Integrated analysis of proteome, phosphotyrosine‐proteome, tyrosine‐kinome, and tyrosine‐phosphatome in acute myeloid leukemia
Bibliographic record
Abstract
Reversible protein-tyrosine phosphorylation is catalyzed by the antagonistic actions of protein-tyrosine kinases (PTKs) and phosphatases (PTPs), and represents a major form of cell regulation. Acute myeloid leukemia (AML) is an aggressive hematological malignancy that results from the acquisition of multiple genetic alterations, which in some instances are associated with deregulated protein-phosphotyrosine (pY) mediated signaling networks. However, although individual PTKs and PTPs have been linked to AML and other malignancies, analysis of protein-pY networks as a function of activated PTKs and PTPs has not been done. In this study, MS was used to characterize AML proteomes, and phospho-proteome-subsets including pY proteins, PTKs, and PTPs. AML proteomes resolved into two groups related to high or low degrees of maturation according to French-American-British classification, and reflecting differential expression of cell surface antigens. AML pY proteomes reflect canonical, spatially organized signaling networks, unrelated to maturation, with heterogeneous expression of activated receptor and nonreceptor PTKs. We present the first integrated analysis of the pY-proteome, activated PTKs, and PTPs. Every PTP and most PTKs have both positive and negative associations with the pY-proteome. pY proteins resolve into groups with shared PTK and PTP correlations. These findings highlight the importance of pY turnover and the PTP phosphatome in shaping the pY-proteome in AML.
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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 | 0.001 |
| 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) | 0.000 | 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".