Human Tumor Xenografts as Predictive Preclinical Models for Anticancer Drug Activity in Humans: Better than Commonly Perceived—But They Can Be Improved
Bibliographic record
Abstract
It is not uncommon for new anti-cancer drugs or therapies to show highly effective, and sometimes even spectacular anti-cancer treatment results using transplantable tumors in mice. These models frequently involve human tumor xenografts grown subcutaneously in immune deficient hosts such as athymic (nude) or severe combined immune deficient (SCID) mice. Unfortunately, such preclinical results are often followed by failure of the drug/therapy in clinical trials, or, if the drug is successful, it usually has only modest efficacy results, by comparison. Not surprisingly, this has provoked considerable skepticism about the value of using such preclinical models for early stage in vivo preclinical drug testing. As a result, a shift has occurred towards developing and using spontaneous mouse tumors arising in transgenic and/or knockout mice engineered to recapitulate various genetic alterations thought to be causative of specific types of respective human cancers. Alternatively, the opinion has been expressed of the need to refine and improve the human tumor xenograft models, e.g., by use of orthotopic transplantation and therefore promotion of metastatic spread of the resultant "primary" tumors. Close inspection of retrospective and prospective studies in the literature, however, reveals that human tumor xenografts-even non metastatic ectopic/subcutaneous "primary" tumor transplants-can be remarkably predictive of cytotoxic chemotherapeutic drugs that have activity in humans, when the drugs are tested in mice using pharmacokinetically clinically equivalent or "rational" drug doses. What may be at variance with clinical activity, however, is the magnitude of the benefit observed in mice, both in terms of the degree of tumor responses and overall survival. It is argued that this disparity can be significantly minimized by use of orthotopic transplant/metastatic tumor models in which treatment is initiated after the primary tumor has been removed and the distant metastases are well established and macroscopic-i.e., the bar is raised and treatment is undertaken on advanced, high volume, metastatic disease. In such circumstances, survival should be used as an endpoint; changes in tumor burden using surrogate markers or micro-imaging techniques can be used as well to monitor effects of therapies on tumor response. Adoption of such procedures would more accurately recapitulate the phase I/II/III clinical trial situation in which treatment is initiated on patients with advanced, high-volume metastatic disease.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".