Identification of alternative protein targets of glutamate-ureido-lysine associated with PSMA tracer uptake in prostate cancer cells
Bibliographic record
Abstract
Prostate-specific membrane antigen (PSMA) is highly overexpressed in most prostate cancers and is clinically visualized using PSMA-specific probes incorporating glutamate-ureido-lysine (GUL). PSMA is effectively absent from certain high-mortality, treatment-resistant subsets of prostate cancers, such as neuroendocrine prostate cancer (NEPC); however, GUL-based PSMA tracers are still reported to have the potential to identify NEPC metastatic tumors. These probes may bind unknown proteins associated with PSMA-suppressed cancers. We have identified the up-regulation of PSMA-like aminopeptidase NAALADaseL and the metabotropic glutamate receptors (mGluRs) in PSMA-suppressed prostate cancers and find that their expression levels inversely correlate with PSMA expression and are associated with GUL-based radiotracer uptake. Furthermore, we identify that NAALADaseL and mGluR expression correlates with a unique cell cycle signature. This provides an opportunity for the future study of the biology of NEPC and potential therapeutic directions. Computationally predicting that GUL-based probes bind well to these targets, we designed and synthesized a fluorescent PSMA tracer to investigate these proteins in vitro, where it shows excellent affinity for PSMA, NAALADaseL, and specific mGluRs associated with poor prognosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".