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Abstract PD09-01: BRCA1 inactivation induces NF-κB in human breast cancer cells and in murine and human mammary glands

2012· article· en· W2095068343 on OpenAlexaff
A. C. SAU, Angel Arnaout, C Pratt

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsRELBCancer researchBreast cancerCancerBiologyCarcinogenesisTumor suppressor geneMammary tumorIκB kinaseKinaseNF-κBSignal transductionNFKB1GeneGeneticsTranscription factor

Abstract

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Abstract Understanding the biological mechanisms underlying the initiation and progression of breast cancer it is an important step for its prevention and treatment. In 2011 in the United States, approximately 230,000 women were diagnosed with breast cancer and 40,000 died. Individuals with mutations in breast cancer-associated gene 1 (BRCA1) have a lifetime risk of developing breast cancer up to 85%. It is well known that BRCA1 participates in DNA damage repair and cell cycle checkpoint control, serving as a tumor suppressor gene to maintain the global genomic stability. However, BRCA1 has also been shown to play a key role in maturation of mammary stem/progenitor cells, which are the targets for carcinogenesis in individuals who have undergone loss of heterozygosity (LOH) for BRCA1. Recently, it has also been shown that NF-κB activity is increased in both mammary carcinoma cell lines and primary human breast cancer tissue. Indeed, in a previous study it has been demonstrated that NF-κB inducible kinase (NIK), p100/p52 and RelB (all components of the alternative NF-κB pathways) were increased in BRCA1-mutated tumors. Here we show that BRCA1-loss or -mutation is responsible for activation of the alternative NF-κB pathway evidenced by NIK and IκB kinase-α (IKKα) phosphorylation, processing of p100 to p52 and p52/RelB nuclear localization. Moreover, increased p52 was also observed after BRCA1 inhibition. A BRCA1-mutated human breast cancer cell line (HCC1937) was also used to understand the role played by NIK in NF-κB alternative pathway activation. Indeed, NIK inhibition in HCC1937 cell line resulted in a decrease in p52 formation. Moreover, a decrease in NIK mRNA level was also observed when wild-type BRCA1 was reconstituted in HCC1937 cells. BRCA1 inactivation in MCF-7 cells also induced NIK phosphorylation and nuclear localization of RelB and p52. Overall, these data show that inactivation of BRCA1 increases NIK mRNA level, associated with induction of the NF-κB alternative pathway. Stem/progenitors cells sorted using the CD24/CD49f immunophenotype derived from BRCA1 knockout mouse mammary glands showed alternative NF-κB pathway activation. Inhibition of IKKα/β using BMS-345541 completely blocked mammary colony formation in a Matrigel assay. Moreover, increased p52 formation was found in mammary stem/progenitor cells and mammary gland paraffin sections obtained from BRCA1 knockout mice. Remarkably, RelB and p100/p52 were highly expressed in 20–50% of the lobular structures in histologically normal breast tissue obtained from human BRCA1 mutation carriers while no staining was evident in normal tissue from non-carrier mastectomy samples. Our data show that BRCA1 inactivation induces alternative NF-κB activation which ultimately promotes the expansion of the mammary progenitor population. These novel findings provide a new basis for functional classification of BRCA1 mutations and a potential method for predicting breast cancer in BRCA1 mutation carriers. Lastly our results suggest that targeting the alternative NF-κB pathway could be of benefit in the prevention of BRCA1-associated breast cancer by limiting progenitor cell expansion. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr PD09-01.

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: Bench or experimental · Consensus signal: Bench or experimental
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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.426
Teacher spread0.349 · 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 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
Published2012
Admission routes1
Has abstractyes

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