ArticleGenome medicine2022
A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting immunotherapy response of advanced bladder cancer through a meta-analysis of six independent cohorts.Nature communications · 2025Pooled it
- PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.Science advances · 2026Article
- Multi-omics analysis linksTranslational cancer research · 2026Article
- Long-term Survivors of Anaplastic Thyroid Cancer: A Genomic Predictive Model.The Journal of clinical endocrinology and metabolism · 2026Article
- Identification of Immune&Driver Molecular Subtypes Optimizes Immunotherapy Strategies for Gastric Cancer.International journal of molecular sciences · 2026Article
- Clinical significance and functional characterization of RRN3 in gastric cancer: insights from pan-cancer analysis and experimental validation.Frontiers in oncology · 2026Article
- Optimizing immunotherapy in advanced gastric cancer: established and emerging biomarkers for precision patient selection.Frontiers in immunology · 2026Review
- PathHDNN: a pathway hierarchical-informed deep neural network framework for predicting immunotherapy response and mechanism interpretation.Genome medicine · 2025Article
- The importance of specificity of drug treatment in head and neck cancer: current developments in personalized medicine to improve safety and tolerability.Expert review of clinical pharmacology · 2025Review
- Exploring a mutation-based signature to predict the benefits of immune checkpoint inhibitors in oncogene-addicted subsets of non-small cell lung cancer: a retrospective study.Respiratory research · 2025Article
- Article
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients.NPJ precision oncology · 2025Article
- ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.Journal of translational medicine · 2025Article
- Article
- Identification of three subtypes of thyroid cancer based on IFN-γ-related genes to reveal their prognostic characteristics.Langenbeck's archives of surgery · 2025Article
- Orchestrating cancer therapy: Recent advances in nanoplatforms harmonize immunotherapy with multifaceted treatments.Materials today. Bio · 2025Review
- Targeting the PD-1/PD-L1 Signaling Pathway for Cancer Therapy: Focus on Biomarkers.International journal of molecular sciences · 2025Review
- Non-B DNA-informed mutation burden as a marker of treatment response and outcome in cancer.British journal of cancer · 2024Article
- Personalized Immunotherapy Achieves Complete Response in Metastatic Adenoid Cystic Carcinoma Despite Lack of Conventional Biomarkers.Current oncology (Toronto, Ont.) · 2024Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundImmune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.
methodsWe constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.
resultsThis study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.
conclusionsThe mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups' differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.
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