Bioinformatics uses computationally intensive approaches to make sense of complex biological data sets. Here we review the role of bioinformatics in 3 areas of biology: genetics, transcriptomics, and microbiomics. Examples of bioinformatics in each area are given with respect to psoriasis and psoriatic arthritis, related inflammatory disorders at the forefront of bioinformatic research in dermatology. While bioinformatic technologies and analyses have traditionally been developed and deployed in siloes, the field of integrative omics is on the horizon. Powered by the advent of machine learning, bioinformatic integration of large data sets has the potential to dramatically revolutionize our knowledge of pathogenetic mechanisms and therapeutic targets.
The application of artificial intelligence (AI) to medicine has considerable potential within dermatology, where the majority of diagnoses are based on visual pattern recognition. Opportunities for AI in dermatology include the potential to automate repetitive tasks; optimize time-consuming tasks; extend limited medical resources; improve interobserver reliability issues; and expand the diagnostic toolbox of dermatologists. To achieve the full potential of AI, however, developers must aim to create algorithms representing diverse patient populations; ensure algorithm output is ultimately interpretable; validate algorithm performance prospectively; preserve human-patient interaction when necessary; and demonstrate validity in the eyes of regulatory bodies.
Atypical fibroxanthoma (AFX) is a dermal spindle-cell sarcoma that is considered a superficial and clinically benign presentation of pleomorphic dermal sarcoma, malignant fibrous histiocytoma, and undifferentiated pleomorphic sarcoma. AFX appears clinically as a discrete red or pink nodule or papule, most commonly on the head and neck region of sun-damaged elderly patients. Histologic findings on routine hematoxylin and eosin staining reveal spindle-shaped, large, and pleomorphic tumor cells throughout the dermis. Immunohistochemistry is not specific for AFX, and the diagnosis is generally one of exclusion. AFX is best treated by complete surgical excision, with Mohs micrographic surgery considered the treatment of choice. Metastasis rarely occurs, but there is a high rate of local recurrence, especially in patients who are immunosuppressed. (C) 2019 Frontline Medical Communications
The incidence of advanced cutaneous squamous cell carcinoma (cSCC) is increasing; of the 1.3 million nonmelanoma skin cancers that arise each year, approximately 20% are cSCC, and between 2-5% of these cases ultimately metastasize. However, there is no established consensus on first-line systemic treatment for those patients who have locally advanced or metastatic disease. Major classes of systemic agents include chemotherapy, epidermal growth factor receptor (EGFR)-targeted therapy, and immunotherapy; each is associated with a distinct set of adverse effects, and availability of data from randomized controlled trials (RCTs) to definitively guide treatment are limited. While several chemotherapeutic agents have been described in case studies or small patient cohorts, only one RCT has been conducted, demonstrating a 34% overall response rate for a cisplatin-based regimen. EGFR-inhibitors evaluated for use in cSCC by RCT include cetuximab, panitumumab, and gefitinib; response rates ranged from 15-31% for these agents. Inhibitors of the immune checkpoint programmed death-1 (PD-1) have yielded promising outcomes in advanced cSCC; indeed, the PD-1 inhibitor cemiplimab recently received FDA approval for use in advanced cSCC. Despite these advances, the preferred regimen for systemic treatment of cSCC remains unclear, particularly in immunocompromised populations. Herein we provide a review of the literature supporting the use of these modalities and a discussion of their clinical utility.
In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural language processing, image recognition, object detection, facial recognition, and action categorization. Indeed, in many of these applications, accuracy has reached or exceeded human levels of performance. Subsequently, a multitude of studies have begun to examine the application of these technologies to health care, and in particular, medical image analysis. Perhaps the most difficult subdomain involves skin imaging because of the lack of standards around imaging hardware, technique, color, and lighting conditions. In addition, unlike radiological images, skin image appearance can be significantly affected by skin tone as well as the broad range of diseases. Furthermore, automated algorithm development relies on large high-quality annotated image data sets that incorporate the breadth of this circumstantial and diagnostic variety. These issues, in combination with unique complexities regarding integrating artificial intelligence systems into a clinical workflow, have led to difficulty in using these systems to improve sensitivity and specificity of skin diagnostics in health care networks around the world. In this article, we summarize recent advancements in machine learning, with a focused perspective on the role of public challenges and data sets on the progression of these technologies in skin imaging. In addition, we highlight the remaining hurdles toward effective implementation of technologies to the clinical workflow and discuss how public challenges and data sets can catalyze the development of solutions.
