Abstract 2776: Establishment of patient non-small cell lung cancer derived models for test of anticancer drugs.
Bibliographic record
Abstract
Abstract BACKGROUND Human tumor xenograft 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 and resulted in a limited number of tumor models available for selection of right models for testing novel agents based on their antitumor mechanism. Recently, we have developed a large number of patient primary non-small cell lung cancer (NSCLC) xenograft models by transplanting patients’ fresh tumor tissues into nude mice, which have been employed for preclinical test of anticancer agents. METHODS The fresh NSCLC tissues were collected from local hospitals. The tumor fragments of 1-2 mm were subcutaneously implanted in the flanks of the nude mice by trocar needle. Sixteen tumor fragments were grafted into four mice from one patient tumor tissue (passage 0). All therapeutic efficacy experiments used female mice bearing passage 5 xenografts. The test drugs included cisplatin, carboplatin, paclitaxel, docetaxel, gemcitabine, erlotinib, gefitinib, and pemetrexed. RESULTS A total of 213 patient primary NSCLC samples were implanted into nude mice, and 107 primary tumor models have been established with a tumor taking rate of 50% for the first passage. The tumor taking rates were higher (80-100) in the later passages. The therapeutic efficacy of the test drugs in these models is consistent with their clinical findings. The histopathology and gene sequence of the established primary tumor xenografts were analyzed; their architecture, histopathological morphology, and genomic mutation status from five generations of xenografts retained the patients’ original tumor characteristics. CONCLUSIONS The patient primary NSCLC models can be established in a large number for right models selection in preclinical setting, and have been employed for test of standard of care drugs and novel anticancer agents. 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 discovery and development programs. Citation Format: Changnian Liu, Connie Sun, Wenwei Li, Wen Zhou, Yong Liu, Rui Zhou, Fang He, Chang Bai. Establishment of patient non-small cell lung cancer derived models for test of anticancer drugs. [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 2776. doi:10.1158/1538-7445.AM2013-2776
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".