International Psoriasis Council

Advancing Knowledge. Improving Care.

Advancing Knowledge. Improving Care.

Focus on Psoriasis

A Report from the SID Virtual Meeting 2021

By Thomas Scharnitz, MD

The Society for Investigative Dermatology (SID) held its 79th Annual Meeting virtually on May 3-8 2021, to ensure international participation and collaboration despite the current global pandemic. Fittingly, the International Psoriasis Council (IPC) opened with a symposium titled “Biomarkers in psoriasis and psoriatic arthritis: Moving towards personalized therapy,” which discussed innovative technological and medical advances in psoriasis research. The SID congress also included many additional noteworthy psoriasis presentations, including the prestigious Eugene M. Farber lecture delivered by IPC Councilor Dr. April Armstrong.






Getting Clear: Psoriasis Advancements and Beyond

IPC Councilor April Armstrong, MD, MPH
Keck School of Medicine, University of Southern California
Studio, California, United States

The Eugene M. Farber lecture honors an investigator whose work is relevant to expanding insights into the pathophysiology and treatment of psoriasis and cutaneous autoimmune disease. This year’s recipient, IPC Councilor Dr. April Armstrong, delivered a presentation titled “Getting Clear: Psoriasis Advancements and Beyond,” specifically focused on therapeutic advances, artificial intelligence, and health care delivery.

Therapeutic advances
Despite the existing approved therapies, many psoriasis patients worldwide remain under- or untreated.1 In 2017, the National Psoriasis Foundation established treatment targets for clinical practice, with goals of very low BSA.2,3 Though advances in pathophysiology knowledge drive therapeutic advancement, therapeutic trials also improve our understanding of vital pathophysiologic pathways.4,5 As a result, new superior psoriasis therapies regularly become available.

A promising novel, topical aryl hydrocarbon receptor modulating agent, tapinarof, decreases Th17 and Th2 cytokines, increases antioxidant activity, and repairs skin barrier.6-8 An emerging oral therapy, the highly selective Tyk2 inhibitor deucravacitinib, modulates the JAK-STAT pathway and decreases IL-12, IL-23, and Type-1 IFN levels.8-12 The only pegylated anti-TNFα biologic, certolizumab, is safe in pregnancy as it does not cross the placenta.13 As a class, IL-17 inhibitors are highly efficacious for both psoriasis and psoriatic arthritis (PsA). Similarly, IL-23 inhibitors have robust efficacy and durability but require infrequent injections.13-15

Artificial Intelligence (A.I.)
As technology advances, we may begin to leverage A.I. to better phenotype patients for individualized therapy. As current remarkable A.I. programs far outperform humans in areas such as complex games, the question arises: can A.I. shape our knowledge and therapies for psoriasis and other dermatologic diseases?

Defined, A.I. is a computer system able to effectively perform tasks that would typically require human intelligence (sensing, reasoning, acting, adapting). ‘Machine learning’ in A.I. uses algorithms whose performance improves as they are exposed to more data over time, and its subset, ‘deep learning,’ utilizes multilayered neural networks to learn from vast amounts of data.16

Machine learning can develop phenotypic clusters with potential therapeutic and prognostic significance without being explicitly programmed.17 An example of machine learning’s utility in medicine can be seen in a Rheumatology secukinumab study where the investigators identified distinct clusters of patients with PsA based on baseline articular, entheseal, and cutaneous disease manifestations. Machine learning detected 13 different phenotypic clusters of patients with psoriatic disease, reporting mean PASI sub scores and % of patients with tender joints across these clusters. Without pre-specified hypotheses, machine learning can discover these clusters based on the data alone.18,19

Health Care Delivery
Though the COVID-19 pandemic thrust telemedicine to the forefront, teledermatology advancements preceded the pandemic. A 2018 RCT of nearly 300 patients by Dr. Armstrong studied face-to-face care versus asynchronous teledermatology, where patients and primary care physicians provided history and still photos with subsequent dermatologist assessment, education, and management. Her group found that the online collaborative model effectively improved clinical outcomes (PASI and BSA responses) for psoriasis patients as in-person care and even slightly outperformed in-person care in the patient’s global assessment.20

Though telehealth models can improve psoriasis care, whether they are sustainable and scalable will depend on reimbursement, technology, medicolegal considerations, special body site evaluation, and workforce considerations. To that end, the IPC is working to provide guidance for clinicians on the use of teledermatology.


