The breakdown of triglycerides, or lipolysis, is a tightly controlled process that regulates fat mobilization in accord with an animal's energy needs. It is well established that lipolysis is stimulated by hormones that signal energy demand and is suppressed by the antilipolytic hormone insulin. However, much still remains to be learned about regulation of lipolysis by intracellular signaling pathways in adipocytes. Here we show that galectin-12, a member of a β-galactoside-binding lectin family preferentially expressed by adipocytes, functions as an intrinsic negative regulator of lipolysis. Galectin-12 is primarily localized on lipid droplets and regulates lipolytic protein kinase A signaling by acting upstream of phosphodiesterase activity to control cAMP levels. Ablation of galectin-12 in mice results in increased adipocyte mitochondrial respiration, reduced adiposity, and ameliorated insulin resistance/glucose intolerance. This study identifies unique properties of this intracellular galectin that is localized to an organelle and performs a critical function in lipid metabolism. These findings add to the significant functions exhibited by intracellular galectins, and have important therapeutic implications for human metabolic disorders.

Measuring health and health-related quality of life (HRQoL) is important for tracking the health of individuals and populations over time. Generic HRQoL measures allow for comparison across health conditions. One form of generic HRQoL measures are profile measures, which provide a description of health across several different domains (such as physical functioning, depression, and pain). Recent advances in health profile measurement include the development of measures based on item response theory. The Patient-Reported Outcomes Measurement Information System (PROMIS®) has been constructed using this theory. Another form of generic HRQoL measures are utility measures, which assess the value of health states. Multi-attribute utility theory provides a framework for valuing disparate domains of health and aggregating them into a single preference-based score. Such a score provides an overall measure of health outcomes as well as a quality of life weight for use in decision analyses and cost-effectiveness analyses. Developing a utility score for PROMIS® would allow simultaneous estimation of both health profile and utility scores using a single measure. The purpose of this paper is to provide a roadmap of the methodological steps necessary to create such a scoring system.

Purpose

We sought to select a parsimonious subset of domains from the patient-reported outcomes measurement information system (PROMIS^®) that could be used for preference-based valuation. Domain selection criteria included face validity, comprehensiveness, and structural independence.

Methods

First, 9 health outcomes measurement experts selected domains appropriate for a general health measure using a modified Delphi procedure. Second, 50 adult community members assessed structural independence of domain pairs. For each pair, the participant was asked if it were possible to have simultaneously good functioning in domain 1 but poor functioning in domain 2, and vice versa. The community members also rated the relative importance of the domains. Finally, the experts selected domains, guided by community members' judgments of structural independence and importance.

Results

After 3 rounds of surveys, the experts agreed on 10 potential domains. The percent of pairs deemed structurally independent by community members ranged from 50 to 95 (mean = 78). Physical Function, Pain Interference, and Depression were retained because of their inclusion in existing preference-based measures and their importance to community members. Four other domains were added because they were important to community members and judged to be independent by at least 67% of respondents: Cognitive Function-Abilities; Fatigue; Ability to Participate in Social Roles and Activities; and Sleep Disturbance.

Conclusion

With input from measurement experts and community members, we selected 7 PROMIS domains that can be used to create a preference-based score.

Background

A subset of melanomas carrying a B-Raf proto-oncogene, serine/threonine kinase gene (BRAF) V600E mutation, which is the most common targetable mutation in melanoma, arise in association with a melanocytic nevus that is also harboring a BRAF V600E mutation. The detailed histomorphologic characteristics of nevi positive for BRAF V600E have not been systematically documented.

Objective

To identify histomorphologic features correlating with BRAF V600E status in nevi.

Methods

We retrospectively identified melanocytic nevi from our laboratory reporting system. We performed a histomorphologic analysis and analysis of BRAF V600E expression by immunohistochemistry.

Results

Thirteen nevi (14.8%) were negative and 76 (86.4%) were positive for BRAF V600E. The nevi positive for BRAF V600E were predominantly dermal (predominantly dermal growth in 55.3% of nevi positive for BRAF V600E and 15.4% of nevi negative for BRAF V600E [P = .01]) and showed a congenital growth pattern (congenital growth pattern in 51.3% of nevi positive for BRAF V600E and 15.4% of nevi negative for BRAF V600E [P = .02]). Compared with nevi negative for BRAF V600E, those that were positive for BRAF V600E often exhibited predominantly nested intraepidermal melanocytes, larger junctional nests, abrupt lateral circumscription, and larger cell size. Architectural disorder and inflammatory infiltrates were seen more often in nevi negative for BRAF V600E. BRAF sequencing of a subset of nevi confirmed the immunohistochemical results.

