Introduction
Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of non-Hodgkin lymphomas with high malignancy and recurrence rates. PTCL encompasses several pathological subtypes, including nodal T-follicular helper cell lymphoma, angioimmunoblastic-type (nTFHL-AI), PTCL-not otherwise specified (PTCL-NOS), ALK-positive anaplastic large cell lymphoma (ALK + ALCL), and ALK-negative ALCL (ALK-ALCL) [ 1 ]. Conventional anthracycline-based CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) or CHOP-like regimens yields disappointing outcomes for most PTCL subtypes, except ALK + ALCL. Patients with advanced stage PTCL have a 5-year overall survival (OS) rate below 30%, and relapsed/refractory PTCL has a median OS of just 5.5 months [ 2 , 3 ]. Due to these inferior prognosis, research efforts are focused on understanding PTCL pathogenesis to develop better targeted therapies.
PTCL frequently harbors mutations in genes involved in epigenetic regulation, particularly methylcytosine dioxygenase TET2 [ 4 , 5 ]. Loss of TET2 is associated with enhanced self-renewal capacity of hematopoietic stem and progenitor cells, along with altered terminal differentiation [ 6 ]. Over 60% of PTCL patients carry TET2 inactivating mutations, which, in synergy with other mutations like RHOA G17V , drive the transformation process and contribute to PTCL development, including in nTFHL [ 7 , 8 ]. The RHOA mutation impairs GTP-binding, disrupting cell polarization, migration, and T-cell receptor (TCR) signaling, while hyperactivating the phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) signaling pathways [ 9 – 12 ]. These dysfunctions drive PTCL pathogenesis and worsen prognosis [ 13 – 15 ].
Azacitidine, a hypomethylating agent with significant therapeutic potential in PTCL, is often combined with chemotherapy. Its mechanism of action involves DNA hypomethylation, which reactivates tumor suppressor genes and induces apoptosis in malignant cells. Notably, azacitidine has demonstrated targeted efficacy against epigenetically dysfunctional tumor cells with TET2 silencing [ 16 , 17 ]. However, despite the combination of azacitidine and CHOP improving outcomes in PTCL, patients with TET2 / RHOA double mutations remains one of the lymphoma subtypes with the worst prognosis [ 17 ]. Given limited efficacy of azacitidine monotherapy, we aimed to find a complementary agent. Supporting this approach, the combination of azacitidine and romidepsin has been shown to increase complete response rates in PTCL to nearly 50%, with higher responses observed in patients harboring DNA methylation-related gene mutations such as DNMT3A and IDH2 [ 18 ].
In this study, we performed a large-scale screening of anti-tumor compounds to evaluate their synergistic effects with azacitidine on cell viability in TET2 and RHOA double-mutated cells. We identified selinexor, an XPO1 inhibitor, as significantly enhancing the anti-tumor efficacy of azacitidine in multiple preclinical models in vitro and in vivo. We demonstrated that azacitidine epigenetically induces SOCS1 expression to inhibit the JAK3/STAT3 signaling pathway, while selinexor enhances this effect by sequestering STAT3 in the nucleus, reducing p-STAT3 Tyr705 levels. Dual targeting of XPO1 and DNA methylation results in synergistic anti-tumor activity and reduces tumor burdens in TET2 / RHOA -mutated murine xenograft models, offering a novel therapeutic strategy for patients with these genetic alterations. Moreover, the clinical case of a TET2 / RHOA -mutated PTCL patient achieving complete remission with this combination therapy further supports its safety and efficacy in a real-world setting.
Methods and materials
Patients
All tumor samples were subjected to WES and/or targeted sequencing ( n = 236, including 130 nTFHL-AI, 54 PTCL-NOS, 24 ALK + ALCL, and 28 ALK-ALCL). The study was approved by the Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University School of Medicine (Approval Number: 2022–317). Written informed consent was obtained from all patients, including the patient presented in the case report, in accordance with the principles of the Declaration of Helsinki. The patient provided specific consent for the use of corresponding clinical data and for the publication of the case details.
Cell culture and transfection
Cell line Jurkat (Human T lymphocyte), H9 (Cutaneous T cell lymphoma), Karpas-299 (ALK + ALCL), DL-40 (ALK- ALCL), and HEK-293 T (Human embryonic kidney cell) were obtained from the American Type Culture Collection (Manassas, VA, USA). Jurkat, H9, and Karpas-299 cells were cultured in RPMI-1640 supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin. DL-40 cells were cultured in RPMI-1640 supplemented with 20% FBS and 1% penicillin–streptomycin. HEK-293 T cells was cultured in Dulbecco’s Modified Eagle Medium supplemented with 10% FBS and 1% penicillin–streptomycin. All the cells were maintained at 37 °C in a humidified atmosphere containing 5% CO₂.
Purified plasmids pCV186/Cherry/Puro-RHOAG17V and pGV655/GFP/Puro-TET2H1380Y were transfected into HEK-293 T cells to collect lentiviral particles. Jurkat, H9, Karpas-299, and DL-40 cells were incubated with lentiviral particles for 72 h with addition of polybrene (8 μg/ml) to establish TET2 MUT / RHOA MUT cells. The stably transduced cells were selected by sorting for double-positive (GFP + and Cherry +) populations using a fluorescence-activated cell sorting instrument.
Compound screens
TET2 MUT / RHOA MUT Jurkat cells were screened with an antitumor compound library ( n = 633, 40 nmol/L) in the presence of 1 μmol/L azacitidine for 24 h. Subsequently, the cells were subjected to Celltiter Glo cell viability assay (Promega, Madison, WI, USA) according to the manufacturer’s instructions. Compounds that, when combined with azacitidine treatment, resulted in the top 15 highest increases in cell death percentage compared to the azacitidine group were selected for further evaluation.
Cell growth inhibition assays and drug combination analysis
Cells were seeded in 96-well plates at a density of 1.0 × 10 5 /mL and incubated for 24 h, then viable cells were detected by CCK-8 (MedChemExpress, Monmouth Junction, NJ, USA) at OD 450 nm. To assess the efficacy of the combination of azacitidine and other compounds (Selleck, Houston, TX, USA), cells were plated, and viability was determined as described above by using twofold dilution matrices of azacitidine and other compounds. The effects of the combinations were calculated by using the zero interaction potency (ZIP) model, which compares observed and expected combination effects [ 19 ]. The ZIP model combines the advantages of the Bliss independence and Loewe additivity models. The output of the ZIP model is the delta synergy score (δ), which is the average combination effect of the drugs over the total dose matrix tested. A δ score of 0 implies both probabilistic independence and dose additivity, whereas a δ score of < 0 indicates antagonistic drug effects and a score of > 0 indicates synergistic drug effects.
Zebrafish xenograft model
Tumor cells were incubated with DiI dye (Vybrant, Molecular Probes, Invitrogen, Eugene, OR, USA). Approximately 300–600 labeled cells per embryo were then injected into the yolk sac of wildtype (WT) Tuebingen zebrafish at 48 h post-fertilization (hpf). Embryos with successful engraftment were randomly assigned to four groups. Subsequently, the death event of xenograft zebrafish model was observed utilizing a fluorescence microscope in the following 7–8 days.
Apoptosis assay by flow cytometry
The collected cells were washed twice with PBS and stained for apoptotic cells using the Apoptosis Detection APC/7-AAD Kit (Elabscience,Wuhan, China) following the manufacturer’s instructions. The apoptotic cells were analyzed using the BDTM LSR II flow cytometry instrument (BD Biosciences, San Jose, CA, USA), and the data was analyzed using FlowJo v10 software (LLC, Ashland, OR, USA). For apoptosis inhibition experiments, cells were pre-treated for 2 h with the pan-caspase inhibitor Z-VAD-FMK (100 μM; S7023, Selleck), the caspase-8 inhibitor Z-IETD-FMK (100 μM; S7314, Selleck), or the caspase-9 inhibitor Z-LEHD-FMK (100 μM; S7313, Selleck) prior to the addition of selinexor and azacitidine.
Protein lysate and western blot
Cells were treated with drugs for 24 h, and then total protein was collected using Pierce IP lysis (Thermo Fisher Scientific, Waltham, MA, USA), supplemented with protease and phosphatase inhibitors (Bimake, Houston, TX, USA). Nuclear and cytoplasmic fractions were isolated using Nuclear and Cytoplasmic Extraction Reagents (Pierce Biotechnology, Rockford, IL, USA). Freshly prepared proteins were resolved by SDS-PAGE, followed by western blotting. The antibody used for WB was purchased from Cell Signaling Technology (Danvers, Massachusetts, USA).
Primary antibodies used for Western blotting were as follows: Caspase-8 (F0067, Selleck), Cleaved Caspase-8 (#9429, Cell Signaling Technology [CST]), Caspase-3 (#9662, CST), Cleaved Caspase-3 (#9664, CST), Caspase-9 (10380–1-AP, Proteintech), Cleaved Caspase-9 (F0326, Selleck), Cytochrome C (10993–1-AP, Proteintech), PARP (66520–1-Ig, Proteintech), p-JAK3 (Tyr980/981) (#5031, CST), JAK3 (#8827, CST), p-STAT3 (Tyr705) (AF00753, AiFang Biological), STAT3 (#12640, CST), SOCS1 (AF03521, AiFang Biological), GAPDH (#2118, CST), and Histone H3 (#9715, CST). HRP-linked Anti-mouse IgG (#7076, CST) and Anti-rabbit IgG (#7074, CST) were used as secondary antibodies. Densitometric analysis was performed using ImageJ software.
RNA-sequencing analysis
Total RNA was extracted from TET2 MUT / RHOA MUT Jurkat cells treated with DMSO (vehicle), selinexor, azacitidine, or their combination for 24 h. RNA libraries were constructed using KAPA RNA HyperPrep Kit with RiboErase (KAPA Biosystems, Wilmington, MA, USA), following the manufacturer’s instructions, and sequenced on the Illumina NovaSeq X Plus System. High-quality trimmed reads were aligned to the reference genome (UCSC hg19) using HISAT2 software (v2.0.4) with guidance from the Ensembl GFF gene annotation file. Differentially expressed genes (DEGs) were identified using edgeR to determine biologically meaningful rankings. Gene set enrichment analysis (GSEA) was performed using KEGG and MSigDB gene sets. The raw RNA-sequencing (RNA-seq) data generated in this study have been deposited in the GSA-Human database under the accession number HRA013471. Data is available from the corresponding author, Wei-Li Zhao (zhao.weili@yahoo.com), upon reasonable request.
RNA extraction, cDNA synthesis, and quantitative PCR (RT-qPCR)
Total RNA was extracted from cultured cells using the FastPure® Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, Nanjing, China). For cDNA synthesis, 1 μg of total RNA was treated with gDNA Wiper Mix (Vazyme) to remove genomic DNA, then incubated with HiScript III qRT SuperMix (Vazyme) for cDNA synthesis. RT-qPCR was performed using the synthesized cDNA, ChamQ Universal SYBR qPCR Master Mix (Vazyme), and primers listed in Supplementary Table 2.
Immunofluorescence
Cells were plated on coverslips, fixed with 4% paraformaldehyde for 10 min at room temperature, and permeabilized with 0.1%Triton X-100 for 15 min. The cells were blocked in 5% bovine serum albumin (BSA) in PBS for 30 min at room temperature, followed by incubation with an anti-STAT3 antibody (#12,640, CST) overnight at 4 °C. After washing, the slides were incubated with Goat Anti-Rabbit IgG H&L (Alexa Fluor® 647) (ab150079, Abcam) and DAPI for nuclear counterstaining. Immunofluorescence analysis was performed using a 60 × objective on an Olympus FV1200 Laser Scanning Confocal Microscope.
