Antitumor effects of the novel docetaxel derivative JJH201601 in glioma: targeting ribosome biogenesis and enhancing temozolomide sensitivity

Abstract

Background

Glioma is a highly aggressive brain tumor with few therapy options. Temozolomide (TMZ) is a standard chemotherapeutic medication, although its effectiveness is limited by innate and acquired drug resistance.

Methods

A series of functional properties were assessed using established glioma cell lines (U87, LN229) and patient-derived subtypes, to investigate cell survival, colony formation, apoptosis, EdU incorporation, and cell cycle distribution. Treatment responses were assessed in a therapeutically relevant setting using glioma organoid models to investigate the synergistic effects of JJH201601 with TMZ, a CDC7 inhibitor (XL413), and a CDK4/6 inhibitor (palbociclib). For pharmacological investigations, glioma xenograft models in nude mice were created by inoculating U87 cells subcutaneously.

Results

JJH201601 dramatically reduced cell viability, induced apoptosis, and caused G1/S transition dysregulation with S-phase accumulation in glioma cells. It suppressed ribosome-related genes (RPL13, RPS9, and RPS27L) while activating the p53 signaling pathway. The combination therapy with TMZ had a synergistic inhibitory effect on tumor growth in both cell lines and glioma organoids. JJH201601 boosted the antiproliferative effects of CDK4/6 and CDC7 inhibitors by lowering Ki-67 expression and disrupting cell cycle checkpoint regulation. JJH201601 has an impact on key glioma cell survival regulators, including CDK2, γ-H2AX, and MCM2, as determined by transcriptome analyses. JJH201601 also had a substantial inhibitory effect on recurrent primary glioma cells.

Conclusions

JJH201601 exhibited anti-glioma activity by suppressing proliferation, promoting apoptosis, affecting ribosome biogenesis-related signaling, and disrupting cell cycle regulation. Its combination with TMZ and selected cell cycle inhibitors showed enhanced inhibitory effects in preclinical glioma models. However, current in vivo evidence is restricted to a subcutaneous xenografts, further orthotopic intracranial glioma studies with pharmacokinetic profiling and comprehensive safety assessments are required to confirm its efficacy for resistant glioma.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12967-026-08630-z.

Introduction

Gliomas are primary brain cancers thought to grow from neural stem cells or progenitor cells with tumorigenic gene alterations [1]. Gliomas account for over 30% of all primary brain tumors, 80% of malignant brain tumors, and are the leading cause of death from primary brain tumors [1]. Glioma therapies include surgical excision, radiation, and temozolomide (TMZ)-based chemotherapy. Because of gliomas’ high proliferation capacity and invasive growth features, total resection is difficult, and radiation and chemotherapy have limited efficacy, with nearly 100% recurrence and an average survival of 15 months [2]. Furthermore, inherent and acquired drug resistance (typically mediated by MGMT expression and DNA repair pathways) restricts long-term efficacy of TMZ [3]. The most frequent glioma has a 5-year survival rate of less than 5%, and the therapeutic medications indicated by the guidelines are extremely limited [4, 5]. Many local and international guidelines advise patients to actively engage in clinical trials to discover more effective treatment approaches and increase their overall survival time and quality of life. As a result, it is critical to find and develop innovative therapeutic strategies that will boost glioma patients’ survival rates and improve their quality of life.

Taxanes, including paclitaxel and docetaxel, are commonly used antimitotic medicines that work by stabilizing microtubules and preventing mitotic progression [6]. Although docetaxel has demonstrated preclinical effectiveness in glioma models, its clinical use is limited due to low blood-brain barrier penetration, multidrug resistance (MDR) caused by efflux pumps such as P-glycoprotein (P-gp), and considerable systemic toxicity [7]. To address these constraints, current research has focused on structural alterations to taxanes, particularly at the C10 position, which is known to influence P-gp recognition and pharmacokinetics [8]. One interesting method is to include functional groups that limit P-gp efflux while maintaining microtubule-binding ability. In this context, a new derivative, DTX-AI (or JJH201601), was created by chemically altering docetaxel’s C10 location with a 4-acetylphenylcarbamate moiety [9].

JJH201601, a Class I new drug in China, is a docetaxel derivative that is currently undergoing Phase I clinical trials for malignant solid tumors (registration number CTR20232054). The chemical formula is C₅₂H₆₀N₂O₁₆, and the molecular weight is 969.04. Previous research has shown that JJH201601 has the same mechanism of action as docetaxel and paclitaxel: it promotes tubule polymerization into stable microtubules while inhibiting their disassembly, resulting in a significant reduction in tubule number and disruption of the microtubule network without affecting centromere number [9]. This class of medications targets the cell cycle and transitions cells in the M phase. They have a larger effect on proliferating cells than non-proliferating cells and rarely decrease DNA, RNA, or protein synthesis. JJH201601 interacts with tubulin in cervical and non-small cell lung cancer cells, boosting tubulin polymerization while blocking microtubule disintegration. This, in turn, influences mitosis in tumor cells by stopping the cell cycle in the G2/M phase, triggering apoptosis, and decreasing cell proliferation [9].

This study reported for the first time the effect of JJH201601 on the proliferation of glioma cell lines, patient-derived primary cells as well as glioma organoids. To test its cytotoxic and cytostatic effects, we performed a variety of in vitro assays, such as cell viability, clonogenicity, apoptosis, cell cycle analysis, and EdU incorporation. We also investigated the combination of JJH201601 with temozolomide and cell cycle inhibitors (CDK4/6 and CDC7). Transcriptome analysis and reverse transcription quantitative polymerase chain reaction (RT-qPCR) revealed that JJH201601 regulates gene pathways related to ribosome biogenesis and cell cycle signaling. In summary, the purpose of this study was to understand the molecular function of JJH201601 in glioma and establish its preclinical application as a combination therapy option.

Materials and methods

Patients and glioma tissue samples

From July 2022 to December 2024, 13 clinical glioma tissue specimens (3 for original glioma and 10 for recurrent glioma) were acquired from the Department of Neurosurgery at Beijing Tiantan Hospital, Capital Medical University. All patients in this study provided written informed consent. The Ethics Committee of Beijing Tiantan Hospital, Capital Medical University, accepted this study, and it was carried out in compliance with the Declaration of Helsinki’s ethical principles for medical research involving humans.

Cell lines, primary glioma cells and reagents

The human U87-MG and LN229 cell lines used in this study were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai). Cell line annotations were retrieved from vendor datasheets and public cell repositories. LN229 cells carry mutated p53 alongside potential homozygous deletions in the p16 and p14ARF tumor suppressors, while retaining wild-type PTEN. By contrast, U87 cells lack documented p53 and IDH1 mutations, but harboring PTEN inactivation, CDKN2A/CDKN2C loss, TERT promoter mutation, and NF1 mutation. Glioma-zzg01, Glioma-zzg02, and Glioma-zzg03 are primary glioma cells isolated from fresh glioma tissues. The pathological information of the primary cell patients is summarized in Table S1. Glioma cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (Biological Industries, Israel) and cultured in an incubator at 37 °C and 5% CO₂. Details of the primary cell culture method are provided in the Supplementary materials. Primary cells were cultured in DMEM/F-12 Nutrient Mixture (Ham, Gibco) supplemented with 5% fetal bovine serum, 1% penicillin/streptomycin (Gibco), 1% B27 (Gibco), 0.5% N2 (Gibco), 10 µg/mL insulin (MedChemExpress, MCE, New Jersey, USA), 10 ng/mL bFGF (PeproTech Inc., New Jersey, USA), 10 ng/mL EGF (PeproTech Inc), and 10 ng/mL PDGF-AB (PeproTech Inc) and incubated at 37 °C in a 5% CO₂ incubator. Mycoplasma contamination was not detected in any of the cells. Docetaxel, temozolomide, palbociclib, and XL413 were purchased from MCE.

Cell transfection

Lipofectamine RNAiMAX (Invitrogen, Waltham, MA, USA) was used to transfer siRNA into LN229 cells. siRNA p53 (5′- CCC ggA CgA UAU UgA ACA A/dT/ /dT/-3′) was from Sangon Biotech company (Shanghai, China). LN229 or U87 cells were transiently transfected with Lipofectamine 3000 (L3000001, Invitrogen) using the following constructs: human p53 (TP53-Myc-DDK-tagged) (RC200003, Origene, Maryland, USA), human RPL13 (Myc-DDK-tagged) (RC203412, Origene), or human RPS9 (Myc-DDK-tagged) (RC200772, Origene).

