Neuro-oncology advances最新文献

{"title":"Non-small cell lung cancer with synchronous brain metastases: Identification of prognostic factors in a retrospective multicenter study (HOT 1701).","authors":"Yoshihito Ohhara, Tetsuya Kojima, Osamu Honjo, Noriyuki Yamada, Toshitaka Sato, Hirofumi Takahashi, Kei Takamura, Taichi Takashina, Noriaki Sukoh, Hisashi Tanaka, Yasutaka Kawai, Yuka Fujita, Keiki Yokoo, Fumihiro Hommura, Toshiyuki Harada, Ryoichi Honda, Toraji Amano, Hirotoshi Dosaka-Akita, Satoshi Oizumi, Ichiro Kinoshita","doi":"10.1093/noajnl/vdae168","DOIUrl":"10.1093/noajnl/vdae168","url":null,"abstract":"<p><strong>Background: </strong>Non-small-cell lung cancer (NSCLC) is associated with a high incidence of brain metastasis (BM), and the prognosis of patients with NSCLC and BM is poor. This study aimed to identify the prognostic factors and elucidate the survival rates of Japanese patients with NSCLC and BM at initial diagnosis.</p><p><strong>Methods: </strong>HOT 1701 is a retrospective multicenter study of patients with NSCLC and BM at initial diagnosis. The medical records of all consecutive patients diagnosed with advanced or recurrent NSCLC and BM at 14 institutions of the Hokkaido Lung Cancer Clinical Study Group Trial (HOT) in Japan were reviewed. The participants were categorized based on the presence or absence of driver mutations. The Kaplan-Meier method was used to estimate median overall survival (OS). Univariate and multivariate analyses were performed to identify prognostic factors in these patients.</p><p><strong>Results: </strong>Among 566 patients with NSCLC and BM, the median OS was 11.8 months. Patients with driver mutations survived longer than those without driver mutations. The univariate and multivariate analyses revealed 6 independent prognostic factors: age ≥65 years, poor performance status, T factor, absence of driver gene mutations, presence of extracranial metastases, and number of BM. According to the prognostic score based on these 6 factors, the patients were stratified into 3 risk groups: low-, intermediate-, and high-risk, with median OS of 27.8, 12.2, and 2.8 months, respectively.</p><p><strong>Conclusions: </strong>We developed a new prognostic model for patients with NSCLC and BM, which may help determine prognosis at diagnosis.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae168"},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635099","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":"Genomic tumor evolution dictates human medulloblastoma progression.","authors":"Yana Ruchiy, Ioanna Tsea, Efthalia Preka, Bronte Manouk Verhoeven, Thale Kristin Olsen, Shenglin Mei, Indranil Sinha, Klas Blomgren, Lena-Maria Carlson, Cecilia Dyberg, John Inge Johnsen, Ninib Baryawno","doi":"10.1093/noajnl/vdae172","DOIUrl":"10.1093/noajnl/vdae172","url":null,"abstract":"<p><strong>Background: </strong>Medulloblastoma (MB) is the most common high-grade pediatric brain tumor, comprised of 4 main molecular subgroups-sonic-hedgehog (SHH), Wnt, Group 3, and Group 4. Group 3 and Group 4 tumors are the least characterized MB subgroups, despite Group 3 having the worst prognosis (~50% survival rate), and Group 4 being the most prevalent. Such poor characterization can be attributed to high levels of inter- and intratumoral heterogeneity, making it difficult to identify common therapeutic targets.</p><p><strong>Methods: </strong>In this study, we generated single-cell sequencing data from 14 MB patients spanning all subgroups that we complemented with publicly available single-cell data from Group 3 patients. We used a ligand-receptor analysis tool (CellChat), expression- and allele-based copy-number variation (CNV) detection methods, and RNA velocity analysis to characterize tumor cell-cell interactions, established a connection between CNVs and temporal tumor progression, and unraveled tumor evolution.</p><p><strong>Results: </strong>We show that MB tumor cells follow a temporal trajectory from those with low CNV levels to those with high CNV levels, allowing us to identify early and late markers for SHH, Group 3, and Group 4 MBs. Our study also identifies <i>SOX4</i> upregulation as a major event in later tumor clones for Group 3 and Group 4 MBs, suggesting it as a potential therapeutic target for both subgroups.