Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma

Domain: biology
Published: 2025-12-15
Editorial outcome: major_revision

Abstract
Chemotherapy often kills a large fraction of cancer cells but leaves behind a small population of drug-tolerant persister cells. These persister cells survive drug treatment through reversible, non-genetic mechanisms and cause tumour recurrence upon cessation of therapy. Here, we report a drug tolerance mechanism regulated by the germ-cell-specific H3K4 methyltransferase PRDM9. Through histone proteomic, transcriptomic, lipidomic, and ChIP-sequencing studies combined with CRISPR knockout and phenotypic drug screen, we identify that chemotherapy-induced PRDM9 upregulation promotes metabolic rewiring in glioblastoma stem cells, leading to chemotherapy tolerance. Mechanistically, PRDM9-dependent H3K4me3 at cholesterol biosynthesis genes enhances cholesterol biosynthesis, which persister cells rely on to maintain homeostasis under chemotherapy-induced oxidative stress and lipid peroxidation. PRDM9 inhibition, combined with chemotherapy, results in strong anti-cancer efficacy in preclinical glioblastoma models, significantly enhancing the magnitude and duration of the antitumor response by eliminating persisters. These findings demonstrate a role of PRDM9 in promoting metabolic reprogramming that enables the survival of drug-tolerant persister cells.

Introduction
Cancer therapy failure and tumour recurrence is, in part, attributed to a cancer cell subpopulation known as drug-tolerant persister cells1. Unlike resistant cells, persister cells do not possess permanent resistance-conferring genetic mutations but instead enter a reversible drug-tolerant state2. Upon cessation of therapy or in periods between therapy cycles (drug holiday), persister cells give rise to a population of cells that are often as sensitive to the drug as the original drug naïve population. Alternatively, over time persister cells may acquire genetic mutations that confer irreversible drug resistance3. The non-genetic mechanisms reported to underlie the drug-tolerant persister phenotype include epigenetic, transcriptomic, translational and metabolic reprogramming4.

Glioblastoma is the most common and lethal primary brain tumour. The DNA-alkylating agent temozolomide, albeit largely ineffective, remains the only approved drug for this disease5. Several lines of pre-clinical evidence indicate that glioblastoma cells can be effectively targeted with microtubule-targeting agents (MTAs) which disrupt fundamental cell functions such as mitosis, migration, and vesicle transport6–9. Moreover, glioblastoma cells are connected via ultralong thin membrane protrusions known as tumour microtubules. Microtubes provide the anatomical basis for transfer of self-renewal factors between cells and their functioning depends on high content of microtubules10,11. However, clinical MTAs like taxanes and vinca alkaloids have limited therapeutic value in neuro-oncology due to their large molecular size and polarity which are incompatible with diffusion across the blood-brain barrier12.

Optical blood-brain-tumour barrier modulation to enhance paclitaxel delivery and the small-molecule MTA lisavanbulin have shown promise in glioblastoma models and trials13–15. These findings underscore the potential of brain-permeable MTA for glioblastoma therapy. Nonetheless, MTAs give rise to drug-tolerant persister cells8,16. Thus, the identification of strategies to combat drug tolerance to MTAs holds the potential to enhance their efficacy, which is also critical for the clinical translation of brain-permeable MTAs.

PRDM9 (PR/SET domain 9) is a zinc finger protein that binds DNA and trimethylates histone H3 only sine 4 and 36 to form H3K4me3 and H3K36me3. PRDM9 is normally expressed only in germ cells, where it regulates DNA recombination in meiosis17. Following fertilisation, meiotic genes are silenced. Yet, the failure of meiotic gene-silencing is common in cancer, with over 200 cancer/testis genes, including PRDM9, found in tumours18,19. Pan-cancer analysis of 32 different cancer types (TCGA) revealed PRDM9 upregulation in 20% of tumours compared to healthy matching tissue, with the highest PRDM9 activity detected in glioblastoma19,20. PRDM9 in cancer has been linked to genomic instability, likely stemming from its known recombination function in meiosis19,20. However, despite PRDM9’s potential as an ideal and safe therapeutic target due to its germ-cell restricted expression, the functional role of PRDM9 in cancer is unknown.

In this work, using a small-molecule microtubule-targeting agent CMPD121 as the lead, we develop a brain-permeable microtubule-targeting agent WJA88. In pursuit of optimising the outcomes of MTA chemotherapy, we show that PRDM9’s methyltransferase activity is critical for the survival of glioblastoma persister cells. Mechanistically, PRDM9-mediated H3K4me3 maintains cholesterol homeostasis under chemotherapy stress. Moreover, inhibiting PRDM9 in combination with brain-permeable MTAs results in strong anti-glioblastoma efficacy and significantly reduces the population of persister cells, the main reason for tumour recurrence.

Results
Glioblastoma persisters display transcriptomic and lipidomic reprogramming
MTAs induce apoptosis in glioblastoma cells8,21, however do not kill all cells. Using the growth-rate (GR) metrics22, we have previously shown that small-molecule MTAs do not reach GR max value of −1, which denotes complete killing efficacy. Across 15 glioblastoma stem cell lines, RKI1 and FPW1 cells were least responsive to MTAs8. Indeed, viable RKI1 and FPW1 cells can still be detected after 14 days of treatment with high concentrations (25x GR50) of CMPD1 or tivantinib (Fig. 1a), both molecules developed as kinase inhibitors but subsequently shown to target microtubules21,23. We have also shown that the size of RKI1 subpopulation surviving MTAs did not decrease when cells were co-treated with inhibitors of drug efflux pumps8. In this study, we observed that surviving cells were morphologically different from the parent cells. As microtubule-targeting agents induce prominent changes in cell morphology, the observed shrinkage caused by disrupted microtubule cytoskeleton can be used as a marker of continuous target engagement in cells24,25.

We used RT-qPCR to confirm that the CMPD1-surviving cells remained in a non-proliferative state throughout the treatment period, evidenced by a decrease in replication genes and upregulation of senescence and quiescence genes, particularly CDKN1A-encoded p21 (Supplementary Fig. 1a, b). However, in drug holiday (i.e., MTA withdrawal) surviving cells recovered their morphology, began to proliferate and re-colonized the wells within 14 days (Fig. 1b). Once a substantial population of recovered cells had been regenerated, we rechallenged these cells with CMPD1 or tivantinib. Consistent with a persister cell classification2, RKI1 and FPW1 persisters yielded MTA-sensitive progeny (Fig. 1c; Supplementary Fig. 1c, d). These observations suggest that we did not select out a subclone with a pre-existing resistance mutation and the fractional killing efficacy of CMPD1 and tivantinib leads to drug-tolerant persisters.

To investigate persisters in an unbiased manner, we compared the transcriptomes of parent and CMPD1-derived RKI1 persister cells by bulk RNA sequencing (RNA-seq). We identified 1,573 differentially expressed genes (DEGs, P adj <0.01; Supplementary Fig. 1e). Gene Ontology of the top 200 DEGs identified that the down-regulated processes were related to cell division and morphogenesis, whereas lipid localisation and transport were the most up-regulated processes (Fig. 1d). Following up on these findings, we found a distinctive lipidomics signature in CMPD1-derived persisters compared to parent cells (Fig. 1e). Significant treatment-related decreases occurred with ceramide (Cer), cholesterol (Chol) and phosphatidylserine (PS), whereas sphingomyelin (SM), lysophosphatidylethanolamine (LPE), and diacylglycerol (DG) increased in persisters (Fig. 1f). Furthermore, carnitine-linked fatty acids, which are substrates for mitochondrial β-oxidation, were more abundant in persisters (Supplementary Fig. 1f), in line with data showing the dependence of melanoma persisters on fatty acid oxidation26. While the transcriptomic analysis of FPW1 persister cells (Supplementary Fig. 1g) revealed several distinct GO pathways being affected by long-term CMPD1 treatment (Supplementary Fig. 1h), we found a high correlation in DEGs between RKI1 and FPW1 persisters (Fig. 1g), with 4794 overlapping DEGs (Supplementary Fig. 1i), suggesting similar transcriptomic profile in two genetically-distinct glioblastoma persister models.

Glioblastoma persisters exhibit increased histone lysine methylation
Epigenetic reprogramming is considered as a critical mechanism driving the reversible drug-tolerant state4 that we observed in glioblastoma persisters (Fig. 1b, c). We therefore profiled histone extracts of parent and CMPD1-derived persister cells by mass-spectrometry (Supplementary Fig. 2a) and constructed H3-centric peptide heatmaps (Fig. 2a, Supplementary Fig. 2b). This comprehensive analysis revealed deposition of replication-independent histone variant H3.3 compared to canonical H3.1/H3.2 in persisters (Fig. 2b), confirmed with immunoblotting (Fig. 2c, Supplementary Fig. 2c). H3.3 preferentially occurs at active gene bodies and has been associated with aggressive metastasis of carcinomas27. Further, we observed significant deacetylation occurring on K27 and K36 (Fig. 2d). In line, H3K27me3 and H3K36me3 were the most prominent modifications in persisters (Fig. 2e, f, Supplementary Fig. 2d). There was no significant modification of the H3K4 and H3K9 methylation marks, except for H3K4me3 levels being low in RKI1 persisters (Fig. 2f, g, Supplementary Fig. 2d, e).

Interestingly, immunoblotting of more distal H3K27 and H3K36 methylation marks revealed an additional faster migrating species of H3 in persisters that was absent in parent cells (Fig. 2f, arrows). This was not detected with antibodies specific for H3K4 and H3K9. Given that H3.3 variant is cleaved during senescence28, generating a shorter H3.3 tail beginning with the amino acid T22; and that persisters had elevated H3.3 deposition (Fig. 2b, c) as well as expression of senescence associated markers (Supplementary Fig. 1a, b), we used H3 cleavage-specific antibody to demonstrate that CMPD1 treatment induced H3 tail cleavage (Fig. 2h). Analysis of H3K4me3 marks revealed that while these marks increased at earlier timepoints, they steeply decline after day 10 (Fig. 2h, Supplementary Fig. 2f), in line with data observed with persister populations (Fig. 2f). Over the course of CMPD1 treatment, we observed negligible changes in H3K9me3 abundance (Fig. 2h), in line with persisters’ H3K9me3 profile determined by MS (Fig. 2a, Supplementary Fig. 2b 2d) and immunoblotting (Fig. 2f). These findings suggest that CMPD1 treatment induces H3K4, H3K27 and H3K36 methylation, accompanied by histone H3 cleavage at the T22 position. As a result, increased H3K27 and H3K36 methylation is maintained in persister cells (Fig. 2f), but H3K4 marks are reduced via histone cleavage later during the treatment (Fig. 2h). This highlights that the epigenetic landscape of persister cells at day 14 is distinct from the earlier changes, underscoring time-dependent chromatin remodelling during the development of drug tolerance. Nevertheless, the deposition of H3.3, coupled with methylation of gene-activating (H3K4, H3K36) as well as gene-repressing (H3K27) residues correlates with the extensive transcriptional changes observed in persisters (Fig. 1d, g; Supplementary Fig. 1e, g).

PRDM9 inhibitor MRK-740 eliminates glioblastoma persisters
Given the significant chromatin remodelling in persister cells, we assessed the sensitivity of MTA-tolerant cells to 37 epigenetic chemical probes (Supplementary Fig. 3a). Structurally related KMT6 (aka EZH2) inhibitors UNC-1999 and GSK-343 attenuated viability of persisters without changing the viability of the parent cells. However, GR curves for orthogonal KMT6 inhibitors UNC-1999, GSK-126, CPI-169, CPI-1205 and tazemetostat revealed minimally increased sensitivity of persisters to KMT6 inhibitors compared to parent cells (Supplementary Fig. 3b). GSK-J4 and KDOBA-67a (targeting KDM6) and JQ1 (targeting BRD4) reduced viability of both parent and persister cells (Supplementary Fig. 3a), consistent with previous findings29 and suggesting that they do not target a mechanism specific to MTA tolerance.

In the viability screen (Supplementary Fig. 3a), we introduced epigenetic probes to persisters after a 14-day treatment with CMPD1 or tivantinib. Considering that methylation of H3K4, H3K27, H3K36 occurs immediately after CMPD1 treatment, with proteolytic cleavage removing H3K4me3 marks (Fig. 2h), we co-treated cells with CMPD1 and inhibitors of writers/erasers of H3K4 (MRK-740, BAY-6035, KDOBA-67a); H3K27 (UNC1999, GSK-J4), H3K36 (EPZ-719) for 14 days and quantified persisters. MRK-740, an inhibitor of H3K4 methyltransferase PRDM930, and EPZ-719, an inhibitor of H3K36 methyltransferase SETD2, significantly reduced the number of persisters, particularly in the RKI1 cell line (Fig. 3a–d). Of note, RKI1 cells carry SETD2 R472H mutation, while FPW1 cells are SETD2 wild-type31. As this mutation is unique to RKI1 and not present in any of the other 11 cell lines in our QCell panel, the efficacy of EPZ-719 may represent a cell line-specific effect rather than a broadly applicable mechanism. This prompted us to focus further on PRDM9 and the anti-persister efficacy of MRK-740.

The finding that inhibition of the H3K4 methyltransferase PRDM9 with MRK-740 reduced the number of persister cells, while inhibition of H3K4 methyltransferase SMYD3 with BAY-6035 did not; and that PRDM9-specific H3K4me3 methylation is critical for persister cell survival, was unexpected, particularly given that previous studies identified H3K4me3 demethylation by KDM5 as a vulnerability in drug-tolerant persisters2,32. In line with these studies, we also found significantly increased KDM5A expression (Supplementary Fig. 3c) in glioblastoma persisters after 14 days of chemotherapy. However, treatment with the KDM5 inhibitor KDOBA-67a or the dual KDM5/6 inhibitor GSK-J4 failed to eliminate persister cells (Fig. 3a, b). Similarly, although the transcripts of histone-cleaving enzyme cathepsin L1 (CTSL1), which also removes H3K4me3, increased over time (Supplementary Fig. 3d), CTSL1 inhibition did not reduce the number of persister cells (Fig. 3e, f). Together, these findings suggest that early PRDM9-dependent H3K4me3 is critical for the survival of persister cells, whereas the subsequent removal of this mark-either through demethylation by KDM5 or histone cleavage by CTSL1-is not a vulnerability of glioblastoma persister cells.

To further establish the efficacy of MRK-740, five glioblastoma stem cell lines, each with distinct combinations of genetic mutations31 were co-treated with CMPD1 and either MRK-740 or matched inactive compound MRK-740-NC which does not inhibit PRDM930, followed by drug holiday (Fig. 3g). In addition to its efficacy in RKI1 cells, MRK-740 reduced the colony area of recovered persisters in FPW1, HW1, SB2b, and MMK1 cells (Fig. 3h). Of note, single MRK-740 induced G0/G1 arrest while present in the cell culture medium (Supplementary Fig. 3e, f), but cells fully recovered during the drug holiday (Supplementary Fig. 3g). MRK-740-NC had no effect on cell viability (Supplementary Fig. 3g) and did not reduce the number of persister-derived colonies (Fig. 3g, h).

To confirm PRDM9 inhibition in cells, we demonstrate that MRK-740 (3µM), but not MRK-740-NC, decreased bulk H3K4me3 in RKI1 cells treated with CMPD1 without reducing H3K4me3 when used as a single agent (Fig. 3i, Supplementary Fig. 3h). To further validate PRDM9 inhibition by MRK-740 in cells and exclude inhibition of other H3K4 methyltransferases, we performed H3K4me3 ChIP-Seq (chromatin immunoprecipitation followed by next-generation sequencing) analyses of RKI1 cells treated with CMPD1 ± MRK-740. The intensity of 27,749 H3K4me3 peaks (q-value < 0.05, Supplementary Data 1) increased in cells treated with CMPD1, while MRK-740, alone or combined with CMPD1, reduced the signal (Supplementary Fig. 3i). Genomic regions with the most significant PRDM9 motif matches (P < 2.67×10-7, MA1723.2, JASPAR database, Supplementary Data 2) identified that 3,872 out of 27,749 H3K4me3 peaks overlap with at least one PRDM9 motif (Supplementary Data 3). Plotting these 3,872 peaks confirmed alignment with the PRDM9 motif center and reduced H3K4me3 signal in cells treated with MRK-740 (Fig. 3j). While 80% of these peaks are located at promoters (Supplementary Fig. 3j) where H3K4me3 are also formed by the COMPASS complex33, H3K4me3 peaks at distal intergenic sites can be attributed solely to PRDM9 activity30. We found near complete eradication of H3K4me3 at intergenic regions in cells treated with MRK-740 (Fig. 3k). These data unequivocally confirm inhibition of PRDM9’s methyltransferase activity by MRK-740 in cells.

