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Record W1975597047 · doi:10.1158/1538-7445.am2013-327

Abstract 327: Establishment of patient primary ovarian cancer xenograft models for test of anticancer agents.

2013· article· en· W1975597047 on OpenAlexaff
Changnian Liu, Chunping Xu, Wenwei Li, Wen Zhou, Yong Liu, Rui Zhou, Ning Zhang

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsOvarian cancerMedicineCarboplatinPrimary tumorDocetaxelCisplatinOvarian tumorCancerTransplantationCancer researchPaclitaxelPathologyOncologyChemotherapyInternal medicineMetastasis

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Human xenograft tumor models established by transplantation of human tumor cell lines into immunodifficient mice have been routinely used for preclinical test of anticancer agents. But tumor cell lines have a relatively low transplantability, which resulted in a limited number of tumor models available for selection of right tumor modes for test of targeted therapeutics. Recently, we have developed a large number of patient primary ovarian tumor xenograft models by transplanting human fresh ovarian tumor tissues into nude mice, which have been employed for preclinical test of clinically used drugs and novel anticancer agents for their chemosensitivity screening. METHODS The fresh ovarian tumor samples were collected from local hospitals. The tumor fragments of 2-3 mm were subcutaneously implanted in the flanks of nude mice by trocar needle. Sixteen tumor fragments were grafted into four mice from one patient tumor tissue (passage 0). The clinically used drugs included cisplatin, carboplatin, paclitaxel, and docetaxel. The histology and gene sequence of the established primary tumor models were analyzed and compared with patients’ original tumors. RESULTS A total of 77 patients’ ovarian tumor samples were implanted into nude mice; and 38 primary tumor models have been established with a tumor taking rates of 49% for the first passage. The tumor taking rates were higher in the later passages ranged from approximately 85-100%. The therapeutic efficacy of the test anticancer drugs was consistent with their clinical findings. The patients’ primary ovarian tumor xenografts from all 5 passages retained a similarity in architecture, histopathological morphology, and genomic mutation status to their patients’ original tumors. CONCLUSIONS The patient primary tumor model system can provide a larger number of models for selection of right models for preclinical testing novel agents based on their anticancer mechanism. The primary tumor models retain a similarity in histology and genomic mutation status to their patients’ original tumors. They may predict more relevant clinical response rate and higher correlation with clinical findings than use of traditional xenograft models established from long-term cultured cancer cell lines. Especially, they have advantages for test of target-oriented therapeutics in new drugs development programs. Citation Format: Changnian Liu, Chunping Xu, Wenwei Li, Wen Zhou, Yong Liu, Rui Zhou, Ning Zhang. Establishment of patient primary ovarian cancer xenograft models for test of anticancer agents. [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 327. doi:10.1158/1538-7445.AM2013-327

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)0.001
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.055
GPT teacher head0.379
Teacher spread0.325 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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