</p><p><strong>Conclusion: </strong>Taken together, our findings highlight MB's inherent tumor heterogeneity and offer promising insights into potential drivers of MB tumor evolution particularly in Group 3 and Group 4 MBs.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae172"},"PeriodicalIF":3.7,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11629688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808959","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":"Biological and prognostic relevance of epigenetic regulatory genes in high-grade gliomas.","authors":"Sonikpreet Aulakh, Joanne Xiu, Andrew Hinton, Sourat Darabi, Michael J Demeure, Soma Sengupta, Santosh Kesari, David M Ashley, Ashley Love Sumrall, Michael J Glantz, David Spetzler","doi":"10.1093/noajnl/vdae169","DOIUrl":"10.1093/noajnl/vdae169","url":null,"abstract":"<p><strong>Background: </strong>High-grade gliomas (HGGs) are the most aggressive type of gliomas and have the poorest outcomes. Chromatin remodeling (CR) genes have been implicated in multiple oncogenic pathways in numerous cancer types. In gliomagenesis, CR genes have been implicated in regulating the stemness of glioma cells, the tumor microenvironment (TME), and resistance to therapies.</p><p><strong>Methods: </strong>We performed molecular profiling of 4244 HGGs and evaluated associations of CR mutations with other cancer-related biomarkers, infiltration by immune cells, and immune gene expression. We also evaluated the association between CR mutations and survival in wild-type <i>IDH</i> HGG patients.</p><p><strong>Results: </strong>Nearly 10% of HGGs carry mutations in CR genes, with a higher prevalence (15%) in HGGs with <i>IDH</i> mutations. Analysis of cooccurrence with other biomarkers revealed that CR-mutated HGGs possess favorable genetic alterations which may have prognostic value. CR-mutated HGGs with wild-type <i>IDH</i> demonstrated colder TME and worse OS overall compared to the CR-wild-type HGGs.</p><p><strong>Conclusions: </strong>Our study reveals the prognostic effects of CR mutations in HGG and points to several biomarker candidates that could suggest sensitivity to emerging therapeutic strategies.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae169"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11565242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649819","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":"Development of the pediatric neuro-oncology services assessment aid: An assessment tool for pediatric neuro-oncology service delivery capacity.","authors":"Revathi Rajagopal, Rosdali Diaz Coronado, Syed Ahmer Hamid, Regina Navarro Martin Del Campo, Frederick Boop, Asim Bag, Alma Edith Benito Reséndiz, Vasudeva Bhat K, Danny Campos, Kenneth Chang, Ramona Cirt, Ludi Dhyani Rahmartani, Jen Chun Foo, Julieta Hoveyan, John T Lucas, Thandeka Ngcana, Rahat Ul Ain, Nuha Omran, Diana S Osorio, Bilal Mazhar Qureshi, Noah D Sabin, Ernestina Schandorf, Patrick Bankah, Mary-Ann Dadzie, Hafisatu Gbadamos, Hend Sharafeldin, Mahendra Somathilaka, Peiyi Yang, Yao Atteby Jean-Jacques, Anan Zhang, Zeena Salman, Miriam Gonzalez, Paola Friedrich, Carlos Rodriguez-Galindo, Ibrahim Qaddoumi, Daniel C Moreira","doi":"10.1093/noajnl/vdae171","DOIUrl":"https://doi.org/10.1093/noajnl/vdae171","url":null,"abstract":"<p><strong>Background: </strong>To enhance the quality of care available for children with central nervous system (CNS) tumors across the world, a systematic evaluation of capacity is needed to identify gaps and prioritize interventions. To that end, we created the pediatric neuro-oncology (PNO) resource assessment aid (PANORAMA) tool.</p><p><strong>Methods: </strong>The development of PANORAMA encompassed 3 phases: operationalization, consensus building, and piloting. PANORAMA aimed to capture the elements of the PNO care continuum through domains with weighted assessments reflecting their importance. Responses were ordinally scored to reflect the level of satisfaction. PANORAMA was revised based on feedback at various phases to improve its relevance, usability, and clarity.