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Translational Cancer Research Nov 2022The aim of this study was to investigate whether texture analysis-based machine learning could be utilized in presurgical differentiation of high-grade gliomas in adults.
BACKGROUND
The aim of this study was to investigate whether texture analysis-based machine learning could be utilized in presurgical differentiation of high-grade gliomas in adults.
METHODS
This is a single-center retrospective study involving 150 patients diagnosed with glioblastoma (GBM) (n=50), anaplastic astrocytoma (AA) (n=50) or anaplastic oligodendroglioma (AO) (n=50). The training group and validation group were randomly divided with a 4:1 ratio. Forty texture features were extracted from contrast-enhanced T1-weighted images using LIFEx software. Two feature-selection methods were separately introduced to select optimal features, including distance correlation (DC) and least absolute shrinkage and selection operator (LASSO). Optimal features selected were fed into linear discriminant analysis (LDA) classifier and support vector machine (SVM) classifier to establish multiple classification models. The performance was evaluated by using the accuracy, Kappa value and area under receiver operating characteristic curve (AUC) of each model.
RESULTS
The overall diagnostic accuracies of LDA-based models were 76.0% (DC + LDA) and 74.3% (LASSO + LDA) in the validation group, while for SVM-based models were 58.0% (DC + SVM) and 63.3% (LASSO + SVM). The combination of DC and LDA reach the highest diagnostic accuracy, AUC of GBM, AA and AO were 0.999, 0.834 and 0.865 separately, indicating that this model could distinguish GBM from AA and AO commendably, whereas the differentiation between AA and AO was relatively difficult.
CONCLUSIONS
This study indicated that MRI texture analysis combined with LDA algorithm has the potential to be utilized in distinguishing the subtypes of high-grade gliomas.
PubMed: 36523299
DOI: 10.21037/tcr-22-1390 -
BMC Medical Imaging May 2022The accurate grading of IDH-mutant astrocytoma is essential to make therapeutic strategies and assess the prognosis of patients. The purpose of this study was to...
BACKGROUND
The accurate grading of IDH-mutant astrocytoma is essential to make therapeutic strategies and assess the prognosis of patients. The purpose of this study was to investigate the usefulness of DWI, SWI and DSC-PWI in grading IDH-mutant astrocytoma.
METHODS
One hundred and seven patients with IDH-mutant astrocytoma who underwent DWI, SWI and DSC-PWI were retrospectively reviewed. Minimum apparent diffusion coefficient (ADC), intratumoral susceptibility signal intensity(ITSS) and maximum relative cerebral blood volume (rCBV) values were assessed. ADC, ITSS and rCBV values were compared between grade 2 vs. grade 3, grade 3 vs. grade 4 and grade 2 + 3 vs. grade 4 tumors. Logistic regression, tenfold cross-validation,and receiver operating characteristic (ROC) curve analyses were used to assess their diagnostic performances.
RESULTS
Grade 4 IDH-mutant astrocytomas showed significantly lower ADC and higher rCBV as compared to grade 3 tumors (adjusted P < 0.001). IDH-mutant grade 3 astrocytomas showed significantly lower ITSS levels as compared with grade 4 tumors (adjusted P < 0.001). ITSS levels between IDH-mutant grade 2 and grade 3 astrocytomas were significantly different (adjusted P = 0.002). Combined the ADC, ITSS and rCBV resulted in the highest AUC for differentiation grade 2 and grade 3 tumors from grade 4 tumors.
CONCLUSION
ADC rCBV and ITSS can be used for grading the IDH-mutant astrocytomas. The combination of ADC ITSS and rCBV could improve the diagnostic performance in grading of IDH-mutant astrocytoma.
