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Copy number alterations of DNA mismatch repair (MMR) genes as novel prognostic markers in localised prostate cancer (CaP).

2016· article· en· W2590320673 on OpenAlexaffabout
Melvin L.K. Chua, Emilie Lalonde, Osman Mahamud, Alejandro Berlín, Alan Dal Pra, Michèle Orain, Valérie Picard, Hélène Hovington, Alain Bergeron, Yves Fradet, Bernard Têtu, Alice Meng, Junyan Zhang, Gaetano Zafarana, Julie Livingstone, Melania Pintilie, Theodorus van der Kwast, Michael Fraser, Paul C. Boutros, Robert G. Bristow

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversité LavalHôtel-Dieu de QuébecUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMSH2MSH6MLH1PMS2MedicineLynch syndromeOncologyCohortProstate cancerInternal medicineDNA mismatch repairCancer researchCancer

Abstract

fetched live from OpenAlex

96 Background: To investigate the prognostic significance of CNA of genes involved in the MMR pathway in localised CaP. Methods: We studied CNA of genes involved in MMR, namely MSH2, MSH3, MSH6, MLH1, PMS2, in 284 patients with intermediate-risk CaP (Toronto cohort), and compared our findings against three public databases (MSKCC and Cambridge cohorts) that included 375 low- to high-risk CaP. The Toronto cohort comprised of 143 and 141 individuals who underwent image-guided radiotherapy (IGRT) and radical prostatectomy (RadP), respectively, while all patients from the public databases underwent RadP. Information on genome-wide copy number alterations (Toronto) was obtained using Affymetrix Oncoscan array. Biochemical relapse-free survival (bRFS) was assessed for clinical outcome. Results: CNA of MSH2, MSH3, MSH6, MLH1, PMS2 were observed in 3.9% (n = 11), 7.7% (n = 22), 3.9% (n = 11), 4.6% (n = 13) and 13.0% (n = 37) of the Toronto cohort, respectively. Distinct patterns of allelic gain and loss were observed for the gene set; gains only for MLH1 and PMS2, and losses only, in all but 1 case, for MSH2, MSH3 and MSH6. In the Toronto cohort, allelic losses of MSH2, MSH3 and MSH6 were determined to be prognostic for poorer bRFS in IGRT patients (HR 2.04, 95% CI 1.01, 4.12, p = 0.048), but not for patients who underwent RadP (HR 1.08, 95% CI 0.49, 2.39, p = 0.84); while gains in MLH1 and PMS2 were not prognostic in either IGRT or RadP patients. A pooled analysis of these genes for all RadP patients from the Toronto and public databases (n = 516) did however indicate that allelic losses of MSH2, MSH3 and MSH6 were significant predictors of poorer bRFS (HR 2.48, 95% CI 1.64-3.77, p < 0.001), but not MLH1 and PMS2 gains. On multi-variable modelling that includes percent genome aberration and pre-treatment PSA levels, allelic losses of MSH2, MSH3 and MSH6remained significant predictors of bRFS for the pooled RadP cohort (HR 1.96, 95% CI 1.27, 3.01, Wald's p < 0.001), but not for IGRT patients (HR 1.50, 95% CI 0.72, 3.12, p = 0.28). Conclusions: We identified a distinct pattern of copy number loss of MSH2, MSH3 and MSH6 genes in localised CaP that appears to be a novel biomarker of failure to definitive treatment.

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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.080
GPT teacher head0.451
Teacher spread0.370 · 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 routes2
Has abstractyes

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