PRDM9 is non-essential in glioblastoma but becomes a chemotherapy-induced vulnerability
To further establish PRDM9 as a critical methyltransferase in persister cells, we examined its expression alongside other H3K4 methyltransferases from the KMT2 family, including SETD1A (KMT2F) and SETD1B (KMT2G), both of which are components of the COMPASS complex33. We found that PRDM9 expression was the most upregulated among H3K4 methyltransferases in persister cells (Fig. 4a), increased in a time-dependent manner following CMPD1 treatment (Fig. 4b) and across six genetically diverse glioblastoma cell lines (Fig. 4c). Given that PRDM9 expression in germ cells is restricted to the prophase, the first phase of meiosis34, and that microtubule-targeting agents, including CMPD induce mitotic arrest21, we reasoned that PRDM9 upregulation could results from the mitotic arrest induced by CMPD1. To test this hypothesis, we used CDK1 inhibitor Ro-3306 to induce mitotic arrest (Fig. 4d)35 and found Ro-3306 induced PRDM9 mRNA upregulation (Fig. 4e). Next, we evaluated whether the timing of MRK-740 administration affects the elimination of persister cells. We established that both simultaneous treatment with CMPD1 and MRK-740, and sequential addition of MRK-740 after CMPD1, reduced the number of persister cells. In contrast, pre-treating cells with MRK-740 before CMPD1 had no effect (Fig. 4f, g). These results suggest that PRDM9 inhibition is only effective once PRDM9 is upregulated during mitotic arrest induced by CMPD1.

Next, we knocked out PRDM9 in RKI1 cells to test whether genetic loss of PRDM9 phenocopies the anti-persister efficacy of MRK-740. As controls, we included SETD1A and SETD1B, two widely studied H3K4 methyltransferases33. DepMap database revealed that SETD1A is a common essential gene (1070/1095 cell lines with Chronos score ≤ -0.5), and 154 cell lines showed probability of SETD1B dependency. However, no cell lines had a probability of PRDM9 dependency (Fig. 4h), aligning with transient cytostatic efficacy of MRK-740 in RKI1 cells (Supplementary Fig. 3f, g). Knocking down SETD1B (Fig. 4i) and PRDM9 (Fig. 4j) had no impact on RKI1 cell fitness (Fig. 4k), suggesting that these genes are not essential in unchallenged cells. However, when these cells were treated with CMPD1 for 14 days, PRDM9 knockout decreased the number of persister cells compared to parent and NTC cells, whereas SETD1B knockout had no significant effect (Fig. 4k, l). Together, these findings suggest that while PRDM9 is not an essential gene in glioblastoma, its expression increases in mitotically arrested cells and the development of a drug-tolerant state during chemotherapy is contingent on this methyltransferase.

PRDM9-dependent H3K4me3 regulates cholesterol biosynthesis genes
To unravel the mechanism by which PRDM9 promotes survival of drug-tolerant persisters, we performed RNA sequencing (RNAseq) of our pharmacological and genetic PRDM9 inhibition models (MRK-740 and PRDM9 knockout) head-to-head. RKI1 cells were treated with CMPD1 ± MRK-740 for 3 days and harvested for analysis. In parallel, RKI1 cells transduced with NTC sgRNA (referred to as NTC cells) and PRDM9(1) sgRNA (referred to as PRDM9 KO cells) were analysed after 3 days treatment with CMPD1. Given that H3K4me3 is a transcription-activating chromatin mark36 and aiming to identify a process exclusive to CMPD1+MRK-740 treatment, which is absent in CMPD1 monotherapy and thus likely to underscore MRK-740-induced persisters’ death, we focused on top 50 down-regulated genes (down-DEGs, P adj < 0.05) in cells treated with CMPD1+MRK-740 versus CMPD1-only cells (Fig. 5a). Gene Ontology (GO) mapped these down-DEGs to cholesterol biosynthesis (Fig. 5b). In line, transcripts for numerous enzymes involved in cholesterol biosynthesis, including HMGCR, HMGCS1, MVD, EBP, SQLE, DHCR7 and DHCR24, were down-regulated upon single MRK-740 treatment (Fig. 5a). Mapping the 50 down-DEGs from Fig. 5a on the waterfall plot ranking differentially expressed genes in PRDM9 KO cells identified DHCR24, DHCR7, EBP and SEMA7A transcripts as most significantly impacted by PRDM9 loss (Fig. 5c).

Next, we reanalysed our H3K4me3 ChIP-seq datasets obtained from RKI1 cells treated with CMPD1 ± MRK-740 to determine whether PRDM9-dependent H3K4me3 peaks localise to cholesterol biosynthesis genes and regulate their transcription. First, we confirmed that the positive correlation between H3K4me3 peak width, which has been found instructive for gene transcription36, and transcripts levels remained constant during all treatments (Supplementary Fig. 4a). Using the K-means clustering of H3K4me3 peak intensities, we divided genes in untreated RKI1 cells into Cluster 1 (genes with H3K4me3) and Cluster 2 (genes without H3K4me3; Supplementary Fig. 4b). Cluster 1 genes had significantly higher mRNA expression compared to Cluster 2 genes (Supplementary Fig. 4c). Additionally, when treated with CMPD1, Cluster 1 genes were kept to a tighter expression range compared to Cluster 2 genes (Supplementary Fig. 4d). This confirms that H3K4me3 maintains transcriptional levels and consistency in RKI1 cells, as reported previously36.

Genome-wide analysis revealed that H3K4me3 peak intensity was highest at the promoters in CMPD1-treated cells and lowest in cells co-treated with CMPD1 and MRK-740 (Fig. 5d, Supplementary Fig. 4e, f). By plotting changes in H3K4me3 peak width at promoters of individual genes versus changes in mRNA expression, we found no correlation (Fig. 5e). This suggests that not all genes’ transcription is sensitive to MRK-740 induced changes in H3K4me3 peak width. However, decreased H3K4me3 intensity (Fig. 5f) and peak width for the top 50 down-DEGs more strongly correlated with decreased mRNA levels (Fig. 5g), indicating that the transcription of cholesterol biosynthesis genes is particularly sensitive to MRK-740 treatment. To identify PRDM9-dependent H3K4me3 marks, a Venn diagram of 14,652 genes annotations with a PRDM9 motif and the 50 down-DEGs revealed that 20 genes, including cholesterol biosynthesis genes DHCR7, DHCR24, EBP, and MVD, contain the PRDM9 binding motif (Fig. 5h). The remaining 30 genes, including cholesterol biosynthesis genes HMGCS1, HMGCR, SQLE, FDPS, despite lacking the PRDM9 motif, could be methylated by PRDM9 via its KRAB domain interactions with CXXC1, a COMPASS complex subunit37,38. Individual genome tracks confirmed that DHCR7 and DHCR24 have PRDM9 motifs at their promoters, while EBP and MVD contain an intronic PRDM9 motif (Fig. 5i). Importantly, reduced H3K4me3 intensity at these genes’ promoters was found in cells co-treated with CMPD1 and MRK-740.

To functionally support that the observed effects of MRK-740 are truly PRDM9-dependent, we performed H3K4me3 ChIP-Seq of NTC and PRDM9 KO cells treated with CMPD1 for 3 days. We found that CMPD1 reduced total H3K4me3 in NTC cells (Supplementary Fig. 4g), an effect not seen in un-transduced RKI1 cells treated with CMPD1 (Supplementary Fig. 3i). Although the reason for the decreased H3K4me3 in CRISPR-Cas9 modified NTC cells remains unclear, the intensity of 23,073 H3K4me3 peaks (q-value < 0.05, Supplementary Data 4, Supplementary Fig. 4g); genome-wide H3K4me3 peaks (Fig. 6a); 1,905 H3K4me3 peaks overlapping with at least one PRDM9 motif (Fig. 6b, Supplementary Data 5); and individual H3K4me3 peaks at intergenic regions overlapping with a PRDM9 motif (Fig. 6c, Supplementary Fig. 4h) was reduced in PRDM9 KO cells, especially when treated with CMPD1. Genome tracks of cholesterol genes DHCR7, DHCR24, EBP, and MVD confirmed most significantly reduced H3K4me3 in PRDM9 KO cells treated with CMPD1 (Fig. 6d). Overall, CMPD1 treatment combined with PRDM9 loss phenocopied the results obtained with CMPD1+MRK-740.

Our results indicate that PRDM9 regulates H3K4me3 and transcription at cholesterol biosynthesis genes. To investigate the clinical relevance of these findings, we accessed H3K4me3 ChIP-Seq data of 19 glioblastoma patients (GSE121723)39. Analysing two patients’ datasets, we found 3,577 H3K4me3 peaks in patient AK124 and 3,565 peaks in patient AK231 to align with the PRDM9 motif centre (Fig. 6e, Supplementary Data 6-7), similar to our results with RKI1 glioblastoma cells. We leveraged parallel H3K36me3 ChIP-seq data, and overlapped intergenic H3K4me3 and H3K36me3 peaks with the PRDM9 motif (Fig. 6f) to confirm PRDM9 activity in glioblastoma, as the co-occurrence of H3K4me3, H3K36me3 and PRDM9 motif in intergenic regions is uniquely associated with PRDM940. Genome wide H3K4me3 signals for each patient (Supplementary Fig. 5a) were consistent with our RKI1 cell data (Fig. 5d), and we found significantly higher H3K4me3 intensities for the top 50 down-DEGs (Supplementary Fig. 5b) compared to the top 50 upregulated genes (Supplementary Fig. 5c, d). DHCR7 and DHCR24 genome tracks revealed strong H3K4me3 peaks at their promoters, aligning with the PRDM9 motif (Fig. 6g), as observed in RKI1 cells (Figs. 5i, 6d). Finally, the widths of H3K4me3 peaks at the DHCR7 and DHCR24 promoters in 19 patients showed a positive correlation with their corresponding mRNA levels (Fig. 6h), supporting our findings obtained with RKI1 glioblastoma cells.

PRDM9 inhibitor MRK-740 depletes cholesterol in persisters
Our data thus far suggest that PRDM9 is a vulnerability in drug-tolerant persister cells in glioblastoma. Mechanistically, we established that PRDM9 regulates transcripts of cholesterol biosynthesis enzymes (Fig. 7a) containing PRDM9 motif (e.g., MVD, EBP, DHCR7, DHCR24) as well as transcripts of cholesterol enzymes lacking PRDM9 motif (e.g., HMGCS1, HMGCR; Fig. 5h). Supporting this, protein levels of HMGCS1, MVD and DHCR24 were reduced following PRDM9 inhibition with MRK-740 or PRDM9 knockout in RKI1 cells (Fig. 7b, Supplementary Fig. 6a), particularly in cells co-treated with CMPD1. HMGCS1, MVD and DHCR24 expression did not significantly change in FPW1 cells treated with CMPD1 ± MRK-740 (Fig. 7c; Supplementary Fig. 6b), consistent with the weaker anti-persister efficacy of MRK-740 in this cell line (Fig. 3h). We extended this investigation to MMK1 cells that showed high sensitivity to CMPD1+MRK-740 (Fig. 3h) and found strongest HMGCS1, MVD and DHCR24 reduction in co-treated cells (Fig. 7d, Supplementary Fig. 6c).

To assess how PRDM9 inhibition impacts cholesterol levels in cells, we quantified cholesterol and its intermediates in RKI1 cells treated with CMPD1 combined with MRK-740 or the inactive MRK-740-NC. Single MRK-740 reduced lathosterol, zymosterol and desmosterol (Supplementary Fig. 6d), leading to ~60% reduction in cholesterol (Fig. 7e). CMPD1 nearly depleted desmosterol (Supplementary Fig. 6d), the final cholesterol precursor in the Bloch pathway, indicating that persisters use the Kandutsch-Russell pathway. Indeed, lathosterol and cholesterol were reduced by 75%, but not depleted in CMPD1-treated cells. Importantly, cholesterol levels were further reduced in cells co-treated with CMPD1+MRK-740 (Fig. 7e). The compound MRK-740-NC, which does not inhibit PRDM930, H3K4me3 (Fig. 3i) and failed to eliminate persister cells (Fig. 3g, h), did not affect cholesterol levels (Fig. 7e). PRDM9 knockout phenocopied cholesterol-lowering efficacy of MRK-740 in RKI1 cells (Fig. 7f). Surprisingly, MRK-740 despite not changing MVD and DHCR24 protein expression in FPW1 cells (Fig. 7c), reduced cholesterol levels in these cells (Fig. 7g), likely via regulating other enzymes in the cholesterol biosynthesis pathways. These findings suggest that PRDM9 regulates cholesterol biosynthesis enzymes either directly, through binding to promoter regions containing the PRDM9 motif (e.g., MVD, DHCR24), or indirectly (e.g., HMGCS1 gene lacking PRDM9 motif). The anti-cholesterol effects of PRDM9 inhibition were more pronounced in RKI1 and MMK1 cells and less evident in FPW1 cells, correlating with the different anti-persister efficacy of MRK-740 across these cell lines. Taken together, these results support a model in which PRDM9 epigenetically regulates cholesterol biosynthesis necessary for the survival of glioblastoma persister cells.

If true, persisters should be sensitive to any agent targeting cholesterol. Statins inhibiting HMGCR in the mevalonate pathway would be the most obvious agents to test this hypothesis. However, the essentiality of mevalonate pathway41, coupled with the fact that statins have pleiotropic cholesterol-independent activity42, would not allow us to specifically validate cholesterol as critical to persister survival when using statins. We therefore targeted DHCR7 and DHCR24, as both are non-essential cancer genes (Supplementary Fig. 7a) and contain PRDM9 motif at their promoters (Fig. 5i). The accumulation of the DHCR7 substrate zymosterol by the DHCR7 inhibitor AY-9944, along with the accumulation of the DHCR24 substrate desmosterol by the DHCR24 inhibitor SH-42, confirmed target engagement. (Supplementary Fig. 7b). As single agents, both AY-9944 and SH-42 reduced cholesterol levels to 20% compared to untreated cells (Supplementary Fig. 7c). However, this reduction had no effect on cell viability (Supplementary Fig. 7d), indicating that in the absence of chemotherapy, cells tolerated disruptions to cholesterol biosynthesis, as observed with single MRK-740 (Supplementary Fig. 3g). Nevertheless, when combined with CMPD1, both DHCR7 and DHCR24 inhibitors significantly reduced persister-derived colonies, phenocopying CMPD1+MRK-740 treatment (Supplementary Fig. 7d).