</p><p><strong>Results: </strong>The operationalization phase identified 14 domains by using 252 questions. The consensus phase involved 15 experts (6 pediatric oncologists, 3 radiation oncologists, 2 neurosurgeons, 2 radiologists, and 2 pathologists). The consensus phase validated the identified domains, questions, and scoring methodology. The PANORAMA domains included national context, hospital infrastructure, organization and service integration, human resources, financing, laboratory, neurosurgery, diagnostic imaging, pathology, chemotherapy, radiotherapy, supportive care, and patient outcomes. PANORAMA was piloted at 13 institutions in 12 countries, representing diverse patient care contexts. Face validity was assessed by examining the correlation between the estimated score by respondents and calculated PANORAMA scores for each domain (<i>r</i> = 0.67, <i>P</i> < .0001).</p><p><strong>Conclusions: </strong>PANORAMA was developed through a systematic, collaborative approach, ensuring its relevance to evaluate core elements of PNO service capacity. Distribution of PANORAMA will enable quantitative service evaluations across institutions, facilitating benchmarking and the prioritization of interventions.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae171"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635183","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":"Effect of antibiotic drug use on outcome and therapy-related toxicity in patients with glioblastoma-A retrospective cohort study.","authors":"Linda Götz, Tananeh Ansafi, Michael Gerken, Monika Klinkhammer-Schalke, Anna Fischl, Markus J Riemenschneider, Martin Proescholdt, Elisabeth Bumes, Oliver Kölbl, Nils Ole Schmidt, Ralf Linker, Peter Hau, Tareq M Haedenkamp","doi":"10.1093/noajnl/vdae170","DOIUrl":"10.1093/noajnl/vdae170","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma (GB) is the most frequent malignant brain tumor and has a dismal prognosis. In other cancers, antibiotic use has been associated with severity of chemotherapy-induced toxicity and outcome. We investigated if these mechanisms are also involved in GB.</p><p><strong>Methods: </strong>We selected a cohort of 78 GB patients who received combined radiochemotherapy. We investigated if exposure to prediagnostic antibiotic use is associated with clinical side effects and laboratory changes during adjuvant therapy as well as overall survival (OS) and progression-free survival (PFS) using chi-square test, binary logistic regression, Kaplan-Meyer analysis, and multivariable Cox regression.</p><p><strong>Results: </strong>Seventeen patients (21.8%) received at least one course of prediagnostic antibiotics and 61 (78.2%) received no antibiotics. We found a higher incidence of loss of appetite (23.5% vs. 4.9%; <i>P</i> = .018) and myelosuppression (41.2% vs. 18.0%; <i>P</i> = .045) in the antibiotic group. Multivariable logistic regression analysis revealed antibiotics to be a predictor for nausea (OR = 6.94, 95% CI: 1.09-44.30; <i>P</i> = .041) and myelosuppression (OR = 9.75, 95% CI: 1.55-61.18; <i>P</i> = .015). Furthermore, lymphocytopenia was more frequent in the antibiotic group (90.0% vs. 56.1%, <i>P</i> = .033). There were no significant differences in OS (<i>P</i> = .404) and PFS (<i>P</i> = .844). Multivariable Cox regression showed a trend toward shorter survival time (<i>P</i> = .089) in the antibiotic group.</p><p><strong>Conclusions: </strong>Our study suggests that antibiotic use affects symptoms and lab values in GB patients. Larger prospective studies are required to investigate if prediagnostic antibiotic use could be a prognostic factor in GB patients.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae170"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570801","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":"Correction to: Effect of bevacizumab on refractory meningiomas: 3D volumetric growth rate versus response assessment in neuro-oncology criteria.","authors":"","doi":"10.1093/noajnl/vdae164","DOIUrl":"https://doi.org/10.1093/noajnl/vdae164","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/noajnl/vdae128.].</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae164"},"PeriodicalIF":3.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383056","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":"Enhancing clinical decision-making: An externally validated machine learning model for predicting isocitrate dehydrogenase mutation in gliomas using radiomics from presurgical magnetic resonance imaging.","authors":"Jan Lost, Nader Ashraf, Leon Jekel, Marc von Reppert, Niklas Tillmanns, Klara Willms, Sara Merkaj, Gabriel Cassinelli Petersen, Arman Avesta, Divya Ramakrishnan, Antonio Omuro, Ali Nabavizadeh, Spyridon Bakas, Khaled Bousabarah, MingDe Lin, Sanjay Aneja, Michael Sabel, Mariam Aboian","doi":"10.1093/noajnl/vdae157","DOIUrl":"10.1093/noajnl/vdae157","url":null,"abstract":"<p><strong>Background: </strong>Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation.