{"title":"Consolidation Radiotherapy for primary CNS lymphoma: the lower, the better.","authors":"Khê Hoang-Xuan","doi":"10.1093/neuonc/noaf274","DOIUrl":"https://doi.org/10.1093/neuonc/noaf274","url":null,"abstract":"","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chunhui Yang, Hong Cai, Wenwen Liu, Jian Wang, Xin You, Yinuo Jin, Mengyi Tang, Dan Liu, Zeming Wu, Peng Gao, Qi Wang
Background: Lung Cancer Leptomeningeal Metastasis (LC-LM) severely impacts patient survival and quality of life, yet current diagnostic methods lack sufficient sensitivity and specificity, particularly for early detection. Cerebrospinal fluid (CSF) metabolomics may reveal specific biomarkers reflecting brain metastasis.
Methods: We performed untargeted metabolomic profiling of CSF samples by high-resolution mass spectrometry (HRMS) in a cohort of 218 participants, including 99 samples from LC-LM (with cancer cells detected in the CSF), 12 samples from the lung cancer parenchymal brain metastases (with no cancer cells detected in the CSF), 27 samples from the control group, 21 samples from the breast cancer LM, 15 samples from patients with LM from other tumors such as melanoma and gastric cancer, and 36 samples from other diseases. Significant metabolites were identified and validated. Subsequently, targeted metabolomics was conducted on serum samples from an independent cohort (n = 233), including 50 LC-LM patients, 150 patients with primary lung cancer (stages I-III), and 33 benign pulmonary nodules.
Results: Untargeted CSF metabolomics revealed a distinct metabolic signature in LC-LM patients. Differential analysis identified metabolites significantly altered in LC-LM, notably elevated lactic acid, N1, N12-diacetylspermine, and altered amino acid metabolites (e.g., L-proline, L-glutamic acid), each demonstrating strong diagnostic accuracy individually, with area under the receiver operating characteristic (ROC) curve (AUC) > 0.90. Machine learning classification models based on CSF metabolite panels achieved perfect diagnostic performance (AUC = 1.00) in distinguishing LC-LM from controls and other groups. Targeted validation of five top metabolites in serum samples confirmed their diagnostic utility, with N1, N12-diacetylspermine achieving an AUC of 0.882, superior to traditional protein biomarkers.
Conclusion: CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.
{"title":"Cerebrospinal Fluid Metabolomics and Machine Learning Identify Novel Biomarkers for Lung Cancer Leptomeningeal Metastasis.","authors":"Chunhui Yang, Hong Cai, Wenwen Liu, Jian Wang, Xin You, Yinuo Jin, Mengyi Tang, Dan Liu, Zeming Wu, Peng Gao, Qi Wang","doi":"10.1093/neuonc/noaf270","DOIUrl":"https://doi.org/10.1093/neuonc/noaf270","url":null,"abstract":"<p><strong>Background: </strong>Lung Cancer Leptomeningeal Metastasis (LC-LM) severely impacts patient survival and quality of life, yet current diagnostic methods lack sufficient sensitivity and specificity, particularly for early detection. Cerebrospinal fluid (CSF) metabolomics may reveal specific biomarkers reflecting brain metastasis.</p><p><strong>Methods: </strong>We performed untargeted metabolomic profiling of CSF samples by high-resolution mass spectrometry (HRMS) in a cohort of 218 participants, including 99 samples from LC-LM (with cancer cells detected in the CSF), 12 samples from the lung cancer parenchymal brain metastases (with no cancer cells detected in the CSF), 27 samples from the control group, 21 samples from the breast cancer LM, 15 samples from patients with LM from other tumors such as melanoma and gastric cancer, and 36 samples from other diseases. Significant metabolites were identified and validated. Subsequently, targeted metabolomics was conducted on serum samples from an independent cohort (n = 233), including 50 LC-LM patients, 150 patients with primary lung cancer (stages I-III), and 33 benign pulmonary nodules.</p><p><strong>Results: </strong>Untargeted CSF metabolomics revealed a distinct metabolic signature in LC-LM patients. Differential analysis identified metabolites significantly altered in LC-LM, notably elevated lactic acid, N1, N12-diacetylspermine, and altered amino acid metabolites (e.g., L-proline, L-glutamic acid), each demonstrating strong diagnostic accuracy individually, with area under the receiver operating characteristic (ROC) curve (AUC) > 0.90. Machine learning classification models based on CSF metabolite panels achieved perfect diagnostic performance (AUC = 1.00) in distinguishing LC-LM from controls and other groups. Targeted validation of five top metabolites in serum samples confirmed their diagnostic utility, with N1, N12-diacetylspermine achieving an AUC of 0.882, superior to traditional protein biomarkers.</p><p><strong>Conclusion: </strong>CSF-based metabolomic profiling combined with machine learning offers a highly accurate and minimally invasive diagnostic tool for LC-LM. Serum validation further supports its translational potential, emphasizing its significance in clinical practice for improving early detection and potentially enhancing patient management and outcomes.