Citations

Background Glioblastoma (GBM) remains the most aggressive primary adult brain cancer attributed to its immunosuppressive nature. Radiation therapy (RT) and concurrent temozolomide chemotherapy are the standard treatments for GBM. Emerging evidence indicates that metformin has potential as an anti-tumor agent that can reshape the immune landscape across various malignancies. We thus postulate that metformin may enhance the anti-tumor effect of RT by modulating the immunosuppressive milieu in GBM. Methods We first explore multiple in vitro conditions, with or without pre-induction, and mimicked a tumor microenvironment to demonstrate a highly context-dependent effect of metformin on the polarization of bone marrow-derived macrophages (BMDMs). We then investigated the antitumor activity and immune-modulatory effects of metformin in combination with RT in GBM-bearing mice. Results The in vitro experiment showed that metformin inhibited the immunosuppressive effects of IL-4/IL-13 but also the immunostimulatory effects of Lipopolysaccharide (LPS) on BMDMs, demonstrating bidirectional immunomodulatory properties that depend on the baseline inflammatory stimulus. In tumor cell co-culture environment, metformin exhibited context-dependent immunoregulatory effects, predominantly promoting M1-associated activation. Notably, concurrent metformin treatment counteracted M2 polarization typically induced by the tumor microenvironment. In a high m-MCSF inducted M2-dominated condition, metformin preferentially shifted the highly polarized M2 phenotype toward an M1-like state in a time and dose-dependent manner but with limited promotion of M1 subtype maturation. RNAseq results on BMDM treated with metformin showed increased homeostasis. Metformin showed dose-dependent immunomodulatory effects when combined with RT in vitro . In syngeneic GBM mouse models, concurrent metformin + RT significantly prolonged survival and reduced tumor burden by reprogramming tumor-associated macrophages (TAMs), elevating intratumoral CD8+ T-cell infiltration and the CD8+/Treg ratio, increasing circulating CD8+ T cells, reversing RT-induced expansion of monocytic myeloid-derived suppressor cells (mMDSCs), and expanding CD4+ and CD8+ effector memory populations in peripheral blood. Conclusion Metformin demonstrates inhibitory effects on M2 phenotype of macrophages. However, it is not a simple M2 inhibitor; instead, it functions primarily as a neutralizer of highly polarized macrophage states by promoting transcriptomic homeostasis. This context-dependent activity enables metformin to relieve the profoundly immunosuppressive TME of GBM to potentiate the anti-tumor effects of RT and by enhancing systemic immune memory.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.

Abstract Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor in adults, with median survival remaining approximately 12–15 months despite aggressive multimodal therapy. Therapeutic resistance and tumor recurrence are driven in part by limited drug penetration across the blood–brain barrier (BBB) and the persistence of brain cancer stem cells (BCSCs), highlighting the need for brain-penetrant therapeutic platforms capable of achieving sustained intratumoral delivery. Here, we developed a dendrimer-based nanotherapeutic by conjugating metformin to a fourth-generation hydroxyl-terminated polyamidoamine dendrimer (P4-MET) to enhance intracranial bioavailability and therapeutic efficacy in GBM. P4-MET exhibited favorable pharmacokinetic properties, including prolonged retention within the tumor microenvironment, and demonstrated enhanced cytotoxicity against GBM cell lines relative to free metformin (f-MET). Mechanistical studies with transcriptomic profiling by RNA sequencing revealed distinct treatment-associated molecular signatures, identifying BOLA2B as the most significantly differentially expressed gene between treatment groups. Specifically, BOLA2B expression was markedly elevated in f-MET–treated cells but not so following P4-MET treatment. Given the established association of BOLA2B with mTORC1 signaling and GPX4-mediated ferroptosis resistance, these findings suggest that P4-MET may, at least in part, enhance therapeutic efficacy by modulating ferroptosis-associated pathways. In orthotopic GBM models, combination treatment with P4-MET and radiotherapy (RT) significantly prolonged overall survival and increased tumor cell death compared with either monotherapy alone, consistent with a synergistic radiosensitizing effect. Importantly, P4-MET demonstrated minimal systemic toxicity, supporting its favorable therapeutic index and translational potential. Collectively, these findings establish P4-MET as a brain-penetrant nanomedicine platform that improves metformin delivery, modulates ferroptosis-related signaling networks, and potentiates radiotherapeutic response in GBM. This study highlights the potential of dendrimer-enabled metabolic nanotherapies to overcome therapeutic resistance in malignant brain tumors.