Cell viability assay and colony formation assay

Cell viability experiments were conducted in 96-well plates with 5 × 10³ cells per well (six replicate wells per group) with specified drug treatments for 24–72 h. At each time point, CCK-8 kit (LABLEAD, Beijing, China) was applied to each well and incubated for 1 h. The OD450 value of the cells in each well was then measured using a Multiskan FC microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).

For colony formation experiments, glioma cells were grown on six-well plates with 200 cells per well for 14 days, with or without the relevant pharmacological treatments. Visible colonies were fixed with 4% paraformaldehyde and then stained with Crystal Violet Staining Solution (Beyotime Biotechnology, Shanghai, China) for 15 min. The colony area was determined using ImageJ software (National Institutes of Health, USA).

Flow cytometry for apoptosis and cell cycle detection

Apoptosis and cell cycle were identified using the Annexin V-FITC apoptosis detection kit (Invitrogen) and PI/RNase Staining Buffer (BD Biosciences, CA, USA) according to the manufacturer’s instructions. The CytoFLEX flow cytometer (Beckman Coulter, Brea, USA) was used to monitor and assess apoptosis and cell cycle in cells.

Development and cultivation of glioma organoids

Tumor tissue was taken from the operating room, suspended in ice-cold culture media, and sent to the laboratory within 30 min of transplantation. The methodology for creating glioma organoids was referenced, as previously described in the literature [10]. Glioma organoids were seeded into six-well ultra-low attachment plates (Corning, NY, USA). Plates were spun at 120 rpm in a humidified incubator at 37 °C with 5% CO₂. The short-term glioma organoid culture media was replaced every 48 h throughout organoid culture. Successfully generated glioma organoids were seeded in 24-well ultra-low attachment plates (Corning) and grown for 3 days following drug treatment (control, 5 nM JJH201601, 0.5 nM docetaxel, 20 µM TMZ, and 5 nM JJH201601 combined with 20 µM TMZ) in a humidified incubator at 37 °C and 5% CO₂, with plates rotating at 120 rpm. Quantification of Ki-67 immunofluorescence in glioma organoids was performed using Aipathwell digital pathology software. Briefly, the whole organoid/tissue region was defined as the region of interest. DAPI-positive nuclei within this region were automatically recognized as the total cell population, and Ki-67-positive red fluorescent signals were identified according to a unified threshold and color-selection standard for all images from the same staining batch. The Ki-67-positive cell rate was calculated as the number of Ki-67-positive cells divided by the total number of DAPI-positive cells. The Ki-67-positive area ratio was calculated as the total Ki-67-positive red fluorescent area divided by the total tissue pixel area.

RNA sequencing

This study’s total RNA extraction, library building, and sequencing were outsourced to Novogene Co., Ltd. In brief, RNA was extracted with Trizol reagent and quality was evaluated. Details are available in the supplemental materials. The index-coded samples were clustered using a cBot Cluster Generation System with TruSeq PE Cluster Kit v3-cBot-HS (Illumina) following the manufacturer’s instructions. Following cluster creation, the library preparations were sequenced using an Illumina Novaseq technology to obtain 150 bp paired end reads.

RT-qPCR

Total RNA was extracted from cultured LN229 cells using Trizol reagent (Invitrogen). Reverse transcription was performed using FastKing RT Kit (Tiangen, Beijing, China). TB Green^® Premix Ex Taq™ II (Tli RNaseH Plus) (Takara Bio, Shiga, Japan) was used and measured by Gentier 96 real-time quantitative PCR system (Xi’an TianLong Science and Technology, Shaanxi, China). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control, and the specific primers for the genes are shown in Table S2.

Western blot assay

Glioma cells were treated with drugs for 24 h. RIPA lysis buffer (Thermo Fisher Scientific), complemented with phosphatase and protease inhibitors (Thermo Fisher Scientific), was used, and the supernatant was collected as total protein. The protein concentration was calculated using the BCA protein assay kit (NCM, Jiangsu, China). The lysate was subjected to 10% SDS-PAGE and transferred to a PVDF membrane. After blocking with rapid blocking solution (NCM), the membrane was incubated with the corresponding primary antibodies: anti-DDK (1:3000; Origene, TA592569S), anti-CDK2 (1:1000; abcam, Cambridge, UK, ab32147), anti-CDK4 (1:1000; abcam, ab108357), anti-MCM2 (1:1000; abcam, ab108935), Anti-MCM2 (phospho S53) (1:1000; abcam, ab109133), anti-Histone H3 (1:1000; CST, Massachusetts, USA, 3638 S), anti-Phospho-Histone H2A.X (Ser139) (1:1000; CST, 2577 S), anti-CDC7 (1:1000; abcam, ab229187), anti-p53 (1:1000; CST, 2527T), Phospho-p53(Ser 392) (1:1000; Selleck, Texas, USA, F2931), p21 (1:1000; CST, 2947T), p27 (1:1000; Immunology, California, USA, YM8191), Cyclin D1 (1:1000; CST, 2978T), Cyclin E1 (1:1000; CST, 20808T), Cyclin E2 (1:1000; CST, 4132T) or anti-β-actin (1:2000; Immunology, YM3028) antibodies were used. After incubation with HRP-conjugated anti-mouse secondary antibody (CST) or anti-rabbit secondary antibody (CST), proteins were detected using ECL reagent (NCM).

5-Ethynyl-2’-deoxyuridine (EdU) staining assay

The EdU detection kit (Servicebio, Hubei, China) was used per the manufacturer’s instructions. First, LN229 cells in logarithmic growth phase were planted in 6-well plates and drug-treated for 24 h. The EdU medium was then prepared and added to the cells, which were incubated for two hours. The cells were then rinsed in PBS, fixed in 4% paraformaldehyde, and neutralized with glycine. Finally, the cells were stained using the EdU kit and DAPI. A laser confocal microscope (Nikon, Tokyo, Japan) was used to observe EdU-positive cells. After scanning the entire slide, Aipathwell software (Servicebio) was used to compute the EdU-positive cell rate. The red light positive rate is calculated as the total number of red light positive cells divided by the total number of cells.

Transwell assay

A transwell assay was conducted using 24-well inserts with 8 μm pores (Corning) to assess cell migration. The upper chamber of a polycarbonate transwell filter was seeded with 1 × 10⁴ cells in 200 µL of serum-free DMEM medium. The lower compartment received DMEM medium containing 5% FBS as a chemoattractant. Drugs or an equal volume of DMSO were introduced into the upper chamber. Cells were cultured for 48 h. The chambers were fixed with 4% paraformaldehyde for 10 min, and non-migrated cells were gently removed using a cotton swab. The cells were then stained with crystal violet (Beyotime). Images were taken with a microscope (Leica-DM2500, Wetzlar, Germany).

Subcutaneous glioma xenograft model

All experiments involving mice were performed in accordance with the ethical standards of the Animal Welfare Ethics Committee of Beijing Neurosurgical Institute, and the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80 − 23, revised in 1996) was carefully implemented throughout the process. To establish the mouse glioma xenograft model, 1 × 10⁷ human U87 cells suspended in 100 µL PBS were subcutaneously inoculated into the right axilla of BALB/c nude mice (4-week-old, female, purchased from Weitong Lihua Experimental Animal Technology Co., Ltd. (certificate number: SCXK (Beijing) 2021-0006)). About 7 days later, mice carrying tumors of about 100–200 mm³ were selected, and 5 mice in each group were randomly divided into the control group, low-concentration JJH201601 group, high-concentration JJH201601 group, TMZ alone group, JJH201601 + TMZ group, and docetaxel group. The mice in the control group received an equivalent drug vehicle (normal saline containing 2.5% Tween-20 and 6.25% ethanol), the mice in the low-concentration JJH201601 group received 20 mg/kg JJH201601 (intraperitoneal injection [ip]) once a week, the mice in the high-concentration JJH201601 group received 40 mg/kg JJH201601 (ip) once a week, the mice in the TMZ group received 20 mg/kg TMZ on a 5-day on/2-day off regimen (3 cycles in total, ip), the mice in the JJH201601 + TMZ group received 20 mg/kg JJH201601 once a week, and 20 mg/kg TMZ on a 5-day on/2-day off regimen, and the mice in the docetaxel group received 20 mg/kg docetaxel (ip) once a week. Tumor volume and body weight were measured every 2 days with a caliper, and the formula was used to calculate tumor volume (mm³) = (length × width²) / 2. Approximately 21 days after the first treatment, all mice were euthanized, and tumor masses were carefully removed, weighed, and processed for immunofluorescence or pathological Hematoxylin and eosin (H&E) staining. Peripheral blood serum was collected for biochemical testing.

