Microarray and Proteomic Analysis of Breast Cancer Cell and Osteoblast Co-cultures
Bibliographic record
Abstract
Dynamic reciprocal interactions between a tumor and its microenvironment impact both the establishment and progression of metastases. These interactions are mediated, in part, through proteolytic sculpting of the microenvironment, particularly by the matrix metalloproteinases, with both tumors and stroma contributing to the proteolytic milieu. Because bone is one of the predominant sites of breast cancer metastases, we used a co-culture system in which a subpopulation of the highly invasive human breast cancer cell line MDA-MB-231, with increased propensity to metastasize to bone, was overlaid onto a monolayer of differentiated osteoblast MC3T3-E1 cells in a mineralized osteoid matrix. CLIP-CHIP® microarrays identified changes in the complete protease and inhibitor expression profile of the breast cancer and osteoblast cells that were induced upon co-culture. A large increase in osteoblast-derived MMP-13 mRNA and protein was observed. Affymetrix analysis and validation showed induction of MMP-13 was initiated by soluble factors produced by the breast tumor cells, including oncostatin M and the acute response apolipoprotein SAA3. Significant changes in the osteoblast secretomes upon addition of MMP-13 were identified by degradomics from which six novel MMP-13 substrates with the potential to functionally impact breast cancer metastasis to bone were identified and validated. These included inactivation of the chemokines CCL2 and CCL7, activation of platelet-derived growth factor-C, and cleavage of SAA3, osteoprotegerin, CutA, and antithrombin III. Hence, the influence of breast cancer metastases on the bone microenvironment that is executed via the induction of osteoblast MMP-13 with the potential to enhance metastases growth by generating a microenvironmental amplifying feedback loop is revealed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".