Single-Cell RNA Sequencing Reveals Dysregulated POSTN+WNT5A+ Fibroblast Subclusters in Prurigo Nodularis.

Prurigo nodularis (PN) is an intensely pruritic, inflammatory skin disease with a poorly understood pathogenesis. We performed single-cell transcriptomic profiling of 28,695 lesional and nonlesional PN cells. Lesional PN has increased dysregulated fibroblasts (FBs) and myofibroblasts. FBs in lesional PN were shifted toward a cancer-associated FB-like phenotype, with POSTN+WNT5A+ cancer-associated FBs increased in PN and similarly so in squamous cell carcinoma. A multicenter cohort study revealed an increased risk of squamous cell carcinoma and cancer-associated FB-associated malignancies (breast and colorectal) in patients with PN. Systemic fibroproliferative diseases (renal sclerosis and idiopathic pulmonary fibrosis) were upregulated in patients with PN. Ligand-receptor analyses demonstrated an FB neuronal axis with FB-derived WNT5A and periostin interactions with neuronal receptors melanoma cell adhesion molecule and ITGAV. These findings identify a pathogenic and targetable POSTN+WNT5A+ FB subpopulation that may predispose cancer-associated FB-associated malignancies in patients with PN.

Risk of incident sleep disorders in patients with prurigo nodularis: A population-level analysis using The Health Improvement Network.

Background: Prurigo nodularis (PN) is a chronic inflammatory skin condition characterized by severely itchy and often painful bumps on the arms, legs, and trunk. It is unknown whether patients with PN have increased risk of developing sleep disorders. Objective: To evaluate the association of PN with sleep disorders. Methods: This retrospective, population-level, matched-cohort study was conducted using The Health Improvement Network. The study included 4193 patients with newly diagnosed PN and 4193 age, sex, and race/ethnicity-matched controls. A Cox regression model was used to assess the development of sleep disorders, including insomnia, sleep apnea, and restless legs syndrome, in patients with PN compared with control patients. Results: Compared with controls, PN was associated with insomnia (adjusted hazard ratio [aHR] = 1.77; 95% CI = 1.48-2.12) and overall sleep disorder (aHR = 1.72; 95% CI = 1.46-2.02), but not with sleep apnea (aHR = 1.51; 95% CI = 0.93-2.44) or restless legs syndrome (aHR = 1.54; 95% CI = 0.92-2.57). Limitations: As a retrospective cohort study, our analysis is subject to potential confounders not already included. Conclusions: PN was associated with subsequent development of insomnia. Thus, clinicians should consider insomnia among patients with PN and develop strategies for treatment and prevention.

Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging.

Deep learning (DL) models have demonstrated state-of-the-art performance in the classification of diagnostic imaging in oncology. However, DL models for medical images can be compromised by adversarial images, where pixel values of input images are manipulated to deceive the DL model. To address this limitation, our study investigates the detectability of adversarial images in oncology using multiple detection schemes. Experiments were conducted on thoracic computed tomography (CT) scans, mammography, and brain magnetic resonance imaging (MRI). For each dataset we trained a convolutional neural network to classify the presence or absence of malignancy. We trained five DL and machine learning (ML)-based detection models and tested their performance in detecting adversarial images. Adversarial images generated using projected gradient descent (PGD) with a perturbation size of 0.004 were detected by the ResNet detection model with an accuracy of 100% for CT, 100% for mammogram, and 90.0% for MRI. Overall, adversarial images were detected with high accuracy in settings where adversarial perturbation was above set thresholds. Adversarial detection should be considered alongside adversarial training as a defense technique to protect DL models for cancer imaging classification from the threat of adversarial images.

Single-cell RNA sequencing reveals dysregulated fibroblast subclusters in prurigo nodularis.

Prurigo nodularis (PN) is an intensely pruritic, chronic inflammatory skin disease that disproportionately affects black patients. However, the pathogenesis of PN is poorly understood. We performed single-cell transcriptomic profiling, ligand receptor analysis and cell trajectory analysis of 28,695 lesional and non-lesional PN skin cells to uncover disease-identifying cell compositions and genetic characteristics. We uncovered a dysregulated role for fibroblasts (FBs) and myofibroblasts as a key pathogenic element in PN, which were significantly increased in PN lesional skin. We defined seven unique subclusters of FBs in PN skin and observed a shift of PN lesional FBs towards a cancer-associated fibroblast (CAF)-like phenotype, with WNT5A+ CAFs increased in the skin of PN patients and similarly so in squamous cell carcinoma (SCC). A multicenter PN cohort study subsequently revealed an increased risk of SCC as well as additional CAF-associated malignancies in PN patients, including breast and colorectal cancers. Systemic fibroproliferative diseases were also upregulated in PN patients, including renal sclerosis and idiopathic pulmonary fibrosis. Ligand receptor analyses demonstrated increased FB1-derived WNT5A and periostin interactions with neuronal receptors MCAM and ITGAV, suggesting a fibroblast-neuronal axis in PN. Type I IFN responses in immune cells and increased angiogenesis/permeability in endothelial cells were also observed. As compared to atopic dermatitis (AD) and psoriasis (PSO) patients, increased mesenchymal dysregulation is unique to PN with an intermediate Th2/Th17 phenotype between atopic dermatitis and psoriasis. These findings identify a pathogenic role for CAFs in PN, including a novel targetable WNT5A+ fibroblast subpopulation and CAF-associated malignancies in PN patients.

