Molecular imaging targets in prostate cancers with neuroendocrine gene signature
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».