Rac-specific guanine nucleotide exchange factor DOCK1 is a critical regulator of HER2-mediated breast cancer metastasis
Bibliographic record
Abstract
Progression of solid tumors to the metastatic stage is accountable for the majority of cancer-related deaths. Further understanding of the molecular mechanisms governing metastasis is essential for the development of antimetastatic regimens. Here, we aimed to identify Rac activators that could promote metastasis downstream of human epithelial growth factor receptor 2 (HER2). We investigated if Dedicator of Cytokinesis 1 (DOCK1), based on its evolutionarily conserved role in receptor tyrosine kinases (RTKs)-mediated Rac activation and cell invasion, could be a regulator of metastasis. We report that high expression of DOCK1 in HER2(+) and basal breast cancer subtypes inversely correlates with human patients' survival. Mechanistically, DOCK1 interacts with HER2 and promotes HER2-induced Rac activation and cell migration. To gain further insight, we developed a HER2 breast cancer mouse model with mammary-gland-specific inactivation of DOCK1. In this in vivo model, a significant decrease in tumor growth and metastasis in lungs was found in animals where DOCK1 is inactivated. Furthermore, we found that DOCK1 is required for maximal activation of two HER2 effectors, c-JUN and STAT3. Using an unbiased gene profiling approach, we identified a mammary tumor DOCK1-associated gene signature enriched for genes implicated in response to IFN type I. This analysis revealed a unique set of genes, including Receptor Transporter Protein 4 (RTP4) and STAT1, for which the expression levels can be used to independently predict breast cancer outcome in HER2(+) patients. Our work demonstrates DOCK1-Rac signaling as an HER2 effector pathway essential for HER2-mediated breast cancer progression to metastasis and offers a therapeutic opportunity to limit the spread of metastatic breast cancers.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".