Next generation patient-derived prostate cancer xenograft models
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
There is a critical need for more effective therapeutic approaches for prostate cancer. Research in this area, however, has been seriously hampered by a lack of clinically relevant, experimental in vivo models of the disease. This review particularly focuses on the development of prostate cancer xenograft models based on subrenal capsule grafting of patients’ tumor tissue into nonobese diabetic/severe combined immunodeficient (NOD/SCID) mice. This technique allows successful development of transplantable, patient-derived cancer tissue xenograft lines not only from aggressive metastatic, but also from localized prostate cancer tissues. The xenografts have been found to retain key biological properties of the original malignancies, including histopathological and molecular characteristics, tumor heterogeneity, response to androgen ablation and metastatic ability. As such, they are highly clinically relevant and provide valuable tools for studies of prostate cancer progression at cellular and molecular levels, drug screening for personalized cancer therapy and preclinical drug efficacy testing; especially when a panel of models is used to cover a broader spectrum of the disease. These xenograft models could therefore be viewed as next-generation models of prostate cancer.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| 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