Faculty Bibliography

Dendritic cells (DCs) facilitate the maintenance of immunological tolerance in the steady state. We report that transcription factor Etv3 is preferentially expressed in mature DCs, including tissue-derived migratory DCs (migDCs), and facilitates their homeostatic maturation and CCR7-dependent migration. Mice with global or DC-specific deletion of Etv3 manifested the expansion of CD25^low regulatory T (T_reg) cells, spontaneous activation of conventional T cells, and multiorgan T cell infiltration. Etv3 deficiency exacerbated TLR7-driven systemic lupus erythematosus (SLE)-like disease, supporting the reported genetic association of human ETV3 with SLE. Etv3-deficient migDCs up-regulated multiple costimulatory molecules, including OX40 ligand (OX40L/TNFSF4), whose blockade partially rescued the T_reg cell abnormalities. These results identify Etv3 as an essential regulator of the tolerogenic function of DCs and implicate it in the regulation of human autoimmunity.

Ultraviolet (UV)-induced DNA mutations generate genetic drivers of cutaneous melanoma and numerous neoantigens that can trigger anti-tumor immunity. Melanoma cells must therefore rapidly evade immune detection by modulating cell-autonomous epigenetic mechanisms and tumor-microenvironment interactions. Although angiogenesis typically facilitates immune infiltration, solid tumors increase vascularization while limiting immune cell entry. By comparing transcription factor (TF) expression across early-stage melanoma, naevi, and other cancers, we found that the homeodomain TF HOXD13 drives a melanoblast-like program upregulated in melanoma and strongly correlated with angiogenesis and immune cell exclusion. Using transcriptomics, 3D chromatin profiling, and in vivo models, we show that HOXD13 promotes tumor growth by enhancing angiogenesis and suppressing T-cell infiltration. HOXD13 orchestrates 3D enhancer-promoter contacts activating VEGFA, SEMA3A, and CD73, which remodel vasculature and elevate immunosuppressive adenosine. Consistently, HOXD13-induced tumor growth is reversed by combined VEGFR and adenosine receptor (AdR) inhibition, revealing a dual pro-angiogenic and immunosuppressive HOXD13 axis with therapeutic relevance.

Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow.

Lymphatic vessels activate anti-tumor immune surveillance and support metastasis. Whether there are distinct lymphatic phenotypes that govern immunity and metastasis remains unclear. Here we reveal that cytotoxic immunity normalizes lymphatic function and uncouples immune and metastatic potential. We demonstrate that intratumoral lymphatic vessel density negatively correlates with cytotoxic immunity and that IFNγ reprograms the intratumoral lymphatic state. Lymphatic deletion of Ifngr1 expanded the intratumoral lymphatic network and drove the emergence of a tip-like state that promotes lymph node metastasis but not dendritic cell migration or response to immune checkpoint blockade (ICB). Mechanistically, IFNγ restrains proliferation and cell state programs through inhibition of mitochondrial respiration. Lymphatic-specific inhibition of mitochondrial complex III restrained the intratumoral tip-like state, blocked metastasis, and enhanced the response to ICB. Our data reveal that IFNγ induces a metabolic and phenotypic switch in tumor-associated lymphatic vessels that blocks regional metastasis and reinforces immune surveillance.

BACKGROUND:As the incidence of frontal fibrosing alopecia (FFA) continues to rise, there is a need for an optimal treatment algorithm for FFA. OBJECTIVE:To produce an international consensus statement on the treatment modalities and prognostic indicators of FFA. METHODS:Sixty-nine hair experts from six continents were invited to participate in a three-round Delphi process. The final stage was held as a virtual meeting facilitated via Zoom. The consensus threshold was set at ≥66%. RESULTS:Of 365 questions, expert consensus was achieved in 204 (56%) questions following completion of the three rounds. Three additional questions were included at the final meeting. The category with the strongest consensus agreement was disease monitoring (9; 100%). Questions pertaining to physical therapies achieved the least category consensus (15; 40%), followed by systemic therapy (45; 43%). LIMITATIONS/CONCLUSIONS:The study lacked sufficient representation from Africa and South America. CONCLUSION/CONCLUSIONS:SOFFIA highlights areas of agreement and disagreement among experts. Robust research is warranted to provide evidence-based treatment recommendations.

