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Record W2083824552 · doi:10.1158/1538-7445.am2013-4050

Abstract 4050: Progesterone receptor signaling induces cellular senescence in ovarian cancer cells.

2013· article· en· W2083824552 on OpenAlexaff
Caroline H. Diep, Nathan J. Charles, C. Blake Gilks, Steve E. Kalloger, Peter A. Argenta, Carol A. Lange

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOvarian cancerCancer researchCancerDownregulation and upregulationSenescenceBiologyEstrogenProgesterone receptorEstrogen receptorEndocrinologyInternal medicineMedicineBreast cancerGene

Abstract

fetched live from OpenAlex

Abstract Despite major advancements in surgical techniques and chemotherapeutics, over 90% of women with advanced ovarian cancer die with recurrent disease, due to the emergence of chemo-resistant tumors (Bukowski et al 2007). In recent years, the progesterone receptor (PR) has become an attractive target in ovarian cancer. PR is typically expressed in normal ovarian surface epithelial (OSE) cells, but the detection of PR mRNA and protein decreases during the transformation to malignancy. However, up to 35% of ovarian tumors express abundant PR. Several independent studies have indicated that the expression of PR in ovarian tumors is associated with longer progression-free survival in ovarian cancer patients (Hempling et al 1998, Munstedt et al 2000, Sinn et al 2011). The detailed molecular mechanisms of PR expression in OSE cells and its anti-tumorigenic effects remain poorly understood. To study the suppressive role of PR in ovarian cancer in the absence of added estrogen (i.e. needed to stimulate PR expression), we created ES-2 ovarian cancer cells stably expressing vector control or GFP-tagged PR-B. Unmodified ER+/PR+ PEO4 ovarian cancer cells were included to validate our findings. Progestin stimulation (R5020; 10 nM) of ES-2 cells stably expressing GFP-PR inhibited the formation of large colonies in soft-agar assays, but yielded a significant increase in the number of viable, very small colonies relative to vehicle-treated and PR-null cohorts. Continuous treatment with R5020 induced cellular senescence characterized by altered cellular morphology, senescence-associated β-galactosidase activity, irreversible G1 cell-cycle arrest, and upregulation of the cell-cycle inhibitor, p21, as well as the Forkhead-box transcription factor, FOXO1. Notably, both PR-B and FOXO1 were detected within the same PRE-containing regions of the p21 upstream promoter. Stable knock-down using lentiviral shRNAs targeting FOXO1 inhibited progestin-induced p21 expression and blocked the development of senescence, suggesting that progestin-induced cellular senescence in PR+ ovarian cancer cells is mediated by FOXO1-dependent p21 expression. Overall, these findings support the concept of PR as a tumor suppressor in ovarian cancer cells that exhibits its inhibitory effects by inducing cellular senescence. Clinical targeting of the PR-FOXO1-p21 signaling pathway may provide a useful strategy to induce irreversible cell cycle arrest and sensitize ovarian cancer cells to existing chemotherapies as part of combination therapy. (This work was supported by grants from the Minnesota Ovarian Cancer Alliance (MOCA), the Cancer Biology Training Grant (NIH T32 CA009138), and the University of Minnesota Clinical and Translational Science Institute (CTSI) F&T Pilot Grant.) Citation Format: Caroline H. Diep, Nathan J. Charles, C. Blake Gilks, Steve E. Kalloger, Peter A. Argenta, Carol A. Lange. Progesterone receptor signaling induces cellular senescence in ovarian cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4050. doi:10.1158/1538-7445.AM2013-4050

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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.091
GPT teacher head0.382
Teacher spread0.291 · 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
Published2013
Admission routes1
Has abstractyes

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