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenjing Zhou, Elena Martinez-Garcia, Katharina Sarnow, Georgia Kanli, Petr V Nazarov, Yaquan Li, Stephanie Schwab, Johannes Meiser, Christian Jaeger, Jakub Mieczkowski, Agnieszka Misztak, Frits A Thorsen, Konrad Grützmann, Boris Mihaljevic, Barbara van Loon, Jubayer A Hossain, Yan Zhang, Zhiyi Xue, Wenjie Li, Shannon S Moreino, Anna Golebiewska, Simone P Niclou, Magnar Bjørås, Saverio Tardito, Justin V Joesph, Taral R Lunavat, Halala S Saed, Marzieh Bahador, Mingzhi Han, Carina Fabian, Hrvoje Miletic, Xingang Li, Gunnar Dittmar, Olivier Keunen, Barbara Klink, Jian Wang, Rolf Bjerkvig
Background: Human brain organoids (BOs) are important models for studying early brain development and neurological disorders. While techniques for creating BOs are advancing, they remain developmental structures. Therefore, when human BOs are used to studying glioma-host interactions, the tumor behavior may be influenced by the BO-developmental microenvironment. Here, we describe the maturation of rat brain organoids (rBOs) into fully differentiated BOs and demonstrate their value as a model for studying glioblastoma (GB)-host interactions and their use in testing therapeutic interventions.
Materials and methods: rBOs were obtained from fetal cortical brains on the 18th day of gestation. Transcriptomic, proteomic, and metabolomic analyses determined their differentiation into maturity. Their developmental trajectory was compared to human BOs derived from induced pluripotent stem cells as well as to rat brain development. Tumor-rBO interactions, including invasion parameters and therapeutic interactions, were studied using five human GB models.
Results: The rBOs develop into organized structures with myelinated neurons, oligodendrocytes, synapses, and glial cells, mirroring the rat brain development. GB invasion in rBOs matched those observed in orthotopic xenografts, enabling real-time assessment of invasion metrics: cellular heterogeneity, single-cell invasion speed, and tumor progression. The BOs had a strong impact on GB transcriptional activity and can be used to study therapeutic interventions. The rBO differentiation status influenced GB invasion capacity.
Conclusions: The rBOs serve as an effective target brain structure for studying GB invasion parameters and for evaluating therapeutic interventions. Their rapid development into mature brain tissue makes rBOs a valuable brain avatar system for studying tumor-host interactions.
{"title":"Development of a highly differentiated rat brain organoid model for exploring glioblastoma invasion dynamics and therapy.","authors":"Wenjing Zhou, Elena Martinez-Garcia, Katharina Sarnow, Georgia Kanli, Petr V Nazarov, Yaquan Li, Stephanie Schwab, Johannes Meiser, Christian Jaeger, Jakub Mieczkowski, Agnieszka Misztak, Frits A Thorsen, Konrad Grützmann, Boris Mihaljevic, Barbara van Loon, Jubayer A Hossain, Yan Zhang, Zhiyi Xue, Wenjie Li, Shannon S Moreino, Anna Golebiewska, Simone P Niclou, Magnar Bjørås, Saverio Tardito, Justin V Joesph, Taral R Lunavat, Halala S Saed, Marzieh Bahador, Mingzhi Han, Carina Fabian, Hrvoje Miletic, Xingang Li, Gunnar Dittmar, Olivier Keunen, Barbara Klink, Jian Wang, Rolf Bjerkvig","doi":"10.1093/neuonc/noaf271","DOIUrl":"https://doi.org/10.1093/neuonc/noaf271","url":null,"abstract":"<p><strong>Background: </strong>Human brain organoids (BOs) are important models for studying early brain development and neurological disorders. While techniques for creating BOs are advancing, they remain developmental structures. Therefore, when human BOs are used to studying glioma-host interactions, the tumor behavior may be influenced by the BO-developmental microenvironment. Here, we describe the maturation of rat brain organoids (rBOs) into fully differentiated BOs and demonstrate their value as a model for studying glioblastoma (GB)-host interactions and their use in testing therapeutic interventions.</p><p><strong>Materials and methods: </strong>rBOs were obtained from fetal cortical brains on the 18th day of gestation. Transcriptomic, proteomic, and metabolomic analyses determined their differentiation into maturity. Their developmental trajectory was compared to human BOs derived from induced pluripotent stem cells as well as to rat brain development. Tumor-rBO interactions, including invasion parameters and therapeutic interactions, were studied using five human GB models.</p><p><strong>Results: </strong>The rBOs develop into organized structures with myelinated neurons, oligodendrocytes, synapses, and glial cells, mirroring the rat brain development. GB invasion in rBOs matched those observed in orthotopic xenografts, enabling real-time assessment of invasion metrics: cellular heterogeneity, single-cell invasion speed, and tumor progression. The BOs had a strong impact on GB transcriptional activity and can be used to study therapeutic interventions. The rBO differentiation status influenced GB invasion capacity.