Candidate Biomarkers for Response to Treatment in Psoriatic Disease.

OBJECTIVE: To determine whether biologic therapy alters serum C-X-C motif chemokine ligand 10 (CXCL10), matrix metalloproteinase 3 (MMP3), S100 calcium-binding protein A8 (S100A8), acid phosphatase 5 (ACP5), and C-C motif chemokine ligand 2 (CCL2) levels in patients with psoriatic arthritis (PsA) and cutaneous psoriasis without arthritis (PsC), and whether baseline levels of these proteins predict response to treatment for PsA. METHODS: We included (1) patients with PsA taking tumor necrosis factor inhibitors (TNFi), interleukin 17 inhibitors (IL-17i), methotrexate (MTX), and those who were untreated with bDMARDs or csDMARDs; (2) patients with PsC taking bDMARDs; and (3) matched patients with PsC who were not treated with bDMARDs or csDMARDs. Serum samples at baseline and at the 3- to 6-month follow-up visit were retrieved from the biobank. Protein levels were quantified using a Luminex multiplex assay. We compared follow-up vs baseline protein levels within groups and change in levels between groups. For the predictive potential of the biomarkers, we developed logistic regression classification models. Response to treatment was defined as (1) achieving low disease activity or remission (according to the Disease Activity Index for Psoriatic Arthritis); (2) ≥ 75% reduction in Psoriasis Area and Severity Index; and (3) ≥ 50% reduction in actively inflamed joint count. RESULTS: In PsA, TNFi reduced serum levels of all 5 proteins, IL-17i increased ACP5 and CCL2, and MTX reduced MMP3. Changes in MMP3 and S100A8 levels were significantly different between untreated PsA and matched biologic-treated PsA (P < 0.05). There were no significant differences between treated or untreated patients with PsC. Baseline levels of CXCL10, MMP3, S100A8, and ACP5 had good predictive value (area under the curve > 0.80) for response to biologics in patients with PsA. CONCLUSION: Treatment with biologics and MTX affect serum CXCL10, MMP3, S100A8, ACP5, and CCL2 levels in patients with PsA. MMP3, S100A8, ACP5, and CXCL10 have potential use as serum biomarkers to predict response to treatment for PsA.

Prediction of Psoriatic Arthritis in Patients With Psoriasis Using DNA Methylation Profiles.

OBJECTIVE: Psoriatic arthritis (PsA) is an immune-mediated inflammatory arthritis, associated with psoriasis, that significantly increases morbidity and mortality risk. We currently lack the means of predicting which patients with psoriasis will develop PsA, and a large number of patients remain undiagnosed. Regulation of gene expression through DNA methylation can potentially trigger and maintain PsA pathophysiological processes. We aimed to identify DNA methylation markers that can predict which patients with psoriasis will develop PsA prior to the onset of musculoskeletal symptoms. METHODS: Genome-wide DNA methylation was assessed in blood samples from patients with psoriasis who went on to develop arthritis (converters) and patients with psoriasis who did not (biologic naive, matched for age, sex, psoriasis duration, and duration of follow-up). Methylation differences between converters and nonconverters were identified by a multivariate linear regression model including clinical covariates (age, sex, body mass index, smoking). Predictive performance of methylation markers was assessed by developing support vector machine classification models with and without the addition of clinical variables. RESULTS: We identified a set of 36 highly relevant methylation markers (false discovery rate: adjusted P < 0.05 and a minimum change in methylation of 0.05) across 15 genes and several intergenic regions. A classification model relying on these markers identified converters and nonconverters with an area under the receiver operating characteristic curve of 0.9644. CONCLUSION: This study shows that DNA methylation patterns at an early stage of psoriatic disease can distinguish between patients who will develop PsA from those who will not during the same follow-up.

Variation in Actionable Pharmacogenetic Markers in Natives and Mestizos From Mexico.

The identification and characterization of pharmacogenetic variants in Latin American populations is still an ongoing endeavor. Here, we investigated SNVs on genes listed by the Pharmacogenomics Knowledge Base in 1284 Mestizos and 94 Natives from Mexico. Five institutional cohorts with NGS data were retrieved from different research projects at INMEGEN, sequencing files were filtered for 55 pharmacogenes present in all cohorts to identify novel and known variation. Bioinformatic tools VEP, PROVEAN, and FATHMM were used to assess, in silico, the functional impact of this variation. Next, we focused on 17 genes with actionable variants that have been clinically implemented. Allele frequencies were compared with major continental groups and differences discussed in the scope of a pharmacogenomic impact. We observed a wide genetic variability for known and novel SNVs, the largest variation was on UGT1A > ACE > COMT > ABCB1 and the lowest on APOE and NAT2. Although with allele frequencies around 1%, novel variation was observed in 16 of 17 PGKB genes. In Natives we identified 59 variants and 58 in Mestizos. Several genes did not show novel variation, on CYP2B6, CYP2D6, and CYP3A4 in Natives; and APOE, UGT1A, and VKORC1 in Mestizos. Similarities in allele frequency, comparing major continental groups for VIP pharmacogenes, hint towards a comparable PGx for drugs metabolized by UGT1A1, DPYD, ABCB1, CBR3, COMT, and TPMT; in contrast to variants on CYP3A5 and CYP2B6 for which significant MAF differences were identified. Our observations offer some discernment into the extent of pharmacogenetic variation registered up-to-date in Mexicans and contribute to quantitatively dissect actionable pharmacogenetic variants in Natives and Mestizos.