| Title |
Pediatric low-grade gliomas can be molecularly stratified for risk
|
|---|---|
| Published in |
Acta Neuropathologica, June 2018
|
| DOI | 10.1007/s00401-018-1874-3 |
| Pubmed ID | |
| Authors |
Rui Ryan Yang, Abudumijiti Aibaidula, Wei-wei Wang, Aden Ka-Yin Chan, Zhi-feng Shi, Zhen-yu Zhang, Danny Tat Ming Chan, Wai Sang Poon, Xian-zhi Liu, Wen-cai Li, Rui-qi Zhang, Yan-Xi Li, Nellie Yuk-Fei Chung, Hong Chen, Jingsong Wu, Liangfu Zhou, Kay Ka-Wai Li, Ho-Keung Ng |
| Abstract |
Pediatric low-grade gliomas (PLGGs) consist of a number of entities with overlapping histological features. PLGGs have much better prognosis than the adult counterparts, but a significant proportion of PLGGs suffers from tumor progression and recurrence. It has been shown that pediatric and adult low-grade gliomas are molecularly distinct. Yet the clinical significance of some of newer biomarkers discovered by genomic studies has not been fully investigated. In this study, we evaluated in a large cohort of 289 PLGGs a list of biomarkers and examined their clinical relevance. TERT promoter (TERTp), H3F3A and BRAF V600E mutations were detected by direct sequencing. ATRX nuclear loss was examined by immunohistochemistry. CDKN2A deletion, KIAA1549-BRAF fusion, and MYB amplification were determined by fluorescence in situ hybridization (FISH). TERTp, H3F3A, and BRAF V600E mutations were identified in 2.5, 6.4, and 7.4% of PLGGs, respectively. ATRX loss was found in 4.9% of PLGGs. CDKN2A deletion, KIAA1549-BRAF fusion and MYB amplification were detected in 8.8, 32.0 and 10.6% of PLGGs, respectively. Survival analysis revealed that TERTp mutation, H3F3A mutation, and ATRX loss were significantly associated with poor PFS (p < 0.0001, p < 0.0001, and p = 0.0002) and OS (p < 0.0001, p < 0.0001, and p < 0.0001). BRAF V600E was associated with shorter PFS (p = 0.011) and OS (p = 0.032) in a subset of PLGGs. KIAA1549-BRAF fusion was a good prognostic marker for longer PFS (p = 0.0017) and OS (p = 0.0029). MYB amplification was also a favorable marker for a longer PFS (p = 0.040). Importantly, we showed that these molecular biomarkers can be used to stratify PLGGs into low- (KIAA1549-BRAF fusion or MYB amplification), intermediate-I (BRAF V600E and/or CDKN2A deletion), intermediate-II (no biomarker), and high-risk (TERTp or H3F3A mutation or ATRX loss) groups with distinct PFS (p < 0.0001) and OS (p < 0.0001). This scheme should aid in clinical decision-making. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 29% |
| United States | 2 | 29% |
| Germany | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 2 | 29% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 15% |
| Student > Ph. D. Student | 8 | 11% |
| Student > Master | 8 | 11% |
| Other | 7 | 10% |
| Student > Bachelor | 5 | 7% |
| Other | 17 | 24% |
| Unknown | 15 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 39% |
| Biochemistry, Genetics and Molecular Biology | 10 | 14% |
| Neuroscience | 6 | 8% |
| Nursing and Health Professions | 3 | 4% |
| Engineering | 2 | 3% |
| Other | 7 | 10% |
| Unknown | 15 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 July 2018.
All research outputs
#7,010,784
of 23,128,387 outputs
Outputs from Acta Neuropathologica
#1,329
of 2,386 outputs
Outputs of similar age
#120,520
of 328,645 outputs
Outputs of similar age from Acta Neuropathologica
#29
of 38 outputs
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