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Record W2491027990 · doi:10.1158/1538-7445.am2016-2921

Abstract 2921: The retinoblastoma protein regulates hypoxia-inducible factor-1α-mediated transcriptional programs, tumor cell invasiveness, tumor growth and metastasis in human breast cancer cells

2016· article· en· W2491027990 on OpenAlexaff
Mandeep Takhar, Mark P. Labrecque, Kevin J. Tam, Anne Haegert, Robert H. Bell, Manuel Altamirano-Dimas, Colin C. Collins, Gratien G. Préfontaine, Michael Cox, Kevin L. Bennewith, Timothy V. Beischlag

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsBC Cancer AgencySimon Fraser University
Fundersnot available
KeywordsAryl hydrocarbon receptor nuclear translocatorCancer researchBiologyGene knockdownRetinoblastomaRetinoblastoma proteinSmall hairpin RNAPI3K/AKT/mTOR pathwayCarcinogenesisSNAI1MetastasisEpithelial–mesenchymal transitionTranscription factorMolecular biologyCellCancerSignal transductionGeneCell biologyCell cycleAryl hydrocarbon receptor

Abstract

fetched live from OpenAlex

Abstract The retinoblastoma protein (Rb) is capable of attenuating the hypoxic response in tumor cells. This process is mediated by the hypoxia inducible factor 1α/2α (HIF1α/2α) and its dimerization partner the aryl hydrocarbon receptor nuclear translocator (ARNT/ HIF1β). Rb modulates HIF activity by virtue of its association with the thyroid hormone receptor/retinoblastoma interacting protein 230 (TRIP230), an essential cofactor of the HIF1α/ARNT transcriptional complex. We used short hairpin RNA (shRNA) technology and microarray analysis to interrogate the Rb-negative and wild-type MCF7 cell transcriptomes and generated lists of genes that were either up- or downregulated in response to both loss of Rb and hypoxia. We found that loss of Rb enhances the expression of hypoxia-regulated genes involved in invasion and epithelial-to-mesenchymal transition and significantly decreases the expression of genes involved in cell anchoring and differentiation in MCF7 and MDA-MB-231 breast cancer cells. Genes were validated using both qRT-PCR and immuno-blot analysis. Additionally, gene ontology analysis revealed that AKT and ERK1/2 are downstream effectors of hypoxic gene programs that are sensitive to loss of Rb. Furthermore, these factors regulate the acquisition of a more invasive phenotype in breast cancer cells. Finally, we found that Rb knockdown in combination with pre-treatment of cells with hypoxia increased growth of tumor foci in the lungs after i.v. injection and increased the development of spontaneous metastases from orthotopically implanted breast tumor cells in female NOD-SCID mice. Primary tumors lacking Rb demonstrated enriched protein levels of genes identified in our arrays when compared to negative control tumors. These results show that Rb is a negative modulator of hypoxia-regulated genetic programs by virtue of its direct effects on the HIF-complex. Understanding the HIF complex and the molecular mechanisms controlling the progression from benign tumors to metastasized and lethal forms will allow us to develop more specific breast cancer therapies. Citation Format: Mandeep K. Takhar, Mark P. Labrecque, Kevin J. Tam, Anne Haegert, Robert H. Bell, Manuel Altamirano-Dimas, Colin C. Collins, Gratien G. Prefontaine, Michael E. Cox, Kevin L. Bennewith, Timothy V. Beischlag. The retinoblastoma protein regulates hypoxia-inducible factor-1α-mediated transcriptional programs, tumor cell invasiveness, tumor growth and metastasis in human breast cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2921.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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
Published2016
Admission routes1
Has abstractyes

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