Cell line-derived mouse xenografts
All animal experiments were conducted in strict accordance with protocols approved by the Animal Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Approval Number: RJ2025036). Four-week-old male NCG mice (Strain NO. T001475) were purchased from GemPharmatech (Nanjing, China) and housed under SPF conditions at the Experimental Animal Center of Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine.
For the Jurkat xenograft model, Jurkat (1 × 10 7 cells per mouse) suspended in 200 μl PBS were subcutaneously injected into the right posterior flanks of six-week-old NCG mice (female, 4 mice per group). When tumor volumes reached approximately 100 mm 3 , the mice were randomly assigned to different treatment groups: vehicle, azacitidine (8 mg/kg, intraperitoneal, once weekly), selinexor (10 mg/kg, oral gavage, twice weekly), or a drug combination in which each compound was administered at the same dose and schedule as the single-agent group.
For the Karpas-299 xenograft model, 5 × 10 6 Karpas-299 cells were subcutaneously injected into the right posterior flanks of six-week-old NCG mice (female, 4 mice per group). When tumors reached approximately 100 mm 3 , mice were randomized into four groups and treated with vehicle, azacitidine (8 mg/kg, intraperitoneal, once weekly), selinexor (10 mg/kg, oral gavage, twice weekly), or the combination, following the same schedule as the Jurkat model.
Tumors and mice body weight were measured twice every week, and the tumor volume was calculated using the modified ellipsoidal formula: tumor volume = ½ × length × width 2 , based on caliper measurements. Mice were monitored for signs of distress throughout the study. The experiment was concluded either after 3 weeks of treatment or when tumors in the control group approached the pre-defined ethical endpoint of 2000 mm 3 . After the final administration, all mice were sacrificed. Subcutaneous tumors and organs were harvested for further analysis.
Immunohistochemistry
Hematoxylin–eosin (HE) staining and immunohistochemical (IHC) staining were performed on 4 μm formalin-fixed, paraffin-embedded tissue sections. Sections were baked at 62 °C for 2 h, deparaffinized in xylene, and rehydrated through a graded ethanol series. Endogenous peroxidase activity was blocked using hydrogen peroxide for 10 min at room temperature. Heat-induced antigen retrieval was performed in Tris–EDTA buffer (pH 9.0) at boiling temperature. After blocking with 5% BSA for 30 min, sections were incubated overnight at 4 °C with primary antibodies against p-STAT3 (Tyr705) (AF00753, AiFang Biological), SOCS1 (AF03521, AiFang Biological), and Cleaved Caspase-3 (#9664, CST). The next day, sections were incubated with Dako EnVision HRP-conjugated secondary antibody (K5007, Dako) at 37 °C for 1 h. Chromogenic detection was conducted using 3,3’-diaminobenzidine (DAB), followed by hematoxylin counterstaining. Slides were dehydrated, cleared in xylene, and mounted with neutral balsam. Tissue sections were scanned using an Aperio GT 450 slide scanner (Leica Biosystems, St. Paul, MN, USA), and images were analyzed with QuPath software (Edinburgh, Scotland, UK). Positive staining fractions were quantified using ImageJ software from five randomly selected fields per sample.
Statistical analysis
All statistical analyses were conducted using GraphPad Prism v9.0 (San Diego, CA, USA) and R software. Data representation is indicated in the figure legends. Bar graphs typically display the mean with all individual data points shown; where specified, they are presented as mean ± standard deviation (SD). Line graphs, such as tumor growth curves, are presented as mean ± SD. Kaplan–Meier analysis with the log-rank test was used to assess the survival of zCDX models. Differential expression analysis of RNA-seq data was performed using the edgeR R package. Drug synergy was quantified using the ZIP model, calculating the δ synergy score. Chi-square test was used to examine the association between categorical variables, such as clinical characteristics. For comparisons involving more than two groups, a one-way ANOVA with Tukey’s post hoc test was used. Two-way repeated-measures ANOVA was used to analyze tumor volume curves over time. Statistical significance was set at p < 0.05. Significance levels are denoted in the figures as follows: * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001.
Results
TET2 and RHOA dual mutations in PTCL patients
We first analyzed whole exome sequencing (WES) ( n = 101) and targeted sequencing ( n = 135) data from 236 patients newly diagnosed with PTCL, including nTFHL-AI ( n = 130), PTCL-NOS ( n = 54), ALK + ALCL ( n = 24), and ALK- ALCL ( n = 28) subtypes. In the comprehensive mutational landscape of PTCL, 34 genes were identified as recurrently mutated, which were detected in more than 5% of all cases (Fig. 1 A). The most frequently mutated genes included TET2 (55%), RHOA (33%), DNMT3A (20%), KMT2C (18%), and IDH2 (16%). Consistent with previous reports, [ 20 , 21 ] TET2 and RHOA dual mutations were highly prevalent in PTCL, with an occurrence rate of 31.3% (74/236 cases). Among the various pathological subtypes, these dual mutations are particularly enriched in nTFHL-AI, with a frequency of 49.2% (64/130), making them characteristic mutations of this subtype (Fig. 1 B). Although patients with TET2 / RHOA double mutations did not exhibit significant differences in survival, [ 22 , 23 ] they showed a notably higher international prognostic index (IPI) in our cohort (Fig. 1 C). Given that RHOA G17V mutation is the most common secondary event in the context of TET2 dysfunction and acts as a key contributor to pathogenesis — posing significant challenges for azacitidine monotherapy — we selected the TET2 / RHOA double mutation ( TET2 MUT RHOA MUT ), the most prevalent genetic alteration in PTCL, as a representative model to screen for drugs that could synergize with azacitidine and enhance its therapeutic efficacy. Fig. 1 Genomic landscape and clinical features of PTCL. A Landscape of somatic mutations detected by WES ( n = 101) and targeted sequencing ( n = 135) in 236 patients with PTCL. Mutations in 159 candidate genes with predicted functional alterations in tumorigenesis are ranked by prevalence in all cases (only mutations with a mutation rate > 5% were shown in the figure). The numbers of mutational burdens in each patient are indicated at the top, and the prevalence of each mutation are depicted on the right. Pathological subtypes are shown at the bottom. B Frequency of concurrent TET2 and RHOA mutations across PTCL subtypes. nTFHL-AI, nodal T-follicular helper cell lymphoma, angioimmunoblastic-type; PTCL-NOS, peripheral T-cell lymphoma, not otherwise specified; ALK + ALCL, ALK-positive anaplastic large cell lymphoma; ALK- ALCL, ALK-negative anaplastic large cell lymphoma. C IPI (0–2 vs. 3–5) according to TET2 / RHOA mutational status. Data was analyzed using chi-square test to compare the proportions between groups. * p < 0.05; *** p < 0.001
