Evidence map›Paper›PMID 27626311›Full record

ArticleOncotarget2016

Immunohistochemical correlates of TP53 somatic mutations in cancer.

Balázs Murnyák, Tibor Hortobágyi

Abstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Correlation of TP53 Mutations With p53 Expression and Ki-67 Index in Pulmonary Large Cell Neuroendocrine Carcinomas.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Balázs MurnyákDivision of Neuropathology, Institute of Pathology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Tibor HortobágyiDivision of Neuropathology, Institute of Pathology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite controversy on the correlation between p53 accumulation and TP53 mutational status, immunohistochemical (IHC) detection of overexpressed protein has long been used as a surrogate method for mutation analysis. The aim of our study was to characterise the IHC expression features of TP53 somatic mutations and define their occurrence in human cancers. A large-scale database analysis was conducted in the IARC TP53 Database (R17); 7878 mutations with IHC features were retrieved representing 60 distinct tumour sites. The majority of the alterations were immunopositive (p <0.001). Sex was known for 4897 mutations showing a female dominance (57.2%) and females carrying negative mutations were significantly younger. TP53 mutations were divided into three IHC groups according to mutation frequency and IHC positivity. Each group had female dominance. Among the IHC groups, significant correlations were observed with age at diagnosis in breast, bladder, liver, haematopoietic system and head & neck cancers. An increased likelihood of false negative IHC associated with rare nonsense mutations was observed in certain tumour sites. Our study demonstrates that p53 immunopositivity largely correlates with TP53 mutational status but expression is absent in certain mutation types.Besides, describing the complex IHC expression of TP53 somatic mutations, our results reveal some caveats for the diagnostic practice.

Indexed as

Genes, p53GenotypePhenotypeAge FactorsDatabases, FactualDNA Mutational AnalysisFemaleHumansImmunohistochemistryMaleMutationNeoplasmsObserver VariationSex FactorsTumor Suppressor Protein p53Tumor Suppressor Protein p53cancerIARC TP53 Databaseimmunohistochemistryp53TP53 mutations

Identifiers

PMID27626311
PMCPMC5323125

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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.