Of note, glioblastoma stem cells are cultured in serum-free media without an external cholesterol supply, and their survival depends on their ability to synthesise cholesterol. These culturing conditions accurately replicate the human brain environment, where the only source of cholesterol is de novo biosynthesis as dietary/peripheral cholesterol cannot penetrate the blood-brain barrier43. To further confirm cholesterol biosynthesis as critical to the survival of drug-tolerant persister cells, we performed rescue experiments. Adding exogenous cholesterol to the cell culture media increased the number of CMPD1-tolerant RKI1 persister cells by 10-fold (Fig. 7h), fully rescued MRK-740-induced cytostatic effect (Supplementary Fig. 7e) and persister cell death upon PRDM9 inhibition with MRK-740 or PRDM9 knockout in RKI1 (Fig. 7h) as well as FPW1 and MMK1 (Fig. 7i) cells. Thus, our findings confirm PRDM9-H3K4me3-dependent cholesterol biosynthesis as a vulnerability in persister cells, yet they also raise an important question: why do persister cells rely on cholesterol biosynthesis, despite their non-proliferative state generally being linked to reduced cholesterol demand44. In line, we observed a reduction in cholesterol precursors and final cholesterol levels in cells treated with CMPD1 for 3 days (Fig. 7e–g) and 14 days (Fig. 1f), even though the transcripts of cholesterol biosynthesis genes either remained unchanged or were upregulated by CMPD1 after 3 days (Fig. 5a) as well as after 14 days (Supplementary Fig. 6e). Given that chemotherapy, including microtubule-targeting agents induces oxidative stress which in turn oxidises lipids9, we questioned whether cholesterol could be sequestered through oxidation during CMPD1 treatment. Indeed, reactive-oxygen species (ROS) dramatically increased within 3 days and remained high until day 14 (Supplementary Fig. 7f), leading to lipid peroxidation in CMPD1-treated cells (Fig. 7j). Upon revisiting the pathway analysis of transcriptomic data from persister cells, we observed that cholesterol homeostasis was indeed upregulated, with a stronger effect in RKI1 and a weaker disruption in FPW1 cells (Supplementary Fig. 7g). While MRK-740 did not generate ROS or exacerbate the oxidative stress caused by CMPD1, it increased the proportion of peroxidised lipids in CMPD1-treated cells, without changing the total lipid levels (Fig. 7j). To further examine the role of oxidative stress in PRDM9-dependent persisters survival, we performed rescue experiments with the antioxidant N-acetylcysteine (NAC) and observed that the addition of NAC into the cell culture media rescued the anti-persister efficacy of MRK-740 or PRDM9 knockout (Supplementary Fig. 7h-i). All in all, these data suggest that CMPD1-triggered oxidative stress lead to cholesterol oxidation. To withstand this disruption of cholesterol homeostasis, persister cells depend on PRDM9-H3K4me3 regulated de novo cholesterol biosynthesis. However, when PRDM9 is inhibited, cholesterol supply is turned off. Combined effects of CMPD1 and MRK-740 have a more pronounced impact on cholesterol homeostasis compared to each agent alone, leading to persister cell death (Fig. 7k).

Brain permeable microtubule-targeting agent in combination with cholesterol depletion increases survival in glioblastoma xenografts
The mechanistic studies were completed with CMPD1, which is an MTA with physicochemical properties compatible with blood-brain barrier permeability21. However, CMPD1 exhibited short (t 1/2 = 4.6 min) half-life in the stability assay using human liver microsomes, preventing its use in vivo (Fig. 8a). To improve its metabolic stability, we performed medicinal chemistry optimisation of CMPD1 (Supplementary Table 1) and developed analogue WJA88 with improved cellular efficacy (EC 50 = 150 nM, A172 cell viability) and metabolic stability (t 1/2 = 34.2 min) compared to CMPD1 (Fig. 8a, b). We confirmed that both CMPD1 and WJA88 inhibit tubulin polymerisation and bind into the colchicine binding site (Supplementary Fig. 8a, b). In functional assays, WJA88 inhibited proliferation of glioblastoma stem cell lines with nanomolar potency (Supplementary Table 2) and attenuated the growth of MMK1 spheroids (Supplementary Fig. 8c). Pharmacokinetic analysis of mice administered a single 50 mg/kg WJA88 dose intraperitoneally revealed that WJA88 had an in vivo half-life (t 1/2) of 71.4 min and peak serum concentration (c max) of 7907 ng/mL (Supplementary Table 3). Distribution across the blood-brain barrier was evaluated in mice dosed with 50 mg/kg WJA88 once intravenously, which showed a brain/plasma ratio of 0.8 and 0.76 at 30 min and 60 min, respectively (Fig. 8c). Therefore, we reasoned that WJA88 with improved stability and sufficient brain uptake could be tested in intracranial models. We first determined WJA88 efficacy and safety in the immunocompetent somatic-cell electroporation model of glioblastoma. In this model, tumour-inducing plasmids, knocking out three most commonly mutated genes of mesenchymal glioblastoma (Nf1, Trp53, Pten), were injected into the lateral ventricle of two-day old, anaesthetised mice pups, followed by electroporation of the brain; leading to the development of tumours that closely recapitulate the histopathological, molecular, and cellular features of human glioblastoma45,46. Three weeks post-electroporation, mice were treated with 50 mg/kg WJA88 for 10 days and euthanised 24 h after the final dose. While WJA88 reduced weight compared to vehicle-treated mice (Supplementary Fig. 8d), mice showed overall good tolerability to WJA88. Importantly, treatment with WJA88 resulted in a significant decrease in proliferative tumour cells (Ki67+/tdTomato+, WJA88 = 10.6% vs Vehicle = 15.4%; Fig. 8d–f), confirming anti-proliferative efficacy of WJA88 in vivo.

Encouraged by in vivo efficacy of WJA88, we investigated whether it might leave behind drug-tolerant persisters cells. Similarly to CMPD1, WJA88, even at a high concentration of 25 µM, led to the emergence of persister cells, and MRK-740 significantly reduced the number of WJA88-tolerant persisters (Fig. 8g, Supplementary Fig. 8e). However, MRK-740 lacks pharmacokinetics suitable for in vivo studies30. As an alternative, we used the Liver X Receptor (LXR) agonist LXR-623 due to its high brain uptake in rodents47. Single LXR-623 (400 mg/kg) suppressed glioblastoma growth by enhancing ABCA1-mediated cholesterol efflux47. We confirmed that LXR-623 increased ABCA1 expression (Supplementary Fig. 8f) and reduced the number of WJA88-tolerant persisters in both RKI1 and FPW1 cells (Fig. 8g). Further, the anti-persister efficacy of LXR-623 was fully rescued by exogenous cholesterol (Supplementary Fig. 8g). Thus, LXR-623 showed a persister-killing efficacy comparable to MRK-740, albeit through different mechanisms: MRK-740 inhibits cholesterol biosynthesis, whereas LXR-623 promotes cholesterol efflux.

To assess the safety of WJA88 in combination with MRK-740 and LXR-623, we examined their impact on the viability of mouse-derived neural progenitor cells and astrocytes (Supplementary Fig. 8h). WJA88 is a microtubule-targeting agent and, like clinical MTAs paclitaxel and vinblastine, is expected to inhibit the proliferation of all dividing cells. Consistent with this, WJA88 suppressed the proliferation of neural stem cells, which are highly proliferative in culture. However, MRK-740 and LXR-623, either alone or in combination, had no effect on neural stem cell viability and did not enhance WJA88 efficacy. Importantly, in astrocytes, none of the treatments, whether alone or combined, affected cell viability. These results suggest that WJA88 may have a safety profile similar to that of clinical MTAs and that the co-treatment with cholesterol depleting agents should be well-tolerated in vivo.

Finally, to test WJA88 and LXR-623 combination in vivo, we used GBM6 cells carrying EGFRvIII mutation and forming aggressive fast-growing tumours in animals48–50. We first confirmed the efficacy of the co-treatment in GBM6 spheroids (Fig. 8h, i). Next, GBM6 cells were surgically engrafted into the cortex of mice, and mice were randomized to treatment cohorts on Day 5. As monotherapy, WJA88 (50 mg/kg) demonstrated short but significant extension in survival relative to vehicle control (median 31 days vs. 28 days: log-rank P < 0.039). LXR-623 (100 mg/kg) monotherapy had negligible effect on survival (median 29 days). Importantly, the combination of WJA88 and LXR-623 effectively inhibited tumour growth (Fig. 8j). Reduced tumour growth translated into prolonged survival of orthotopic tumour-bearing mice treated with the combination therapy compared with monotherapies or vehicle control (median 40.5 days vs. 28 days: log-rank P < 0.0001; Fig. 8k). The WJA88 and LXR-623 combination was well tolerated with no significant change in body weight (Fig. 8l). In contrast, the minor reduction (up to 5%) in body weight observed with both WJA88 and LXR-623 when administered as standalone treatments was reversed when these two agents were combined. Furthermore, only mild effects on blood parameters were observed in mice receiving WJA88 and LXR-623 (Supplementary Fig. 8i). Taken together, these findings imply that the reliance on cholesterol following MTA chemotherapy was more prominent in glioblastoma cells compared with cells of other vital organs.

Methods
Ethical compliance
This research complies with all relevant ethical regulations. GBM6 xenograft studies at the Telethon Kids Institute (Perth) were approved by the Telethon Kids Institute Animal Ethics Committee (AEC362) and conducted in accordance with the Australian Code for the Care and Use of Animals for Scientific Purposes. Under this protocol, mice with brain tumours were required to be euthanised at the first emergence of symptoms, which is considered the maximum allowable tumour burden; this limit was not exceeded. The electroporation glioblastoma model studies at QIMR Berghofer (Brisbane) were approved by the QIMR Berghofer Animal Ethics Committee (A2308-608) and performed in compliance with the same Code. In these studies, animals were euthanised immediately after the two-week treatment period, well before reaching the humane endpoint of approximately 10 weeks after tumour initiation. Experiments to derive mouse neural stem cell lines were also conducted at QIMR Berghofer (Brisbane) with approval from the QIMR Berghofer Animal Ethics Committee (A2304-602).

Cell lines
Glioblastoma stem cell lines RKI1, FPW1, HW1, WK1, MMK1, JK2, and SB2b were derived from glioblastoma patient specimens. Patient tumour tissue was collected from patients undergoing surgery at the Royal Brisbane and Women’s Hospital (RBWH), with human ethics approval from the QIMR Berghofer Medical Research Institute and RBWH Human Research Ethics Committees. All human studies have been performed in accordance with the ethical standards laid down in the 2013 version of the 1964 Declaration of Helsinki. Written informed consent from patients for the generation and use of these cell lines, including consent to publish patient information, age and gender has been obtained. These details, as well as characterisation via RNA sequencing, mutational profiling, subtype assignment and proteomic data are available online at https://www.qimrberghofer.edu.au/our-research/commercialisation/q-cell/ and in ref. 31. GBM6 glioblastoma cells (EGFR/EGFRvIII over-expression) were obtained from Paul Mischel, Ludwig Institute of Cancer Research, USA. Cells were cultured in KnockOut DMEM/F-12 basal medium kit with neural supplement, EGF (20 ng/mL) and FGF-β (10 ng/mL) (ThermoFisher Scientific, Cat# A1050901). GlutaMAX-I CTS (2 mM) (ThermoFisher Scientific, Cat# 11995) supplemented with 1X N-2 supplement (Thermo Fisher Scientific, Cat# 17502048), 1X GlutaMAX (ThermoFisher Scientific, Cat# 35050061), 1% fetal bovine serum (ThermoFisher Scientific, Cat# 10099141), and 100 U/mL penicillin-streptomycin (ThermoFisher Scientific, Cat# 15140122). Cells were cultured in this medium for nine days with a 60% medium change every 2–4 days. Astrocytic phenotype was confirmed through the loss of proliferation marker Ki-67, and morphological changes visualised through glial fibrillary acidic protein expression. No cell line authentication was performed for any models used in this study. All cell cultures were routinely tested for mycoplasma infection, and the cumulative length of culturing did not exceed 10 passages.

Chemical probes and inhibitors
CMPD1 (CAS# 41179-33-3, Cat# sc-203138) was purchased from Santa Cruz. Colchicine (CAS# 64-86-8, Cat# 1364), paclitaxel (CAS# 33069-62-4, Cat# 1097) and vinblastine (CAS# 143-67-9, Cat# 1256) were purchased from Tocris. SGC Probe Set (Supplementary Fig. 3a; Cat# 17748), CPI-169 (CAS# 1450655-76-1, Cat# 18299), EPZ6438 (tazemetostat, CAS# 1403254-99-8, Cat# 16174), GSK126 (CAS# 1346574-57-9, Cat# 15415), AY-9944 (CAS# 366-93-8, Cat# 14611), SH-42 (CAS# 2143952-36-5, Cat# 34677), BAY-6035 (CAS# 2247890-13-5, Cat# 25925-10MG) and Ro-3306 (CAS# 872573-93-8, Cat# 15149) were purchased from Cayman Chemicals. MRK-740 (CAS# 2387510-80-5, Cat# HY-114209) and EPZ-719 (CAS# 2697176-16-0, Cat# HY-139626) were purchased from MedChemExpress. Tivantinib (CAS# 905854-02-6, Cat# S2753) was purchased from Selleckchem. CPI-1205 (CAS# 1621862-70-1, Cat# A16357) was purchased from AdooQ Bioscience. MRK-740-NC (CAS# 2421146-31-6, Cat# SML2536) and Cathepsin L Inhibitor I (CTSLi) (CAS# 108005-94-3, Cat# 219421) was purchased from Sigma Aldrich. LXR-623 (CAS# 875787-07-8, Cat# 21117) was purchased from Sapphire Biosciences. Cholesterol complexed to methyl-beta-cyclodextrin (Cat# C4951) was purchased from Sigma Aldrich.

Animal models
GBM6 xenografts. BALB/c Nude mice (Animal Resource Centre, Perth, Western Australia) were housed thorough the study in standard cages with a 12 h light/dark cycle and ad libitum access to food and water. Following a 7-day acclimatisation period with routine health checks, mice were orthotopically xenografted with tumour cells and closely monitored - twice daily initially, then daily - before imaging and treatment commenced. Throughout the treatment phase, animals were scored three times weekly across multiple health parameters (e.g., body weight, body posture, social behaviour, skin condition, grimace scoring, respiration, hydration, eyes, ears and nose, diarrhea, hydrocephalus, surgical wound gaping, infection), with humane euthanasia performed for severe or tumour-related symptoms. Analgesia (ibuprofen) was provided pre- and post-surgery, and weekly imaging under isoflurane was conducted, with non-recovering mice euthanised when tumour burden was high. To establish xenografts, mice were injected with 3×10^5 GBM6 cells in the cortex, and tumour imaging was done on Day 5. On Day 6, animals were randomly grouped into the treatment cohorts on Day 5. As monotherapy, WJA88 (50 mg/kg) demonstrated short but significant extension in survival relative to vehicle control (median 31 days vs. 28 days: log-rank P < 0.039). LXR-623 (100 mg/kg) monotherapy had negligible effect on survival (median 29 days). Importantly, the combination of WJA88 and LXR-623 effectively inhibited tumour growth (Fig. 8j). Reduced tumour growth translated into prolonged survival of orthotopic tumour-bearing mice treated with the combination therapy compared with monotherapies or vehicle control (median 40.5 days vs. 28 days: log-rank P < 0.0001; Fig. 8k). The WJA88 and LXR-623 combination was well tolerated with no significant change in body weight (Fig. 8l). In contrast, the minor reduction (up to 5%) in body weight observed with both WJA88 and LXR-623 when administered as standalone treatments was reversed when these two agents were combined. Furthermore, only mild effects on blood parameters were observed in mice receiving WJA88 and LXR-623 (Supplementary Fig. 8i). Taken together, these findings imply that the reliance on cholesterol following MTA chemotherapy was more prominent in glioblastoma cells compared with cells of other vital organs.

