The newly identified MEK1 tyrosine phosphorylation target MACC1 is druggable by approved MEK1 inhibitors to restrict colorectal cancer metastasis
Bibliographic record
Abstract
Cancer metastasis causes >90% of cancer deaths and remains a major treatment challenge. Here we deciphered the impact of tyrosine phosphorylation of MACC1, a causative driver for cancer metastasis, for cancer cell signaling and novel interventions to restrict cancer metastasis. We identified MACC1 as new MEK1 substrate. MEK1 directly phosphorylates MACC1, leading to accelerated and increased ERK1 activation. Mutating in silico predicted hierarchical MACC1 tyrosine phosphorylation sites abrogates MACC1-induced migration, invasion, and MET expression, a transcriptional MACC1 target. Targeting MEK1 by RNAi or clinically applicable MEK1 inhibitors AZD6244 and GSK1120212 reduces MACC1 tyrosine phosphorylation and restricts MACC1-induced metastasis formation in mice. Although MEK1 levels, contrary to MACC1, are not of prognostic relevance for CRC patients, MEK1 expression was found indispensable for MACC1-induced metastasis. This study identifies MACC1 as new MEK1 substrate for tyrosine phosphorylation decisively impacting cell motility, tumor growth, and metastasis. Thus, MAP kinase signaling is not linear leading to ERK activation, but branches at the level of MEK1. This fundamental finding opens new therapeutic options for targeting the MEK1/MACC1 axis as novel vulnerability in patients at high risk for metastasis. This might be extended from CRC to further solid tumor entities.
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 | 0.000 | 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 | 0.001 |
| 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".