Biologic therapies have transformed the management of psoriasis, but clinical outcome is variable leaving an unmet clinical need for predictive biomarkers of response. Here we perform in-depth ...immunomonitoring of blood immune cells of 67 patients with psoriasis, before and during therapy with the anti-TNF drug adalimumab, to identify immune mediators of clinical response and evaluate their predictive value. Enhanced NF-κBp65 phosphorylation, induced by TNF and LPS in type-2 dendritic cells (DC) before therapy, significantly correlates with lack of clinical response after 12 weeks of treatment. The heightened NF-κB activation is linked to increased DC maturation in vitro and frequency of IL-17
T cells in the blood of non-responders before therapy. Moreover, lesional skin of non-responders contains higher numbers of dermal DC expressing the maturation marker CD83 and producing IL-23, and increased numbers of IL-17
T cells. Finally, we identify and clinically validate LPS-induced NF-κBp65 phosphorylation before therapy as a predictive biomarker of non-response to adalimumab, with 100% sensitivity and 90.1% specificity in an independent cohort. Our study uncovers important molecular and cellular mediators underpinning adalimumab mechanisms of action in psoriasis and we propose a blood biomarker for predicting clinical outcome.
Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic ...basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.
Acne vulgaris is a highly heritable skin disorder that primarily impacts facial skin. Severely inflamed lesions may leave permanent scars that have been associated with long-term psychosocial ...consequences. Here, we perform a GWAS meta-analysis comprising 20,165 individuals with acne from nine independent European ancestry cohorts. We identify 29 novel genome-wide significant loci and replicate 14 of the 17 previously identified risk loci, bringing the total number of reported acne risk loci to 46. Using fine-mapping and eQTL colocalisation approaches, we identify putative causal genes at several acne susceptibility loci that have previously been implicated in Mendelian hair and skin disorders, including pustular psoriasis. We identify shared genetic aetiology between acne, hormone levels, hormone-sensitive cancers and psychiatric traits. Finally, we show that a polygenic risk score calculated from our results explains up to 5.6% of the variance in acne liability in an independent cohort.
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large ...number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the 'lowest number of feature subset' with the 'maximal average AUC over the nested cross validation' and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.
Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a ...pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.
Targeted biologic therapies can elicit an undesirable host immune response characterized by the development of antidrug antibodies (ADA), an important cause of treatment failure. The most widely used ...biologic across immune-mediated diseases is adalimumab, a tumor necrosis factor inhibitor. This study aimed to identify genetic variants that contribute to the development of ADA against adalimumab, thereby influencing treatment failure. In patients with psoriasis on their first course of adalimumab, in whom serum ADA had been evaluated 6-36 months after starting treatment, we observed a genome-wide association with ADA against adalimumab within the major histocompatibility complex (MHC). The association signal mapped to the presence of tryptophan at position 9 and lysine at position 71 of the HLA-DR peptide-binding groove, with both residues conferring protection against ADA. Underscoring their clinical relevance, these residues were also protective against treatment failure. Our findings highlight antigenic peptide presentation via MHC class II as a critical mechanism in the development of ADA against biologic therapies and downstream treatment response.
Abstract Introduction and aims Obesity is a chronic inflammatory state that is highly prevalent in people with psoriasis. There is evidence to suggest that increasing levels of adiposity promotes the ...risk of psoriasis development and worsens disease severity. The degree to which adiposity influences psoriasis varies between individuals, and it is also unclear which key genetic factors underpin this variation. Identifying this could be mechanistically informative in uncovering immunological pathways connecting adiposity and psoriasis. This project explores the hypothesis that the influence of adiposity on psoriasis has a genetic basis. Methods Firstly, we examined the comparative strength of association between a range of measures of adiposity and psoriasis in a population-based study (UK Biobank, n = 336 806). Secondly, we performed a genome-wide by environment interaction study (GWEIS) meta-analysis examining the interaction between key measures of adiposity and psoriasis susceptibility in two large population-based studies UK Biobank and HUNT (n = 164 753). Thirdly, in view of this association between human leucocyte antigen (HLA)-C*06:02 and higher adiposity levels in psoriasis, we wanted to explore whether biological differences existed between HLA-stratified subgroups. To explore this, we performed HLA-C*06:02-stratified Mendelian randomization to establish whether a difference in causal effect of adiposity on psoriasis existed between subgroups. Results We found that measures of central obesity (including impedance and dual-energy x-ray absorptiometry/magnetic resonance imaging measures) were most strongly associated with both psoriasis risk and severity. A sexual dimorphism was observed, with a stronger effect in men compared with women. Through GWEIS meta-analysis, we found a significant interaction between HLA-C*06:02-status and waist circumference on psoriasis risk (P = 3.69e−08). No significant difference in causal effect of body mass index or waist circumference on psoriasis risk were found in HLA-C*06:02-stratified subgroups. Conclusions These findings underscore the significance of central adiposity in influencing psoriasis risk. This study also emphasizes both the value and limitations of HLA-C*06:02 as a potential biomarker for distinguishing metabolically-driven psoriasis.
Biologic therapies can be highly effective for the treatment of severe psoriasis, but response for individual patients can vary according to drug. Predictive biomarkers to guide treatment selection ...could improve patient outcomes and treatment cost-effectiveness.
We sought to test whether HLA-C*06:02, the primary genetic susceptibility allele for psoriasis, predisposes patients to respond differently to the 2 most commonly prescribed biologics for psoriasis: adalimumab (anti–TNF-α) and ustekinumab (anti–IL-12/23).
This study uses a national psoriasis registry that includes longitudinal treatment and response observations and detailed clinical data. HLA alleles were imputed from genome-wide genotype data for 1326 patients for whom 90% reduction in Psoriasis Area and Severity Index score (PASI90) response status was observed after 3, 6, or 12 months of treatment. We developed regression models of PASI90 response, examining the interaction between HLA-C*06:02 and drug type (adalimumab or ustekinumab) while accounting for potentially confounding clinical variables.
HLA-C*06:02–negative patients were significantly more likely to respond to adalimumab than ustekinumab at all time points (most strongly at 6 months: odds ratio OR, 2.95; P = 5.85 × 10−7), and the difference was greater in HLA-C*06:02–negative patients with psoriatic arthritis (OR, 5.98; P = 6.89 × 10−5). Biologic-naive patients who were HLA-C*06:02 positive and psoriatic arthritis negative demonstrated significantly poorer response to adalimumab at 12 months (OR, 0.31; P = 3.42 × 10−4). Results from HLA-wide analyses were consistent with HLA-C*06:02 itself being the primary effect allele. We found no evidence for genetic interaction between HLA-C*06:02 and ERAP1.
This large observational study suggests that reference to HLA-C*06:02 status could offer substantial clinical benefit when selecting treatments for severe psoriasis.
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