Abstract A19: Establishment of patient primary tumor-derived xenograft models for testing anticancer agents.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".