Histopathological examination

Glioma tissues, nude mouse tumour tissues, and glioma organoids were fixed in 4% paraformaldehyde for 24 h. H&E staining was used on paraffin-embedded slices to analyze glioma tissue, naked mouse tissue, and glioma organoids. All photos were acquired with an upright light microscope (Nikon). Slides were scanned with a scanner (3DHISTECH), and images were captured with CaseViewer.

Immunofluorescence

Glioma tissues, nude mouse tumor tissues, and glioma organoids were placed in 4% paraformaldehyde for 24 h or embedded frozen tissue in Tissue-Tek OCT compound (Servicebio). Immunofluorescence staining of tissue and glioma organoids sections can be performed according to previously published protocols [10]. The primary antibodies were anti-DCX (Catalog No. 13925-1-AP; Proteintech, Hubei, China), anti-HOPX (Catalog No. 11419-1-AP; Proteintech), anti-OLIG2 (Catalog No. 13999-1-AP; Proteintech), anti-S100β (Catalog No. GB113884-100; Servicebio), anti-GFAP (Catalog No. GB15100-100; Servicebio), anti-Ki67 (Catalog No. GB151499-100; Servicebio), anti-NESTIN (Catalog No. GB12138-100; Servicebio), anti-BLBP (Catalog No. GB121213-100; Servicebio), anti-SOX2 (Catalog No. GB11249-100; Servicebio), and anti-CDK2 (Catalog No. YM8146; Immunoway). Images were acquired using an upright fluorescence microscope (Nikon) Slides were scanned using a scanner (3DHISTECH), and images were acquired using CaseViewer.

Pharmacokinetic studies

Five mice in each group were euthanized 0.25, 1, and 2 h after intravenous injection of 12.5 mg/kg JJH201601 and intraperitoneal injection of 20 mg/kg JJH201601, respectively. Blood was collected and centrifuged at 4000 rpm for 10 min to obtain plasma. After perfusion with physiological saline, brain tissue was removed, weighed, and homogenized with methanol/water (1/1, v/v) at a ratio of 1:5 (g/mL).

The concentration of JJH201601 in plasma and brain tissue was determined using methanol precipitation and acetonitrile precipitation methods, respectively. The specific steps were as follows: 10 µL of plasma or tissue homogenate was added to 100 ng/mL docetaxel as an internal standard, followed by 100 µL of methanol or acetonitrile and 10 µL of water. The mixture was vortexed at room temperature for 30s, centrifuged at 13,000 rpm for 5 min, and 50 µL of the supernatant was injected into an CMS9030 C18 column (2.1 × 50 mm, 3 μm). The column temperature was maintained at 45℃ during the analysis. The mobile phase consisted of methanol (solvent A) and water (10 mM ammonium acetate, solvent B) at a flow rate of 0.4 mL/min. The liquid chromatography gradient for plasma was set as follows: 0.00 min, 55%A, 45%B; 0.50 min, 55%A, 45%B; 0.60 min, 95%A, 5%B; 1.60 min, 95%A, 5%B; 1.70 min, 55%A, 45%B; 3.00 min, 55%A, 45%B The liquid chromatography gradient for brain tissue was set as follows: 0.00 min, 55%A, 45%B; 0.50 min, 55%A, 45%B; 1.20 min, 95%A, 5%B; 2.00 min, 95%A, 5%B; 2.10 min, 55%A, 45%B; 3.50 min, 55%A, 45%B.

The column elution was monitored using a Sciex Exion LC™ UHPLC tandem 6500 QTRAP mass system (AB SCIEX, CA, USA) in positive ion electrospray ionization (ESI) mode. The detected ion pairs were: JJH201601 m/z 991.5 → 710.1, and docetaxel m/z 830.4 → 549.1. Mass spectrometry analysis was conducted in positive electrospray ionization mode with scheduled multiple reaction monitoring (sMRM) for quantification, using docetaxel as the internal standard. Full instrumental parameters are listed in the Supporting Information. The acceptable limits for accuracy and precision were: relative error of nominal concentration less than 15%, coefficient of variation less than 15%, and the lowest acceptable concentration less than 20%. Concentrations measured in plasma fell within the calibration range of 200–16,000 ng/mL, and those in brain tissue within the calibration range of 2–800 ng/mL. The data were acquired and processed on an Analyst software (AB SCIEX, CA, USA, version 1.7.2).

Statistical analysis

Statistical analysis was performed using GraphPad Prism 9.0 (GraphPad Software). Data are presented as mean ± SD. Nonparametric unpaired t-test was used to calculate the P value of the difference between 2 independent data sets. One-way analysis of variance (ANOVA) was used to analyze the significance between 3 or more independent data sets. P < 0.05 was considered statistically significant. CalcuSyn software (v2.1) was employed to identify the synergistic effect of TMZ and JJH201601, as opposed to additive or antagonistic effects.

Results

JJH201601 reduced cell viability and caused apoptosis and cell cycle transition in glioma cells

To explore the therapeutic potential and mechanistic pathways of JJH201601 across distinct genetic backgrounds, we selected two established human glioma cell lines with complementary genotypes: U87 (p53-wildtype) and LN229 (p53-mutant). These well-characterized genetic profiles provide a rigorous framework to evaluate whether the efficacy of JJH201601 is dependent on specific baseline tumor suppressors or oncogenic signaling cascades.

The half maximal inhibitory concentration (IC50) of JJH201601, docetaxel, and TMZ was calculated to find the best medication combination. The CCK-8 assay findings revealed that JJH201601 decreased the viability of glioma cells in a dose- and time-dependent manner (Fig. 1A). Table 1 shows the IC50s of JJH201601, docetaxel, and TMZ on U87 and LN229 cells for 72 h, as well as JJH201601’s IC50 for 24 h. When the JJH201601 concentration was reduced to 1 nmol/L, it was still cytotoxic to LN229 cells, but no apparent viability inhibition was observed in U87 cells, indicating that different glioma cell lines have slightly different sensitivities to JJH201601, and compared to TMZ, JJH201601 exhibited better efficacy (Fig. 1A). The IC50 for LN229 and U87 cells was lower after 72 h than at 24 h, showing that JJH201601 had a time-dependent effect on glioma cells (Fig. 1B). Then we chose relatively low JJH201601 concentrations (5 nM, IC50 of LN229 at 72 h) and higher concentrations (50 nM) to test its cell viability in patient-derived primary glioma cells (glioma-zzg01 and glioma-zzg02) and primary astrocytoma (glioma-zzg03). The CCK-8 experiment revealed that JJH201601 reduced the viability of primary glioma cells in a dose-dependent manner (p < 0.0001 and p < 0.05) (Fig. 1C). JJH201601 demonstrated a strong inhibitory effect on a variety of glioma cells.

Fig. 1.

Effects of JH201601 on proliferation, apoptosis and cell cycle of glioma cells. (A) CCK-8 assay to detect the survival rate of LN229 and U87 cells after treatment with 0 (Control, 0.01% DMSO), 0.001, 0.01, 0.05, 0.1, 0.5, 1, 10, 20, 50 µmol/L JJH201601; 0 (Control, 0.01% DMSO), 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 1.5, 10, 20 µmol/L docetaxel; 0 (Control, 0.01% DMSO), 1, 5, 10, 15, 20, 30, 40, 50, 60, 80 (only for U87), 100 (only for U87) µmol/L TMZ for 72 h. The cell proliferation inhibition rate of each treatment group and each detection group on the same day was compared. GraphPad Prism 9.0 software, and the IC50 of JH201601 in LN229 and U87 cells was calculated by the “log (inhibitor) vs. normalized Response-Variable Slope” method. (B) CCK-8 method was used to detect the survival rate of LN229 and U87 cells treated with 0 (Control, 0.01% DMSO), 0.001, 0.025, 0.125, 0.5, 0.625, 1, 3.125, 15.625 µmol/L JH201601 and 0 (Control, 0.01% DMSO), 0.01, 0.05, 0.25, 1.25 µmol/L JH201601 for 24 h, and the cell proliferation inhibition rate of each treatment group was compared with the cell proliferation inhibition rate of each detection group on the same day. The IC50 of JH201601 in LN229 and U87 cells was calculated using the “log (inhibitor) vs. normalized response variable slope” method using GraphPad Prism 9.0 software. (C) CCK-8 assay: 0 (Control, 0.01% DMSO), 0.005, 0.05 µmol/L JJH201601; 0 (Control, 0.01% DMSO), 0.0005, 0.005 µmol/L docetaxel; 0 (Control, 0.01% DMSO), 20, 30 (only for glioma-zzg01 and glioma-zzg02), 200 (only for glioma-zzg03) µmol/L TMZ treatment for 72 h to determine the survival rate of the three primary cells. (D) The left figure is a representative image of the cell cycle detected by flow cytometry. LN229 and U87 cells were treated with 2 and 9 nmol/L JJH201601 for 24 h, respectively. The right figure shows the calculation and comparison of the percentage of cells in the G0/1 (purple peak on the left), S (yellow flat peak in the middle), and G2/M (green peak on the right) phases in each group. (E) The left figure shows a representative image of cell apoptosis detected by flow cytometry. LN229 cells were treated with 2 nmol/L JJH201601 for 72 h. The right figure shows the comparison of the percentage of total apoptotic cells. DMSO dimethyl sulfoxide, IC50 half inhibitory concentration. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

Table 1.

