Y, Liu Y, Xu H, Jiang G, Van der Jeught K, Fang Y, et al. Heterozygous deletion of chromosome 17p renders prostate cancer vulnerable to inhibition of RNA polymerase II. Nat Commun. 2018. Oct 22;9(1):4394. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  • 8.Liu Y, Zhang X, Han C, Wan G, Huang X, Ivan C, et al. TP53 loss creates therapeutic vulnerability in colorectal cancer. Nature. 2015. Apr 30;520(7549):697–701. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Singh RK, Jones RJ, Shirazi F, Hong S, Wang H, Wan J, et al. HDP-101, a Novel BCMA-targeted Antibody Conjugated to α-Amanitin, is Active against Myeloma with Preferential Efficacy against Pre-clinical Models of Deletion 17p. Clin Lymphoma Myeloma Leuk. 2019. Oct 1;19(10):e152. [Google Scholar]
  • 10.Schmidt M, Rohe A, Platzer C, Najjar A, Erdmann F, Sippl W. Regulation of G2/M Transition by Inhibition of WEE1 and PKMYT1 Kinases. Mol J Synth Chem Nat Prod Chem. 2017. Nov 23;22(12):2045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Nakajima H, Toyoshima-Morimoto F, Taniguchi E, Nishida E. Identification of a Consensus Motif for Plk (Polo-like Kinase) Phosphorylation Reveals Myt1 as a Plk1 Substrate*. J Biol Chem. 2003. Jul 11;278(28):25277–80. [DOI] [PubMed] [Google Scholar]
  • 12.Wang S, Xiong Y, Luo Y, Shen Y, Zhang F, Lan H, et al. Genome-wide CRISPR screens identify PKMYT1 as a therapeutic target in pancreatic ductal adenocarcinoma. EMBO Mol Med. 2024. May;16(5):1115–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Gallo D, Young JTF, Fourtounis J, Martino G, Álvarez-Quilón A, Bernier C, et al. CCNE1 amplification is synthetic lethal with PKMYT1 kinase inhibition. Nature. 2022. Apr;604(7907):749–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Szmyd R, Niska-Blakie J, Diril MK, Renck Nunes P, Tzelepis K, Lacroix A, et al. Premature activation of Cdk1 leads to mitotic events in S phase and embryonic lethality. Oncogene. 2019;38(7):998–1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sunada S, Saito H, Zhang D, Xu Z, Miki Y. CDK1 inhibitor controls G2/M phase transition and reverses DNA damage sensitivity. Biochem Biophys Res Commun. 2021. Apr 23;550:56–61. [DOI] [PubMed] [Google Scholar]
  • 16.Fagundes R, Teixeira LK. Cyclin E/CDK2: DNA Replication, Replication Stress and Genomic Instability. Front Cell Dev Biol. 2021. Nov 24;9:774845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Yang X, Hu X, Yin J, Li W, Fu Y, Yang B, et al. Comprehensive multi-omics analysis reveals WEE1 as a synergistic lethal target with hyperthermia through CDK1 super-activation. Nat Commun. 2024. Mar 7;15(1):2089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yuan W, Ni J, Wen H, Shi W, Chen X, Huang H, et al. Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability. BJOG Int J Obstet Gynaecol. 2022. Nov;129 Suppl 2:14–22. [DOI] [PubMed] [Google Scholar]
  • 19.Larrayoz M, Garcia-Barchino MJ, Celay J, Etxebeste A, Jimenez M, Perez C, et al. Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma. Nat Med. 2023. Mar;29(3):632–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Chng WJ, Price-Troska T, Gonzalez-Paz N, Van Wier S, Jacobus S, Blood E, et al. Clinical significance of TP53 mutation in myeloma. Leukemia. 2007. Mar;21(3):582–4. [DOI] [PubMed] [Google Scholar]
