Defining domains within the Afadin scaffold protein that promote breast and colorectal cancer metastasis
Bibliographic record
Abstract
One of the most lethal aspects of cancer, estimated to account for 90% of all cancer-related deaths, is the emergence of metastatic disease.Metastasis is the process through which cancer cells leave the primary tumor and colonize distal sites in the body.A prominent site of metastasis common to breast and colorectal cancer is the liver.Previous work has identified Afadin (AF6) as a multi-domain scaffolding protein that plays an integral role in development of lung and liver metastases in breast cancer, a novel finding contrasting with previous studies that suggest Afadin acts as a tumor suppressor.Elevated Afadin expression in primary breast tumors has been correlated with reduced breast cancer specific survival, overall survival and lung metastasis-free survival.Afadin exists as either a short (sAF6) or a long (lAF6) isoform, sharing a common domain structure including: two RAS-association (RA) domains, a forkhead-associated (FHA) domain, a dilute domain (DIL), a PDZ domain and two proline rich regions (PRR).The long isoform has an additional proline rich region in its carboxy-terminus, called the F-actin binding (FAB) domain.Given that these domains allow Afadin to link cell junction components and regulate intracellular signaling networks, we hypothesize that, in the context of triple negative breast cancer, Afadin acquires a pro-metastatic function outside of its role as a junctional protein.Indeed, previous results from our laboratory indicate that in breast cancer cells stable knockout of Afadin has minimal effect on primary tumor growth of but impairs spontaneous metastasis to the lungs and liver, while potentially promoting anchorage independent growth of colorectal cancer cells through interaction with Claudin-2.Building off this work, we are interested in defining the functional domains that contribute to the pro-metastatic functions of AF6.lab, Dr. Siegel's infectious enthusiasm for scientific research reminded me why I was drawn to this discipline in the first place.His insights, patient guidance, and unwavering support have helped shape this project into what it is today.His encouragement and confidence
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".