Abstract P2-11-10: Validation of the Breast Cancer Index (BCI) prognostic models optimized for late distant recurrence in postmenopausal women with early-stage HR+ breast cancer in the TEAM trial
Notice bibliographique
Résumé
Abstract Background: Women with HR+ breast cancer experience a persistent risk of distant recurrence (DR) even after completion of 5 years of adjuvant endocrine therapy, with more than 50% of DR occurring after 5 years (late DR). The prognostic genomic signatures currently being used in the clinic were not developed or optimized specifically for late DR. We have previously shown that the Breast Cancer Index (BCI) and BCIN+ prognostic models were significantly prognostic for risk of overall (0-10y) and late (5-10y) distant recurrence (DR) in N0 and N1 HR+ patients in the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial. Here, the prognostic performance of the BCI and BCIN+ models with alternative cut-points optimized for late DR were evaluated in patients from the TEAM trial, who were free from DR for at least 5 years. Methods: BCI testing was performed blinded to clinical outcome. The pre-specified alternative cut-points 4.4 and 1.8 for BCI and BCIN+ models were determined previously from Trans-aTTom and IDEAL studies, respectively (ESMO 2021). Kaplan-Meier analysis and log-rank test were used to evaluate the prognostic significance of BCI/BCIN+ risk groups based on DR. Univariate and multivariate Cox models were used to estimate hazard ratios (HRs) and the associated 95% confidence intervals (CIs). Results: 1285 HR+ N0 (median age 69.2, 54.2% T1, 92.5% G2-3, 21.3% chemotherapy) and 1762 N1 (median age 68.5, 49.7% T1, 80.8% G2-3, 42.6% chemotherapy) patients who remained free from DR at 5 years post randomization were included in the current analysis. For N0 patients, BCI identified 439 (34%) and 846 (66%) patients as low and high-risk with late 10-year DR rates of 3.8% (95% CI: 1.5-6.0%) and 9.1% (95% CI: 6.8-11.4%), respectively (HR: 2.6, 95% CI: 1.4-5.0; p=0.0025). For N1 patients, BCIN+ identified 287 (16%) and 1475 (84%) patients as low and high-risk with late 10-year DR rates of 3.4% (95% CI: 1.2-5.5%) and 12.3% (95% CI: 10.4-14.2%), respectively (HR: 3.5, 95% CI: 1.8-6.9; p< 0.0001). Similar results were observed in the HER2- patients. Notably, BCI/BCIN+ remained a statistically significant prognostic factor in the multivariate analysis after controlling for age, tumor size, grade, treatment. (Table). Conclusions: Compared to the original BCI/BCIN+ models, the optimized BCI and BCIN+ models showed improved prognostic performance for identifying low-risk patients with a very low risk of late DR (< 4%), for both N0 and N1 patients. These results provide further validation of BCI clinical utility as an aid in the decision-making for extended endocrine therapies for HR+ breast cancer, particularly in patients with N1 disease that may be spared extended endocrine treatment. Table Citation Format: John MS Bartlett, Keying Xu, Jenna Wong, Gregory R. Pond, Yi Zhang, Melanie Spears, Ranelle Salunga, Elizabeth Mallon, Karen J. Taylor, Annette Hasenburg, Christos Markopoulos, Luc Dirix, Caroline Seynaeve, Cornelis J.H. van de Velde, Daniel Rea, Catherine A. Schnabel, Kai Treuner, Jane Bayani. Validation of the Breast Cancer Index (BCI) prognostic models optimized for late distant recurrence in postmenopausal women with early-stage HR+ breast cancer in the TEAM trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-11-10.
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 | 0,016 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».