| Literature DB >> 27999674 |
Emily L Williams1,2, Madeleine L Stimpson1,2, Peter L Collins1,3, Doyo G Enki4, Ashish Sinha3, Richard W Lee1,2, Ashwin D Dhanda1,5,6.
Abstract
BACKGROUND: Glucocorticoids (GCs) remain the first line treatment for almost all non-infectious inflammatory diseases, ranging from acute asthma to rheumatoid arthritis. However, across all conditions, patients have a variable response to GCs with approximately 30% being non-responders. This group of GC resistant patients is typically exposed to high-dose GCs and their side-effects before more appropriate immunotherapy is instituted. Hence, there is a pressing clinical need for a predictive biomarker of GC responsiveness. The availability of such a tool would also enable patient stratification for the conduct of smart clinical trials in GC resistance. Lymphocyte GC sensitivity has been shown to be closely associated with clinical GC sensitivity in a number of inflammatory diseases. However, the method for determining in vitro GC response is not standardized and requires the use of specialist equipment, including a radioisotope to quantify cellular proliferation, making it challenging to translate into clinical practice.Entities:
Keywords: Biomarker; Bromodeoxyuridine; Glucocorticoid sensitivity; Proliferation
Year: 2016 PMID: 27999674 PMCID: PMC5157083 DOI: 10.1186/s40364-016-0079-y
Source DB: PubMed Journal: Biomark Res ISSN: 2050-7771
Fig. 1Distribution of Imax for DILPA a and BLISS assay b. Frequencies have been binned in 10% groups in 101 healthy control samples and the normal distribution curve overlaid. The mean and standard deviation for DILPA is 48% and 26 and for BLISS 32% and 21, respectively. Their distributions are significantly different (unpaired t-test p < 0.001)
Fig. 2Bland and Altman test of agreement for DILPA and BLISS. Bland and Altman plot of average versus difference for DILPA and BLISS (adjusted by the inverse regression model of 6.8 + [5.3 × DILPA]). Bias (mean difference between DILPA and BLISS) is −0.2% with 95% limits of agreement from −59.2 to 58.7% (dashed lines)
Fig. 3BLISS has a high accuracy in predicting DILPA. Receiver operating characteristic for BLISS (adjusted by inverse regression model) in predicting DILPA with diagonal reference line (dashed). AUROC = 0.82 (95% CI 0.73–0.92; p < 0.001 compared to reference line)
Contingency table comparing classification of GC sensitive (GC-S) and resistant (GC-R) subjects by DILPA and BLISS assays
| DILPA | |||
| GC-R (Imax < 60%) | GC-S Imax > 60%) | ||
| BLISS | GC-R (Imax < 39%) | 57 | 10 |
| GC-S (Imax > 39%) | 12 | 22 | |
Sensitivity: 83% (95% confidence interval 71–90%)
Specificity: 69% (50–83%)
Positive Predictive Value: 85% (74–92%)
Negative Predictive Value: 65% (46–80%)