MétaCan
Menu
Back to cohort
Record W2993693045 · doi:10.2967/jnumed.119.231068

Differential Expression of Glucose Transporters and Hexokinases in Prostate Cancer with a Neuroendocrine Gene Signature: A Mechanistic Perspective for <sup>18</sup>F-FDG Imaging of PSMA-Suppressed Tumors

2019· article· en· W2993693045 on OpenAlexafffund
Martin Bakht, Jessica M. Lovnicki, Janice Tubman, Keith Stringer, Jonathan Chiaramonte, Michael R. Reynolds, Iulian Derecichei, Rosa‐Maria Ferraiuolo, Bre‐Anne Fifield, Dorota Lubanska, So Won Oh, Gi Jeong Cheon, Cheol Kwak, Chang Wook Jeong, Keon Wook Kang, John F. Trant, Colm Morrissey, Ilsa M. Coleman, Yuzhuo Wang, Hojjat Ahmadzadehfar, Xuesen Dong, Lisa A. Porter

Bibliographic record

VenueJournal of Nuclear Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British ColumbiaUniversity of Windsor
FundersCanadian Institutes of Health Research
KeywordsNeuroendocrine tumorsProstate cancerCancer researchGlucose transporterGene signatureBiologyGlucokinaseGene expressionGeneCancerEndocrinologyBiochemistryGeneticsInsulin

Abstract

fetched live from OpenAlex

Although the incidence of de novo neuroendocrine prostate cancer (PC) is rare, recent data suggest that low expression of prostate-specific membrane antigen (PSMA) is associated with a spectrum of neuroendocrine hallmarks and androgen receptor (AR) suppression in PC. Previous clinical reports indicate that PCs with a phenotype similar to neuroendocrine tumors can be more amenable to imaging by <sup>18</sup>F-FDG than by PSMA-targeting radioligands. In this study, we evaluated the association between neuroendocrine gene signature and <sup>18</sup>F-FDG uptake–associated genes including glucose transporters (GLUTs) and hexokinases, with the goal of providing a genomic signature to explain the reported <sup>18</sup>F-FDG avidity of PSMA-suppressed tumors. <b>Methods:</b> Data-mining approaches, cell lines, and patient-derived xenograft models were used to study the levels of 14 members of the <i>SLC2A</i> family (encoding GLUT proteins), 4 members of the hexokinase family (genes <i>HK1</i>–<i>HK3</i> and <i>GCK</i>), and PSMA (<i>FOLH1</i> gene) after AR inhibition and in correlation with neuroendocrine hallmarks. Also, we characterize a neuroendocrine-like PC (NELPC) subset among a cohort of primary and metastatic PC samples with no neuroendocrine histopathology. We measured glucose uptake in a neuroendocrine-induced in vitro model and a zebrafish model by nonradioactive imaging of glucose uptake using a fluorescent glucose bioprobe, GB2-Cy3. <b>Results:</b> This work demonstrated that a neuroendocrine gene signature associates with differential expression of genes encoding GLUT and hexokinase proteins. In NELPC, elevated expression of <i>GCK</i> (encoding glucokinase protein) and decreased expression of <i>SLC2A12</i> correlated with earlier biochemical recurrence. In tumors treated with AR inhibitors, high expression of <i>GCK</i> and low expression of <i>SLC2A12</i> correlated with neuroendocrine histopathology and PSMA gene suppression. GLUT12 suppression and upregulation of glucokinase were observed in neuroendocrine-induced PC cell lines and patient-derived xenograft models. A higher glucose uptake was confirmed in low-PSMA tumors using a GB2-Cy3 probe in a zebrafish model. <b>Conclusion:</b> A neuroendocrine gene signature in neuroendocrine PC and NELPC associates with a distinct transcriptional profile of GLUTs and hexokinases. PSMA suppression correlates with GLUT12 suppression and glucokinase upregulation. Alteration of <sup>18</sup>F-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 preclinical method for monitoring nonradioactive 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.286
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Nuclear MedicineSame topicProstate Cancer Treatment and ResearchFrench-language works237,207