Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.10.1 (ERK)
 95,504 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

KIAA1549-BRAF fusion gene and isocitrate dehydrogenase (IDH) mutations are considered two mutually exclusive genetic events in pilocytic astrocytomas and diffuse gliomas, respectively. We investigated the presence of the KIAA1549-BRAF fusion gene in conjunction with IDH mutations and 1p/19q loss in 185 adult diffuse gliomas. Moreover BRAF(v600E) mutation was also screened. The KIAA1549-BRAF fusion gene was evaluated by reverse-transcription polymerase chain reaction (RT-PCR) and sequencing. We found IDH mutations in 125 out 175 cases (71.4%). There were KIAA1549-BRAF fusion gene in 17 out of 180 (9.4%) cases and BRAF(v600E) in 2 out of 133 (1.5%) cases. In 11 of these 17 cases, both IDH mutations and the KIAA1549-BRAF fusion were present, as independent molecular events. Moreover, 6 of 17 cases showed co-presence of 1p/19q loss, IDH mutations and KIAA1549-BRAF fusion. Among the 17 cases with KIAA1549-BRAF fusion gene 15 (88.2%) were oligodendroglial neoplasms. Similarly, the two cases with BRAF(v600E) mutation were both oligodendroglioma and one had IDH mutations and 1p/19q co-deletion. Our results suggest that in a small fraction of diffuse gliomas, KIAA1549-BRAF fusion gene and BRAF(v600E) mutation may be responsible for deregulation of the Ras-RAF-ERK signaling pathway. Such alterations are more frequent in oligodendroglial neoplasm and may be co-present with IDH mutations and 1p/19q loss.
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PMID:KIAA1549-BRAF fusions and IDH mutations can coexist in diffuse gliomas of adults. 2259 44

Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.
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PMID:Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. 2423 9

It has become increasingly evident that morphologically similar gliomas may have distinct clinical phenotypes arising from diverse genetic signatures. To date, glial tumours from the Tunisian population have not been investigated. To address this, we correlated the clinico-pathology with molecular data of 110 gliomas by a combination of HM450K array, MLPA and TMA-IHC. PTEN loss and EGFR amplification were distributed in different glioma histological groups. However, 1p19q co-deletion and KIAA1549:BRAF fusion were, respectively, restricted to Oligodendroglioma and Pilocytic Astrocytoma. CDKN2A loss and EGFR overexpression were more common within high-grade gliomas. Furthermore, survival statistical correlations led us to identify Glioblastoma (GB) prognosis subtypes. In fact, significant lower overall survival (OS) was detected within GB that overexpressed EGFR and Cox2. In addition, IDH1R132H mutation seemed to provide a markedly survival advantage. Interestingly, the association of IDHR132H mutation and EGFR normal status, as well as the association of differentiation markers, defined GB subtypes with good prognosis. By contrast, poor survival GB subtypes were defined by the combination of PTEN loss with PDGFRa expression and/or EGFR amplification. Additionally, GB presenting p53-negative staining associated with CDKN2A loss or p21 positivity represented a subtype with short survival. Thus, distinct molecular subtypes with individualised prognosis were identified. Interestingly, we found a unique histone mutation in a poor survival young adult GB case. This tumour exceptionally associated the H3F3A G34R mutation and MYCN amplification as well as 1p36 loss and 10q loss. Furthermore, by exhibiting a remarkable methylation profile, it emphasised the oncogenic power of G34R mutation connecting gliomagenesis and chromatin regulation.
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PMID:Molecular Diagnostic and Prognostic Subtyping of Gliomas in Tunisian Population. 2695 5