Retinoid and carotenoid status in serum and liver among patients at high-risk for liver cancer

BACKGROUND: Approximately 2.7 million Americans are chronically infected with hepatitis C virus (HCV). HCV patients with cirrhosis form the largest group of persons at high risk for hepatocellular carcinoma (HCC). Increased oxidative stress is regarded as a major mechanism of HCV-related liver disease progression. Deficiencies in retinoid and carotenoid antioxidants may represent a major modifiable risk factor for disease progression. This study aims to identify key predictors of serum antioxidant levels in patients with HCV, to examine the relationship between retinoid/carotenoid concentrations in serum and hepatic tissue, to quantify the association between systemic measures of oxidative stress and antioxidant status, and to examine the relationship between retinoids and stellate cell activation. METHODS: Patients undergoing liver biopsy (n = 69) provided fasting blood, fresh tissue, urine and completed a diet history questionnaire. Serum and questionnaire data from healthy volunteers (n = 11), normal liver tissue from public repositories and patients without liver disease (n = 11) were also collected. Urinary isoprostanes, serum and tissue retinoid concentrations were obtained by UHPLC-MS-MS. Immunohistochemistry for αSMA was performed on FFPE sections and subsequently quantified via digital image analysis. Associations between urinary isoprostanes, αSMA levels, and retinoids were assessed using Spearman correlation coefficients and non-parametric tests were utilized to test differences among disease severity groups. RESULTS: There was a significant inverse association between serum retinol, lycopene, and RBP4 concentrations with fibrosis stage. Serum β-carotene and lycopene were strongly associated with their respective tissue concentrations. There was a weak downward trend of tissue retinyl palmitate with increasing fibrosis stage. Tissue retinyl palmitate was inversely and significantly correlated with hepatic αSMA expression, a marker for hepatic stellate cell activation (r = −0.31, P < 0.02). Urinary isoprostanes levels were inversely correlated with serum retinol, β-carotene, and RBP4. CONCLUSIONS: A decrease in serum retinol, β-carotene, and RBP4 is associated with early stage HCV. Retinoid and carotenoid levels decline as disease progresses, and our data suggest that this decline occurs early in the disease process, even before fibrosis is apparent. Measures of oxidative stress are associated with fibrosis stage and concurrent antioxidant depletion. Vitamin A loss is accompanied by stellate cell activation in hepatic tissue. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12876-016-0432-5) contains supplementary material, which is available to authorized users

Development of a Nuclear Morphometric Signature for Prostate Cancer Risk in Negative Biopsies

Background: Our objective was to develop and validate a multi-feature nuclear score based on image analysis of direct DNA staining, and to test its association with field effects and subsequent detection of prostate cancer (PCa) in benign biopsies.Methods: Tissue sections from 39 prostatectomies were Feulgen-stained and digitally scanned (400×), providing maps of DNA content per pixel. PCa and benign epithelial nuclei were randomly selected for measurement of 52 basic morphometric features. Logistic regression models discriminating benign from PCa nuclei, and benign from malignant nuclear populations, were built and cross-validated by AUC analysis. Nuclear populations were randomly collected 5 mm from cancer foci, and from cancer-free prostates, HGPIN, and PCa Gleason grade 3–5. Nuclei also were collected from negative biopsy subjects who had a subsequent diagnosis of PCa and age-matched cancer-free controls (20 pairs).Results: A multi-feature nuclear score discriminated cancer from benign cell populations with AUCs of 0.91 and 0.79, respectively, in training and validation sets of patients. In prostatectomy samples, both nuclear- and population-level models revealed cancer-like features in benign nuclei adjacent to PCa, compared to nuclei that were more distant or from PCa-free glands. In negative biopsies, a validated model with 5 variance features yielded significantly higher scores in cases than controls (P = 0.026).Conclusions: A multifeature nuclear morphometric score, obtained by automated digital analysis, was validated for discrimination of benign from cancer nuclei. This score demonstrated field effects in benign epithelial nuclei at varying distance from PCa lesions, and was associated with subsequent PCa detection in negative biopsies.Impact: This nuclear score shows promise as a risk predictor among men with negative biopsies and as an intermediate biomarker in Phase II chemoprevention trials. The results also suggest that subvisual disturbances in nuclear structure precede the development of pre-neoplastic lesions.</p

Development of a Nuclear Morphometric Signature for Prostate Cancer Risk in Negative Biopsies

