Abstract 2060: Quantifying vascular biomarkers with contrast-enhanced molecular ultrasound imaging
Bibliographic record
Abstract
Abstract Introduction: Molecular imaging has the potential to dramatically impact all facets of patient care, from early disease detection to treatment monitoring and follow-up, as a tool for the characterization and measurement of key biomolecules in vivo. In ultrasound, functional and molecular imaging is possible through the use of microbubbles (MB), a contrast agent that can be transformed into targeting agents that bind to vascular biomarkers of interest. In this study, we evaluate whether targeted ultrasound contrast enhanced imaging can provide a quantitative measure of surface receptor expression in endothelial cell populations. Methods: The biomarker endoglin (Eng) was selected for targeting due to its involvement in the processes of development, vascular regulation and disease, including tumor angiogenesis. Endoglin wildtype (Eng+/+), heterozygous null (Eng+/-) and null (Eng-/-) mouse embryonic endothelial cells were cultured on glass slides and mounted in parallel plate flow chambers. MicroMarker microbubbles (endoglin targeted: MBE, isotype control: MBC or untargeted: MBU at 1x107 MB/mL in PBS) were perfused across the cells at 4 mL/min, corresponding to a shear stress of 2 dynes/cm2. Cell and bubble numbers were determined from bright field and phase images (Nikon, 40x), with adhesion quantified as the number of MB/cell. Binding of microbubbles was also assessed in late-gestational stage, isolated, living embryos (Eng+/+, Eng+/-). The highly regulated and controlled activities of normal angiogenesis and vasculogenesis in the mouse embryo make it an excellent surrogate for complex and heterogeneous tumor microenvironments, while genetic manipulation enables the generation of a variety of useful transgenic models. Nonlinear contrast-specific ultrasound imaging, performed at 21MHz with a Vevo-2100 scanner (VisualSonics Inc.), was used to collect contrast mean power ratios (CMPR, representative measure of MB binding) within the brains of each embryo 4 minutes after a bolus injection of MBE, MBC or MBU. Results: Expression levels in cells and embryos were significantly different across genotypes, with endoglin reduced by half in Eng+/- and totally absent in Eng-/- samples. In vitro, microbubble adhesion was found to vary significantly (p<0.05) across genotype populations, with minimal attachment of MBC and MBU compared to MBE. Endoglin-targeted binding was approximately two-fold higher (median = 0.96 MBE/cell) in Eng+/+ compared to Eng+/- (median = 0.42 MBE/cell) cells. In embryo studies, we observed minimal signal from MBC and MBU, while MBE binding was found to be significantly higher in Eng+/+ embryos (CMPR+/+ = 9.71 + 0.66, 95% CI) compared to Eng+/- embryos (CMPR+/- = 5.51 + 0.64, 95% CI). In conclusion, these results suggest that molecular ultrasound is capable of reliably differentiating between molecular genotypes and relating receptor densities to quantifiable molecular ultrasound levels. Citation Format: Janet M. Denbeigh, Brian A. Nixon, John J.Y. Lee, Mirjana Jerkic, Philip A. Marsden, Michelle Letarte, Mira C. Puri, F. Stuart Foster. Quantifying vascular biomarkers with contrast-enhanced molecular ultrasound imaging. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2060. doi:10.1158/1538-7445.AM2014-2060
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".