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Record W2160378267 · doi:10.1158/1078-0432.ccr-11-3281

Molecular Pathways: Tumor Cells Co-opt the Brain-Specific Metabolism Gene <i>CPT1C</i> to Promote Survival

2012· review· en· W2160378267 on OpenAlexaff
Patrick T. Reilly, Tak W. Mak

Bibliographic record

VenueClinical Cancer Research · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsGeneBiologyMetabolismCancer researchBrain cancerCancerGeneticsCell biologyEndocrinology

Abstract

fetched live from OpenAlex

The metabolic adaptations of cancer cells are receiving renewed attention as potential targets for therapeutic exploitation. Recent work has highlighted the importance of fatty acid catabolism through β-oxidation to cellular energy homeostasis. In this article, we describe recent preclinical studies suggesting that a gene usually expressed only in the brain, carnitine palmitoyltransferase (CPT)1C, promotes cancer cell survival and tumor growth. CTP1C confers rapamycin resistance on breast cancer cells, indicating that this gene may act in a pathway parallel to mTOR-enhanced glycolysis. Because of CPT1C's normally brain-restricted expression and the inability of most drugs to pass the blood-brain barrier, CPT1C may be an ideal candidate for specific small-molecule inhibition. We further speculate that concurrent targeting of CPT1C activity and glycolysis in tumor cells could be a highly effective anticancer approach.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.253
GPT teacher head0.480
Teacher spread0.228 · 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

Citations65
Published2012
Admission routes1
Has abstractyes

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