A novel knock-in prostate cancer model demonstrates biology similar to that of human prostate cancer and suitable for preclinical studies
Bibliographic record
Abstract
Preclinical studies of prostate cancer (CaP) have employed a genetically engineered mouse model, since there is no naturally occurring CaP in rodents. We have previously reported a new knock-in mouse adenocarcinoma prostate (KIMAP) model. In this study, we demonstrate that the new model possesses a tumor architecture of heterogeneity and multifocality similar to that of human CaP, by utilizing a new compound scoring system to compare with the PSP94 (approved gene symbol Msmb) gene-directed transgenic mouse CaP model (TGMAP). KIMAP mice showed a balanced distribution of tumor extent, which penetrated the prostate gland. Comparative studies on cDNA microarrays demonstrated that KIMAP tumors were upregulated with higher contents of immunoresponse genes, whereas PSP-TGMAP tumors had neuroendocrine (NE) differentiation. The majority of KIMAP mice did not progress to NE CaP, which was observed only at a very late stage and a low frequency. Several tumor marker genes characteristic of human CaP were uniquely identified in KIMAP tumors, including hepsin, maspin, Nkx3.1, CD10 and PSP94 (similar to PSA), etc. The differences between these two CaP models are attributed to the introduction of a single endogenous knock-in mutation. Due to the similarities between human CaP tumors and the PSP-KIMAP tumors, this preclinical model may supplement the current transgenic models to study CaP more accurately.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".