Electroporation model of glioblastoma. Time-mated CD-1 females (embryonic day 14.5) were purchased from Australian BioResources (Sydney, NSW, Australia) and group housed in standard cages with a 12 h light/dark cycle and ad libitum access to food and water. Both male and female mice pups were utilised for brain electroporation. The hGFAPmin-SpCas9-T2A-PBase and PB_U6-Nf1, Pten, Trp53-EF1a-tdTomato (NPP-tdTomato) plasmid (kindly provided by Prof. Simona Parrinello, University College London, UK)45,46 as well as pPB-mProm1-Venus-p27k-plasmids (kindly provided by Associate Prof. Luca Tiberi, Center for Integrative Biology Trento, Italy)64 were used for the generation of a high-grade glioma tumours. All plasmids for in vivo injections were transformed into NEB® Stable Competent E. coli (New England Biolabs, Cat# C3040I) according to the recommended protocol with 100 ng of plasmid DNA. Transformed bacteria were grown at 30°C for 24 h. Extraction and purification were performed using and following the protocol of the EndoFree Plasmid Maxi Kit (Qiagen, Cat# 12362). Final plasmid DNA was eluted in MilliQ water and DNA concentration was measured via Nanodrop (ND-ONE, Thermo Fisher Scientific). Plasmids were run on a gel to confirm correct plasmid size. Further plasmid validation was performed via Sanger sequencing, confirming correct DNA sequences. In vitro transfection of HEK293T cells (obtained from the American Type Culture Collection) was performed to confirm correct fluorescent protein expression (NPP-tdTomato) or successful integration capacity (PBase) through multiple cell passages, following the protocol for Lipofectamine 2000 (Thermo Fisher Scientific, Cat# 11668019). To generate the somatic-cell electroporation model (NPP-tdTomato model)45,46, DNA-mixes were prepared containing; PiggyBase and piggyBacs at a 1:1:1 ratio (total DNA 2.44 mg/ml), saline and Fast Green FCF (0.5 µg/µl, Sigma-Aldrich Cat# F7258). CD-1 mice pups (postnatal day 2) were anaesthetised on ice for 5-10 min. Using a microinjector (µPUMP, PV850 WPI), approximately 1 µl DNA-mix was injected into the right lateral ventricle (lambda -1, 5 D/V, + 0.8 M/L) with the following settings; injection pressure: 1.0 PSI, compensation pressure: 0.2 PSI, duration: manual. PBS-wetted tweezertrodes (7 mm, Fisher Biotec, Cat# 45-0488) were placed in front of the ears on each side of the head, with the positive electrode on the injected side of the pup’s head. Electroporation settings were the following: 100 V square wave, 50 ms pulses, 800 ms intervals, 5 times46. According to individual cohorts, the tail of each pup was tattooed (green/black dot) using 30G needles. For full recovery from anaesthesia, pups were warmed by hand, checked for breathing reflex and placed on a heating mat. Pups were rubbed with cage litter before being returned to the mother to reduce cannibalisation incidences. After weaning, mice received individual ear notches and were observed daily for 7 days, then monitored three times per week, with increased monitoring in response to clinical signs. Animals are assessed using defined scoring criteria, and those reaching independent humane endpoints are to be promptly euthanised. No mice reached these criteria in this study.

WJA88 treatment. Captisol (Cydex Pharmaceuticals) was dissolved in MilliQ at 40% (0.4 g/mL), rotated for dissolution (35 rpm, 30 min at RT) and then sterile filtered (0.22 µm PES filter), after which it was stored at 4°C. WJA88 was dissolved in 40% Captisol to a stock concentration of 5 mg/mL and sonicated for 1 h at 37°C protected from light, after which it was stored as single-use aliquots at -80°C. At three weeks post-electroporation, mice were divided in treatment groups for WJA88 (n=5) or its vehicle (n=5) and treated daily in two five-day treatment cycles with a two-day break in between. WJA88 or 40% Captisol were administered to mice via intraperitoneal injection (1% body weight) with WJA88 administered at a final dose of 50 mg/kg. Mice were euthanised 24 h after the last dose via intraperitoneal injection of pentobarbital (162.5 mg/mL) and lidocaine (10 mg/mL), followed by transcardial perfusion with PBS and then 4% paraformaldehyde.

Immunostaining. Fixed mouse brains were cut coronally in 40 µm sections using a VT1000S Vibratome (Leica). For antigen retrieval of mouse brains, sections were placed in sodium citrate solution (10 mM, pH 6.0) on a heating block at 95°C for 8 min. Sections were cooled, rinsed with PBS and blocked for 2 h at room temperature (RT) in 2% donkey serum with 0.2% Triton-X-100 in PBS (blocking buffer). All antibodies were prepared in blocking buffer. Sections were washed with PBS and stained with primary antibody Ki67 (mouse, 1:100, #550609, BD Pharmingen), overnight at 4°C. After three times 20 min washes with PBS, sections were incubated with anti-mouse 647 secondary antibody (donkey, 1:500, #715-606-151, Jackson ImmunoResearch) for 3 h at RT. Following another 20 min PBS wash, sections were stained with DAPI (1:5000, 62248, Thermo Fisher Scientific) in PBS for 20 min at RT, before a final PBS wash. Sections were mounted onto Superfrost slides (SF41296SP, Thermo Fisher Scientific) using fluorescence mounting medium (S3023, Dako).

Microscopy. For whole-tumour imaging of tdTomato signal and DAPI staining, brain sections were imaged with a Stellaris confocal microscope (Leica) using a 10x objective and a 0.75% zoom factor, taking z planes every 2.41 µm across a range of 33.72 µm, which were then used to generate a maximum projection image. For Ki67 immunostaining, brain sections were imaged with a spinning disk confocal microscope (ANDOR WD Revolution) using a 20x objective, taking 10 µm z planes with a 2 µm step size. One position of three different brain sections per brain were imaged, including mid-SVZ (bregma ~0.745 mm), late SVZ (either bregma ~0.245 mm) and either late SVZ (bregma ~0.38 mm) or the early-mid hippocampus region (bregma ~-1.455 mm). Positions were based on highest tumour cell density, which included the corpus callosum and caudoputamen. One quarter of each image was analysed in one plane. Overall tdTomato+ and tdTomato+Ki67+ cells were counted manually using ImageJ. The proportion of tdTomato+Ki67+ cells across three positions was calculated for each animal and the average proportion for each treatment group was calculated.

Statistical analysis. To assess change in weight of mice treated with WJA88 compared to vehicle, fold-change in weight was calculated relative to day 1 and a two-way mixed effects ANOVA with Holm-Sidak multiple comparisons was performed. To assess change in the proportion of Ki67+ tumour cells between WJA88 and vehicle treated tumours an unpaired t-test with Welch’s correction was performed.

Persister generation and expansion
FPW1 and RKI1 cells (1.5×10^4 cells/cm^2) were treated with CMPD1 (25 µM) or tivantinib (25 µM) for 14 days. Every 3 days, the media containing CMPD1 or tivantinib was replaced. At Day 14, drug-tolerant persister (DTP) cells were collected for further analyses. Alternatively, DTP cells were allowed to recover in drug-free media (drug holiday) until they regained their morphology and expansion started when cells resembled their parental counterparts. Images were taken using Zeiss AxioVert.A1 microscope using the Zeiss ZEN software.

Persisters quantification
Cells (1.5×10^5 cells/well) were seeded on black imaging 24-well plates (Eppendorf) and treated with tested compounds for 14 days. To remove dead cells, media was replaced with fresh media containing tested compounds every 3 days. Untreated (Day 0) and treated (Day 14) cells were stained with Nuclear-ID red stain (Enzo Lifesciences) at 1:1,000 dilution in StemPro media. Cells were incubated with the stain for 30 min prior to washing with PBS. Alternatively, when DAPI stain was used, on Day 14 cells were fixed in 1% PFA/PBS for 10 min, washed three times with PBS, stained and permeabilised in 1 µg/mL DAPI and 0.1% Triton-X-100 in PBS for 15 min. Cells were imaged using Zeiss Axio Scope.A1 and Zeiss ZEN software. ImageJ Fiji was used for quantification (nine images per well).

Cell viability and GR metrics
Neural stem cells, astrocytes, treatment-naïve glioblastoma cells (all at 2×10^3 cells/well) or drug-tolerant persister cells (8×10^3 cells/well) were treated with DMSO or tested compounds at 8-point dilution row for 5 days. CellTitre-Blue (Promega, Cat# G808B) was added (1:10) and incubated at 37°C for 2–4 h. Fluorescence was measured with a Tecan M200 PRO+ microplate reader (Tecan) at Ex/Em 530/590 with readings collected on Tecan Magellan software. Data were normalised to DMSO-treated controls (set as 1). Per-division GR 50, GR max, h, and GR AOC metrics were calculated from cell viability data on Day 0 and Day 5 using the GR calculator online tool22. Graphs were recreated using GR values from the GR calculator and Prism v10.1.2 (GraphPad).

Colony formation assay
Cells (1×10^4 cells/well) were treated with the tested compounds for 7 days, then washed with PBS and allowed to recover in drug-free media for an additional 7–21 days (depending on the cell line). Once visible colonies were formed in CMPD1-only treatments, colonies were stained with 1% toluidine blue (>4 h, 4°C). Plates were imaged using Bio-Rad Gel Doc and colony area quantified using the ImageJ software.

Spheroid growth assay
MMK1 and GBM6 cells (10×10^3 cells/well) were seeded into 96-well round bottom low attachment plates and centrifuged at 190g for 3 min. Cells were left undisturbed for 48 h to form spheroids, then treated with tested compounds or DMSO (vehicle) for 28–35 days, with drug-containing media replenished every 4 days. Spheroids were imaged on an AxioVert A1 microscope immediately prior to treatment and every 7 days following the initiation of treatment to determine spheroid diameter using ImageJ. After 28 days of treatment, a PrestoBlue (Thermofisher Scientific, Cat# A13261) assay was performed according to the manufacturer’s instructions with an incubation period of 3 h. Fluorescence was measured with a FLUOstar Omega microplate reader (BMG Labtech), and readings were normalised to the average of DMSO-treated vehicle controls. Graphing and statistical analyses were performed with GraphPad Prism v10.1.2.

Incucyte Live Cell Imaging
Cells (4×10^4 cells/well) were treated as described and placed into the Sartorius Incucyte SX5 at 37°C and 5% CO2. 16 images were taken per well every 2 h. Confluency was calculated using the Sartorius Incucyte 2022A software.

RNA sequencing
RNA extraction was performed with RNeasy Minikit (Qiagen, Cat# 74104), according to manufacturer’s instructions, with additional on column DNA digestion using RNase free DNase set (Qiagen, Cat# 79254). For the sequencing of DTPs (Fig. 1), sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina (New England Biolabs, Cat# E770) following manufacturer’s instructions. For sequencing of RKI1 cells treated with CMPD1 ± MRK-740 or RKI1 cells transduced with NTC/PRDM9 sgRNA (Fig. 5), RNA extracted from samples underwent library preparation using mRNA stranded library preparation kit (Illumina, Cat# 20040532) with associated indexes and anchors, following manufacturer’s instructions. Libraries were quantified using NEBnext Ultra II DNA library preparation kit for Illumina (New England Biolabs, Cat# E7630L). For sequencing in Fig. 1, the libraries were sequenced on an Illumina Novaseq. For Fig. 5, the libraries were sequenced on Illumina Nextseq 2000 instrument and Illumina Nextseq 1000/2000 Control Software Suite v1.7.1. FASTQ generation, alignments and counts were processed on instrument using Illumina Dragen RNA (v4.2.7) pipeline. Heatmaps were generated using Pheatmap package, gene sets were mapped to gene ontology terms using clusterProfiler package and plots were generated using ggplot2 package, all in RStudio. Gene annotation packages used in RStudio included biomaRt, org.Hs.eg.db, EnsDb.Hsapiens.v86 and AnnotationDbi. Venn diagrams were generated with Venny software. For data in (Fig. 5c). batch correction was performed using RUV and a set of stably expressed genes using Combat package in RStudio65. Samples were processed in three biological replicates with significance determined through adjusted P-value.

LC-MS/MS analysis of histone peptides
All solvents, acids and bases used were HPLC-grade (Sigma-Aldrich) and all reactions were carried out in Protein-LoBind Eppendorf tubes. Chromatin-bound fractions from snap-frozen cell pellets were extracted using the Sub-Cellular Fractionation Kit (Thermo Fisher Scientific, Cat# 78840) as per manufacturer’s instructions. The samples were supplemented with 50 mM sodium bicarbonate, Protease Inhibitor Cocktail, 1 mM PMSF, 1 mM sodium orthovanadate and 10 mM sodium butyrate (All Sigma-Aldrich). Protein concentration was measured using Pierce BCA Protein Assay (Thermo Fisher Scientific, Cat# 23225). Proteins were precipitated with methanol:chloroform, air-dried, reconstituted to ~2.5 mg/mL in 50 mM sodium bicarbonate (pH 8.0) and sonicated (SonoPlus Mini 20 (Bandelin)). Histones were propionylated and trypsinised for bottom-up analysis as described by Sidoli et al. Samples were then acidified with 1% trifluoroacetic acid and desalted using Oasis HLB Sep-Pak columns (Waters) and vacuum dried. For LC/MS-MS, 1 µg of propionylated histones in loading buffer (3% acetonitrile, 0.1% TFA) was injected onto a 30 cm × 75 µm inner diameter column packed in-house with 1.9 µm C18AQ particles (Dr Maisch GmbH, HPLC) using a Dionex Ultimate 3000 nanoflow UHPLC. Peptides were separated using a linear gradient of 5–35% buffer B over 120 min at 300 nL/min at 55°C (buffer A: 0.1% (v/v) formic acid; buffer B: 80% (v/v) acetonitrile and 0.1% (v/v) formic acid). All MS analyses were performed using a Q-Exactive HFX mass spectrometer. For Data-Dependent Acquisition (DDA): after each full-scan MS1 (R=120,000 at 200 m/z, 300–1600 m/z; 3×10^6 AGC; 110 ms max injection time), up to 10 most abundant precursor ions were selected for MS/MS (R=45,000 at 200 m/z; 2×10^5 AGC; 86 ms max injection time; 30 normalised collision energy; peptide match preferred; exclude isotopes; 1.3 m/z isolation window; minimum charge state of +2; dynamic exclusion of 15 s). For DIA: after each full-scan MS1 (R=60,000 at 200 m/z (300–1600 m/z; 3×10^6 AGC; 100 ms max injection time), 54×10 m/z isolations windows (loop count = 27) in the 390–930 m/z range were sequentially isolated and subjected to MS/MS (R=15,000 at 200 m/z, 5×10^5 AGC; 22 ms max injection time; 30 normalised collision energy). 10 m/z isolation window placements were optimised in Skyline66 to result in an inclusion list starting at 395.4296 m/z with increments of 10.00455 m/z. This resulted in a duty cycle of ~2.2 s. Database searches were performed using Mascot v2.4 against the human SwissProt database (May 2019; 559,634 entries) using a precursor-ion and product-ion mass tolerance of ±10 ppm and ±0.02 Da, respectively. The enzyme was specified as ArgC with a maximum of 1 missed cleavage. Variable modifications were set as: acetyl(K), propionyl(K), monomethyl+propionyl(K), dimethyl(K) and trimethyl(K), propionyl(N-term), oxidation(M), carbamidomethyl(C). All DIA data were processed using Skyline (v20.1)66. Reference spectral libraries were built in Skyline using Mascot.dat files using the BiblioSpec algorithm67. A False Discovery Rate (FDR) of 5% was set and a reverse decoy database was generated using Skyline68.

Data normalisation and statistical analysis
Processed MS1 quantifications were first Log transformed (base 2) and then quantile-normalised across samples. Data from FPW1 and RKI1 were subsequently analysed independently. For data from each cell type, Combat R package69 was used to remove experimental batch effects69. Batch corrected data were used for downstream analyses including hierarchical clustering, heatmap visualisation and bar graphs. ChIP sequencing
RKI1 cells treated with CMPD1 ± MRK-740 or RKI1 cells transduced with NTC/PRDM9 sgRNA ± CMPD1 were harvested, cell pellets were cross-linked with 0.75% PFA/PBS for 10 min, quenched in 1 M glycine, washed in PBS, and snap frozen in liquid nitrogen. Cells were lysed in ChIP lysis buffer (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA pH 8, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, Protease Inhibitor Cocktail) and sonicated to shear chromatin to ~300 bp DNA fragment size. DNA fragment size was checked via 1.5% agarose gel electrophoresis against a 100–1000 bp DNA. Input samples were aliquoted for later DNA extraction processing. Chromatin immunoprecipitation was carried out with H3K4me3 antibody (Cell Signalling Technology, Cat# 9751) added 1:50 to chromatin lysates. The samples were agitated overnight at 4°C with bead capture of antibody bound chromatin fragments using Dynabeads Protein A (Thermofisher Scientific, Cat# 10002D). The samples were then washed in the following buffers: 1) low salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.0, 150 mM NaCl); 2) high salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.0, 500 mM NaCl); 3) LiCl wash buffer (0.25 M LiCl, 1% NP-40, 1% sodium deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8.0). DNA protein complexes were eluted from antibody captured beads in 1% SDS, 100 mM NaHCO3. Chromatin fragments were subject to overnight de-crosslinking in 0.32 M NaCl and RNA digestion with 10 µg RNAse A at 65°C. Protein was digested using 20 µg Proteinase K for 1 h at 60°C. DNA was extracted using ChIP Clean and Concentrate Kit (Zymo Research, Cat# D5205). Input and ChIP fragments were quantified using QUBIT dsDNA kit (Thermofisher Scientific, Cat# Q33230) and 5 ng DNA fragments was subjected to library preparation using NEBNext Ultra II DNA library preparation kit for Illumina (New England Biolabs, Cat# E7645S). Libraries were quantified, sequenced on Illumina Nextseq 2000 instrument and Illumina Nextseq 1000/2000 Control Software Suite v1.7.1. Illumina Dragen BCL Convert (v4.2.7) was used to generate FASTQ sequences for further downstream analysis using Deeptools 2.0 pipeline. Briefly, FASTQ files were aligned to hg38 homo sapiens reference genome, then bigWig density files were generated from the BAM alignments for subsequent downstream analyses. pyGenomeTracks was used for generating individual gene tracks of H3K4me3 enrichments. To identify PRDM9 motif occurrences across the genome, we obtained the reference genome sequence (hg38 for RKI1, hg19 for patient data GSE121723) in FASTA format, and homo sapiens PRDM9 motif matrix MA1723.2 was scored against it using FIMO from MEME Suite v5.5.770. MACS2 call peak was used to calculate H3K4me3 ChIP seq peak widths. Downstream analysis was conducted in RStudio using dpylr, ChIPseeker, GenomicRanges, EnsDb.Hsapiens.v86, AnnotationDbi and biomaRt packages. ChIP-seq experiments were analysed in biological duplicate (n=2) according to ENCODE guidelines.

