Abstract PD10-08: PD10-08 Immune cell infiltration associated with poor anti-proliferative response to aromatase inhibitors in postmenopausal women with primary ER-positive HER2-negative breast cancer
Notice bibliographique
Résumé
Abstract Background: Aromatase inhibitors (AIs) are one of the main treatment strategies for the clinical management of estrogen receptor-positive (ER+) breast cancer (BC). Despite prolonged time to recurrence and initial clinical responses, >20% of patients eventually relapse, and previous studies have shown an association of poor anti-proliferative response to AIs and worse outcome. High immune activity in ER+ tumors may be associated with worse outcome, in contrast to ER-negative BC where immune infiltration is a feature associated with better outcome. Our work focused on understanding the correlations between immune cell infiltration and response to AI. Methods: All patients with ER+ HER2- tumors within the bottom 15% of Ki67 anti-proliferative responders to AIs (poor responders [PRs]; n=177) were selected from the PeriOperative Endocrine Therapy for Individualizing Care (POETIC) trial and matched on baseline Ki67 levels to good responders (GRs) within the 50% showing the best response (n=190). Response to AI was measured by the Ki67 percentage change after 2 weeks of treatment. PRs were further divided into groups expressing high ESR1 (PRs ESR1HIGH; n=119) and low ESR1 (PRs ESR1LOW; n=58) levels to represent PR subgroups that showed partial or no response to AIs. The percentage of stromal tumor-infiltrating lymphocytes (TILs) was assessed. Multiple immunofluorescence was performed for ER, CD3, CD20, CD68, FOXP3, and CD3/FOXP3 in 15 baseline samples from each of the GR, PR ESR1HIGH, and PR ESR1LOW populations and immune cell density in stromal or tumor compartments was estimated. Spearman correlations of TILs with Consensus tumor microenvironment (TME) deconvolution and Molecular Signatures Database hallmark gene sets were conducted. The relationship between the immune markers’ density and genes, hallmark gene sets and Consensus TME was assessed. Results: The percentage of TILs was significantly higher in the PR ESR1HIGH and PR ESR1LOW compared to the GRs (adjusted p< 0.05). As expected, TILs were highly correlated with T cells (particularly T-regulatory cells) and immune hallmark gene sets. There was a tendency for higher density of each of the immune markers in PRs compared to GRs, with significant differences being observed in stromal B-cell marker CD20 density (p< 0.05). Analysis showed a significant correlation between TILs and stromal FOXP3 marker density (FDR< 0.05), and stromal biomarker density was highly correlated to the gene expression of the encoding genes of the same tumors (CD3/CD3D, FOXP3/FOXP3, and CD20/MS4A1) (FDRs< 0.05). There was also a strong and significant correlation between the stromal expression of CD20, CD3, FOXP3, and CD3/FOXP3 with the immune hallmark gene sets (FDRs< 0.05). Finally, the immune phenotyping showed the expected correlations with TME deconvolution, with particularly strong correlations of CD20 and CD3 with B- and T-cell gene signatures, respectively (FDRs< 0.05). Conclusions: Different immune features indicated a broad involvement of several immune cell types in PRs to AIs, suggesting that the immune system might be associated with resistance of ER+ breast tumors to AI treatment. Spatial gene expression profiling is ongoing to characterize these tumors further and investigate potential mechanisms of AI resistance. Citation Format: Anastasia Alataki, Gene Schuster, Lila Zabaglo, Perry Maxwell, Elena López-Knowles, Elizabeth Folkerd, David Evans, Kally Sidhu, Holly Tovey, Nicholas Turner, Stephen Johnston, Maggie Chon U Cheang, John Robertson, Manuel Salto-Tellez, Ian Smith, Judith Bliss, Mitch Dowsett. PD10-08 Immune cell infiltration associated with poor anti-proliferative response to aromatase inhibitors in postmenopausal women with primary ER-positive HER2-negative breast cancer [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 PD10-08.
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 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».