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Copy number alterations of <i>P53</i>, <i>RB1,</i> and <i>MDM2</i> as prognostic markers in intermediate-risk prostate cancer.

2016· article· en· W2496102636 on OpenAlexaff
Osman Mahamud, Melvin L.K. Chua, Emilie Lalonde, Jonathan So, Alan Dal Pra, Alejandro Berlín, Michèle Orain, Valérie Picard, Hélène Hovington, Alain Bergeron, Yves Fradet, Bernard Têtu, Gaetano Zafarana, Alice Meng, Julie Livingstone, Melania Pintilie, Michael Fraser, Theodorus van der Kwast, Paul C. Boutros, Robert G. Bristow

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversité LavalHôtel-Dieu de QuébecUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineProstate cancerCancerProstateCohortOncologyImage-guided radiation therapyInternal medicineProstatectomyMdm2Radiation therapyBiologyGeneticsGene

Abstract

fetched live from OpenAlex

117 Background: We interrogated copy number alternations (CNA) of p53, Rb1 and MDM2 as prognostic determinants of biochemical failure in localized intermediate-risk prostate cancer. Methods: Using Affymetrix Oncoscan array technology, we characterized copy number alterations (CNA) for 284 D’Amico-classified intermediate-risk prostate cancers. Of the 284 patients, 143 underwent image-guided radiotherapy (IGRT), while 141 underwent radical prostatectomy (RadP). Biochemical-relapse free survival (bRFS) was assessed as a clinical end-point, with biochemical failure defined using the Phoenix and AUA criteria for IGRT and RadP patients, respectively. Results: We observed allelic losses ofp53 and Rb1 in 23.9% (n = 68) and 31.0% (n = 88) and allelic gains of MDM2 in 3.17% (n = 9) in our cohort, respectively. 7.7% (n = 22), 1.1% (n = 3) and 0.4% (n = 1) of all cases exhibited concurrent losses of p53 and Rb1, p53 loss and MDM2 gain, and concurrent p53/Rb1 loss and MDM2 gain, respectively. Patients with allelic losses of p53, Rb1 and allelic gain of MDM2 loci exhibited increased percent genome aberration (PGA) (Mean 9.2 vs. 6.1 p < 0.001; 10.1 vs. 5.4 p < 0.001; 18.1 vs. 6.5 p < 0.001 respectively). Rb1 loss and MDM2 gain were not significant predictors of bRFS, independent of treatment modality. Allelic loss of p53 was predictive of poor bRFS in the RadP cohort (HR = 1.86, 95% CI 1.10-3.16, p = 0.022), but not for IGRT patients (HR = 1.16, 95% CI 0.63-2.12, p = 0.629). Additionally, patients in the RadP cohort with concurrent losses of p53 and Rb1 also had a higher likelihood of poorer bRFS (HR = 2.32, 95% CI 1.1-4.93, p = 0.029). On multivariate analysis, incorporating PGA and pre-treatment PSA, concurrent p53/Rb1 losses, but not single p53 loss, was prognostic for bRFS in patients who underwent RadP (p53/Rb1 losses, HR = 2.16, 95% CI 1.0-4.65, Wald's p = 0.05; p53loss, HR = 1.62, 95% CI 0.94-2.79, Wald's p = 0.08). Conclusions: In a cohort of men with intermediate-risk prostate cancers, we identified an unfavourable subgroup of patients harbouring concurrent copy number losses of p53 and Rb1 that was associated with an adverse prognosis of biochemical failure following radical prostatectomy.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.385
Teacher spread0.364 · 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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Citations0
Published2016
Admission routes1
Has abstractyes

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