LC-MS/MS analysis of lipids and cholesterol
RKI1 parent and CMPD1-derived drug-tolerant persister cells (Fig. 1e) were collected, homogenized in sample extraction buffer (50 mM Hepes pH 7.4, 25 mM KCl, Protease Inhibitor Cocktail) by sonicating for 5 min (30 s on/30 s off) at 4°C with a Qsonica Q800R2 sonicating bath. Protein concentration was determined with the BCA assay. Lipids were extracted from 200 µL lysate (~200 µg protein) using the methyl-tert-butyl ether (MTBE)/methanol/water protocol as previously described71. Cell homogenate was combined with 250 µL methanol containing 0.01% 3,5-di-tert-4-butylhydroxyltoluene (BHT), internal standards and 850 µL MTBE, sonicated in a 4°C water bath for 30 min, and phase separation was induced by centrifuging at 2000g for 5 min. The upper organic phase was transferred to a 5 mL glass tube, and the aqueous phase was re-extracted with 500 µL MTBE and 150 µL methanol. The organic phase from the second extraction was combined with the first, after which the extracts were dried in a Savant SC210 SpeedVac dessicator (Thermo Fisher Scientific). Lipids were reconstituted in 400 µL of 80% (v/v) methanol: 20% water containing 0.01% BHT, 1 mM ammonium formate, and 0.1% formic acid, and stored at -80 °C. Lipids were detected in multiple reaction monitoring mode on a TSQ Altis triple quadrupole mass spectrometer with a Vanquish HPLC (Thermo Fisher Scientific) and Waters Acquity UPLC CSH 2.1×100 mm C18 column (1.7 µm particle size), as previously described72. Run time was 25 min with a flow rate of 0.28 ml/min, using mobile phases A (10 mM ammonium formate, 0.1% formic acid, 60% acetonitrile and 40% water) and B (10 mM ammonium formate, 0.1% formic acid, 10% acetonitrile and 90% isopropanol) with the following binary gradient: 0–3 min, 20% B; 3-5.5 min, ramp to 45% B; 5.5–8 min, ramp to 65% B; 8–13 min, ramp to 85% B; 13-14 min, ramp to 100% B; 14-20 min, hold at 100% B; 20–25 min, decrease to 20% B and hold to 25 min. Acylcarnitine (AcCa), ceramide, sphingomyelin (SM), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and lysophosphatidylethanolamine (LPE) species were identified as the [M+H]+ precursor ion, with production m/z values of 85.0 for AcCa, 184.1 for PC, LPC and SM, 264.3 for ceramide and SM, and neutral loss of 141.0 for LPE. Diacylglycerol (DG) was detected as the [M+NH4]+ precursor ion, with product ions corresponding to neutral loss of 35.0 and RCOOH+NH4. Cholesterol was detected as precursor m/z 369.4 and production m/z 161.1. Phosphatidylethanolamine (PE), phosphatidylinositol (PI), and phosphatidylserine (PS) species were detected as the [M-H]- precursor ion, with productions corresponding to the fatty acyl anion. TraceFinder 4.1 (Thermo Fisher) was used to integrate the peaks. The molar amount of each lipid was calculated with reference to its class-specific internal standard, then normalized to protein content. For targeted cholesterol analysis (Fig. 7f, g), lipids were extracted as described above with the addition of 5 nmoles of d7-cholesterol internal standard, followed by reconstitution in 360 µL of 25% 1-butanol: 55% Methanol: 20% H2O containing 0.01% BHT. 300 µL was transferred to fused-insert glass HPLC vials. Lipids were detected in multiple reaction monitoring mode on a TSQ Altis triple quadrupole mass spectrometer coupled to a Vanquish HPLC. Lipids were resolved on a Phenomenex Kinetex 3×100 mm (2.6 µm particle size) C8 column. Run time was 12.5 minutes, with a flow rate of 0.5 mL/min using the following binary gradient: 0-4.1 min 90% B; 4.1-4.2 100% B; 4.2-10.5 min 100% B; 10.5–10.6 min 90% B; 10.6-12.5 90% B, where mobile phase A was 1 mM ammonium formate and 0.1% formic acid in water, and mobile phase B was 1 mM ammonium formate and 0.1% formic acid in methanol. Cholesterol was detected as precursor m/z 369.4 and production m/z 161.1. Lipids were quantified in TraceFinder and expressed relative to the internal standard and protein amount per sample.

GC/MS analysis of cholesterol
RKI1 cells treated with CMPD ± MRK-740 ± MRK-740-NC (Fig. 7e, Supplementary Fig. 6d, Supplementary Fig. 7b, c) were collected, cell pellets washed in PBS, lysed with 10 M NaOH containing protease inhibitors and proteins quantified by BCA assay following manufacturers’ instruction. Heavy isotope standards (100 ng per sample) and methanol supplemented with 0.02% BHT (8:1 v/v) were added to each sample and samples were heated at 60 °C for 1 h in 1 M KOH. The reaction was neutralised with 300 mM HCl, and sterols extracted in hexane. The samples were dried, derivatised (60°C, 1 h) in acetonitrile:BSTFA (1:3 v/v) and toluene (4:1 v/v) was added. Derivatives were analysed by gas chromatography (Agilent 8890) and mass spectrometry (Agilent 5977 System). Briefly, derivatized samples (1 µl) were injected into a fused silica capillary column (60 m x 0.25 mm internal diameter) coated with cross-linked 50% phenylmethylsiloxane (film thickness 0.25 um; Restek Rxi-5ms) using a splitless injection for sterol intermediate analysis or a 30:1 split ratio for cholesterol analysis. The helium carrier gas flow rate was 1 mL/min. Selected ion monitoring was performed by using the electron-ionization (EI) mode at 70 eV. Quantification was performed by monitoring specific ions at standard retention times and compared using the peak area ratio of the sterol vs the internal standard, on the Agilent Masshunter software. Data was then normalised to protein quantifications. The following standards were used: Cholesterol-d7 (Cat# 791645), Zymosterol-d5 (Cat# 700072), Zymostenol-d7 (Cat# 700117), Desmosterol-d6 (Cat# 700040P) (all Avanti Polar Lipids), Lathosterol-d4 (Cat# D-5546), 7-Alpha-Hydroxycholesterol-d7 (Cat# D-4064), 7-Beta-hydroxycholesterol-d7 (Cat# D-4123) (all CDN isotopes), 24-hydroxycholesterol-d7 (Cat#, D730), 27-hydroxycholesterol-d5 (Cat# D733) (all Medical Isotopes), Squalene-d6 (Cat# TRC-S683802), Lanosterol-d6 (Cat# TR-L174584), 7-dehydrocholesterol-d7 (Cat# TRC-D229457), 25-hydroxycholesterol-d6 (Cat# TRC-H918032) (all TRC).

Western blotting
For histone immunoblots in Fig. 2, histones were extracted using the Histone Extraction Kit (Abcam, Cat# ab113476) as per manufacturer’s instructions, and protein concentrations were determined. 1 µg of histone extracts were resolved (30 min, 200 V) on 12% Bolt Bis-Tris gels (Cat# NW00127BOX) and transferred onto nitrocellulose membranes (Cat# IB301002) using iBlot 2 protein transfer system (Cat# IB21001) at 15 V, 7 min (all Thermofisher Scientific). For whole cell lysates, cell pellets were lysed in RIPA buffer containing protease inhibitor PMSF, protease inhibitor cocktail and phosphatase inhibitors (sodium orthovanadate), sonicated to remove viscosity, then protein concentration was measured. Samples were prepared with Bolt LDS loading buffer (Cat# B0007) and Bolt sample reducing agent (Cat# B0009) (both Thermofisher Scientific). Samples were heated at 95°C for 5 min. 20–30 µg of total protein were resolved (2 h, 95 V) on 4-12% Bolt Bis-Tris gels (Cat# NW04120BOX) and dry transferred onto PVDF membranes (Cat# IB24001) at 20 V, 7 min (all Thermofisher Scientific). Membranes were blocked with 5% skim milk powder in TBST, washed in TBST, and incubated with primary antibody in 5% BSA in TBST overnight at 4°C. Membranes were washed in TBST and incubated with secondary antibody for 1 h at room temperature. Detection was performed with Immobilon Western HRP Substrate Luminol-Peroxidase kit (Merck Millipore, Cat# WBKLS0500) and the ChemiDoc MP Imaging System (Bio-Rad). Densitometry quantification was done in Image Lab software (Bio-Rad). The following antibodies were purchased from Cell Signalling Technologies: GAPDH (1:2000, Cat# 97166), H3K4me1 (1:1000, Cat# 5326), H3K4me2 (1:1000, Cat# 9725), H3K4me3 (1:1000, Cat# 9751S), H3K27me1 (1:1000, Cat# 84932), H3K27me3 (1:1000, Cat# 9733), Histone H3 (1:2000, Cat# 4499), Cleaved Histone H3 (Thr22) (1:1000, Cat# 12576), DHCR24 (1:1000, Cat# 2033), HMGCS1 (1:1000, Cat# 36877S), SETD1B (1:1000, Cat# 44922S), Rabbit IgG HRP-linked (1:2000, Cat# 7074) and Mouse IgG HRP-linked (1:2000, Cat# 7076). The following antibodies were purchased from ABCAM: H3K4me3 (1:1000, Cat# ab12209), H3K9me1 (1:1000, Cat# ab9045), H3K9me2 (1:1000, Cat# ab1220), H3K9me3 (1:1000, Cat# ab8898), H3K27me2 (1:1000, Cat# ab24684), H3K36me1 (1:1000, Cat# ab9048), H3K36me2 (1:1000, Cat# ab9049), H3K36me3 (1:1000, Cat# ab9050), Histone H3.3 (1:1000, Cat# ab176840) and MVD (1:1000, Cat# ab129061). Histone H3.1/3.2 (1 µg/mL, Cat# ABE154) antibody was purchased from Merck Millipore and antibody against PRDM9 (1:1000, Cat# MA5-51196) was purchased from Thermo Fisher.

RT-qPCR
After RNA extraction, cDNA was generated using Applied Biosystems High-Capacity cDNA Reverse Transcription kit (Thermofisher Scientific, Cat# 4368814) as per manufacturer’s instructions. RT-qPCR was performed using Quantitect validated primers (Qiagen) or custom primers, with KAPA SYBR FAST Universal 2× qPCR Master Mix (Kapa Biosystems, (Cat# KK4602). RT-PCR was run on LightCycler 480 (Roche). The cycling condition were as follows: 10 min at 95°C followed by 45 cycles, each consisting of 10 s at 95°C and 30 s at 60°C. Samples were run in triplicate. Threshold cycles (CT) were calculated using the LightCycler® 480 software. Relative quantification using the comparative CT method was used to analyse to the data output. GAPDH were used as loading controls. Values were expressed as fold change over corresponding values for the control by the 2-ΔΔCT method. The following primers were used, all from Qiagen Quantitect Primer Assay (Cat# 249900): Hs_ACTB_1_SG (Identifier: QT00095431), Hs_GAPDH_1_SG (Identifier: QT00079247), Hs_CDKN1A_SG (Identifier: QT00062090), Hs_PML_1_SG (Identifier: QT00090447), Hs_YPEL3_1_SG (Identifier: QT00078589), Hs_BHLHE41_1_SG (Identifier: QT00032697), Hs_CDKN1B_2_SG (Identifier: QT00998445), Hs_NR2F1_1_SG (Identifier: QT00089355), Hs_SOX2_1_SG (Identifier: QT00237601), Hs_SOX9_1_SG (Identifier: QT00001498), Hs_ORC1_1_SG (Identifier: QT00005341), Hs_NEK2_2_SG (Identifier: QT01668394), Hs_MCM5_1_SG (Identifier: QT00084000), Hs_KMT2A_2_SG (Identifier: QT00247464), Hs_KMT2B_1_SG (Identifier: QT00015778), Hs_KMT2C_1_SG (Identifier: QT00029316), Hs_KMT2D_2_SG (Identifier: QT01762096), Hs_KMT2E_1_SG (Identifier: QT00053900), Hs_KIAA0339_1_SG (KMT2F/SETD1A, Identifier: QT00042427), Hs_KIAA1076_1_SG (KMT2G/SETD1B; Identifier: QT01016799), Hs_ASH1L_1_SG (KMT2H, Identifier: QT00083335), Hs_BUB1_1_SG (Identifier: QT00082929), Hs_ABCA1_SG (Identifier: QT00074606). In Fig. 4a, Hs_PRDM9_1_SG (KMT8B, Identifier: QT01023631) was used. In Fig. 4b, c, e the PRDM9 custom designed primers (Forward: TGAAAGAATTGTCAAGAACAGCA; Reverse: CTCCTTCTTCCTGAGTTCCAGT) were used (manufactured by Integrated DNA Technologies). KDM5A custom designed primers (Forward: ACCCCAACGTGCTAATGGAG; Reverse: TCCAATGGGCAACCAGTCAG) and CTSL1 custom designed primers (Forward: TCTGCTGGCCTTGAGGTTTT; Reverse: GGCAATTCCCAGGCAAAAGG) were manufactured by Integrated DNA Technologies.

Retroviral packaging and CRISPR-Cas9 infection
SET1A, SET1B, PRDM9 and non-targeting control (NTC) sgRNAs were cloned by traditional restriction digestion into pLentiCRISPRv2 (Addgene plasmid 52961). HEK293T cells (7.2×10^5 per well, 6-well plate, one for each sgRNA) were seeded and once cells reached 70–90% confluency, cells were transfected using Lipofectamine 3000 (Thermofisher Scientific, Cat# L3000015) with packaging the plasmids pCAG-VSVG (Addgene plasmid 35616) and psPAX2 (Addgene plasmid 12260) and the respective sgRNA pLentiCRISPRv2 vector at a 1:3:3 ratio. 16 h after transfection the medium was replenished with fresh medium. At 48 h post transfection the lentivirus-containing supernatant was collected and filtered through a 0.45 µm ultra-low protein binding filter and concentrated using PEG according to manufacturer’s instructions. The concentrated lentivirus was stored at -80°C. RKI1 cells (7×10^4 cells/well) were incubated with polybrene-containing (8 µg/mL) media and lentiviral sgRNA particles for 16 hours, followed by resting the cells in fresh media for 24 h. Cells were challenged in puromycin-containing media (1 µg/mL) for 72 h, or until all untransduced cells were eliminated. Puromycin-containing media was replaced with fresh media and knock-out/control cell lines were expanded. To validate gene knockout, genomic DNA was extracted from cell pellets with the ISOLATE II Genomic DNA Kit (Bioline, Cat# BIO-52067), subjected to PCR amplification and Sanger sequencing of each sgRNA target site and comparison to untransduced RKI1 cells through Synthego ICE analysis tool (Supplementary Fig. 9)73. sgRNA sequences and primers against gDNA are listed below.

