Generation of Organ-conditioned Media and Applications for Studying Organ-specific Influences on Breast Cancer Metastatic Behavior
Bibliographic record
Abstract
Breast cancer preferentially metastasizes to the lymph node, bone, lung, brain and liver in breast cancer patients. Previous research efforts have focused on identifying factors inherent to breast cancer cells that are responsible for this observed metastatic pattern (termed organ tropism), however much less is known about factors present within specific organs that contribute to this process. This is in part because of a lack of in vitro model systems that accurately recapitulate the organ microenvironment. To address this, an ex vivo model system has been established that allows for the study of soluble factors present within different organ microenvironments. This model consists of generating conditioned media from organs (lymph node, bone, lung, and brain) isolated from normal athymic nude mice. The model system has been validated by demonstrating that different breast cancer cell lines display cell-line specific and organ-specific malignant behavior in response to organ-conditioned media that corresponds to their in vivo metastatic potential. This model system can be used to identify and evaluate specific organ-derived soluble factors that may play a role in the metastatic behavior of breast and other types of cancer cells, including influences on growth, migration, stem-like behavior, and gene expression, as well as the identification of potential new therapeutic targets for cancer. This is the first ex vivo model system that can be used to study organ-specific metastatic behavior in detail and evaluate the role of specific organ-derived soluble factors in driving the process of cancer metastasis.
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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 | 0.000 | 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 | 0.000 |
| 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".