Synergistic cytotoxicity of selinexor and azacitidine in TET2/RHOA -mutated T-lymphoma cells
Azacitidine has been reported to exhibit significant selectivity in targeting patients with TET2 mutations [ 17 , 22 ]. To identify drugs that synergistically induce cell death in TET2 MUT RHOA MUT T-lymphoma cells in combination with azacitidine, we performed a screening in TET2 MUT RHOA MUT Jurkat cells using an anti-tumor compound library ( n = 633, 40 nmol/L) (Supplementary Table 1) in the presence of 1 μmol/L azacytidine (Fig. 2 A). The selection of Jurkat cells for the compound screen was based on their partial responses to azacitidine [ 22 ]. After 24 h of treatment, cell viability was assessed with the Celltiter Glo cell viability assay. We highlighted the top 15 compounds that exhibited significant inhibitory effects in conjunction with azacytidine, involving targets such as cyclin-dependent kinase, proteasome, topoisomerase, XPO1, and microtubule system (Fig. 2 B and 2 C). To further explore whether these drugs show synergistic rather than merely additive effects, we selected 5 out of 15 clinically available drugs with 5 different targeting mechanisms for synergy experiments with azacitidine in TET2 MUT RHOA MUT Jurkat and H9 cells (Fig. 2 C). The effects of the drug combinations were evaluated using the Zero Interaction Potency (ZIP) model, which compares the observed effects of the combination with the expected effects based on individual drug activities (Supplemental Fig. 1) [ 19 ]. The results indicated that the combination of selinexor and azacitidine yielded the highest ZIP δ synergy score (Fig. 2 D) and most synergistic area score (Fig. 2 E) across both TET2 MUT RHOA MUT cell lines, demonstrating a potent and robust synergistic effect in PTCL. Fig. 2 Synergy of selinexor and azacitdine in TET2 MUT / RHOA MUT in vitro and in vivo preclinical models. A A schematic outline of the compound screen. B BTop 15 compound candidates from the screen that exhibited significant anti-tumor effects in combination with azacitidine. C C5 out of 15 compound candidates with various targets selected for further synergistic analysis. D , E Heatmaps of ZIP δ synergy scores ( D ) and most synergistic area scores ( E ) for the five selected compounds in combination with azacitidine in TET2 MUT / RHOA MUT Jurkat and H9 cells after 48 h. F - I Inhibition of cell viability in TET2 MUT / RHOA MUT Jurkat ( F ), H9 ( G ), Karpas-299 ( H ), and DL-40 ( I ) cells treated with a dose range of azacytidine (AZA) and/or selinexor (SEL) for 24 h. Data is presented as the mean of three independent experiments, with individual data points shown. Statistical significance was determined by two-way ANOVA with Tukey’s multiple comparisons test. J , K Survival of TET2 MUT / RHOA MUT Jurkat (J, n = 90 for each group) and H9 (K, n = 60 for each group) zebrafish CDX (zCDX) models upon AZA and/or SEL treatment alone or in combination. Survival curves were estimated using the Kaplan–Meier method and compared between groups using the log-rank test. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; ns, not significant
Consequently, we prioritized the combination of selinexor and azacitidine for further investigation. We first determined in TET2 MUT RHOA MUT Jurkat and H9 cells that the combination treatment was significantly more effective at inhibiting cell viability than either single agent across a range of concentrations (Fig. 2 F and G, Supplementary Fig. 2 A and 2B). To confirm that this synergy extended to more PTCL subtypes, we repeated these experiments in TET2 MUT RHOA MUT Karpas-299 (ALK + ALCL) and DL-40 (ALK- ALCL) cells. The potent synergistic effect was successfully validated, with the combination therapy again showing significantly greater inhibition of cell viability compared to either azacitidine or selinexor alone (Fig. 2 H and I, Supplementary Fig. 2C-F). Notably, the drug combination that achieved an inhibition rate closest to 50% in each cell line was selected for subsequent experiments. Furthermore, preclinical zebrafish CDX models harboring TET2 and RHOA mutations exhibited extended survival in the combination treatment group compared to both the untreated and single-agent groups (Fig. 2 J and K).
Finally, to investigate whether the TET2 / RHOA double mutation status is a key determinant of this synergy, we compared the drug response between WT and TET2 MUT RHOA MUT Jurkat and H9 cells (Supplementary Fig. 3). While a modest synergistic interaction was observed in WT cells (Supplementary Fig. 3 A and 3 C), the synergy was markedly stronger in the TET2 MUT RHOA MUT cells, with higher mean synergy scores and larger synergistic areas (Supplementary Fig. 3B and 3D). This enhanced sensitivity was mirrored in the apoptosis analysis. Flow cytometry analysis revealed that the combination treatment induced apoptosis in WT Jurkat and H9 cells, though to a lesser extent than in their mutant counterparts (Supplementary Fig. 3E-H). A direct comparison of the apoptotic response confirmed that TET2 MUT RHOA MUT cells were significantly more sensitive to the pro-apoptotic effects of both selinexor alone and the combination treatment (Supplementary Fig. 3I). These findings strongly suggest that the TET2 / RHOA mutational status acts as a key sensitizing factor for the therapeutic efficacy of the selinexor and azacitidine combination.
