Abstract P5-04-03: Targeting glycoprotein non-metastatic B (GPNMB) to overcome EGFR-mediated resistance to Mek inhibition in triple negative breast cancer
Notice bibliographique
Résumé
Abstract Background: Triple negative breast cancer (TNBC) is an aggressive subtype that constitutes ∼15% of all BC. Currently there are no targeted therapies available for patients with TNBC and these patients have a poor prognosis. As such, there is much interest in developing targeted therapies for this disease. Recently we identified GPNMB as a transmembrane protein that promotes breast tumor growth and metastasis. CDX-011 is an antibody drug conjugate that targets GPNMB, and has recently shown promising clinical activity in patients with GPNMB+TNBC. In subset analyses of the EMERGE trial, patients with high GPNMB expressing TNBC had a median OS of 10 vs. 5.5 months for CDX011 versus chemotherapy, respectively. Response rates to CDX011 correlated with degree of GPNMB expression. These findings support the hypothesis that TNBC with high GPNMB will respond better to CDX011. As such, we sought to identify therapies with intrinsic activity against TNBC that would also induce GPNMB expression, in order to synergize with CDX-011. Recently there has been much interest in targeting the MAPK pathway in TNBC. We have recently shown that MAPK pathway inhibition induces GPNMB expression in melanoma. Therefore we sought to determine whether mek inhibition induced GPNMB in TNBC and the targeted therapies could synergize with CDX-011. Results: We interrogated the TCGA breast dataset to determine whether the MAPK pathway is more frequently altered in TNBC. Indeed, we find that the MAPK pathway is altered in 93% of basal BC compared to 56%, 82%, and 81% of Lum A, LumB, and Her2 subtypes, respectively. We used immunoblot and FACS analysis to assess GPNMB expression in response to MAPK-inhibition. We found that mek inhibitors (trametinib, cobimetinib) markedly induced GPNMB protein expression in several TNBC cell lines. RTK upregulation has been proposed as an adaptive resistance mechanism to mek inhibition in TNBC. Indeed, we find that EGFR is upregulated in response to Mek inhibition in MDA-MB-468 and Hs578T cells. Using shRNA to knockdown GPNMB expression in MDA-MB-468 cells or ectopic GPNMB overexpression in Hs578t cells, we found that GPNMB is both necessary and sufficient for enhanced EGFR activation in response to Mek inhibition in TNBC. Interestingly, we also find that Hs578T cells overexpressing GPNMB show less growth inhibition in response to Mek inhibitors compared to control cells in vitro. These in vitro data are corroborated by our analyses of 1097 breast tumors from the TCGA dataset. GPNMB alterations were found in 7% of all BC and correlates significantly with increased EGFR, Mek and Erk activation. Finally, we are investigating the efficacy of combining trametinib with CDX011 to treat TNBC using in vivo mouse models. Preliminary data from this experiment suggest that MDA-MB-468 tumors treated with both drugs are more growth restricted than tumors treated with either drug alone. Final data will be presented at the meeting. Conclusions: Mek inhibition induces GPNMB expression in TNBC. GPNMB promotes EGFR activation and protects from mek-inhibitor induced growth inhibition. The combination of a mek inhibitor with CDX011 shows promise in pre-clinical models and warrants further investigation in clinical trials. Citation Format: Rose AA, Annis MG, Maric G, Siegel PM. Targeting glycoprotein non-metastatic B (GPNMB) to overcome EGFR-mediated resistance to Mek inhibition in triple negative breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-04-03.
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) | 0,002 | 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 ».