Abstract LB-225: RNA molecular signatures as predictive biomarkers of response to monotherapy pembrolizumab in patients with metastatic triple-negative breast cancer: KEYNOTE-086
Notice bibliographique
Résumé
Abstract Background: Response to anti-programmed death 1/programmed death ligand 1 (PD-L1) therapy is associated with tumor expression of PD-L1 and an 18-gene T-cell-inflamed gene expression profile (GEP) across several tumor types. The association and utility of the GEP, as calculated using baseline RNA-seq data as a predictor of pembrolizumab response, was evaluated in patients with triple-negative breast cancer (TNBC) enrolled in the KEYNOTE-086 trial (NCT02447003). Additionally, a 37-gene tissue -resident memory (TRM) T-cell signature was evaluated and compared with the GEP. Methods: In the phase II KEYNOTE-086 study, patients with previously treated, metastatic TNBC (independent of PD-L1 status; cohort A, n=170) and treatment-naive, PD-L1-positive (combined positive score ≥1) TNBC (cohort B, n=84) were treated with pembrolizumab monotherapy. Using RNA-seq data, the GEP and TRM signature scores were calculated prospectively, merged with clinical outcome data, evaluated for their level of correlation with each other, and tested for their association with pembrolizumab response (best overall response [BOR], progression-free survival [PFS], and overall survival [OS]) in cohorts A and B after adjusting for Eastern Cooperative Oncology Group performance status. The independent predictive value of the TRM was also assessed after adjusting for the explanatory value of the GEP. Results: RNA-seq data from both baseline tumor specimens and clinical data were available for 154/254 pembrolizumab-treated patients in KEYNOTE-086 (12 [7.8%] were considered responders). The GEP and TRM signature scores were highly correlated (Spearman correlation, 0.89; Kendall’s tau, 0.72), suggesting that they measure linked immune phenomena in the tumor microenvironment (TME). The GEP showed a statistically significant association with clinical outcome (BOR AUROC, 0.76 [95% CI, 0.65-0.86], P=0.004; PFS, P<0.001; OS, P<0.001). A similar result was found for the TRM signature and clinical outcome (BOR AUROC, 0.76 [95% CI, 0.64-0.88], P=0.003; PFS, P<0.001; OS, P<0.001). Testing of TRM in models that adjusted for the explanatory value of the GEP showed no evidence of additional predictive value for the TRM signature beyond the GEP. Conclusions: Using RNA-seq-based data, we confirmed that inflammatory state signatures measuring the TME are associated with response to pembrolizumab in TNBC. Both signatures evaluated (GEP and TRM) were significantly associated with clinical outcome but were highly correlated with each other and did not show independent explanatory value. Results confirmed that there may be multiple ways to measure the inflammatory state of the TME, but understanding their relative clinical utility and potential use in conjunction with PD-L1 via immunohistochemistry will require larger, randomized studies. Citation Format: Sherene Loi, Peter Schmid, Javier Cortés, David W. Cescon, Eric P. Winer, Deborah Toppmeyer, Hope S. Rugo, Michelino De Laurentiis, Rita Nanda, Hiroji Iwata, Ahmad Awada, Antoinette Tan, Chunsheng Zhang, Andrey Loboda, Andrew Albright, Razvan Cristescu, Maureen Lane, Anran Wang, Jared Lunceford, Gursel Aktan, Vassiliki Karantza, Sylvia Adams. RNA molecular signatures as predictive biomarkers of response to monotherapy pembrolizumab in patients with metastatic triple-negative breast cancer: KEYNOTE-086 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-225.
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 | 0,001 | 0,001 |
| 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 | 0,001 | 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) | 0,001 | 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 ».