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Modulation of the plasma lipidomic profile with simvastatin in metastatic castration-resistant prostate cancer (mCRPC).

2022· article· en· W4213324432 on OpenAlexaff
Blossom Mak, Hui‐Ming Lin, Kate Mahon, Anthony M. Joshua, Martin R. Stockler, Howard Gurney, Francis Parnis, Alison Yan Zhang, Tahlia Scheinberg, Gary Wittert, Lisa M. Butler, Andrew J. Hoy, Peter J. Meikle, Lisa G. Horvath

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSimvastatinMedicineProstate cancerInternal medicineOncologyStatinLipid profileSphingomyelinAndrogen deprivation therapyEndocrinologyCancerCholesterol

Abstract

fetched live from OpenAlex

154 Background: Elevated circulating sphingolipids are associated with poorer outcomes across the natural history of prostate cancer (PC), including metastatic relapse in localised PC, earlier androgen deprivation failure in metastatic hormone-sensitive PC, and shorter overall survival (OS) in mCRPC. We have derived and validated a poor prognostic 3-lipid signature (3LS) [consisting of ceramide Cer(d18:1/24:1), sphingomyelin SM(d18:2/16:0) and phosphatidylcholine PC(16:0/16:0)], which was independently associated with shorter radiographic progression-free survival (rPFS) and OS in men with mCRPC commencing taxanes or androgen receptor signaling inhibitors (ARSI). Statins significantly reduce plasma levels of ceramides, sphingomyelin and cholesterol in cardiovascular disease. We hypothesised that this therapy could change the poor prognostic lipid profile of patients with mCRPC. This study assessed whether the addition of simvastatin to standard treatment for mCRPC modulates the circulating lipidomic profile. Methods: This investigator-initiated, multi-centre, single arm, pilot study enrolled men with mCRPC commencing taxanes or ARSI for disease progression, who were not on a lipid-lowering agent. Men were treated with simvastatin 40mg orally once daily for 12 weeks, commencing on day 1 of treatment for mCRPC. Plasma was taken at baseline and after 12 weeks of simvastatin, and underwent lipidomic profiling of ̃800 lipids. Differences in lipid levels between baseline and post-simvastatin samples and between those with and without the 3LS were assessed using t-tests. Results: 27 men (74% on taxanes, 26% on ARSI) were recruited between May 2018 to March 2021. 46% of the men had the poor prognostic 3LS at baseline, of whom 45% lost the 3LS after simvastatin. Comparison between all paired baseline and post-simvastatin samples showed significant reduction (p < 0.05) in free cholesterol, cholesteryl esters and some sphingolipids (sphingomyelins, hexosylceramides) with simvastatin treatment. Baseline profiles with the 3LS displayed significantly higher levels (p < 0.05) of ceramides, hexosylceramides and sphingomyelins, relative to baseline profiles without the 3LS. Men who lost the 3LS after treatment (n = 5) demonstrated significant reductions in ceramides (23-45%, p≤0.046), hexosylceramides (27-52%, p≤0.049) and sphingomyelins (28-44%, p≤0.047). These changes were not seen in men with persistent 3LS after treatment (n = 6). Conclusions: Simvastatin in addition to standard treatment for mCRPC can modulate the circulating lipidomic profile and eliminate the presence of a poor prognostic 3LS in 45% of participants with the 3LS. Further prospective randomised control studies are required to determine if modulation of the 3LS by simvastatin can improve clinical outcomes. Clinical trial information: ACTRN12617000965303.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.381
Teacher spread0.337 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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