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Record W2783014571 · doi:10.1136/bmj.j5757

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts

2018· article· en· W2783014571 on OpenAlexfundno aff
Tyler M. Seibert, Chun Chieh Fan, Yunpeng Wang, Verena Zuber, Roshan Karunamuni, J. Kellogg Parsons, Rosalind A. Eeles, Douglas F. Easton, Zsofia Kote‐Jarai, Ali Amin Al Olama, Sara Benlloch Garcia, Kenneth Muir, Henrik Grönberg, Fredrik Wiklund, Markus Aly, Johanna Schleutker, Csilla Sipeky, Teuvo L.J. Tammela, Børge G. Nordestgaard, Sune F. Nielsen, Maren Weischer, Rasmus Bisbjerg, Martin Andreas Røder, Peter Iversen, Timothy J. Key, Ruth C. Travis, David E. Neal, Jenny Donovan, Freddie C. Hamdy, Paul D.P. Pharoah, Nora Pashayan, Kay‐Tee Khaw, Christiane Maier, Walther Vogel, Manuel Luedeke, Kathleen Herkommer, Adam S. Kibel, Cezary Cybulski, Dominika Wokołorczyk, Wojciech Kluźniak, Lisa Cannon‐Albright, Hermann Brenner, Katarina Ćuk, Kai-Uwe Saum, Jong Y. Park, Thomas A. Sellers, Chavdar Slavov, Radka Kaneva, Vanio Mitev, Jyotsna Batra, Judith A. Clements, Amanda B. Spurdle, Manuel R. Teixeira, Paula Paulo, Sofia Maia, Hardev Pandha, Agnieszka Michael, Andrzej Kierzek, David S. Karow, Ian G. Mills, Ole A. Andreassen, Anders M. Dale

Bibliographic record

VenueBMJ · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIIWorld Cancer Research FundDeutsche KrebshilfeCancerfondenCanadian Institutes of Health ResearchU.S. Department of DefenseAssociazione Italiana per la Ricerca sul CancroVetenskapsrådetSwedish Cancer FoundationBundesministerium für Bildung und ForschungNational Institute for Health and Care ResearchFrancis Crick InstituteCancer Research UKWellcome TrustMedical Research CouncilBreast Cancer Research FoundationPetrus och Augusta Hedlunds StiftelseStockholms Läns LandstingDeutsches KrebsforschungszentrumNational Institutes of HealthOvarian Cancer Research FundNorges ForskningsrådHellenic Health FoundationProstate Cancer FoundationEuropean Commission
KeywordsProstate cancerScale (ratio)HazardMedicineComputer scienceCancerOncologyData scienceInternal medicineBiologyCartographyGeography

Abstract

fetched live from OpenAlex

OBJECTIVES: To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. DESIGN: Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. SETTING: Multiple institutions that were members of international PRACTICAL consortium. PARTICIPANTS: All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. MAIN OUTCOME MEASURES: Prediction with hazard score of age of onset of aggressive cancer in validation set. RESULTS: ). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. CONCLUSIONS: Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.350
Teacher spread0.316 · 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 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".

Quick stats

Citations227
Published2018
Admission routes1
Has abstractyes

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