Rules for PP2A-controlled phosphosignalling and drug responses
Bibliographic record
Abstract
Abstract Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation. These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2A-modulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".