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Record W2009623312 · doi:10.1016/j.juro.2015.02.2182

MP61-01 FUNCTIONAL ROLE OF THE KALLIKREIN 6 REGION OF THE KALLIKREIN LOCUS IN GENETIC PREDISPOSITION FOR AGGRESSIVE (GLEASON ≥8) PROSTATE CANCER: FINE-MAPPING AND METHYLATION STUDY IN A CANADIAN COHORT AND THE SWISS ARM OF THE EUROPEAN RANDOMIZED STUDY FOR PROSTATE CANCER SCREENING

2015· article· en· W2009623312 on OpenAlexaboutno aff
Laurent Briollais, Hilmi Özçelik, Maciej Kwiatkowski, Jingxiong Xu, Sevtap Savas, Ekaterina Olkhov‐Mitsel, Franz Recker, Cynthia Kuk, Sally Hanna, Neil Fleshner, Tristan Juvet, Matt Friedlander, Hong Li, Karen Chadwick, John Trachtenberg, Ants Toi, Theodorus van der Kwast, Eleftherios P. Diamandis, Bharati Bapat, Alexandre R. Zlotta

Bibliographic record

VenueThe Journal of Urology · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerCohortCancerInternal medicine

Abstract

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You have accessJournal of UrologyProstate Cancer: Basic Research IV1 Apr 2015MP61-01 FUNCTIONAL ROLE OF THE KALLIKREIN 6 REGION OF THE KALLIKREIN LOCUS IN GENETIC PREDISPOSITION FOR AGGRESSIVE (GLEASON ≥8) PROSTATE CANCER: FINE-MAPPING AND METHYLATION STUDY IN A CANADIAN COHORT AND THE SWISS ARM OF THE EUROPEAN RANDOMIZED STUDY FOR PROSTATE CANCER SCREENING Laurent Briollais, Hilmi Ozcelik, Maciej Kwiatkowski, Jingxiong Xu, Sevtap Savas, Ekaterina Olkhov-Mitsel, Franz Recker, Cynthia Kuk, Sally Hanna, Neil E Fleshner, Tristan Juvet, Matt Friedlander, Hong Li, Karen Chadwick, John Trachtenberg, Ants Toi, Theodorus H van der Kwast, Eleftherios P Diamandis, Bharati Bapat, and Alexandre R. Zlotta Laurent BriollaisLaurent Briollais More articles by this author , Hilmi OzcelikHilmi Ozcelik More articles by this author , Maciej KwiatkowskiMaciej Kwiatkowski More articles by this author , Jingxiong XuJingxiong Xu More articles by this author , Sevtap SavasSevtap Savas More articles by this author , Ekaterina Olkhov-MitselEkaterina Olkhov-Mitsel More articles by this author , Franz ReckerFranz Recker More articles by this author , Cynthia KukCynthia Kuk More articles by this author , Sally HannaSally Hanna More articles by this author , Neil E FleshnerNeil E Fleshner More articles by this author , Tristan JuvetTristan Juvet More articles by this author , Matt FriedlanderMatt Friedlander More articles by this author , Hong LiHong Li More articles by this author , Karen ChadwickKaren Chadwick More articles by this author , John TrachtenbergJohn Trachtenberg More articles by this author , Ants ToiAnts Toi More articles by this author , Theodorus H van der KwastTheodorus H van der Kwast More articles by this author , Eleftherios P DiamandisEleftherios P Diamandis More articles by this author , Bharati BapatBharati Bapat More articles by this author , and Alexandre R. ZlottaAlexandre R. Zlotta More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2182AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Novel prostate cancer (PCa) markers that can identify individuals at increased risk of harboring an aggressive form of the disease are needed. We previously demonstrated that a novel locus in the KLK6 region was strongly associated with PCa aggressiveness. We further explored the functional role of this KLK6 locus. METHODS 380 PCa cases were accrued from the Swiss arm of the European Randomized Study of Screening for PCa and 540 from Toronto. We genotyped (Illumina platform) 123 tag SNPs selected from the entire KLK region using a stringent definition for PCa aggressiveness (GS<8 vs ≥8). A total of 880 SNPs imputed from the 1,000 genomes data and 47 SNPs imputed from Hapmap provided a dense map of the KLK region. We quantified the KLK6 tissue methylation levels using the MethyLight assay (percent DNA methylation, PMR) in 115 pairs of PCa and normal tissue samples (Toronto). The methylation data for KLK6 were analyzed as PMR score in the tumor tissue – PMR score in the normal tissue (defined as PMRdiff). A PMR score was calculated for the KLK6 gene locus by dividing the KLK6 gene:Alu-C4 ratio of a sample by the KLK6 gene:Alu-C4 ratio of commercially available fully methylated DNA and multiplying by 100. RESULTS Five SNPs in very strong linkage disequilibrium (LD) in the KLK6 gene (rs113640578, rs79324425, rs11666929, rs28384475, rs3810287) were highly associated with PCa aggressiveness when discriminating between GS<8 vs ≥8 in the Swiss cohort (p=9.5x10-5) and the Toronto cohort (6.5x10-3 to 1x10-2). Using bioinformatics, two additional SNPs (rs78353057 and rs201147694) were in strong linkage equilibrium with these five SNPs. Haploreg analyses showed that all 7 SNPs may have regulatory functions; rs11666929 was in a RAD21 protein binding site; 3 SNPs were in DNAse sensitive regions and 4 at potential histone modification sites. Based on the covariance analysis of the GS, we found a significant interaction between SNP rs113640578 in KLK6 and PMRdiff, (P=0.048) at the tissue level supporting that the increase of GS due to KLK6 methylation was significantly higher in individuals carrying the rare allele of rs113640578 compared to those having the common allele. CONCLUSIONS Our fine-mapping study has identified a novel locus in the KLK6 region strongly associated with PCa aggressiveness and functional results suggest that this locus resides within the transcription factor binding sites of the gene. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e746-e747 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Laurent Briollais More articles by this author Hilmi Ozcelik More articles by this author Maciej Kwiatkowski More articles by this author Jingxiong Xu More articles by this author Sevtap Savas More articles by this author Ekaterina Olkhov-Mitsel More articles by this author Franz Recker More articles by this author Cynthia Kuk More articles by this author Sally Hanna More articles by this author Neil E Fleshner More articles by this author Tristan Juvet More articles by this author Matt Friedlander More articles by this author Hong Li More articles by this author Karen Chadwick More articles by this author John Trachtenberg More articles by this author Ants Toi More articles by this author Theodorus H van der Kwast More articles by this author Eleftherios P Diamandis More articles by this author Bharati Bapat More articles by this author Alexandre R. Zlotta More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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 designObservational
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
Published2015
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