Mutation-specific non-canonical pathway of PTEN as a distinct therapeutic target for glioblastoma.

PTEN is one of the most frequently altered tumor suppressor genes in malignant tumors. The dominant-negative effect of PTEN alteration suggests that the aberrant function of PTEN mutation might be more disastrous than deletion, the most frequent genomic event in glioblastoma (GBM). This study aimed to understand the functional properties of various PTEN missense mutations and to investigate their clinical relevance. The genomic landscape of PTEN alteration was analyzed using the Samsung Medical Center GBM cohort and validated via The Cancer Genome Atlas dataset. Several hotspot mutations were identified, and their subcellular distributions and phenotypes were evaluated. We established a library of cancer cell lines that overexpress these mutant proteins using the U87MG and patient-derived cell models lacking functional PTEN. PTEN mutations were categorized into two major subsets: missense mutations in the phosphatase domain and truncal mutations in the C2 domain. We determined the subcellular compartmentalization of four mutant proteins (H93Y, C124S, R130Q, and R173C) from the former group and found that they had distinct localizations; those associated with invasive phenotypes ('edge mutations') localized to the cell periphery, while the R173C mutant localized to the nucleus. Invasive phenotypes derived from edge substitutions were unaffected by an anti-PI3K/Akt agent but were disrupted by microtubule inhibitors. PTEN mutations exhibit distinct functional properties regarding their subcellular localization. Further, some missense mutations ('edge mutations') in the phosphatase domain caused enhanced invasiveness associated with dysfunctional cytoskeletal assembly, thus suggesting it to be a potent therapeutic target.

Differences in Perspectives of Medical Device Adverse Events: Observational Results in Training Program Using Virtual Cases.

BACKGROUND: Medical device adverse event reporting is an essential activity for mitigating device-related risks. Reporting of adverse events can be done by anyone like healthcare workers, patients, and others. However, for an individual to determine the reporting, he or she should recognize the current situation as an adverse event. The objective of this report is to share observed individual differences in the perception of a medical device adverse event, which may affect the judgment and the reporting of adverse events. METHODS: We trained twenty-three participants from twelve Asia-Pacific Economic Cooperation (APEC) member economies about international guidelines for medical device vigilance. We developed and used six virtual cases and six questions. We divided participants into six groups and compared their opinions. We also surveyed the country's opinion to investigate the beginning point of 'patient use'. The phases of 'patient use' are divided into: 1) inspecting, 2) preparing, and 3) applying medical device. RESULTS: As for the question on the beginning point of 'patient use,' 28.6%, 35.7%, and 35.7% of participants provided answers regarding the first, second, and third phases, respectively. In training for applying international guidelines to virtual cases, only one of the six questions reached a consensus between the two groups in all six virtual cases. For the other five questions, different judgments were given in at least two groups. CONCLUSION: From training courses using virtual cases, we found that there was no consensus on 'patient use' point of view of medical devices. There was a significant difference in applying definitions of adverse events written in guidelines regarding the medical device associated incidents. Our results point out that international harmonization effort is needed not only to harmonize differences in regulations between countries but also to overcome diversity in perspectives existing at the site of medical device use.

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noninvasive methods helps establish the appropriate treatment strategy. We aimed to predict IDH1 mutation status using quantitative radiomic data in patients with GBM. METHODS: Between May 2010 and June 2015, we retrospectively identified 88 patients with newly diagnosed GBM. After semiautomatic segmentation of the lesions, we extracted 31 features from preoperative multiparametric magnetic resonance images. We also determined IDH1 mutation status using targeted sequencing and immunohistochemistry. A training cohort (n = 88) was used to train machine learning-based classifiers, with internal validation. The machine-learning technique was then validated in an external dataset of 35 patients with GBM. RESULTS: We detected the IDH1 mutation in 12 out of 88 GBMs. Multiparametric radiomic profiles revealed that the IDH1 mutation was associated with a smaller enhancing area volume and a larger necrotic area volume. Using the machine learning-based classification algorithms, we identified 70.3%-87.3% of prediction rate of IDH1 mutation status and found 66.3%-83.4% accuracy in the external validation set. CONCLUSIONS: We demonstrate that machine learning algorithms can predict IDH1 mutation status in GBM using preoperative multiparametric magnetic resonance images.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.