Limitations

Limitations include the study's retrospective design and the small sample size of nevi negative for BRAF V600E.

Conclusions

BRAF V600E is associated with distinct histomorphologic features in nevi. These features may contribute to improving the accuracy of classification and diagnosis of melanocytic neoplasms.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

PURPOSE:Health status descriptive systems based on item response theory (IRT), such as the Patient-Reported Outcomes Measurement Information System (PROMIS®), have item banks to measure domains of health. We developed a method to present such banks for health-state valuation. METHODS:We evaluated four different presentation approaches: a single item (1S), 2 items presented separately (2S), 2 items presented together (2T), or 5 items presented together (5T). We evaluated these four approaches in three PROMIS item banks (depression, physical function, and sleep disturbance). Adult community members valued health-state descriptions using the visual analog scale and standard gamble methods. We compared the approaches by the range of item bank theta scores captured, participants' assessments of difficulty (1 = very easy to 7 = very hard), and exit interviews. RESULTS:Participants (n = 118) ranged in age from 18 to 71; 63% were female and 54% were white. The 1S approach captured the smallest range of theta scores. A monotonic relationship between theta score and mean standard gamble estimate was found with all approaches except 2S. Across all 3 item banks, mean difficulty assessments were 2.35 (1S), 2.69 (2T), 2.78 (5T), and 2.80 (2S). In exit interviews, participants generally found all four approaches similarly meaningful and realistic. CONCLUSIONS:Creating health descriptions by presenting 2 items maximized the range of theta while minimizing difficulty and maintaining a monotonic relationship with utility estimates. We recommend this approach for valuation of IRT-based descriptive systems such as PROMIS.

Background

Health-related quality of life (HRQL) preference-based scores are used to assess the health of populations and patients and for cost-effectiveness analyses. The National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS^®) consists of patient-reported outcome measures developed using item response theory. PROMIS is in need of a direct preference-based scoring system for assigning values to health states.

Objective

To produce societal preference-based scores for 7 PROMIS domains: Cognitive Function-Abilities, Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities.

Setting

Online survey of a US nationally representative sample ( n = 983).

Methods

Preferences for PROMIS health states were elicited with the standard gamble to obtain both single-attribute scoring functions for each of the 7 PROMIS domains and a multiplicative multiattribute utility (scoring) function.

Results

The 7 single-attribute scoring functions were fit using isotonic regression with linear interpolation. The multiplicative multiattribute summary function estimates utilities for PROMIS multiattribute health states on a scale where 0 is the utility of being dead and 1 the utility of "full health." The lowest possible score is -0.022 (for a state viewed as worse than dead), and the highest possible score is 1.

Limitations

The online survey systematically excludes some subgroups, such as the visually impaired and illiterate.

Conclusions

A generic societal preference-based scoring system is now available for all studies using these 7 PROMIS health domains.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we used single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi and primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative two-dimensional analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included up-regulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 patients with melanoma via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene regulatory landscapes pertinent to cancer progression and patient survival.

Brassinosteroids (BRs) are essential phytohormones for plant growth and development. BRs are perceived by the cell surface receptor kinase BRI1, and downstream signal transduction through multiple components leads to activation of the transcription factors BZR1 and BZR2/BES1. BZR1 activity is highly controlled by BR through reversible phosphorylation, protein degradation, and nucleocytoplasmic shuttling. To further understand the molecular function of BZR1, we performed tandem affinity purification of the BZR1 complex and identified BZR1-associated proteins using mass spectrometry. These BZR1-associated proteins included several known BR signaling components, such as BIN2, BSK1, 14-3-3λ, and PP2A, as well as a large number of proteins with previously unknown functions in BR signal transduction, including the kinases MKK5 and MAPK4, histone deacetylase 19, cysteine proteinase inhibitor 6, a DEAD-box RNA helicase, cysteine endopeptidases RD21A and RD21B, calmodulin-binding transcription activator 5, ubiquitin protease 12, cyclophilin 59, and phospholipid-binding protein synaptotagmin A. Their interactions with BZR1 were confirmed by in vivo and in vitro assays. Furthermore, MKK5 was found to phosphorylate BZR1 in vitro. This study demonstrates an effective method for purifying proteins associated with low-abundance transcription factors, and identifies new BZR1-interacting proteins with potentially important roles in BR response.