ArticleNature communications2021
Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Cutting edges and therapeutic opportunities on tumor-associated macrophages in lung cancer.Frontiers in immunology · 2022Pooled it
- Post‑translational modification‑governed immune states in cancer immunity: Biomarker implications for checkpoint competence, tumor visibility and immunotherapy resistance (Review).International journal of oncology · 2026Review
- Transposable elements and homotypic niches drive immune dynamics and resistance in melanoma epigenetic-based immunotherapy.Science advances · 2026Article
- A longitudinal, multi-omic atlas reveals the emergence of a spatially organized immunosuppressive ecosystem in resistant melanoma.Cell reports. Medicine · 2026Article
- Article
- T-cell receptor clonotypic diversity and specialization in digestive system cancers.NPJ precision oncology · 2026Article
- Ligand-receptor pair-based signature score derived from on-treatment tumor specimens predicts immune checkpoint blockade response in metastatic melanoma.Discover oncology · 2026Article
- Adaptive individualized gene pair signatures distinguishing melanoma and predicting response to immune checkpoint blockade.iScience · 2025Article
- Evaluating Gene Fusions as Prognostic Biomarkers and Therapeutic Targets in Immune Checkpoint Blockade-Treated Advanced Melanoma: A Retrospective Analysis.Cancer research communications · 2025Article
- Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer.Nature cancer · 2025Article
- Machine Learning-Based Prognostic Signature in Breast Cancer: Regulatory T Cells, Stemness, and Deep Learning for Synergistic Drug Discovery.International journal of molecular sciences · 2025Article
- Immunometabolism signature derived from on-treatment tumor specimens predicts immune checkpoint blockade response in metastatic melanoma.Discover oncology · 2025Article
- Alterations in PD-L1 succinylation shape anti-tumor immune responses in melanoma.Nature genetics · 2025Article
- Protocol for using scCURE to construct an immunotherapy outcome prediction model.STAR protocols · 2024Article
- Observational
- Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis.Computational and structural biotechnology journal · 2024Article
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Immunogenic cell death signatures from on-treatment tumor specimens predict immune checkpoint therapy response in metastatic melanoma.Scientific reports · 2024Article
- Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024Article
- ALDH1A3-acetaldehyde metabolism potentiates transcriptional heterogeneity in melanoma.Cell reports · 2024Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45-0.69 and 0.85-0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.