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Abstract IA03: DICER1: From ontogenesis to oncogenesis

2014· article· en· W2160970614 on OpenAlexaff
William D. Foulkes, Mona K. Wu, Leanne de Kock, Leora Witkowski, Nelly Sabbaghian, Evan Weber, Nancy Hamel, John R. Priest

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsGermlineCancer researchBiologyGene silencingGeneticsRhabdomyosarcomaMedicineGenePathologySarcoma

Abstract

fetched live from OpenAlex

Abstract The DICER1 syndrome, also known as the pleuropulmonary blastoma familial tumor dysplasia syndrome (PPB-FTDS) (OMIM #601200), is a recently described entity comprising a number of rare to ultra-rare tumors arising mainly in childhood or adolescence. The most frequent and characteristic disorders are pleuropulmonary blastoma, cystic nephroma and ovarian Sertoli-Leydig cell tumors. Some aspects of the syndrome were identified in the 1970s and 1990s, but discovery in 2009 by Hill et al of heterozygous disease-associated germ-line DICER1 mutations in affected kindred brought the syndrome into focus. Several studies since 2009 have extended the phenotypes to include more common conditions such as multinodular goiter and Wilms tumor, as well as much rarer entities such as cervical embryonal rhabdomyosarcoma, pineoblastoma and pituitary blastoma. The critical molecular defect appears to be impairment of DICER1's RNase III endonuclease function, which normally would cleave precursor microRNAs to their final mature length. These microRNAs function by targeted silencing and/or degradation of specific messenger RNAs. DICER1 may be considered an unusual type of tumor suppressor gene, in that the first inherited “hit” usually cripples one allele completely, whereas a second somatic “hit” is nearly always limited to the RNase III domains (and is in fact often even more focused on the metal-binding domains of RNase IIIb). These second hits are most commonly a single base substitution leading to an amino acid change, which functionally impairs the protein without overall protein loss. In this presentation I will summarize the current knowledge on the role of DICER1 mutations in cancer and will describe the edges of the known associated phenotypes. Citation Format: William D. Foulkes, Mona Wu, Leanne De Kock, Leora Witkowski, Nelly Sabbaghian, Evan Weber, Nancy Hamel, John R. Priest. DICER1: From ontogenesis to oncogenesis. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Susceptibility and Cancer Susceptibility Syndromes; Jan 29-Feb 1, 2014; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(23 Suppl):Abstract nr IA03. doi:10.1158/1538-7445.CANSUSC14-IA03

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.008

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 head0.126
GPT teacher head0.445
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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