IC50 values of JJH201601, docetaxel, and TMZ in glioma cells

Cell line	JJH201601(µM) 24 h	JJH201601(µM) 72 h	Docetaxel(µM) 72 h	TMZ(µM) 72 h
LN229	0.018	0.0048	0.00046	20.01
U87	0.037	0.018	2.82	88.01

In U87 cells, we chose a JJH201601 concentration of 9 nM, which corresponds to the IC25-IC30 level; in LN229 cells, we chose a JJH201601 concentration of roughly 2 nM, which also corresponds to the IC25-IC30 level for future investigations. Flow cytometry revealed that JJH201601 significantly caused G1/S transition dysregulation with S-phase accumulation in LN229 and U87 cells, increasing S-phase cells (P < 0.05 or P < 0.0001) (Fig. 1D). JJH201601 treatment significantly increased apoptosis in LN229 and U87 cells (P < 0.0001) (Fig. 1E; Table 2). This is consistent with JJH201601’s significant cytotoxicity in gliomas, as previously documented.

Table 2.

Cell cycle alteration after JJH201601 treatment

Cell line	Group	Percentage of cells in different cell cycle phases (%)
		Phase G1	Phase S	Phase G2
LN229	Control	77.84 ± 2.41	11.69 ± 3.43	10.47 ± 1.06
	0.002 µM	68.9 ± 0.56	21.28 ± 0.71	9.82 ± 0.15
U87	Control	41.81 ± 4.32	7.32 ± 0.84	50.87 ± 3.71
	0.009 µM	54.47 ± 5.5	32.37 ± 0.97	13.17 ± 4.55

Combination therapy of TMZ and JJH201601 showed significant synergistic effects on glioma cells

The next step is to investigate the impact of administering the two medications together on glioma cells. For the TMZ combination experiment, we selected a lower concentration of JJH201601, specifically half of its IC50 value in 24 h. As demonstrated in Fig. 2A, CCK-8 assays revealed that JJH201601 combined with TMZ treatment significantly reduced glioma cell viability compared to TMZ monotherapy. The combination index (CI) was calculated using CalcuSyn software to evaluate whether the interaction between the two drugs was synergistic, additive, or antagonistic. A CI value less than 1 indicates synergism, a CI value equal to 1 indicates an additive effect, and a CI value greater than 1 indicates antagonism. The results showed that the CI of JJH201601 and TMZ in LN229 cell lines (CI = 0.4604) and primary glioma cells (CI = 0.0917) was less than 1, indicating that the two work together synergistically. Fig. S1 showed some representative CalcuSyn results in LN229 and glioma-zzg01 cells. To confirm the anti-tumor activity, we also performed a colony formation assay. The results were similar to CCK-8 assay, indicating that the number of colonies in the JJH201601 combined with TMZ treatment group was much lower than in the other groups (Fig. 2B and C). These results suggest that JJH201601 can enhance the inhibitory effect of TMZ on glioma cell growth under the experimental conditions tested.

Fig. 2.

Combined treatment of JJH201601 and TMZ showed significant synergistic effects on glioma cells. (A) CCK-8 assay was used to evaluate the viability of glioma cells after 24 h of treatment with the indicated concentrations of drugs. The cultured cells were divided into control group, JJH201601 group, TMZ group, and JJH201601 combined with TMZ group. (B) Colony formation assay was performed on glioma cells after 14 days of treatment with the indicated concentrations of drugs. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

Preclinical evaluation of the therapeutic effect of JJH201601 in patient-derived glioma organoid models

Patient-derived organoids (PDOs) have shown great promise in improving drug screening and predicting drug sensitivity in cancer treatment. To assess the clinical therapeutic potential of JJH201601, we successfully produced glioma PDOs. Figure 3A depicts a schematic picture of the dynamic production and culture of glioma organoids. PDOs with diameters of 200 to 500 μm can be successfully manufactured within two weeks. We then stained for glial cell markers GFAP, OLIG2, DCX, S100β, glioma stem cell markers SOX2, Nestin, HOPX, BLBP, and tumour proliferation marker Ki-67. All glioma PDOs expressed the appropriate markers (Fig. 3B). We also stained PDOs and their corresponding parental tumors with H&E, which revealed striking similarities in glioma histology between the two tissues (Fig. 3C).

Fig. 3.

Preclinical evaluation of the therapeutic effects of JJH201601 and TMZ in patient-derived glioma organoid models. (A) Top, schematic diagram of the PDO culture process. Bottom is a representative image of the glioma PDO culture process. Scale bar, 20 μm. (B) Immunofluorescence staining was performed to detect GFAP, OLIG2, DCX, S100B, SOX2, Nestin, HOPX, BLBP, and Ki-67 protein markers in tissue sections of glioma PDOs. (C) Representative H&E staining images of glioma PDOs and parental glioma tumor tissue sections are provided. (D) Representative images of glioma PDOs after 3 days of treatment with the indicated drugs to observe and analyze the morphology and size of the organoids. Scale bar, 20 μm. (E) Immunofluorescence staining for Ki-67 was performed to evaluate the proliferation index of PDOs in each treatment group. (F) The proliferation index of PDOs in each treatment group was evaluated through statistical analysis of the red-positive cells and red-positive areas of Ki-67. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

In addition, we assessed the anticancer activity of the glioma PDO model following several therapies. PDOs were examined under a microscope for 3 days following various treatments. Figure 3D shows that following treatment with JJH201601, docetaxel, and JJH201601 combined with TMZ, the organoids’ structure shrank or even aggregated and stacked, but after TMZ monotherapy, the organoids relaxed or disintegrated. In addition, the diameter of the organoids in the JJH201601 combined with TMZ group was significantly smaller than other groups. Glioma PDOs from each group were harvested and fixed for Ki-67 immunofluorescence analysis. Compared to the control group, the JJH201601 group dramatically reduced Ki-67 expression in organoids, while the JJH201601 combined with TMZ group showed a more evident decrease in Ki-67 density. These data suggest that JJH201601 monotherapy can effectively limit organoid growth, while combined TMZ treatment is more effective (Fig. 3E, F).

Effects of JJH201601 on the expression levels of cell cycle and ribosome-related pathway proteins in glioma cells

In comparison with the control group, 28 upregulated genes and 109 downregulated genes were found. Functional annotation of differentially expressed genes in LN229 cells treated with JJH201601 (threshold: |log2(FoldChange)| ≥ 1 & padj ≤ 0.05) revealed significant GO and Reactome enrichment terms, with the top 30 terms listed for each function. We discovered that rRNA processing was a major enrichment for drug-disease effects (Fig. 4A). To further understand the regulatory role of JJH201601 in the ribosome-related pathway, LN229 (0 and 2 nM), U87 (0 and 9 nM), and glioma-zzg01 (0 and 2 nM) cells were treated with JJH201601 for 24 h, and ribosome-related gene mRNA expression levels were measured by RT-qPCR. Compared to the control group, JJH201601 treatment significantly reduced the expression of ribosome-related genes RPL13, RPS9, and RPS27L (Fig. 4B). These three genes can activate p53 through the RP-MDM2-p53 pathway. Western blotting was used to identify the expression levels of p53 and phospho-p53 (Ser 392) associated proteins in glioma cells. JJH201601 treatment significantly raised the expression of p53 and phospho-p53 (Ser 392) in both cell lines (Fig. 4C).

Fig. 4.