  1. Armstrong AW, Robertson AD, Wu J, Schupp C, Lebwohl MG. Undertreatment, treatment trends, and treatment dissatisfaction among patients with psoriasis and psoriatic arthritis in the United States: findings from the National Psoriasis Foundation surveys, 2003-2011. JAMA Dermatol. 2013;149(10):1180-1185.
  2. Armstrong AW, Siegel MP, Bagel J, et al. From the medical board of the national psoriasis foundation: treatment targets for plaque psoriasis. Journal of the American Academy of Dermatology. 2017;76(2):290-298
  3. Amatore F, Villani A-P, Tauber M, Viguier M, Guillot B, Psoriasis Research Group of the French Society of Dermatology (Groupe de Recherche sur le Psoriasis de la Société Française de Dermatologie). French guidelines on the use of systemic treatments for moderate-to-severe psoriasis in adults. J Eur Acad Dermatol Venereol. 2019;33(3):464-483
  4. Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. The Lancet. 2021;397(10281):1301-1315.
  5. Armstrong AW, Read C. Pathophysiology, clinical presentation, and treatment of psoriasis: a review. JAMA. 2020;323(19):1945-1960.
  6. Smith SH, Jayawickreme C, Rickard DJ, et al. Tapinarof is a natural ahr agonist that resolves skin inflammation in mice and humans. Journal of Investigative Dermatology. 2017;137(10):2110-2119.
  7. Furue M, Tsuji G, Mitoma C, et al. Gene regulation of filaggrin and other skin barrier proteins via aryl hydrocarbon receptor. J Dermatol Sci. 2015;80(2):83-88.
  8. Tsuji G, Takahara M, Uchi H, et al. Identification of ketoconazole as an AhR-Nrf2 activator in cultured human keratinocytes: the basis of its anti-inflammatory effect. J Invest Dermatol. 2012;132(1):59-68.
  9. Wrobleski ST, Moslin R, Lin S, et al. Highly selective inhibition of tyrosine kinase 2 (Tyk2) for the treatment of autoimmune diseases: discovery of the allosteric inhibitor bms-986165. J Med Chem. 2019;62(20):8973-8995.
  10. Papp K, Gordon K, Thaçi D, et al. Phase 2 trial of selective tyrosine kinase 2 inhibition in psoriasis. N Engl J Med. 2018;379(14):1313-1321.
  11. Burke JR, Cheng L, Gillooly KM, et al. Autoimmune pathways in mice and humans are blocked by pharmacological stabilization of the TYK2 pseudokinase domain. Sci Transl Med. 2019;11(502).
  12. Efficacy and Safety of Deucravacitinib (BMS-986165), an Oral, Selective Tyrosine Kinase 2 Inhibitor, in Patients with Active Psoriatic Arthritis: Results from a Phase 2, Randomized, Double-Blind, Placebo-Controlled Trial. ACR Meeting Abstracts. Accessed May 9, 2021.
  13.  2021 Board Immersion Course.
  14. Kreymborg K, Böhlmann U, Becher B. IL-23: changing the verdict on IL-12 function in inflammation and autoimmunity. Expert Opin Ther Targets. 2005;9(6):1123-1136.
  15. Oppmann B, Lesley R, Blom B, et al. Novel p19 protein engages IL-12p40 to form a cytokine, IL-23, with biological activities similar as well as distinct from IL-12. Immunity. 2000;13(5):715-725.
  16. Singh S. Cousins of Artificial Intelligence. Towards Science Data. Published May 27, 2018.
  17. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233-234.
  18. McInnes I, Kormaksson M, Pournara E, et al. Clinically Relevant Patient Clusters Identified by Machine Learning Tools in a Large Database from the Secukinumab Psoriatic Arthritis Clinical Development Program. 2019 ACR/ARP Annual Meeting. Poster 2855.
  19. Kormaksson M, Pournara E, Ligozio G, et al. Op0114 machine learning tools identify patient clusters and swollen and tender joint correlation patterns in a large database from the secukinumab psoriatic arthritis clinical development program. Annals of the Rheumatic Diseases. 2019;78(Suppl 2):131-131.
  20. Armstrong AW, Chambers CJ, Maverakis E, et al. Effectiveness of online vs. in-person care for adults with psoriasis. JAMA Netw Open. 2018;1(6).


Disease Models for Biomarker Research

IPC Councilor Nicole Ward, PhD
Case Western Reserve University
Cleveland, Ohio, United States

Though psoriatic arthritis (PsA) occurs in up to 30% of psoriasis patients, we currently cannot predict who will develop PsA until they begin to show irreversible joint damage. There is a significant overlap of differentially expressed genes (DEGs) amongst different animal models of psoriasis with human psoriasis.1,2 To date, 13 psoriasis mouse models develop PsA-like phenotype, and in most models, the skin inflammation predates the joint disease.

Dr. Ward discussed her lab’s goal to model personalized medicine and drug responsiveness by comparing mouse phenotypes with psoriasis patient endotypes. Specific to this lecture, Dr. Ward’s lab examined whether mouse models can predict which psoriasis patients will develop PsA.

Her lab has developed three mouse models (KC-Tie2, IL-17C, and KLK6) that develop characteristic psoriasiform skin changes and respond to therapeutics. However, only the KLK6 model displays a similar phenotype to PsA patients (dactylitis, osteopenia, kyphosis, degenerative sacroiliac changes) with both peripheral and axial disease. KLK6 mouse skin also shows increases in key disease signature cytokines (IL-22, IL-17A, etc.) and transcriptionally shows tight overlap with human disease.3-7

Using RNA-Seq, Dr. Ward is attempting to identify novel human disease biomarkers using transcriptomics and bioinformatics. Her lab compared genes for the KLK6 mice against the two other mouse models, lesional and non-lesional skin in cutaneous-only patients (PsC) versus those with PsA, lesional modulation with infliximab over ten weeks, and blood of PsC vs. PsA patients.7

In KLK6 mice, they identified 19 transcripts, of which 11 were increased and eight decreased. While some had been previously associated with psoriasis, others had not. Their current goal is to generate a list of putative PsA Biomarkers to test in a well-defined patient cohort via comparison of blood and skin from healthy controls, PsC, PSA, osteoarthritis, rheumatoid arthritis, and other inflammatory diseases. This will allow testing for biomarker success that only occurs in PsA patients. Once a potential biomarker is identified, prospective testing in a new patient cohort will test success at predictability could develop a PsA biomarker.