Flow cytometry for reactive oxygen species, lipid peroxidation and cell cycle
Flow cytometric analyses of reactive oxygen species using CellROXTM Green Reagent (Thermo Fisher Scientific, Cat# C10444), lipid peroxidation using Image-iT Lipid Peroxidation kit (Thermo Fisher Scientific, Cat# C10445) were carried out following manufacturer’s instructions. Briefly, following treatments, cells were stained for 30 min, harvested, washed in PBS and resuspended in media in flow cytometry tubes. For ROS quantification, staining intensity was assessed in the FITC channel (495/525) using a BD LSR Fortessa X-20. Lipid peroxidation was assessed in the FITC channel (495/525) for oxidised lipid species and Texas Red channels (586/603) for total lipid staining using a BD LSR Fortessa X-20. Cell cycle distribution was determined using propidium iodide (Sigma-Aldrich, Cat# 81845) staining. Briefly, cells were harvested, fixed overnight at -20°C in 70% ethanol, washed in PBS, incubated in RNAse A (1 µg/mL; 30 min, 37°C) and stained in Propidium Iodide (1 µg/mL in PBS, 20 min, room temperature, protected from light), and resuspended in flow buffer (2% FBS in PBS) for fluorescence measurements of DNA content in Texas Red channel (586/603) using a BD LSR Fortessa X-20 instrument and BD FACSDiv software. Data were analysed using FlowJo v10.8.1.

Tubulin polymerization assay
Tubulin polymerization assay was done using an assay kit (Cytoskeleton, Cat# BK006P) per manufacturer’s instructions. Briefly, porcine brain tubulin (10 mg/ml) was thawed in water before placing on ice and used within 2 h. Tested compounds were incubated at 37°C with reaction mixture containing tubulin, in 55 µL reaction volume, and fluorescence over time was measured using TECAN Infite® 1000 PRO plate reader (Männedorf, Switzerland) with Tecan Magellan software.

[3H]Colchicine binding assay
A radioligand competition assay was performed using the centrifugal gel filtration method. The reaction mixture (12 µL) contained 2 µM purified tubulin and 0.1 µCi (1 µl of 0.1 mCi/ml, specific activity 80%) [3H]colchicine in PEM buffer (100 mM PIPES, 10 mM CaCl2, 1 mM Na-EGTA, and 1 mM MgCl2 [pH 6.9]). Competition binding reaction was started by the addition of 2 µL of tested compounds dissolved in dH2O (final concentration of 10 µM). Reaction mixtures were incubated at room temperature for 45 min, unlabelled radioactive material was filtered by using 75 µL spin column Zeba Micro Desalt Spin Column (Pierce Biotechnology) following manufacturer’s guidelines. Elute was added to the scintillation fluid to measure radioactivity of [3H]-colchicine bound tubulin using a scintillation counter.

In vitro metabolic stability
All reactions were performed in 200 µL reaction volume in duplicate. 10 µM of tested compounds were incubated with human liver microsomes (0.4 mg/ml) in potassium phosphate buffer (0.1 M, pH 7.4) at 37°C with gentle shaking for 5 min. Assay was initiated by adding 12 µL of NADPH regenerating system (containing final concentrations of 1 mM NADP, 3 mM glucose-6-phosphate, 3.3 mM MgCl2 and 0.4 u/ml glucose-6-phosphate dehydrogenase). Reaction was quenched by adding 130 µL of ice-cold methanol, vortexed vigorously and centrifuged at 15,000g at 4°C for 10 min. 5 µL of the supernatant was analysed using an Agilent 1260 LC system coupled to a QTRAP 6500 mass spectrometer. For LC, zorbax Extend-C18 (2.1×50 mm 1.8 um) column was used in reversed-phase mode at flow rate of 200 µL/min, with gradient elution starting with 10% of phase B (0.1% formic acid in water) and 90% of phase A (0.1% formic acid in acetonitrile). The amount of phase B was linearly increased from 10% to 90% in 5 min followed by 2 min at 90% B then back to initial conditions at 8 min. The MS detector was operated with an ESI positive ionization mode. Source temperature and capillary voltage were set at 300°C and 4000 V, respectively. The Analyst software was used to control the instruments and data acquisition. Assaying of test compounds was carried out utilizing the mode of multiple reaction monitoring (MRM) using the following conditions for each compound. Ion transitions were 350.3 to 241 for CMPD1, 350.3 to 230 for analogue 17, 335 to 106 for WJA88 and 335 to 185 for analogue 10. Fragmentor voltage was set to 145 V with collision energy of 30 for all the compounds.