Psoriasis associated with asthma and allergic rhinitis: a US-based cross-sectional study using the All of US Research Program.

Psoriasis is a common chronic inflammatory disease with multiple known comorbidities. Increasing evidence suggests some mechanistic overlap in the immunopathogenesis of psoriasis and some cases of asthma and allergic rhinitis (AR), but the potential association between psoriasis and asthma and AR has not been thoroughly investigated. The study aimed to investigate the association between psoriasis and asthma and AR. We used data from the NIH All of US Research Program, a nationwide longitudinal cohort of US adults, collected from 2018 to present. The source population comprised a demographically and socioeconomically diverse cohort of over 300,000 Americans. We used multivariable logistic regression models to examine the association between psoriasis and asthma and AR, after adjusting for sociodemographic variables, body mass index, and smoking status. In total, 235,551 participants (mean [SD] age, 54.7 [16.6] years; 59.3% female), including 5165 individuals with psoriasis and 230,386 individuals without psoriasis, were included in our analysis. Participants with psoriasis had significantly higher prevalence of asthma (26.1% vs. 12.9%; P < 0.001) and AR (31.8% vs. 13.4%; P < 0.001) compared to participants without psoriasis. Psoriasis was significantly associated with both asthma [adjusted odds ratio (aOR) 2.22; 95% confidence interval (CI) 2.08-2.37] and AR (aOR, 2.57; 95% CI 2.42-2.73). In subgroup analyses, associations remained stable in multivariable analyses after stratification by age, sex, and income. Psoriasis is associated with both asthma and AR in our sample of US adults. Further research is needed to explore potentially unifying inflammatory pathways among psoriasis, asthma, and AR.

DNA binding and RAD51 engagement by the BRCA2 C-terminus orchestrate DNA repair and replication fork preservation.

The tumor suppressor BRCA2 participates in DNA double-strand break repair by RAD51-dependent homologous recombination and protects stressed DNA replication forks from nucleolytic attack. We demonstrate that the C-terminal Recombinase Binding (CTRB) region of BRCA2, encoded by gene exon 27, harbors a DNA binding activity. CTRB alone stimulates the DNA strand exchange activity of RAD51 and permits the utilization of RPA-coated ssDNA by RAD51 for strand exchange. Moreover, CTRB functionally synergizes with the Oligonucleotide Binding fold containing DNA binding domain and BRC4 repeat of BRCA2 in RPA-RAD51 exchange on ssDNA. Importantly, we show that the DNA binding and RAD51 interaction attributes of the CTRB are crucial for homologous recombination and protection of replication forks against MRE11-mediated attrition. Our findings shed light on the role of the CTRB region in genome repair, reveal remarkable functional plasticity of BRCA2, and help explain why deletion of Brca2 exon 27 impacts upon embryonic lethality.

Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology.

PURPOSE: Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to cause misclassifications by DL models. The purpose of the study is to investigate the robustness of DL models trained on diagnostic images using adversarial images and explore the utility of an iterative adversarial training approach to improve the robustness of DL models against adversarial images. METHODS: We examined the impact of adversarial images on the classification accuracies of DL models trained to classify cancerous lesions across three common oncologic imaging modalities. The computed tomography (CT) model was trained to classify malignant lung nodules. The mammogram model was trained to classify malignant breast lesions. The magnetic resonance imaging (MRI) model was trained to classify brain metastases. RESULTS: Oncologic images showed instability to small pixel-level changes. A pixel-level perturbation of 0.004 (for pixels normalized to the range between 0 and 1) resulted in most oncologic images to be misclassified (CT 25.6%, mammogram 23.9%, and MRI 6.4% accuracy). Adversarial training improved the stability and robustness of DL models trained on oncologic images compared with naive models ([CT 67.7% v 26.9%], mammogram [63.4% vs 27.7%], and MRI [87.2% vs 24.3%]). CONCLUSION: DL models naively trained on oncologic images exhibited dramatic instability to small pixel-level changes resulting in substantial decreases in accuracy. Adversarial training techniques improved the stability and robustness of DL models to such pixel-level changes. Before clinical implementation, adversarial training should be considered to proposed DL models to improve overall performance and safety.

Comparison of radiomic feature aggregation methods for patients with multiple tumors.

Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.