ReviewTrends in cancer2020
TP53 Mutations and Outcomes in Breast Cancer: Reading beyond the Headlines.
Review in Trends in cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 3 of them syntheses that pooled it.
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
131 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis.Systematic reviews · 2024Pooled it
- Pooled it
- A systematic review of recommendations on screening strategies for breast cancer due to hereditary predisposition: Who, When, and How?Cancer medicine · 2021Pooled it
- Prognostic and immune microenvironment analysis of cuproptosis-related LncRNAs in breast cancer.Functional & integrative genomics · 2023Trial
- Targeting SUV39H2 exon skipping reverses resistance to CDK4/6 inhibitors via transcriptional activation of p21.Science advances · 2026Article
- Covalent Targeting and Thermostabilization of Oncogenic R280K and R273H Mutants p53 by Small Molecule RVJB59.ChemMedChem · 2026Article
- Succinylation related gene signature enables risk stratification and prognostic modeling in breast cancer patients.Amino acids · 2026Article
- Review
- Article
- Article
- Integrating multi-omics and clinical management in breast cancer: a comprehensive review.Discover oncology · 2026Review
- Multi-omics network inference with a Gaussian copula model.BMC bioinformatics · 2026Article
- Development of a LiDia-SEQ™ platform compatible breast cancer panel and testing with metastatic breast cancer patient samples: A rapid, sample-to-result, fully integrated next-generation sequencing (NGS)-based platform, LiDia-SEQ, for use at the point-of-need.The journal of liquid biopsy · 2026Article
- Construction of a Lysine Lactylation- and DNA Damage Repair-Related Gene Signature to Predict the Prognosis and Drug Sensitivity of Breast Cancer Patients.International journal of molecular sciences · 2026Article
- Circadian clock control of breast cancer hallmarks: molecular mechanisms and therapeutic implications.Cancer metastasis reviews · 2026Review
- Benchmarking computational methods for multi-omics biomarker discovery in cancer.Briefings in bioinformatics · 2026Article
- Predicting protein cascade expression from H&E images.PLoS computational biology · 2026Article
- EpGAT: integrating epigenetics and 3D genome structure to predict alternative splicing and polyadenylation.Briefings in bioinformatics · 2026Article
- Evaluation of non-canonical p53 functions in DNA replication and recombination for variant classification.Cell death & disease · 2026Article
- The oncogenome of the domestic cat.Science (New York, N.Y.) · 2026Article
71 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
3 authors.
Funding
Abstract
TP53 is the most frequently mutated gene in breast cancer, but its role in survival is confounded by different studies concluding that TP53 mutations are associated with negative, neutral, or positive outcomes. Closer examination showed that many studies were limited by factors such as imprecise methods to detect TP53 mutations and small cohorts that combined patients treated with drugs having very different mechanisms of action. When only studies of patients receiving the same treatment(s) were compared, they tended to agree. These analyses reveal a role for TP53 in response to different treatments as complex as its different biological activities. We discuss studies that have assessed the role of TP53 mutations in breast cancer treatment and limitations in interpreting reported results.
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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.