Abstract PS18-31: Identification of cell surface proteins in triple negative breast cancer for the development of novel targeted therapies
Notice bibliographique
Résumé
Abstract Triple negative breast cancer (TNBC) stands out due to its aggressive course and high metastatic rates. No consistent protein biomarker has been described for TNBC, resulting in a scarcity of adjuvant therapies for the afflicted patients. Hence, there is an urgent need to determine novel TNBC-associated proteins for the development of new therapeutics and the identification of novel molecular mechanisms driving this malignancy. Cell surface proteins represent attractive targets for novel therapies, due to their easily accessible localization and their involvement in essential signaling pathways. Objectives: We aimed to uncover novel TNBC-associated cell surface proteins involved in carcinogenesis and metastasis for the development of novel targeted therapeutics. Rationale: Taking advantage of the fact that cell surface proteins are often N-glycosylated, we employed hydrazide chemistry to isolate N-glycopeptides. The glycoproteome of six immortalized TNBC cell lines and five ‘healthy’ controls (HC) (the immortalized cell line MCF10A and four patient-derived human mammary epithelial cell lines) was analyzed using LC-MS/MS and label-free quantification. A data mining strategy was employed to select candidates of interest. We knocked-down (k.d.) the top five candidates of interest (using siRNA technology) and evaluated cell growth using growth-curve analysis. Plexin B3 (PLXNB3) was selected for subsequent functional validations. The effects of PLXNB3 k.d. (using siRNA and CRISPR technology) on cancer cell growth, apoptosis and adhesion were interrogated using western blotting and standard cell biology protocols.Results: The N-glycoproteomics approach led to the identification of 1044 glycoproteins, with over 70% described as plasma membrane/secreted proteins. Gene Ontology analysis revealed that the TNBC-associated glycoproteome was enriched in biological processes such as: mesenchyme development, MAPK signaling, positive regulation of proliferation and regulation of neuron projection development. Candidates of interest were selected from our list by focusing on those plasma membrane proteins that were enriched in TNBC cells compared to HC according to our label-free quantified proteomics data, and that showed limited expression in healthy tissues according to the Human Protein Atlas. We further restricted the list of candidates to those proteins whose k.d. impaired cancer cell growth, with little effect on the HC. PLXNB3, an understudied protein typically expressed in healthy neuronal tissues, was selected for in depth functional analysis. Elevated mRNA PLXNB3 levels in breast cancer patients is associated with poorer overall survival compared to low mRNA expression (Integrated TCGA Pan-Cancer Clinical Data Resource, 2018). PLXNB3 k.d. (using siRNA and CRISPR technologies) impaired TNBC cell growth (in both adherent and spheroid cultures) and was associated with elevated levels of cleaved-caspase 3 and cleaved-caspase 7 compared to scrambled controls. Furthermore, PLXNB3 k.d. negatively impacted TNBC cell adhesion to extracellular matrixes compared to scrambled controls. Subsequent tests will evaluate PLXNB3’s role in tumor growth and metastasis in vivo, using orthotopic tumor models in immunocompromised mice. Citation Format: Laura Kuhlmann, Ankit Sinha, Vladimir Ignatchenko, Andrew Macklin, Lydia Liu, Meinusha Govindarajan, Amanda Khoo, Salvador Mejia-Guerrero, Jennifer Cruickshank, Hal Berman, Thomas Kislinger. Identification of cell surface proteins in triple negative breast cancer for the development of novel targeted therapies [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS18-31.
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 | 0,001 |
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 ».