Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

Distance-based Classification and Regression Trees for the analysis of complex predictors in health and medical research

Predicting patient outcomes based on patient characteristics and care processes is a common task in medical research. Such predictive features are often multifaceted and complex, and are usually simplified into one or more scalar variables to facilitate statistical analysis. This process, while necessary, results in a loss of important clinical detail. While this loss may be prevented by using distance-based predictive methods which better represent complex healthcare features, the statistical literature on such methods is limited, and the range of tools facilitating distance-based analysis is substantially smaller than those of other methods. Consequently, medical researchers must choose to either reduce complex predictive features to scalar variables to facilitate analysis, or instead use a limited number of distance-based predictive methods which may not fulfil the needs of the analysis problem at hand. We address this limitation by developing a Distance-Based extension of Classification and Regression Trees (DB-CART) capable of making distance-based predictions of categorical, ordinal and numeric patient outcomes. We also demonstrate how this extension is compatible with other extensions to CART, including a recently published method for predicting care trajectories in chronic disease. We demonstrate DB-CART by using it to expand upon previously published dose-response analysis of stroke rehabilitation data. Our method identified additional detail not captured by the previously published analysis, reinforcing previous conclusions. We also demonstrate how by combining DB-CART with other extensions to CART, the method is capable of making predictions about complex, multifaceted outcome data based on complex, multifaceted predictive features.

A Knock-Down Cell-Based Study for the Functional Analysis of Chloride Intracellular Channel 1 (CLIC1): Integrated Proteomics and Microarray Study

BACKGROUND

Previously, we detected that chloride intracellular channel 1 (CLIC1) was involved in the pathogenesis of atopic dermatitis (AD).

OBJECTIVE

In this study, we aimed to use high-throughput screening (HTS) approaches to identify critical factors associated with the function of CLIC1 in knock-down cells.

METHODS

We down-regulated CLIC1 in human A549 cells via siRNA and then conducted serial HTS studies, including proteomics integrated with a microarray and the implementation of bioinformatics algorithms.

RESULTS

Together, these approaches identified several important proteins and genes associated with the function of CLIC1. These proteins and genes included tumor rejection antigen (gp96) 1, nucleophosmin, annexin I, keratin 1 and 10, FLNA protein, enolase 1, and metalloprotease 1, which were found using two-dimensional electrophoresis (2-DE) proteomics. Separately, NTNG1, SEMA5A, CLEC3A, GRPR, GNGT2, GRM5, GRM7, DNMT3B, CXCR5, CCL11, CD86, IL2, MNDA, TLR5, IL23R, DPP6, DLGAP1, CAT, GSTA1, GSTA2, GSTA5, CYP2E1, ADH1A, ESR1, ARRDC3, A1F1, CCL5, CASP8, DNTT, SQSTM1, PCYT1A, and SLCO4C1 were found using a DNA microarray integrated with PPI mapping.

CONCLUSION

CCL11 is thought to be a particularly critical gene among the candidate genes detected in this study. By integrating the datasets and utilizing the strengths of HTS, we obtained new insights into the functional role of CLIC1, including the use of CLIC1-associated applications in the treatment of human diseases such as AD.

To protect or adversely affect? The dichotomous role of the NLRP1 inflammasome in human disease

NLRP1 is an inflammasome forming pattern recognition receptor (PRR). When activated by pathogen- and damage- associated molecular patterns (PAMPS/DAMPS), NLRP1 inflammasome formation leads to inflammation through the production of proinflammatory cytokines IL-18 and IL-1β. As with other inflammasome forming NLR family members, NLRP1 also regulates cell death processes, termed pyroptosis. The domain structure of NLRP1 differs between mice and humans, making it possible for the function of the inflammasome to differ between species and adds complexity to the study of this NLR family member. In humans, mutations in both coding and non-coding regions of the NLRP1 gene are linked to a variety of diseases. Likewise, interruption of NLRP1 inhibitors or changes in the prevalence of NLRP1 activators can also impact disease pathobiology. Adding to its complexity, the NLRP1 inflammasome plays a dichotomous role in human diseases, functioning to either attenuate or augment miscellaneous biological processes in a tissue specific manner. For example, NLRP1 plays a protective role in the gastrointestinal tract by modulating the microbiome composition; however, it augments neurological disorders, cardio-pulmonary diseases, and cancer through promoting inflammation. Thus, it is critical that the role of NLRP1 in each of these disease processes be robustly defined. In this review, we summarize the current research landscape to provide a better understanding of the mechanisms associated with NLRP1 function and dysfunction in human disease pathobiology. We propose that a better understanding of these mechanisms will ultimately result in improved insight into immune system dysfunction and therapeutic strategies targeting inflammasome function in multiple human diseases.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

An Application of Sequential Meta-Analysis to Gene Expression Studies

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.

