Genetic evolution of uveal melanoma guides the development of an inflammatory microenvironment

Experimental cancer immunology and therap

Benchmarked performance charts using principal components analysis to improve the effectiveness of feedback for audit data in HIV care

Abstract Background Feedback tools for clinical audit data that compare site-specific results to average performance over all sites can be useful for quality improvement. Proposed tools should be simple and clearly benchmark the site’s performance, so that a relevant action plan can be directly implemented to improve patient care services. We aimed to develop such a tool in order to feedback data to UK HIV clinics participating in the 2015 British HIV Association (BHIVA) audit assessing compliance with the 2011 guidelines for routine investigation and monitoring of adult HIV-1- infected individuals. Methods HIV clinic sites were asked to provide data on a random sample of 50–100 adult patients attending for HIV care during 2014 and/or 2015 by completing a self-audit spreadsheet. Outcomes audited included the proportion of patients with recorded resistance testing, viral load monitoring, adherence assessment, medications, hepatitis testing, vaccination management, risk assessments, and sexual health screening. For each outcome we benchmarked the proportion for a specific site against the average performance. We produced performance charts for each site using boxplots for the outcomes. We also used the mean and differences from the mean performance to produce a dashboard for each site. We used principal components analysis to group correlated outcomes and simplify the dashboard. Results The 106 sites included in the study provided information on a total of 7768 patients. Outcomes capturing monitoring of treatment of HIV-infection showed high performance across the sites, whereas testing for hepatitis, and risk assessment for cardiovascular disease and smoking, management of flu vaccination, sexual health screening, and cervical cytology for women were very variable across sites. The principal components analysis reduced the original 12 outcomes to four factors that represented HIV care, hepatitis testing, other screening tests, and resistance testing. These provided simplified measures of adherence to guidelines which were presented as a 4 bar dashboard of performance. Conclusion Our dashboard performance charts provide easily digestible visual summaries of locally relevant audit data that are benchmarked against the overall mean and can be used to improve feedback to HIV services. Feedback from clinicians indicated that they found these charts acceptable and useful

Correlation between <i>HLA-</i> gene expression and the expression of the genes encoding the HLA transcriptional regulators and peptide-loading machinery.

<p>Correlation between <i>HLA-</i> gene expression and the expression of the genes encoding the HLA transcriptional regulators and peptide-loading machinery.</p

Chromosomal aberrations and HLA expression.

<p>Comparison between chromosomal aberrations and the expression of HLA class I and II antigens in a set of 27 primary UM. Tumors are divided according to their chromosome 3 and 6 status (disomy or monosomy of chromosome 3, and disomy of chromosome 6 or gain of 6p). HLA gene expression was determined using an Illumina microarray (A) and protein expression by immunohistochemistry (B) in UM. Additionally, HLA gene expression was determined using qPCR, which served to validate the Illumina findings (C). Four data points of the qPCR that are outside the axis limits (> 11 and < 24) are not shown (HLA-A, D3D6p: 17; HLA-B, D3D6p: 24, and M3D6p: 12; B2M, D3D6p: 13). Only significant p-values are shown, all other comparisons between the groups were not significant (p-values not shown). Error-bars represent the interquartile range. Results were obtained using the Mann-Whitney U tests.</p

Tumor infiltrating immune cells.

<p>Association between a low or high density of tumor-infiltrating CD3+ (A), and CD68+ (B)cells and several HLA and HLA-related genes (Illumina array) in primary UM. CD3 (cells/mm²) and CD68 (pixels x 10³/mm²) scores were dichotomized at the median.</p

Schematic illustration of tumor characteristics and infiltrate.

<p>UM with monosomy 3 attract an infiltrate, producing different cytokines, including Interferon-gamma. The tumor cell (UM cell) responds by increasing HLA class I and II levels, as well as rendering the infiltrating immune cells ineffective (immune suppression) and creating a tumor-favorable environment, with amongst others, stimulation of angiogenesis.</p

Effect of the absence of human leukocytes.

<p>Gene-expression (log 2 intensity values) of HLA-B, HLA-DRA, CD3D (as marker for T-cells), and CD163 (as marker for macrophages), of the original tumors (patient) compared to the xenografts (xeno). MP’s are primary tumors; MM’s are metasisis.</p

Prognostic parameters in uveal melanoma and their association with BAP1 expression

AIM: To determine whether BAP1 gene and protein expression associates with different prognostic parameters in uveal melanoma and whether BAP1 expression correctly identifies patients as being at risk for metastases, following enucleation of the primary tumour. METHODS: Thirty cases of uveal melanoma obtained by enucleation between 1999 and 2004 were analysed for a variety of prognostic markers, including histological characteristics, chromosome aberrations obtained by fluorescence in situ hybridisation (FISH) and single nucleotide polymorphism (SNP) analysis and gene expression profiling. These parameters were compared with BAP1 gene expression and BAP1 immunostaining. RESULTS: The presence of monosomy of chromosome 3 as identified by the different chromosome 3 tests showed significantly increased HRs (FISH on isolated nuclei cut-off 30%: HR 11.6, p=0.002; SNP analysis: HR 20.3, p=0.004) for death due to metastasis. The gene expression profile class 2, based on the 15-gene expression profile, similarly provided a significantly increased HR for a poor outcome (HR 8.5, p=0.005). Lower BAP1 gene expression and negative BAP1 immunostaining (50% of 28 tumours were immunonegative) were both associated with these markers for prognostication: FISH cut-off 30% monosomy 3 (BAP1 gene expression: p=0.037; BAP1 immunostaining: p=0.001), SNP-monosomy 3 (BAP1 gene expression: p=0.008; BAP1 immunostaining: p=0.002) and class 2 profile (BAP1 gene expression: p<0.001; BAP1 immunostaining: p=0.001) and were themselves associated with an increased risk of death due to metastasis (BAP1 gene expression dichotomised: HR 8.7, p=0.006; BAP1 immunostaining: HR 4.0, p=0.010). CONCLUSIONS: Loss of BAP1 expression associated well with all of the methods currently used for prognostication and was itself predictive of death due to metastasis in uveal melanoma after enucleation, thereby emphasising the importance of further research on the role of BAP1 in uveal melanoma