Topics: Astrocytoma; Brain Neoplasms; Glioblastoma; Humans; Magnetic Resonance Imaging; Perfusion; Retrospective Studies
PubMed: 35644621
DOI: 10.1186/s12880-022-00832-3 -
Clinical and Translational Medicine Feb 2024Paediatric and adult astrocytomas are notably different, where clinical treatments used for adults are not as effective on children with the same form of cancer and... (Review)
Review
Paediatric and adult astrocytomas are notably different, where clinical treatments used for adults are not as effective on children with the same form of cancer and these treatments lead to adverse long-term health concerns. Integrative omics-based studies have shown the pathology and fundamental molecular characteristics differ significantly and cannot be extrapolated from the more widely studied adult disease. Recent clinical advances in our understanding of paediatric astrocytomas, with the aid of next-generation sequencing and epigenome-wide profiling, have led to the identification of key canonical mutations that vary based on the tumour location and age of onset. These driver mutations, in particular the identification of the recurrent histone H3 mutations in high-grade tumours, have confirmed the important role epigenetic dysregulations play in cancer progression. This review summarises the current updates of the classification, epidemiology, pathogenesis and clinical management of paediatric astrocytoma based on their grades and the ongoing clinical trials. It also provides novel insights on genetic and epigenetic alterations as diagnostic biomarkers, highlighting the potential of targeting these pathways as therapeutics for this devastating childhood cancer.
Topics: Adult; Humans; Child; Brain Neoplasms; Astrocytoma; Histones; Epigenesis, Genetic; Epigenomics
PubMed: 38299304
DOI: 10.1002/ctm2.1560 -
Indian Journal of Pathology &... May 2022Glioblastoma is the most common malignant central nervous system (CNS) tumor in adults. Acute common clinical symptoms include headache, seizure, behavior changes, focal... (Review)
Review
Glioblastoma is the most common malignant central nervous system (CNS) tumor in adults. Acute common clinical symptoms include headache, seizure, behavior changes, focal neurological deficits, and signs of increased intracranial pressure. The classic MRI finding of glioblastoma is an irregularly shaped, rim-enhancing or ring-enhancing lesion with a central dark area of necrosis. This constellation of features correlates with microscopic findings of tumor necrosis and microvascular proliferation. Besides these common features, several well-recognized histological subtypes include giant cell glioblastoma, granular cell glioblastoma, gliosarcoma, glioblastoma with a primitive neuronal component, small cell glioblastoma, and epithelioid glioblastoma. While glioblastoma was historically classified as isocitrate dehydrogenase (IDH)-wildtype and IDH-mutant groups, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) and the fifth edition of the WHO Classification of Tumors of the Central Nervous System clearly updated the nomenclature to reflect glioblastoma to be compatible with wildtype IDH status only. Therefore, glioblastoma is now defined as "a diffuse, astrocytic glioma that is IDH-wildtype and H3-wildtype and has one or more of the following histological or genetic features: microvascular proliferation, necrosis, Telomerase reverse transcriptase promoter mutation, Epidermal growth factor receptor gene amplification, +7/-10 chromosome copy-number changes (CNS WHO grade 4)."
Topics: Adult; Astrocytoma; Brain Neoplasms; Central Nervous System Neoplasms; Glioblastoma; Humans; Isocitrate Dehydrogenase; Mutation; Necrosis; World Health Organization
PubMed: 35562131
DOI: 10.4103/ijpm.ijpm_1109_21 -
Frontiers in Oncology 2021To investigate the diagnostic ability of radiomics-based machine learning in differentiating atypical low-grade astrocytoma (LGA) from anaplastic astrocytoma (AA).
PURPOSE
To investigate the diagnostic ability of radiomics-based machine learning in differentiating atypical low-grade astrocytoma (LGA) from anaplastic astrocytoma (AA).
METHODS
The current study involved 175 patients diagnosed with LGA (n = 95) or AA (n = 80) and treated in the Neurosurgery Department of West China Hospital from April 2010 to December 2019. Radiomics features were extracted from pre-treatment contrast-enhanced T1 weighted imaging (T1C). Nine diagnostic models were established with three selection methods [Distance Correlation, least absolute shrinkage, and selection operator (LASSO), and Gradient Boosting Decision Tree (GBDT)] and three classification algorithms [Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and random forest (RF)]. The sensitivity, specificity, accuracy, and areas under receiver operating characteristic curve (AUC) of each model were calculated. Diagnostic ability of each model was evaluated based on these indexes.