Advancements in smartphone technologies and the use of specialized health care applications offer an exciting new era to promote melanoma awareness to the public and improve education and prevention strategies. These applications also afford an opportunity to power meaningful research aimed at improving image diagnostics and early melanoma detection. Here, we summarize our experience associated with developing and managing the implementation of MoleMapper (TM), a research-based application that not only provides an efficient way for users to digitally track images of moles and facilitate skin self-examinations but also provides a platform to crowd-source research participants and the curation of mole images in efforts to advance melanoma research. Obtaining electronic consent, safeguarding participant data, and employing a framework to ensure collection of meaningful data represent a few of the inherent difficulties associated with orchestrating such a wide-scale research enterprise. In this review, we discuss strategies to overcome these and other challenges leading to the implementation of MoleMapper (TM). (C) 2019 Frontline Medical Communications
Emerging technology is fundamentally changing how individuals interact with the health care system. Web-based searches, mobile applications, social media, and directto- consumer genetic testing companies are facilitating information exchange at a higher rate than ever before, creating a macroscopic shift in the mechanisms by which individuals seek health information. The visual nature of skin disease enables individuals to browse, share, and search based on images, adding another dimension to how dermatological information is transferred. These trends carry important implications on user health care behavior, and so it is vital for health care professionals to stay attuned to the morphing characteristics of their patients' health management in order to continue to provide high-quality, patient-centered care.
The influences of information technology have touched almost all aspects of our lives, and health care delivery has been no exception. Law, policy, and regulation have driven the adoption of electronic medical records, particularly over the past decade, driving fundamental changes to the practice of medicine in general and dermatology in particular. This article reviews the history of these changes, the regulations that drove these changes, the intended and unintended consequences of these initiatives, and our insights into the appropriate roles for policy and regulation to drive positive change.
Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.
Skinomics is a field of bioinformatics applied specifically to skin biology and, by extension, to dermatology. Skinomics has been expanding into extensive genome-wide association studies, eg, of psoriasis, proteomics, lipidomics, metabolomics, metagenomics, and the studies of the microbiome. Here, the current state of the field of transcriptomics is reviewed, including the studies of the gene expression in human skin under several healthy and disease conditions. Specifically, transcriptional studies of epidermal differentiation, skin aging, effects of cytokines, inflammation with emphases on psoriasis and atopic dermatitis, and wound healing are reviewed. The transition from microarrays to NextGen sequencing is noted and potential future directions suggested.
In this chapter, we present the use of whole slide imaging (WSI) and dermoscopy in the field of dermatology. Image digitization has allowed for increasing computer-assisted clinical decision-making. An introduction to common digital imaging data sources such as WSI and dermoscopy is provided. We also review some commonly used image quantification methods and their potential applications in dermatology. Finally, we review how machine learning approaches utilize novel large dermatology image datasets.
Atypical fibroxanthoma (AFX) is a dermal spindle-cell sarcoma that is considered a superficial and clinically benign presentation of pleomorphic dermal sarcoma, malignant fibrous histiocytoma, and undifferentiated pleomorphic sarcoma. AFX appears clinically as a discrete red or pink nodule or papule, most commonly on the head and neck region of sun-damaged elderly patients. Histologic findings on routine hematoxylin and eosin staining reveal spindle-shaped, large, and pleomorphic tumor cells throughout the dermis. Immunohistochemistry is not specific for AFX, and the diagnosis is generally one of exclusion. AFX is best treated by complete surgical excision, with Mohs micrographic surgery considered the treatment of choice. Metastasis rarely occurs, but there is a high rate of local recurrence, especially in patients who are immunosuppressed.
