P5-11-11: Automated Quantification Methods Improve the Accuracy of PR as an Independent Prognostic Factor in Tamoxifen Treated Breast Cancer Patients.
Notice bibliographique
Résumé
Abstract Background Adjuvant endocrine therapy reduces the risk of recurrence and death in hormone receptor positive breast cancer patients. However, 40–50% of estrogen receptor (ER) positive tumors are resistant to endocrine therapy. We have previously shown that quantitative measurement of ER protein expression has limited value as a prognostic marker in tamoxifen treated patients. The presence of progesterone receptor (PR) expression has shown promise as prognostic and/or predictive marker for endocrine therapy, including adjuvant tamoxifen, but reports are contradictory. Automation of scoring methods will improve the accuracy of PR scoring and its value as a prognostic factor. Methods: This retrospective study evaluated breast cancer patients treated with adjuvant tamoxifen (n=458) from 1985–2000 at the Tom Baker Cancer Centre, Calgary, Canada. Tissue microarrays were assembled from formalin fixed paraffin embedded tumor tissue. Clinico-pathologic data was obtained from chart review. Five-year disease-free survival (DFS) was the primary outcome. DAB-based PR staining was used to generate Allred PR scores, as well as to generate scores for PR percent area expression and PR integrated optical density (PR IOD) using the DAKO ACIS III scanner and image analysis software. PR tumor nuclear pixel intensity density scores were obtained using fluorescence-based PR staining, scanned and quantified with a HistoRx PM2000 scanner andAQUA image analysis software. Continuous variables were dichotomized using Xtile. Results: In our tamoxifen cohort, 5-year DFS was associated with tumor grade [HR 4.9(3.5−6.9), p<0.001], tumor size [HR 2.7(1.9−3.8), p<0.001], lymph node status [HR 5.7(4.0−8.0), p<0.001] and ER status [HR 2.4(1.3−4.7), p=0.008]. Low PR was associated with significantly worse DFS regardless of the method used to quantify its expression. Methods of analysis requiring less subjective input had stronger associations between PR expression and outcome. The subjective identification of tumor and subjective biomarker quantification used in Allred scoring [HR 2.2(1.4−3.3), p<0.001] was less effective than ACIS scoring [PR percent area: HR 2.8(1.8−4.3), p<0.001; PR IOD: HR 2.9(1.9−4.6), p<0.001], which only requires subjective identification of the tumor. All methods were inferior to fluorescence-based AQUA scoring [HR 4.2(2.7−6.5), p<0.001], which automatically detects the tumor nuclear area using pan-cytokeratin and DAPI staining and measures the PR expression within this compartment. Multivariate analysis, which included age, tumor size, tumor grade, lymph node status, and ER status, confirmed that AQUA [HR 3.3(2.0−5.6), p<0.001] was superior to ACIS PR percent area [HR 2.3(1.4−3.9), p=0.002] and ACIS PR IOD [HR 2.7(1.6−4.8), p<0.001], all of which were superior to Allred scoring [HR 1.2(0.7−2.1), p=0.511]. Conclusions: We conclude that: 1) PR is an independent prognostic marker in our cohort, 2) using less subjective, automated quantitative scoring methods improves the value of PR as a prognostic biomarker, 3) AQUA appears superior to other automated methods and that 4) the incorporation of digital image analysis into practice would improve the prognostic value of PR expression in the clinical setting. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-11-11.
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 | 0,004 |
| 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 | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».