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Record W7066542686

Interaction of combined administration of multi-targeted kinase inhibitors with ionizing radiation

2014· dissertation· en· W7066542686 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsIonizing radiationRadiation therapyCancerSorafenibIn vivoBreast cancerRadiation sensitivityCancer cellKinase
DOInot available

Abstract

fetched live from OpenAlex

Along with surgery and chemotherapy, radiation therapy is one of the three cancer treatment modalities to treat patients. Over the recent decades, thanks to technological progress, radiation techniques have been improved drastically. However, the same effort has not been put into the biology of the tumours and how different tumours interact with radiation beams. Moreover, it has been only recently that attention has been drawn towards the chemo-radiation or combination of radiation with molecular targeting agents. In this thesis we have studied the combination of ionizing radiation with a class of small molecule inhibitors that target defects in the MAPK/PI3K pathway in breast cancer cells. These pathways are often over-activated in human malignancies including breast cancer. Two of the multiple receptors involved in these pathways are EGFR and VEGFR, which have been shown to be over-expressed in cancerous tumours and have been associated with poor prognosis as well as drug and radiation resistance. The two inhibitors we have used in this study are ZRBA1, a combi-molecule that targets EGFR and also induces DNA lesions, and Sorafenib (Nexavar), which is an inhibitor of several RTKs including VEGFR and also Raf kinase. Using breast cancer cells, we have shown that these multi-functional inhibitors increased the sensitivity of cancer cells towards radiation as they induced a strong G2/M cell cycle arrest and apoptosis. Our in vivo results show that ZRBA1 and Sorafenib, if combined with radiation, can significantly increase tumour growth delay. When radiation is administered concurrently with ZRBA1, a significant tumour growth delay of 47 days is observed. Moreover, ZRBA1 in combination with radiation not only induced the DNA single and double strand breaks, but also delayed DNA repair process contributing to its higher potency against breast cancer cells. Interestingly, Sorafenib when it is combined with radiation has more persistent anti-tumour effect in our in vivo model.Overall, our results suggest that the combined administration of multi-targeting molecular inhibitors, a systemic targeted therapy, with radiation, a local and regional therapy, could be beneficial for patients as they potentiate the radiation response while not increasing adverse side effects.The results of these preclinical studies contribute to a better understanding of how radiation interacts with small molecule inhibitors such as ZRBA1 and Sorafenib and provide the rational basis for further preclinical and clinical studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.239
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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