The expanding therapeutic landscape for inflammatory skin diseases, combined with rising healthcare costs, has increased the need for a comprehensive method to assess and compare treatment performance in daily practice care. In this context, drug survival—the time a patient remains on a given treatment—has gained increasing attention in dermatology because it comprehensively reflects treatment effectiveness, safety, and patient and clinician preferences. However, methodological approaches and reporting practices vary considerably across drug survival studies in dermatology. In this article, we aim to provide an updated and comprehensive guide for clinical researchers on drug survival methodology, offering recommendations for performing, reporting, and interpreting drug survival analyses in a standardized manner, while highlighting best practices within dermatology. We provide guidance on fundamental concepts of drug survival analysis, including Kaplan–Meier curves, reporting standards, censoring, stratification, regression analyses, and sample size calculations. In addition, to further enhance the quality of analyses, we discuss advanced topics such as restricted mean survival time, competing risks, time-dependent effects and covariates, and meta-analyses of drug survival data. Key concepts will be illustrated using a synthetic dataset, comprising patients with psoriasis treated with biologics, complemented by references to published studies that clarify relevant methodological concepts and provide real-world dermatological examples.
UVR is a major environmental trigger of cutaneous inflammation. Although DNA damage has long been considered the main mediator of UV pathology, a growing body of evidence indicates that RNA is a primary driver of UV-induced inflammation. UV exposure induces RNA damage and dysregulates RNA metabolism, activating the ribotoxic stress response and promoting the generation and accumulation of immunostimulatory self-RNAs. RNA metabolism also plays a role in UV-related dermatologic pathologies, including photosensitive inflammatory skin disorders and skin carcinogenesis. In this review, we summarize the current knowledge and new advances on the role of RNA in UV-induced inflammation, with an emphasis on both mechanisms and translational implications. Together, these insights suggest that targeting RNA pathways may represent a new approach for treating or preventing UV-induced skin inflammation and inflammatory skin diseases.
Loss-of-function (LOF) variants of the FLG gene are risk factors for atopic dermatitis. However, owing to the repetitive nature of exon 3, these variants are difficult to characterize. We sought to determine whether the use of long-read technologies would enable better characterization of the FLG gene and improve the detection of LOF variants. We sequenced a diverse clinical cohort of 192 samples collected in Belgium, Ireland, and Rwanda using long-read sequencing as well as publicly available data. We identified both common and rare LOF variants linked to atopic dermatitis in our clinical cohort, a number of compound heterozygotes, as well as 1 previously undescribed LOF variant. We also observed 20 previously undescribed FLG alleles, which varied in length by up to 13 kb. These were mostly found in samples of sub-Saharan origin and appear to be products of nonallelic homologous recombination. Despite the use of a long-read–based approach, we did not detect any FLG LOF variants in our African cohort, highlighting differences in FLG LOF burden and atopic dermatitis genetic risk between populations. Our approach has the potential to be a useful tool to identify FLG variants contributing to atopic dermatitis in various human populations.
