Abstract PD10-07: PD10-07 Low plasma estradiol, low expression of estrogen responsive genes and TP53 mutations are associated with poor anti-proliferative response to aromatase inhibitors
Notice bibliographique
Résumé
Abstract Background: Aromatase inhibitors (AIs) are highly effective at reducing recurrences and mortality in postmenopausal patients with estrogen receptor positive breast cancer (ER+ BC). Poor anti-proliferative (Ki67) response or ER+ BCs to AIs after 2 weeks is associated with worse long-term outcomes. Factors that relate to the degree of the response may identify markers and/or mechansims of resistance. Methods: The PeriOperative Endocrine Therapy for Individualizing Care (POETIC) trial randomized 4,480 with ER+ BC to 2 weeks’ AI before surgery or no presurgical treatment. All patients within the bottom 15% of Ki67 responders to AI (poor responders [PRs]; n=177 with RNA extracted) were selected from and matched to good responders (GRs) within the 50% showing the best response (n=190). Matching was based on baseline Ki67 levels as measured by immunohistochemistry (IHC). Response to AI was measured by the percentage change in Ki67 after 2 weeks’ treatment. PRs were further divided into groups expressing high ESR1 (PRs ESR1HIGH; n=119) and low ESR1 (PRs ESR1LOW; n=58) levels since there were very few GRs with low ESR1. RNAseq, targeted exome DNA sequencing of 87 BC/resistance related genes and measurement of plasma estradiol levels by mass spectrometry were performed to understand mechanisms of de novo resistance. Intrinsic subtypes were estimated from RNAseq data. Results: More than 90% of PRs ESR1LOW were non-luminal subtypes with low expression of estrogen-responsive genes. In contrast, 11% of PRs ESR1HIGH were non-luminal compared to 4% of GRs but only HER2-enriched subtypes were significantly higher in PR ESR1HIGH (p=0.05, Fisher exact). While AI treatment had limited impact on Ki67 IHC values in PRs ESR1HIGH, PGR expression was more than 2-fold lower after 2 weeks of AI. Gene-set enrichment analysis showed significantly lower expression of estrogen-response genes in PRs ESR1HIGH compared to GRs (FDR< 10-9) at baseline despite similar percentage of Luminal subtypes in PRs ESR1HIGH and GRs. Plasma estradiol levels were correlated with expression of estrogen-response genes (FDR=0.01) and levels were significantly lower in PRs ESR1HIGH compared to GRs (p=0.003, Mann Whitney). PRs ESR1HIGH had significantly more mutations in RB1, TP53, ARID1B and DNAH11 genes (p< 0.05, Fisher exact). TP53 mutations were significantly enriched in Luminal-A PRs ESR1HIGH compared to GRs (22% and 3% respectively; p=0.003, Fisher exact), but not in Luminal-B tumors (23% and 15% mutated respectively). Discussion and conclusions: In approximately 33% of PRs, de novo AI resistance was associated with and most likely due to low expression of ER/ESR1 and estrogen-responsive genes in non-luminal tumors. In the remaining tumors, AI treatment still impacted some estrogen responsive genes but had limited downstream impact on suppressing proliferation. This might be due to mutations including in TP53 that limit suppression of proliferation downstream of estrogen signaling. The proportion of Luminal tumors in GRs and PRs ESR1HIGH was similar, suggesting better outcome of Luminal-A tumors on AI is likely due to their better intrinsic prognosis rather than better response to endocrine therapy. Citation Format: Eugene F. Schuster, Elena López-Knowles, Anastasia Alataki, Lila Zabaglo, Elizabeth Folkerd, David Evans, Kally Sidhu, Holly Tovey, Perry Maxwell, Nicholas Turner, Stephen Johnston, Manuel Salto-Tellez, Maggie Chon U Cheang, John Robertson, Ian Smith, Judith Bliss, Mitch Dowsett. PD10-07 Low plasma estradiol, low expression of estrogen responsive genes and TP53 mutations are associated with poor anti-proliferative response to aromatase inhibitors [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 PD10-07.
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».