Total shoulder arthroplasty versus reverse total shoulder arthroplasty: Outcome comparison in osteoarthritis patients with or without concurrent rotator cuff deficiency.

Osteoarthritis (OA) is a common shoulder disorder that impacts shoulder functions. Shoulder arthroplasty is often required to restore function and quality of life. Reverse total shoulder arthroplasty (RSA), which was originally designed mainly for irreparable rotator cuff damage, has gained popularity in recent years for the treatment of advanced shoulder OA instead of the clinically standard total shoulder arthroplasty (TSA). However, this RSA has some nonnegligible flaws such as higher complications rate and economic cost, not mention the following problems caused by irreversible physical structural damage. Therefore, the employment of RSA needs to be carefully considered. This study aimed to compare TSA and RSA in OA patients with or without rotator cuff damage to better guide clinical decision making. We believe the radical use of RSA in patients without rotator cuff deficiency may cause more harm than good. We queried the Nationwide Inpatient Sample (NIS) database from 2011 to 2014 to collect information on OA patients who received TSA and RSA. Patients were divided into 2 groups of comparison according to the presence of rotator cuff deficiency and matched with propensity score analysis. A total of 57,156 shoulder arthroplasties were identified. RSA patients in the rotator cuff deficiency group had significant higher transfusion rates and longer hospital stays. RSA patients without rotator cuff deficiency had a statistically significantly higher number of implant-related mechanical complications, acute upper respiratory infections and postoperative pain. Overall, RSA incurred higher costs in both groups. For OA patients with rotator cuff deficiencies, RSA has its benefits as complication rates were comparable to TSA. For those patients without rotator cuff deficiencies, the use of RSA should be reconsidered as there were more complications with higher severity.

Assessment of RAS Dependency for BRAF Alterations Using Cancer Genomic Databases.

Importance: Understanding RAS dependency and mechanisms of RAS activation in non-V600 BRAF variant cancers has important clinical implications. This is the first study to date to systematically assess RAS dependency of BRAF alterations with real-world cancer genomic databases. Objective: To evaluate RAS dependency of individual BRAF alterations through alteration coexistence analysis using cancer genomic databases. Design and Setting: A cross-sectional data analysis of 119 538 nonredundant cancer samples using cancer genomics databases including GENIE (Genomics Evidence Neoplasia Information Exchange) and databases in cBioPortal including TCGA (The Cancer Genome Atlas) (accessed March 24, 2020), in addition to 2745 cancer samples from Mayo Clinic Genomics Laboratory (January 1, 2015, to July 1, 2020). Frequencies and odds ratios of coexisting alterations of RAS (KRAS, NRAS and HRAS) and RAS regulatory genes (NF1, PTPN11 and CBL) were calculated for individual BRAF alterations, and compared according to the current BRAF alteration classification; cancer type specificity of coexisting alterations of RAS or RAS regulatory genes was also evaluated. Main Outcomes and Measures: Primary outcome measurement is enrichment of RAS (KRAS, NRAS and HRAS) alterations in BRAF variant cancers. Secondary outcome measurement is enrichment of RAS regulatory gene (NF1, PTPN11, and CBL) in BRAF variant cancers. Results: A total of 2745 cancer samples from 2708 patients (female/male ratio: 1.0) tested by Mayo Clinic Genomics Laboratory and 119 538 patients (female/male ratio: 1.1) from GENIE and cBioPortal database were included in the study. In 119 538 nonredundant cancer samples, class 1 BRAF alterations and BRAF fusions were found to be mutually exclusive to alterations of RAS or RAS regulatory genes (odds ratio range 0.03-0.13 and 0.03-0.73 respectively), confirming their RAS independency. Both class 2 and class 3 BRAF alterations show variable and overlapping levels of enriched RAS alterations (odds ratio range: 0.03-5.9 and 0.63-2.52 respectively), suggesting heterogeneity in RAS dependency and a need to revisit BRAF alteration classification. For RAS-dependent BRAF alterations, the coexisting alterations also involve RAS regulatory genes by enrichment analysis (for example, S467L shows an odds ratio of 8.26 for NF1, 9.87 for PTPN11, and 15.23 for CBL) and occur in a variety of cancer types with some coalterations showing cancer type specificity (for example, HRAS variations account for 46.7% of all coexisting RAS alterations in BRAF variant bladder cancers, but 0% in non-small cell lung cancers). Variant-level assessment shows that BRAF alterations involving the same codon may differ in RAS dependency. In addition, RAS dependency of previously unclassified BRAF alterations could be assessed. Conclusions and Relevance: Current BRAF alteration classification based on in vitro assays does not accurately predict RAS dependency in vivo for non-V600 BRAF alterations. RAS-dependent BRAF variant cancers with different mechanisms of RAS activation suggest the need for different treatment strategies.

Data-driven Sublanguage Analysis for Cancer Genomics Knowledge Modeling: Applications in Mining Oncological Genetics Information from Patients' Genetic Reports.

Despite an abundance of information in clinical genetic testing reports, information is oftentimes not well documented/utilized for decision making. Unstructured information in genetic reports can contribute to long-term patient management and future translational research. Thus, we proposed a knowledge model that could manage unstructured information in medical genetic reports and facilitate knowledge extraction, curation and updating. For this pilot study, we used a dataset including 1,565 cancer genetics reports of Mayo Clinic patients. We used a previously developed, data-driven discovery pipeline that involves both semantic annotation and co-occurrence association analysis to establish a knowledge model. We showed that compared to genetic reports, around 56% of testing results are missing or incomplete in the clinical notes. We built a genetic report knowledge model and highlighted four key semantic groups including "Genes and Gene Products" and "Treatments". Coverage of term annotation was 99.5%. Accuracies of term annotation and relationship extraction were 98.9% and 92.9% respectively.