Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman.

BACKGROUND: Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE: To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN: Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS: Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES: Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS: A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS: Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Epidemiology and treatment patterns of idiopathic multicentric Castleman disease in the era of IL-6-directed therapy.

The epidemiology of human herpesvirus-8-negative/idiopathic multicentric Castleman disease (iMCD) remains incompletely understood. Prior epidemiologic studies of CD and iMCD have been hampered by difficulties in accurate case ascertainment resulting from a lack of uniform diagnostic criteria and a disease-specific International Classification of Diseases (ICD) code. In this study, we provide reliable estimates of CD and iMCD in the United States using a novel claims-based algorithm that includes a CD-specific ICD (10th revision) diagnosis code (D47.Z2) supported by the presence of ≥2 claims codes corresponding to the minor criteria from the international evidence-based diagnostic criteria for iMCD. We additionally analyzed the treatment classes and patterns in the clinical course of patients with iMCD. Using an administrative claims database of 30.7 million individuals enrolled between 1 January 2017 and 31 December 2018, we identified 254 patients with iMCD, with an estimated annual incidence and prevalence of 3.4 (95% confidence interval [CI], 1.4-9.2) and 6.9 (95% CI, 3.7-13.3) cases per million, respectively. Among patients with iMCD, 39% received corticosteroid monotherapy, 33.1% received no iMCD-directed treatment, and 9.8% received interleukin-6 (IL-6)-targeted therapy with tocilizumab or siltuximab. Siltuximab, which is the only US Food and Drug Administration-approved treatment and established first-line treatment recommendation, was used in only 8.7% of patients with iMCD. This study provides the most up-to-date understanding of the iMCD disease burden in the United States and identifies a major unmet treatment need for IL-6-directed therapy in this vulnerable cohort.

Current and Future Projections of Amyotrophic Lateral Sclerosis in the United States Using Administrative Claims Data.

BACKGROUND: Various methodologies have been reported to assess the real-world epidemiology of amyotrophic lateral sclerosis (ALS) in the United States. The aim of this study was to estimate the prevalence, incidence, and geographical distribution of ALS using administrative claims data and to model future trends in ALS epidemiology. METHODS: We performed a retrospective analysis of deidentified administrative claims data for >100 million patients, using 2 separate databases (IBM MarketScan Research Databases and Symphony Health Integrated DataVerse [IDV]), to identify patients with ALS. We evaluated disease prevalence, annual incidence, age- and population-controlled geographical distribution, and expected future trends. RESULTS: From 2013 to 2017, we identified 7,316 and 35,208 ALS patients from the MarketScan databases and IDV, respectively. Average annual incidence estimates were 1.48 and 1.37 per 100,000 and point prevalence estimates were 6.85 and 5.16 per 100,000 and in the United States for the MarketScan databases and IDV, respectively. Predictive modeling estimates are reported out to the year 2060 and demonstrate an increasing trend in both incident and prevalent cases. CONCLUSIONS: This study provides incidence and prevalence estimates as well as geographical distribution for what the authors believe to be the largest ALS population studied to date. By using 2 separate administrative claims data sets, confidence in our estimates is increased. Future projections based on either database demonstrate an increase in ALS cases, which has also been seen in other large-scale ALS studies. These results can be used to help improve the allocation of healthcare resources in the future.

A Pilot Comparative Study of Quantitative Ultrasound, Conventional Ultrasound, and MRI for Predicting Histology-Determined Steatosis Grade in Adult Nonalcoholic Fatty Liver Disease.

OBJECTIVE: The purpose of this study is to explore the diagnostic performance of two investigational quantitative ultrasound (QUS) parameters, attenuation coefficient and backscatter coefficient, in comparison with conventional ultrasound (CUS) and MRI-estimated proton density fat fraction (PDFF) for predicting histology-confirmed steatosis grade in adults with nonalcoholic fatty liver disease (NAFLD). SUBJECTS AND METHODS: In this prospectively designed pilot study, 61 adults with histology-confirmed NAFLD were enrolled from September 2012 to February 2014. Subjects underwent QUS, CUS, and MRI examinations within 100 days of clinical-care liver biopsy. QUS parameters (attenuation coefficient and backscatter coefficient) were estimated using a reference phantom technique by two analysts independently. Three-point ordinal CUS scores intended to predict steatosis grade (1, 2, or 3) were generated independently by two radiologists on the basis of QUS features. PDFF was estimated using an advanced chemical shift-based MRI technique. Using histologic examination as the reference standard, ROC analysis was performed. Optimal attenuation coefficient, backscatter coefficient, and PDFF cutoff thresholds were identified, and the accuracy of attenuation coefficient, backscatter coefficient, PDFF, and CUS to predict steatosis grade was determined. Interobserver agreement for attenuation coefficient, backscatter coefficient, and CUS was analyzed. RESULTS: CUS had 51.7% grading accuracy. The raw and cross-validated steatosis grading accuracies were 61.7% and 55.0%, respectively, for attenuation coefficient, 68.3% and 68.3% for backscatter coefficient, and 76.7% and 71.3% for MRI-estimated PDFF. Interobserver agreements were 53.3% for CUS (κ = 0.61), 90.0% for attenuation coefficient (κ = 0.87), and 71.7% for backscatter coefficient (κ = 0.82) (p < 0.0001 for all). CONCLUSION: Preliminary observations suggest that QUS parameters may be more accurate and provide higher interobserver agreement than CUS for predicting hepatic steatosis grade in patients with NAFLD.