Effects of JJH201601 on the expression of proteins in glioma cell cycle and ribosome-related pathways. (A) Pathway enrichment analysis based on the differentially expressed mRNAs identified in transcriptomics studies. GO (left) and Reactome (right) enrichment terms show the enrichment of JJH201601 on glioma ribosome-related pathways. (B) Top, relative mRNA levels of RPL13, RPS9, and RPS27L in LN229 (2 nM), U87 (9nM), and glioma-zzg01 (2nM) cells treated with JJH201601 for 24 h; bottom, relative mRNA levels of cyclinD1, cyclinE1, and cyclinE2 in LN229 (2 nM), U87 (9nM), and glioma-zzg01 (2nM) cells treated with JJH201601 for 24 h. (C) Western blot analysis of the protein expression levels of p53, phospho-p53 (Ser 392), CDK2, cyclinD1, cyclinE1, cyclinE2, p21, and p27 in LN229 and U87 cells after 24 h of JJH201601 treatment. LN229 cells were treated with JJH201601 (0, 1nM, 2nM, 9 nM, and 18 nM) for 24 h, and U87 cells were treated with JJH201601 (0, 9 nM, 19 nM, and 37 nM) for 24 h. (D) Western blotting proved that knockdown p53 with siRNA dramatically inhibited its protein expression. (E) CCK-8 assay: JJH201601(0.018 µmol/L) was administered for 24 h to determine the survival rate of LN229 and p53 siRNA transfected LN229 cells. (F) The expression levels of DDK and p53 in LN229 and U87 cells 48 h after RPL13, RPS9, and p53 overexpressed were detected via western blot. (G) CCK-8 assay: JJH201601 was administered for 24 h to determine the survival rate on LN229 and U87 cells transfected with the RPL13-OE, RPS9-OE, and p53-OE plasmid for 48 h. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

According to the results of cell cycle detection by flow cytometry, JJH201601 increased glioma cells’ G1/S phase transition. LN229 cells were treated with JJH201601 (0 and 2nM) for 24 h, and the expression levels of cell cycle-related gene mRNA were determined by RT-qPCR. JJH201601 therapy significantly raised the expression of cell cycle-related genes cyclin D1, cyclin E1, and cyclin E2 in both LN229 and U87 cells (Fig. 4B). Compared to the control group, JJH201601 administration significantly raised the expression of cell cycle-related proteins CDK2, cyclin D1, cyclin E1, and cyclin E2, while considerably decreasing the expression of p21 and p27 in both LN229 and U87 cells (Fig. 4C). Collectively, these findings indicate that JJH201601 induces G1/S transition dysregulation and S-phase accumulation, accompanied by altered checkpoint regulation and replication stress-related signaling.

To further verify p53 sensitizes glioma cells to JJH201601, siRNA was used to genetically inhibit p53 in LN229 cells. It was found that inhibition of p53 upregulation with siRNA against p53 effectively prevented JJH201601 induced glioma cell death (Fig. 4D and E). To further confirm the role of mechanism linking ribosome biogenesis inhibition to p53 activation in JJH201601, we induced overexpression of the three genes by transfecting two types of glioma cell lines with RPL13, RPS9, and p53 plasmids. Compared with cells in the vector, the p53 protein expression level of the two cell lines transfected with RPL13 and RPS9 was significantly reduced, and the survival rate was higher after treatment with JJH201601 (Fig. 4F and G). However, the survival rate of the two cell lines transfected with p53 was even lower after treatment with JJH201601 (Fig. 4G). Collectively, these findings suggest that JJH201601 inhibits glioma cell growth by activating the p53-dependent antitumor pathway through inhibition of ribosome biosynthesis.

CDK4/6 and CDC7 inhibition enhances JJH201601-induced G1/S transition dysregulation and replication stress

To see if targeting cell cycle-related pathways could overcome resistance to JJH201601, we treated the LN229 cells with the CDK4/6 inhibitor palbociclib. First, we investigated the IC50 value of palbociclib in the LN229 cell line using CCK-8 (0.055 µM) for 24 h (Fig. S2A). The combination of JJH201601 and palbociclib had a CI value smaller than one in glioma cells (Fig. S2B). Next, we treated LN229 cells with both low and high concentration combinations. Western blot results revealed that the combined treatment of JJH201601 and palbociclib significantly reduced the expression of CDK2 protein in glioma cells compared to the JJH201601 alone treatment group and the control group (Fig. 5A). Subsequently, we performed EdU incorporation experiments. Since only cells in the S-phase can absorb EdU, immunofluorescence experiments were used to quantitatively analyze the labeled cells in different stages of the cell cycle (S-phase to G2/M phase to G0/G1 phase). It was found that compared with JJH201601 alone, JJH201601 combined with palbociclib significantly reduced the number of EdU+ cells in LN229 cells (P < 0.0001) (Fig. 5B).

Fig. 5.

JJH201601 overcomes the G1/S transition by combining a CDK4/6 inhibitor and a CDC7 inhibitor. (A) The protein level of CDK2 was analyzed by western blot in LN229 cells treated with low-concentration combinations (0.002 µM JJH201601, 0.025 µM palbociclib, 0.002 µM JJH201601 + 0.025 µM palbociclib) and high-concentration combinations (0.006 µM JJH201601, 0.075 µM palbociclib, 0.006 µM JJH201601 + 0.075 µM palbociclib). (B) EdU proliferation assay was performed after JJH201601 treatment in LN229 cells. Red fluorescent cells represent cells in the mitotic S-phase, and blue fluorescent cells represent all cells. (C) Immunoblot analysis of proteins (including CDC7, CDK2, γH2AX, histone H3, total and phospho MCM2, and MCM2 (phospho S53)) in lysates collected from LN229 cells after JJH201601 (0.002 µM and 0.006 µM), XL413 (0.0002 µM and 0.0006 µM), and JJH201601 + XL413 (0.002 + 0.0002 µM and 0.006 µM + 0.0006 µM) treatment. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

CDC7 is a serine-threonine kinase that phosphorylates the minichromosome maintenance protein (MCM) complex, hence beginning DNA replication. CDC7 governs the activation of cell cycle replication checkpoints, maintains DNA replication forks, and mediates the DNA damage response [11]. To further confirm whether JJH201601 induces G1/S transition through a CDC7-dependent pathway, we used CCK-8 to detect the 24 h IC50 value of the CDC7 inhibitor XL413 on LN229 cells (0.6468 µM) (Fig. S2C). The combination of JJH201601 and XL413 resulted in a CI value of less than 1 in glioma cells (Fig. S2D). Western blot results showed that CDC7 protein expression in glioma cells treated with the combination of JJH201601 and XL413 was significantly decreased (Fig. 5C). Therefore, we suppose that JJH201601 induces G1/S transition through a CDC7-dependent pathway. To reveal the molecular mechanism of the synergistic effect of XL413 and JJH201601, we then investigated the DNA damage response status of LN229 cells treated with a combination of JJH201601 and XL413. Western blot results showed that the expression of γ-H2AX protein was significantly increased and the expression of H3 protein was significantly decreased in LN229 cells after combined treatment with JJH201601 and XL413 compared with the JJH201601 single treatment group and the control group (Fig. 5C), indicating that DNA damage was aggravated. In addition, the phosphorylation of MCM2 at Ser53 was significantly inhibited after combined treatment of JJH201601 and XL413 (Fig. 5C). Taken together, these results suggest that combined treatment of CDC7 inhibition with JJH201601 may enhance DNA damage and replication stress, thereby improving the efficacy of JJH201601 as a therapeutic regimen.

JJH201601 inhibits glioma growth and enhances TMZ efficacy in vivo

The chemotherapeutic effect of JJH201601 was investigated further utilizing a nude mice xenograft glioma model. U87 cells were implanted subcutaneously in nude mice to create a subcutaneous glioma model. The subcutaneous inoculation plan of glioma cells and the systemic therapy scheme after tumor growth are shown in Fig. 6A. The experimental nude mice were randomly divided into six groups: control group, low-concentration JJH201601 group, high-concentration JJH201601 group, TMZ alone group, JJH201601 + TMZ group, and docetaxel group. The tumor volume of the JJH201601 group and the TMZ group was significantly reduced from the second day after the start of treatment. And the tumor volume of the high-concentration JJH201601 group was smaller than that of the low-concentration group, and this difference became more evident over time. In addition, the average tumor volume of the combined group was significantly smaller than that of the JJH201601 group and the TMZ group (Fig. 6B). Similar to the changes in tumor volume, the average tumor weight (at the end of treatment) of the JJH201601 group and the TMZ group was significantly smaller than that of the control group, and significantly larger than that of the combination group (P < 0.05 or P < 0.0001) (Fig. 6C). The above results show that JJH201601 can effectively inhibit the growth of U87 cells in vivo, and the combination of JJH201601 and TMZ has a stronger inhibitory effect on the growth of the U87 xenograft model. The fluorescence intensity of Ki-67 in each drug administration group was significantly reduced, while the fluorescence intensity of CDK2 in the low and high concentration groups of JJH201601 was significantly increased, indicating that JJH201601 upregulated the expression of CDK2 in vivo (Fig. 6D and E).

