Abstract PD10-08: Quantitative and Standardized Measurement of Estrogen Receptor Predicts Response to Radiation Therapy in Breast Cancer
Notice bibliographique
Résumé
Abstract Background: It has been repeatedly demonstrated that ER-positive patients have a significantly increased response to tamoxifen, however, continuous association between relative risk and quantitative ER expression has yet to be fully established. Additionally, although it has been hypothesized that estrogen receptor positivity and tamoxifen treatment may play a role in radiation therapeutic response, an assessment of whether quantitative levels of ER predict response to radiation therapy has yet to be determined. Materials and Methods: Fluorescence immunohistochemistry with AQUA® technology was used to quantitatively assess ER expression on a cohort (n = 568) of retrospectively collected breast cancer specimens from patients treated with tamoxifen (20mg) ± radiotherapy (RT). Staining, image acquisition and AQUA analysis was performed at two independent sites using two different standardized digital pathology platforms. AQUA technology is a completely standardized and objective platform with minimized operator interaction that provides tumor-specific, quantitative and continuous expression score data. Results: A comparison between completely independent sample staining and quantitative assessment at two sites showed highly significant correlation, with AQUA score values approaching unity (Pearson's R=0.94; linear slope = 0.999) and indistinguishable means (p=0.93). Assessment of the same tissue slides on two different instrument platforms (HistoRx PM2000 v. Aperio Scanscope FL) also showed highly significant correlation (Pearson's R=0.95; linear slope = 1.01; p = 0.89). Continuous ER AQUA scores showed a highly significant association with 5-year disease-free survival by itself (HR = 0.80 (95%CI: 0.70-0.91); p=0.001) and when put into a model with nodal status and tumor size (HR=0.80 (95%CI: 0.68 — 0.94); p=0.006). The cohort was then divided at the median AQUA score representing relative low and high ER expressing patients. The low ER expressing group showed significant benefit from RT for 5-year disease-free survival (HR = 0.56 (95%CI: 0.32-0.95); p=0.03) and maintained significance at the 10% level when nodal status and tumor size were adjusted for in the model (HR = 0.60 (95%CI: 0.32 — 1.10); p=0.097). In contrast, the high ER expressing group showed no benefit (HR = 0.81 (95%CI: 0.43-1.52); p=0.51) for radiation treatment. No benefit for either group was observed for 15-year overall survival. Discussion: Taken together, these data demonstrate that quantification of ER, beyond simple positive/negative characterization, could provide valuable predictive information for the treatment of breast cancer, specifically for predicting a group more likely to respond to radiation therapy and sparing patients from a potentially harmful treatment. These data will need further validation on an independent cohort designed to differentiate radiation response. Furthermore, these data indicate that true quantification of ER expression provides a continuous recurrence risk assessment for patients being treated with tamoxifen. Because these data are standardized across sites and imaging platforms, misclassification of patients is significantly reduced as compared to the current standard by which ER expression is determined. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD10-08.
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 | 0,001 | 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 | 0,001 | 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 ».