Abstract S4-06: HER2 status as predictive marker for AI vs Tam benefit: A TRANS-AIOG meta-analysis of 12129 patients from ATAC, BIG 1-98 and TEAM with centrally determined HER2
Notice bibliographique
Résumé
Abstract There is now significant evidence emerging from the pivotal trials of AIs versus Tamoxifen (AIOG) demonstrating the value of meta-analysis of key clinical questions. The "Trans-AIOG" group has been tasked with the exploration of key molecular/biomarker questions that are pertinent to meta-analyses of biomarkers (past/present/future) in AIOG trials. HER2 has been long proposed as a marker of endocrine "resistance". Data from three trials, before the era of HER-directed therapy, suggest a potential role for HER2 to select patients for treatment with upfront AIs. However the individual trials lack power to test treatment-by-HER2 interaction due to sample size and low HER2+ve rates. A meta-analysis of the predictive value of HER2 status, specifically within the first 3 years of endocrine therapy, has the potential to inform patient selection for upfront or sequential strategies with AIs. The pre-existing standardization of methodology for HER2 (IHC/FISH) facilitates analysis of existing data from BIG-1-98, TEAM and ATAC for this key marker. Analysis plan: Following a prospectively-designed analysis plan, patient-level data from 3 randomized phase III trials (ATAC, BIG 1-98, TEAM) comparing AIs to tamoxifen during the first 2-3 years of adjuvant treatment were collected at the CRCTU (Birmingham UK), accounting for both the established time-dependency of relapse in HER2+ve, anti-endocrine treated patients and to address the clinical question of "upfront" vs "sequential" strategies for AIs. For each trial, covariate-adjusted Cox models estimated HER2-by-treatment (AI vs Tam) interaction on distant recurrence-free interval-censored at 2-2.75 years follow-up. A meta-analysis of the HER2-by-treatment interaction terms and of treatment effects according to HER2 status was performed. Results: 12129 patients with centrally-confirmed ER and HER2 status, 1092 (9%) HER2+ve, with 473 (4%; 111 among HER2+ve) distant recurrences were analyzed. The meta-analysis estimated a pooled HER2-by-treatment interaction of 1.61 (95% CI 1.01,2.57), reflecting treatment effect hazard ratio(AI/Tam) of HR=1.13 (0.75,1.71) among HER2+ve and HR=0.70 (0.56,0.87) among HER2-ve. There was heterogeneity among interaction terms (I-squared=59%, p=.09) that resulted from treatment effect heterogeneity among HER2+ve subgroup (I2=71%, p=.03), not the HER2-ve subgroup (I2=0%). The results for disease-free survival were similar. Conclusion: An individual patient data meta-analysis across 3 trials (ATAC, BIG 1-98, TEAM) conducted prior to standard use of HER2-directed adjuvant therapy demonstrated a marginally-significant interaction between HER2 status and treatment with AIs vs Tamoxifen in the 2-2.75 years prior to potential "switching" between Tamoxifen and AIs. Patients with HER2-ve cancers experienced improved outcomes when treated with AIs vs Tamoxifen whilst patients with HER+ve cancers fared no better, or slightly worse, during AI treatment. However, the small number of HER2+ve cancers and events even in this meta-analysis may explain a large degree of heterogeneity in the treatment effects within the HER2+ve subgroups across the 3 trials. Other causes, perhaps related to subtle differences between AIs, cannot be excluded. Citation Format: Bartlett JMS, Ahmed I, Regan MM, Sestak I, Mallon EA, Dell'Orto P, Thürlimann BJK, Seynaeve C, Putter H, Brookes CL, Forbes JF, Colleoni MA, Bayani J, van de Velde CJH, Viale G, Cuzick J, Dowsett M, Rea DW, On Behalf of the Translational Aromatase Inhibitor Overview Group (Trans-AIOG). HER2 status as predictive marker for AI vs Tam benefit: A TRANS-AIOG meta-analysis of 12129 patients from ATAC, BIG 1-98 and TEAM with centrally determined HER2. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr S4-06.
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 | 0,025 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,053 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».