Selinexor and azacitidine synergize in TET2/RHOA -mutated PTCL to induce cell death via intrinsic and extrinsic apoptotic pathways
To ascertain whether the inhibition of cell growth by combined treatment with selinexor and azacitidine is associated with apoptosis or other forms of cell death, we treated TET2 MUT RHOA MUT PTCL cell lines with low concentrations of azacitidine and/or selinexor for 24 h and analyzed the proportion of apoptotic cells by flow cytometry (Fig. 3 A, Supplementary Fig. 4). Across all four cell lines, the combination treatment induced a significantly higher percentage of apoptotic cells compared to either single agent or the control, suggesting a robust synergistic pro-apoptotic effect by selinexor and azacitidine. Additionally, electron microscopy of Jurkat cells revealed a significant presence of apoptotic bodies in the co-treated group, further confirming that apoptosis was induced by the combination treatment (Fig. 3 B). To determine whether the induced apoptosis involved the intrinsic and/or extrinsic pathways, we measured the expression levels of key apoptosis-related proteins by Western blot (Fig. 3 C). Consistent with the flow cytometry results, quantitative analysis showed that the combination treatment markedly increased the active, cleaved forms of key effector proteins, including caspase 3 (Fig. 3 D, Supplementary Fig. 5 A) and PARP (Supplementary Fig. 5B and 5 C). Furthermore, the levels of cleaved caspase 9 (Fig. 3 E, Supplementary Fig. 5D) and cytochrome C (Supplementary Fig. 5E), markers of the intrinsic apoptotic pathway, as well as cleaved caspase 8 (Fig. 3 F, Supplementary Fig. 5 F), a marker of the extrinsic apoptotic pathway, were significantly elevated in the combination group compared to the single-agent groups. This pattern was consistently observed across all tested cell lines. Fig. 3 Selinexor synergistically enhances azacitidine-mediated intrinsic and extrinsic apoptosis. A Flow cytometry analysis of apoptosis. Left: Representative flow cytometry plots of Annexin V (APC) and 7-AAD staining in TET2 MUT / RHOA MUT Jurkat cells after 24 h treatment. Right: Quantification of total apoptosis (early and late) across all four TET2 MUT / RHOA MUT cell lines (Jurkat, H9, Karpas-299, and DL-40). Data is presented as the mean of three independent experiments, with individual data points shown. Statistical significance was determined by two-way ANOVA with Tukey’s multiple comparisons test. B Electron microscopy of TET2 MUT / RHOA MUT Jurkat cells showing that AZA and SEL synergistically promote apoptosis (*indicated apoptotic bodies). C Representative Western blot analysis of apoptosis-related proteins in TET2 MUT / RHOA MUT cell lines, including intrinsic (cleaved caspase-9, cytochrome c), extrinsic (cleaved caspase-8), and general apoptosis markers (cleaved PARP, cleaved caspase-3). D - F Quantitative analysis of key apoptosis markers. Bar graphs represent the expression of cleaved caspase-3 ( D ), cleaved caspase-9 ( E ), and cleaved caspase-8 ( F ) in TET2 MUT / RHOA MUT cell lines. Western blotting results were quantified using densitometry. The band intensities of each target protein were normalized to the corresponding GAPDH band intensity. Data is presented as the mean of three independent experiments, with individual data points shown. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. G Apoptosis inhibition assay. Bar graph shows inhibition rate of TET2 MUT / RHOA . MUT cell lines treated with indicated caspase inhibitors. Data is presented as the mean of three independent experiments, with individual data points shown. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001
To functionally confirm the dependence on caspase activation, we utilized specific inhibitors for caspase-9 (Z-LEHD-FMK), caspase-8 (Z-IETD-FMK), and a pan-caspase inhibitor (Z-VAD-FMK). Pre-treatment with these inhibitors significantly rescued the cells from the cytotoxic effects of the combination treatment (Fig. 3 G). Collectively, these findings confirmed that the synergistic apoptosis induced by the combination of selinexor and azacitidine involves the activation of both intrinsic and extrinsic pathways.
Selinexor and azacitidine exert synergistic effects by inhibition of the JAK3/STAT3 signaling pathway
To elucidate the mechanism underlying the synergistic effects of selinexor and azacitidine, we conducted transcriptomic analyses by performing RNA-seq on TET2 MUT RHOA MUT Jurkat cells treated with DMSO, azacitidine, and/or selinexor for 24 h. The combination treatment displayed a significantly greater number of differentially expressed genes (DEGs) than the DMSO-treated and single-agent treated cells, suggesting a unique mechanism driving the synergistic effects (Fig. 4 A). Gene set enrichment analysis (GSEA) revealed that the combination treatment significantly upregulated apoptosis-related gene sets and downregulated the JAK/STAT signaling pathway (Fig. 4 B and C). To validate these transcriptomic findings, we first performed qPCR on all highlighted DEGs in Jurkat cells, which showed high concordance with the RNA-seq data (Fig. 4 D and Supplementary Fig. 6). We then confirmed the expression of key representative genes— BIRC3 , FOS , IFNAR , and SOCS1 —across all four cell lines. This analysis revealed a consistent pattern of FOS and SOCS1 upregulation, and BIRC3 and IFNAR downregulation, in response to the combination treatment (Fig. 4 D), confirming that these key transcriptional changes are conserved across different T-cell malignancy subtypes. Fig. 4 Selinexor and azacitidine exert synergistic effects by inhibiting the JAK3/STAT3 signaling pathway. A Venn diagram showing differentially expressed genes across various drug combinations in TET2 MUT / RHOA MUT Jurkat cells. B Pathways enriched in AZA and SEL treated TET2 MUT / RHOA MUT Jurkat cells identified by GSEA analysis. C Heatmap of representative apoptosis-related and JAK/STAT genes from RNA-seq data. D The mRNA expression levels of key representative genes in TET2 MUT / RHOA MUT cell lines. Data is presented as relative mRNA expression (fold change) and shown as the mean of three independent experiments, with individual data points overlaid. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. E Representative Western blot analysis of JAK/STAT pathway proteins in TET2 MUT / RHOA MUT cell lines. F Quantitative analysis of JAK3 (upper panel) and STAT3 (lower panel) phosphorylation in TET2 MUT / RHOA MUT cell lines. Data is presented as the mean of three independent experiments, with individual data points shown. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. G Representative Western blot analysis of STAT3 subcellular localization in TET2 MUT / RHOA MUT cell lines. GAPDH served as the cytoplasmic loading control, and Histone H3 served as the nuclear loading control. H Immunofluorescence showing the intracellular distribution of STAT3 protein in TET2 MUT / RHOA MUT cell lines. Scale bar = 20 μm. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001
We next sought to confirm these pathway alterations at the protein level across our panel of PTCL cell lines. Critically, quantitative analysis revealed that the combination treatment resulted in a synergistic and profound reduction in the phosphorylation of both JAK3 (p-JAK3) and its downstream target STAT3 (p-STAT3) (Fig. 4 E and 4 F, Supplementary Fig. 7 A and 7B). Consistent with the transcriptomic data, SOCS1 protein expression was significantly upregulated by azacitidine, both alone and in combination with selinexor (Fig. 4 E and Supplementary Fig. 7 C). This change was likely due to azacitidine-induced demethylation, which enhances SOCS1 transcription, as supported by subsequent qPCR experiments. However, the reduction in levels of p-JAK3 and p-STAT3 Tyr705 in the selinexor-treated and co-treated groups did not fully align with SOCS1 changes, suggesting that other mechanisms are contributing beyond the effects of azacitidine alone.
