Abstract 4318: Biomarker evaluation during an image-guided intracranial murine glioma study of radiation and sunitinib
Bibliographic record
Abstract
Abstract Introduction: Tumour angiogenesis, a hallmark of glioblastoma multiforme, serves as a potential therapeutic target. Sunitinib (SU) is a multi-targeted tyrosine kinase inhibitor with antiantiogenic activity that may enhance radiation (RT) effects. In preclinical studies using intracranial (IC) mouse tumour models, a major challenge is the ability to evaluate IC tumour size, growth and vascular changes over time. This study aims to use an image-guided experimental design to evaluate the effects of RT and/or SU in an IC murine glioma model using serial micro-MRI and urine biomarkers with corresponding pathology, tumour growth delay (TGD) and survival (OS)outcomes. Methods: U87 glioma cells were inoculated by IC injection in the right frontal lobe of 58 NOD SCID mice. Imaging was performed on a 7-Tesla Bruker BioSpec on day 7 post-injection (treatment D0), D3, 7, 10, 14, 17 and 21. Mice with visible tumours on D0 T2-weighted (T2-w) imaging were stratified by tumour size to treatments: (1) control (CTRL) - placebo (2) RT - RT + placebo (3) SU - oral gavage SU (4) RT + SU. RT 8 Gy was delivered in 1 fraction to the right hemi-brain on D1 under image-guidance. SU 0.8 mg or placebo was administered by oral gavage. Serial imaging/analysis included: (1) T2-w imaging (2) Quantitative T1 (3) DCE-MRI - initial area under the DCE curve at 60 seconds (iAUC60) (4) diffusion weighted imaging - apparent diffusion coefficient (ADC) (5) contrast-enhanced T1-w imaging (T1-gad). Expression of angiogenesis markers were measured in serial urine samples. Pathological evaluation included tumour size, cell density, vessel density/permeability and apoptosis. Results: Fifty-two mice with tumours were assigned to treatment arms such that mean tumour volume for each arm was 0.58 - 0.61mm3 (p>0.05) on D0. No mice expired with serial contrast-enhanced imaging. OS was significantly better with RT (p=0.008) and SU+RT (p=0<0.001) than CTRL, and SU+RT better than SU (p=0.018) and RT (p=0.05). Exponential tumour growth occurred in CTRLs but TGD was noted in all treatment arms until D14. Changes in iAUC60 showed trends/differences between treatment arms only at early timepoints: (1) RT - rise by D3 (2) SU - stable during SU (3) SU+RT - decrease of 30.1+6.8% by D3, maintained during SU. Baseline tumour ADC values were 8±5% above CL brain across all animals. Rise in ADC was greatest with RT and SU+RT (43±10%, 28±7%) than CTRL and SU (16±4%, 19±19%) at D10. Conclusion: This study demonstrates the potential utility of image-guidance to augment murine intracranial tumour studies of RT and AA therapy. Survival was improved with combined SU+RT. Promising early biomarkers of response identified to date include DCE at D3, and ADC at D10, warranting further investigation in a larger cohort. Further study of these markers may improve temporal characterization of vessel normalization and enable studies to optimize scheduling of antiangiogenic therapies with radiation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4318.
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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) | 0.001 | 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) | 0.001 | 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".