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Record W2100761288 · doi:10.1158/0008-5472.can-04-0341

A Novel Founder CHEK2 Mutation is Associated with Increased Prostate Cancer Risk

2004· article· en· W2100761288 on OpenAlexaff
Cezary Cybulski, Tomasz Huzarski, Bohdan Górski, Bartłomiej Masojć, Marek Mierzejewski, Tadeusz Dębniak, Bartłomiej Gliniewicz, Joanna Matyjasik, Elżbieta Złowocka, Grzegorz Kurzawski, Andrzej Sikorski, Michał Posmyk, Marek Szwiec, R. Czajka, Steven A. Narod, Jan Lubiński

Bibliographic record

VenueCancer Research · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCoalition for Research in Women's Health
Fundersnot available
KeywordsCHEK2Prostate cancerMissense mutationProstateMedicineInternal medicineAlleleOdds ratioCancerOncologyGeneticsMutationBiologyGeneGermline mutation

Abstract

fetched live from OpenAlex

Variants in the CHEK2 have been found to be associated with prostate cancer risk in the United States and Finland. We sequenced CHEK2 gene in 140 Polish patients with prostate cancer and then genotyped the three detected variants in a larger series of prostate cancer cases and controls. CHEK2 truncating mutations (IVS2 + 1G>A or 1100delC) were identified in 9 of 1921 controls (0.5%) and in 11 of 690 (1.6%) unselected patients with prostate cancer [odds ratio (OR) = 3.4; P = 0.004]. These mutations were found in 4 of 98 familial prostate cases (OR = 9.0; P = 0.0002). The missense variant I157T was also more frequent in men with prostate cancer (7.8%) than in controls (4.8%), but the relative risk was more modest (OR = 1.7; P = 0.03). I157T was identified in 16% of men with familial prostate cancer (OR = 3.8; P = 0.00002). Loss of the wild-type CHEK2 allele was not observed in any of prostate cancers from five men who carried CHEK2-truncating mutations. Our results provide evidence that the two truncating mutations of CHEK2 confer a moderate risk of prostate cancer in Polish men and that the missense change appears to confer a modest risk.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.374
Teacher spread0.333 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations156
Published2004
Admission routes1
Has abstractyes

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