Next-generation sequencing in dermatology

Over the past decade, Next-Generation Sequencing (NGS) has advanced our understanding, diagnosis, and management of several areas within dermatology. NGS has emerged as a powerful tool for diagnosing genetic diseases of the skin, improving upon traditional PCR-based techniques limited by significant genetic heterogeneity associated with these disorders. Epidermolysis bullosa and ichthyosis are two of the most extensively studied genetic diseases of the skin, with a well-characterized spectrum of genetic changes occurring in these conditions. NGS has also played a critical role in expanding the mutational landscape of cutaneous squamous cell carcinoma, enhancing our understanding of its molecular pathogenesis. Similarly, genetic testing has greatly benefited melanoma diagnosis and treatment, primarily due to the high prevalence of BRAF hot spot mutations and other well-characterized genetic alterations. Additionally, NGS provides a valuable tool for measuring tumor mutational burden, which can aid in management of melanoma. Lastly, NGS demonstrates promise in improving the sensitivity of diagnosing cutaneous T-cell lymphoma. This article provides a comprehensive summary of NGS applications in the diagnosis and management of genodermatoses, cutaneous squamous cell carcinoma, melanoma, and cutaneous T-cell lymphoma, highlighting the impact of NGS on the field of dermatology

Hyper IgE syndrome‐related disease treated with dupilumab: A case report

Abstract Phosphoglucomutase 3 (PGM3) catalyzes the glycosylation of immune system precursor proteins. Its impairment leads to severe infections and other developmental, musculoskeletal, and nervous system defects. We present a case of a 2‐month‐old female patient with recurrent infections and diffuse eczematous dermatitis recalcitrant to corticosteroids. A next‐generation sequencing NGS gene panel for inherited immune dysfunction syndromes revealed multiple variants of unknown significance in key immune regulators, specifically heterozygous mutation c.337C⟩G (p.Pro113Ala) on exon 4 of PGM3 as a novel variant in the PGM3 associated diseases. Off‐label use of dupilumab resulted in rapid improvement

Disseminated cryptococcosis presenting with cutaneous involvement in an immunocompromised patient

Cryptococcosis is a fungal infection that typically affects immunocompromised patients. It most commonly affects the lungs and may then disseminate to the central nervous system, bone, skin, and adrenal glands. Herein, we describe a 69-year-old man who presented with skin lesions as the initial manifestation of disseminated cryptococcosis. Initial workup led to an assumption that the patient was immunocompetent. Later in the clinical course, idiopathic depletion of CD4 T cells was discovered. This case highlights that disseminated cryptococcosis may present with cutaneous symptoms even when there is no evidence of pulmonary or central nervous system involvement and may be the first sign of an underlying cellular immune dysfunction

Disseminated cryptococcosis presenting with cutaneous involvement in an immunocompromised patient

Cryptococcosis is a fungal infection that typically affects immunocompromised patients. It most commonly affects the lungs and may then disseminate to the central nervous system, bone, skin, and adrenal glands. Herein, we describe a 69-year-old man who presented with skin lesions as the initial manifestation of disseminated cryptococcosis. Initial workup led to an assumption that the patient was immunocompetent. Later in the clinical course, idiopathic depletion of CD4 T cells was discovered. This case highlights that disseminated cryptococcosis may present with cutaneous symptoms even when there is no evidence of pulmonary or central nervous system involvement and may be the first sign of an underlying cellular immune dysfunction

Improvement of steatotic rat liver function with a defatting cocktail during ex situ normothermic machine perfusion is not directly related to liver fat content.

There is a significant organ shortage in the field of liver transplantation, partly due to a high discard rate of steatotic livers from donors. These organs are known to function poorly if transplanted but make up a significant portion of the available pool of donated livers. This study demonstrates the ability to improve the function of steatotic rat livers using a combination of ex situ machine perfusion and a "defatting" drug cocktail. After 6 hours of perfusion, defatted livers demonstrated lower perfusate lactate levels and improved bile quality as demonstrated by higher bile bicarbonate and lower bile lactate. Furthermore, defatting was associated with decreased gene expression of pro-inflammatory cytokines and increased expression of enzymes involved in mitochondrial fatty acid oxidation. Rehabilitation of marginal or discarded steatotic livers using machine perfusion and tailored drug therapy can significantly increase the supply of donor livers for transplantation

Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortalityResearch in context

Summary: Background: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. Methods: We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). Findings: We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS  5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. Interpretation: The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. Funding: This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript