Neutrophil extracellular traps (NETs) are important pieces of equipment for neutrophils. Excess NETs play promoting roles in cancer-associated thrombosis (CAT). Therefore, directing NETs formation is a promising therapeutic strategy in thrombosis and related diseases. Diosmetin, an antioxidant flavonoid derived from dietary sources, might be involved in NETs formation and CAT.
Firstly, the tests of cell-free DNA and Immunofluorescence were applied to evaluate the NETs levels of neutrophils. Luminol-based chemiluminescence and the DCFH-DA probe were used to detect the levels of reactive oxygen species (ROS) in neutrophils. Then, network pharmacological analysis and molecular docking were used to predict potential target molecules of diosmetin. The RT-qPCR was performed to measure the levels of Nrf2 and HO-1. A series of functional assays of neutrophils were used to examine the effect of diosmetin on other neutrophil functions. Finally, an animal model of deep vein thrombosis was constructed to assess the effect of diosmetin on thrombosis.
Diosmetin reduced NETs and ROS levels in neutrophils. Then, molecular mechanisms analysis suggested that Nrf2 might be the primary target of diosmetin. Diosmetin treatment increased the levels of Nrf2 and HO-1 in NETs-generating neutrophils. An inhibitor of Nrf2 diminished the negative effect of diosmetin on NETs generation. Lastly, the murine thrombosis model results indicated that diosmetin treatment reduced thrombosis via NETs formation.
Diosmetin exerts as anti-NETs effect through Nrf2 signaling in neutrophils, showing the therapeutic potential in thromboembolism and related pathological processes, such as CAT.
© 2025 The Author(s). Thoracic Cancer published by John Wiley & Sons Australia, Ltd.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.
Copyright: © 2024 Benzekry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Androgen deprivation therapy (ADT) is pivotal in treating recurrent prostate cancer and is often combined with external beam radiation therapy (EBRT) for localized disease. However, for metastatic castration-resistant prostate cancer, EBRT is typically only used in the palliative setting, because of the inability to radiate all sites of disease. Systemic radiation treatments that preferentially irradiate cancer cells, known as radiopharmaceutical therapy or targeted radionuclide therapy (TRT), have demonstrable benefits for treating metastatic prostate cancer. Here, we explored the use of a novel TRT, 90Y-NM600, specifically in combination with ADT, in murine prostate tumor models.
6-week-old male FVB mice were implanted subcutaneously with Myc-CaP tumor cells and given a single intravenous injection of 90Y-NM600, in combination with ADT (degarelix). The combination and sequence of administration were evaluated for effect on tumor growth and infiltrating immune populations were analyzed by flow cytometry. Sera were assessed to determine treatment effects on cytokine profiles.
ADT delivered prior to TRT (ADT→TRT) resulted in significantly greater antitumor response and overall survival than if delivered after TRT (TRT→ADT). Studies conducted in immunodeficient NRG mice failed to show a difference in treatment sequence, suggesting an immunological mechanism. Myeloid-derived suppressor cells (MDSCs) significantly accumulated in tumors following TRT→ADT treatment and retained immune suppressive function. However, CD4+ and CD8+ T cells with an activated and memory phenotype were more prevalent in the ADT→TRT group. Depletion of Gr1+MDSCs led to greater antitumor response following either treatment sequence. Chemotaxis assays suggested that tumor cells secreted chemokines that recruited MDSCs, notably CXCL1 and CXCL2. The use of a selective CXCR2 antagonist, reparixin, further improved antitumor responses and overall survival when used in tumor-bearing mice treated with TRT→ADT.
The combination of ADT and TRT improved antitumor responses in murine models of prostate cancer, however, this was dependent on the order of administration. This was found to be associated with one treatment sequence leading to an increase in infiltrating MDSCs. Combining treatment with a CXCR2 antagonist improved the antitumor effect of this combination, suggesting a possible approach for treating advanced human prostate cancer.
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors prior to surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet such trials are rarely preceded by preclinical testing involving surgery. Here we used a mouse model of spontaneous metastasis after surgical removal to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Longitudinal data consisted of measurements of presurgical primary tumor size and postsurgical metastatic burden in 128 mice (104 for model training, 24 for validation), following variable neoadjuvant treatment schedules over a 14-day period. A nonlinear mixed-effects modeling approach was used to quantify inter-animal variability. Machine learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our simulations showed that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery. Surprisingly, machine-learning algorithms demonstrated only limited predictive power of tested biomarkers on the mathematical parameters. These results suggest that presurgical modeling might be an effective tool to screen biomarkers prior to clinical trial testing. Mathematical modeling combined with artificial intelligence techniques represent a novel platform for integrating preclinical surgical metastasis models in outcome prediction of neoadjuvant treatment. Major findings Using simulations from a mechanistic mathematical model compared with preclinical data from surgical metastasis models, we found anti-tumor effects of neoadjuvant RTKI treatment can differ between the primary tumor and metastases in the perioperative setting. Model simulations with variable drug doses and scheduling of neoadjuvant treatment revealed a contrasting impact on initial primary tumor debulking and metastatic outcomes long after treatment has stopped and tumor surgically removed. Using machine-learning algorithms, we identified the limited power of several circulating cellular and molecular biomarkers in predicting metastatic outcome, uncovering a potential fast-track strategy for assessing future clinical biomarkers by paring patient studies with identical studies in mice.

Systemic radiation treatments that preferentially irradiate cancer cells over normal tissue, known as targeted radionuclide therapy (TRT), have shown significant potential for treating metastatic prostate cancer. Preclinical studies have demonstrated the ability of external beam radiation therapy (EBRT) to sensitize tumors to T cell checkpoint blockade. Combining TRT approaches with immunotherapy may be more feasible than combining with EBRT to treat widely metastatic disease, however the effects of TRT on the prostate tumor microenvironment alone and in combinfation with checkpoint blockade have not yet been studied.
C57BL/6 mice-bearing TRAMP-C1 tumors and FVB/NJ mice-bearing Myc-CaP tumors were treated with a single intravenous administration of either low-dose or high-dose 90Y-NM600 TRT, and with or without anti-PD-1 therapy. Groups of mice were followed for tumor growth while others were used for tissue collection and immunophenotyping of the tumors via flow cytometry.
90Y-NM600 TRT was safe at doses that elicited a moderate antitumor response. TRT had multiple effects on the tumor microenvironment including increasing CD8 +T cell infiltration, increasing checkpoint molecule expression on CD8 +T cells, and increasing PD-L1 expression on myeloid cells. However, PD-1 blockade with TRT treatment did not improve antitumor efficacy. Tregs remained functional up to 1 week following TRT, but CD8 +T cells were not, and the suppressive function of Tregs increased when anti-PD-1 was present in in vitro studies. The combination of anti-PD-1 and TRT was only effective in vivo when Tregs were depleted.
Our data suggest that the combination of 90Y-NM600 TRT and PD-1 blockade therapy is ineffective in these prostate cancer models due to the activating effect of anti-PD-1 on Tregs. This finding underscores the importance of thorough understanding of the effects of TRT and immunotherapy combinations on the tumor immune microenvironment prior to clinical investigation.
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.