Neuro-oncology advances最新文献

{"title":"Watch-and-wait approach versus adjuvant treatment after radical awake resection in selected adult-type grade 3 gliomas, <i>isocitrate dehydrogenase</i> mutant: A case-matched cohort.","authors":"Angela Elia, Alexandre Roux, Bénédicte Trancart, Alessandro Moiraghi, Maimiti Seneca, Edouard Dezamis, Pascale Varlet, Fabrice Chretien, Catherine Oppenheim, Marc Zanello, Johan Pallud","doi":"10.1093/noajnl/vdae189","DOIUrl":"https://doi.org/10.1093/noajnl/vdae189","url":null,"abstract":"<p><strong>Background: </strong>Following large resection, proposing a watch-and-wait strategy in selected grade 3 glioma, <i>isocitrate dehydrogenase</i> (<i>IDH)</i>-mutant patients is an emerging practice. We compared the watch-and-wait approach to the standard postoperative adjuvant oncological treatment for grade 3 gliomas, <i>IDH</i>-mutant.</p><p><strong>Methods: </strong>Observational, retrospective, single-institution cohort (2011-2023) of 106 consecutive adult patients harboring supratentorial grade 3 gliomas, <i>IDH</i>-mutant treated by maximal awake resection and who received a watch-and-wait approach (surgery group) or an adjuvant oncological treatment (oncological group) postoperatively. Case-matched analysis (1:1) criteria between the surgery group and oncological group: extent of resection, tumor volume, Karnofsky Performance Status (KPS) score, tumor location and size, and age.</p><p><strong>Results: </strong>Patients of the surgery group (<i>n</i> = 26) had significantly better KPS scores, less preoperative neurological and/or neurocognitive deficits, less hyperperfusion, less corpus callosum infiltration, smaller tumor volume, higher rate of total resection, and smaller residual tumor than patients of the oncological group (<i>n</i> = 80). The 5-year progression-free survival (66.2 vs. 77.9 months, <i>P</i> = .713) and the 5-year overall survival (88.9 vs. 83.9 months, <i>P</i> = .291) did not differ between surgery and oncological groups. In the whole series, a preoperative KPS score >70, a total resection, and the oligodendroglioma subtype were independent predictors of longer progression-free survival and overall survival. After case matching, no difference in survival was observed between watch-and-wait and oncological treatment both in astrocytomas (<i>n</i> = 14 per group) and oligodendrogliomas (<i>n</i> = 12 per group).</p><p><strong>Conclusions: </strong>Watch-and-wait following radical resection appears to be feasible in highly selected grade 3 gliomas, <i>IDH</i>-mutant patients without impairing survival both in astrocytoma and in oligodendroglioma subgroups.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae189"},"PeriodicalIF":3.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning prediction of brain metastasis invasion pattern on brain magnetic resonance imaging scans.","authors":"Keyhan Najafian, Benjamin Rehany, Alexander Nowakowski, Saba Ghazimoghadam, Kevin Pierre, Rita Zakarian, Tariq Al-Saadi, Caroline Reinhold, Abbas Babajani-Feremi, Joshua K Wong, Marie-Christine Guiot, Marie-Constance Lacasse, Stephanie Lam, Peter M Siegel, Kevin Petrecca, Matthew Dankner, Reza Forghani","doi":"10.1093/noajnl/vdae200","DOIUrl":"10.1093/noajnl/vdae200","url":null,"abstract":"<p><strong>Background: </strong>Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination.</p><p><strong>Methods: </strong>From an initial cohort of 329 patients, a subset of 132 patients met the inclusion criteria for this retrospective study. We evaluated the ability of an expert neuroradiologist to reliably predict BMIP. Thereafter, the dataset was randomly divided into training/validation (80% of cases) and test subsets (20% of cases). The ground truth for BMIP was the histopathologic evaluation of resected specimens. Following MRI sequence co-registration, advanced feature extraction techniques deriving hand-crafted radiomic features with traditional ML classifiers and convolution-based deep learning (CDL) models were trained and evaluated. Different ML approaches were used individually or using ensembling techniques to determine the model with the best performance for BMIP prediction.</p><p><strong>Results: </strong>Expert evaluation of brain MRI scans could not reliably predict BMIP, with an accuracy of 44%-59% depending on the semantic feature used. Among the different ML and CDL models evaluated, the best-performing model achieved an accuracy of 85% and an F1 score of 90%.</p><p><strong>Conclusions: </strong>ML approaches can effectively predict BMIP, representing a noninvasive MRI-based approach to guide the management of patients with brain metastases.