Efficacy of PACE4 pharmacotherapy in JHU-LNCaP-SM preclinical model of androgen independent prostate cancer
Bibliographic record
Abstract
Prostate cancer (PCa) is a complex disease progressing from in situ to invasive or metastatic tumors while also being capable of modulating its androgen dependence. Understanding how novel therapies are working across the different stages of the disease is critical for their proper positioning in the spectrum of PCa treatments. The targeting of proprotein convertase PACE4 (Paired basic Amino Acid-Cleaving Enzyme 4) has been proposed as a novel approach to treat PCa. Animal studies performed on LNCaP xenografts, an androgen-dependent model, already yielded positive results. In this study, we tested PACE4 inhibition on JHU-LNCaP-SM, a newly described androgen-independent model, in cell-based and xenograft assays. Like LNCaP, JHU-LNCaP-SM cells express PACE4 and its oncogenic isoform PACE4-altCT. Using isoform-specific siRNAs, downregulation of PACE4-altCT resulted in JHU-LNCaP-SM growth inhibition. Furthermore, JHU-LNCaP-SM responded to the PACE4 pharmacological inhibitor known as C23 in cell-based assays as well as in athymic nude mice xenografts. These data support the efficacy of PACE4 inhibitors against androgen independent PCa thereby demonstrating that PACE4 is a key target in PCa. The JHU-LNCaP-SM cell line represents a model featuring important aspects of androgen-independent PCa, but it also represents a very convenient model as opposed to LNCaP cells for in vivo studies, as it allows rapid screening due to its high implantation rate and growth characteristics as xenografts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".