Abstract PD14-08: Effectiveness of aromatase inhibitors versus tamoxifen in lobular compared to ductal carcinoma: Individual patient data meta-analysis of 9328 women with central histopathology, and 7654 women with e-Cadherin status
Notice bibliographique
Résumé
Abstract Background: In post-menopausal women with hormone receptor (HR) positive early breast cancer, aromatase inhibitors (AIs) are more effective than tamoxifen as endocrine therapy. However, some trial reports indicate greater benefit from AIs in lobular than ductal cancers. Invasive lobular cancer can be identified using conventional microscopy and/or immunohistochemistry for e-Cadherin status. We performed an individual patient data meta-analysis to explore possible differential treatment benefits for AI vs tamoxifen in women with lobular vs ductal hormone receptor positive breast cancer. Methods: Individual patient data were collected from three randomised controlled trials (BIG 01-98, TEAM and ATAC) of AI vs tamoxifen for postmenopausal women with estrogen receptor positive breast cancer, as well as results of central pathology review and e-Cadherin expression. Central pathology and e-Cadherin data were available on 9328 and 7654 women. Local pathology data was available for TEAM, BIG 01-98. Data were analysed using the same methodology as the previous EBCTCG meta-analysis of AI vs tamoxifen: results of different methods of diagnosing ductal vs lobular cancer were cross tabulated, and outcomes analysed using log-rank methods, yielding event rate ratios (RR) and confidence intervals. Interactions were evaluated using standard tests for heterogeneity; the primary outcomes were time to any invasive breast cancer recurrence, and time to distant recurrence. Results: Rates of lobular cancer were higher when assessed by central pathology (BIG 01-98 16%; ATAC 16%; TEAM 12%) than e-Cadherin (15% vs 14% vs 9%). Methods agreed in over 80% of cases classified as ductal using either pathology or e-Cadherin, while the agreement rate for lobular cancers was only about 50%. A similar pattern was seen comparing local pathology with either central pathology or e-Cadherin. Consequently, analyses were stratified by pathology and e-Cadherin both separately and together. Consistent with the previous meta-analysis there was a significant reduction in recurrence for AI compared to tamoxifen (RR 0.73 (0.61-0.87) p=0.0004). Exploration of interaction found no evidence of heterogeneity of treatment effect on recurrence by pathology (ductal HR 0.76 (0.64-0.89); lobular HR 0.76 (0.50-1.15) interaction p>0.99; nor by e-Cadherin status (interaction p=0.9). No significant interactions were seen on other endpoints. Conclusion: Analyses of three large trials of adjuvant AI vs tamoxifen found discordance in identifying patients with lobular carcinoma by local or central pathology or e-Cadherin status, indicating variability in the consistency of diagnosis. The trials included showed a benefit for AI over tamoxifen in line with the previous meta-analysis, but with no evidence of differential efficacy in lobular compared to ductal carcinomas, however measured. These data cannot rule out smaller quantitative interactions or differences in site of recurrence: however, in contrast to earlier reports, this meta-analysis of the totality of the data does not identify ductal/lobular cancer as a predictive marker for differential endocrine treatment benefit. Citation Format: Robert K Hills, Steffi Oesterreich, Otto Metzger, David Dabbs, Hongchao Pan, Jeremy Braybrooke, Richard Gray, Richard Peto, Rosie Bradley, Ewan Straiton, Richard Berry, Daniel Rea, David Cameron, Jack Cuzick, Meredith Regan, Mitch Dowsett, Ivana Sestak, Jonas Bergh, Sandra M Swain, John Bartlett, Early Breast Cancer Trialists' Collaborative Group. Effectiveness of aromatase inhibitors versus tamoxifen in lobular compared to ductal carcinoma: Individual patient data meta-analysis of 9328 women with central histopathology, and 7654 women with e-Cadherin status [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD14-08.
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 | 0,023 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,054 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| 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 ».