Surgery remains one of the key treatment modalities for melanoma. Wide excision of the primary site with sentinel lymph node biopsy for selected patients has been recognized as the standard surgical approach for patients with early-stage disease. Controversies persist regarding margin width, indications for sentinel lymph node biopsy, and surgical management of regional nodal basins. Additionally, new therapies such as intralesional therapies as well as new systemic therapies are changing the role for surgery in patients with recurrent local-regional as well as metastatic disease. In this chapter, we discuss the current recommendations as well as the topics of debate in the surgical management of melanoma.
The world is flat when it comes to aesthetic treatments, meaning women and men from all walks of life, regardless of culture and ethnicity or even socioeconomic status, are seeking ways to improve their appearance, prevent aging, and rejuvenate their skin. Year after year, statistics show a steady increase in people of color undergoing aesthetic treatments, with neurotoxins, fillers, laser resurfacing, and body contouring being the most sought-after procedures. When treating this cohort of patients, however, dermatologists need to be sensitized to how a patient's ethnicity affects facial structure, the tissue reaction to treatments, and patient's specific expectations for recommended therapies. A balance between tolerability and efficacy needs to be struck to minimize risk for adverse effects such as postinflammatory hyperpigmentation, which can negatively impact a patient's experience and quality of life.
Immune checkpoint inhibitors have dramatically transformed melanoma treatment options. However, intrinsic and acquired resistance remain fundamental limitations to extending the benefits to all patients. Understanding molecular and clinical features that correlate with response to treatment (biomarkers) may unravel therapeutic resistance, assist in treatment decision-making, and facilitate drug development. An intensive effort to characterize these biomarkers is underway. Herein, we highlight promising molecular biomarkers involving the tumor microenvironment, host immune response, and microbiome. We particularly focus on anti-programmed death-1 therapy but will also briefly cover anti-cytotoxic T lymphocyte antigen-4 and novel combination therapies.
Psoriasis is chronic inflammatory skin condition that imposes a significant physical and psychosocial burden on patients. Moderate to severe psoriasis often requires systemic treatments, including oral systemic therapies and biologics. An addition to the treatment repository for psoriasis is oral small molecules, which include apremilast, tofacitinib, and ponesimod. Of these 3 medications, only apremilast is currently approved for the treatment of psoriasis. Long-term safety data for apremilast suggest that it has a tolerable safety profile and leads to significant improvement in patients with psoriasis; however, there are few head-to-head comparisons with other oral systemic medications. Tofacitinib and ponesimod have demonstrated clinical efficacy in treating psoriasis; however, further studies are required to understand the benefit-risk profile of these medications in psoriasis patients. (C) 2018 Frontline Medical Communications
Cutaneous inflammatory conditions such as psoriasis, atopic dermatitis, alopecia areata, vitiligo, and connective tissue diseases often remain a challenge to treat. Although there is an in-depth understanding of the clinical presentation of these diseases, much less is known regarding the pathophysiology. This has limited the effective treatment options for patients. A more detailed understanding of the pathogenesis of each disease will lead to newer targeted medications with less morbidity. Though there are different pathways involved in these diseases, the Janus Kinase (JAK)-Signal Transducer and Activator of Transcription proteins (STAT) signaling pathway is common to them all. Therefore, this review article endeavors to substantiate the immunopathology and clinical utility of the JAK inhibitors as treatments for different chronic inflammatory diseases of the skin.
Mycosis fungoides is the most common and therefore quintessential cutaneous lymphoma and is typically characterized by an epidermotropic infiltrate of atypical monoclonal CD4+ lymphocytes. Classical histopathologic findings include epidermotropism, lymphocytes with convoluted nuclear contours and surrounding perinuclear “halos,” and papillary dermal fibrosis. Atypical lymphocytes may occasionally form Pautrier’s microabscesses with tagging of lymphocytes along the basal keratinocytes. Unfortunately, a variety of benign inflammatory infiltrates, as well as other cutaneous lymphomas, may demonstrate some similar histopathologic findings. Herein, we review the wide array of epidermotropic T-cell lymphomas and discuss distinguishing features between these entities. We also offer an algorithmic approach utilizing histopathologic, immunophenotypic, and molecular techniques that can be used for analyzing an epidermotropic T-cell infiltrate in order to render a specific diagnosis.