Factors affecting the accuracy of a class prediction model in gene expression data

BACKGROUND

Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer.

RESULTS

Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation.

CONCLUSIONS

We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models.

Discovery in genetic skin disease: the impact of high throughput genetic technologies

The last decade has seen considerable advances in our understanding of the genetic basis of skin disease, as a consequence of high throughput sequencing technologies including next generation sequencing and whole exome sequencing. We have now determined the genes underlying several monogenic diseases, such as harlequin ichthyosis, Olmsted syndrome, and exfoliative ichthyosis, which have provided unique insights into the structure and function of the skin. In addition, through genome wide association studies we now have an understanding of how low penetrance variants contribute to inflammatory skin diseases such as psoriasis vulgaris and atopic dermatitis, and how they contribute to underlying pathophysiological disease processes. In this review we discuss strategies used to unravel the genes underlying both monogenic and complex trait skin diseases in the last 10 years and the implications on mechanistic studies, diagnostics, and therapeutics.

In-vivo imaging of psoriatic lesions with polarization multispectral dermoscopy and multiphoton microscopy

Psoriasis is a skin autoimmune disease characterized by hyperkeratosis, hyperproliferation of the epidermis and dilatation of dermal papillary blood vessels. Healthy skin (5 volunteers) and psoriatic lesions (3 patients) were visualized in vivo, with high contrast and resolution, with a Polarization Multispectral Dermoscope and a Multiphoton Microscope. Psoriatic features were identified and quantified. The effective diameter of the superficial blood vessels was measured at 35.2 ± 7.2 μm and the elongated dermal papillae had an effective diameter of 64.2 ± 22.6 μm. The methodologies developed could be employed for quantitative diagnostic purposes and furthermore serve as a monitoring method of the effect of personalized treatments.

Skinomics: past, present and future for diagnostic microarray studies in dermatology

Easily accessible, skin was among the first targets analyzed using 'omics' and dermatology embraced the approaches very early. Microarrays have been used to define disease markers, identify transcriptional changes and even trace the course of treatment. Melanoma and psoriasis have been explored using microarrays. Particularly noteworthy is the multinational mapping of psoriasis susceptibility loci. The transcriptional changes in psoriasis have been identified using hundreds of biopsies. Epidermal keratinocytes have been studied because they respond to UV light, infections, inflammatory and immunomodulating cytokines, toxins and so on. Epidermal differentiation genes are being characterized and are expressed in human epidermal stem cells. Exciting discoveries defining human skin microbiomes have opened a new field of research with great medical potential. Specific to dermatology, the non-invasive skin sampling for microarray studies, using tape stripping, has been developed; it promises to advance dermatology toward 'omics' techniques directly applicable to the personalized medicine of the future.

Looking within the lesion: Large scale transcriptional profiling of psoriatic plaques

Psoriasis is a lifelong, chronic, recurring and highly variable skin disease. Psoriatic plaques are formed through induction of inflammation in the epidermis and deregulation of keratinocyte proliferation and differentiation. This results in red or silvery scaly patches on the surface of the epidermis. To look within the lesions and define the changes in gene expression in psoriasis, investigators compared the transcriptomes of psoriatic plaques, of uninvolved skin of patients and of skin from healthy individuals. In several large studies with many patients, the genes expressed at much higher level in psoriatic plaques included those responsible for the cell cycle, keratinocyte differentiation, and response to wounding; conversely, lipid and fatty acid metabolism enzymes were expressed at reduced levels. The nonlesional and healthy skin appeared fairly similar. The largest study included paired biopsies from 85 individual patients. The same group used transcription profiling to follow the course of treatment in a set of patients, and correlated changes in the transcriptome of blood samples of psoriatic patients. Importantly, a noninvasive technique involving tape-stripping of skin, has been shown effective in transcriptional studies of psoriasis. Current efforts are focused on deconvoluting the contributions of various cell types in psoriasis, keratinocytes, lymphocytes, fibroblasts etc. Taken as a whole, these efforts will lead to personalized medicine, i.e., to specific, individualized treatments of patients with psoriasis.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Putting together the psoriasis puzzle: an update on developing targeted therapies