The vision is to ultimately develop a diagnostic test for a biomarker to predict which patients will develop PsA, with goals for personalized medicine.7 Then, artificial intelligence and machine learning could potentially identify cause and effect, correlation, mechanisms of action and comorbidities, drug responders, and more.


  1. Swindell WR, Johnston A, Carbajal S, et al. Genomewide expression profiling of five mouse models identifies similarities and differences with human psoriasis. PLoS One. 2011;6(4):e18266.
  2. Swindell WR, Remmer HA, Sarkar MK, et al. Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance. Genome Med. 2015;7(1).
  3. Lundberg KC, Fritz Y, Johnston A, et al. Proteomics of skin proteins in psoriasis: from discovery and verification in a mouse model to confirmation in humans. Mol Cell Proteomics. 2015;14(1):109-119.
  4. Ward NL, Loyd CM, Wolfram JA, Diaconu D, Michaels CM, McCormick TS. Depletion of antigen-presenting cells by clodronate liposomes reverses the psoriatic skin phenotype in KC-Tie2 mice. Br J Dermatol. 2011;164(4):750-758.
  5. Fritz Y, Klenotic PA, Swindell WR, et al. Induction of alternative proinflammatory cytokines accounts for sustained psoriasiform skin inflammation in il-17c+il-6ko mice. J Invest Dermatol. 2017;137(3):696-705.
  6. Johnston A, Fritz Y, Dawes SM, et al. Keratinocyte overexpression of IL-17C promotes psoriasiform skin inflammation. J Immunol. 2013;190(5):2252-2262.
  7. Billi AC, Ludwig JE, Fritz Y, et al. KLK6 expression in skin induces PAR1-mediated psoriasiform dermatitis and inflammatory joint disease. J Clin Invest. 2020;130(6):3151-3157.


Psoriasis and Psoriatic Arthritis Biomarkers

Vinod Chandran, MD PhD
University of Toronto
Toronto, Ontario, Canada

Psoriasis and psoriatic arthritis (PsA) are heterogenous diseases.1 Though there is overlap in many genetic pathways (IL-17, IL-23, etc.), PsA synovium gene expression differs from that of skin.2 Given the heterogeneity of psoriatic disease, biomarker research is becoming increasingly employed.

Biomarkers are objectively measured and evaluated characteristics that can serve as indicators of normal biologic processes, pathogenic processes, or pharmacologic responses to therapies.3-5 Prediction, a time-denominated inference about an unknown present or future state based on available information, is vital in biomarkers. Three common types of medical predictions include risk, diagnosis, and prognosis. Optimal predictive diagnostic tests are significant, accurate, generalizable, and easily deployed.5-7 For psoriatic disease, biomarkers that can predict risk, assess activity, and monitor therapeutic response could revolutionize diagnosis and treatment.

Machine learning is a budding tool in biomarker research. Patrick et al. show you can predict PsA with high confidence in at least 10% of patients, based on GWAS in several cohorts across 200 markers. A challenge is that when precision was high, the sensitivity was low, but this could be corrected by combining various methods.8

Various methods are being utilized in biomarker discovery. Single-cell genome studies identified that CXCL10 is a possible vital biomarker to predict PsA development. Patients who developed PsA had overexpression of baseline synovium and serum CXCL10 levels that decreased over time, whereas those with the cutaneous-limited disease did not.9-11 Proteomics are also being utilized. Cretu et al. identified many markers that are overexpressed in PsA skin and synovial fluid when comparing skin biopsies and synovial fluid.12 A challenge with serum proteomics is correcting for high dynamic range, which is targeted and can be expensive.13 There is also a recent focus on metabolomic fingerprinting. Dr. Chandran’s group discovered apparent differences when comparing psoriasis to PSA serum metabolites, but disease activity may explain much of the differences for these potential diagnostic markers.14

Response to treatment is clearly of interest in biomarker research. Using genotype markers in a large cohort, Dand et al. found that adalimumab had a better response in HLA-C*06:02 negative patients than ustekinumab, especially if they had PsA. The opposite was also true.15 Rahmati et al. observed clear gene expression profiles between IL-17 responders and non-responders using transcriptomic markers. They found that Rho-GTPase pathway was important and strongly signaled specific in IL-17, but not in TNF-α, response.16 In a study using immune cell phenotyping, Miyagawa et al. found that with strategic biologic treatment based on phenotypic differences in helper T cells (vs. usual practice), ACR20 response is significantly improved (85% vs. 55%).17

In summary, biomarkers have great potential to improve defined clinical outcomes in psoriasis. Though initial studies are promising, many clinically translational challenges still exist, and research remains in its infancy.


