Imaging and modulating the biodistribution of therapeutic agents for cancer gene therapy
Bibliographic record
Abstract
Longitudinal bioluminescence imaging (BLI) was first utilized in establishing the spatiotemporal patterns associated with EBV-specific transcription within adenoviral gene therapy vectors, both replicating and non-replicating, delivered systemically and intratumourally (Chapter 2). BLI was then utilized to rapidly screen four compounds that could potentially modulate adenoviral biodistribution favourably. STI571, a small molecule tyrosine kinase inhibitor, was found to enhance tumour uptake of adenoviruses via reduction of tumour interstitial fluid pressure. Afterwards, BLI was combined with fluorescence imaging to develop an imaging method that could be applied to small disseminated cancers (Chapter 3). EBV-positive NPC xenografts were stably transfected with a red fluorescent protein, while a luciferase-expressing EBV-specific adenovirus were delivered systemically; viral expression was observed in all fluorescent tumours, implanted in several locations. Lastly, automated tiling fluorescence microscopy was utilized to develop a method, including the generation of novel algorithms, to quantitate microdistribution patterns of a systemically delivered fluorescent antisense oligodeoxynucleotide in four solid tumour models (Chapter 5). The aim was to elucidate the factors which might account for the differential distribution of this molecule in the four models, including two NPC models previously studied in a therapeutic context. A secondary aim was to demonstrate a quantifiable change in distribution following the administration of ZD6126, a vascular disrupting agent. This thesis has successfully developed methods to assess changes in biodistribution, both at the macroscopic and microscopic scales, of the cancer therapeutics in current development. These methods are applicable to other types of studies, including the engraftment of stem-cells following local irradiation, and the elucidation of the behaviour of other oncolytic viruses in tumour models. Molecular imaging allows for the direct visualization of numerous parameters in cancer. The modalities available allow for the inspection of molecular processes on scales ranging from the whole organism to the individual cell. The objective of this thesis is to develop and apply imaging techniques involving both bioluminescence and fluorescence imaging to cancer gene therapy, specifically directed towards investigating Epstein-Barr virus (EBV) positive nasopharyngeal carcinoma (NPC) in preclinical models. Exogenous genes are introduced by recombinant adenoviral vectors, and endogenous genes are suppressed by antisense oligonucleotides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".