Data from Kinome Profiling in Pediatric Brain Tumors as a New Approach for Target Discovery
Bibliographic record
Abstract
Abstract Progression in pediatric brain tumor growth is thought to be the net result of signaling through various protein kinase-mediated networks driving cell proliferation. Defining new targets for treatment of human malignancies, without a priori knowledge on aberrant cell signaling activity, remains exceedingly complicated. Here, we introduce kinome profiling using flow-through peptide microarrays as a new concept for target discovery. Comprehensive tyrosine kinase activity profiles were identified in 29 pediatric brain tumors using the PamChip kinome profiling system. Previously reported activity of epidermal growth factor receptor, c-Met, and vascular endothelial growth factor receptor in pediatric brain tumors could be appreciated in our array results. Peptides corresponding with phosphorylation consensus sequences for Src family kinases showed remarkably high levels of phosphorylation compared with normal tissue types. Src activity was confirmed applying Phos-Tag SDS-PAGE. Furthermore, the Src family kinase inhibitors PP1 and dasatinib induced substantial tumor cell death in nine pediatric brain tumor cell lines but not in control cell lines. Thus, this study describes a new high-throughput technique to generate clinically relevant tyrosine kinase activity profiles as has been shown here for pediatric brain tumors. In the era of a rapidly increasing number of small-molecule inhibitors, this approach will enable us to rapidly identify new potential targets in a broad range of human malignancies. [Cancer Res 2009;69(14):5987–95]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".