Profiling lysine ubiquitination by selective enrichment of ubiquitin remnant-containing peptides.

Protein ubiquitination plays critical roles in many biological processes. However, functional studies of protein ubiquitination in eukaryotic cells are limited by the ability to identify protein ubiquitination sites. Unbiased high-throughput screening methods are necessary to discover novel ubiquitination sites that play important roles in cellular regulation. Here, we describe an immunopurification approach that enriches ubiquitin remnant-containing peptides to facilitate downstream mass spectrometry (MS) identification of lysine ubiquitination sites. This approach can be utilized to identify ubiquitination sites from proteins in a complex mixture.

Fluorescence imaging of cellular metabolites with RNA.

Genetically encoded sensors are powerful tools for imaging intracellular metabolites and signaling molecules. However, developing sensors is challenging because they require proteins that undergo conformational changes upon binding the desired target molecule. We describe an approach for generating fluorescent sensors based on Spinach, an RNA sequence that binds and activates the fluorescence of a small-molecule fluorophore. We show that these sensors can detect a variety of different small molecules in vitro and in living cells. These RNAs constitute a versatile approach for fluorescence imaging of small molecules and have the potential to detect essentially any cellular biomolecule.

RNA mimics of green fluorescent protein.

Green fluorescent protein (GFP) and its derivatives have transformed the use and analysis of proteins for diverse applications. Like proteins, RNA has complex roles in cellular function and is increasingly used for various applications, but a comparable approach for fluorescently tagging RNA is lacking. Here, we describe the generation of RNA aptamers that bind fluorophores resembling the fluorophore in GFP. These RNA-fluorophore complexes create a palette that spans the visible spectrum. An RNA-fluorophore complex, termed Spinach, resembles enhanced GFP and emits a green fluorescence comparable in brightness with fluorescent proteins. Spinach is markedly resistant to photobleaching, and Spinach fusion RNAs can be imaged in living cells. These RNA mimics of GFP provide an approach for genetic encoding of fluorescent RNAs.

Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling.

Protein ubiquitination is a post-translational modification (PTM) that regulates various aspects of protein function by different mechanisms. Characterization of ubiquitination has lagged behind that of smaller PTMs, such as phosphorylation, largely because of the difficulty of isolating and identifying peptides derived from the ubiquitinated portion of proteins. To address this issue, we generated a monoclonal antibody that enriches for peptides containing lysine residues modified by diglycine, an adduct left at sites of ubiquitination after trypsin digestion. We use mass spectrometry to identify 374 diglycine-modified lysines on 236 ubiquitinated proteins from HEK293 cells, including 80 proteins containing multiple sites of ubiquitination. Seventy-two percent of these proteins and 92% of the ubiquitination sites do not appear to have been reported previously. Ubiquitin remnant profiling of the multi-ubiquitinated proteins proliferating cell nuclear antigen (PCNA) and tubulin alpha-1A reveals differential regulation of ubiquitination at specific sites by microtubule inhibitors, demonstrating the effectiveness of our method to characterize the dynamics of lysine ubiquitination.

Nitrosothiol reactivity profiling identifies S-nitrosylated proteins with unexpected stability.

Nitric oxide (NO) regulates protein function by S-nitrosylation of cysteine to form nitrosothiols. Nitrosothiols are highly susceptible to nonenzymatic degradation by cytosolic reducing agents. Here we show that although most protein nitrosothiols are rapidly degraded by cytosolic reductants, a small subset form unusually stable S-nitrosylated proteins. Our findings suggest that stable S-nitrosylation reflects a protein conformation change that shields the nitrosothiol. To identify stable protein nitrosothiols, we developed a proteomic method for profiling S-nitrosylation. We examined the stability of over 100 S-nitrosylated proteins, and identified 10 stable nitrosothiols. These proteins remained S-nitrosylated in cells after NO synthesis was inhibited, unlike most S-nitrosylated proteins. Taken together, our data identify a class of NO targets that form stable nitrosothiols in the cell and are likely to mediate the persistent cellular effects of NO.

Pharmacologic manipulation of nitric oxide signaling: targeting NOS dimerization and protein-protein interactions.

Nitric oxide (NO) is an endogenously-produced small molecule that has critical roles in cellular signaling and a variety of physiological processes in many tissues, including the brain, the vasculature, and the immune system. In several medical disorders, NO has been implicated in disease pathology, in most cases due to persistent activation or overproduction of one of three NO synthase (NOS) isoforms. Although NOS inhibitors that are both potent and cell-permeable have been developed, none is currently used in the treatment of any disorder. One reason that NOS inhibitors fail to have therapeutic efficacy may be linked to their very low isoform-selectivity. An additional possibility is that NOS inhibitors, even if they exhibit isoform selectivity, might indiscriminately affect beneficial and pathological NO signaling pathways. In this review, we discuss emerging approaches in the development of isoform-specific NOS-directed therapeutics including dimerization inhibitors, novel L-arginine (L-Arg) binding site inhibitors, and dimer stabilization. Additionally, we suggest novel strategies for the future including targeting subcellular localization of NOS and protein-protein interactions with NOS effectors.