Personalized models of breast cancer desmoplasia reveal biomechanical determinants of drug penetration
Bibliographic record
Abstract
ABSTRACT Breast cancer desmoplasia heterogeneity contributes to high disease mortality due to discrepancies in treatment efficacy between patients. Personalized in vitro breast cancer models can be used for high throughput testing and ranking of therapeutic strategies to normalize the aberrant microenvironment in a patient-specific manner. Here, tumoroids assembled from patient-derived cells cultured in microphysiological systems including perfusable microvasculature reproduce key aspects of stromal and vascular dysfunction. Increased hyaluronic acid and collagen deposition, loss of vascular glycocalyx and reduced perfusion, and elevated interstitial fluid pressure in the models result in impaired drug distribution to tumor cells. We demonstrate the application of these personalized models as tools to rank molecular therapies for the normalization of the tumoroid microenvironment and to discover new therapeutic targets such as IL8 and CD44, which may ultimately improve drug efficacy in breast cancer patients.
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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".