</p><p><strong>Conclusions: </strong>The rBOs serve as an effective target brain structure for studying GB invasion parameters and for evaluating therapeutic interventions. Their rapid development into mature brain tissue makes rBOs a valuable brain avatar system for studying tumor-host interactions.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander P Landry, Justin Z Wang, Vikas Patil, Andrew Ajisebutu, Chloe Gui, Leeor S Yefet, Yosef Ellenbogen, Jeff Liu, Yasin Mamatjan, Qingxia Wei, Olivia Singh, Sheila Mansouri, Felix Ehret, David Capper, Aaron A Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Jill S Barnholtz-Sloan, Andrew E Sloan, Lola B Chambless, Alireza Mansouri, Serge Makarenko, Stephen Yip, Derek S Tsang, Andrew Gao, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh
Background: DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed.
Methods: We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade.
Results: We first demonstrated that all risk groups benefit from gross total resection. All "high-risk, RT sensitive" tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (p = 0.003); after STR, from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (p = 0.004). "Low-risk, RT sensitive cases" (n = 1207, 61.8%) also benefitted from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, p = 0.03), suggesting that RT be considered in these patients. Neither "low-risk RT resistant" (n = 84, 4.3%) nor "high-risk RT resistant" (n = 356, 18.2%) cases benefitted from RT, and the latter group was associated with universally poor outcomes.
Conclusions: We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.
{"title":"A framework for using DNA methylation-based modelling for the clinical management of cranial meningioma.","authors":"Alexander P Landry, Justin Z Wang, Vikas Patil, Andrew Ajisebutu, Chloe Gui, Leeor S Yefet, Yosef Ellenbogen, Jeff Liu, Yasin Mamatjan, Qingxia Wei, Olivia Singh, Sheila Mansouri, Felix Ehret, David Capper, Aaron A Cohen-Gadol, Ghazaleh Tabatabai, Marcos Tatagiba, Felix Behling, Jill S Barnholtz-Sloan, Andrew E Sloan, Lola B Chambless, Alireza Mansouri, Serge Makarenko, Stephen Yip, Derek S Tsang, Andrew Gao, Kenneth Aldape, Farshad Nassiri, Gelareh Zadeh","doi":"10.1093/neuonc/noaf237","DOIUrl":"https://doi.org/10.1093/neuonc/noaf237","url":null,"abstract":"<p><strong>Background: </strong>DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed.</p><p><strong>Methods: </strong>We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade.</p><p><strong>Results: </strong>We first demonstrated that all risk groups benefit from gross total resection. All \"high-risk, RT sensitive\" tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (p = 0.003); after STR, from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (p = 0.004). \"Low-risk, RT sensitive cases\" (n = 1207, 61.8%) also benefitted from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, p = 0.03), suggesting that RT be considered in these patients. Neither \"low-risk RT resistant\" (n = 84, 4.3%) nor \"high-risk RT resistant\" (n = 356, 18.2%) cases benefitted from RT, and the latter group was associated with universally poor outcomes.</p><p><strong>Conclusions: </strong>We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145557504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel J Silver, Gunnar H D Poplawski, Justin D Lathia
{"title":"Opposing Functions of White Matter in Glioblastoma.","authors":"Daniel J Silver, Gunnar H D Poplawski, Justin D Lathia","doi":"10.1093/neuonc/noaf266","DOIUrl":"https://doi.org/10.1093/neuonc/noaf266","url":null,"abstract":"","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145513478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"\"TREAT\"ing Seizures as an Important Endpoint.","authors":"Mei-Yin Polley, David Schiff","doi":"10.1093/neuonc/noaf268","DOIUrl":"https://doi.org/10.1093/neuonc/noaf268","url":null,"abstract":"","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145513381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Zeyen, Inga Krause, Andreas Decker, Florian Kroh, Sebastian Regnery, Johannes Weller, Niklas Schaefer, Mousa Zidan, Anna-Laura Potthoff, Matthias Schneider, Lea L Friker, Jochen Keupp, Christoph Katemann, Julian P Layer, Christina Schaub, Eleni Gkika, Hartmut Vatter, Torsten Pietsch, Alexander Radbruch, Ulrich Herrlinger, Daniel Paech
Background: Differentiating progressive disease (PD) from treatment-related effects (TRE) in glioblastoma remains challenging, particularly at single time point evaluations. TRE can occur at any disease stage, and its underlying biology is poorly understood. This study evaluates the clinical feasibility and diagnostic performance of amide proton transfer-weighted (APTw) MRI in this challenge.
