Early Changes in Protein Expression Detected by Mass Spectrometry Predict Tumor Response to Molecular Therapeutics
Bibliographic record
Abstract
Biomarkers that predict therapeutic response are essential for the development of anticancer therapies. We have used matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to directly analyze protein profiles in mouse mammary tumor virus/HER2 transgenic mouse frozen tumor sections after treatment with the erbB receptor inhibitors OSI-774 and Herceptin. Inhibition of tumor cell proliferation and induction of apoptosis and tumor reduction were predicted by a >80% reduction in thymosin beta4 and ubiquitin levels that were detectable after 16 hours of a single drug dose before any evidence of in situ cellular activity. These effects were time- and dose-dependent, and their spatial distribution in the tumor correlated with that of the small-molecule inhibitor OSI-774. In addition, they predicted for therapeutic synergy of OSI-774 and Herceptin as well as for drug resistance. These results suggest that drug-induced early proteomic changes as measured by MALDI-MS can be used to predict the therapeutic response to established and novel therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".