Abstract 2777: Advantages of patient primary tumor models versus tumor cell lines derived models for testing anticancer therapeutics.
Bibliographic record
Abstract
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
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".