BackgroundGenetic alterations in pediatric primary brain tumors can be used as diagnostic and prognostic markers and are the basis for the development of new target therapies that, ideally, would be associated with lower mortality and morbidity. This study evaluates the incidence and interplay of the presence of BRAF V600E mutation and chromosomal 9p21 deletions in a series of 100 pediatric gliomas, aiming to determine the role of these alterations in recurrence and malignant transformation, and to verify if they could be used in the clinical set for stratifying patients for tailored therapies and surveillance.MethodsSanger sequencing was used for the assessment of BRAF mutations at exon 15 and Fluorescent In Situ Hybridization (FISH) with BAC: RP11–14192 for the detection of 9p21 alterations. Expression levels of the CDKN2A and MTAP by real-time PCR were evaluated in cases with 9p21 deletions. Statistical analysis of genetic and clinical data was performed using Graph Pad Prism 5 and SPSS Statistics 24 software.ResultsIn our cohort it was observed that 7 /78 (8,9%) of the low-grade tumors recurred and 2 (2,6%) showed malignant transformation. BRAF V600E mutations were detected in 15 cases. No statistically significant correlations were found between the presence of BRAF V600E mutation and patient’s morphologic or clinical features. Deletions at 9p21 abrogating the CDKN2A/B and MTAP loci were rare in grade I gliomas (12.2%, p = 0.0178) but frequent in grade IV gliomas (62.5%, p = 0.0087). Moreover it was found that deletions at these loci were correlated with a shorter overall survival (p = 0.011) and a shorter progression-free survival (p = 0.016).ConclusionsIt was demonstrated that in these tumors BRAF V600E mutated and that CDKN2A/B MTAP co-deletions may be used for stratifying patients for a stricter surveillance. The Investigating and defining if glial tumors with CDKN2A/B and MTAP homozygous loss may be vulnerable to new forms of therapy, namely those affecting the methionine salvage pathway, was proven to be of importance.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5120-0) contains supplementary material, which is available to authorized users.

Background: The best curative treatment for hepatocellular carcinoma (HCC) is liver transplant (LT), but the limited number of organs available for LT dictates strict eligibility criteria. Despite this patient selection stringency, current criteria often fail in pinpointing patients at risk of HCC relapse and in identifying good prognosis patients that could benefit from a LT. HepatoPredict kit was developed and clinically validated to forecast the benefit of LT in patients diagnosed with HCC. By combining clinical variables and a gene expression signature in an ensemble of machine learning algorithms, HepatoPredict stratifies HCC patients according to their risk of relapse after LT. Methods: Aiming at the characterization of the analytical performance of HepatoPredict kit in terms of sensitivity, specificity and robustness, several variables were tested which included reproducibility between operators and between RNA extractions and RT-qPCR runs, interference of input RNA levels or varying reagent levels. The described methodologies, included in the HepatoPredict kit, were tested according to analytical validation criteria of multi-target genomic assays described in guidelines such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. Furthermore, a new retrained version of the HepatoPredict algorithms is also presented and tested. Results: The results of the analytical performance demonstrated that the HepatoPredict kit performed within the required levels of robustness (p > 0.05), analytical specificity (inclusivity ≥ 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The introduced operator, equipment, input RNA and reagents into the assay had no significant impact on HepatoPredict classifier results. As demonstrated in a previous clinical validation, a new retrained version of the HepatoPredict algorithm still outperformed current clinical criteria, in the accurate identification of HCC patients that more likely will benefit from a LT. Conclusions: Despite the variations in the molecular and clinical variables, the prognostic information obtained with HepatoPredict kit and does not change and can accurately identify HCC patients more likely to benefit from a LT. HepatoPredict performance robustness also validates its easy integration into standard diagnostic laboratories.