</p><p><strong>Methods: </strong>This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry.</p><p><strong>Results: </strong>The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847.</p><p><strong>Conclusions: </strong>The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae157"},"PeriodicalIF":3.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11630777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808950","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":"Empowering the next generation in neuro-oncology: Introduction of the EANO Career Boost Initiative.","authors":"Philipp Lohmann, Johnny Duerinck, Matthijs van der Meulen, Dan Mitrea, Susan Short, Marjolein Geurts","doi":"10.1093/noajnl/vdae167","DOIUrl":"https://doi.org/10.1093/noajnl/vdae167","url":null,"abstract":"","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae167"},"PeriodicalIF":3.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515558","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":"Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O⁶-methylguanine-methyltransferase promoter methylation status.","authors":"Virendra Kumar Yadav, Suyash Mohan, Sumeet Agarwal, Laiz Laura de Godoy, Archith Rajan, MacLean P Nasrallah, Stephen J Bagley, Steven Brem, Laurie A Loevner, Harish Poptani, Anup Singh, Sanjeev Chawla","doi":"10.1093/noajnl/vdae159","DOIUrl":"10.1093/noajnl/vdae159","url":null,"abstract":"<p><strong>Background: </strong>It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.</p><p><strong>Methods: </strong>GBM patients (<i>n</i> = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O⁶-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (<i>n</i> = 55) or PsP (<i>n</i> = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.</p><p><strong>Results: </strong>The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.</p><p><strong>Conclusions: </strong>Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae159"},"PeriodicalIF":3.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584923","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":"A phase 1 dose escalation of pritumumab in patients with refractory or recurrent gliomas or brain metastases.","authors":"Jose Carrillo, Jaya Mini Gill, Charles Redfern, Ivan Babic, Natsuko Nomura, Dhaval K Shah, Sean Carrick, Santosh Kesari","doi":"10.1093/noajnl/vdae166","DOIUrl":"10.1093/noajnl/vdae166","url":null,"abstract":"<p><strong>Background: </strong>This phase 1 (NCT04396717) open-label, multicenter study, evaluated Pritumumab, a IgG1 monoclonal antibody, in patients with gliomas and brain metastases. The primary objective was to evaluate the safety and/or tolerability and to identify a recommended phase 2 dose (RP2D) of Pritumumab.</p><p><strong>Methods: </strong>Adult patients with recurrent gliomas or brain metastases were enrolled in the dose cohort that was open at the time of their consent. Study treatment consisted of pritumumab administered intravenously weekly on days 1, 8, 15, and 22 in 28-day cycles. Safety, pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity were evaluated.</p><p><strong>Results: </strong>Fifteen patients received Pritumumab in the recurrent setting. Pritumumab was well tolerated, with no serious adverse events related to Pritumumab reported. The most common drug-related toxicities were constipation and fatigue. There were no dose-limiting toxicities observed, and a maximum tolerable dose was not reached. Thus, the maximum feasible dose and recommended phase 2 dose of Pritumumab was established at 16.2 mg/kg weekly. Out of eleven patients evaluated for efficacy, one patient (9.1%) demonstrated partial response based on response assessment in neuro-oncology criteria, and disease stabilization was seen in 3 patients (27.3%).</p><p><strong>Conclusions: </strong>Pritumumab was well tolerated with no DLTs observed up to 16.2 mg/kg weekly. Further studies are warranted to determine clinical benefit in patients.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae166"},"PeriodicalIF":3.7,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502913/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515555","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}