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Record W2330244165 · doi:10.1158/1538-7445.am2013-3063

Abstract 3063: MicroRNA-605: A novel genetic modifier in Li-Fraumeni Syndrome.

2013· article· en· W2330244165 on OpenAlexaff
Badr Idsaid, David Malkin

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPenetrancemicroRNAGeneticsBiologySingle-nucleotide polymorphismCancerCancer researchSNPLi–Fraumeni syndromeMutationGermline mutationGeneGenotypePhenotype

Abstract

fetched live from OpenAlex

Abstract Background: Li-Fraumeni syndrome (LFS) is a rare autosomal-dominant disorder, typically involving germ-line mutations in the prototypical tumour suppressor gene, TP53. Despite the high penetrance of TP53 mutations, LFS patients display striking differences in the age of disease onset, suggesting the presence of additional genetic modifiers. To date, functional polymorphisms in TP53 (Arg72Pro), MDM2 (SNP 309), as well as DNA copy number variations (CNVs) and telomere attrition, have been associated with cancer risk in LFS. However, it is likely that additional modifiers exist and that a combination of these low-penetrance risk factors act together to predispose TP53 mutation carriers to a spectrum of susceptibilities. Recently, the TP53 signalling network has been linked to a small number of non-coding RNAs, called microRNAs (miRNAs), whose main function is to negatively regulate gene expression. One particular member of this family, called miR-605, has been found to target the MDM2 gene, an important inhibitor of p53 function. Importantly, a common Single Nucleotide Polymorphism (SNP) within miR-605 was also found to modulate gastrointestinal cancer risk. By affecting miR-605 expression, this SNP could impact cancer risk in TP53 mutation carriers. Hypothesis: In this study, we hypothesized that SNP-mediated miR-605 deregulation might modify cancer risk in LFS by eliciting important transcriptional changes in the levels of p53 and/or MDM2. Methods: miR-605 was overexpressed in two TP53 mutant cell lines (RD and Rh30) in order to assess its tumor suppressive functions. MDM2 and TP53 protein expression were analyzed through Western Blotting. Additionally, blood-derived DNA samples from 47 Caucasian TP53 mutation carriers were genotyped for the miR-605 variant by SNP-RFLP. Results: Overexpressing miR-605 in two TP53 mutated cell lines led to a significant attenuation in cell viability as measured by MTS assay. The capacity of these cell lines to form macroscopic colonies was also compromised following transfection by a miR-605 commercial mimic. Moreover, miR-605-transfected cells also displayed higher sensitivity to a wide range of chemotherapeutic drugs. In RD cells, MDM2 downregulation was detected following miR-605 overexpression but TP53 levels remained unchanged. Genotyping our aforementioned study population revealed the G-allele of the miR-605 SNP to be associated with an earlier age of cancer onset (p=0.02). The median age of cancer onset in carriers of the G-allele (n= 15) was 3.75 years compared to 21.5 years in patients with the common homozygous A/A genotype (n= 32). Conclusion: Together, our results identify miR-605 deregulation as a potential cancer modifying event in TP53 mutation carriers. Future work will focus on elucidating the functional consequences of the miR-605 SNP whilst also pursuing a more general understanding of miR-605 tumor suppression. Citation Format: Badr Idsaid, David Malkin. MicroRNA-605: A novel genetic modifier in Li-Fraumeni Syndrome. [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 3063. doi:10.1158/1538-7445.AM2013-3063

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.082
GPT teacher head0.378
Teacher spread0.296 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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