Psoriasis vulgaris is a chronic, debilitating skin disease that affects millions of people worldwide. There is no mouse model that accurately reproduces all facets of the disease, but the accessibility of skin tissue from patients has facilitated the elucidation of many pathways involved in the pathogenesis of psoriasis and highlighted the importance of the immune system in the disease. The pathophysiological relevance of these findings has been supported by genetic studies that identified polymorphisms in genes associated with NFκB activation, IL-23 signaling and T helper 17 (Th17)-cell adaptive immune responses, and in genes associated with the epidermal barrier. Recently developed biologic agents that selectively target specific components of the immune system are highly effective for treating psoriasis. In particular, emerging therapeutics are focused on targeting the IL-23–Th17-cell axis, and several agents that block IL-17 signaling have shown promising results in early-phase clinical trials. This review discusses lessons learned about the pathogenesis of psoriasis from mouse-and patient-based studies, emphasizing how the outcomes of clinical trials with T-cell-targeted and cytokine-blocking therapies have clarified our understanding of the disease.

The role of the palatine tonsils in the pathogenesis and treatment of psoriasis

Psoriasis is a common chronic skin disease with strong genetic associations and environmental triggers. Patients with psoriasis develop sore throats much more frequently than nonpsoriatic individuals and it is well documented that streptococcal throat infections can trigger the onset of psoriasis, and such infections cause exacerbation of chronic psoriasis. It is now generally accepted that psoriatic lesions are caused by abnormal reactivity of specific T lymphocytes in the skin. However, it has been shown in recent years that activation of specific immunity is always preceded by activation of nonspecific innate immune mechanisms, and that abnormalities in the innate immune system can cause dysregulation in specific immune responses. Here we explore the possible immune mechanisms that are involved in the link between infection of the tonsils and this inflammatory skin disease. Moreover, we survey the literature and discuss the suitability of tonsillectomy as a treatment for psoriasis.

Molecular diagnostic strategies: a role in the practice of dermatology

Molecular diagnostic strategies are gaining wider acceptance and use in dermatology and dermatopathology as more practitioners in this field develop an understanding of the principles and applications of genomic technologies. Molecular testing is facilitating more accurate diagnosis, staging, and prognostication, in addition to guiding the selection of appropriate treatment, monitoring of therapy, and identification of novel therapeutic targets, for a wide variety of skin diseases.

Heterogeneity of inflammatory and cytokine networks in chronic plaque psoriasis

The clinical features of psoriasis, characterized by sharply demarcated scaly erythematous plaques, are typically so distinctive that a diagnosis can easily be made on these grounds alone. However, there is great variability in treatment response between individual patients, and this may reflect heterogeneity of inflammatory networks driving the disease. In this study, whole-genome transcriptional profiling was used to characterize inflammatory and cytokine networks in 62 lesional skin samples obtained from patients with stable chronic plaque psoriasis. We were able to stratify lesions according to their inflammatory gene expression signatures, identifying those associated with strong (37% of patients), moderate (39%) and weak inflammatory infiltrates (24%). Additionally, we identified differences in cytokine signatures with heightened cytokine-response patterns in one sub-group of lesions (IL-13-strong; 50%) and attenuation of these patterns in a second sub-group (IL-13-weak; 50%). These sub-groups correlated with the composition of the inflammatory infiltrate, but were only weakly associated with increased risk allele frequency at some psoriasis susceptibility loci (e.g., REL, TRAF3IP2 and NOS2). Our findings highlight variable points in the inflammatory and cytokine networks known to drive chronic plaque psoriasis. Such heterogeneous aspects may shape clinical course and treatment responses, and can provide avenues for development of personalized treatments.

Advances in the treatment of moderate-to-severe plaque psoriasis

PURPOSE

Advances in the treatment of moderate-to-severe plaque psoriasis, including new biological agents and related drugs, are reviewed.

