MétaCan
Menu
Back to cohort
Record W2083633779 · doi:10.1158/1538-7445.am2013-3829

Abstract 3829: TP53 mutation status defines two distinct classes of diffuse anaplastic Wilms tumor.

2013· article· en· W2083633779 on OpenAlexaff
Mariana Maschietto, Tasnim Chagtai, Sergey D. Popov, Neil J. Sebire, Gordan Vujanić, Sandra Hing, Paul E. Grundy, Jeffrey S. Dome, Kathy Pritchard‐Jones, Richard D. Williams

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSanger sequencingWilms' tumorExonMutationAnaplasiaBiologyMicrodissectionMedicineCancer researchPathologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Wilms tumor (WT) is a paediatric renal tumor with a relapse rate of 10 to 15%. Tumor stage and the histological presence of diffuse anaplasia (DA) are the most important adverse prognostic factors in WT. Previous studies have described TP53 mutations at a frequency of up to 75% in DA WT cases, however only small cohorts were analysed. The aim of this study was to evaluate the frequency of TP53 mutations in a large group of DA WT and correlate these data with tumor and patient characteristics. DA WTs from 61 patients were analysed for TP53 mutations using Sanger sequencing. A paediatric pathologist confirmed the presence of anaplastic cells in the specimen used for DNA extraction. Sequence alterations were classified as damaging or neutral mutations with reference to databases of known mutations and functional prediction algorithms (SIFT, PolyPhen2). Loss of 17p, where TP53 is located, was evaluated using CGH or SNP arrays and gene expression profiling was carried out using cDNA microarrays. Kaplan-Meier Survival analysis and Breslow's test were used to compare TP53 alterations with clinico-pathological characteristics. Of the 61 cases, 29 (47.5%) had at least one damaging TP53 mutation and 26 of these also had TP53 loss. Most of the cases (20 out of 29, 68.9%) presented TP53 mutations located within exons 5 to 8. These exons comprise the DNA-binding domain where the hotspots are located. The other 32 DA WT cases did not harbour mutations but six had 17p loss. Patients with DA WT that carry TP53 mutations and/or 17p loss had a worse response to treatment than those patients that had wild type TP53 (p<0.01). The CGH profiles showed an increase in the number of gains and losses seen in DA WT with TP53 mutations, compared to those without, suggesting a more unstable genome. Gene expression profiling revealed 71 differentially expressed genes between DA WT with and without mutations. These genes may be associated with the anaplastic phenotype, but their role in the pathogenesis of WT remains to be confirmed. Evaluating a large series of anaplastic WT, we found that the frequency of TP53 mutations in DA WT was lower than previously reported. However, samples with apparent wild type TP53 will be further evaluated by deep-sequencing to determine if mutations are present in a minority cell population. The relatively high frequency of wild type TP53 in DA WT suggests that TP53 mutations are neither necessary nor sufficient to generate anaplasia. Grants: Cancer Research UK; EU FP7 P-medicine, Sophie Barringer Trust and GOSHCC. Citation Format: Mariana Maschietto, Tasnim Chagtai, Sergey D. Popov, Neil Sebire, Gordan Vujanic, Sandra Hing, Paul Grundy, Jeffrey S. Dome, Kathy Pritchard-Jones, Richard D. Williams. TP53 mutation status defines two distinct classes of diffuse anaplastic Wilms tumor. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3829. doi:10.1158/1538-7445.AM2013-3829

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.351
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicRenal and related cancersFrench-language works237,207