The Mystery of Multiple Masses: A Case of Anaplastic Astrocytoma.
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ABSTRACT:
SUBMITTER: Sethi P
PROVIDER: S-EPMC5522018 | biostudies-other | 2017 Jun
REPOSITORIES: biostudies-other
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Project description:Anaplastic astrocytoma (AA) is a diffusely infiltrating, malignant, astrocytic, primary brain tumor. AA is currently defined by histology although future classification schemes will include molecular alterations. AA can be separated into subgroups, which share similar molecular profiles, age at diagnosis and median survival, based on 1p/19q co-deletion status and IDH mutation status. AA with co-deletion of chromosomes 1p and 19q and IDH mutation have the best prognosis. AA with IDH mutation and no 1p/19q co-deletion have intermediate prognosis and AA with wild-type IDH have the worst prognosis and share many molecular alterations with glioblastoma. Treatment of noncodeleted AA based on preliminary results from the CATNON clinical trial consists of maximal safe resection followed by radiotherapy with post-radiotherapy temozolomide (TMZ) chemotherapy. The role of concurrent TMZ and whether IDH1 subgroups benefit from TMZ is currently being evaluated in the recently completed randomized, prospective Phase III clinical trial, CATNON.
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Project description:We report on a patient with atypical parkinsonism due to coexistent Lewy body disease (LBD) and diffuse anaplastic astrocytoma. The patient presented with a mixed cerebellar and parkinsonian syndrome, incomplete levodopa response, and autonomic failure. The clinical diagnosis was multiple system atrophy (MSA). Supportive features of MSA according to the consensus diagnostic criteria included postural instability and early falls, early dysphagia, pyramidal signs, and orofacial dystonia. Multiple exclusion criteria for a diagnosis of idiopathic Parkinson's disease (iPD) were present. Neuropathological examination of the left hemisphere and the whole midbrain and brainstem revealed LBD, neocortical-type consistent with iPD, hippocampal sclerosis, and widespread neoplastic infiltration by an anaplastic astrocytoma without evidence of a space occupying lesion. There were no pathological features of MSA. The classification of atypical parkinsonism was difficult in this patient. The clinical features and disease course were confounded by the coexistent tumor, leading to atypical presentation and a diagnosis of MSA. We suggest that the initial features were due to Lewy body pathology, while progression and ataxia, pyramidal signs, and falls were accelerated by the occurrence of the astrocytoma. Our case reflects the challenges of an accurate diagnosis of atypical parkinsonism, the potential for confounding co-pathology and the need for autopsy examination to reach a definitive diagnosis.
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Project description:Anaplastic astrocytoma WHO grade III (A3) is a lethal brain tumor that often occurs in middle aged patients. Clinically, it is challenging to distinguish A3 from glioblastoma multiforme (GBM) WHO grade IV. To reveal the genetic landscape of this tumor type, we sequenced the exome of a cohort of A3s (n=16). For comparison and to illuminate the genomic landscape of other glioma subtypes, we also included in our study diffuse astrocytoma WHO grade II (A2, n=7), oligoastrocytoma WHO grade II (OA2, n=2), anaplastic oligoastrocytoma WHO grade III (OA3, n=4), and GBM (n=28). Exome sequencing of A3s identified frequent mutations in IDH1 (75%, 12/16), ATRX (63%, 10/16), and TP53 (82%, 13/16). In contrast, the majority of GBMs (75%, 21/28) did not contain IDH1 or ATRX mutations, and displayed a distinct spectrum of mutations. Finally, our study also identified novel genes that were not previously linked to this tumor type. In particular, we found mutations in Notch pathway genes (NOTCH1, NOTCH2, NOTCH4, NOTCH2NL), including a recurrent NOTCH1-A465Tmutation, in 31% (5/16) of A3s. This study suggests genetic signatures will be useful for the classification of gliomas.
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Project description:BackgroundOllier disease is a rare, nonfamilial disorder that primary affects the long bones and cartilage of joints with multiple enchondromas. It is associated with a higher risk of central nervous system (CNS) malignancies; although the incidence is unknown.Case descriptionHere, we present the case of a 55-year-old woman who developed an anaplastic astrocytoma with a known diagnosis of Ollier disease with a survival time of over 3 years.ConclusionThis report draws attention to the rarity of this disease and the paucity of information regarding CNS involvement in Ollier disease, as well as reviews the current literature.