Given that selinexor is a nuclear export protein inhibitor, we hypothesized that it contributes to STAT3 inhibition by preventing its nuclear export, thereby sequestering it away from cytoplasmic kinases like JAK3. The STAT family has been identified as a key cargo of XPO1-mediated transport, [ 24 , 25 ] so we measured STAT3 levels in both the cytoplasm and nucleus of all four cell lines. Results from both Western blot (Fig. 4 G and Supplementary Fig. 7D) and immunofluorescence (IF) (Fig. 4 H) consistently indicated that selinexor treatment, alone or in combination, led to a significant accumulation of STAT3 in the nucleus. This suggested that selinexor effectively blocked STAT3 nuclear export, preventing its re-phosphorylation and activation in the cytoplasm, thus providing a clear mechanism for its synergistic effect with azacitidine in dampening JAK3/STAT3 signaling. Collectively, selinexor and azacitidine synergistically promote apoptosis in TET2 / RHOA -mutated PTCL cells by jointly inhibiting the JAK3/STAT3 signaling pathway.
Selinexor and azacitidine exert JAK3/STAT3-mediated synergistic effects in CDX murine models
To validate the therapeutic synergy of selinexor and azacitidine in vivo, we established subcutaneous CDX models using both TET2 MUT RHOA MUT Jurkat and Karpas-299 cells (Fig. 5 A). Once tumors were established, selinexor (10 mg/kg, oral gavage, twice weekly) and azacitidine (8 mg/kg, intraperitoneal, once weekly) were administered either alone or in combination for a total of 3 weeks. Fig. 5 Selinexor and azacitidine exert synergistic effects in CDX murine models by inhibiting the JAK3/STAT3 signaling pathway. A Schematic of the in vivo CDX experimental design. B , C Tumor volume growth curves for the TET2 MUT / RHOA MUT Jurkat ( B ) and Karpas-299 ( C ) xenograft models. Data is presented as mean ± SD ( n = 4 mice per group). Statistical significance was determined by two-way repeated-measures ANOVA. D , E Final tumor weights at the study endpoint for the Jurkat ( D ) and Karpas-299 ( E ) models. Data is presented as mean ± SD, with individual data points shown ( n = 4 mice per group). Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. F Representative images of hematoxylin and eosin (HE) staining and immunohistochemistry (IHC) for p-STAT3 Tyr705 , SOCS1, and cleaved caspase-3 (c-caspase3). Scale bar = 25 μm. G - I Quantitative analysis of IHC staining. Bar graphs show the quantified positive staining area fraction for p-STAT3 ( G ), SOCS1 ( H ), and c-caspase3 ( I ). Data is presented as mean ± SD from five randomly selected fields per tumor. Statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; ns, not significant
Consistent with our in vitro findings, the combination treatment demonstrated profound anti-tumor efficacy in CDX models. Tumor growth was significantly inhibited by the combination treatment compared to vehicle or single-agent treatments throughout the study period in both xenografts (Fig. 5 B and C). This resulted in a dramatic and statistically significant reduction in final tumor weights at the study endpoint for both Jurkat (Fig. 5 D) and Karpas-299 models (Fig. 5 E). Importantly, the combination treatment was well-tolerated, with no significant body weight loss or signs of organ toxicity observed in either model (Supplementary Fig. 8).
Histological analysis of excised tumors was performed to confirm the on-target mechanism in vivo (Fig. 5 F). Hematoxylin and eosin (HE) staining showed a marked reduction in tumor burden in the combination groups for both xenograft models. Furthermore, immunohistochemistry (IHC) revealed a molecular signature consistent with our proposed mechanism. Tumors from combination-treated mice displayed a significant reduction in STAT3 phosphorylation (p-STAT3), a robust increase in SOCS1 expression, and a marked elevation of the apoptosis marker cleaved caspase-3 (c-caspase3), as confirmed by quantitative analysis (Fig. 5 G-I).
In summary, these in vivo findings strongly confirmed the synergistic anti-tumor efficacy of selinexor and azacitidine in TET2 MUT RHOA MUT Jurkat xenograft models, providing further evidence for the clinical potential of this combination treatment.