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae200"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison.","authors":"Santiago Cepeda, Roberto Romero, Lidia Luque, Daniel García-Pérez, Guillermo Blasco, Luigi Tommaso Luppino, Samuel Kuttner, Olga Esteban-Sinovas, Ignacio Arrese, Ole Solheim, Live Eikenes, Anna Karlberg, Ángel Pérez-Núñez, Olivier Zanier, Carlo Serra, Victor E Staartjes, Andrea Bianconi, Luca Francesco Rossi, Diego Garbossa, Trinidad Escudero, Roberto Hornero, Rosario Sarabia","doi":"10.1093/noajnl/vdae199","DOIUrl":"10.1093/noajnl/vdae199","url":null,"abstract":"<p><strong>Background: </strong>The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms.</p><p><strong>Methods: </strong>To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used.</p><p><strong>Results: </strong>The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast ET, 0.77 for edema, and 0.81 for surgical cavities. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort.</p><p><strong>Conclusions: </strong>Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae199"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of a brain tumor consortium of Africa: Advancing collaborative research and advocacy for brain tumors in Africa.","authors":"Lateef A Odukoya, Kwadwo Darko, Francis Zerd, Nathalie C Ghomsi, Gloria Kabare, David O Kamson, Jeanette E Eckel-Passow, Robert B Jenkins, Gaspar J Kitange, Andrea O Akinjo, Kabir B Badmos, Olufemi Bankole, Olufemi E Idowu, Claire Karekezi, Elias Edrick, Chukwuyem Ekhator, Victoria M Katasi, Desmond A Brown, Jason Huse, Henry Llewellyn, Margreth Magambo, Michael Magoha, Umaru Barrie, Advera Ngaiza, Arsene D Nyalundja, Minda Okemwa, Lawrence Osei-Tutu, Bernard Petershie, W Elorm Yevudza, Charles C Anunobi, Liadi Tiamiyu, Gbetoho Fortuné Gankpe, Kashaigili Heronima, Dominique Higgins, Kristin Schroeder, Teddy Totimeh, James Balogun, Beverly Cheserem, Arnold B Etame, Ekokobe Fonkem","doi":"10.1093/noajnl/vdae198","DOIUrl":"10.1093/noajnl/vdae198","url":null,"abstract":"<p><strong>Background: </strong>Brain tumors represent a significant global health challenge, with rising incidence and mortality impacting individuals worldwide and contributing to cancer-related morbidity and mortality. In Africa, this burden is exacerbated by limited access to advanced diagnostics, treatment options, and multidisciplinary care, compounded by the absence of standardized cancer registration and tumor biobanking. The introduction of molecular diagnostics, as outlined in the 2021 World Health Organization central nervous system (CNS) tumor classification, adds complexity to brain tumor management, particularly in regions with scarce resources.</p><p><strong>Methods: </strong>To address these issues, the Brain Tumor Consortium for Africa (BTCA) was established in 2023, bringing together experts to improve CNS tumor diagnosis, patient care, and research. The initial project, conducted via an electronic questionnaire, aimed to assess neuro-oncology capacity across Sub-Saharan Africa.</p><p><strong>Results: </strong>The study revealed significant gaps, with a limited number of institutions incorporating molecular subtyping into their diagnostic algorithms. The consortium's efforts focus on enhancing local data use, informing public policy, and promoting collaboration to advance neuro-oncology practices in Africa. By fostering a network enlisting the expertise of collaborators in the fields of neurosurgery, neurology, neuropathology, anatomic pathology, and medical and radiation oncology, the BTCA seeks to improve brain tumor management through better diagnostics, infrastructure, and policy advocacy. Future directions include expanding molecular diagnostic capabilities, standardizing brain tumor biobanking, enhancing data collection, and advocating for improved brain tumor care in national health agendas.</p><p><strong>Conclusions: </strong>The BTCA represents a pioneering model of collaboration and innovation in addressing the unique challenges of brain tumor care in Africa.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae198"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postoperative NEOadjuvant TEMozolomide followed by chemoradiotherapy versus upfront chemoradiotherapy for glioblastoma multiforme (NEOTEM) trial: Interim results.","authors":"Azadeh Sharifian, Ali Kazemian, Mostafa Farzin, Nikan Amirkhani, Borna Farazmand, Soheil Naderi, Alireza Khalilian, Ahmad Pourrashidi, Ghazaleh Amjad, Kasra Kolahdouzan, Romina Abyaneh, Paola Anna Jablonska, Reza Ghalehtaki","doi":"10.1093/noajnl/vdae195","DOIUrl":"10.1093/noajnl/vdae195","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor survival rates despite current treatments. The standard of care (SOC) includes surgery, followed by radiotherapy plus concurrent and adjuvant chemotherapy with temozolomide (TMZ). This phase II trial assessed the safety and efficacy of neoadjuvant TMZ (nTMZ) before and during chemoradiotherapy in newly diagnosed GBM patients.