Personalized Vascularized Models of Breast Cancer Desmoplasia Reveal Biomechanical Determinants of Drug Delivery to the Tumor
Bibliographic record
Abstract
Desmoplasia in breast cancer leads to heterogeneity in physical properties of the tissue, resulting in disparities in drug delivery and treatment efficacy among patients, thus contributing to high disease mortality. Personalized in vitro breast cancer models hold great promise for high-throughput testing of therapeutic strategies to normalize the aberrant microenvironment in a patient-specific manner. Here, tumoroids assembled from breast cancer cell lines (MCF7, SKBR3, and MDA-MB-468) and patient-derived breast tumor cells (TCs) cultured in microphysiological systems including perfusable microvasculature reproduce key aspects of stromal and vascular dysfunction causing impaired drug delivery. Models containing SKBR3 and MDA-MB-468 tumoroids show higher stromal hyaluronic acid (HA) deposition, vascular permeability, interstitial fluid pressure (IFP), and degradation of vascular HA relative to models containing MCF7 tumoroids or models without tumoroids. Interleukin 8 (IL8) secretion is found responsible for vascular dysfunction and loss of vascular HA. Interventions targeting IL8 or stromal HA normalize vascular permeability, perfusion, and IFP, and ultimately enhance drug delivery and TC death in response to perfusion with trastuzumab and cetuximab. Similar responses are observed in patient-derived models. These microphysiological systems can thus be personalized by using patient-derived cells and can be applied to discover new molecular therapies for the normalization of the tumor microenvironment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".