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MP28-02 QUANTITATIVE MEASUREMENT OF PTEN LOSS IMPROVES RISK ASSESSMENT IN PROSTATE CANCER

2019· article· en· W2934740771 on OpenAlexaboutno aff
Tamara Jamaspishvili, Palak Patel, Yi Niu, Thiago Vidotto, Isabelle Caven, Rachel Livergant, Winnie Fu, Véronique Ouellet, Clarissa Gondim Picanço, Madhuri Koti, Nathan E. How, Fred Saad, Anne‐Marie Mes‐Masson, Tamara L. Lotan, Jeremy A. Squire, Yingwei Peng, David M. Berman, Rodolfo Borges dos Reis

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerPTENOncologyRisk assessmentInternal medicineCancer

Abstract

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You have accessJournal of UrologyProstate Cancer: Markers II (MP28)1 Apr 2019MP28-02 QUANTITATIVE MEASUREMENT OF PTEN LOSS IMPROVES RISK ASSESSMENT IN PROSTATE CANCER Tamara Jamaspishvili*, Palak Patel, Yi Niu, Thiago Vidotto, Isabelle Caven, Rachel Livergant, Winnie Fu, Veronique Ouellet, Clarissa Picanço, Madhuri Koti, Nathan How, Fred Saad, Anne-Marie Mes-Masson, Tamara Lotan, Jeremy Squire, Yingwei Peng, David Berman, and Rodolfo Reis Tamara Jamaspishvili*Tamara Jamaspishvili* More articles by this author , Palak PatelPalak Patel More articles by this author , Yi NiuYi Niu More articles by this author , Thiago VidottoThiago Vidotto More articles by this author , Isabelle CavenIsabelle Caven More articles by this author , Rachel LivergantRachel Livergant More articles by this author , Winnie FuWinnie Fu More articles by this author , Veronique OuelletVeronique Ouellet More articles by this author , Clarissa PicançoClarissa Picanço More articles by this author , Madhuri KotiMadhuri Koti More articles by this author , Nathan HowNathan How More articles by this author , Fred SaadFred Saad More articles by this author , Anne-Marie Mes-MassonAnne-Marie Mes-Masson More articles by this author , Tamara LotanTamara Lotan More articles by this author , Jeremy SquireJeremy Squire More articles by this author , Yingwei PengYingwei Peng More articles by this author , David BermanDavid Berman More articles by this author , and Rodolfo ReisRodolfo Reis More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555707.15264.f6AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Loss of the PTEN tumor suppressor is a powerful prognostic biomarker in prostate cancer. However, the significance of tumour heterogeneity and partial loss are not clearly defined, nor are interactions between PTEN loss and the TMPRSS2-ERG fusions, the most common genetic aberration found in prostate cancer. Taking into account TMPRSS2-ERG status, we aimed to define and quantify patterns of PTEN loss that best account for the risk of recurrence in low and intermediate-risk prostate cancer patients who underwent radical prostatectomy. METHODS: Tissue microarrays comprising a training (n=410) and validation (n=272) cohorts were constructed and PTEN protein levels were measured using automated clinical grade immunohistochemistry assays. PTEN loss was quantified per cancer cell and per cancer core using digital and visual scoring. Thresholds for PTEN loss were determined by log-rank statistics and Kaplan-Meier survival estimator. Cox’s proportional hazards models were used to determine the prognostic significance of selected cut-offs of PTEN loss along with multiple pathological and clinical variables. RESULTS: PTEN loss in >65% of cancer cells (digital scoring)/per case or >50% of TMA cancer cores/per case (visual scoring) were associated with a 50% reduction in recurrence free survival (RFS) (% cells, HR=4.22, p<0.001 and % cores, HR=2.75, p=0.002 in multivariate analysis, respectively). Median RFS was 10.2 yrs for any PTEN loss (p<0.001) vs 5.4 yrs for >65% cells with PTEN loss (p<0.001) in Kaplan-Meier analysis. Cases with PTEN loss but without TMPRSS2-ERG fusion had the shortest RFS (4.1 yrs) compared to cases with TMPRSS2-ERG fusion (10 yrs; p=0.001). Finally, PTEN loss was found almost exclusively in dominant tumor foci (50/54 cases). CONCLUSIONS: Degree of PTEN protein loss is strongly associated with disease progression. PTEN loss is independently associated with increased risk of disease progression regardless ERG status. Its high level of intra-focal heterogeneity and strong association with dominant foci indicates that PTEN assessment is vulnerable to sampling error and might influence on prognostic assessment of biopsy samples. Any PTEN loss may not be a “red flag” for poor prognosis. Quantitative assessment of PTEN loss may improve risk stratification of patients with localized prostate cancer. Source of Funding: Work by T.J., P.P. and D.M.B. was awarded by Prostate Cancer Canada (PCC) and is proudly funded by the Movember Foundation-Grant #T2014-01. T.J. was supported by a Transformative Pathology Fellowship funded by the Ontario Institute for Cancer Research (OICR) through funding provided by the Government of Ontario. P.P was supported by Terry Fox Transdisciplinary Fellowship. V.O., A.-M.M.-M and F.S. are researchers of the Centre de recherche du Centre hospitalier de l’Universitéde Montréal which receives support from the FRQS. Biobanking was done in collaboration with the Réseau de Recherche sur le cancer of the Fonds de Recherche Québec - Santé (FRQS) that is affiliated with the Canadian Tumor Repository Network (CTRNet). TMA construction was supported by the Terry Fox Research Institute. F. Saad holds the Montreal University Research Chair in Prostate Cancer. J.A.S. and T.V. are supported by FAPESP grant no. 2015/09111-5. J.S. by CNPq Bolsa Produtividade em Pesquisa - Nàvel: PQ-1B grant no. 306864/2014-2. M.K. is supported by funding from Prostate Cancer Cancer, Terry Fox Research Institute-Canadian Prostate Cancer Biomarker Network and Canadian Institutes for Health Research. Kingston, Canada; Kingston, Canada; Dalian, China, People’s Republic of; Kingston, Canada; Montreal, Canada; São Paulo, Brazil; Kingston, Canada; Montreal, Canada; Baltimore, MD; São Paulo, Brazil; Kingston, Canada; São Paulo, Brazil© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e403-e403 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tamara Jamaspishvili* More articles by this author Palak Patel More articles by this author Yi Niu More articles by this author Thiago Vidotto More articles by this author Isabelle Caven More articles by this author Rachel Livergant More articles by this author Winnie Fu More articles by this author Veronique Ouellet More articles by this author Clarissa Picanço More articles by this author Madhuri Koti More articles by this author Nathan How More articles by this author Fred Saad More articles by this author Anne-Marie Mes-Masson More articles by this author Tamara Lotan More articles by this author Jeremy Squire More articles by this author Yingwei Peng More articles by this author David Berman More articles by this author Rodolfo Reis More articles by this author Expand All 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)0.003
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.014

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 head0.012
GPT teacher head0.287
Teacher spread0.275 · 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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Published2019
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