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Enregistrement W2934740771 · doi:10.1097/01.ju.0000555707.15264.f6

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

2019· article· en· W2934740771 sur 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

Notice bibliographique

RevueThe Journal of Urology · 2019
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Lipids, and Metabolism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineProstate cancerPTENOncologyRisk assessmentInternal medicineCancer

Résumé

récupéré en direct d'OpenAlex

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 ...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,113

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,000
Communication savante0,0030,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0340,014

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,287
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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