Cellular and molecular heterogeneity in the liver has been increasingly recognized to drive liver fibrosis progression, but the particular events that occur initially in response to liver injury and trigger immune cell recruitment remain unclear. Here, we identify epigenetically aberrant liver sinusoidal endothelial cells (LSECs) as key players in this process. Mechanistically, the epigenetic readers like bromodomain-containing protein 4–dependent (BRD4-dependent) super enhancers (SEs) activate proinflammatory genes, including promyelocytic leukemia (PML). The PML protein, in turn, binds BRD4 and amplifies proinflammatory angiocrine signaling through phase separation–dependent SE activation via PML/BRD4 condensate formation. In mouse models, LSEC-specific depletion of the PML/BRD4 complex mitigates liver inflammation and fibrosis. Single-cell RNA-seq reveals that epigenetically aberrant LSECs exhibit a reprogrammed proinflammatory angiocrine landscape in mouse fibrotic livers. TIMP1 + LSECs promote the recruitment of CD63 + monocyte–derived macrophages (MoMFs) during liver fibrosis progression. Thereby, PML/BRD4 in LSECs governs inflammatory immune cell recruitment in liver fibrosis. Pharmacological BRD4 inhibition or epigenetic PML-SE repression alleviates liver inflammation and fibrosis. In conclusion, PML/BRD4-mediated SE activation via phase separation drives proinflammatory angiocrine signaling in LSECs, initiating the inflammatory cascade and subsequent immune cell recruitment during liver fibrosis.

Abstract The incidence of venous thromboembolism (VTE) in patients with acute lymphoblastic leukemia (ALL) receiving asparaginase-based induction is high despite primary thromboprophylaxis. Our aim was to derive and externally validate a VTE risk prediction model in patients with ALL receiving asparaginase-based induction. We conducted a multicenter retrospective cohort study of patients (aged ≥18 years) with newly diagnosed ALL receiving asparaginase-based induction. The derivation and external validation cohorts included 306 and 94 patients, respectively. Primary outcome was VTE at any site. A cause-specific Cox proportional hazards model stratified by thromboprophylaxis and center was performed to identify VTE risk factors in the derivation cohort. A risk prediction model for VTE at 30 days was derived using variables with P value < .05 in the multivariable model and was tested in the validation cohort. VTE risk factors on multivariable analysis in the derivation cohort included D-dimer ≥1 μg fibrinogen equivalent unit per mL (hazard ratio [HR], 2.64; 95% confidence interval [CI], 1.07-6.5) and hemoglobin (HR for each 1 g/dL increment, 1.19; 95% CI, 1.06-1.34) at ALL diagnosis. A VTE risk score based on these variables distinguished between a 4% (95% CI, 0.72-12) and 20% (95% CI, 14-27) 30-day cumulative incidence of VTE in the derivation cohort, with similar findings in the validation cohort (area under the curve, 0.56). The negative predictive value for VTE at 30 days was 96% and 93% in the derivation and validation cohorts, respectively, and the positive predictive value was 20% in both. We derived and validated a model using D-dimer and hemoglobin, which stratifies VTE risk in patients with ALL receiving asparaginase-based induction.

