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Record W2016892262 · doi:10.1158/1538-7445.am10-198

Abstract 198: Two novel insertion polymorphisms of the BRM gene are associated with loss of BRM expression and lung cancer risk

2010· article· en· W2016892262 on OpenAlexaff
Colin Rogers, Geoffrey Liu, Daniela Muñoz, Dangxiao Cheng, Azad K. Kalam, Maryam Mirshams, Zhou Chen, Wei Xu, Heidi Roberts, Frances A. Shepherd, Ming‐Sound Tsao, David Reisman

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiologyLung cancerCancer researchGene silencingCancerChromatin remodelingCell cycleChromatinGeneticsGeneMolecular biologyMedicineOncology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: BRM, a catalytic subunit of the SWI/SNF chromatin remodeling complex, regulates expression/function of key signal transduction pathways with anticancer functions. In 15-25% of lung cancers, BRM protein expression is lacking. When BRM null mice (who display distinct cell cycle abnormalities) are exposed to carcinogens, they develop 10-fold more tumors then control mice. BRM silencing does not appear to be driven by mutational changes. We hypothesized that BRM silencing may be related to sequence variants in the BRM promoter region. METHODS: We compared novel BRM promoter polymorphisms to BRM protein expression in cancer cell lines and tumors of lung cancer patients. We then performed a case-control analysis of these polymorphisms with lung cancer risk. RESULTS: Sequencing of human cancer cell lines identified a 7-bp (rs34480940; located −741 from transcription start site) and 6-bp (rs3832613; located −1321) BRM insertion polymorphisms. In Caucasians, minor allele frequencies (MAF) of 45% were found for both polymorphisms. Cancer cell lines were evaluated for BRM protein expression (Western blot). Twelve BRM staining (BRM-positive) and twelve non-staining (BRM-negative) cancer cell lines were genotyped. In the BRM-negative cell lines, 11/12 cell lines were homozygous variant for at least one of the two BRM promoter polymorphisms (5/12 were double homozygous; 6/12 were homozygous for one polymorphism). In contrast, the BRM-positive cell lines yielded a good mix of genotypes for both polymorphisms. In lung cancer tissues, all ten BRM-negative samples carried at least one homozygous variant (eight were double homozygous variant, while two were homozygous for one polymorphism). In contrast, genotyping of normal adjacent tissue of twelve BRM-positive samples yielded population-normal MAFs of 42% for BRM −741 and 46% for BRM −1321. Tumour-normal tissue genotyping concordance rate was 86%. In the case-control analysis, 484 ever-smoker lung cancer cases were compared to 715 age and gender frequency-matched ever-smoker controls. Compared with a wildtype reference, carrying one homozygous variant was associated with an adjusted odds ratio (aOR) of 1.40 (95%CI:0.95-2.05; p=0.09); carrying two homozygous variants was associated with aOR=2.19 (1.40-3.43; p=0.0006), after adjusting for age, gender, and smoking variables. CONCLUSIONS: Homozygous variants of two novel BRM promoter polymorphisms are tightly associated with loss of BRM expression in lung cancer tissues and cancer cell lines. These homozygous variants are also associated with lung cancer risk in ever-smokers. Since epigenetic silencing of BRM can be reversed by various compounds (e.g. HDAC inhibitors, novel compounds from screening libraries), reversing BRM silencing could be developed as part of a chemoprevention strategy in smokers who carry the homozygous variants of these two BRM promoter polymorphisms. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 198.

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)0.000
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 score0.060
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.345
Teacher spread0.320 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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