ProMPt: A modular preclinical platform for functional modelling of prostate cancer heterogeneity and therapeutic vulnerabilities
Bibliographic record
Abstract
ABSTRACT Prostate cancer progression is driven by heterogenous genetic, phenotypic, and microenvironmental programs that remain challenging to model experimentally. Existing systems such as genetically engineered mouse models, xenografts, and patient-derived organoids have each advanced mechanistic insight but are limited by genetic scope, scalability, or lack of immune context. To overcome these constraints we developed ProMPt, a genetically-defined syngeneic mouse modelling platform that captures combinations of the most recurrent clinical prostate cancer genomic alterations to enable scalable in vitro and in vivo interrogation of prostate cancer evolution. Tumours derived from ProMPt organoids recapitulate the histologic and molecular diversity of human disease. Cross - species transcriptomic integration and multivariate single-cell analysis under defined culture permutations revealed conserved phenoscapes, highlighting a central role for MYC in disease progression and therapy resistance. Guided by these insights, preclinical intervention studies demonstrated that combined MAPK inhibition and blockade of protein translation synergistically suppressed tumour growth in castration-resistant models. This combination not only suppressed proliferation but also remodelled the tumour immune landscape, underscoring its dual epithelial and microenvironmental effects. Together, these findings establish ProMPt as a versatile framework for linking genotype, lineage plasticity, and therapeutic vulnerability in prostate cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".