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Project description:Anaplastic astrocytomas are aggressive glial cancers that present poor prognosis and high recurrence. Heterozygous IDH1 R132H mutations are common in adolescent and young adult anaplastic astrocytomas. In a majority of cases, the IDH1 R132H mutation is unique to the tumor, although rare cases of anaplastic astrocytoma have been described in patients with mosaic IDH1 mutations (Ollier disease or Maffucci syndrome). Here, we present two siblings with IDH1 R132H mutant high grade astrocytomas diagnosed at 14 and 26 years of age. Analysis of IDHR132H mutations in the siblings' tumors and non-neoplastic tissues, including healthy regions of the brain, cheek cells, and primary teeth indicate mosaicism of IDHR132H. Whole exome sequencing of the tumor tissue did not reveal any other common mutations between the two siblings. This study demonstrates the first example of IDH1 R132H mosaicism, acquired during early development, that provides an alternative mechanism of cancer predisposition.
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Project description:BackgroundCo-occurrence of multiple sclerosis (MS) and glial tumours (GT) is uncommon although occasionally reported in medical literature. Interpreting the overlapping radiologic and clinical characteristics of glial tumours, MS lesions, and progressive multifocal leukoencephalopathy (PML) can be a significant diagnostic challenge.Case presentationWe report a case of anaplastic astrocytoma mimicking PML in a 27-year-old patient with a 15-year history of MS. She was treated with interferon, natalizumab and finally fingolimod due to active MS. Follow-up MRI, blood and cerebrospinal fluid examinations, and biopsy were conducted, but only the latter was able to reveal the cause of progressive worsening of patient's disease.ConclusionsAnaplastic astrocytoma misdiagnosed as PML has not yet been described. We suppose that the astrocytoma could have evolved from a low grade glioma to anaplastic astrocytoma over time, as the tumour developed adjacent to typical MS plaques. The role of the immunomodulatory treatment as well as other immunological factors in the malignant transformation can only be hypothesised. We discuss clinical, laboratory and diagnostic aspects of a malignant GT, MS lesions and PML. The diagnosis of malignant GT must be kept in mind when an atypical lesion develops in a patient with MS.
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Project description:BackgroundThe 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.MethodsThis 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.ResultsThe 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.ConclusionsThis study indicated that MRI texture analysis combined with LDA algorithm has the potential to be utilized in distinguishing the subtypes of high-grade gliomas.
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Project description:Introduction: Glioblastoma and anaplastic astrocytoma (ANA) are two of the most common primary brain tumors in adults. The differential diagnosis is important for treatment recommendations and prognosis assessment. This study aimed to assess the discriminative ability of texture analysis using machine learning to distinguish glioblastoma from ANA. Methods: A total of 123 patients with glioblastoma (n = 76) or ANA (n = 47) were enrolled in this study. Texture features were extracted from contrast-enhanced Magnetic Resonance (MR) images using LifeX package. Three independent feature-selection methods were performed to select the most discriminating parameters:Distance Correlation, least absolute shrinkage and selection operator (LASSO), and gradient correlation decision tree (GBDT). These selected features (datasets) were then randomly split into the training and the validation group at the ratio of 4:1 and were fed into linear discriminant analysis (LDA), respectively, and independently. The standard sensitivity, specificity, the areas under receiver operating characteristic curve (AUC) and accuracy were calculated for both training and validation group. Results: All three models (Distance Correlation + LDA, LASSO + LDA and GBDT + LDA) showed promising ability to discriminate glioblastoma from ANA, with AUCs ?0.95 for both the training and the validation group using LDA algorithm and no overfitting was observed. LASSO + LDA showed the best discriminative ability in horizontal comparison among three models. Conclusion: Our study shows that MRI texture analysis using LDA algorithm had promising ability to discriminate glioblastoma from ANA. Multi-center studies with greater number of patients are warranted in future studies to confirm the preliminary result.
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Project description:Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.
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Project description: Not available
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