</p><p><strong>Methods: </strong>Newly diagnosed GBM patients who underwent maximal safe resection were randomized into 2 groups. One received nTMZ before standard chemoradiotherapy and adjuvant TMZ (intervention). The other received standard chemoradiotherapy followed by adjuvant TMZ (control). Primary endpoints were progression-free survival (PFS) at 6 and 12 months. Secondary endpoints included overall survival, radiological and clinical responses, and adverse events.</p><p><strong>Results: </strong>Of 35 patients, 16 were in the intervention group and 19 in the control group. Median PFS was 9 months (95% CI: 3.93-14.06) versus 3 months (95% confidence interval [CI]: 1.98-4.01) in the control and intervention groups (<i>P</i> = .737), with a high progression rate (73.4%) during nTMZ treatment. The 6-month PFS rates were 58% versus 25% (<i>P</i> = .042), and 12-month PFS rates were 26% versus 25% (<i>P</i> = .390) in the control and intervention groups, respectively. Patients with unmethylated O⁶-methylguanine-DNA methyltransferase (MGMT) and those with good performance status (PS) had significantly worse PFS with nTMZ.  However, those who underwent larger extent of resection exhibited significantly better PFS  with nTMZ. Adverse events were similar between groups.</p><p><strong>Conclusions: </strong>Neoadjuvant TMZ before SOC chemoradiotherapy did not improve outcomes for newly diagnosed GBM patients and is unsuitable for those with unmethylated MGMT and good PS. However, It may benefit patients with near or gross total resection. Further research is needed to refine GBM treatment strategies.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae195"},"PeriodicalIF":3.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11632829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of timing of temozolomide chemoradiotherapy for newly diagnosed glioblastoma on patient overall survival: A multicenter retrospective study.","authors":"Arthur C K Lau, Brandon L H Chan, Carly S K Yeung, Lai-Fung Li, Danny T M Chan, Michael W Y Lee, Tony K T Chan, Jason M K Ho, Ka-Man Cheung, Teresa P K Tse, Sarah S N Lau, Joyce S W Chow, Natalie M W Ko, Herbert H F Loong, Aya El-Helali, Wai-Sang Poon, Peter Y M Woo","doi":"10.1093/noajnl/vdae194","DOIUrl":"10.1093/noajnl/vdae194","url":null,"abstract":"<p><strong>Background: </strong>The optimal timing of initiating adjuvant temozolomide (TMZ) chemoradiotherapy after surgery in patients with glioblastoma is contentious. This study aimed to determine whether the timing of adjuvant treatment affects their overall survival (OS).</p><p><strong>Methods: </strong>Consecutive adult patients with histologically-confirmed newly diagnosed glioblastoma treated with adjuvant TMZ chemoradiotherapy across all neurosurgical centers in Hong Kong between 2006 and 2020 were analyzed. The surgery-to-chemoradiotherapy (S-CRT) interval was defined as the date of the first surgery to the date of initiation of adjuvant TMZ chemoradiotherapy.</p><p><strong>Results: </strong>Four hundred and forty-one patients were reviewed. The median S-CRT interval was 40 days (interquartile range [IQR]: 33-47) and the median overall survival (mOS) was 16.7 months (95% CI: 15.9-18.2). The median age was 58 years (IQR: 50-63). Multivariable Cox regression with restricted cubic splines identified a nonlinear relationship between the S-CRT interval and mOS. <i>Post hoc</i> analysis-derived S-CRT intervals revealed that early CRT (<5 weeks; adjusted hazard ratio [aHR]: 1.11; 95% CI 0.90-1.37) or late CRT (>9-12 weeks; aHR 1.07; 95% CI 0.67-1.71) were not significantly associated with OS. Subgroup analyses for the extent of resection (EOR) and p<i>MGMT</i> methylation status revealed no significant difference in treatment timing on OS.</p><p><strong>Conclusion: </strong>The timing of adjuvant TMZ chemoradiotherapy, if commenced within 12 weeks after glioblastoma diagnosis, did not influence OS regardless of EOR or p<i>MGMT</i> methylation status. Clinical judgment should be exercised in optimizing the timing of initiating adjuvant therapy.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae194"},"PeriodicalIF":3.