Abstract 3207: Automated quantitative analysis of p53, cyclin D1 and pErk expression in breast carcinoma does not differ from expert pathologist scoring and correlates well with clinico-pathological characteristics
Notice bibliographique
Résumé
Abstract Prognosis and risk assessment of breast cancer patients are currently driven by TNM stage, ER/PR/HER2 expression, tumor grade and lymphovascular invasion (LVI). However there is critical need for improved biomarker assessment platforms to better predict systemic treatment response. One roadblock is the lack of semi-quantitative methods to reliably measure expression, activity and localization of biomarkers in formalin-fixed tumor specimens. The present study assesses reliability of automated IHC scoring compared to manual scoring of routine and non-routine biomarkers (HER2, cyclin D1, p53 and phospho(p)-ERK) on a human breast cancer tissue microarray according to REMARK guidelines, and correlates these markers with clinical-pathological data. Using a triplicate core TMA of formalin-fixed paraffin embedded tissues, we investigated 63 primary invasive breast cancers, for which ER/PR/HER2 status, LVI, grade and recurrence status were recorded. IHC was performed on the TMA for the above biomarkers (pH 6 citrate buffer conditions). Histologic (H) scores (% positive tumor area × staining intensity 0-3) were determined manually by two independent evaluators with resolution of discordant cases by a senior pathologist. Excellent replicability was observed between H scores for each marker compared on replicate slides, as determined by Spearman correlations (0.79-0.82). Each TMA slide was then scanned into the Ariol Imaging System, algorithms were trained for each marker, and H scores were calculated. Pearson correlation coefficients (with data left as continuous) and Kappa statistics (with dichotomized data) were used for inter-method comparisons. Associations between biomarker positivity and clinical data were assessed by Fisher's exact test. Excellent concordance between manual and automated Ariol scores was observed for all four markers based on Kappa statistics (0.667-0.813) and Pearson correlation coefficients (0.790-0.885). Distinct proportions of tumor cases showed any positive staining for membranous HER2 (19/63), nuclear p53 (16/56), cyclin D1 (26/57) and pERK (32/59). A statistically significant association of pERK positivity with absence of LVI (p=0.0025) and lymph node negativity (p=0.0006) was observed. In contrast, pERK positivity was associated with high-grade tumors (p=0.0040), consistent with a role of pERK in poorly differentiated high-grade primary tumors. p53 over-expression, characteristic of dysfunctional p53 in breast cancer, was also associated with high tumor grade (p=0.0074). Thus automated quantitation of immunostaining yields objective results that do not differ from pathologists’ scoring, and provide meaningful associations with clinico-pathological data. (Supported by CIHR, PSI, and Queen's Dept. Pathol. & Mol. Med.) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3207. doi:10.1158/1538-7445.AM2011-3207
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».