Brain uptake and pharmacokinetics of WJA88
Brain uptake of WJA88 was assessed in male CD-1 mice (n=3 per timepoint) following administration of a single bolus dose of 50 mg/kg by the intravenous route. Terminal blood and brain samples were collected from sub-groups of mice at 30 min and 60 min post-dose, the concentration of WJA88 in each sample was determined using LC-MS/MS. Pharmacokinetic parameters were assessed in male CD-1 mice (n=3) following administration of a single 50 mg/kg dose intraperitoneally. Blood samples were collected at seven time-points up to 24 h, transferred into tubes containing K2-EDTA and WJA88 concentration in each sample was determined with LC-MS/MS. Pharmacokinetic parameters were calculated using Phoenix WinNonlin 6.3 software. All experiments and calculations were performed by WUXI, Study No. 410466-20191217-MPK.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this article, authors uncover the role of PRDM9, a methyltransferase expressed primarily on germ cells, in mediating resistance to microtubule-targeting agents (MTA) in glioblastoma. They show that PRDM9 induces H3K4-me3 in GBM cells following MTA administration, which positively regulates cholesterol production to counteract chemotherapy-induced oxidative stress.\nWhile PRDM9 has been previously reported to play a role in the chromosomal instability in cancer, it has never been linked with treatment resistance or cholesterol metabolism.\nAltogether, the authors present exciting data and go as far as developing a novel stable and blood-brain permeable MTA analog. They tested the developed compound in combination with a PRDM9 inhibitor in an aggressive GBM animal model and showed significant survival benefits of this strategy. On the other hand, the data opens some interesting unexplored angles, and some of the data would benefit from further validation.\nMajor comments:\n1) Authors provide a very interesting observation: in drug-tolerant persisters (DTP) cells that survived 14d treatment, H3K4-me3 was significantly downregulated in one DTP cell line and unaffected in the second cell line. Additionally, cholesterol was significantly downregulated in 14d treated cells. On the other hand, in cells that received only 3d of treatment, H3K4-me3 was upregulated, as was cholesterol. However, the underlying reasons why DTP cells cease to depend on cholesterol and H3K4-me3 after the 6th day of MTA treatment remain unexplored.\na. What are levels of PRDM9 on 14d and 6d time points?\nb. What changes occur between days 3 and 6 that reduce cell dependence on cholesterol?\nc. The importance of timely implementation of PRDM9 inhibition to prevent cells from becoming resistant to MTA is a very interesting point that should be emphasized more.\n2) Authors suggest that the upregulation of cholesterol helps to compensate for cholesterol oxidation due to ROS. This does not necessarily align with evidence that cholesterol is essential only up to 6d of treatment. The proposed mechanism can benefit from further validation and can solve the discrepancy indicated in the previous question.\na. What are the ROS levels at 3d, 6d and 14d timepoints?\nb. Can cell proliferation be rescued by adding antioxidants in cells treated with CMPD1 and MRK-740/PRDM9 KO?\nMinor comments:\n1) Genetic manipulation to prevent cholesterol production will be a good second line of evidence to support DepMap data and data observed in the presence of inhibitor and ensure that findings are not related to the inhibitor's off-target effect.\n2) In Figure 2F total H3 should be presented.\n3) PRDM9 is unlikely to be the main writer regulating H3K4 methylation. While the overall focus on this non-essential enzyme is well justified and explained, understanding whether other H3K4-me3 writers (SET1, MLL) are affected in a similar fashion will provide important insight into the resistance mechanism. Do MTAs activate all the writes of the H3K4-me3, or only PRDM9? This is even more important since data show that MRK-740 caused H3K4-me3 depletion only in CMPD1-treated cells but not in naïve ones.\n4) It would be beneficial to additionally validate the safety of newly developed compound in normal human astrocytes and neural stem cells.\n5) In Figure 3 I, the effect of the KO should be analyzed in relation to the NT control and not the parent cell line. If NT and transfection cause cytotoxicity, that has to be considered when judging the effect of PRDM9 KO.\n6) Having representative images of the tumour bioluminescence in animal model will make in vivo data more impactful.\n7) Some western blots (Figure 2 E and G, Figure 3 E, Figure 6 B - C) lack quantification, which should be fixed to provide statistical robustness.\n8) For the cases where densitometry quantification for the western blots is provided, were these numbers normalized to the total protein and/or total histone levels before being compared between groups?", "reviewer_2": "Joun et al. have taken a multi-scale integrative approach to understand how epigenetic changes support survival of glioblastoma cells resistant to microtubule-targeting (MT) drugs. The presence of drug-tolerant persistent cells post-chemotherapy is the primary cause for tumour re-occurrence. Understanding the molecular mechanisms that drive drug-resistance is valuable for designing combination therapies to increase the effectiveness of the treatment. This study has uncovered an interesting link between epigenetics and cellular metabolism in conferring drug resistance to microtubule-targeting drugs. The authors have also leveraged their discovery to demonstrate the efficacy of combining inhibition of cholesterol efflux along with microtubule depolymerization in increasing the survival of preclinical xenograft models. The manuscript is well written and easy to follow. However, there are some missing links, and all key findings of this study mainly come from one glioblastoma cell line which makes the generalisation of this discovery questionable. Moreover, the dynamics of H3K4me3 within this treatment window raises a concern on the contribution of histone methylation vs. histone proteolytic cleavage in conferring MT-drug resistance (details below). The in vivo studies are also preliminary, convincing evidence of the these xenografts being a faithful model of glioblastoma in patients is lacking. Recapitulating key results in a few additonal glioblastoma cells, validating the in vivo work, and clarifying the H3K4me3 dynamics in MT-drug resistance will strengthen the findings and provide higher-confidence in generalizing the discovery for glioblastoma biology.\nMajor concerns:\n(1) The authors demonstrate down regulation of cholesterol biosynthetic genes in RKI1-persister cells compared to the parent cells on a transcriptomic level and further confirm the functional impact by quantifying the lipidomic profile. Does this observation hold true for the second set of cells (FPW1) used in this study? RT-PCR of cholesterol biosynthetic enzymes in persister vs. parent FPW1 will be necessary to understand the changes in metabolism associated with developing MT-drug tolerance.\n(2) The authors have profiled the histone extracts of parent and persister cells. It will be very useful for the scientific community to have the information on all histone peptides identified in this experiment in the form of a table.\n(3) In fig 2f, the faster migration H3 band is missing in H3K27me3 and H3K27me2 blot. Can this be explained? Alternatively, a blot for total H3 in parent vs. persister will be helpful in making the point of proteolytically cleaved isoform and potentially the proportions of two isoforms present in the persistent cells.\n(4) When does the proteolytically cleaved H3 variant appear within the 14-day treatment window. To understand the dynamics of H3K4me3 loss vs. proteolytic cleavage of H3 at T22, a WB against H3K4me3 and total H3 along the time course (upto 14 days of treatment) must be shown.\n(5) Furthermore, how do the levels of PRDM9 change over the 14-day CMPD1 treatment? The authors show the levels of PRDM9 increase 72h post-treatment which is in-line with initial increase in H3K4me3 levels. However, in the persister H3K4me3 levels are lower. Is this due to lower PRDM9 or increased proteolysis of H3?\n(6) The blots showing histones (fig 2f, 2g, 2h) as loading controls are washed out and unclear. Better quality images are required to make informed conclusions.\n(7) The authors have included inhibitors of writers and/or erasers of H3K27me2/3 (UNC1999, GSK-J4 and KDOBA-67a) and H3K4me3 (MRK-70) in combination with CMPD1. Since, they observe a substantial increase in H3K36me3 mark in persisters, inclusion of inhibitors of writers or erasers of this mark will be necessary to delineate the contribution of these epigenetic changes in development of drug-resistance.\n(8) The authors show that MRK-70 treatment stalls cellular growth. Does this compound also cause an arrest of cells in G2/M?\n(9) The authors show that MRK-740 alone or in combination with CMPD1 dramatically reduces cholesterol biosysthetic genes and is due to reduction of H3K4me3 deposition in their gene bodies. Is this observation due to lower amounts of H3K4me3 or increased proteolysis of H3 at T22?\n(10) In the study cited on H3 proteolytic cleavage - PMID: 25394905, it is shown that CTSL1 protease is responsible for the cleavage of H3. Do the transcript levels of this protease go up in the persisters? Does treatment of cells with CTSL1 inhibitor increase the vulnerability of parent cells?\n(11) Why were RKI1 cells not used for xenograft experiments? There is no reference for GBM6, what is the evidence that xenografts of this line faithfully model glioblasoma of the patient they have been derived from? There are no histology pictures and/or molecular data (over and above EGFR status which is not specific of the tumour entity) to convince readers that this is indeed a tumour with features of glioblastoma. Also here, it is difficult to generalise findings if only one line/xenografts is used.\n(12) Would treatment of xenograft models with MT-drug in combination with higher-cholesterol diet improve the survival of mice? This is an essential point to be addressed.\nMinor\n(1) Statistical analysis description in the methods section need to be more detailed to inform on the type of analysis that has been performed for histone peptide analysis, RNA seq, ChipSeQ and lipodomics.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Drug resistance continues to be the most significant hurdle to finding a cure for cancer, accounting for an estimated 90% of all cancer-related deaths. The speed with which cancer cells can gain resistance to both conventional chemotherapy and targeted medicines is alarming and there is an urgent need to fully understand the underlaying mechanism drug acquired resistance in tumors. Non-genetic mechanisms have lately emerged as major causes of anticancer treatment resistance. Among these, the drug-tolerant persister cells phenotype is gaining increasingly interest as it plays a several non-genetic role in cancer therapeutic resistance. Within this context the authors describe a drug tolerance mechanism in glioblastoma persister cells regulated by the germ-cell-specific H3K4 methyltransferase PRDM9. The topic of the manuscript is timely / pertinent, and the data here presented is sound and supports the majority of the scientific claims and findings mentioned throughout the text.\nThe authors have performed transcriptomics and lipidomic analysis and complementary western blot analyses aiming a better understanding the mechanistically role of PRDM9-mediated H3K4me3 in maintaining cholesterol homeostasis and contributing to cancer cells survival under chemotherapy stress. However in order to establish a holistic role of the role o PRDM9 during drug tolerance it is important that the authors complement their work with a large scale multiomics approaches specific proteomics and metabolomics. However, in order to show a comprehensive role for PRDM9 during drug tolerance, the authors should supplement their results using large-scale multiomics methods, specifically proteomics and metabolomics. Specifically, multiomics of untreated RK11 cells versus treatment with CMPD1 +/- MRK-740 and the knockout mutant. These experiments not only would validate some of the results described in the study but provide a holistic system biology overview of the impact of PRDM9 inhibition and ultimately provide an excellent opportunity to identify additional key players contributing to persisters phenotype and potential drug targets.", "reviewer_5": "Joun and colleagues contributed to shed light on epigenetic mechanisms underlying chemotherapy tolerance of glioblastoma persister cell, although the study is restricted to microtubule-targeting agents. Through epigenetic and transcriptomic analysis authors unveiled chemo-induction of germline methyltransferase PRDM9, and its role in promoting the metabolic rewiring of glioblastoma persister cells and consequently chemotherapy tolerance. In detail, chemotherapy induces oxidative stress and cholesterol oxidation. In this state, glioblastoma persister cells upregulate PRDM9, leading to an increase of the activating mark H3K4me3 on genes involved in de novo cholesterol biosynthesis. The inhibition of PRDM9 disrupts the cholesterol supply, leading to persister cell death.\nThe study is clear, well conducted, supports the conclusions and claims, and, importantly, the topic is highly relevant and of potential clinical interest.\nThere are few comments below that need to be addressed:\n1. The authors generate glioblastoma persister cells after the treatment with CMPD1 and Tivantinib. It is not clear why the authors include also Tivantinib, if the work is focused entirely on microtubule-targeting agents, namely CMPD1?\n2. The authors perform a bulk RNA-seq analysis on parental and CMPD1-derived persister cells. The analysis is limited to one cell line. It will be better to perform the same analysis on more cell lines to confirm the transcriptional profile founded in RKI1 cells.\n3. How do the authors explain the different trend of H3K9me3 after 3 and 6 day of CMPD1 treatment in RKI1 and FPW1 cells? (fig. 2h)\n4. The knockouts for SETD1A, SETD1B and PRDM9 must be assessed (e.g., by western blot) to verify the real inactivation of the genes\n5. To test if CMPD1 induces mitotic arrest, it might be more quantitative to perform the analysis of cell cycle with propidium iodide.\n6. Line 427: The authors state that the loss of H3K4me3 in CMPD1-derived persister cells is caused by the proteolytic cleavage of histone tails, rather than the activities of KDM5 demethylases. Did the authors check the expression of histone demethylases in glioblastoma persister cells since it was demonstrated an increased expression of KDM5A in persister cells of other tumor type (Sharma et al, 2010)?\n7. Fig.2G: please improve quality\n8. Fig. 3C: quality need to be improved, the quantification does not match what is visible in the figure.\n9. Line 172: It is clear why UNC1999, KDOBA-67a and GSK-Ja have been selected foe co-treatment. However, it is not clear how MRK-740 was chosen since it is not included in previous screening."}, "author_response": "NCOMMS-24-67473: Histone methyltransferase PRDM9 promotes survival of drug-tolerant persister cells in glioblastoma\nRESPONSE TO REVIEWERS’ COMMENTS\nReviewer #1\n1) Authors provide a very interesting observation: in drug-tolerant persisters (DTP) cells that survived\n14d treatment, H3K4me3 was significantly downregulated in one DTP cell line and unaffected in the\nsecond cell line. Additionally, cholesterol was significantly downregulated in 14d treated cells. On\nthe other hand, in cells that received only 3d of treatment, H3K4me3 was upregulated, as was\ncholesterol. However, the underlying reasons why DTP cells cease to depend on cholesterol and\nH3K4me3 after the 6th day of MTA treatment remain unexplored.\na. What are levels of PRDM9 on 14d and 6d time points? In a new Figure 4b (results section page\n8) we show continuing PRDM9 mRNA increase at 3, 6, 10 and 14 days of treatment.\nb. What changes occur between days 3 and 6 that reduce cell dependence on cholesterol? We\nwould like to clarify that cholesterol levels decrease in cells treated with CMPD1 both at day 3\n(Figures 7e-g) and at day 14 (Figure 1f). In addition to reduced metabolic activity in non-proliferating\ncells, our data show that this decrease is also driven by chemotherapy-induced oxidative stress and\nlipid peroxidation (Figure 7j). We are not suggesting that DTPs become independent of cholesterol\nbetween day 3 and 6; rather, we propose that PRDM9-dependent cholesterol biosynthesis provides\nsufficient new cholesterol to replace oxidised cholesterol and support DTP survival (Figure 7k).\nc. The importance of timely implementation of PRDM9 inhibition to prevent cells from becoming\nresistant to MTA is a very interesting point that should be emphasized more. Based on this\nsuggestion, we performed a DTP assay where cells were treated with CMPD1 and MRK-740 either\nsimultaneously, pre-treated with MRK-740 for 3 days before CMPD1, or treated with MRK-740 3\ndays after CMPD1 (new Figure 4f). We found that both simultaneous treatment and delayed addition\nof MRK-740 after CMPD1 reduced the number of persister cells, whereas pre-treatment with MRK-\n740 had no effect. These results suggest that PRDM9 inhibition is only effective after its upregulation\nduring CMPD1-induced mitotic arrest (Results, page 8).\n2) Authors suggest that the upregulation of cholesterol helps to compensate for cholesterol oxidation\ndue to ROS. This does not necessarily align with evidence that cholesterol is essential only up to 6d\nof treatment. The proposed mechanism can benefit from further validation and can solve the\ndiscrepancy indicated in the previous question. We propose that PRDM9 upregulation sustains\ncholesterol biosynthesis in non-proliferating cells to compensate for cholesterol oxidation induced by\nCMPD1; however, this does not lead to an overall increase in cholesterol levels (Figure 7e-g, CMPD1\nvs Parent/NTC). We do not show evidence of cholesterol upregulation or that cholesterol is essential\nonly up to 6 days of treatment.\na. What are the ROS levels at 3d, 6d and 14d timepoints? In the new Supplementary Figure 7f, we\nshow a 2.5-fold increase in ROS levels following 3 days of CMPD1 treatment, which remains\nelevated at this level on days 6, 10, and 14 post-treatment (Results, page 13).\nb. Can cell proliferation be rescued by adding antioxidants in cells treated with CMPD1 and MRK-\n740/PRDM9 KO? In the new Supplementary Figure 7h-I (Results, page 13), we show that adding\nthe antioxidant N-acetyl-cysteine (NAC) increases the number of persister cells and rescues the anti-\npersister effects of MRK-740 and PRDM9 knockout, confirming our working model in Figure 7k.\nBecause NAC is unstable and loses activity upon oxidation, it was replenished daily, and we\nquantified surviving cells after 7 days instead of the standard 14 days (DTP assay). This explains\nthe weaker efficacy of MRK-740 and PRDM9 knockout in the Supplementary Figure 7h-i compared\nto MRK-740 efficacy in e.g., Figure 7h (14 days).\nMinor comments:\n1) Genetic manipulation to prevent cholesterol production will be a good second line of evidence to\nsupport DepMap data and data observed in the presence of inhibitor and ensure that findings are\nnot related to the inhibitor's off-target effect. In the new Figure 7f, we show that PRDM9 KO\nsignificantly reduced cholesterol levels in RKI1 cells, phenocopying the efficacy of MRK-740\n(Results, page 11).\n2) In Figure 2F total H3 should be presented. Immunoblots of total H3 added have been added to\nFigure 2f.\n3) PRDM9 is unlikely to be the main writer regulating H3K4 methylation. While the overall focus on\nthis non-essential enzyme is well justified and explained, understanding whether other H3K4-me3\nwriters (SET1, MLL) are affected in a similar fashion will provide important insight into the resistance\nmechanism. Do MTAs activate all the writes of the H3K4-me3, or only PRDM9? This is even more\nimportant since data show that MRK-740 caused H3K4-me3 depletion only in CMPD1-\ntreated cells but not in naïve ones. We compared the expression of eight KMT2 methyltransferases (which\nmethylate H3K4), including MLL1 (KMT2A), SETD1A (KMT2F), and SETD1B (KMT2G), to PRDM9\nin RKI1 and FPW1 persisters. PRDM9 showed the highest upregulation, followed by MLL1, while\nthe expression of other KMT2 members remained unchanged (new Figure 4a, Results page 8),\nsupporting PRDM9 as the major H3K4 methyltransferase in our glioblastoma models.\n4) It would be beneficial to additionally validate the safety of newly developed compound in normal\nhuman astrocytes and neural stem cells. We were unable to test in normal human astrocytes (NHA),\nas Lonza, the main supplier, has discontinued import of NHAs to Australia. Instead, we evaluated\nour novel chemotherapy agent WJA88 in combination with MRK-740 and LXR-623 in mouse-derived\nneural progenitor cells and astrocytes (new Supplementary Figure 8g). WJA88 is a microtubule-\ntargeting agent and, like clinical MTAs paclitaxel and vinblastine, is expected to inhibit the\nproliferation of all dividing cells. Consistent with this, WJA88 suppressed the proliferation of neural\nstem cells, which are highly proliferative in culture. However, MRK-740 and LXR-623, either alone\nor in combination, had no effect on neural stem cell viability and did not enhance WJA88 efficacy.\nImportantly, in astrocytes, none of the treatments, whether alone or combined, affected cell viability.\nThese results suggest that WJA88 may have a safety profile similar to that of clinical MTAs (Results,\npage 14).\n5) In Figure 3 I, the effect of the KO should be analyzed in relation to the NT control and not the\nparent cell line. If NT and transfection cause cytotoxicity, that has to be considered when judging the\neffect of PRDM9 KO. In addition to 3 data points presented in the original Figure 3I (now Figure 4k–\nl), we repeated the DTP assays using NTC and PRDM9 knockout cells three more time. NTC\ntransfection caused evident cytotoxicity in 1 out of 6 repeats. When compared to NTC controls,\nPRDM9 knockout significantly reduced DTP survival in the PRDM9(1) KO line, with a p-value of\n0.098 observed for PRDM9(2) KO.\n6) Having representative images of the tumour bioluminescence in animal model will make in vivo\ndata more impactful. We agree that bioluminescence images can enhance in vivo data; however,\nwe primarily use it as a guide for starting treatments and monitor general trends. Changes in hypoxia,\nvascularisation, immune infiltration, or extracellular pH induced by treatments can provide a false\nimpression of treatment response when measured by imaging. Survival, in contrast, is an\nunambiguous outcome measure and our preferred readout. Further, this experiment was conducted\nduring Covid restrictions, limiting imaging frequency due to reduced animal facility access. We\nprovide images from the electroporation glioblastoma model we have done for the rebuttal\nexperiments (new Figure 8d-e).\n7) Some western blots (Figure 2 E and G, Figure 3 E, Figure 6 B - C) lack quantification, which\nshould be fixed to provide statistical robustness. All Western blots presented in the main figures are\naccompanied by corresponding quantification in the Supplementary Figures. For easy reference,\neach figure legend includes a note indicating where the related quantification can be found.