Abstract P1-19-05: Capivasertib (AZD5363) in combination with fulvestrant in PTEN-mutant ER+ metastatic breast cancer
Notice bibliographique
Résumé
Abstract Background: Loss of function in the tumor suppressor gene, PTEN, activates PI3K/AKT signaling, driving tumor growth. Somatic mutations in PTEN occur in 5-10% of estrogen-receptor-positive (ER+) breast cancer (BC), and PTEN loss/inactivation is associated with an aggressive BC phenotype and poor outcome. Capivasertib, a pan-AKT kinase inhibitor, has shown antitumor activity in solid tumors. In ER+ BC, suppression of PI3K/AKT signaling results in a compensatory increase in ER-dependent transcription, potentially limiting the efficacy of AKT inhibitors when given as monotherapy. We therefore investigated concurrent inhibition of AKT and ER with combination therapy of capivasertib and fulvestrant in PTEN-mutant ER+ metastatic BC (MBC). Methods: In an expansion cohort (part F) of a Phase I study (NCT01226316), oral capivasertib 400 mg twice daily, 4 days on 3 days off, and fulvestrant at labeled dose, was administered to ER+ MBC patients (pts) with tumors harboring a deleterious PTEN alteration (identified in tissue/plasma by local next-generation sequencing [NGS], with central NGS and immunohistochemistry [IHC] performed retrospectively). Pts were enrolled in fulvestrant-naïve (FN) or fulvestrant-resistant (FR) cohorts (max 24 pts/cohort). Key objectives included safety and efficacy based on 24-week clinical benefit rate (CBR). Results: At data cut-off, 31 pts (12 FN; 19 FR) received treatment. Median number of prior metastatic regimens was 7. FN pts had higher rates of visceral disease (100%) and prior chemotherapy receipt (median 4 [range 0-8]) than FR pts (84%; median 2 [1-7]), respectively]. CBR and median progression-free survival (PFS) were 17% and 2.6 months in FN pts, and 37% and 4.1 months in FR pts, respectively (Table). Twenty-four patients (77%) had PTEN mutations and 7 (23%) had PTEN gene deletions determined by local NGS. Central plasma NGS confirmed 79% (19/24) of the PTEN mutations, and IHC confirmed complete loss of the PTENprotein in 85% (22/26) of cases. Treatment-related grade ≥3 adverse events (AEs) occurred in 32%, most frequently diarrhea and maculopapular rash (both n=2 pts). Treatment-related AEs resulted in dose reduction in 2 pts. Table. Clinical efficacyFN, n=12FR, n=19ORR, % (95% CI)8 (0.2, 39)21 (6, 46)CBR, % (95% CI)17 (2, 48)37 (16, 62)Confirmed response, n (%)1 (8)4 (21)Stable disease ≥24 weeks, n (%)2 (17)3 (16)Median PFS, months (95% CI)2.6 (1.2–4.2)4.1 (1.5–6.7)Median duration of response, months (95% CI)5.5 (NC–NC)6.9 (1.4–NC)Data cut-off was 21 March 2019. Median time from last fulvestrant administration to study entry in FR pts (n=19) was 13.8 months (range 0.7–28.2). CBR was defined as confirmed responders and those with stable disease ≥24 weeks. NC, not calculable (because of limited pt numbers); ORR, objective response rate Conclusions: Capivasertib plus fulvestrant is clinically active in heavily pretreated PTEN-mutated ER+ MBC, including in pts with prior resistance to fulvestrant. Efficacy appeared marginally better in FR than FN pts, possibly due to enrichment of pts with more aggressive disease in the FN cohort. Further analyses of the relationship between genomic features, such as concurrent mutations with drug activity, will be reported. Citation Format: Lillian M Smyth, Gerald Batist, Funda Meric-Bernstam, Peter Kabos, Iben Spanggaard, Ana Lluch, Alison Schram, Andrea Varga, Andrea Wong, Helen Ambrose, Alan Barnicle, T. Hedley Carr, Elza C de Bruin, Carolina Salinas-Souza, Andrew Foxley, Joana Hauser, Justin PO Lindemann, Rhiannon Maudsley, Robert McEwen, Michele Moschetta, Martine Roudier, Gaia Schiavon, Pedram Razavi, Udai Banerji, Sarat Chandarlapaty, José Baselga, David M Hyman. Capivasertib (AZD5363) in combination with fulvestrant in PTEN-mutant ER+ metastatic breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-19-05.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».