  1. McGonagle D, McDermott MF. A proposed classification of the immunological diseases. PLoS Med. 2006;3(8):e297.
  2. Nerviani A, Boutet M-A, Tan WSG, et al. IL-23 skin and joint profiling in psoriatic arthritis: novel perspectives in understanding clinical responses to IL-23 inhibitors. Ann Rheum Dis. Published online November 26, 2020.
  3. Laterza OF, Hendrickson RC, Wagner JA. Molecular biomarkers. Drug Information J. 2007;41(5):573-585.
  4. Litman T. Personalized medicine—concepts, technologies, and applications in inflammatory skin diseases. APMIS. 2019;127(5):386-424.
  5. Davis KD, Aghaeepour N, Ahn A.H., et al. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nature Reviews Neurology. 2020;16(7):381-400.
  6. Burke HB. Predicting clinical outcomes using molecular biomarkers. Biomark Cancer. 2016;8:89-99.
  7. Šimundić A-M. Measures of diagnostic accuracy: basic definitions. EJIFCC. 2009;19(4):203-211.
  8. Patrick MT, Stuart PE, Raja K, et al. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nature Communications. 2018;9(1):4178.
  9. Abji F, Pollock RA, Liang K, Chandran V, Gladman DD. Brief report: cxcl10 is a possible biomarker for the development of psoriatic arthritis among patients with psoriasis. Arthritis Rheumatol. 2016;68(12):2911-2916.
  10. Abji F, Lee K-A, Pollock RA, et al. Declining levels of serum chemokine (C-x-c motif) ligand 10 over time are associated with new-onset of psoriatic arthritis in patients with psoriasis: a new biomarker?. British Journal of Dermatology. 2020;183(5):920-927.
  11. Penkava F, Velasco-Herrera MDC, Young MD, et al. Single-cell sequencing reveals clonal expansions of proinflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nature Communications. 2020;11(1):4767.
  12. Cretu D, Prassas I, Saraon P, et al. Identification of psoriatic arthritis mediators in synovial fluid by quantitative mass spectrometry. Clin Proteomics. 2014;11(1):27.
  13. McArdle A, Qasim Butt A, Szentpetery A, et al. Developing clinically relevant biomarkers in inflammatory arthritis: A multiplatform approach for serum candidate protein discovery. Proteomics Clin Appl. 2016;10(6):691-698.
  14. Chandran V, Looby N, Reyes-Garces N, et al. Serum Metabolomic Analysis of Psoriatic Arthritis Using Solid Phase Microextraction – Liquid Chromatography – High-Resolution Mass Spectrometry Identifies Putative Disease Activity Markers [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10). Accessed May 13, 2021.
  15. Dand N, Duckworth M, Baudry D, et al. HLA-C*06:02 genotype is a predictive biomarker of biologic treatment response in psoriasis. J Allergy Clin Immunol. 2019;143(6):2120-2130.
  16. Rahmati S, O’Rielly DD, Li Q, et al. Rho-GTPase pathways may differentiate treatment response to TNF-alpha and IL-17A inhibitors in psoriatic arthritis. Scientific Reports. 2020;10(1):21703.
  17. Miyagawa I, Nakayamada S, Nakano K, et al. Precision medicine using different biological DMARDs based on characteristic phenotypes of peripheral T helper cells in psoriatic arthritis. Rheumatology (Oxford). 2019;58(2):336-344.


Machine learning approaches for psoriasis research

Alex Tsoi, PhD
University of Michigan
Ann Arbor, Michigan, United States

Though many models in high throughput data have advanced biomedicine, data modeling still faces challenges as complex systems may only contribute a modest effect size from each feature.1 In this compelling lecture, Dr. Tsoi focused on three aspects of machine learning in psoriasis modeling: genetically regulated components of gene expression, risk of psoriatic arthritis, and drug responses.

Modeling genetically regulated components of gene expression
Identifying genome-wide association study (GWAS) signals is easier than interpreting the results. Many psoriasis-associated loci will not change the function of a protein but rather change expression levels for the genes they target. The objective is to identify causal genes from associated loci by modeling the genetically regulated component and then impute other genotyped individuals’ transcriptome to be associated with the phenotype of interest. Modeling programs, therefore, prioritize genes that are likely to be causal for the phenotype.2

Once a model is created, you can apply it to independent datasets. With a larger sample size, the predictive performance matches the genetic heritability of the expression profiles with more refined confidence intervals. Dr. Tsoi’s group, for example, used expansive Genotype-Tissue Expression (GTEx) portal RNA-seq to improve predictive performance of their cohort, then used association analysis to reveal genes from 13 existing and five new psoriasis-associated loci.3

Modeling risk of PsA among psoriasis patients
In addition to using genetic data to model profiles, potential clinical implications are vital. Dr. Tsoi’s team used machine learning to create a model that predicts which psoriasis patients will develop PsA. They performed a meta-analysis of PsA and cutaneous-limited psoriasis (PsC) vs. controls, then used GWAS to find new genome-wide significant loci for PsA or PsC. Their model showed 98% precision with 33% recall for the top 10% of patients predicted to develop PsA and 83% precision with 55% recall for the top 20%. Importantly, integrating genetic data allowed for dynamic predictions.4

Modeling drug response using genomic data
In recent work, Dr. Tsoi’s team used cytokine signatures to characterize different skin types in psoriasis and atopic dermatitis. From existing transcriptome psoriatic lesional skin data, they implemented a self-organizing map to highlight heterogeneity among patients. Using RNA-seq, they found the baseline transcriptome expression levels of uninvolved skin has implication for anti-TNF PASI response.5 In unpublished work (currently under review) they observed impressive prediction results via cytokine-stimulated signatures to model PASI-75 response; for the top 20% of patients predicted to be PASI-75 responders, they displayed 80% accuracy in prediction.

Challenges of machine learning
Machine learning is not without challenges. The sample size is critical to provide robust information modeling for better parameter estimation and cross-validation. Reproducibility in different settings is crucial, and we need to continue associating molecular data with clinical response. A “multi-omics” (genome, proteome, transcriptome, etc.) integrative approach to refine models will continue to improve machine learning.


