ReviewSignal transduction and targeted therapy2021
Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence.
Review in Signal transduction and targeted therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06001528 (Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer), which is not on this map. Cited by 78 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Who cites it
78 citing papers in PubMed.
- Identifying breast cancer subtypes and exploring spatial expression patterns of the key subtype-specific gene based on pathological images, single-cell sequencing, and spatial transcriptomic data.Breast cancer (Tokyo, Japan) · 2026Article
- Deciphering lung cancer at high resolution: a narrative review of applications of single-cell and spatial transcriptomics sequencing.Translational lung cancer research · 2026Review
- Advances in delivery technologies-powered cancer vaccines.Bioactive materials · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Decoding the tumor immune landscape: emerging TIL subsets as prognostic biomarkers and therapeutic targets.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Multi-omics data mining combined with experimental validation reveals ferroptosis- and autophagy-associated hub genes as diagnostic candidates and immune modulators in atherosclerosis.Animal models and experimental medicine · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Towards Precision Oncology: How Advances in Cancer Genomics, Immunobiology and Artificial Intelligence Will Change Molecular Diagnostics.Biomedicines · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Analytical Techniques as Indicators of Biomarkers in Proteomics Cancer Diagnosis.Anti-cancer agents in medicinal chemistry · 2026Review
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- What affects natural killer cell activity: a cross-sectional study.Frontiers in immunology · 2026Article
- CyTOF profiling of bone marrow immune dynamics across myeloma stages.Oncoimmunology · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Antibody-drug conjugates in cancer therapy: current landscape, challenges, and future directions.Molecular cancer · 2025Review
- Single-cell multi-omics in biliary tract cancers: decoding heterogeneity, microenvironment, and treatment strategies.Molecular biomedicine · 2025Review
- Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.Journal of translational medicine · 2025Article
- Postoperative immune checkpoint inhibitors plus anti-angiogenesis for hepatitis B virus-associated hepatocellular carcinoma: Analyzing the evidence and future prospects.World journal of gastrointestinal oncology · 2025Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.
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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.