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Record W2057494314 · doi:10.1158/1535-7163.targ-11-a19

Abstract A19: Establishment of patient primary tumor-derived xenograft models for testing anticancer agents.

2011· article· en· W2057494314 on OpenAlexaff
Changnian Liu, Wen‐Wei Li, Bin Li, Rong Liu, Wen Zhou, Fang He, Chang Bai, Rui Zhou, Connie Sun

Bibliographic record

VenueMolecular Cancer Therapeutics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsMedicineIrinotecanGemcitabineDocetaxelIn vivoPaclitaxelCancerCisplatinDoxorubicinErlotinibCancer researchPrimary tumorPharmacologyColorectal cancerChemotherapyInternal medicineMetastasisBiology

Abstract

fetched live from OpenAlex

Abstract Background: While the human tumor xenograft models established by inoculation of human cancer cell lines into immunodeficient mice have been widely used for test of novel cytotoxic anticancer agents, new drug development has moved from general cytotoxic agents to molecular target-directed therapeutics. Consequently, there is a need to identify tumor types and individual patient tumors that express the target and could benefit from more selective therapies in clinical trials. Therefore, the in vivo models used in preclinical development should be “disease-oriented” and target-directed. Recently, we developed xenograft tumor models by transplanting human fresh tumor fragments into nude mice, which have been used for test of clinically used and novel anticancer drugs. Methods: The fresh tumor samples were collected from local hospitals. The tumor fragments of 1–2 mm were subcutaneously implanted in the flanks of the Balb/c nude mice. In the first passage, tumors derived from male patients were implanted into male mice, and tumors from women were inoculated into female mice. The histology and genomic mutation status were compared between original patients' tumors and the genografts. All therapeutic efficacy experiments, with the exception of prostate cancer, used female mice. The positive control drugs tested included cisplatin, paclitaxel, docetaxel, irinotecan, doxorubicin, 5-FU, gemcitabine, and erlotinib. Results: A total of 537 human tumor samples have been implanted into nude mice, 221 patient tumor-derived models have been established. The tumor taking rates of the first passage were colorectal (69%), ovarian (64%), esophagel (63%), small cell lung cancer (60%), non-small cell lung cancer (54%), gastric (25%), kidney (17%), glioblastoma (16%), breast (12%), liver (12%), and acute lymphocytic leukemia (25%). The tumor taking rates were higher in the later passages for the various tumor types, ranged from approximately 50–100%. The test anticancer drugs produced tumor inhibition rates ranged from 20–90%, which were consistent with their clinical findings. The patient-tumor xenografts from all five generations presented the same histopathological morphology and genomic mutation status to their counterparts of the human primary tumors. Conclusions: These results suggest that patient-tumor derived xenograft tumor models provide a unique renewable source of tumor material for test of novel anticancer agents and may give a better predictive value than the traditional human tumor xenograft models established by inoculation of cancer cell lines. Especially, they have advantages for test of target-oriented therapeutics in new drugs development programs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A19.

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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.070
GPT teacher head0.308
Teacher spread0.239 · 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
Published2011
Admission routes1
Has abstractyes

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