Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines
Bibliographic record
Abstract
A "bottom-up" proteomics approach and a two-dimensional (strong cation exchange followed by reversed-phase) LC-MS/MS strategy on a linear ion trap (LTQ) were utilized to identify and compare expressions of extracellular and membrane-bound proteins in the conditioned media of three breast cell lines (MCF-10A, BT474, and MDA-MB-468). Proteomics analysis of the media identified in excess of 600, 500, and 700 proteins in MCF-10A, BT474, and MDA-MB-468, respectively. We successfully identified the internal control proteins, kallikreins 5, 6, and 10 (ranging in concentration from 2 to 50 microg/liter) in MDA-MB-468 conditioned medium as validated by ELISA and confidently identified Her-2/neu in BT474 cells. Subcellular localization was determined based on Genome Ontology terms for all the 1,139 proteins of which 34% were classified as extracellular and membrane-bound. Proteomics analysis of MDA-MB-468 cell lysate demonstrated that only 5% of all identified proteins were extracellular. This confirmed our hypothesis that examining the CM of cell lines, as opposed to the cell lysates, leads to a significant enrichment in secreted proteins. Tissue specificity, functional classifications, and spectral counting were performed. Elafin, a protease inhibitor, identified in the conditioned media of BT474 and MDA-MB-468 and the three kallikreins (KLK5, KLK6, and KLK10) were validated using an immunoassay on various serum and biological samples. Some of the secreted proteins identified have established roles in breast cancer development (cell growth, differentiation, and metastasis) and/or are linked to early onset breast cancer. Our approach to mining for low abundance molecules could identify proteins in various stages of breast cancer development. Many of the identified proteins are potentially useful to investigate as circulating serum breast cancer biomarkers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".