  • 21.Walker BA, Mavrommatis K, Wardell CP, Ashby TC, Bauer M, Davies F, et al. A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia. 2019;33(1):159–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Hyun SY, Jang YJ. p53 activates G1 checkpoint following DNA damage by doxorubicin during transient mitotic arrest. Oncotarget. 2014. Dec 31;6(7):4804–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Senturk E, Manfredi JJ. p53 and Cell Cycle Effects After DNA Damage. Methods Mol Biol Clifton NJ. 2013;962:49–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Benada J, Bulanova D, Azzoni V, Petrosius V, Ghazanfar S, Wennerberg K, et al. Synthetic lethal interaction between WEE1 and PKMYT1 is a target for multiple low-dose treatment of high-grade serous ovarian carcinoma. NAR Cancer. 2023. Sep;5(3):zcad029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Xu H, George E, Gallo D, Medvedev S, Wang X, Datta A, et al. Targeting CCNE1 amplified ovarian and endometrial cancers by combined inhibition of PKMYT1 and ATR. Nat Commun. 2025. Apr 1;16(1):3112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chow JPH, Poon RYC. The CDK1 inhibitory kinase MYT1 in DNA damage checkpoint recovery. Oncogene. 2013. Oct;32(40):4778–88. [DOI] [PubMed] [Google Scholar]
  • 27.Do K, Doroshow JH, Kummar S. Wee1 kinase as a target for cancer therapy. Cell Cycle. 2013. Oct 1;12(19):3348–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ghelli Luserna di Rorà A, Cerchione C, Martinelli G, Simonetti G. A WEE1 family business: regulation of mitosis, cancer progression, and therapeutic target. J Hematol Oncol J Hematol Oncol. 2020. Sep 21;13(1):126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Bruyer A, Dutrieux L, de Boussac H, Martin T, Chemlal D, Robert N, et al. Combined inhibition of Wee1 and Chk1 as a therapeutic strategy in multiple myeloma. Front Oncol. 2023;13:1271847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Szychowski J, Papp R, Dietrich E, Liu B, Vallée F, Leclaire MÈ, et al. Discovery of an orally bioavailable and selective PKMYT1 inhibitor RP-6306. J Med Chem. 2022. Aug 11;65(15):10251–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Li M, Lulla AR, Wang Y, Tsavaschidis S, Wang F, Karakas C, et al. Low–Molecular Weight Cyclin E Confers a Vulnerability to PKMYT1 Inhibition in Triple-Negative Breast Cancer. Cancer Res. 2024. Nov 15;84(22):3864–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Morelli E, Fulciniti M, Samur MK, Ribeiro CF, Wert-Lamas L, Henninger JE, et al. A MIR17HG-derived long noncoding RNA provides an essential chromatin scaffold for protein interaction and myeloma growth. Blood. 2023. Jan 26;141(4):391–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Thakurta A, Ortiz M, Blecua P, Towfic F, Corre J, Serbina NV, et al. High subclonal fraction of 17p deletion is associated with poor prognosis in multiple myeloma. Blood. 2019. 14;133(11):1217–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bolli N, Li Y, Sathiaseelan V, Raine K, Jones D, Ganly P, et al. A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma. Blood Cancer J. 2016. Sep 2;6(9):e467. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Hebraud B, Magrangeas F, Cleynen A, Lauwers-Cances V, Chretien ML, Hulin C, et al. Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience. Blood. 2015. Mar 26;125(13):2095–100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Corre J, Cleynen A, Robiou du Pont S, Buisson L, Bolli N, Attal M, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia. 2018. Dec;32(12):2636–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Balmus G, Karp NA, Ng BL, Jackson SP, Adams DJ, McIntyre RE. A high-throughput in vivo micronucleus assay for genome instability screening in mice. Nat Protoc. 2015. Jan;10(1):205–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Terradas M, Martín M, Genescà A. Impaired nuclear functions in micronuclei results in genome instability and chromothripsis. Arch Toxicol. 2016. Nov;90(11):2657–67. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

1
NIHMS2207133-supplement-1.pdf (4.4MB, pdf)
2
NIHMS2207133-supplement-2.pdf (1.6MB, pdf)
3
NIHMS2207133-supplement-3.xlsx (11.5KB, xlsx)
4
NIHMS2207133-supplement-4.xlsx (8.9MB, xlsx)

Data Availability Statement

The de-identified and annotated processed data elements can be requested from the corresponding author or https://www.myelomaresearch.net/faq.

RESOURCES