RESULTS
Nine radiomics-based machine learning models with promising diagnostic performances were established. For LDA-based models, the optimal one was the combination of LASSO + LDA with AUC of 0.825. For SVM-based modes, Distance Correlation + SVM represented the most promising diagnostic performance with AUC of 0.808. And for RF-based models, Distance Correlation + RF were observed to be the optimal model with AUC of 0.821.
CONCLUSION
Radiomic-based machine-learning has the potential to be utilized in differentiating atypical LGA from AA with reliable diagnostic performance.
PubMed: 34141605
DOI: 10.3389/fonc.2021.521313 -
European Journal of Medical Genetics Jan 2020Mutations in LZTR1, already known to be causal in familial schwannomatosis type 2, have been recently involved in a small proportion of patients with autosomal dominant...
Mutations in LZTR1, already known to be causal in familial schwannomatosis type 2, have been recently involved in a small proportion of patients with autosomal dominant and autosomal recessive Noonan syndrome. LZTR1 is also a driver gene in non syndromal glioblastoma. We report a 26-year-old patient with typical Noonan syndrome, and the dominantly transmitted c.850C > T (p.(Arg284Cys)) variant in LZTR1. An oligoastrocytoma was diagnosed in the patient at the age of 22 years; recurrence of the tumor occurred at age 26, as a ganglioblastoma. The patient had been transiently treated with growth hormone between ages 15 and 17. Considering the implication of LZTR1 in sporadic tumors of the nervous system, we hypothesize that gliomas are a possible complication of LZTR1-related Noonan syndrome. This report also supports a possible link between occurrence of a cerebral tumor in Noonan syndrome and a previous treatment with growth hormone.
Topics: Adolescent; Adult; Astrocytoma; Female; Genetic Predisposition to Disease; Glioblastoma; Humans; Male; Mutation; Noonan Syndrome; Pedigree; Transcription Factors
PubMed: 30664951
DOI: 10.1016/j.ejmg.2019.01.007 -
Astrocytoma and glioblastoma IDH1-wildtype cells colonize tumor vessels and deploy vascular mimicry.Ultrastructural Pathology Jul 2023Gliomas are the most prevalent type of malignant brain tumors with a very dismal prognosis. Angiogenesis in glioma has recently gotten more attention and its molecular...
Gliomas are the most prevalent type of malignant brain tumors with a very dismal prognosis. Angiogenesis in glioma has recently gotten more attention and its molecular aspects have been published; however, these were not complemented with ultrastructural evidence. Our ultrastructural examination of glioma vessels reveals several unique and critical features related to their mechanisms of progression and metastasis strategy. The detailed ultrastructural survey of 18 isocitrate dehydrogenase-wildtype (IDH1-wt) glioblastomas and 12 isocitrate dehydrogenase-mutant (IDH1-mt) High-grade gliomas indicated that tumor vessels of both types had undergone deformities such as the thickening of the vessel wall (VW) and proliferation of the basement membrane, contour distortions, abnormal and discontinuous basal lamina, tumor cells' invasion and colonization of VW, disappearance of endothelial cells (ECs), pericytes, and smooth muscle cells, as well as the formation of a continuous ring of tumor cells attached to the luminal side of VW in numerous cases. The latter feature is a clear sign of vascular mimicry (VM) that was previously suggested in gliomas but never shown by TEM. Additionally, the vascular invasion was carried out by a large number of tumor cells and was accompanied by the accumulation of tumor lipids in the vessels' lumina and VWs; these two features are distinct for gliomas and may alter the course of the clinical presentation and overall prognosis. This raises the issue of how to specifically target tumor cells involved in vascular invasion in order to optimize prognosis and overcome these mechanisms employed by the tumor cells.