Efficacy of azacitidine and selinexor combination therapy in a TET2/RHOA -mutated PTCL patient
Based on our compelling preclinical data, here we report a case of a 51-year-old male diagnosed with nTFHL-AI, classified as Ann Arbor stage IV with an IPI score of 2 (Fig. 6 A). IHC analysis of the pretreatment tumor tissue revealed strongly positive p-STAT3 Tyr705 (Fig. 6 B) and negative SOCS1 (Fig. 6 C) expression, suggesting that this patient could benefit from combination treatment with selinexor and azacitidine. Baseline positron emission tomography-computed tomography (PET-CT) scan at diagnosis revealed extensive metabolic activity (Fig. 6 D). The patient initially received standard CHOP chemotherapy for Cycle 1. Next-generation sequencing of tumor tissue revealed concurrent TET2 and RHOA mutations. As a result, selinexor and azacitidine were introduced alongside the CHOP regimen from Cycles 2 to 6. Notably, PET-CT at both interim (Fig. 6 E) and end-of-treatment (Fig. 6 F) evaluations showed a marked reduction in metabolic activity and demonstrated metabolic complete remission according to the Lugano criteria. The treatment regimen was generally well-tolerated. The patient experienced Grade 4 neutropenia following the initial cycle of standard CHOP regimen alone. With the introduction of the combination treatment from Cycle 2 onwards, mandatory supportive care, including G-CSF prophylaxis, was administered. During this period, the observed toxicities were manageable, with a maximum of Grade 3 neutropenia and Grade 1 gastrointestinal reactions. No new or unexpected severe adverse events were attributed to the addition of selinexor and azacitidine. Following complete remission, the patient underwent autologous hematopoietic stem cell transplantation (ASCT) as a consolidative treatment. This case further underscores the promising therapeutic efficacy of the selinexor and azacitidine combination with CHOP regimen in patients harboring these dual mutations. Fig. 6 Treatment response and PET-CT imaging in a TET2 / RHOA -mutated PTCL patient undergoing azacitidine and selinexor combination treatment. A Timeline of treatment regimen. PET-CT, positron emission tomography-computed tomography; CHOP, cyclophosphamide, doxorubicin, vincristine, prednisone; AS-CHOP, azacitidine and selinexor combined with CHOP; ASCT, autologous hematopoietic stem cell transplantation. B Immunohistochemical staining for p-STAT3 Tyr705 of the pretreatment tumor tissue. C Immunohistochemical staining for SOCS1 of the pretreatment tumor tissue. D Baseline PET-CT scan at diagnosis showing extensive metabolic activity. E Interim PET-CT after Cycle 3 showing marked reduction in metabolic activity. F End-of-treatment PET-CT demonstrating metabolic complete remission
Discussion
PTCL remains a challenging malignancy to treat, with poor outcomes persisting despite the availability of standard chemotherapy regimens such as CHOP. The profound molecular heterogeneity of PTCL underscores the critical need for therapies that target specific genetic drivers of the disease.
TET2 and RHOA are among the most mutated genes in PTCL and frequently co-occur, rendering them key molecular markers in this disease. Notably, previous studies have reported that these two mutations act synergistically to drive lymphomagenesis, as demonstrated in murine models where the combination of TET2 deletion and RHOA G17V mutation led to the development of PTCL-like tumors [ 8 , 26 ]. This makes the TET2 / RHOA double mutation model an optimal platform for drug screening.
Azacitidine has demonstrated efficacy as a hypomethylating agent, particularly in patients harboring TET2 mutations, however, its effectiveness remains limited, especially in patients with co-occurring RHOA mutations. In this study, we identified selinexor, an XPO1 inhibitor, as a promising agent that synergizes with azacitidine to enhance anti-tumor effects in PTCL cells harboring TET2 and RHOA double mutations. XPO1 mediates the nuclear export of various tumor suppressor proteins, oncogenic proteins, and transcription factors. Inhibition of XPO1 leads to the retention of these proteins in the nucleus, thereby augmenting tumor suppressor pathways and inhibiting oncogenic signaling [ 27 ]. Selinexor has been evaluated in combination with other agents, such as topoisomerase II inhibitors in multiple myeloma, [ 28 ] ibrutinib in chronic lymphocytic leukemia, [ 29 ] and cyclin-dependent kinase 4/6 inhibitors in liver cancer, [ 30 ] showing efficacy across various genetic backgrounds, including loss-of-function mutations in tumor suppressor genes and oncogenic mutations. For instance, selinexor is effective in targeting TP53 deletions in diffuse large B-cell lymphoma, [ 31 ] BTK mutations in chronic lymphocytic leukemia, [ 29 ] and BCR-ABL1 gene fusions in chronic myeloid leukemia [ 32 ]. Our systematic screen and subsequent validation demonstrated that the addition of selinexor profoundly enhances the anti-tumor effects of azacitidine. This synergy was consistently observed across a panel of T-cell malignancy cell lines, and was validated in two independent murine xenograft models, highlighting the robustness of this combination.
A key finding of our study is that the TET2 / RHOA double mutation appears to act as a predictive biomarker for sensitivity to this combination. While the drugs exhibited a modest synergistic effect in WT cells, the degree of synergy and apoptosis induction was significantly enhanced in cells harboring the mutations. This suggests that the oncogenic signaling programs driven by these mutations create a specific vulnerability that can be exploited by the dual inhibition of XPO1 and DNA methylation, providing a strong rationale for patient stratification in future clinical trials.
Mechanistically, the synergistic effects of selinexor and azacitidine can be attributed to the co-inhibition of the JAK3/STAT3 signaling pathway, a key regulator of survival and proliferation in PTCL cells. Our transcriptomic analysis revealed that the combination treatment downregulated the JAK/STAT signaling pathway, while upregulating genes involved in apoptosis. One key observation was the azacitidine-induced upregulation of SOCS1 , a negative regulator of the JAK/STAT signaling pathway. SOCS1 acts by inhibiting JAK-mediated phosphorylation of STAT3, thus disrupting downstream signaling. Selinexor complemented this effect by blocking the nuclear export of STAT3, resulting in its sequestration within the nucleus and further reducing the phosphorylation levels of JAK3 and STAT3. This dual mechanism effectively suppressed the pro-survival signals from the JAK3/STAT3 axis, providing a robust explanation for the observed synergy between selinexor and azacitidine.
