IGF-IR targeted cancer gene therapy
Bibliographic record
Abstract
Since the declaration of âWar on cancerâ in 1971, and with the insight provided by recent advances in human genetics and in molecular technology , the field of cancer biology has been expanded rapidly providing hope that a cure for this lethal disease will be found. One of the major contributions of the field of cell biology to the understanding of malignant diseases has been the identification of growth factors and their receptors as major promoters of transformation and malignant progression. In particular, the appreciation of the central role that receptor tyrosine kinases (RTK) play in different cancers has led to development of effective therapeutic reagents. One of the RTK implicated in malignant progression is the receptor for the type 1 insulin like growth factor (IGF-IR) that has been identified as a target for anti-cancer treatments. Cancer gene therapy is a rapidly developing modality for cancer therapy. Many gene therapy strategies have been developed generally targeting the genes and proteins involved in cancer initiation and progression. To design a successful gene therapy strategy requires an understanding of the molecular basis of cancer progression and knowledge of human and animal genetics and physiology. In the present work, I have introduced two different strategies to inhibit liver metastases formation using established human and murine cancer cell lines. The first strategy is based on targeting the IGF-IR in tumor cells using an antisense technology (chapter 2). This strategy was also shown to be applicable in cancer gene therapy of glioblastoma growing in the brain (chapter 3). Very interestingly and for the first time, we showed that reduction of IGF-IR expression levels in glioma cells can induce a state of dormancy, providing a unique model to study this clinically important phenomenon. As the second strategy, I designed a novel soluble IGF-IR molecule. I showed that expression of this molecule in tumor cells caused an inhibitio
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".