Time-resolved Phosphoproteome Analysis of Paradoxical RAF Activation Reveals Novel Targets of ERK
Bibliographic record
Abstract
Small molecules targeting aberrant RAF activity, like vemurafenib (PLX4032), are highly effective against cancers harboring the V600E BRAF mutation and are now approved for clinical use against metastatic melanoma. However, in tissues showing elevated RAS activity and in RAS mutant tumors, these inhibitors stimulate RAF dimerization, resulting in inhibitor resistance and downstream "paradoxical" ERK activation. To understand the global signaling response of cancer cells to RAF inhibitors, we profiled the temporal changes of the phosphoproteome of two colon cancer cell lines (Colo205 and HCT116) that respond differently to vemurafenib. Comprehensive data mining and filtering identified a total of 37,910 phosphorylation sites, 660 of which were dynamically modulated upon treatment with vemurafenib. We established that 83% of these dynamic phosphorylation sites were modulated in accordance with the phospho-ERK profile of the two cell lines. Accordingly, kinase substrate prediction algorithms linked most of these dynamic sites to direct ERK1/2-mediated phosphorylation, supporting a low off-target rate for vemurafenib. Functional classification of target proteins indicated the enrichment of known (nuclear pore, transcription factors, and RAS-RTK signaling) and novel (Rho GTPases signaling and actin cytoskeleton) ERK-controlled functions. Our phosphoproteomic data combined with experimental validation established novel dynamic connections between ERK signaling and the transcriptional regulators TEAD3 (Hippo pathway), MKL1, and MKL2 (Rho serum-response elements pathway). We also confirm that an ERK-docking site found in MKL1 is directly antagonized by overlapping actin binding, defining a novel mechanism of actin-modulated phosphorylation. Altogether, time-resolved phosphoproteomics further documented vemurafenib selectivity and identified novel ERK downstream substrates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".