Methods: Following the integration of APTw MRI into the routine clinical workflow for brain tumor imaging, we screened a total of 870 scans from 626 patients. APTw signal (voxel-based measurement) was automatically quantified in gadolinium-enhanced T1w and FLAIR regions of interest using a deep learning-based approach for 3D tumor segmentations. PD and TRE were compared using unpaired t-tests, and diagnostic accuracy was assessed via ROC- and logistic regression analysis.
Results: Among 256 MRI scans of 143 patients with glioblastoma, 65 scans showed PD (n = 42) or TRE (n = 23). The median APTw signal was higher in PD (2.23%) vs TRE (1.76%; p = 0.001). ROC analysis showed an area under the curve (AUC) of 0.82. In patients with early PD or TRE (<6 months post-radiotherapy), the AUC increased to 0.93. Anti-angiogenic therapy decreased APTw signal (p < 0.01). Combining APTw MRI with DWI and PWI improved diagnostic accuracy (AUC = 0.90).
Conclusions: APTw MRI is a non-invasive imaging tool that is feasible for clinical routine and aids in differentiation of early progression from pseudoprogression in glioblastoma. Its diagnostic accuracy decreases with application of anti-angiogenic treatment and at later follow-up time points. Highest diagnostic accuracy was found in a multimodal approach combining APTw MRI, PWI and DWI.
{"title":"Amide proton transfer-weighted (APTw) CEST MRI in clinical routine for single time point diagnosis of pseudoprogression in IDH-wildtype glioblastoma.","authors":"Thomas Zeyen, Inga Krause, Andreas Decker, Florian Kroh, Sebastian Regnery, Johannes Weller, Niklas Schaefer, Mousa Zidan, Anna-Laura Potthoff, Matthias Schneider, Lea L Friker, Jochen Keupp, Christoph Katemann, Julian P Layer, Christina Schaub, Eleni Gkika, Hartmut Vatter, Torsten Pietsch, Alexander Radbruch, Ulrich Herrlinger, Daniel Paech","doi":"10.1093/neuonc/noaf261","DOIUrl":"https://doi.org/10.1093/neuonc/noaf261","url":null,"abstract":"<p><strong>Background: </strong>Differentiating progressive disease (PD) from treatment-related effects (TRE) in glioblastoma remains challenging, particularly at single time point evaluations. TRE can occur at any disease stage, and its underlying biology is poorly understood. This study evaluates the clinical feasibility and diagnostic performance of amide proton transfer-weighted (APTw) MRI in this challenge.</p><p><strong>Methods: </strong>Following the integration of APTw MRI into the routine clinical workflow for brain tumor imaging, we screened a total of 870 scans from 626 patients. APTw signal (voxel-based measurement) was automatically quantified in gadolinium-enhanced T1w and FLAIR regions of interest using a deep learning-based approach for 3D tumor segmentations. PD and TRE were compared using unpaired t-tests, and diagnostic accuracy was assessed via ROC- and logistic regression analysis.</p><p><strong>Results: </strong>Among 256 MRI scans of 143 patients with glioblastoma, 65 scans showed PD (n = 42) or TRE (n = 23). The median APTw signal was higher in PD (2.23%) vs TRE (1.76%; p = 0.001). ROC analysis showed an area under the curve (AUC) of 0.82. In patients with early PD or TRE (<6 months post-radiotherapy), the AUC increased to 0.93. Anti-angiogenic therapy decreased APTw signal (p < 0.01). Combining APTw MRI with DWI and PWI improved diagnostic accuracy (AUC = 0.90).</p><p><strong>Conclusions: </strong>APTw MRI is a non-invasive imaging tool that is feasible for clinical routine and aids in differentiation of early progression from pseudoprogression in glioblastoma. Its diagnostic accuracy decreases with application of anti-angiogenic treatment and at later follow-up time points. Highest diagnostic accuracy was found in a multimodal approach combining APTw MRI, PWI and DWI.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145513488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mounting evidence implicates medroxyprogesterone acetate in meningioma risk, but mechanisms require further investigation.","authors":"Brooke C Braman, David R Raleigh","doi":"10.1093/neuonc/noaf267","DOIUrl":"https://doi.org/10.1093/neuonc/noaf267","url":null,"abstract":"","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145505916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deanna Tiek, Xiao Song, Runxin Wu, Xiaozhou Yu, Maya Walker, Yingyu Mao, Derek Sisbarro, Qiu He, Assa Magassa, Amandeep Singh, Junxuan Lu, Arun K Sharma, Jason Miska, Bo Hu, Marcelo G Bonini, Xiaoyu Zhang, Shi-Yuan Cheng
Background: Cysteine is a multifunctional amino acid that can be oxidized affecting disulfide bond formation, redox signaling, and protein function. Reactive oxygen species (ROS) and the metabolic environment dictate cysteine uptake and oxidation status - especially in redox sensitive pathways. As many chemotherapeutic agents increase ROS, including the standard for glioblastoma (GBM), temozolomide (TMZ), we hypothesized that TMZ-resistant (TMZ-R) GBM would have increased ROS affecting cysteine reactivity that could be therapeutically targeted.