  1. Tsoi LC, Stuart PE, Tian C, et al. Large-scale meta-analysis characterizes genetic architecture for common psoriasis associated variants. Nature Communications. 2017;8(1):15382.
  2. Gamazon ER, Wheeler HE, Shah KP, et al. A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics. 2015;47(9):1091-1098.
  3. Tsoi LC, Yang J, Liang Y, et al. Transcriptional determinants of individualized inflammatory responses at anatomically separate sites. J Allergy Clin Immunol. 2018;141(2):805-808.
  4. Patrick MT, Stuart PE, Raja K, et al. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nature Communications. 2018;9(1):4178.
  5. Tsoi LC, Rodriguez E, Degenhardt F, et al. Atopic dermatitis is an il-13-dominant disease with greater molecular heterogeneity compared to psoriasis. J Invest Dermatol. 2019;139(7):1480-1489.


Single cell transcriptomic analysis of the peripheral neutrophil compartment in psoriatic arthritis reveals heterogeneity and novel potential therapeutic targets; 1:20:39

Cheryl Cameron, PhD
Case Western Reserve University
Cleveland, Ohio, United States

  • Background: Much research has been done on immunopathogenic mechanisms associated with psoriatic arthritis (PsA), and many therapeutic approaches are directed towards lymphocytes, including inhibition of the IL-17 axis.1 Neutrophil subsets are elevated in psoriatic disease, but their role in the disease process has yet to be fully elucidated.
  • Aim: Investigate the gene expression of neutrophils in PsA and the response of therapeutic targeting of these neutrophils.
  • Methods: Obtained whole blood from high- and low-PASI psoriasis patients and PsA patients. Performed negative enrichment of neutrophils, single-cell RNA-seq, and comparative analysis of single-cell gene expression profiles. Validated neutrophil-specific transcriptomic signatures at the protein level. Built linear regression models with clinical and immunologic data from cohort subjects. Mechanistically dissected neutrophil-specific pathways via pharmacologic and small molecule perturbation of identified pathways.
  • Results:
    • Dimension Reduction (uniform manifold approximation and projection, UMAP) showed cell classification based on gene expression, focusing on neutrophil clusters. By overlaying patient classification of cutaneous psoriasis only (PsA-No) vs. psoriatic arthritis (PsA-Yes), separated the neutrophil clusters with strong effect.
    • Clustering analysis showed three significant clusters with predominantly PsA-No neutrophils and 3 with PsA-Yes neutrophils. neutrophils display a unique pattern of gene expression; metabolic regulation (PsA-Yes) vs. interferon response (PsA-No).
    • Pseudotime trajectories of neutrophils showed early stages were populated by primarily PsA-Yes neutrophils, but along the trajectory (in multiple cell fate decisions made) end with predominantly PsA-No neutrophils (higher expression of interferon response genes).
  • Conclusions: Neutrophils display unique patterns of gene expression, which differ in cutaneous psoriasis and psoriatic arthritis. Novel therapeutic targets for preventing or reversing neutrophil-mediated mechanisms driving psoriatic arthritis may be possible.


  1. Bravo A, Kavanaugh A. Bedside to bench: defining the immunopathogenesis of psoriatic arthritis. Nat Rev Rheumatol. 2019;15(11):645-656.


Perspective of psoriatic disease patients on novel COVID-19 vaccines

George Gondo, MA
National Psoriasis Foundation
Portland, Oregon, United States


  • Background: Rapid online surveys may help illuminate patient perspectives about the SARS-COV-2 virus and the COVID-19 pandemic, allowing clinicians to address these concerns.1 Novel COVID-19 vaccines are highly effective in preventing infection, transmission, and poor outcomes.
  • Aim: Examine the likelihood of receiving the COVID-19 vaccine amongst psoriasis patients and demographic and clinical factors that influence the possibility of receiving the vaccine, identify vaccine-hesitant individuals, and develop effective interventions to change their perspectives/hesitancy.
  • Methods: Data from online survey random stratified sample n > 1400 with psoriasis. Survey asked: the likelihood of receiving the vaccine, history of flu vaccine in last 12 months, current therapies, comorbidities diagnosed by physician, standard demographics. Chi square tests used to identify demo and clinical variables with increasing likelihood of receiving the vaccine.
  • Results:
    • 52.7% ‘very likely’ or ‘likely’ to receive COVID-19 vaccine
    • 18.5% ‘very unlikely’ or ‘unlikely’ to receive COVID-19 vaccine
    • Likelihood to receive vaccine was higher among those who received flu vaccine (65%), males, Caucasians, 65+ years old, mild psoriasis, acceptable PsA (Psoriatic Arthritis Impact of Disease (PsAID) ≤ 4), income $75,000+
    • Interestingly, factors not associated with higher likelihood: Biologic use (p = 0.985), disease type (p = 0.77), high-risk comorbidity for worse COVID-19 outcome (p = 0.122)
  • Conclusions:
    • A large segment of patients with psoriasis plan not to get the vaccine; > 25% of patients are at least ‘somewhat unlikely’ to receive it.
    • Further intervention and outreach are necessary to address the hesitancy.
    • Healthcare providers, including dermatologists and rheumatologists, are viewed as trusted information sources by patients and can relay effective vaccine information.
    • The National Psoriasis Foundation, in conjunction with a COVID-19 task force, can help inform these conversations.


  1. Geldsetzer P. Use of rapid online surveys to assess people’s perceptions during infectious disease outbreaks: a cross-sectional survey on COVID19. J Med Internet Res 2020; 22: e18790.


Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide

IPC Councilor James T. Elder, MD, PhD
University of Michigan,
Ann Arbor, Michigan, United States

  • Background: Genome-wide association studies (GWAS) have revealed > 80 psoriasis susceptibility regions, but most identified loci have been from European-origin (EUR) and Chinese cohorts.
  • Aim: Acquire GWAS data from psoriasis patients of South Asian descent (SAS, India, and Pakistan) to help prioritize causal genetic variants by exploiting differences in haplotype structure.
  • Methods:
    • Genotyping done on Illumina arrays. GWAS Imputation included 9.02M markers, with r≥ 0.7 were used. Spearman’s ρ used to correlate effect sizes for known psoriasis loci between the EUR and SAS populations. Transethnic meta-analysis of the SAS sample was performed with an established EUR sample with 15,967 cases and 28,194 controls.1
    • HLA imputation was optimized using combinations of multiple panels. Association between MHC variants and psoriasis was tested by logistic regression.
  • Results:
    • Effect sizes were highly correlated in EUR and SAS GWAS.
    • Genetic signal strengths in the ‘classical MHC’ far exceeded those in the ‘extended MHC’ and outside the MHC, allowing identification and strength ranking of multiple MHC signals via logistic regression
    • Psoriasis association ranks were similar, but not identical, in SAS and EUR (HLA-C > HLA-B > HLA-A). HLA-C*06 is the most significant in both populations.
    • The lead variants for the HLA-C signals are in complete linkage disequilibrium (L.D.) between SAS and EUR (r2 = 1.00 in both populations), while the HLA-A lead variants are in moderate L.D. (r= 0.70 in SAS and 0.49 in EUR)
    • Non-coding variants, with a high probability of being causal, influence the expression of HLA-C and HLA-B and manifest different regulatory behavior in immunocytes vs. non-immunocytes.
  • Conclusions: This study reveals elements of typical genetic architecture shared across ethnicities in psoriasis. It highlights the value of trans-ethnic meta-analysis to uncover new disease-associated variants and to enhance the fine-mapping of susceptibility loci within and outside the MHC.


  1. Patrick MT, Stuart PE, Raja K, et al. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nat Commun. 2018;9(1):4178.


Comparing the pediatric and adult plaque psoriasis transcriptomes; Poster #687

Wynnis Tom, MD
University of California San Diego
San Diego, California, United States

  • Background: Pediatric psoriasis may have lesional differences depending on early-life triggers and disease persistence. There may also be differences in pathogenesis, as IL-22 producing T-cells are more involved in pediatric disease (vs. IL-17 in adults).1
  • Aim: Examine similarities and differences in the pediatric plaque psoriasis transcriptome compared to adult disease. Assess expression of genes relevant to treatment, particularly systemic agents for moderate to severe disease.
  • Methods: Children aged two months to ≤ 18 years with plaque psoriasis compared to age, sex, and race matched children, and to adults aged ≥ 40 years with plaque psoriasis with adult-onset disease (≥ 25 years). 5mm punch biopsies of lesional and non-lesional skin were performed, and gene expression levels were determined using RNAseq (Hi-Seq 2500). Top differentially expressed genes and pathway analysis were confirmed using qRT-PCR and immunohistochemical staining.
  • ResultsPediatric vs. Adult disease
    • Expression of 34,465 genes was compared; found 1,842 genes are the same. In pediatric disease, 1,055 genes were upregulated, and 787 genes were downregulated.
    • Via Metascape Enrichment Pathway analysis found the upregulated genes were more involved in forming the cornified envelope, defense response, microbial and humoral response, and metal sequestration by antimicrobial proteins. For the downregulated genes: keratinization and metabolism of lipids.
    • Like adult disease, found involvement of IL-17 and IL-23 pathway genes and downregulation of loricrin and FLG2.
    • Not very many genes with significant fold change. Three overlapping genes that differed from adult disease: IL-22, CXCL12, CD177. In pediatric psoriasis:
      • No difference in expression of IL-22, but found pediatric psoriasis may be more influenced by neutrophil innate immunity.
      • CD177 upregulated; plays a role in PMN activation.
      • CXCL12 downregulated; chemoattractant active on T-lymphocytes and monocytes, but not neutrophils.
    • Overall expression and fold change very similar for treatment relevant gene expression (TNF, IL-12, IL12/23, IL-17, JAK pathways)
  • Conclusions:
    • The pediatric plaque psoriasis transcriptome is very similar to the adult plaque psoriasis transcriptome, with the involvement of the IL-17 signaling, formation of the cornified envelope, and microbial defense pathways.
    • Pediatric disease may be more influenced by neutrophil innate immunity.
    • Children are expected to respond similarly to existing biologic and small molecule therapies.


  1. Cordoro KM, Hitraya-Low M, Taravati K, et al. Skin-infiltrating, interleukin-22-producing T cells differentiate pediatric psoriasis from adult psoriasis. J Am Acad Dermatol. 2017;77(3):417-424.