<div><p>Background</p><p>Our objective was to develop and validate a multi-feature nuclear score based on image analysis of direct DNA staining, and to test its association with field effects and subsequent detection of prostate cancer (PCa) in benign biopsies.</p><p>Methods</p><p>Tissue sections from 39 prostatectomies were Feulgen-stained and digitally scanned (400×), providing maps of DNA content per pixel. PCa and benign epithelial nuclei were randomly selected for measurement of 52 basic morphometric features. Logistic regression models discriminating benign from PCa nuclei, and benign from malignant nuclear populations, were built and cross-validated by AUC analysis. Nuclear populations were randomly collected <1 mm or >5 mm from cancer foci, and from cancer-free prostates, HGPIN, and PCa Gleason grade 3–5. Nuclei also were collected from negative biopsy subjects who had a subsequent diagnosis of PCa and age-matched cancer-free controls (20 pairs).</p><p>Results</p><p>A multi-feature nuclear score discriminated cancer from benign cell populations with AUCs of 0.91 and 0.79, respectively, in training and validation sets of patients. In prostatectomy samples, both nuclear- and population-level models revealed cancer-like features in benign nuclei adjacent to PCa, compared to nuclei that were more distant or from PCa-free glands. In negative biopsies, a validated model with 5 variance features yielded significantly higher scores in cases than controls (<i>P</i> = 0.026).</p><p>Conclusions</p><p>A multifeature nuclear morphometric score, obtained by automated digital analysis, was validated for discrimination of benign from cancer nuclei. This score demonstrated field effects in benign epithelial nuclei at varying distance from PCa lesions, and was associated with subsequent PCa detection in negative biopsies.</p><p>Impact</p><p>This nuclear score shows promise as a risk predictor among men with negative biopsies and as an intermediate biomarker in Phase II chemoprevention trials. The results also suggest that subvisual disturbances in nuclear structure precede the development of pre-neoplastic lesions.</p></div

Frequency histograms of multifeature scores (MFS_n) for nuclei from various malignant and benign tissue compartments.

<p>Fitted multifeature scores were generated for each nucleus from a logistic regression model comparing all cancer nuclei to normal-far nuclei (>5 mm from a cancer focus) from 20 prostatectomy specimens, with 27 covariate features selected by backwards elimination. Scores were calculated for populations of nuclei obtained from specific histological compartments in 20 RP and 8 cystoprostatectomy specimens. The frequency distributions for normal-far nuclei are significantly different from each other benign type (Kolmogorov-Smirnov D statistic <0.0001).</p

Frequency histograms for MFS_n benign and cancer nuclei from two selected subjects.

<p>MFS_n scores are shifted upward for cancer nuclei as expected; however, variance for MFS_n is also greater among cancer nuclei, reflecting pleomorphism.</p

A multifeature nuclear morphometric score (MFSp) accurately discriminates cancer vs. benign cell populations: AUC results for two one-step logistic regression models.

*<p>Model A: Five features selected by backwards elimination: (FeretY_ave, MaxDiameter_ave, Elongation_ave, Slope_ave, ODKurtosis_ave).</p>**<p>Model B: Five features (SumOD_sd, MaxDiameter_sd, TSD_sd, TEntropy_sd, No.MedDensityObjects_sd), selected after competition among all models with ≤5 covariates based on leave-one-out AUC.</p

Comparing populations of benign nuclei from negative biopsies in a case-control study: subsequent diagnosis of prostate cancer is associated with a “cancer-like” nuclear morphometric score^* (MFSp). Scores derived from external datasets.

*<p>Model A: 5 features selected by backwards elimination (FeretY_ave, MaxDiameter_ave, Elongation_ave, Slope_ave and ODKurtosis_ave).</p>**<p>Model B: 5 features (SumOD_sd, MaxDiam_sd, TSD_sd, TEntropy_sd, No.MedDensityObjects_sd), selected after competition among all models with ≤5 covariates based on leave-one-out AUC.</p

Additional file 3: Figure S2. of Retinoid and carotenoid status in serum and liver among patients at high-risk for liver cancer

Boxplots (10th and 90th Percentile) of urinary isoprostanes by fibrosis stage. (EPS 102 kb

Additional file 1: Table S1. of Retinoid and carotenoid status in serum and liver among patients at high-risk for liver cancer

Clinical assay results for the study population by fibrosis stage - median (interquartile range). (DOCX 38 kb