Fig. 6.

JJH201601 inhibits glioma growth and sensitizes glioma to temozolomide. (A) The regimen is to subcutaneously inoculate glioma cells and perform systemic treatment after tumor formation. The experimental nude mice were randomly divided into 6 groups, namely the control group (n = 5), low-concentration JJH201601 group (20 mg/kg) (n = 5), high-concentration JJH201601 group (40 mg/kg) (n = 5), TMZ alone group (n = 5), combination group (20 mg/kg JJH201601 + 20 mg/kg TMZ) (n = 5), and docetaxel group (20 mg/kg) (n = 5). Drug treatment was maintained for 3 cycles. (B) The tumor volume in vivo was measured with a vernier caliper every 2 days, and the weight changes of glioma-bearing mice in each group during drug treatment were recorded. (C) After 21 days of treatment, the nude mice were killed and the glioma lesions were removed entirely. Left, the weight of fresh glioma lesions. Right, representative images of glioma lesions in each group of mice. (D) Representative images of Ki-67 immunofluorescence staining of tumor tissue sections of glioma-bearing mice in each group. Scale bar, 100 μm. (E) Representative images of CDK2 immunofluorescence staining of tumor tissue sections of glioma-bearing mice in each group. Scale bar, 100 μm Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

To further evaluate the potential application of JJH201601 in the field of biomedicine, we conducted an in vivo biosafety test of the compound. The results showed that no abnormal pathological changes were found in the tissues of the main organs (liver, kidney and brain), and no significant changes were observed in the liver and kidney function indicators (Fig. S3A and S3B). In addition, the body weight of mice in the control group, the JJH201601 low-concentration group, and the JJH201601 high-concentration group did not change significantly (Fig. 6B). At the same time, the same concentrations of docetaxel and TMZ caused a significant decrease in the body weight of mice. These results suggest that JJH201601 treatment is safe for glioma treatment.

Given the critical importance of brain exposure for assessing the potential application of JJH201601 in gliomas, we further measured the concentrations in plasma and brain tissue in mice following intravenous and intraperitoneal administration, and the data are presented in Table 3. Following intravenous administration, the concentrations of JJH201601 in plasma and brain tissue gradually decreased at 0.25 h, 1 h, and 2 h, while the brain tissue-to-plasma concentration ratio increased from 0.47% to 0.67%. Following intraperitoneal administration, the concentrations in plasma and brain tissue gradually increased at 0.25 h, 1 h, and 2 h, with the brain tissue-to-plasma concentration ratio reaching a maximum of 0.89%. These results indicate that although the brain tissue-to-plasma concentration ratio remains low, JJH201601 can still be detected in brain tissue after systemic administration. Therefore, these data preliminarily confirm limited brain exposure and support further pharmacokinetic and optimization of blood-brain barrier penetration studies.

Table 3.

Plasma and brain tissue concentrations and brain-to-plasma ratios of JJH201601 in mice following intravenous and intraperitoneal administration

Time (h)	Intravenous injection			Intraperitoneal injection
	Plasma (ng/mL)	Brain tissue (ng/mL)	Brain/plasma ratio(%)	Plasma (ng/mL)	Brain tissue (ng/mL)	Brain/plasma ratio(%)
0.25	5755 ± 985.04	26.82 ± 4.51	0.47	779.75 ± 156.51	3.97 ± 1.22	0.51
1	3524 ± 376.94	18.98 ± 3.04	0.54	1106.50 ± 611.94	9.84 ± 2.71	0.89
2	2230 ± 145.09	14.96 ± 2.52	0.67	1983.25 ± 1160.82	11.38 ± 5.48	0.57

Preclinical evaluation of the therapeutic efficacy of JJH201601 in a patient-derived primary cell model of recurrent glioma

Patient-derived primary cells have shown great potential in advancing drug screening and predicting drug sensitivity for cancer treatment. To evaluate the clinical therapeutic potential of JJH201601 treatment, we have successfully generated recurrent glioma primary cells. We examined the effect of JJH201601 on the migration of recurrent glioma primary cells (Fig. 7A). They showed reduced migration after being treated with JJH201601 at different concentrations.

Fig. 7.

Preclinical evaluation of the therapeutic efficacy of JJH201601 in patient-derived recurrent glioma primary cell models. (A) Representative images of Transwell migration assays showing LN229 cells treated with JJH201601 (0.005 µM and 0.05 µM), Docetaxel (0.0005 µM and 0.005 µM), and TMZ (20 µM and 200 µM). Scale bar, 200 μm. (B) Calculation of the inhibition rate of 10 patient-derived recurrent glioma primary cells treated with JJH201601 (0.005 µM, 0.05 µM, and 0.5 µM), Docetaxel (0.0005 µM, 0.005 µM, and 0.05 µM), and TMZ (2 µM, 20 µM, and 200 µM). (C) Classification of recurrent glioma primary cells according to IDH1, IDH2, TERT promoter C250T and EGFR amplification, and quantification of the efficacy of JJH201601 treatment in each group. (D) CCK-8 assay: No.5 and No.10 patients treated with low-concentration combinations (0.002 µM JJH201601, 0.025 µM palbociclib, 0.002 µM JJH201601 + 0.025 µM palbociclib) and high-concentration combinations (0.006 µM JJH201601, 0.075 µM palbociclib, 0.006 µM JJH201601 + 0.075 µM palbociclib), JJH201601 (0.002 µM and 0.006 µM), XL413 (0.0002 µM and 0.0006 µM), and JJH201601 + XL413 (0.002 + 0.0002 µM and 0.006 µM + 0.0006 µM) treatment. Data are shown as the mean ± s.e.m. and are representative of at least three independent experiments performed in quintuplicate cultures. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001; ANOVA and Student’s t-test

Further, we tested the drug treatment sensitivity of a group of recurrent glioma primary cells from 10 patients. To accurately characterize the drug resistance profile of the 10 primary cells of recurrent gliomas, we comprehensively evaluated the patients’ clinical treatment history and molecular characteristics (Table S1). Clinical data showed that 60% (6/10) of the patients had a clear history of standardized TMZ treatment before tumor recurrence, while 4 patients did not receive radiotherapy or chemotherapy after surgery for their initial glioma due to personal reasons. Molecular analysis indicated that 70% (7/10) of the recurrent samples had MGMT promoter unmethylation, a key indicator of intrinsic TMZ resistance. Furthermore, immunohistochemical evaluation revealed that 80% (8/10) of the samples lacked expression of mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, and PMS2), indicating a prevalent MMR deficiency in these samples—a classic mechanism driving acquired TMZ resistance in gliomas. Combined with the presence of other resistance-related mutations (such as p53 and TERT promoter mutations), these findings definitively confirm the biological basis of glioma models with potential TMZ-insensitive features. The results showed that treatment including JJH201601 also had a significant effect on recurrent glioma cases, and the inhibition rate increased with increasing concentration. The average inhibition rate of the JJH201601 treatment group was significantly higher than that of the TMZ monotherapy group (Fig. 7B).

We also analyzed the correlation between the genetic background and the response rate of the glioma primary cell cohort. Our results showed that recurrent glioma patients with IDH1 mutations exhibited a lower response to JJH201601 treatment (Fig. 7C), suggesting that this particular genetic profile may not benefit from JJH201601. However, these IDH1-mutant cases were not uniformly characterized by p53 abnormality, suggesting that the reduced response in IDH-mutant models cannot be explained solely by p53 status. These findings indicate that JJH201601 sensitivity may be influenced by multiple genetic factors, including IDH status, p53 abnormality, MGMT promoter methylation, and MMR status. In addition, because of the limited sample size, these genotype-response associations should be interpreted as exploratory.