IL-23 maintains tissue-resident memory Th17 cells in murine and psoriatic skin; Poster #036

Sarah Whitley, MD, PhD
University of Pittsburgh
Pittsburgh, Pennsylvania, United States

  • Background: IL-23 from CD301b+ dermal dendrocytes (dDCs) is both necessary and sufficient for IL-17 production by dermal γδ T cells following Candida albicans skin infection.1 The TRPV1+ nerve circuit is enough to expand IL-17 producing γδ and cD4 T cells, induce IL-23 and other types 17 cytokines and elicit psoriasiform skin in mice.2
  • Hypothesis: IL-23 is a critical TRM17 maintenance factor, and TRM17 depletion is a significant mechanism of IL-23 inhibitor efficacy and durability.1,3
  • Method: (17Fate)-reporter mice expressing the red fluorescent protein ‘tdTomato,’ with a reporter molecule to track antigen-specific Th17 cells following infection were infected with C. albicans known to elicit a host protective TRM17 response. tdTomato T cells present in the skin were then phenotyped at various time points after infection.
  • Results:
    • Cutaneous C. albicans infection generates skin-resident TRM17 that persist for a long time
    • TRM17-mediated protection from C. albicans reinfection requires IL-23
    • IL-23 maintains TRM17 in the skin after clearance from C. albicans infection and promotes in situ proliferation of TRM17 in the skin
    • IL-23 from CD301b+ DCs is required for cutaneous TRM17 maintenance
    • Treatment with IL-23 inhibitors depletes TRM17 from lesional psoriasis skin
  • Conclusions: IL-23 is required to maintain TRM17 in murine and human psoriasis skin, and impairing TRM17 proliferation strongly reduces psoriatic inflammation. Targeting local immune circuits supporting IL-23 production by cutaneous D.C.s could reduce psoriasis relapse and lead to curative treatments. Locally administered anti-IL-23 or anti-CGRP (inhibits IL-17 produced by CD301b+ dDC) may have similar efficacy as systemic biologics.


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A specific mutation in TRPM4 predisposes mice to psoriasiform dermatitis; Poster #153

Daisuke Yamada, MD PhD
University of California Davis School of Medicine
Sacramento, California, United States

  • Background: TRPM4 is an intracellular Ca2+ activated, nonselective cation channel, and it contributes to the migration and proliferation of cells. Reports suggest a possible role of TRPM4 in psoriasis pathogenesis. Gain-of-function mutations can produce progressive erythrokeratodermia, which is similar to psoriasis clinically and histologically.1-6
  • Aim: To assess if TRPM4 equivalent mutant mice (I1029M, obtained via CRISPR/Cas9) have increased susceptibility to form psoriasiform dermatitis (PsD)
  • Results: TRPM4 Heterozygous-I1029M mice exhibited the following (vs. wild type mice)
    • Keratinocytes were more likely to be in proliferating cell cycle status
    • Enhanced susceptibility to PsD in response to imiquimod (IMQ)
    • Increased inflammation, characterized by accumulation of CCR6-expressing γδ low T cells, and higher expression levels of IL-17A
    • Increased numbers of IL-17A-producing T cells and accumulation of dendritic cells
    • Glibenclamide, a TRPM4 inhibitor, ameliorated IMQ-induced PsD in the mutant mice
  • Proposed mechanism: TRPM4 mutant dendritic cells migrate to lymph nodes, leading to expansion of IL-17A+ and γδ T cells, which then migrate to lesional skin to induce PsD.
  • Conclusion: TRPM4 mutant mice are more susceptible to psoriasiform dermatitis. TRPM4 may be a future therapeutic target in psoriasis.


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  5. Wang H, Xu Z, Lee BH, et al. Gain-of-function mutations in trpm4 activation gate cause progressive symmetric erythrokeratodermia. J Invest Dermatol. 2019;139(5):1089-1097.
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Bile acids improve psoriasiform dermatitis; Poster #192

Zhenrui Shi, MD PhD
University of California Davis Department of Dermatology
Sacramento, California, United States

  • Wahlström A, Sayin SI, Marschall H-U, Bäckhed F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 2016;24(1):41-50.
  • Hang S, Paik D, Yao L, et al. Bile acid metabolites control Th17 and T reg cell differentiation. Nature. 2019;576(7785):143-148.
  • Itoh S, Kono M, Akimoto T. Psoriasis treated with ursodeoxycholic acid: three case reports. Clin Exp Dermatol. 2007;32(4):398-400.
  • Ely PH. Is psoriasis a bowel disease? Successful treatment with bile acids and bioflavonoids suggests it is. Clin Dermatol. 2018;36(3):376-389.


  1. Wahlström A, Sayin SI, Marschall H-U, Bäckhed F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 2016;24(1):41-50.
  2. Hang S, Paik D, Yao L, et al. Bile acid metabolites control Th17 and T reg cell differentiation. Nature. 2019;576(7785):143-148.
  3. Itoh S, Kono M, Akimoto T. Psoriasis treated with ursodeoxycholic acid: three case reports. Clin Exp Dermatol. 2007;32(4):398-400.
  4. Ely PH. Is psoriasis a bowel disease? Successful treatment with bile acids and bioflavonoids suggests it is. Clin Dermatol. 2018;36(3):376-389.


COVID19 Related Outcomes in Psoriasis and Psoriasis Arthritis Patients: A Multicenter Analysis; Poster #254

Rahul Raiker, BS
West Virginia University School of Medicine
Morgantown, West Virginia, United States

  • Background: Currently, there is little literature on the outcomes of COVID-19 patients with Psoriasis (Pso) and Psoriatic Arthritis (PsA), especially multicenter data from large sample sizes.
  • Aim: Investigate the risk of COVID complications in both Pso and PsA patients.
  • Methods: Retrospective cohort study using TriNetX database (63 million records), from validated ICD-10 and serology codes per CDC guidelines, from January 20, 2020, to November 6, 2020. Calculated adjusted risk ratios (aRR) with 95% confidence intervals. 45-day “severe COVID complications” defined as a composite of mortality and ventilation. Subgroup analyses and Kaplan-Meier analysis were conducted to analyze 45-day severe COVID probability between those with and without PsO or PsA.
  • Results: Matched samples of PsO-COVID (n = 2288), PsA-COVID (n = 502), and systemic immunosuppressant use ≥ one year showed no significant difference compared to those without psoriatic disease in hospitalization, sepsis, mortality, or severe COVID.
    • Notably, compared to non-PsO patients, PsO-COVID patients had a significantly lower risk of ARDS and mechanical ventilation and improved 45-day severe COVID probability.
  • Conclusion: Patients with cutaneous psoriasis, psoriatic arthritis, and psoriasis-related immunosuppressant use ≥ one year were not at higher risk for COVID complications or severe infection than those without psoriatic disease. Additional studies are needed. 