Signaling interactions between the dermal papilla (DP) and neighboring stem cells in the hair germ (HGSCs) and bulge (BuSCs) regulate new follicle growth in the hair cycle. To study these interactions, the three populations had been profiled together by now-outdated microarrays or separately by bulk RNA-sequencing. Recent single-cell transcriptomics established signatures of DP, BuSCs and HGSCs, but low detection sensitivity limited the depth of gene expression discovery. Here, we define the transcriptomes of DP, BuSCs, HGSCs, epidermal, follicle and dermal fibroblast cells-after flow-sorting each population from four neighboring mouse back skin regions-to gain deeper insights into the unique gene expression programs of SCs and their instructive niche. With cross-comparisons of 56 whole-transcriptome measurements, we classify cell type-specific molecular signatures of enriched genes with unprecedented sensitivity. Joint analysis with signatures from 15 leading studies published in the last 20 years revealed many previously undescribed DP, BuSC and HGSC genes in mouse and human counterparts. With ligand-receptor mapping and CellChat analyses we then uncover comprehensive cell-cell communication insights. Finally, we provide a new installment of our Hair-Gel repository along with numerous signature and other gene tables for easy exploration of gene expression in hair follicle SCs and their niche.

PURPOSE/OBJECTIVE:Patients with stage II and resected stage III melanomas have variable clinical outcomes, evidence of underlying biological differences in tumors and/or the patients themselves, beyond stage. The approval of adjuvant immunotherapy for stage IIB/C and resected stage III/IV disease (and adjuvant targeted therapy for resected stage III disease), has created a pressing need to develop biomarkers to accurately distinguish patients at low- versus high-risk for recurrence and death from melanoma. MicroRNAs (miRNAs) are promising biomarkers because of their stability in tissues/fluids and their demonstrated functional and prognostic roles in melanoma. We hypothesized that miRNA expression could be integrated into prognostic models that would more accurately classify 5-year survival outcomes than clinical factors alone. PATIENTS AND METHODS/METHODS:Using a Nanostring miRNA Expression Assay, we analyzed 715 primary melanomas from patients with stage II or stage III disease within the InterMEL consortium, and examined associations between miRNA expression and melanoma-specific death. RESULTS:When integrated into clinical prognostic models for 5-year melanoma-specific survival, miRNA signatures improved the area under the receiver operating characteristic curve (AUC) for stage II patients from 0.71 for a 'clinical factors-only' model to 0.81 for a 'clinical plus miRNA' model in an independent test set, an improvement of 0.10 with 95% CI (0.03, 0.19). The improvement was more modest for stage III patients that were included in the analysis. CONCLUSIONS:Incorporating miRNA expression in primary melanomas may enhance the accuracy of clinical prognostic models, and potentially aid the selection of melanoma patients for adjuvant treatment and clinical trials.

PURPOSE/UNASSIGNED:Cancer treatment has been revolutionized by immune checkpoint inhibitors (ICI). However, a subset of patients do not respond and/or they experience significant adverse events. Attempts to integrate reliable biomarkers of ICI response as part of standard care have been hampered by limited generalizability. We previously reported our supervised machine learning (ML) model in a retrospective cohort of metastatic melanoma. EXPERIMENTAL DESIGN/UNASSIGNED:In this study, we expanded our testing to include larger cohorts of patients with melanoma accrued at several sites, including patients enrolled in clinical trials in both adjuvant and metastatic settings. We examined pretreatment hematoxylin and eosin slides from 639 patients with stage III/IV melanoma treated with ICIs [anti-cytotoxic T-lymphocyte-associated protein 4 (n = 212), anti-programmed death 1 (n = 271), or the combination (n = 156)]. We tested the generalizability of our supervised ML algorithm to predict response to ICIs in the metastatic melanoma cohort and then developed a self-supervised ML model to identify the histologic morphologies associated with patients' survival following ICI use in adjuvant and metastatic melanoma cohorts. RESULTS/UNASSIGNED:We predicted the response to ICI treatment with an AUC of 0.72. The deep convolutional neural network classified patients into high and low risk based on their likelihood of progression-free survival (P < 0.0001). We uncovered a novel association of specific histomorphologic tumor features-epithelioid histology and a low tumor-stroma ratio-with survival following ICI treatment. CONCLUSIONS/UNASSIGNED:Our data support the generalizability of our developed ML algorithm in predicting response to ICI treatment in patients with metastatic unresectable melanoma. We also showed, for the first time, tumor features associated with patients' overall survival.