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11630800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The landscape of immune checkpoint inhibitor clinical trials in glioblastoma: A systematic review.","authors":"Ethan Schonfeld, John Choi, Andrew Tran, Lily H Kim, Michael Lim","doi":"10.1093/noajnl/vdae174","DOIUrl":"https://doi.org/10.1093/noajnl/vdae174","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma is characterized by rapid tumor growth and high invasiveness. The tumor microenvironment of glioblastoma is highly immunosuppressive with both intrinsic and adaptive resistance mechanisms that result in disease recurrence despite current immunotherapeutic strategies.</p><p><strong>Methods: </strong>In this systematic review of clinical trials involving immunotherapy for glioblastoma using ClinicalTrials.gov and PubMed databases from 2016 and onward, we explore immunotherapeutic modalities involving immune checkpoint blockade (ICB).</p><p><strong>Results: </strong>A total of 106 clinical trials were identified, 18 with clinical outcomes. ICB in glioblastoma has failed to improve overall survival compared to the current standard of care, including those therapies inhibiting multiple checkpoints. Among all immune checkpoint trials, targets included programmed cell death protein-1 (PD-1) (35/48), PD-L1 (12/48), cytotoxic T-lymphocyte-associated protein-4 (6/48), TIGIT (2/48), B7-H3 (2/48), and TIM-3 (1/48). Preliminary results from combination immunotherapies (32.1% of all trials) demonstrated improved treatment efficacy compared to monotherapy, specifically those combining checkpoint therapy with another immunotherapy modality.</p><p><strong>Conclusions: </strong>Clinical trials involving ICB strategies for glioblastoma have not demonstrated improved survival. Comparison of therapeutic efficacy across trials was limited due to heterogeneity in the study population and outcome operationalization. Standardization of future trials could facilitate comparison across immunotherapy modalities for robust meta-analysis. Current immunotherapy trials have shifted focus toward combination strategies; preliminary results suggest that they are more encouraging than mono-modality immunotherapies. Given the intrinsic heterogeneity of glioblastoma, the utilization of immune markers will be key for the development of future immunotherapy approaches.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae174"},"PeriodicalIF":3.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical application of machine-based deep learning in patients with radiologically presumed adult-type diffuse glioma grades 2 or 3.","authors":"Tomás Gómez Vecchio, Alice Neimantaite, Erik Thurin, Julia Furtner, Ole Solheim, Johan Pallud, Mitchel Berger, Georg Widhalm, Jiri Bartek, Ida Häggström, Irene Y H Gu, Asgeir Store Jakola","doi":"10.1093/noajnl/vdae192","DOIUrl":"10.1093/noajnl/vdae192","url":null,"abstract":"<p><strong>Background: </strong>Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in <i>IDH</i> mutation prediction in patients with radiologically presumed dLGG.</p><p><strong>Methods: </strong>Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States. Collected data included patients' age, sex, tumor molecular characteristics (<i>IDH</i>, and 1p19q), and routine preoperative radiological images. A clinical model was built using multivariable logistic regression with the variables age and tumor location. DL models were built using MRI data only, and 4 DL architectures used in glioma research. In the final validation test, the clinical model and the best DL model were scored on an external validation cohort with 155 patients from the Erasmus Glioma Dataset.</p><p><strong>Results: </strong>The mean age in the recruited and external cohorts was 45.0 (SD 14.3) and 44.3 years (SD 14.6). The cohorts were rather similar, except for sex distribution (53.5% vs 64.5% males, <i>P</i>-value = .03) and <i>IDH</i> status (30.9% vs 12.9% <i>IDH</i> wild-type, <i>P</i>-value <.01). Overall, the area under the curve for the prediction of <i>IDH</i> mutations in the external validation cohort was 0.86, 0.82, and 0.87 for the clinical model, the DL model, and the model combining both models' probabilities.</p><p><strong>Conclusions: </strong>In their current state, when these complex models were applied to our clinical scenario, they did not seem to provide a net gain compared to our baseline clinical model.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae192"},"PeriodicalIF":3.