A Phosphoproteomics Approach to Identify Candidate Kinase Inhibitor Pathway Targets in Lymphoma-Like Primary Cell Lines
Bibliographic record
Abstract
Mass spectrometry-based technologies are increasingly utilized in drug discovery. Phosphoproteomics in particular has allowed for the efficient surveying of phosphotyrosine signaling pathways involved in various diseases states, most prominently in cancer. We describe a phosphotyrosine-based proteomics screening approach to identify signaling pathways and tyrosine kinase inhibitor targets in highly tumorigenic human lymphoma-like primary cells. We identified several receptor tyrosine kinase pathways and validated SRC family kinases (SFKs) as potential drug targets for targeted selection of small molecule inhibitors. BMS-354825 (dasatinib) and SKI-606 (bosutinib), second and third generation clinical SFK/ABL inhibitors, were found to be potent cytotoxic agents against tumorigenic cells with low toxicity to normal pediatric stem cells. Both SFK inhibitors reduced ERK1/2 and AKT phosphorylation and induced apoptosis. This study supports the adaptation of high-end mass spectrometry techniques for the efficient identification of candidate tyrosine kinases as novel therapeutic targets in primary cancer cell lines.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".