Abstract 746: Development of novel formats of anti-AXL ADCs
Notice bibliographique
Résumé
Abstract AXL is a member of the Tyro3- AXL -Mer (TAM) receptor tyrosine kinase subfamily which has been associated with several cellular functions including growth, migration, aggregation, and anti-inflammation (Li Y et al., 2009) via activation through its ligand, Gas6. Originally identified as an oncogene in chronic myelogenous leukemia, expression of Axl has been reported to be upregulated in a variety of cancers including breast, gastric, prostate, ovarian, and lung. AXL expression has been shown to be negatively associated with patient survival and implicated in epithelial-to-mesenchymal transition, a process closely linked with invasive motility and metastasis of malignant cells. Several recent studies have reported that AXL is overexpressed during resistance to chemotherapy and other molecularly targeted therapies, as well as during hypoxia (Schoumacher M et al 2017). Consequently, Axl is promising therapeutic target for development into ADCs for the first or second line treatment of cancer. At the National Research Council (NRC) of Canada, we have selected mouse monoclonal (mAbs) or llama derived single domain antibodies (sdAbs) that are selective for the extracellular domain of human Axl and which have been shown to be internalized into Axl expressing cells for antibody drug conjugate (ADC) development using a surrogate ADC screen. These antibodies were also characterized based on their binding domains and ability to compete with Gas6, the extracellular ligand for Axl. A panel of Axl-specific ADCs was subsequently directly conjugated to maytansine, the microtubule disrupting agent through a noncleavable linker. Axl-DM1 ADCs were shown to efficiently induce cytotoxicity in vitro, in Axl expressing cells including breast (MDA-MB 231), lung (NCI-H292) and ovarian (SKOV3) cell lines with sub nM potencies achieved with both sdAb and conventional mAb-based ADCs. When tested in vivo using ovarian (SKOV3) and breast cancer (MDA-MB 231) xenograft models, Axl-DM1 ADCs were shown to have potent anti-tumor activity. Biparatopic antibodies (i.e. those which target two non-overlapping epitopes) directed against HER2 have been previously shown to induce target clustering and promote robust internalization, lysosomal trafficking, and degradation (LI JY et al 2016). Since these properties are important in the mechanism of action of ADCs, we have further tested anti-Axl antibodies in various configurations and have identified potent biparatopic antibodies for further development into ADCs. Citation Format: Maria L. Jaramillo, Myriam Banville, Alma Robert, Luc Meury, Maurizio Acchione, Anne Marcil, Christine Gadoury, Cunle Wu, Yves Fortin, Kevin Henry, Joseph Schrag, The-Minh Tu, Binbing (Erica) Ling, Jacqueline Slinn, Maria Moreno. Development of novel formats of anti-AXL ADCs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 746.
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) | 0,003 | 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 ».