MétaCan
Menu
Back to cohort
Record W1969729521 · doi:10.1186/bcr3422

Progesterone metabolites regulate induction, growth, and suppression of estrogen- and progesterone receptor-negative human breast cell tumors

2013· article· en· W1969729521 on OpenAlexafffund
John P. Wiebe, Guihua Zhang, Ian Welch, Heather-Anne T. Cadieux-Pitre

Bibliographic record

VenueBreast Cancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanadian Breast Cancer Research AllianceSusan G. KomenBreast Cancer Alliance
KeywordsProgesterone receptorSurgical oncologyEstrogenInternal medicineBreast cancerEstrogen receptorOestrogen receptorEndocrinologyMedicineCancer researchCell growthBiologyCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Of the nearly 1.4 million new cases of breast cancer diagnosed each year, a large proportion is characterized as hormone receptor negative, lacking estrogen receptors (ER) and/or progesterone receptors (PR). Patients with receptor-negative tumors do not respond to current steroid hormone-based therapies and generally have significantly higher risk of recurrence and mortality compared with patients with tumors that are ER- and/or PR-positive. Previous in vitro studies had shown that the progesterone metabolites, 5α-dihydroprogesterone (5αP) and 3α-dihydroprogesterone (3αHP), respectively, exhibit procancer and anticancer effects on receptor-negative human breast cell lines. Here in vivo studies were conducted to investigate the ability of 5αP and 3αHP to control initiation, growth, and regression of ER/PR-negative human breast cell tumors. METHODS: ER/PR-negative human breast cells (MDA-MB-231) were implanted into mammary fat pads of immunosuppressed mice, and the effects of 5αP and 3αHP treatments on tumor initiation, growth, suppression/regression, and histopathology were assessed in five separate experiments. Specific radioimmunoassays and gas chromatography-mass spectrometry were used to measure 5αP, 3αHP, and progesterone in mouse serum and tumors. RESULTS: Onset and growth of ER/PR-negative human breast cell tumors were significantly stimulated by 5αP and inhibited by 3αHP. When both hormones were applied simultaneously, the stimulatory effects of 5αP were abrogated by the inhibitory effects of 3αHP and vice versa. Treatment with 3αHP subsequent to 5αP-induced tumor initiation resulted in suppression of further tumorigenesis and regression of existing tumors. The levels of 5αP in tumors, regardless of treatment, were about 10-fold higher than the levels of 3αHP, and the 5αP:3αHP ratios were about fivefold higher than in serum, indicating significant changes in endogenous synthesis of these hormones in tumorous breast tissues. CONCLUSIONS: The studies showed that estrogen/progesterone-insensitive breast tumors are sensitive to, and controlled by, the progesterone metabolites 5αP and 3αHP. Tumorigenesis of ER/PR-negative breast cells is significantly enhanced by 5αP and suppressed by 3αHP, the outcome depending on the relative concentrations of these two hormones in the microenvironment in the breast regions. The findings show that the production of 5αP greatly exceeds that of 3αHP in ER/PR-negative tumors and that treatment with 3αHP can effectively block tumorigenesis and cause existing tumors to regress. The results provide the first hormonal theory to explain tumorigenesis of ER/PR-negative breast tissues and support the hypothesis that a high 3αHP-to-5αP concentration ratio in the microenvironment may foster normalcy in noncancerous breast regions. The findings suggest new diagnostics based on the relative levels of these hormones and new approaches to prevention and treatment of breast cancers based on regulating the levels and action mechanisms of anti- and pro-cancer progesterone metabolites.

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 score0.055
Threshold uncertainty score0.741

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 studies0.0000.001
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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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

Citations46
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueBreast Cancer ResearchSame topicEstrogen and related hormone effectsFrench-language works237,207