To further validate the mechanism of action of JJH201601 in recurrent glioma models with relatively low sensitivity to JJH201601, we selected patient-derived primary recurrent glioma cell lines No. 5 and No. 10 and conducted combination therapy experiments with the CDK4/6 inhibitor palbociclib and the CDC7 inhibitor XL413, respectively (Fig. 7D). In both primary cell models, low-dose JJH201601 monotherapy or combination therapy had limited inhibitory effects on cell viability. However, compared with monotherapy, high-dose combination therapy with palbociclib further reduced cell viability, indicating that the CDK4/6 inhibitor enhanced the antiproliferative effect of JJH201601 in these relatively insensitive recurrent glioma cells. Similarly, combination therapy with XL413 also showed a stronger inhibitory effect than monotherapy. These findings suggest that primary recurrent glioma cells with reduced sensitivity to JJH201601 can regain drug sensitivity by targeting G1/S phase regulation and replication licensing.

Discussion

JJH201601 is a novel docetaxel derivative that has demonstrated some chemotherapeutic effects on solid tumors (cervical cancer and non-small cell lung cancer) in vitro and in vivo in prior studies. It has a considerably higher safety profile in vivo than docetaxel [9]. This study found that JJH201601 had significant anti-glioma efficacy via several routes. JJH201601 dramatically reduced the cell viability of established glioma cell lines (U87 and LN229) and patient-derived primary glioma cells in a dose- and time-dependent manner, inducing apoptosis. Furthermore, patient-derived glioma organoid models demonstrated that JJH201601 (either alone or in combination with TMZ) dramatically reduced organoid size and the proliferation marker Ki-67 expression. We also discovered that JJH201601, coupled with the standard glioma chemotherapy medication TMZ, had strong synergistic effects on growth inhibition and colony formation. In terms of molecular mechanism, our findings indicated that JJH201601 suppressed ribosome biogenesis-related genes (RPL13, RPS9, and RPS27L) while activating the p53 pathway. JJH201601 induced a G1/S transition, and its antiproliferative impact was amplified when administered in combination with CDK4/6 or CDC7 inhibitors, suggesting that it may be involved in controlling the cell cycle process.

We observed that JJH201601 induced different cell cycle patterns in LN229 and U87 cells. In LN229 cells, the proportion of cells in G1-phase decreased while the proportion of cells in S-phase increased, whereas in U87 cells, both G1 and S-phase cells accumulated significantly. A plausible explanation is that LN229 cells proliferate relatively quickly, allowing G1-phase cells to rapidly enter S-phase, resulting in a lower number of G1-phase cells at the time of measurement. In contrast, U87 cells proliferate more slowly, and cells preparing for DNA replication need to remain in G1-phase for a longer time before entering subsequent phases, leading to the simultaneous accumulation of G1 and S-phase cells.

It should be noted that the cell cycle phenotype induced by JJH201601 should not be interpreted as simple G1/S arrest. The increased S-phase fraction, together with upregulation of cyclin D1, cyclin E1/E2, and CDK2 and downregulation of p21 and p27, indicates abnormal activation of the G1/S regulatory program. This pattern may reflect dysregulated G1/S transition or compensatory cell cycle stress feedback rather than a stable checkpoint-mediated arrest. Moreover, the enhanced γ-H2AX expression and reduced MCM2 phosphorylation observed after combined treatment with the CDC7 inhibitor XL413 suggest impaired DNA replication licensing and replication stress. Therefore, we interpret the JJH201601-induced cell cycle phenotype as G1/S transition dysregulation followed by S-phase accumulation and replication stress, which may contribute to its antiproliferative activity.

Docetaxel, a semi-synthetic taxane derivative extracted from the needles of the European berry taxus, has certain efficacy against tumors [12]. Docetaxel can also inhibit the Bcl-2 activity of lung cancer cells, thereby upregulating the activities of caspase-3 and caspase-9 and inducing cell apoptosis. These three signaling proteins also play an important role in the apoptosis of glioma cells [13, 14]. Furthermore, previous studies have shown that docetaxel inhibits cell growth by inducing apoptosis and dose-dependent G2/M cell cycle arrest [15], which differs from the mechanism of G1/S phase alteration induced by JJH201601 in our model. However, the safety of its systemic administration is a cause for concern.

Recent clinical trials have utilized targeted therapies, such as immunotherapy and gene therapy, as adjunctive approaches to counteract the effects of immune dysregulation by stimulating the patient’s immune system. Due to the existence of the blood-brain barrier, the complexity of the tumor microenvironment, the heterogeneity of tumor tissues, and the drug tolerance, classic targets such as the p53 and retinoblastoma (RB) pathways, as well as gene alterations in the epidermal growth factor receptor (EGFR), have all failed [16]. Several immunotherapy strategies are currently being used for cancer treatment (e.g., immune checkpoint inhibitors, chimeric antigen receptor (CAR) T cell therapy, oncolytic virus-based therapies, and therapeutic anti-tumor vaccination) [17–20]. Although neuroglial tumor immunotherapy has shown promising results in preclinical and early clinical trials, it faces specific limitations and challenges that have hindered its success in further III-stage trials. For instance, there is high heterogeneity within and between tumors, an inherent immunosuppressive microenvironment, unique tissue-specific interactions between the central nervous system and the peripheral immune system, the existence of the blood-brain barrier (which is a physical barrier for drug delivery), and the immunosuppressive effect of standard treatments [21].

Building on these pharmacological properties and challenges, JJH201601—engineered through structural optimization of docetaxel—has shown enhanced efficacy and reduced toxicity in glioma models. Moreover, JJH201601 enhances the sensitivity of glioma cells to TMZ and exhibits synergy with cell cycle inhibitors such as CDK4/6 and CDC7 inhibitors, suggesting its potential utility in combination therapies targeting treatment-resistant gliomas.

The potent activity of JJH201601 in glioma cells is consistent with previous studies showed that this derivative modifies the C10 site to overcome multidrug resistance and reduce systemic toxicity [9]. The cell cycle transition observed after JJH201601 treatment is consistent with the mechanism reported for docetaxel analogs, which stabilize microtubules and block mitotic progression [9]. Transcriptomic and molecular analyses revealed that JJH201601 mechanistically affects ribosome biogenesis and cell cycle progression. Downregulation of ribosomal proteins, such as RPL13, RPS9, and RPS27L, and activation of p53 via the RP-MDM2-p53 pathway confirm earlier findings that ribosomal stress serves as a suppressive mechanism in various tumors, including gliomas [22–25]. This suggests that JJH201601 impairs protein synthesis and triggers a stress response. Furthermore, upregulation of cyclin D1, cyclin E1/E2, and CDK2, as well as inhibition of p21 and p27, indicated a disruption of G1/S transition control. Notably, JJH201601 induced a G1/S transition, which is distinct from the G2/M arrest typically associated with taxanes [26], suggesting that it may uniquely regulate cell cycle checkpoints through a CDC7-dependent mechanism. This was further confirmed by experiments combining JJH201601 with the CDK4/6 inhibitor palbociclib and the CDC7 inhibitor XL413. Results showed that JJH201601 enhanced cell proliferation inhibition, increased DNA damage markers (γH2AX), and impaired DNA replication licensing (decreased phosphorylated MCM2) [27, 28]. These results confirm previous studies that the CDC7-MCM2 axis is essential for cell cycle progression and suppression of tumor proliferation and that JJH201601 has therapeutic potential. However, the role of the CDC7-MCM2 axis in glioma remains unclear [29, 30]. Importantly, the synergistic effects with TMZ observed in cell viability and colony formation assays reflect the clinical strategy of combining DNA-damaging agents with microtubule-targeting drugs to enhance efficacy [31, 32]. Additional validation in patient-derived primary recurrent glioma cells further supports the association between cell cycle dysregulation and replication stress in the efficacy of JJH201601. These findings suggest that decreased sensitivity to JJH201601 may be partly related to persistent G1/S phase progression or incomplete replication checkpoint inhibition. Therefore, pharmacological inhibition of CDK4/6 or CDC7 may enhance JJH201601 activity by interfering with cell cycle progression, impairing DNA replication permission, and exacerbating replication stress. Importantly, these data also highlight the heterogeneity of primary recurrent glioma cells and suggest that combination therapy strategies may be necessary for models with relatively weak responses to JJH201601 monotherapy. However, since only two relatively insensitive patient-derived models were tested, these conclusions remain exploratory and require further validation in a large cohort of recurrent gliomas with matched genomic characteristics and functional drug sensitivity data.

