Temporal Profiling of Lapatinib-suppressed Phosphorylation Signals in EGFR/HER2 Pathways
Bibliographic record
Abstract
Lapatinib is a clinically potent kinase inhibitor for breast cancer patients because of its outstanding selectivity for epidermal growth factor receptor (EGFR) and EGFR2 (also known as HER2). However, there is only limited information about the in vivo effects of lapatinib on EGFR/HER2 and downstream signaling targets. Here, we profiled the lapatinib-induced time- and dose-dependent phosphorylation dynamics in SKBR3 breast cancer cells by means of quantitative phosphoproteomics. Among 4953 identified phosphopeptides from 1548 proteins, a small proportion (5-7%) was regulated at least twofold by 1-10 μm lapatinib. We obtained a comprehensive phosphorylation map of 21 sites on EGFR/HER2, including nine novel sites on HER2. Among them, serine/threonine phosphosites located in a small region of HER2 (amino acid residues 1049-1083) were up-regulated by the drug, whereas all other sites were down-regulated. We show that cAMP-dependent protein kinase is involved in phosphorylation of this particular region of HER2 and regulates HER2 tyrosine kinase activity. Computational analyses of quantitative phosphoproteome data indicated for the first time that protein-protein networks related to cytoskeletal organization and transcriptional/translational regulation, such as RNP complexes (i.e. hnRNP, snRNP, telomerase, ribosome), are linked to EGFR/HER2 signaling networks. To our knowledge, this is the first report to profile the temporal response of phosphorylation dynamics to a kinase inhibitor. The results provide new insights into EGFR/HER2 regulation through region-specific phosphorylation, as well as a global view of the cellular signaling networks associated with the anti-breast cancer action of lapatinib.
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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 | 0.000 | 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 | 0.000 |
| 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".