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631182/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiomic analyses reveal new targets of polycomb repressor complex 2 in Schwann lineage cells and malignant peripheral nerve sheath tumors.","authors":"Minu M Bhunia, Christopher M Stehn, Tyler A Jubenville, Ethan L Novacek, Alex T Larsson, Mahathi Madala, Suganth Suppiah, Germán L Velez-Reyes, Kyle B Williams, Mark Sokolowski, Rory L Williams, Samuel J Finnerty, Nuri A Temiz, Ariel Caride, Aditya V Bhagwate, Nagaswaroop K Nagaraj, Jeong-Heon Lee, Tamas Ordog, Gelareh Zadeh, David A Largaespada","doi":"10.1093/noajnl/vdae188","DOIUrl":"10.1093/noajnl/vdae188","url":null,"abstract":"<p><strong>Background: </strong>Malignant peripheral nerve sheath tumors (MPNSTs) can arise from atypical neurofibromas (ANF). Loss of the polycomb repressor complex 2 (PRC2) is a common event. Previous studies on PRC2-regulated genes in MPNST used genetic add-back experiments in highly aneuploid MPNST cell lines which may miss PRC2-regulated genes in <i>NF1</i>-mutant ANF-like precursor cells. A set of PRC2-regulated genes in human Schwann cells (SCs) has not been defined. We hypothesized that PRC2 loss has direct and indirect effects on gene expression resulting in MPNST, so we sought to identify PRC2-regulated genes in immortalized human Schwann cells (iHSCs).</p><p><strong>Methods: </strong>We engineered <i>NF1</i>-deficient iHSCs with loss of function <i>SUZ12</i> or <i>EED</i> mutations. RNA sequencing revealed 1327 differentially expressed genes to define PRC2-regulated genes. To investigate MPNST pathogenesis, we compared genes in iHSCs to consistent gene expression differences between ANF and MPNSTs. Chromatin immunoprecipitation sequencing was used to further define targets. Methylome and proteomic analyses were performed to further identify enriched pathways.</p><p><strong>Results: </strong>We identified potential PRC2-regulated drivers of MPNST progression. Pathway analysis indicates many upregulated cancer-related pathways. We found transcriptional evidence for activated Notch and Sonic Hedgehog (SHH) signaling in PRC2-deficient iHSCs. Functional studies confirm that Notch signaling is active in MPNST cell lines, patient-derived xenografts, and transient cell models of PRC2 deficiency. A combination of MEK and γ-secretase inhibition shows synergy in MPNST cell lines.</p><p><strong>Conclusions: </strong>We identified PRC2-regulated genes and potential drivers of MPNSTs. Our findings support the Notch pathway as a druggable target in MPNSTs. Our identification of PRC2-regulated genes and pathways could result in more novel therapeutic approaches.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae188"},"PeriodicalIF":3.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pediatric spinal high-grade glioma in the pediatric precision oncology registry INFORM: Identification of potential therapeutic targets.","authors":"Elke Pfaff, Kathrin Schramm, Mirjam Blattner-Johnson, Barbara C Jones, Sebastian Stark, Gnana Prakash Balasubramanian, Christopher Previti, Robert J Autry, Petra Fiesel, Felix Sahm, David Reuss, Andreas von Deimling, Cornelis M van Tilburg, Kristian W Pajtler, Till Milde, Uta Dirksen, Christof M Kramm, André O von Bueren, Monica C Munthe-Kaas, Ingrid Øra, Stefan M Pfister, Olaf Witt, David T W Jones","doi":"10.1093/noajnl/vdae185","DOIUrl":"10.1093/noajnl/vdae185","url":null,"abstract":"<p><strong>Background: </strong>High-grade glioma (HGG) of the spinal cord constitutes rare tumors in the pediatric population. Knowledge of the molecular profile of this pediatric HGG (pedHGG) subgroup is limited and the clinical outcome is poor. Therefore, the aim of this study is to provide more profound investigations of molecular characteristics and clinical features of these tumors.</p><p><strong>Methods: </strong>Between January 2015 and October 2023, 17 spinal tumors with HGG histology were analyzed by the Individualized Therapy For Relapsed Malignancies in Childhood (INFORM) precision oncology registry. Comprehensive molecular profiling (including next-generation sequencing approaches and DNA methylation analysis) was performed. Clinical data provided by the treating centers were evaluated regarding treatment approaches and outcomes.</p><p><strong>Results: </strong>Subgroup classification based on DNA methylation analysis revealed molecular HGG subgroups in 12/17 cases, while 2/17 were classified as molecular low-grade glioma (LGG) and 3/17 were not unequivocally classifiable. Typical genetic alterations described in pedHGG usually presenting at other localizations were also present in the counterparts located in the spinal cohort. Alterations that might serve as a promising target for personalized therapy approaches were identified in a subset of tumors.</p><p><strong>Conclusion: </strong>With this cohort of 12 molecularly confirmed spinal pedHGG cases, we provide a compilation of genomic as well as clinical features of this rare subgroup, contributing to a better understanding and eventually to future treatment approaches.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"7 1","pages":"vdae185"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}