Tissue culture‐based breast cancer biomarker discovery platform
Bibliographic record
Abstract
Current cancer biomarkers suffer from low diagnostic sensitivity and specificity and have not yet made a major impact on reducing cancer burden. Proteomic methods based on mass spectrometry have matured significantly over the past few years and hold promise to deliver candidate markers for diagnosis, prognosis or monitoring therapeutic response. Because of the complex nature of biological fluids such as plasma, biomarker discovery efforts using proteomics have not as yet delivered any novel tumor markers. Recently, there has been a rise in the number of publications utilizing a cell culture-based model of cancer to identify novel candidate tumor markers. The secretome of cancer cell lines constitutes an important class of proteins that can act locally and systemically in the body. Secreted proteins, in addition to serving as serological markers, play a central role in physiology and pathophysiology. In this review, we focus on the proteomics of breast cancer and the different strategies to mine for biomarkers, with particular emphasis on a cell culture-based model developed in our laboratory.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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".