Topics: Humans; Glioblastoma; Isocitrate Dehydrogenase; Endothelial Cells; Astrocytoma; Glioma; Brain Neoplasms; Mutation
PubMed: 37144386
DOI: 10.1080/01913123.2023.2205927 -
Brain Pathology (Zurich, Switzerland) Jan 2021Pleomorphic xanthoastrocytoma (PXA) is a rare astrocytoma predominantly affecting children and young adults. We performed comprehensive genomic characterization on a...
Pleomorphic xanthoastrocytoma (PXA) is a rare astrocytoma predominantly affecting children and young adults. We performed comprehensive genomic characterization on a cohort of 67 patients with histologically defined PXA (n = 53, 79%) or anaplastic PXA (A-PXA, n = 14, 21%), including copy number analysis (ThermoFisher Oncoscan, n = 67), methylation profiling (Illumina EPIC array, n = 43) and targeted next generation sequencing (n = 32). The most frequent alterations were CDKN2A/B deletion (n = 63; 94%) and BRAF p.V600E (n = 51, 76.1%). In 7 BRAF p.V600 wild-type cases, alternative driver alterations were identified involving BRAF, RAF1 and NF1. Downstream phosphorylation of ERK kinase was uniformly present. Additional pathogenic alterations were rare, with TERT, ATRX and TP53 mutations identified in a small number of tumors, predominantly A-PXA. Methylation-based classification of 46 cases utilizing a comprehensive reference tumor allowed assignment to the PXA methylation class in 40 cases. A minority grouped with the methylation classes of ganglioglioma or pilocytic astrocytoma (n = 2), anaplastic pilocytic astrocytoma (n = 2) or control tissues (n = 2). In 9 cases, tissue was available from matched primary and recurrent tumors, including 8 with anaplastic transformation. At recurrence, two tumors acquired TERT promoter mutations and the majority demonstrated additional non-recurrent copy number alterations. Methylation class was preserved at recurrence. For 62 patients (92.5%), clinical follow-up data were available (median follow-up, 5.4 years). Overall survival was significantly different between PXA and A-PXA (5-year OS 80.8% vs. 47.6%; P = 0.0009) but not progression-free survival (5-year PFS 59.9% vs. 39.8%; P = 0.05). WHO grade remained a strong predictor of overall survival when limited to 38 cases defined as PXA by methylation-based classification. Our data confirm the importance of WHO grading in histologically and epigenetically defined PXA. Methylation-based classification may be helpful in cases with ambiguous morphology, but is largely confirmatory in PXA with well-defined morphology.
Topics: Adolescent; Adult; Aged; Astrocytoma; Brain Neoplasms; Child; Child, Preschool; Cohort Studies; DNA Methylation; Female; Humans; Male; Middle Aged; Neoplasm Grading; Young Adult
PubMed: 32619305
DOI: 10.1111/bpa.12874 -
Scientific Reports Jun 2023Pleomorphic xanthoastrocytoma (PXA) is a rare subset of primary pediatric glioma with 70% 5-year disease free survival. However, up to 20% of cases present with local...
