Characterisation of the epidermal growth factor receptor, vascular and stromal biomarkers in mesothelioma for oncogenic targeted therapies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".