SUMMARY

Most patients with psoriasis have mild disease that can be treated with topical agents alone; however, over one third of patients have more-extensive disease, called moderate-to-severe plaque psoriasis. Although effective, traditional therapies, including methotrexate, cyclosporine, acitretin, and phototherapy, have serious adverse effects that limit both the initiation and duration of treatment, necessitating sequential treatment regimens. With the increasing knowledge of the immune nature of the disease, biological agents that target T lymphocytes, tumor necrosis factor (TNF)-α, interleukin (IL)-12, and IL-23 have been used successfully in moderate-to-severe psoriasis. Etanercept, adalimumab, and infliximab are also highly effective in the treatment of moderate-to-severe plaque psoriasis. Ustekinumab, a new agent that targets IL-12 and IL-23, was approved for marketing in 2009 and offers similar efficacy and safety profiles to the anti-TNF agents. While the rapid onset and apparent lack of long-term toxicity of biological agents make them major advances in the treatment of more severe forms of psoriasis, the lack of extensive experience with these agents in patients with psoriasis leaves several unresolved issues that must be addressed before their exact place in therapy can be determined.

CONCLUSION

With the development of biological therapies over the past 10 years, health care providers have a much broader choice of highly effective agents with which to treat patients suffering from moderate-to-severe plaque psoriasis. Though costly to use, biological agents offer considerable advantages over previously available systemic therapies.

A pharmacoeconomic analysis of severe psoriasis therapy: a review of treatment choices and cost efficiency

INTRODUCTION

Psoriasis is a chronic, inflammatory disease afflicting 2% of the US population; it results in significant morbidity. The annual healthcare costs related to psoriasis are an estimated $11.3 billion and, with an expanding biologic market, an updated costs analysis is needed.

AREAS COVERED

Current treatments, including systemic agents (acitretin, cyclosporine, methotrexate), phototherapies and all available biologics (adalimumab, etanercept, infliximab, alefacept, ustekinumab) appropriate for severe psoriasis are described mechanistically and with regard to their efficacy, quality-of-life improvements and side effects. A cost-efficacy model considering US health-system-based annual costs, clinical and quality-of-life improvements was created. Reported Psoriasis Area and Severity Index improvement of 75% from baseline (PASI-75) scores, Dermatology Life Quality Index (DLQI) improvements and estimated costs of medications are described. Annual costs ranged from $1330 for methotrexate to $48,731 for high-dose etanercept. The lowest cost per achieving DLQI minimally important difference was from phototherapy; the highest was from alefacept. The lowest costs per patient achieving PASI-75 was from methotrexate and the highest was from alefacept.

EXPERT OPINION

Phototherapies and methotrexate offer high efficacy for their costs. Therapeutic approaches must be individualized for each patient given all considerations described.

Guidelines of care for the management of psoriasis and psoriatic arthritis: section 6. Guidelines of care for the treatment of psoriasis and psoriatic arthritis: case-based presentations and evidence-based conclusions

Psoriasis is a common, chronic, inflammatory, multisystem disease with predominantly skin and joint manifestations affecting approximately 2% of the population. In the first 5 parts of the American Academy of Dermatology Psoriasis Guidelines of Care, we have presented evidence supporting the use of topical treatments, phototherapy, traditional systemic agents, and biological therapies for patients with psoriasis and psoriatic arthritis. In this sixth and final section of the Psoriasis Guidelines of Care, we will present cases to illustrate how to practically use these guidelines in specific clinical scenarios. We will describe the approach to treating patients with psoriasis across the entire spectrum of this fascinating disease from mild to moderate to severe, with and without psoriatic arthritis, based on the 5 prior published guidelines. Although specific therapeutic recommendations are given for each of the cases presented, it is important that treatment be tailored to meet individual patients' needs. In addition, we will update the prior 5 guidelines and address gaps in research and care that currently exist, while making suggestions for further studies that could be performed to help address these limitations in our knowledge base.

Pharmacogenetics of psoriasis

Psoriasis is an inflammatory hyperproliferative skin disorder with a strong genetic predisposition. While many effective modalities are currently available for treating psoriasis, response to therapy is quite variable among patients. The genetic component underlying the response to pharmacotherapy in psoriasis is slowly beginning to emerge and represents a specialized field of genetics referred to as pharmacogenetics. The identification of genetic variants has the potential to improve the management of patient care by identifying which patients should avoid a specific drug and which patients should be administered a modified dose. A suitable approach in implementing such a strategy could potentially reduce medical costs and improve success of drug therapy. This article summarizes the clinical aspects of psoriasis, its genetic susceptibility and highlights the current landscape of genetic targets for psoriasis pharmacotherapy.