\n8) For the cases where densitometry quantification for the western blots is provided, were these\nnumbers normalized to the total protein and/or total histone levels before being compared between\ngroups? All immunoblots in Figure 2 and Figure 3e were normalized to total H3 before comparison\nbetween groups. All other immunoblots were normalised to housekeeping proteins (Lamin B1,\nGAPDH, or HSP90). This normalisation method is indicated in the figure legends of the\nSupplementary Figures presenting the quantification data.\nReviewer #2\n(1) The authors demonstrate down regulation of cholesterol biosynthetic genes in RKI1-persister\ncells compared to the parent cells on a transcriptomic level and further confirm the functional impact\nby quantifying the lipidomic profile. Does this observation hold true for the second set of cells (FPW1)\nused in this study? FPW1 cells were less sensitive to all interventions tested in this study. As shown\nin new Figure 7i, exogenous cholesterol supplementation increased DTP numbers by only 1.3-fold\nin FPW1 cells, compared to a 10-fold increase in RKI1 cells (Figure 7h). However, cholesterol\nsupplementation rescued the anti-persister efficacy of PRDM9 inhibition with MRK-740 (Figure 7i),\nand MRK-740 significantly reduced cholesterol levels in FPW1 cells (Figure 7g). We extended our\nstudy to the MMK1 cell line: PRDM9 inhibition downregulated cholesterol biosynthesis enzymes\n(new Figure 7d), cholesterol supplementation did not increase DTP numbers but rescued MRK-740\nefficacy (new Figure 7i). Further, chemotherapy upregulated PRDM9 mRNA in six glioblastoma stem\ncell lines (Figure 4c), and MRK-740 showed anti-persister efficacy in five genetically diverse\nglioblastoma cell lines (Extended Figure 3h). Together, these findings support a broader role for\nPRDM9 in regulating cholesterol biosynthesis across glioblastoma models. While persister cells in\nall three cell lines depend on cholesterol supply, this dependency appears stronger in RKI1 and\nMMK1 persisters. In contrast, FPW1 persisters may rely on additional survival mechanisms beyond\ncholesterol. This has been added to the Discussion on page 15.\nRT-PCR of cholesterol biosynthetic enzymes in persister vs. parent FPW1 will be necessary to\nunderstand the changes in metabolism associated with developing MT-drug tolerance. RNA\nsequencing was performed on RKI1 and FPW1 parental and persister cells, revealing that transcripts\nfor cholesterol biosynthesis enzymes are predominantly upregulated in persister cells (new\nSupplementary Figure 6e), consistent with the transcriptomic profile of cells treated with CMPD1 for\n3 days. Despite this transcriptional upregulation, cholesterol levels are reduced after both 3 and 14\ndays of CMPD1 treatment (persisters). This supports our working model that the decrease in\ncholesterol is caused by oxidation, and that, to survive, persister cells compensate by upregulating\ncholesterol biosynthesis pathways (Results, page 12-13).\n(2) The authors have profiled the histone extracts of parent and persister cells. It will be very useful\nfor the scientific community to have the information on all histone peptides identified in this\nexperiment in the form of a table. The mass spectrometry proteomics data, including all histone\npeptides identified in persister cells, have been deposited in the ProteomeXchange Consortium via\nthe PRIDE partner repository under the dataset identifier PXD050643. This will allow the scientific\ncommunity full access to the complete list of histone peptides identified in persisters.\n(3) In fig 2f, the faster migration H3 band is missing in H3K27me3 and H3K27me2 blot. Can this be\nexplained? Alternatively, a blot for total H3 in parent vs. persister will be helpful in making the point\nof proteolytically cleaved isoform and potentially the proportions of two isoforms present in the\npersistent cells. We have added total H3 immunoblots to Figure 2f, which also show a faster-\nmigrating band consistent with cleaved histone H3. Accurately quantifying the proportion of full-\nlength versus cleaved H3 is challenging, as the bands run closely together, making separate\nquantification difficult.\nThe absence of the faster-migrating band in the H3K27me2 and H3K27me3 blots is likely due to\nlower antibody sensitivity. Similarly, this band is not visible in the total H3 immunoblots in Figures\n2g-h, which were generated using a different lot of the total H3 antibody (same supplier, different\nbatch). Although cleaved H3 is clearly detected with the specific Cleaved H3 (T22) antibody, it is not\napparent in the total H3 blots (new Figure 2h). This also suggests that the amount of cleaved H3 is\nsubstantially lower than that of full-length H3.\n(4) When does the proteolytically cleaved H3 variant appear within the 14-day treatment window. To\nunderstand the dynamics of H3K4me3 loss vs. proteolytic cleavage of H3 at T22, a WB against\nH3K4me3 and total H3 along the time course (up to 14 days of treatment) must be shown. New\nFigure 2h shows the dynamics of H3K4me3, H3K9me3, and H3 cleavage at days 3, 6, 10, and 14\nof treatment. Histone cleavage begins as early as day 3, peaks at day 10, and then declines.\nH3K4me3 levels display distinct patterns in RKI1 and FPW1 cells but are elevated at days 3, 6, and\n10, then steeply decline. In contrast, changes in H3K9me3 are minimal, also aligning with Figure 2f.\nTogether, these data suggest that H3K4 methylation and its removal via histone cleavage may occur\nconcurrently, but the net effect is an overall increase in H3K4me3 up to day 10 in both cell lines\n(Results, page 5-6).\n(5) Furthermore, how do the levels of PRDM9 change over the 14-day CMPD1 treatment? The\nauthors show the levels of PRDM9 increase 72h post-treatment which is in-line with initial increase\nin H3K4me3 levels. However, in the persister H3K4me3 levels are lower. Is this due to lower PRDM9\nor increased proteolysis of H3? New Figure 4b shows a time course (days 3, 6, 10, and 14) of\nprogressively increasing PRDM9 mRNA levels. However, this does not correlate with H3K4me3\ndynamics (Figure 2h), as H3K4me3 abundance first increases and then declines. Data in the new\nSupplementary Figure 3c–d demonstrate an increase in the H3K4 demethylase KDM5 and the\nhistone-cleaving protease CTSL1 upon CMPD1 treatment. Hence, both enzymes may contribute to\nthe decline in H3K4me3 observed at later time points (Results, page 7).\n(6) The blots showing histones (fig 2f, 2g, 2h) as loading controls are washed out and unclear. Better\nquality images are required to make informed conclusions. The loading controls were Amido Black\nstains of membranes but have been replaced with total H3 immunoblots.\n(7) The authors have included inhibitors of writers and/or erasers of H3K27me2/3 (UNC1999, GSK-\nJ4 and KDOBA-67a) and H3K4me3 (MRK-740) in combination with CMPD1. Since, they observe a\nsubstantial increase in H3K36me3 mark in persisters, inclusion of inhibitors of writers or erasers of\nthis mark will be necessary to delineate the contribution of these epigenetic changes in development\nof drug-resistance. New Figure 3c shows that inhibiting the H3K36 methyltransferase SETD2 with\nEPZ-719 reduces persister cell survival, particularly in RKI1 cells, which carry the SETD2 R472H\nmutation, while FPW1 cells are SETD2 wild-type. As this mutation is unique to RKI1 and not present\nin any of the other 11 cell lines in our QCell panel, the efficacy of EPZ-719 may represent a cell line-\nspecific effect rather than a broadly applicable mechanism. This prompted us to focus further on\nPRDM9 and the anti-persister efficacy of MRK-740 (clarified in Results, page 6).\nH3K36me3 is demethylated by KDM4, and while we initially tested the KDM4 inhibitor QC6352\n(Supplementary Figure S3a), we recently found that its anti-cancer activity is driven by an off-target\n(manuscript under review/revision, Nature Chemical Biology). Due to its promiscuity, we chose not\nto include QC6352 data here, as its efficacy cannot be solely attributed to KDM4 inhibition.\n(8) The authors show that MRK-70 treatment stalls cellular growth. Does this compound also cause\nan arrest of cells in G2/M? New Supplementary Figure 3e shows that MRK-740 increases the\npercentage of cells in the G /G phase while reducing the proportion of cells in S phase (Results,\n0 1\npage 7).\n(9) The authors show that MRK-740 alone or in combination with CMPD1 dramatically reduces\ncholesterol biosysthetic genes and is due to reduction of\nH3K4me3 deposition in their gene bodies. Is this observation due\nto lower amounts of H3K4me3 or increased proteolysis of H3 at\nT22? Based on H3K4me3 ChIP-seq results, we propose that the\nprimary mechanism involves reduced H3K4me3 deposition at\ncholesterol biosynthesis gene promoters (Figure 5i). However,\nwe tested whether MRK-740 induces H3 cleavage - and found\nthat it does not (data on the right, not included in the paper).\n(10) In the study cited on H3 proteolytic cleavage - PMID: 25394905, it is shown that CTSL1 protease\nis responsible for the cleavage of H3. Do the transcript levels of this protease go up in the persisters?\nDoes treatment of cells with CTSL1 inhibitor increase the vulnerability of parent cells? CTSL1\ntranscript levels increased over the course of CMPD1 treatment, reaching a maximum increase in\nDTPs (day 14; new Supplementary Figure 3d). However, inhibition of CTSL1 did not reduce the\nnumber of persister cells (new Figure 3e-f).\n(11) Why were RKI1 cells not used for xenograft experiments? There is no reference for GBM6, what\nis the evidence that xenografts of this line faithfully model glioblasoma of the patient they have been\nderived from? There are no histology pictures and/or molecular data (over and above EGFR status\nwhich is not specific of the tumour entity) to convince readers that this is indeed a tumour with\nfeatures of glioblastoma. Also here, it is difficult to generalise findings if only one line/xenografts is\nused. The median survival for RKI1 xenografts is 248 days - the longest among the 12 glioblastoma\nstem cell lines in our QCell panel. BAH1 and MMK1 xenografts show median survivals of 210 and\n157 days, respectively (Ref #31; Sci Rep 9, 4902, 2019). Based on our findings that WJA88+LXR-\n623 significantly extended survival in the aggressive GBM6 model (nearly doubling it), we anticipated\na similarly pronounced effect in RKI1/BAH1/MMK1 xenografts, which would require an extremely\nlengthy study. We used GBM6, a well-established PDX model that closely recapitulates key\nbiological and clinical features of human GBM, as supported by references #48, 49, 50 cited in the\nmanuscript (Results, page 15). GBM6 tumours grow rapidly in vivo and exhibit highly aggressive\nbehaviour, making them suitable for time-efficient experimental workflows. In addition, GBM6 model\nwas previously employed in studies of brain-penetrant microtubule-targeting agents such as\nlisavanbulin (Ref #48, Neuro Oncol 24, 384, 2022), which supports comparability across studies.\nIn the revised manuscript, we present data obtained with a new glioblastoma model generated by\nbrain electroporation and knockout of the three most commonly mutated genes in mesenchymal\nglioblastoma (Nf1, Trp53, Pten). This model produces tumours that closely recapitulate the\nhistopathological and molecular features of human glioblastoma within 3 weeks (Ref #45, 46: STAR\nProtoc 5, 102928, 2024; Cell Rep 42,112472, 2023). To our knowledge, this model has not previously\nbeen used to test new therapeutic modalities.\nWe applied it to evaluate the combination of WJA88 and LXR-623, following the same treatment\nprotocol as in the GBM6 mice. However, because the animals in this model are young pups, unlike\nthe adult mice used in the GBM6 model, they did not tolerate twice-daily oral gavage administration\nof LXR-623, and this treatment arm had to be terminated. We did, however, establish the effect of\nWJA88 alone in reducing the pool of proliferating cells in these tumours, confirming both in vivo\ntarget engagement and safety (new Figure 8d-e, Supplementary Figure S8d).\nAs LXR-623 was used solely as a surrogate for the PRDM9 inhibitor MRK-740 (unsuitable for in vivo\nuse) to generate proof-of-concept, and given both our inability to administer it in young mice and its\nprevious failure in human clinical trials, we believe that further investment of time and resources into\nanimal models would not be justified or ethically appropriate. The aim of this study is to present\nproof-of-concept evidence for PRDM9 as a vulnerability in persister cells and stimulate the\ndevelopment of clinically viable PRDM9 inhibitors.\n(12) Would treatment of xenograft models with MT-drug in combination with higher-cholesterol diet\nimprove the survival of mice? This is an essential point to be addressed. Cholesterol\nsupplementation to the cell culture media increases persister survival (Figure 7h; new Figure 7i), so\nwe would expect that animals on a high-cholesterol diet would have more persister cells post-\nchemotherapy, leading to shorter survival and/or more aggressive tumour recurrence due to an\nexpanded DTP pool. However, as the blood-brain barrier is impermeable to peripheral cholesterol,\nglioblastomas are unlikely to be affected by a high-cholesterol diet. This is included on page 12.\nMinor\n(1) Statistical analysis description in the methods section need to be more detailed to inform on the\ntype of analysis that has been performed for histone peptide analysis, RNA seq, ChipSeQ and\nlipodomics. Statistical considerations for specific experiments including RNA-seq, ChIP-seq, histone\npeptide LC-MS/MS, and lipidomics have been included in the Data Analysis section on page 32.\nReviewer #4\nThe authors have performed transcriptomics and lipidomic analysis and complementary western blot\nanalyses aiming a better understanding the mechanistically role of PRDM9-mediated H3K4me3 in\nmaintaining cholesterol homeostasis and contributing to cancer cells survival under chemotherapy\nstress. However in order to establish a holistic role of the role o PRDM9 during drug tolerance it is\nimportant that the authors complement their work with a large scale multiomics approaches specific\nproteomics and metabolomics. Specifically, multiomics of untreated RK11 cells versus treatment\nwith CMPD1 +/- MRK-740 and the knockout mutant. These experiments not only would validate\nsome of the results described in the study but provide a holistic system biology overview of the\nimpact of PRDM9 inhibition and ultimately provide an excellent opportunity to identify additional key\nplayers contributing to persisters phenotype and potential drug targets. We agree that a multi-omics\napproach would provide valuable insights into the role of PRDM9 in glioblastoma and potentially\nother cancers, and we plan to further explore this in future studies. However, as this manuscript\nalready presents a substantial amount of data, including 8 main figures and 8 supplementary figures,\neach with at least 8 panels; we felt that additional omics analyses were not essential for validating\nthe oncogenic role of PRDM9-regulated cholesterol biosynthesis.\nReviewer #5\n1. The authors generate glioblastoma persister cells after the treatment with CMPD1 and Tivantinib.\nIt is not clear why the authors include also Tivantinib, if the work is focused entirely on microtubule-\ntargeting agents, namely CMPD1? Tivantinib was originally developed as a c-Met inhibitor, but later\nstudies revealed its anti-cancer activity is due to microtubule targeting (ref #23). Similarly, CMPD1\nwas developed as an MK2 inhibitor, but we validated its mechanism of action via targeting\nmicrotubules (ref #21). Therefore, both CMPD1 and tivantinib function as small-molecule MTAs, and\ntivantinib was included as a control. This is now clarified in the Results on the page 4.\n2. The authors perform a bulk RNA-seq analysis on parental and CMPD1-derived persister cells.\nThe analysis is limited to one cell line. It will be better to perform the same analysis on more cell\nlines to confirm the transcriptional profile founded in RKI1 cells. We performed transcriptomic\nanalysis of persisters derived from FPW1 cells (new Supplementary Figure 1g, h) and compared\nDEGs with those from RKI1-derived persisters. The analysis revealed a high degree of correlation\nand overlap (new Figure 1g, new Supplementary Figure 1i), suggesting a shared persister\ntranscriptional profile (Results, page 5). This is particularly noteworthy given the completely distinct\ngenetic backgrounds of the two cell lines: RKI1 (MYC amplification; ATRX D808G; SETD2 R472H)\nand FPW1 (PTEN R130Q; CDKN2A/2B deletion; NF1 splice mutation).\n3. How do the authors explain the different trend of H3K9me3 after 3 and 6 day of CMPD1 treatment\nin RKI1 and FPW1 cells? (fig. 2h) As noted above, RKI1 and FPW1 have distinct genetic\nbackgrounds, which can influence histone methylation patterns. In the original Figure 2h, RKI1 cells\nshowed a decrease in H3K9me3 (n=2), while FPW1 cells showed an apparent increase, albeit with\nhigh variability between replicates. These data were obtained from whole cell lysates. Given the\nsemi-quantitative nature of immunoblotting, the potentially transient dynamics of H3K9 methylation,\nand the genetic differences between the two cell lines, variability in their H3K9me3 response is not\nunexpected. Yet, to clarify this, we extended and repeated the time-course experiment and used\nhistone extracts for better quality. The immunoblots in the new Figure 2h have been updated to show\nH3K9me3 levels at days 3, 6, 10, and 14. Again, we observed variability in H3K9me3 signal following\nCMPD1 treatment, and the quantification revealed only minimal overall changes in both RKI1 and\nFPW1 cells, consistent with the findings in Figure 2f (Results, page 5-6).\n4. The knockouts for SETD1A, SETD1B and PRDM9 must be assessed (e.g., by western blot) to\nverify the real inactivation of the genes. Knockout of SETD1A, a common essential gene, was\ndetrimental to RKI1 cells, leaving no viable cells for analysis. Immunoblots of successful knockout of\nthe non-essential genes SETD1B and PRDM9 are shown the new Figures 4i-j (Results, page 8).\n5. To test if CMPD1 induces mitotic arrest, it might be more quantitative to perform the analysis of\ncell cycle with propidium iodide. New Figure 4d shows mitotic arrest induced by CMPD1 and Rho-\n3306 (Results, page 8).\n6. Line 427: The authors state that the loss of H3K4me3 in CMPD1-derived persister cells is caused\nby the proteolytic cleavage of histone tails, rather than the activities of KDM5 demethylases. Did the\nauthors check the expression of histone demethylases in glioblastoma persister cells since it was\ndemonstrated an increased expression of KDM5A in persister cells of other tumor type (Sharma et\nal, 2010)? New Supplementary Figure S3c shows that KDM5A mRNA levels remain unchanged at\nday 3 and 6 but increase at days 10 and 14 (DTP time point) post-CMPD1 treatment. New\nSupplementary Figure 3d shows that CTSL1 mRNA expression begins to increase from day 3, and\nimmunoblotting shows histone cleavage as early as day 3. This suggests that the H3K4me3 may be\ndriven by both KDM5A and CTSL1. However, since inhibition of either CTSL1 or KDM5 did not\neliminate persisters (Figure 3a, new Figure 3f), we did not investigate further which enzyme, if not\nboth, contributes to the H3K4me3 decrease in persisters. Importantly, our findings indicate that early\nchanges in response to chemotherapy, i.e. increased H3K4 methylation, rather than alterations found\nin established persister cells (i.e. H3K4me3 demethylation), are the key targets for preventing the\nemergence of glioblastoma persister cells. This is discussed on page 15.\n7. Fig.2G: please improve quality. The original Figure 2g included immunoblots showing histone\ncleavage in DTPs, with Amido Black staining used as the loading control (hence the washed-out\nappearance). Since these samples were not run using the total H3 antibody, and because we\nperformed time-course experiments for histone cleavage, where day 14 corresponds to the DTP\nsamples, we removed the original DTP histone cleavage blots. The time-course immunoblots in the\nnew Figure 2h show histone cleavage across multiple time points, including day 14 (DTP timepoint).\n8. Fig. 3C: quality need to be improved, the quantification does not match what is visible in the figure.\nThe quantification corresponds to the right panel of the original Figure 3c, showing CMPD1 ± MRK-\n740 ± MRK-740-NC treatments. Untreated cells and those treated with MRK-740 or MRK-740-NC\nalone (left panel of the original Figure 3c) were not quantified, and were included only as controls to\ndemonstrate that MRK-740 and MRK-740-NC alone do not inhibit proliferation. To avoid confusion,\nthe single-agent MRK-740 and MRK-740-NC images have been moved to Supplementary Figure\n3h. We quantified colony areas only for the CMPD1, CMPD1 + MRK-740, and CMPD1 + MRK-740-\nNC treatment groups, showing that co-treatment with MRK-740 (but not MRK-740-NC) significantly\nreduces colony formation compared to CMPD1 alone (set as 1 in the quantification). Figure 3c has\nalso been updated to include additional cell line (MMK1) and is now presented as Figure 3h.\n9. Line 172: It is clear why UNC1999, KDOBA-67a and GSK-J4 have been selected foe co-treatment.\nHowever, it is not clear how MRK-740 was chosen since it is not included in previous screening.\nMRK-740 was included in the initial screen shown in Supplementary Figure 3a but was tested only\nin RKI1 cells. It showed no efficacy, which is consistent with our later findings that there is a limited\ntherapeutic window for targeting PRDM9, likely most effective when administered concurrently (new\nFigure 4f). For the DTP assays presented in Figure 3a–d, we selected epigenetic inhibitors targeting\nwriters and erasers of H3K4, H3K27, and H3K36 marks (clarified in the manuscript, page 6):\nUNC1999 targets H3K27 methyltransferase KMT6 (EZH2)\nGSK-J4 targets H3K27 demethylases KDM6\nMRK-740 targets H3K4 methyltransferase PRDM9\nBAY-6035 targets H3K3 methyltransferase SMYD3\nKDOBA-67a targets H3K4 demethylase KDM5\nEPZ-719 targets H3K36 methyltransferase SETD2\nH3K36me3 is demethylated by KDM4, and while we initially tested the KDM4 inhibitor QC6352\n(Supplementary Figure S3a), we later found that its anti-cancer activity is driven by an off-target\neffect (manuscript under revision, Nature Chemical Biology). Due to its promiscuity, we chose not to\ninclude QC6352 in this DTP study, as its efficacy cannot be solely attributed to KDM4 inhibition.\n7"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have put forth a commendable effort to thoughtfully address the various comments and concerns that were raised during the review process. Their diligent revisions have significantly enhanced the overall quality and clarity of the manuscript. The improvements made are evident and demonstrate a deep commitment to elevating the work, resulting in a substantially more coherent and comprehensive presentation of their research findings.", "reviewer_2": "The authors have adequately addressed the points I had raised in my review.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "As response to my previous suggestion the authors simply stated that: \"we felt that additional omics analyses were not essential for validating the oncogenic role of PRDM9-regulated cholesterol biosynthesis\"", "reviewer_5": "The authors provided additional experiments and explanations that clarified my doubts and satisfied my revision. I do not have additional revision."}, "author_response": ""}]

Decision letter