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Record W2140280994 · doi:10.1158/1535-7163.mct-11-0402

ABT-898 Induces Tumor Regression and Prolongs Survival in a Mouse Model of Epithelial Ovarian Cancer

2011· article· en· W2140280994 on OpenAlexafffund
Nicole Campbell, James Greenaway, Jack Henkin, Jim Petrik

Bibliographic record

VenueMolecular Cancer Therapeutics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsOvarian cancerDebulkingMedicineCancerStage (stratigraphy)Ovarian tumorInternal medicineAsymptomaticDiseaseMalignancyCancer researchOncologyPathologyBiology

Abstract

fetched live from OpenAlex

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy and is often not diagnosed until late stages due to its asymptomatic nature. Women diagnosed with EOC typically undergo surgical debulking followed by chemotherapy; however, disease recurrence often occurs. In this study, we evaluated the ability of the thrombospondin-1 mimetic peptide, ABT-898, to regress established, late-stage tumors in a mouse model of human EOC. Ovarian tumors were induced and ABT-898 treatment was initiated at time points that were representative of late stages of the disease to study tumor regression. ABT-898 induced tumor regression and reduced the morbidity of treated animals compared with controls. Analysis of tumors from ABT-898-treated animals showed reduced abnormal tumor vasculature, decreased expression of the proangiogenic compound VEGF, and reduced tumor tissue hypoxia. ABT-898 treatment initiated at late-stage disease also significantly prolonged disease-free survival compared with control animals. Results from this study show that ABT-898 is capable of regressing established ovarian tumors in an animal model of the disease. As most women are detected at advanced stage EOC, ABT-898 may improve our treatment of ovarian cancer.

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 score0.009
Threshold uncertainty score0.792

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 studies0.0000.000
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 head0.046
GPT teacher head0.291
Teacher spread0.245 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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