Characterisation of the epidermal growth factor receptor, vascular and stromal biomarkers in mesothelioma for oncogenic targeted therapies
Notice bibliographique
Résumé
Malignant Mesothelioma (MM) is an aggressive malignancy of the pleura and other serosal surfaces with limited treatment options. The mainstay of medical treatment is the combination of cisplatin and pemetrexed chemotherapy. Despite initial responses to chemotherapy, nearly all patients will progress. Only recently, the results of the Mesothelioma Avastin Cisplatin Pemetrexed Study (MAPS) demonstrated a statistically The tumour microenvironment represents an important target in mesothelioma given the absence of oncogenic drivers within the tumour itself. Towards this, I have investigated an anti-EGFR monoclonal antibody [ABT-806 (mAb806)] developed by co-supervisor Scott and now licenced to Abbott (AbbVie), which selectively binds an epitope of wild type (wtEGFR) only found in the overexpressed or amplified EGFR, or its oncogenic truncation mutant EGFRvIII, on cancer cell surfaces. The presence of overexpressed wtEGFR that binds the ABT-806 antibody was also found to be present in a number of MM samples. In this thesis, I aim to characterise specific biomarkers in MM, with particular focus on vascular markers and EGFR expression in MM (Chapter 3). The tumour microenvironment that comprises the interface between the tumour and stroma is of particular interest. This thesis focused on EGFR and angiogenesis given the access to novel anti-EGFR antibody drug conjugates. Angiogenesis, assessed by microvessel density (MVD) has previously been reported by several groups to be a poor prognostic factor in MM. The results of an angiogenic and stromal biomarkers analysis in a large mesothelioma patient cohort are consistent with the literature. I identified high Chalkley count of CD31 immunohistochemistry staining (more than or equal to 5) and increased PDGF-CC expression are associated with a poorer prognosis in 326 MM patients. CD31 was found to be an independent prognostic marker in the multivariate analysis. This project seeks to build on previous work establishing these antibodies as novel therapies for cancer and determine their potential in MM, a disease where we have previously established ligand expression. Using our large repository of MM tissues, I have investigated the expression of the selected targets overexpressed/ amplified EGFR to establish the potential for use of these targets in mesothelioma (Chapter 4). I have also created a MM patient derived xenograft (PDX) library via patient samples derived from the institute and our collaborators from Toronto, Princess Margaret Hospital. Via the library of MM PDXs generated during the course of this PhD, I have evaluated EGFR and mAb806 immunohistochemistry to help select specific models to investigate the validity and feasibility of targeting EGFR in MM using novel mAb806 based antibody drug conjugates (ADCs) (Chapter 5). These novel anti-EGFR ADCs were explored in mesothelioma cell line xenografts, then taken to a panel of novel PDX models to further characterise these mAb806 based ADCs to determine if there are any signals of clinical efficacy (Chapter 6). I have demonstrated the efficacy and selectivity of these anti-EGFR compounds with MM cell line MSTO 211H, as well as 2 other PDX models. The cell line and the PDX model which are mAb806 IHC positive had demonstrated significant tumour suppression and therapeutic efficacy with these mAb806 drug conjugates. These findings contribute towards the understanding and development of potential prognostic biomarkers of interest in MM. The data presented in this thesis also provides novel insight into anti-EGFR antibody drug conjugates in mesothelioma and reveal potential targets for development of targeted therapies in this disease where none was thought feasible.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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) | 0,000 | 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 tête enseignante, 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 ».