Abstract P3-11-05: Predictive gene signatures of adjuvant capecitabine benefit in triple negative breast cancer in the FinXX trial
Notice bibliographique
Résumé
Abstract Background: Recent studies have demonstrated a benefit of adjuvant capecitabine in early stage breast cancer, particularly in patients with residual disease after neoadjuvant chemotherapy. Subset analyses suggest that patients with triple negative breast cancer (TNBC) may be more likely to benefit with capecitabine in this setting. However, more precise biomarkers to predict which patients are most likely to benefit from capecitabine are needed. Methods: The NanoString Breast Cancer 360TM (BC360) panel was used to quantify mRNA expression in FFPE tissue samples from patients with TNBC in the FinXX trial. Gene signature scores were analyzed using prespecified algorithms developed by NanoString. 30 additional custom genes related to capecitabine metabolism and function were added. Patients in FinXX trial were randomized to receive either 3 cycles of docetaxel followed by 3 cycles of cyclophosphamide, epirubicin, and fluorouracil (T+CEF) or 3 cycles of docetaxel plus capecitabine followed by 3 cycles of cyclophosphamide, epirubicin, and capecitabine (TX+CEX). Cox proportional hazard ratio (HR) was used to determine the association of each gene signature with recurrence free survival (RFS). Results: A total of 111 samples from patients with TNBC in the FinXX trial were available for gene expression analysis. 57 patients were treated with T+CEF and 54 patients were treated with TX+CEX. The median age was 52 years and median follow up was 10.2 years. Consistent with the previous analysis of the FinXX trial, patients with TNBC had nonsignificant but favorable RFS with capecitabine (HR 0.60, 95% CI 0.27-1.3, p 0.2). Among 39 individual genes and metagene signatures generated with the BC360 panel, there were 4 gene signatures significantly associated with improved RFS favoring an addition of capecitabine with TX+CEX compared to T+CEF. These were the cytotoxic cell signature (HR 0.37, 95%CI 0.15-0.92, p 0.03), endothelial signature (HR 0.18, 95%CI 0.04-0.83, p 0.03), mast cell signature (HR 0.43, 95%CI 0.21-0.88, p 0.02), and PDL2 gene (HR 0.29, 95%CI 0.09-0.99, p 0.05). PAM50 intrinsic subtype was not predictive of capecitabine benefit within this TNBC subset. Moreover, we identified additional individual 12 genes that were significantly associated with capecitabine benefit (p-FDR<0.05). Among these genes, high expression of CES1, which encodes an enzyme that activates capecitabine, was significantly associated with improved RFS when treated with capecitabine (HR 0.71, 95%CI 0.32-1.61 with p-interaction=0.04). Conclusion: Our analyses demonstrated potential predictive individual genes and metagene signatures that may be used to identify TNBC patients who are more likely to benefit from adjuvant capecitabine. Interestingly, several gene signatures related to immune response and genes related to capecitabine activation were associated with improved outcome in TNBC patients treated with capecitabine in the FinXX trial. While these findings are compatible with basic science reports, future studies are needed to validate the significance of these gene signatures as predictive biomarkers for capecitabine benefit. Citation Format: Chumsri S, Asleh K, Brauer HA, Mashadi-Hossein A, Lauttia LS, Lindman H, Nielsen TO, Joensuu H, Thompson EA. Predictive gene signatures of adjuvant capecitabine benefit in triple negative breast cancer in the FinXX trial [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-11-05.
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 | 0,002 | 0,001 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».