Molecular imaging targets in prostate cancers with neuroendocrine gene signature
Bibliographic record
Abstract
The main treatment option for castration-resistant prostate cancer (CRPC) is androgen receptor pathway inhibition (ARPI). Selection pressure and lineage plasticity of ARPI could lead to neuroendocrine (NE) differentiation of prostate cancer (PC), promoting the more prevalent subtype of CRPC which is termed treatment-induced neuroendocrine prostate cancer (NEPC) or t-NEPC. Treatment options for NEPC are limited to platinum- and cisplatin-based combinations and median survival of NEPC patients is much lower than patients with CRPC. Early identification of NEPC and novel targeting options could be valuable. The transmembrane protein prostate-specific membrane antigen (PSMA) is an appealing target for molecular imaging and therapy of PC since it is over-expressed in a majority of PC tumors and metastatic lesions. Targeting PSMA is feasible by a wide variety of radio-ligands. The PSMA ligands can also be labeled with therapeutic radionuclides which can irradiate PSMA-expressing cells. Despite the positive implications of PSMA for many forms of advanced AdPC there are clinical reports supporting that PSMA-targeted imaging is not able to delineate NEPC tumors. Previous clinical reports indicate that PCs with a phenotype related to NE tumors can be more responsive to imaging by 18F-Fluorodeoxyglucose (FDG) rather than PSMA-targeting radioligands. In this work, we evaluated the association between NE gene signature and FDG uptake-associated genes including glucose transporters (GLUTs) and hexokinases, with the goal of providing a genomic signature to explain the reported FDG-avidity of PSMA-suppressed tumors. We use data mining approaches, cell lines and patient-derived xenograft (PDX) models to study the levels of 14 members of the SLC2A family (encoding GLUT proteins), 4 members of the hexokinase family (genes: HK1 to 3 and GCK) and PSMA (FOLH1 gene) following AR-inhibition and in correlation with NE hallmarks. Also, we characterize a NE-like PC (NELPC) subset among a cohort of primary and metastatic PC samples with no NE histopathology. We measured glucose uptake in a NE-induced in vitro model and a zebrafish model by non-radioactive imaging of glucose uptake using fluorescent glucose bioprobe, GB2-Cy3. This work demonstrates that a NE gene signature associates with differential expression of genes encoding GLUT and hexokinase proteins. In NELPC, elevated expression of GCK (encoding glucokinase protein) and decreased expression of SLC2A12 correlated with earlier biochemical recurrence. In tumors treated with AR-inhibitors, enhanced expression of GCK and low expression of SLC2A12 correlated with NE histopathology and PSMA gene suppression. GLUT12-suppression and enhanced expression of glucokinase were observed in NE-induced PC cell lines and PDX models. A higher glucose uptake was confirmed in low-PSMA tumors using a GB2-Cy3 probe in a zebrafish model. In summary, a NE gene signature in NEPC and NELPC associates with a distinct transcriptional profile of GLUTs and HKs. In transcriptomic level, PSMA-suppression correlates with GLUT12-suppression and glucokinase-upregulation. Alteration of FDG uptake-associated genes correlated positively with higher glucose uptake in AR and PSMA-suppressed tumors. Zebrafish xenograft tumor models are an accurate and efficient pre-clinical method for monitoring non-radioactive glucose uptake.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".