Background: Patients with acute lymphoblastic leukemia (ALL) receiving L-asparaginase (ASP) based induction therapy have a high risk of venous thromboembolism (VTE). Studies investigating universal thromboprophylaxis strategies have yielded sub-optimal results. Risk prediction models are needed to identify high-risk patients who would be candidates for intensification of thromboprophylaxis, and lower risk patients who are unlikely to benefit. Aims: Derive and externally validate a risk prediction model for VTE in patients with ALL receiving ASP therapy. Methods: We conducted a multicenter, international, cohort study of patients with newly-diagnosed ALL receiving ASP-based induction therapy (≥18yrs). The derivation and external validation cohorts included 306 and 97 patients without VTE prior to ASP therapy, respectively. Patients on therapeutic anticoagulation at index were excluded. Patients were followed from date of first ASP dose for 100 days post ALL diagnosis or until VTE, therapeutic anticoagulation or death. Candidate predictors were documented at ALL diagnosis and included demographics, disease and treatment characteristics, VTE history, comorbidities, thromboprophylaxis, complete blood count, fibrinogen and d-dimer. D-dimer was only analyzed within the 7 derivation cohort centers who routinely tested this at ALL diagnosis with <20% missing values. Primary outcome was VTE at any site (centrally confirmed). Secondary outcomes included major bleeding by ISTH criteria. Cause-specific Cox proportional hazard regression was performed to identify VTE risk factors in the derivation cohort while treating death as a competing event. Variables with p-value <0.1 on univariable analysis were included in a multivariable model and subsequently used in the risk model if p-value <0.05. Lastly, the derived VTE risk prediction model was tested in the external validation cohort for association with time to VTE using cause-specific Cox proportional hazard model. All analyses were stratified by center and use of low molecular weight heparin prophylaxis (at any dose on the date of first ASP dose). Results: The cumulative incidence of VTE in the derivation cohort was 17% at 30 days (95% confidence interval [CI] 13% - 21%) and 22% at 90 days (95% CI 17% - 27%). The multivariable model included hyperlipidemia (hazard ratio [HR] 1.35; 95% CI 0.56 - 3.27), body mass index ≥ 25 kg/m² (HR 1.49; 95% CI 0.75 - 2.95), platelet count ≥ 25 x 10⁹/L (HR 1.42; 95% CI 0.67 - 3.00), increasing hemoglobin per 1 g/dL (HR 1.17; 95% CI 1.03 - 1.32) and d-dimer ≥ 1 µg FEU/mL (HR 2.68; 95% CI 1.08 - 6.66). Hemoglobin as a continuous variable and d-dimer ≥ 1 µg FEU/mL were included in the final model and used to create a risk score which was weighted by estimated log-HR sum of these risk factors. The lowest risk quartile (risk score below 2.1) successfully differentiated between a lower 30-day cumulative VTE incidence (4%, 95% CI 0.72% - 12%) compared with the other quartiles (20%, 95% CI 14% - 27%; HR 4.40, 95% CI 1.54 - 12.5) which were defined as high risk (score ≥2.1). The risk score's negative predictive value (NPV) for VTE at 30 days was 96% and positive predictive value (PPV) was 20%. The validation cohort (n=97 from 3 external cohorts with routine d-dimer measurement) included 16 (16.5%) patients in the low-risk group and 81 (83.5%) in the high-risk group, with a 30-day VTE incidence of 6.3% (95% CI 0% - 17%) and 21% (95% CI 12% - 30%), respectively (HR 6.22; 95% CI 0.85 - 45.8; p = 0.073)). The NPV (93.7%) and PPV (21%) were comparable to the derivation cohort. The 30-day incidence of major bleeding was 0% (95% CI NR - NR) in low-risk patients (HR 1.36; 95% CI 0.27 - 6.77) and 2.0% (95% CI 0.55% - 5.4%) in the high-risk group. Conclusion: Patients with ALL receiving ASP induction therapy have a 17% VTE risk at 30 days. We derived and externally validated a VTE risk prediction model based upon d-dimer and hemoglobin. This model successfully identifies at least 16.5% of patients with ALL who have a low VTE risk after ASP treatment. Using this model, novel prevention strategies which may be associated with cost and risk could now be tailored to patients with higher risk. Prospective studies are needed to validate the model's performance and role in VTE risk management.