bims-malgli Biomed News
on Biology of malignant gliomas
Issue of 2023‒12‒31
five papers selected by
Oltea Sampetrean, Keio University
  1. Whole tumor analysis reveals early origin of the TERT promoter mutation and intercellular heterogeneity in TERT expression.
  2. Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.
  3. Developing a computable phenotype for glioblastoma.
  4. IRE1 endoribonuclease signaling promotes myeloid cell infiltration in glioblastoma.
  5. Consensus framework for conducting phase I/II clinical trials for children, adolescents, and young adults with pediatric low-grade glioma: Guidelines established by the International Pediatric Low-Grade Glioma Coalition Clinical Trial Working Group.
  1. Neuro Oncol. 2023 Dec 23. pii: noad231. [Epub ahead of print]
    Whole tumor analysis reveals early origin of the TERT promoter mutation and intercellular heterogeneity in TERT expression.
    Christina L Appin, Chibo Hong, Abigail K Suwala, Stephanie Hilz, Radhika Mathur, David A Solomon, Ivan V Smirnov, Nicholas O Stevers, Anny Shai, Albert Wang, Mitchel S Berger, Susan M Chang, Joanna J Phillips, Joseph F Costello.
      BACKGROUND: The TERT promoter mutation (TPM) is acquired in most IDH-wildtype glioblastomas (GBM) and IDH-mutant oligodendrogliomas (OD) enabling tumor cell immortality. Previous studies on TPM clonality show conflicting results. This study was performed to determine whether TPM is clonal on a tumor-wide scale.METHODS: We investigated TPM clonality in relation to presumed early events in 19 IDH-wildtype GBM and 10 IDH-mutant OD using three-dimensional comprehensive tumor sampling. We performed Sanger sequencing on 264 tumor samples and deep amplicon sequencing on 187 tumor samples. We obtained tumor purity and copy number estimates from whole exome sequencing. TERT expression was assessed by RNA-seq and RNAscope.
    RESULTS: We detected TPM in 100% of tumor samples with quantifiable tumor purity (219 samples). Variant allele frequencies (VAF) of TPM correlate positively with chromosome 10 loss in GBM (R = 0.85), IDH1 mutation in OD (R = 0.87), and with tumor purity (R = 0.91 for GBM; R = 0.90 for OD). In comparison, oncogene amplification was tumor-wide for MDM4- and most EGFR-amplified cases but heterogeneous for MYCN and PDGFRA, and strikingly high in low-purity samples. TPM VAF was moderately correlated with TERT expression (R = 0.52 for GBM; R = 0.65 for OD). TERT expression was detected in a subset of cells, solely in TPM-positive samples, including samples equivocal for tumor.
    CONCLUSION: On a tumor-wide scale, TPM is among the earliest events in glioma evolution. Intercellular heterogeneity of TERT expression, however, suggests dynamic regulation during tumor growth. TERT expression may be a tumor cell-specific biomarker.
    Keywords:   TERT ; glioblastoma; oligodendroglioma; sequencing
    DOI:  https://doi.org/10.1093/neuonc/noad231
  2. Neuro Oncol. 2023 Dec 28. pii: noad259. [Epub ahead of print]
    Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.
    Martha Foltyn-Dumitru, Tobias Kessler, Felix Sahm, Wolfgang Wick, Sabine Heiland, Martin Bendszus, Philipp Vollmuth, Marianne Schell.
      BACKGROUND: While the association between diffusion and perfusion MRI and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival.METHODS: A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient (ADC) normalized relative cerebral blood volume (nrCBV), and relative cerebral blood flow (rCBF) were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using Partition Around Medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership.
    RESULTS: Using the training dataset (231/289) we identified two stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (p=0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (p≤ 0.004 each).
    CONCLUSIONS: Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion and perfusion MRI in predicting survival rates of glioblastoma patients.
    Keywords:  diffusion; glioblastoma; machine learning; perfusion; prognostic biomarker
    DOI:  https://doi.org/10.1093/neuonc/noad259
  3. Neuro Oncol. 2023 Dec 23. pii: noad249. [Epub ahead of print]
    Developing a computable phenotype for glioblastoma.
    Sandra Yan, Kaitlyn Melnick, Xing He, Tianchen Lyu, Rachel S F Moor, Megan E H Still, Duane A Mitchell, Elizabeth A Shenkman, Han Wang, Yi Guo, Jiang Bian, Ashley P Ghiaseddin.
      BACKGROUND: Glioblastoma (GBM) is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are non-specific. The aim of this study was to create a computable phenotype (CP) for GBM from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR).METHODS: We used the UF Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords.
    RESULTS: We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule.
