ReviewNature reviews. Clinical oncology2024
Computational immunogenomic approaches to predict response to cancer immunotherapies.
Review in Nature reviews. Clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed.
- Review
- MS4A4A promotes macrophages M2 polarization via NF-κB /JAK-STAT6 axis, resulting GBM malignant progression.Oncogene · 2026Article
- Explainable machine learning identifies features and thresholds predictive of immunotherapy response.Scientific reports · 2026Article
- GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer.International journal of molecular sciences · 2026Article
- Organ-Specific Migration License (OSML) theory: a novel paradigm for spatiotemporal regulation and intervention of cross-organ immune cell migration in tumor immune responses.Cell communication and signaling : CCS · 2026Review
- Clinically interpretable nomogram combining body composition and clinicopathological features for one year survival prediction in advanced solid tumors.Scientific reports · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.Microbiology spectrum · 2026Article
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Towards the tumor microenvironment scoring methods for immune checkpoint inhibitor response.NPJ precision oncology · 2026Article
- Multi-omics classification of acute myeloid leukemia guides drug combinations to overcome Venetoclax resistance.Cancer drug resistance (Alhambra, Calif.) · 2026Article
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- PredIG: an interpretable predictor of T-cell epitope immunogenicity.Genome medicine · 2025Article
- Tumor mutational burden modulates the prognostic effect of RAS mutations in metastatic colon cancer: mechanistic insights and genotype-phenotype correlations.Journal of translational medicine · 2025Article
- Genome instability and crosstalk with the immune response.Genome medicine · 2025Review
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- Mapping the role of cytokine signaling at single-cell and structural resolution in uveal melanoma.Genes and immunity · 2025Article
- A free energy perturbation-assisted machine learning strategy for mimotope screening in neoantigen-based vaccine design.Briefings in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
Indexed as
Identifiers
37907723What OpenQuestion holds
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.