Abstract PD17-08: Pooled gene expression analysis and association with treatment response in patients with HR+/HER2− advanced breast cancer in the MONALEESA-2, -3, and -7 trials
Notice bibliographique
Résumé
Abstract Background: The Phase III MONALEESA (ML)-2, -3, and -7 trials showed significant improvement in progression-free survival (PFS) and overall survival (OS) with ribociclib (RIB) + endocrine therapy (ET) over placebo (PBO) + ET in patients (pts) with HR+/HER2− advanced breast cancer (ABC); improvement in OS with cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) has been observed in some, but not all clinical trials. Gene expression analyses for each separate ML study were reported previously. Given the differences in CDK4 vs CDK6 inhibition between RIB and other CDK4/6i, we evaluated the association between cell cycle (CC)–related genes and outcomes based on pooled analysis of gene expression using tumor samples from the ML-2, -3, and -7 trials. Methods: Gene expression data were generated from pre-treatment archival tumor samples (primary, 73%; metastatic, 27%) with a customized NanoString nCounter panel (781 genes) including genes involved in CC, other signaling pathways, and breast cancer biology. Samples were pooled from 1139 pre- and postmenopausal pts with HR+/HER2− ABC across the 3 ML studies, which included pts on first- and second-line therapy. Data were categorized into training (80%) and test (20%) datasets. The training dataset was used to analyze each gene (modeled continuously) individually for an association with PFS, and genes with a gene × treatment (tx) interaction P value <.10 were evaluated in the test dataset. Genes or gene signatures were classified by tertiles based on expression level (low/medium/high). For each tertile, median (m) PFS was calculated by the Kaplan-Meier method, and hazard ratios (HRs) of tx benefit (RIB vs PBO) were estimated. A Cox proportional hazards model adjusting for clinical covariates was used. A machine learning approach (elastic net survival model with stability selection), which used available gene expression data and select clinical factors and their interactions with tx arms, was applied to predict PFS. Results: This report focused on CC-related genes and signatures. Gene expression levels of CDKN2B and the expression ratio of CCND1/CDKN2A showed a predictive relationship with benefit from RIB in both training and test sets (Table). PFS benefit with RIB was consistent regardless of the CDK4/CDK6 expression ratio or level of expression of CCNE1, CDK2, RB1, combined CC-related genes, E2F gene signatures, RB gene signature, combined DNA-replication genes, or combined proliferation-related genes. A machine learning approach identified a clinico-genomic signature that was prognostic for PFS benefit with RIB. Selected variables included gene expression levels of FXBO5, PGR, RBBP8, and STC2 and several clinical features (tx arm, de novo disease, prior ET, and visceral disease). Pts with a low signature score had a longer mPFS vs pts with a high signature score, in the RIB (HR, 0.37; 95% CI, 0.22-0.62) and PBO (HR, 0.30; 95% CI, 0.15-0.59) arms. Conclusion: In the largest pooled analysis of the association of gene expression profile data with CDK4/6i tx response in pts with HR+/HER2− ABC, the PFS benefit with RIB + ET over ET alone was consistent irrespective of expression levels of most CC genes. Variation in magnitude of RIB benefit was observed, depending on CDKN2B expression levels, CCND1/CDKN2A expression ratio, and machine learning–derived signature scores. The clinico-genomic CDK4/6i signature requires validation in additional datasets. Table 1: Progression-Free Survival by Gene Expression Subgroup Citation Format: Aditya Bardia, Faye Su, Nadia Solovieff, Fabrice Andre, Carlos Arteaga, Patrick Neven, Yoon-Sim Yap, Yen-Shen Lu, Stephen K. Chia, Dennis Slamon, Seock-Ah Im, Arunava Chakravartty, Agnes Lteif, Tetiana Taran, Debu Tripathy. Pooled gene expression analysis and association with treatment response in patients with HR+/HER2− advanced breast cancer in the MONALEESA-2, -3, and -7 trials [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 PD17-08.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 0,000 |
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 tête enseignante, 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 ».