ArticleFrontiers in immunology2022
Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 36 citations in OpenAlex.
- Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.iScience · 2026Article
- Machine learning on transcription factor expression profiles for precision breast cancer therapy.Cancer cell international · 2025Article
- Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer.Cancer cell international · 2025Article
- Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis.Frontiers in immunology · 2025Article
- Artificial intelligence-assisted RNA-binding protein signature for prognostic stratification and therapeutic guidance in breast cancer.Frontiers in immunology · 2025Article
- Development of a tertiary lymphoid structure-based prognostic model for breast cancer: integrating single-cell sequencing and machine learning to enhance patient outcomes.Frontiers in immunology · 2025Article
- Identification of a novel immune-related gene signature by single-cell and bulk sequencing for the prediction of the immune landscape and prognosis of breast cancer.Cancer cell international · 2024Article
- Machine learning unveils key Redox signatures for enhanced breast Cancer therapy.Cancer cell international · 2024Article
- Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy.BMC immunology · 2024Article
- Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.Frontiers in immunology · 2024Article
- Machine learning based anoikis signature predicts personalized treatment strategy of breast cancer.Frontiers in immunology · 2024Article
- Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.Frontiers in immunology · 2024Article
- Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model.Frontiers in oncology · 2024Article
- Deciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction.Frontiers in immunology · 2024Article
- Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential.Frontiers in oncology · 2024Article
- Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.Frontiers in immunology · 2024Article
- Integrating PANoptosis insights to enhance breast cancer prognosis and therapeutic decision-making.Frontiers in immunology · 2024Article
- Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer.Frontiers in immunology · 2024Article
- Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.Frontiers in immunology · 2024Article
- Establishment and validation of an immune infiltration predictive model for ovarian cancer.BMC medical genomics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is characterized by some types of heterogeneity, high aggressive behaviour, and low immunotherapeutic efficiency. Detailed immune stratification is a prerequisite for interpreting resistance to treatment and escape from immune control. Hence, the immune landscape of breast cancer needs further understanding. We systematically clustered breast cancer into six immune subtypes based on the mRNA expression patterns of immune signatures and comprehensively depicted their characteristics. The immunotherapeutic benefit score (ITBscore) was validated to be a superior predictor of the response to immunotherapy in cohorts from various datasets. Six distinct immune subtypes related to divergences in biological functions, signatures of immune or stromal cells, extent of the adaptive immune response, genomic events, and clinical prognostication were identified. These six subtypes were characterized as immunologically quiet, chemokine dominant, lymphocyte depleted, wounding dominant, innate immune dominant, and IFN-γ dominant and exhibited features of the tumor microenvironment (TME). The high ITBscore subgroup, characterized by a high proportion of M1 macrophages:M2 macrophages, an activated inflammatory response, and increased mutational burden (such as mutations in TP53, CDH1 and CENPE), indicated better immunotherapeutic benefits. A low proportion of tumor-infiltrating lymphocytes (TILs) and an inadequate response to immune treatment were associated with the low ITBscore subgroup, which was also associated with poor survival. Analyses of four cohorts treated with immune checkpoint inhibitors (ICIs) suggested that patients with a high ITBscore received significant therapeutic advantages and clinical benefits. Our work may facilitate the understanding of immune phenotypes in shaping different TME landscapes and guide precision immuno-oncology and immunotherapy strategies.
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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.