Lessons from patient-derived xenografts for better in vitro modeling of human cancer
Bibliographic record
Abstract
The development of novel cancer therapeutics is often plagued by discrepancies between drug efficacies obtained in preclinical studies and outcomes of clinical trials. The inconsistencies can be attributed to a lack of clinical relevance of the cancer models used for drug testing. While commonly used in vitro culture systems are advantageous for addressing specific experimental questions, they are often gross, fidelity-lacking simplifications that largely ignore the heterogeneity of cancers as well as the complexity of the tumor microenvironment. Factors such as tumor architecture, interactions among cancer cells and between cancer and stromal cells, and an acidic tumor microenvironment are critical characteristics observed in patient-derived cancer xenograft models and in the clinic. By mimicking these crucial in vivo characteristics through use of 3D cultures, co-culture systems and acidic culture conditions, an in vitro cancer model/microenvironment that is more physiologically relevant may be engineered to produce results more readily applicable to the clinic.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".