This study integrates gene expression data from vitiligo and melanoma patients to construct a high-precision immune phenotype prediction model—the Vitiligo Gene Signature (VGS)—and identifies Fulvestrant as a candidate drug capable of enhancing anti-PD-L1 therapeutic effects using the deep learning platform DLEPS. Fulvestrant demonstrates significant immune-enhancing effects across multiple solid tumor models, offering a novel solution to the issue of resistance to immune checkpoint therapy.
Literature Overview
This paper, 'Vitiligo Signature-Based Drug Screening Identifies Fulvestrant as a Novel Immunotherapy Combination Strategy,' published in Advanced Science, reviews and summarizes the development of a vitiligo gene signature (VGS) to predict responses to immune checkpoint blockade (ICB) therapy and the use of a deep learning system to identify compounds that enhance anti-PD-L1 treatment. The study finds that Fulvestrant effectively enhances CD8+ T cell infiltration in melanoma, breast cancer, and colorectal cancer models, offering a new combination strategy for overcoming resistance to immunotherapy.
Background Knowledge
Immune checkpoint blockade (ICB) therapy has demonstrated durable clinical benefits in multiple cancers, yet only 10–30% of patients benefit, and many tumors are resistant to ICB. Several studies have attempted to use biomarkers such as PD-L1 expression and tumor mutation burden (TMB) to predict ICB efficacyacies, but these biomarkers show limited predictive performance across different cancer types. Meanwhile, immune-related adverse events (irAEs), such as vitiligo, correlate positively with anti-tumor immune activation, suggesting their potential as biomarkers. Patients with vitiligo frequently exhibit CD8+ T cell infiltration, activation of the IFN-γ pathway, and increased expression of chemokines like CCL5, CXCL9/10—features highly similar to those of an activated tumor microenvironment (TME), indicating that vitiligo-related gene expression programs could be used to predict ICB efficacy. Furthermore, inducing a 'vitiligo-like' immune signature might convert 'cold' tumors into 'hot' tumors, thereby enhancing ICB therapy. Based on this hypothesis, the study integrates multi-omics data to construct a VGS and uses the DLEPS platform to identify drugs that can induce this signature, ultimately validating the immune-enhancing effects of Fulvestrant.
Research Methods and Experiments
The research team first identified 262 vitiligo progression-related genes using WGCNA and differential expression gene (DEG) analysis. By further integrating survival data from melanoma patients and performing univariate Cox regression analysis, 18 prognosis-related genes were identified. Ultimately, 10 genes (GP1BA, ANKS4B, CCDC87, CA8, HLA-DOB, RHEBL1, NLRP7, GZMH, HERPUD1, MAP2K1) were selected as the VGS. The DLEPS platform was then employed to identify drugs that can induce this signature, with Fulvestrant emerging as the top candidate. The therapeutic efficacy of Fulvestrant combined with anti-PD-L1 was validated in B16-F10, 4T1, and CT26 mouse models. Multi-faceted analysis, including scRNA-seq, immunofluorescence, and flow cytometry, was conducted to assess immune cell infiltration and activation of signaling pathways.
Key Conclusions and Perspectives
Research Significance and Prospects
This study introduces a novel, biologically grounded biomarker, VGS, for patient stratification in ICB therapy and proposes Fulvestrant as a combination treatment to enhance ICB efficacy, showing strong translational potential. Future studies should focus on optimizing the dosing and administration schedule of Fulvestrant and exploring its combination efficacy in other cancer types. Moreover, the VGS-based drug screening strategy could be applied to other immune-related disease models, promoting the development of personalized immunotherapy.
Conclusion
Through systematic analysis of gene expression profiles in vitiligo and melanoma patients, this study successfully constructed the VGS biomarker and identified Fulvestrant as a candidate drug capable of inducing immune activation via the DLEPS deep learning platform. Experimental validation confirms that Fulvestrant enhances CD8+ T cell infiltration and reshapes the tumor immune microenvironment by inhibiting M2 macrophage polarization, thereby improving ICB efficacy. These findings provide new theoretical and practical avenues for predicting immunotherapy responses and developing combination strategies, offering important clinical and translational value. Future work should explore the application of this strategy in non-melanoma cancers and validate the safety and efficacy of Fulvestrant combined with ICB in clinical trials.
