Oncogenic Fusion Tyrosine Kinases as Molecular Targets for Anti-Cancer Therapy
Bibliographic record
Abstract
Deregulated activation of protein tyrosine kinases (PTKs) is a frequent event underlying malignant transformation in many types of cancer. The formation of oncogenic fusion tyrosine kinases (FTKs) resulting from genomic rearrangements, represents a common mechanism by which kinases escape the strict controls that normally regulate their expression and activation. FTKs are typically composed of an N-terminal dimerisation domain, provided by the fusion partner protein, fused to the kinase domain of receptor or non-receptor tyrosine kinases (non-RTKs). Since FTKs do not contain extracellular domains, they share many characteristics with non-RTKs in terms of their properties and approaches for therapeutic targeting. FTKs are cytoplasmic or sometimes nuclear proteins, depending on the normal distribution of their fusion partner. FTKs no longer respond to ligand and are instead constitutively activated by dimerisation induced by the fusion partner. Unlike RTKs, FTKs cannot be targeted by therapeutic antibodies, instead they require agents that can cross the cell membrane as with non-RTKs. Here we review the PTKs known to be expressed as FTKs in cancer and the strategies for molecularly targeting these FTKs in anti-cancer therapy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".