Modeling Therapy of Late or Early‐Stage Metastatic Disease in Mice
Bibliographic record
Abstract
Overview An ongoing problem in oncology drug development is the frequent failure of preclinical therapy models involving treatment of tumor‐bearing mice, which show positive results, to predict similar success in patients enrolled in clinical trials. There are numerous possible reasons for causing such high rates of false positives. One is the failure to reproduce the clinical circumstances of treating systemic metastatic disease, whether microscopic or macroscopic in nature—but especially the latter. Thus, it is still common practice to treat mice with established primary tumors, whether they are transplanted or spontaneous, of mouse or human origin, or derived from cell lines or tumor tissue grafts—including human patient‐derived xenografts (PDXs). In this chapter, we summarize recent progress in developing mouse models of spontaneous metastases, especially of late‐stage disease, after surgical resection of primary tumors, including the use of human tumor cell lines, PDXs, and genetically engineered mouse models (GEMMs). Some limited therapy results using such models, and how they retrospectively or prospectively correlated with relevant phase III clinical trial outcomes, are discussed. A limited database indicates the possible benefits of using such models for predictive investigational therapeutic studies relevant to the treatment of patients with metastatic disease. Some limitations of such models are also discussed.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".