The JAK/STAT cascade is constitutively activated in PTCL [ 33 , 34 ], as demonstrated by IHC and gene expression profiling [ 34 ], with frequent alterations in genes such as JAK3 , STAT3 , STAT5B , JAK1 , and JAK2 [ 22 ]. The most common mutations were found in STAT5B , STAT3 , and JAK3 [ 22 , 35 ]. This activation promotes the upregulation of genes essential for cell growth, survival, angiogenesis, and immune evasion. Moskowitz et al . conducted a phase 2 biomarker-driven trial ( NCT02974647 ) to evaluate the efficacy of ruxolitinib, a JAK1/2 inhibitor, in relapsed/refractory PTCL [ 36 ]. The study assessed the clinical benefit rate, defined as complete or partial responses or stable disease lasting at least 6 months. Ruxolitinib showed greater efficacy in patients with JAK / STAT mutations or active signaling, with a clinical benefit rate of 45–53%, compared to 13% in the cohort without JAK/STAT activation. This suggested that STAT3 overactivation can occur even in the absence of direct JAK / STAT mutations and that targeting this pathway is effective in PTCL. Here we found that in the genetic background of TET2 / RHOA mutations, the combination of selinexor and azacitidine exerts significant inhibitory effects specifically on JAK3, rather than JAK1/2. As ruxolitinib is less effective against JAK3, this combination treatment overcomes the limitation of JAK1/2 inhibitors by effectively targeting JAK3. Recently, Waarts et al . developed an advanced ex vivo CRISPR screening platform to identify vulnerabilities in clonal hematopoiesis, showing that JAK inhibitors can eliminate TET2 -mutated clones, suggesting a strategy to prevent malignancy [ 37 ]. This underscores a link between TET2 mutations and JAK pathway inhibition. Additionally, JAK / STAT mutations in nTFHL-AI patients were associated with worse progression-free survival [ 38 ]. Given the frequent occurrence of TET2 / RHOA mutations in nTFHL-AI, this further suggests a potential link between JAK/STAT signaling pathway dysregulation and these mutations.
Our preclinical findings are strongly supported by the clinical case presented, where a patient with TET2 / RHOA -mutated nTFHL-AI achieved a complete remission after the addition of selinexor and azacitidine to their chemotherapy regimen, with a manageable safety profile. Given that both drugs are clinically approved, this combination represents a readily translatable strategy.
We acknowledge several limitations in our study. Our in vitro models were established through the lentiviral-mediated overexpression of mutant proteins, which may not perfectly recapitulate the heterozygous mutational state found in patients. Future studies using CRISPR/Cas9-mediated knock-in models could provide further validation. Additionally, while our findings were consistent across multiple T-cell malignancy subtypes, our study lacked cell line models for nTFHL-AI and PTCL-NOS, primarily due to the difficulty in establishing long-term cultures from these histological subtypes. Nevertheless, the robust efficacy observed in PTCL cell lines and the high prevalence of TET2 / RHOA mutations in nTFHL-AI provide a strong impetus for evaluating this combination in broader clinical settings, including with patient-derived xenografts.
In conclusion, our study provides a compelling preclinical rationale for combining selinexor and azacitidine to treat TET2 / RHOA -mutated PTCL. By co-targeting the JAK3/STAT3 signaling pathway through distinct but complementary mechanisms, this combination offers a promising new therapeutic avenue for a patient population with a high unmet medical need.
Supplementary Information
Supplementary Material 1: Supplementary Figure 1. The synergy scores of azacytidine with 5 compound candidates. Supplementary Figure 2. Selinexor and azacitidine show synergistic inhibition of cell viability in T-cell lymphoma cell lines. Supplementary Figure 3. Selinexor and azacitidine synergy is enhanced in TET2 / RHOA -mutant cells. Supplementary Figure 4. Synergistic induction of apoptosis in PTCL cell lines. Supplementary Figure 5. Validation of combination-induced apoptosis across multiple cell lines. Supplementary Figure 6. RT-qPCR validation of RNA-seq data. Supplementary Figure 7. Validation of JAK/STAT pathway inhibition across multiple cell lines. Supplementary Figure 8. Toxicity assessment of combined selinexor and azacitidine treatment in CDX murine models. Supplementary Table 1. 633 anti-tumor compounds. Supplementary Table 2. Primer sequences. Supplementary Material 2.
Publisher’s Note
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Ting-Ting Xu and Ming-Ci Cai contributed equally to this work.
Authors’ contributions
T.-T.X. performed the experiments and analyzed the data. M.-C.C. S.C. and Y.-H.H. collected and analyzed clinical data, prepared biological samples. Q.-Q.Z. guided the drug screening experiment. P.-P.X. and L.W. helped with study design and bioinformatic analysis. Y.-R.Q. and W.-L.Z. conceived the study, supervised research and edited the manuscript.
Funding
This study was supported in part by research funding from the National Key R&D Program of China (2022YFC2502600), the National Natural Science Foundation of China (8230010085, 82130004, and 81900193), and Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR1032B).
Data availability
The raw RNA-sequencing (RNA-seq) data generated in this study have been deposited in the GSA-Human database under the accession number HRA013471. Data is available from the corresponding author, Wei-Li Zhao (zhao.weili@yahoo.com), upon reasonable request.
Declarations
Ethics approval and consent to participate
The study involving human samples and clinical data was approved by the Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University School of Medicine (Approval Number: 2022–317). Written informed consent was obtained from all participants in accordance with the principles of the Declaration of Helsinki. All animal experiments were conducted under protocols approved by the Animal Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Approval Number: RJ2025036).
Consent for publication
All authors have agreed to publish this manuscript.
Competing interests
The authors declare no competing interests.
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