ArticleClinical cancer research : an official journal of the American Association for Cancer Research2024
Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Unraveling the tapestry: Lessons from multi-omics and spatial biology in hepatocellular cancer.Hepatology (Baltimore, Md.) · 2026Review
- Development of functional spatial mapping of the tumour immune microenvironment (FuncO:TiME) in advanced melanomas.BJC reports · 2026Article
- The spatial architecture of tumor immunity: Emerging predictive biomarkers across cancer.Immunologic research · 2026Review
- Beyond the single biopsy: Sampling dynamic disease ecosystems for patient stratification and translational validation.Clinical and translational medicine · 2026Article
- The melanocytic transcriptomic state is independently associated with poor overall survival in patients with metastatic melanoma.Science advances · 2026Article
- Spatial transcriptomics in cancer research: insights into tumorigenesis, diagnosis and therapeutics.Cell death discovery · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- A Stretchable, Transparent, Photothermally Stimulated Laser-Induced Graphene Patch for Noninvasive Skin Tumor Treatment.ACS nano · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Toward Personalized Response Monitoring in Melanoma Patients Treated with Immunotherapy and Target Therapy.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.International journal of molecular sciences · 2025Review
- Spatial signatures for predicting immunotherapy outcomes using multi-omics in non-small cell lung cancer.Nature genetics · 2025Article
- Aquaglyceroporin-7 ameliorates sorafenib resistance and immune evasion in hepatocellular carcinoma through inhibition of lipid accumulation.Cellular and molecular life sciences : CMLS · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
- Sphingolipid metabolism-related genes for the diagnosis of metabolic syndrome by integrated bioinformatics analysis and Mendelian randomization identification.Diabetology & metabolic syndrome · 2025Article
- AnInternational journal of molecular sciences · 2025Article
- Multifaceted investigations of PSMB8 provides insights into prognostic prediction and immunological target in thyroid carcinoma.PloS one · 2025Article
- Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeWe aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. EXPERIMENTAL
designWe collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification.
resultsWe achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment.
conclusionsWe conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.
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