ATM mutations improve radio-sensitivity in wild-type isocitrate dehydrogenase-associated high-grade glioma: retrospective analysis using next-generation sequencing data

ATM mutations improve radio-sensitivity in wild-type isocitrate dehydrogenase-associated high-grade glioma: retrospective analysis using next-generation sequencing data

Background: To identify the relationship between somatic ataxia-telangiectasia mutated (ATM) mutation and improved radio-sensitivity, we retrospectively reviewed the next-generation sequencing data from patients diagnosed with glioma isocitrate dehydrogenase (IDH) -wildtype high quality.


Methods: We included 39 individuals (IDH) -wildtype high-grade glioma (diffuse astrocytoma n = 2, n = 10 anaplastic astrocytoma and glioblastoma n = 27) had no gross tumor resection and radiation therapy undergo the median total dose of 60 Gy in 30 fractions. ATM gene mutation status was obtained through generation sequencing using TruSight cancer panel 170. Tumor progression was defined by Response Assessment in Neuro-Oncology (Rano) criteria as well as neurological and clinical findings.
Results: Among the 39 samples, ATM mutations (mut ATM (+)) was detected in 26% of cases (n = 10).

No significant differences were observed in the patient or tumor characteristics. Among the 10 patients in the ATM mut (+), there are 6 patients with glioblastoma and four patients with anaplastic astrocytoma. Most mutations are missense mutations (n ​​= 8, 80%). With a median follow-up of 16.5 mo (interquartile range, 11.4 to 19.8), ATM mut (+) exhibited one year of control in the field of 100% compared with 44.1% in ATM mut (-) group (p = 0.002). There was no difference in the rate of out-of-field control or overall survival between the two groups (p = 0.861 and p = 0.247, respectively).


Conclusions: Our results indicate that ATM mutations may be involved in the increase in radio-sensitivity with good control in the field despite the aggressive nature of IDH-wildtype high-grade glioma. Further studies are needed to uncover the potential role of ATM as a candidate biomarker and therapeutic target in high-grade glioma.

 ATM mutations improve radio-sensitivity in wild-type isocitrate dehydrogenase-associated high-grade glioma: retrospective analysis using next-generation sequencing data
ATM mutations improve radio-sensitivity in wild-type isocitrate dehydrogenase-associated high-grade glioma: retrospective analysis using next-generation sequencing data

Whole-genome sequencing and Mycobacterium tuberculosis: Challenges in sample preparation and analysis of sequencing data

The number of patients with tuberculosis (TB) caused by resistant strains are still alarming. Therefore, it is necessary to determine the resilience of faster and more accurate, than with the currently used phenotypic and genotypic methods.

In recent years, technological advances have been made and the method of whole genome sequencing (WGS) has been introduced as part of routine clinical laboratory diagnostics. Comparing various mycobacterial genome variation with reference genome leads to a consistent evaluation of molecular epidemiology and resistance of Mycobacterium tuberculosis (M. tuberculosis) to a variety of anti-TB drugs.

The quality of sequencing data obtained is closely related to the type of samples and the methods used for DNA extraction and sequencing library preparation. In addition, the correct interpretation of the results is also affected by bioinformatics data processing. A large number of bioinformatics pipeline that is currently available, the sensitivity varies due to the different size of the database containing the relevant mutation. This review focuses on the individual steps in the workflow including sequencing and factors that may affect the interpretation of the final results.


comparative analysis suggests that miR-375 and miR-7 expression was substantially higher in the case of the control of non-Nen Nen. Correlation analysis showed that NENs from diverse anatomical sites have convergent program miRNA expression, may reflect the morphological and functional similarities.

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Using machine learning approach, we identified 17 miRNAs to distinguish 15 Nen type of pathological and then build a classifier multilayer, correctly identified 217 (98%) of the 221 samples and overturning a histological diagnosis.

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