Gliomas are differentiated into two major disease subtypes, astrocytoma or oligodendroglioma, which are then characterized as either IDH (isocitrate dehydrogenase)-wild type or IDH-mutant due to the dramatic differences in prognosis and overall survival. Here, we investigated the genetic background of IDH1-mutant gliomas using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. In astrocytoma patients, we found that IDH1 is often co-mutated with TP53, ATRX, AMBRA1, PREX1, and NOTCH1, but not CHEK2, EGFR, PTEN, or the zinc finger transcription factor ZNF429. The majority of the mutations observed in these genes were further confirmed to be either drivers or pathogenic by the Cancer-Related Analysis of Variants Toolkit (CRAVAT). Gene expression analysis showed down-regulation of DRG2 and MSN expression, both of which promote cell proliferation and invasion. There was also significant over-expression of genes such as NDRG3 and KCNB1 in IDH1-mutant astrocytoma patients. We conclude that IDH1-mutant glioma is characterized by significant genetic changes that could contribute to a better prognosis in glioma patients.

Arginine methylation is a form of posttranslational modification that regulates many cellular functions such as development, DNA damage repair, inflammatory response, splicing, and signal transduction, among others. Protein arginine methyltransferase 5 (PRMT5) is one of nine identified methyltransferases, and it can methylate both histone and non-histone targets. It has pleiotropic functions, including recruitment of repair machinery to a chromosomal DNA double strand break (DSB) and coordinating the interplay between repair and checkpoint activation. Thus, PRMT5 has been actively studied as a cancer treatment target, and small molecule inhibitors of its enzymatic activity have already been developed. In this report, we analyzed all reported PRMT5 mutations appearing in cancer cells using data from the Catalogue of Somatic Mutations in Cancers (COSMIC). Our goal is to classify mutations as either drivers or passengers to understand which ones are likely to promote cellular transformation. Using gold standard artificial intelligence algorithms, we uncovered several key driver mutations in the active site of the enzyme (D306H, L315P, and N318K). In silico protein modeling shows that these mutations may affect the affinity of PRMT5 for S-adenosylmethionine (SAM), which is required as a methyl donor. Electrostatic analysis of the enzyme active site shows that one of these mutations creates a tunnel in the vicinity of the SAM binding site, which may allow interfering molecules to enter the enzyme active site and decrease its activity. We also identified several non-coding mutations that appear to affect PRMT5 splicing. Our analyses provide insights into the role of PRMT5 mutations in cancer cells. Additionally, since PRMT5 single molecule inhibitors have already been developed, this work may uncover future directions in how mutations can affect targeted inhibition.

Background Patients’ subjective experiences during clinical interactions may affect their engagement in healthcare, and better understanding of the issues patients consider most important may help improve service quality and patient-staff relationships. While diagnostic imaging is a growing component of healthcare utilization, few studies have quantitatively and systematically assessed what patients deem most relevant in radiology settings. To elucidate factors driving patient satisfaction in outpatient radiology, we derived quantitative models to identify items most predictive of patients’ overall assessment of radiology encounters. Methods Press-Ganey survey data (N = 69,319) collected over a 9-year period at a single institution were retrospectively analyzed, with each item response dichotomized as “favorable” or “unfavorable.” Multiple logistic regression analyses were performed on 18 binarized Likert items to compute odds ratios (OR) for those question items significantly predicting Overall Rating of Care or Likelihood of Recommending. In a secondary analysis to identify topics more relevant to radiology than other encounter types, items significantly more predictive of concordant ratings in radiology compared to non-radiology visits were also identified. Results Among radiology survey respondents, top predictors of Overall Rating and Likelihood of Recommending were items addressing patient concerns or complaints (OR 6.8 and 4.9, respectively) and sensitivity to patient needs (OR 4.7 and 4.5, respectively). When comparing radiology and non-radiology visits, the top items more predictive for radiology included unfavorable responses to helpfulness of registration desk personnel (OR 1.4–1.6), comfort of waiting areas (OR 1.4), and ease of obtaining an appointment at the desired time (OR 1.4). Conclusions Items related to patient-centered empathic communication were the most predictive of favorable overall ratings among radiology outpatients, while underperformance in logistical issues related to registration, scheduling, and waiting areas may have greater adverse impact on radiology than non-radiology encounters. Findings may offer potential targets for future quality improvement efforts.