Neuroendocrine differentiation of prostate cancer leads to PSMA suppression
Why this work is in the frame
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Bibliographic record
Abstract
Prostate-specific membrane antigen (PSMA) is overexpressed in most prostate adenocarcinoma (AdPC) cells and acts as a target for molecular imaging. However, some case reports indicate that PSMA-targeted imaging could be ineffectual for delineation of neuroendocrine (NE) prostate cancer (NEPC) lesions due to the suppression of the PSMA gene (FOLH1). These same reports suggest that targeting somatostatin receptor type 2 (SSTR2) could be an alternative diagnostic target for NEPC patients. This study evaluates the correlation between expression of FOLH1, NEPC marker genes and SSTR2. We evaluated the transcript abundance for FOLH1 and SSTR2 genes as well as NE markers across 909 tumors. A significant suppression of FOLH1 in NEPC patient samples and AdPC samples with high expression of NE marker genes was observed. We also investigated protein alterations of PSMA and SSTR2 in an NE-induced cell line derived by hormone depletion and lineage plasticity by loss of p53. PSMA is suppressed following NE induction and cellular plasticity in p53-deficient NEPC model. The PSMA-suppressed cells have more colony formation ability and resistance to enzalutamide treatment. Conversely, SSTR2 was only elevated following hormone depletion. In 18 NEPC patient-derived xenograft (PDX) models we find a significant suppression of FOLH1 and amplification of SSTR2 expression. Due to the observed FOLH1-supressed signature of NEPC, this study cautions on the reliability of using PMSA as a target for molecular imaging of NEPC. The observed elevation of SSTR2 in NEPC supports the possible ability of SSTR2-targeted imaging for follow-up imaging of low PSMA patients and monitoring for NEPC development.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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