Lessons from patient-derived xenografts for better in vitro modeling of human cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| 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 | 0.000 |
| 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 it