Optimizing metastatic-cascade-dependent Rac1 targeting in breast cancer: Guidance using optical window intravital FRET imaging
Bibliographic record
Abstract
Assessing drug response within live native tissue provides increased fidelity with regards to optimizing efficacy while minimizing off-target effects. Here, using longitudinal intravital imaging of a Rac1-Förster resonance energy transfer (FRET) biosensor mouse coupled with in vivo photoswitching to track intratumoral movement, we help guide treatment scheduling in a live breast cancer setting to impair metastatic progression. We uncover altered Rac1 activity at the center versus invasive border of tumors and demonstrate enhanced Rac1 activity of cells in close proximity to live tumor vasculature using optical window imaging. We further reveal that Rac1 inhibition can enhance tumor cell vulnerability to fluid-flow-induced shear stress and therefore improves overall anti-metastatic response to therapy during transit to secondary sites such as the lung. Collectively, this study demonstrates the utility of single-cell intravital imaging in vivo to demonstrate that Rac1 inhibition can reduce tumor progression and metastases in an autochthonous setting to improve overall survival.
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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 | 0.000 | 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".