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Record W2319222851 · doi:10.1158/1538-7445.am2013-2777

Abstract 2777: Advantages of patient primary tumor models versus tumor cell lines derived models for testing anticancer therapeutics.

2013· article· en· W2319222851 on OpenAlexaff
Changnian Liu, Wenwei Li, Wen Zhou, Rong Liu, Rui Zhou, Fang He, Chunping Xu, Chang Bai

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMicropharma (Canada)
Fundersnot available
KeywordsMedicinePrimary tumorPemetrexedIrinotecanGemcitabineCancerDocetaxelCancer researchCarboplatinCisplatinStromal tumorPancreatic cancerOncologyInternal medicineColorectal cancerChemotherapyMetastasisStromal cell

Abstract

fetched live from OpenAlex

Abstract BACKGROUND While the tumor xenograft models derived from human cancer cell lines in immunodeficient mice have been widely used for testing cytotoxic anticancer agents, new drug development has moved from cytotoxic agents to molecular target-directed therapeutics. Consequently, there is a need to identify tumor types and individual patient tumors that express the target that will benefit tumor model selection and be more indicative for clinical trials. Therefore, the tumor models used in preclinical development should be “disease-oriented” and target-directed. Recently, we have developed a large number of patient primary tumor xenograft models by transplanting patients’ fresh tumor tissues into immunodeficient mice, which have been used for testing novel therapeutics. METHODS The fresh tumor samples were collected from local hospitals. The tumor fragments of 1-2 mm were subcutaneously implanted in the flanks of nude mice. The histopathology and genomic mutation status of primary tumor xenografts were analyzed and compared with patients’ original tumors. The tested clinically used drugs included cisplatin, carboplatin, paclitaxel, docetaxel, irinotecan, doxorubicin, 5-FU, gemcitabine, gefitinib, erlotinib, pemetrexed, Erbitux, and Avastin. RESULTS A total of 1,120 patients’ primary tumor tissues have been implanted into immunodeficient mice and 348 patient tumor-derived models have been established. The tumor taking rates of the different tumor types in the first passage were colorectal (52%), ovarian (49%), esophageal (67%), small cell lung cancer (89%), non-small cell lung cancer (45%), gastric (27%), kidney (17%), glioblastoma (21%), breast (11%), liver (12%), pancreatic (50%), lymphoma (33%), and leukemia (23%). The tumor taking rates were higher in the later passages for the various tumor types, ranged from approximately 80-100%. The clinically used drugs produced tumor inhibition rates ranged from 20-90%, which were consistent with their clinical findings. The patient primary tumor xenografts presented a similar histopathological morphology and the same genomic mutation status to their counterparts of the patients’ original tumors. CONCLUSIONS The results suggest that patient primary tumor-derived xenograft model system can provide a larger number of models within the same tumor histological type for models selection based on antitumor mechanism of test agents. They also provide a unique renewable source of tumor material for target identification and biomarker evaluation. The preclinical results obtained from primary tumor models may give a better predictive value than the traditional human tumor xenograft models established by inoculation of in vitro cultured cancer cell lines. Especially, they have advantages for testing target-oriented therapeutics in new drugs development programs. Citation Format: Changnian Liu, Wenwei Li, Wen Zhou, Rong Liu, Rui Zhou, Fang He, Chunping Xu, Chang Bai. Advantages of patient primary tumor models versus tumor cell lines derived models for testing anticancer therapeutics. [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 2777. doi:10.1158/1538-7445.AM2013-2777

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.108
GPT teacher head0.389
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 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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