The main reason for the failure of many new drug clinical trials is the huge gap between existing preclinical models and patient treatment conditions. Existing preclinical models cannot accurately represent human diseases and do not take into account the molecular and cellular heterogeneity between tumors of different patients, which may be even more complex in malignant glioma [33]. Therefore, new methods are urgently needed for personalized treatment. Primary glioma cell lines, derived directly from surgical procedures and cultured with minimal passages, are closer to the primary tumor than commercially available cell lines and provide an excellent source of material for various types of research [34]. Numerous studies have utilized primary cell cultures derived from patient tumor tissue and established cell lines based on the WHO glioma classification to investigate alterations in cellular and molecular processes in gliomas [35, 36]. These studies have also been used to validate molecular signatures or therapeutic approaches, conduct drug screening, identify drug candidates, and subsequently tailor treatment [31, 37]. Compared to primary cells, organoids, three-dimensional cell cultures derived from patient tumor cells, can maximize the preservation of the pathological and genetic characteristics of the original tumor tissue, while maintaining high culture success rates and requiring shorter culture times. They hold great potential for advancing drug screening and predicting drug sensitivity for cancer treatments [38]. Tumor organoids can serve as a valuable tool for predicting drug sensitivity and can identify patients who are most likely to respond positively to specific drugs [39]. Our research team has successfully established a glioma organoid model and conducted sensitivity testing for combined drug therapies. The results showed that JJH201601 had a significant inhibitory effect on organoids derived from glioma patients, and significantly improved sensitivity when combined with TMZ. In addition, we successfully established primary cells from 10 patients with recurrent gliomas and verified the therapeutic effect of JJH201601. We summarized clinical treatment history and molecular features associated with TMZ response. Six patients had received TMZ before recurrence, whereas four did not receive postoperative radiotherapy or chemotherapy after the initial surgery. Seven recurrent samples were MGMT-unmethylated or showed low-level methylation, suggesting reduced TMZ sensitivity. In addition, mutations in DNA MMR pathway genes (MSH2, MSH6, MLH1, and PMS2) are linked to acquired resistance to TMZ [40]. Among these 7 cases, 5 also showed loss or absence of MMR protein expression, one showed weak or heterogeneous MLH1 expression with retained MSH2, MSH6, and PMS2, and one retained MMR protein expression. Several recurrent samples also harbored TERT promoter alterations, p53 abnormality, or EGFR positivity. These findings suggest that this cohort was enriched for recurrent gliomas with potential TMZ-insensitive or resistance-associated features. However, because recurrence alone does not define TMZ resistance, these models should not be regarded as molecularly confirmed TMZ-resistant gliomas. JJH201601 showed inhibitory activity in recurrent glioma primary cells, including cases with prior TMZ exposure and MGMT-unmethylated status. Future studies using larger, molecularly defined TMZ-resistant cohorts are needed to determine whether JJH201601 is effective against TMZ-resistant gliomas.

The genetic background of each glioma model may influence the response to JJH201601. U87 and LN229 cells have distinct molecular profiles, particularly in p53 status, which is important because our data suggest that JJH201601 activates a p53-related stress response after ribosome biogenesis inhibition. In patient-derived recurrent glioma primary cells, IDH-mutant samples showed relatively lower sensitivity to JJH201601, possibly reflecting their distinct molecular context, including potential coexistence with p53-related alterations. However, because of the limited sample size, the current study cannot define p53, IDH mutation, MGMT promoter methylation, or MMR status as definitive predictors of JJH201601 sensitivity. We therefore interpret the genotype-response relationship as exploratory and suggest that future studies should integrate comprehensive genomic profiling with functional drug-sensitivity testing to identify molecular determinants of JJH201601 response.

This study makes significant advances in glioma treatment and cancer pharmacology. First, JJH201601, a docetaxel derivative, has high efficacy and low toxicity, which addresses a significant clinical limitation of standard taxanes. Second, the synergistic effect of JJH201601 and TMZ supports further evaluation of JJH201601-based combination strategies in glioma models. In addition, experiments using recurrent primary glioma cells with relatively low sensitivity to JJH201601 suggest that targeting CDK4/6- or CDC7-related cell cycle pathways may enhance JJH201601-induced growth inhibition. However, whether JJH201601 can overcome established TMZ chemoresistance remains to be validated in well-defined TMZ-resistant glioma models. Third, by clarifying JJH201601’s impacts on the ribosome biogenesis pathway and its downstream effects on p53 activation and cell cycle control, we establish a molecular link between ribosomal stress and glioma suppression. Fourth, this study presents a novel dual-targeting strategy in which JJH201601 is paired with CDK4/6 or CDC7 inhibitors to improve therapeutic efficacy, setting the groundwork for future combination medication development. Finally, we tested JJH201601’s therapeutic potential in primary cells obtained from patients with recurrent glioma. Taken together, these findings confirm that JJH201601 is a good candidate for the treatment of initial gliomas, and may also be effective in treating recurrent gliomas. They also provide new opportunities for addressing the cell cycle and translational regulation in high-grade gliomas. These findings promote future preclinical research and clinical trials of JJH201601, especially in combination treatments and precision oncology frameworks.

Despite the optimistic findings, this study has several drawbacks. First, while patient-derived cells and organoids are functional in vitro models, they do not adequately represent the complexities of the glioma microenvironment, such as the blood-brain barrier and immunological interactions. Moreover, although section-based Ki-67 immunofluorescence preserved organoid spatial architecture, future studies using flow cytometry or single-cell analysis would allow more quantitative evaluation of proliferative cell populations. Second, JJH201601’s in vivo efficacy has only been briefly assessed in a subcutaneous glioma xenograft model; further research employing orthotopic intracranial models will be required for more clinically significant assessments. Third, whereas RNA sequencing findings indicate ribosome-associated gene suppression, the direct transcriptional regulators and upstream signaling pathways are unknown. Nevertheless, the current data are not sufficient to establish JJH201601 as a therapeutic strategy for resistant gliomas. Rather, they support further mechanistic and pharmacological evaluation of JJH201601, particularly in orthotopic intracranial models and in studies designed to assess central nervous system exposure, long-term toxicity, and optimal combination regimens. What’s more, although docetaxel is known to have limited brain penetration because of P-gp-mediated efflux at the blood–brain barrier, our results showed that JJH201601 was detectable in mice brain tissue after both intravenous and intraperitoneal administration. However, the brain-to-plasma ratios remained low, indicating measurable but limited brain exposure. Therefore, these data support the need for further head-to-head pharmacokinetic comparisons with docetaxel and more detailed blood–brain barrier penetration studies in both intact and destroyed blood–brain barrier.

In conclusion, this study demonstrated that the novel docetaxel derivative JJH201601 exerted anti-glioma effects in experimental models by inhibiting cell viability, inducing apoptosis, altering cell cycle progression, suppressing ribosome biogenesis-related genes, and enhancing TMZ-associated growth inhibition. Patient-derived glioma organoids and recurrent glioma primary cells further supported its activity in clinically relevant in vitro models. However, because the current in vivo evidence was limited to a subcutaneous xenograft model, and pharmacokinetic properties and central nervous system delivery remain undefined, these findings should be regarded as preliminary. Further orthotopic intracranial studies and pharmacological investigations are required to determine whether JJH201601 has translational potential for glioma treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

M.C., N.J., and Z.Z. conceived and designed the research. M.C. and Z.Z. carried out the investigation. R.J., M.C., M.S., L.Z., and N.J. performed data curation and formal analysis. R.J., M.C., D.L., M.S., S.Y., and N. J. developed the research methodology. R.J., D.L., M.S., S.M., and S.Y. completed the software and visualization work. R.J., D.L., and L.Z. conducted the validation of the analysis and experiments. All authors wrote, reviewed, and revised the manuscript. R.J., M.C., S.M., and L.Z. supervised the research work. M.C., N.J., and Z.Z. took charge of project administration. D.L., M.S., S.M., S.Y., L.Z., N.J., and Z.Z. provided critical research resources and technical suggestions. N.J. and Z.Z. secured the funding acquisition for the study. All authors read and approved the final manuscript.

Funding

This work was supported by the capital health research and development of special (2022-2-2047) and horizontal research project of Beijing Tiantan Hospital Affiliated to Capital Medical University (HX-A-2024072).

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files. The datasets and code generated during the current study are openly available. Accession numbers or DOIs are provided in the relevant sections of the Methods.

Declarations

Ethics approval and consent to participate

Approval for this study was issued by the Beijing Tiantan Hospital institutional review board [approval number: KY2024-346-03]. All patients in this study provided written informed consent. All protocols adhered to the Declaration of Helsinki and applicable national guidelines. All the animal samples used in this study were collected according to the protocol approved by the Beijing Neurosurgical Institute Laboratory Animal Welfare and Ethics Committee (No. BNI202404002).

Consent for publication

All authors consent to being listed as authors.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.