Biologic and nonbiologic systemic treatment of psoriasis are protective against solid organ, hematologic, and cutaneous cancer in a large multi-institution cohort; Poster #387

Nicholas Theodosakis, MD, PhD
Massachusetts General Hospital Department of Dermatology
Boston, Massachusetts, United States

  • Background: There are known links between chronic inflammation and cancer, and previous studies have suggested a generally increased risk for cancer in the psoriasis population. However, there are numerous confounders.
  • Aim: To explore the effect of systemic therapy for psoriasis patients on solid and hematologic malignancy incidence.
  • Methods: Adult psoriasis patients in the Mass General Brigham Enterprise Data Warehouse and Research Patient Data Registry (n = 69,391), with at least one associated ICD-9/10 code consistent with a diagnosis of psoriasis and at least two prescriptions for systemic therapy ≥ 30 days from January 1, 1990, and October 1, 2020. Cox proportional hazard model was used to account for cancer variation incidence.
  • Results: Reductions in non-cutaneous cancer seen in following arms: only-biologic therapy with hazard ratio 0.41 (CI: 0.32-0.53, p < 0.001), exclusively non-biologic therapy HR 0.64 (0.57-0.73, p < 0.001), and mixed biologic/non-biologic therapy HR 0.60 (0.51-0.70, p < 0.001). Similar results were seen in cutaneous-limited cancer.
  • Conclusions: Patients treated with both biologic and non-biologic systemic therapy showed lower cancer incidence than patients receiving no systemic therapy when adjusted for adjusted comorbidities and demographics. Male gender, Caucasian race, and history of CKD, COPD, CVD, or pneumonia were significantly associated with a higher malignancy rate


Racial/Ethnic Diversity in U.S. Clinical Trials for Acne, A.D. and Psoriasis, Poster #560

Anjana Sevagamoorthy MBBS, MPH
University of Pennsylvania
Philadelphia, Pennsylvania, United States

  • Background: The 2020 U.S. Census projects that non-White racial groups will represent 33% of the U.S. population by 2060. Despite federal efforts, diversity in clinical trials is inadequate. The reporting and racial/ethnic distribution of U.S. participants in dermatologic clinical trials are not well described.
  • Aim: Examine racial and ethnic diversity in dermatologic trials.
  • Methods: Comprehensive review of acne, A.D., and psoriasis clinical trials published between January 1, 2014, and July 3, 2019. The literature search was performed using the PubMed, Scopus, EMBASE, and Web of Science databases.
  • Results: Weighted percentage of U.S. participants in psoriasis clinical trials (n = 35) included: 15.1% non-White and 18.6% Hispanic. Non-White participant results were below the U.S. population estimate of 23.6%.
  • Conclusion: Among clinical trials that report race and ethnicity, non-White individuals are underrepresented in psoriasis trials (as well as acne, atopic dermatitis trials). Efforts to increase the reporting of race/ethnicity and ensure adequate representation of racial/ethnic minorities in dermatologic clinical trials are needed.


Psoriasis patients with subclinical atherosclerosis parse into distinct endotypes by differential gene expression; Poster #663

Dustin P. DeMeo
Case Western Reserve University School of Medicine
Cleveland, Ohio, United States

  • Background: Psoriasis is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). Coronary artery calcification scores (CACS) calculated via Computed Tomography and reported as Agaston Units are a surrogate for chronic subclinical atherosclerosis.
  • Aim: Identify shared molecular targets for psoriasis/ASCVD co-therapy
  • Methods: Cohort from the Center of Research Translation Psoriasis Study in Cleveland. Psoriasis compared to control among all patients, and high (≥ Agaston) vs. low (< 100 Agaston) CACS compared among psoriasis patients.
  • Results:
    • Higher CACS is associated with increased age, male sex, higher CVD risk scores, and elevated mean corpuscular volume (MCV). Patients are well controlled for cutaneous psoriasis but not CACS.
    • Differentially Expressed Genes (DEGs): For psoriasis-↑CACS (vs. Psoriasis ↓CACS)
      • Gene Set Variation Analysis (GSVA) found increased expression with largest fold-changes and smallest p-values in IFN-α, IFN-γ, and PI3K
    • DEGs: For psoriasis-↑CACS (vs. Psoriasis control)
      • 13 genes in common. 11 genes increase in expression together, where upregulation of high-CACS genes is more than double the control. Over half of genes belong to the MSigDB hallmark-IFN pathways, and DEGs partially explain phenotypes.
  • Conclusions: Controlling psoriasis does not necessarily control ASCVD. Patients with higher CACS may have elevated MCV, molecular signature of increased IFN-α, IFN-γ, and PI3K, and molecular sub-endotypes correlated with rising Agaston scores. DEGs overlapping between psoriasis and psoriasis-↑CACS are significantly enriched for interferon/anti-viral pathways.