Pleomorphic xanthoastrocytoma (PXA) is a rare subset of primary pediatric glioma with 70% 5-year disease free survival. However, up to 20% of cases present with local recurrence and malignant transformation into more aggressive type anaplastic PXA (AXPA) or glioblastoma. The understanding of disease etiology and mechanisms driving PXA and APXA are limited, and there is no standard of care. Therefore, development of relevant preclinical models to investigate molecular underpinnings of disease and to guide novel therapeutic approaches are of interest. Here, for the first time we established, and characterized a patient-derived xenograft (PDX) from a leptomeningeal spread of a patient with recurrent APXA bearing a novel CDC42SE2-BRAF fusion. An integrated -omics analysis was conducted to assess model fidelity of the genomic, transcriptomic, and proteomic/phosphoproteomic landscapes. A stable xenoline was derived directly from the patient recurrent tumor and maintained in 2D and 3D culture systems. Conserved histology features between the PDX and matched APXA specimen were maintained through serial passages. Whole exome sequencing (WES) demonstrated a high degree of conservation in the genomic landscape between PDX and matched human tumor, including small variants (Pearson's r = 0.794-0.839) and tumor mutational burden (~ 3 mutations/MB). Large chromosomal variations including chromosomal gains and losses were preserved in PDX. Notably, chromosomal gain in chromosomes 4-9, 17 and 18 and loss in the short arm of chromosome 9 associated with homozygous 9p21.3 deletion involving CDKN2A/B locus were identified in both patient tumor and PDX sample. Moreover, chromosomal rearrangement involving 7q34 fusion; CDC42SE-BRAF t (5;7) (q31.1, q34) (5:130,721,239, 7:140,482,820) was identified in the PDX tumor, xenoline and matched human tumor. Transcriptomic profile of the patient's tumor was retained in PDX (Pearson r = 0.88) and in xenoline (Pearson r = 0.63) as well as preservation of enriched signaling pathways (FDR Adjusted P < 0.05) including MAPK, EGFR and PI3K/AKT pathways. The multi-omics data of (WES, transcriptome, and reverse phase protein array (RPPA) was integrated to deduce potential actionable pathways for treatment (FDR < 0.05) including KEGG01521, KEGG05202, and KEGG05200. Both xenoline and PDX were resistant to the MEK inhibitors trametinib or mirdametinib at clinically relevant doses, recapitulating the patient's resistance to such treatment in the clinic. This set of APXA models will serve as a preclinical resource for developing novel therapeutic regimens for rare anaplastic PXAs and pediatric high-grade gliomas bearing BRAF fusions.
Topics: Humans; Child; Proto-Oncogene Proteins B-raf; Heterografts; Phosphatidylinositol 3-Kinases; Proteomics; Neoplasm Recurrence, Local; Astrocytoma; Glioma; Mutation; Chromosome Aberrations; Brain Neoplasms; Membrane Proteins; Intracellular Signaling Peptides and Proteins
PubMed: 37280243
DOI: 10.1038/s41598-023-36107-2 -
Acta Neuropathologica Oct 2023Pilocytic astrocytoma (PA), the most common pediatric brain tumor, is driven by aberrant mitogen-activated protein kinase signaling most commonly caused by BRAF gene...
Pilocytic astrocytoma (PA), the most common pediatric brain tumor, is driven by aberrant mitogen-activated protein kinase signaling most commonly caused by BRAF gene fusions or activating mutations. While 5-year overall survival rates exceed 95%, tumor recurrence or progression constitutes a major clinical challenge in incompletely resected tumors. Here, we used similarity network fusion (SNF) analysis in an integrative multi-omics approach employing RNA transcriptomic and mass spectrometry-based proteomic profiling to molecularly characterize PA tissue samples from 62 patients. Thereby, we uncovered that PAs segregated into two molecularly distinct groups, namely, Group 1 and Group 2, which were validated in three non-overlapping cohorts. Patients with Group 1 tumors were significantly younger and showed worse progression-free survival compared to patients with group 2 tumors. Ingenuity pathways analysis (IPA) and gene set enrichment analysis (GSEA) revealed that Group 1 tumors were enriched for immune response pathways, such as interferon signaling, while Group 2 tumors showed enrichment for action potential and neurotransmitter signaling pathways. Analysis of immune cell-related gene signatures showed an enrichment of infiltrating T Cells in Group 1 versus Group 2 tumors. Taken together, integrative multi-omics of PA identified biologically distinct and prognostically relevant tumor groups that may improve risk stratification of this single pathway driven tumor type.
Topics: Child; Humans; Multiomics; Proteomics; Astrocytoma; Brain Neoplasms; Action Potentials
PubMed: 37656187
DOI: 10.1007/s00401-023-02626-5