Methods: Here, to study the metabolic state within drug sensitive and resistant GBM, we used metabolite tracing with 13C-Cyst(e)ine, specialized cysteine reactivity proteomics and CRISPR screening with drug treatments to determine the efficacy of targeting cysteine metabolic pathways with our designer selenium drug in both patient derived cell lines and patient derived xenograft GBM orthotopic models.
Results: We show that TMZ-R have increased cyst(e)ine uptake, cysteine reactivity, and sensitivity to selenium (Se)-containing compounds - which can bind cysteine - in vitro and in vivo. We show that in TMZ-R models selenium compound treatment increases the need for thioredoxin reductases where co-treatment of Se compounds and the thioredoxin inhibitor auranofin significantly improves overall survival in mouse models.
Conclusions: Overall, our findings show a unique metabolic environment in TMZ-R models where designer brain penetrant Se-containing compounds target cysteine reactivity within proteins necessary for cancer cell survival and hold therapeutic potential.
{"title":"Cysteine addiction in drug resistant glioblastoma and therapeutic targeting with designer selenium compounds.","authors":"Deanna Tiek, Xiao Song, Runxin Wu, Xiaozhou Yu, Maya Walker, Yingyu Mao, Derek Sisbarro, Qiu He, Assa Magassa, Amandeep Singh, Junxuan Lu, Arun K Sharma, Jason Miska, Bo Hu, Marcelo G Bonini, Xiaoyu Zhang, Shi-Yuan Cheng","doi":"10.1093/neuonc/noaf265","DOIUrl":"https://doi.org/10.1093/neuonc/noaf265","url":null,"abstract":"<p><strong>Background: </strong>Cysteine is a multifunctional amino acid that can be oxidized affecting disulfide bond formation, redox signaling, and protein function. Reactive oxygen species (ROS) and the metabolic environment dictate cysteine uptake and oxidation status - especially in redox sensitive pathways. As many chemotherapeutic agents increase ROS, including the standard for glioblastoma (GBM), temozolomide (TMZ), we hypothesized that TMZ-resistant (TMZ-R) GBM would have increased ROS affecting cysteine reactivity that could be therapeutically targeted.</p><p><strong>Methods: </strong>Here, to study the metabolic state within drug sensitive and resistant GBM, we used metabolite tracing with 13C-Cyst(e)ine, specialized cysteine reactivity proteomics and CRISPR screening with drug treatments to determine the efficacy of targeting cysteine metabolic pathways with our designer selenium drug in both patient derived cell lines and patient derived xenograft GBM orthotopic models.</p><p><strong>Results: </strong>We show that TMZ-R have increased cyst(e)ine uptake, cysteine reactivity, and sensitivity to selenium (Se)-containing compounds - which can bind cysteine - in vitro and in vivo. We show that in TMZ-R models selenium compound treatment increases the need for thioredoxin reductases where co-treatment of Se compounds and the thioredoxin inhibitor auranofin significantly improves overall survival in mouse models.</p><p><strong>Conclusions: </strong>Overall, our findings show a unique metabolic environment in TMZ-R models where designer brain penetrant Se-containing compounds target cysteine reactivity within proteins necessary for cancer cell survival and hold therapeutic potential.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":" ","pages":""},"PeriodicalIF":13.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145482633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}