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Record W2966095624 · doi:10.3389/fonc.2019.00753

The Flick of a Switch: Conferring Survival Advantage to Breast Cancer Stem Cells Through Metabolic Plasticity

2019· review· en· W2966095624 on OpenAlexafffund
H. Walsh, Brianne M. Cruickshank, Justin M. Brown, Paola Marcato

Bibliographic record

VenueFrontiers in Oncology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsEpigeneticsCarcinogenesisCancer researchBiologyCancer stem cellBreast cancerEpithelial–mesenchymal transitionStem cellAnaerobic glycolysisTranscription factorChromatinCancer cellMetastasisTumor microenvironmentCell biologyCancerBiochemistryGeneticsGene

Abstract

fetched live from OpenAlex

cell surface marker expression and increased aldehyde dehydrogenase activity. In general, bulk breast tumor cells possess altered energetics characterized by aerobic glycolysis. In contrast, breast CSCs appear to have adaptive metabolic plasticity that allows these tumor-initiating cells to switch between glycolysis and oxidative phosphorylation, depending on factors present in the tumor microenvironment (e.g., hypoxia, reactive oxygen species, availability of glucose). In this article, we review the regulatory molecules that may facilitate the metabolic plasticity of breast CSCs. These regulatory factors include epigenetic chromatin modifiers, non-coding RNAs, transcriptional repressors, transcription factors, energy and stress sensors, and metabolic enzymes. Furthermore, breast cancer cells acquire CSC-like characteristics and altered energetics by undergoing epithelial-mesenchymal transition (EMT). This energy costly process is paired with reprogrammed glucose metabolism by epigenetic modifiers that regulate expression of both EMT and other metabolism-regulating genes. The survival advantage imparted to breast CSCs by metabolic plasticity suggests that targeting the factors that mediate the energetic switch should hinder tumorigenesis and lead to improved patient outcomes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.036
GPT teacher head0.341
Teacher spread0.305 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2019
Admission routes2
Has abstractyes

Explore more

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