Targeting SNARE-Mediated Vesicle Transport to Block Invadopodium-Based Cancer Cell Invasion
Bibliographic record
Abstract
During metastasis, cancer cells can invade extracellular matrix (ECM) through a process mediated by matrix-degrading protrusions of the plasma membrane, termed invadopodia. Formation of invadopodia correlates with cells’ invasive and metastatic potential, and thus presents a potential target for therapeutic approaches to target metastatic progression. Invadopodia formation is dependent on the recruitment of proteins involved in intracellular signaling, actin cytoskeleton remodeling, and proteolytic matrix modification. The latter includes matrix degrading enzymes such as MT1-MMP, MMP2, and MMP9. These essential invadopodium-associated enzymes are required for localized matrix degradation, and their localization at invadopodia is central to invadopodium-based cancer cell invasion. Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) facilitate intracellular vesicle traffic, including that involved in the transport of invadopodium-associated proteins, and in so doing promote modification of ECM and modulation of signaling pathways involved in the movement of cancer cells. Specific SNARE complexes have been found to support invadopodia formation, and these complexes are, in turn, regulated by associated proteins that interact specifically with SNAREs. Targeting SNARE regulatory proteins thus provides a possible approach to disrupt SNARE-dependent delivery of invadopodial proteins, including MT1-MMP, to sites of ECM modification. Here, we review recent studies of SNARE regulators that hold potential as targets for the development of anti-metastatic therapies for patients burdened with invadopodia-forming cancer types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".