Xenografts of Human Hepatocellular Carcinoma: A Useful Model for Testing Drugs
Bibliographic record
Abstract
PURPOSE: Our aims were to establish and characterize primary human hepatocellular carcinoma xenografts. They were used to screen new drugs and improve our current treatment regimens used in hepatocellular carcinoma. EXPERIMENTAL DESIGN: Primary hepatocellular carcinomas were used to create the xenografts. Western blotting was used to determine the changes in proteins in these xenografts before and after therapies. Apoptotic and cell proliferation were analyzed by immunohistochemistry. RESULTS: Seven lines of xenografts were established from primary human hepatocellular carcinomas. Lines 4-1318, 2-1318, 2006, and 26-1004 grew rapidly in severe combined immunodeficient (SCID) mice and doubled its volume every 48 to 72 hours. Series 5-1318 (5-1318, 30-1004, and 29-1104) grew relatively slowly in SCID mice and required approximately 6 to 10 days to double its tumor volume. Western blot analysis revealed that the growth rate of these xenografts was associated with abnormal expression of proteins associated with the cell cycle, signaling pathways, and tumor suppressor genes. Although hepatocellular carcinoma xenografts expressed the receptors for androgens, estrogens, and progesterone, their growth rate was not affected by either castration or sex steroid hormone supplementation. Cisplatin, oxaliplatin, vitamin D analogue EB1089, and Iressa had no effects on the growth rate in SCID mice. Although 5-fluorouracil exerted mild growth inhibition of these xenografts, i.p. delivery of 2-chloroethyl-3-sarcosinamide-1-nitrosourea (SarCNU) or doxorubicin resulted in a significant growth inhibition. Doxorubicin-induced growth suppression was associated with elevation of p53 and p21(Cip1/Waf1). In addition to up-regulation of p53 and p21(Cip1/Waf1), SarCNU also increased the levels of phosphorylated cdc-2 at Tyr15. CONCLUSION: Hepatocellular carcinoma xenografts are powerful tools for screening drugs and SarCNU may be useful in the treatment of this fatal disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".