    CONCLUSIONS: We developed a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance which minimizes possible biases from misclassification errors.
    Keywords:  Computable phenotype; Electronic Health Records (EHRs); Glioblastoma; Structured data; Unstructured data
    DOI:  https://doi.org/10.1093/neuonc/noad249
  4. Neuro Oncol. 2023 Dec 28. pii: noad256. [Epub ahead of print]
    IRE1 endoribonuclease signaling promotes myeloid cell infiltration in glioblastoma.
    Joanna Obacz, Jérôme Archambeau, Elodie Lafont, Manon Nivet, Sophie Martin, Marc Aubry, Konstantinos Voutetakis, Raphael Pineau, Rachel Boniface, Daria Sicari, Diana Pelizzari-Raymundo, Gevorg Ghukasyan, Eoghan McGrath, Efstathios-Iason Vlachavas, Matthieu Le Gallo, Pierre Jean Le Reste, Kim Barroso, Tanya Fainsod-Levi, Akram Obiedat, Zvi Granot, Boaz Tirosh, Juhi Samal, Abhay Pandit, Luc Négroni, Nicolas Soriano, Annabelle Monnier, Jean Mosser, Aristotelis Chatziioannou, Véronique Quillien, Eric Chevet, Tony Avril.
      BACKGROUND: Intrinsic or environmental stresses trigger the accumulation of improperly folded proteins in the endoplasmic reticulum (ER), leading to ER stress. To cope with this, cells have evolved an adaptive mechanism named the unfolded protein response (UPR) which is hijacked by tumor cells to develop malignant features. Glioblastoma (GB), the most aggressive and lethal primary brain tumor, relies on UPR to sustain growth. We recently showed that IRE1 alpha (referred to IRE1 hereafter), one of the UPR transducers, promotes GB invasion, angiogenesis and infiltration by macrophage. Hence, high tumor IRE1 activity in tumor cells predicts worse outcome. Herein, we characterized the IRE1-dependent signaling that shapes the immune microenvironment towards monocytes/macrophages and neutrophils.METHODS: We used human and mouse cellular models in which IRE1 was genetically or pharmacologically invalidated and which were tested in vivo. Publicly available datasets from GB patients were also analyzed to confirm our findings.
    RESULTS: We showed that IRE1 signaling, through both the transcription factor XBP1s and the regulated IRE1-dependent decay (RIDD) controls the expression of the ubiquitin-conjugating E2 enzyme UBE2D3. In turn, UBE2D3 activates the NFκB pathway, ensuing chemokine production and myeloid infiltration in tumors.
    CONCLUSION: Our work identifies a novel IRE1/UBE2D3 pro-inflammatory axis that plays an instrumental role in GB immune regulation.
    Keywords:  ER stress; IRE1; chemokines; glioblastoma; inflammation
    DOI:  https://doi.org/10.1093/neuonc/noad256
  5. Neuro Oncol. 2023 Dec 26. pii: noad227. [Epub ahead of print]
    Consensus framework for conducting phase I/II clinical trials for children, adolescents, and young adults with pediatric low-grade glioma: Guidelines established by the International Pediatric Low-Grade Glioma Coalition Clinical Trial Working Group.
    Sabine Mueller, Jason Fangusaro, Arzu Onar Thomas, Thomas S Jacques, Pratiti Bandopadhayay, Peter de Blank, Roger J Packer, Maryam Fouladi, Antoinette Schouten van Meeteren, David Jones, Arie Perry, Yoshiko Nakano, Darren Hargrave, David Riedl, Nathan J Robinson, Marita Partanen, Michael J Fisher, Olaf Witt.
      Within the last few decades, we have witnessed tremendous advancements in the study of pediatric low-grade gliomas (pLGG), leading to a much-improved understanding of their molecular underpinnings. Consequently, we have achieved successful milestones in developing and implementing targeted therapeutic agents for treating these tumors. However, the community continues to face many unknowns when it comes to the most effective clinical implementation of these novel targeted inhibitors or combinations thereof. Questions encompassing optimal dosing strategies, treatment duration, methods for assessing clinical efficacy, and the identification of predictive biomarkers remain unresolved. Here, we offer the consensus of the international pLGG coalition (iPLGGc) clinical trial working group on these important topics and comment on clinical trial design and endpoint rationale. Throughout, we seek to standardize the global approach to early clinical trials (phase I and II) for pLGG, leading to more consistently interpretable results as well as enhancing the pace of novel therapy development and encouraging an increased focus on functional endpoints as well and quality of life for children faced with this disease.
    Keywords:  Consensus recommendation; Early phase clinical trial; Low grade glioma
    DOI:  https://doi.org/10.1093/neuonc/noad227
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