Early stage cancer cell invasion: signaling, biomarkers and therapeutic targeting
Bibliographic record
Abstract
The process of primary cancer invasion of distant organs is multifactorial and multistep. Successful therapeutic management of invasive cancers remains hampered by the multitude of overlapping signaling pathways that initiate and drive cancer cell migration. A crucial early event by which cancer cells switch from localized to invasive states is initiated by the acquisition of autonomous motile properties; a process driven by dynamic assemblies and disassemblies of multiple focal adhesion, cytoskeleton and motor proteins. Several of the protein complexes involved are tightly regulated through posttranslational modifications and intermolecular collisions with partners that occur in a time- and space-dependent manner. These concerted mechanisms are essential for the regulation of cell shape, cell polarity, and cell motility and migration in response to chemotactic signals. This review summarizes the current knowledge in the field and potential clinical implications for molecular pathology and cancer therapeutics. It is not meant to be comprehensive; aspects related to basic signaling are not dealt with extensively in this review. However, the reader is referred to excellent reviews that provide coverage of these topics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".