Recent insights into the therapeutic strategies targeting the pseudokinase PTK7 in cancer
Bibliographic record
Abstract
The generation of drugs counteracting deregulated protein kinases has been a major focus in cancer therapy development. Breakthroughs in this effort have produced many therapeutic agents to the benefit of patients, mostly through the development of chemical or antibody-based drugs targeting active kinases. These strategies are challenged when considering catalytically inactive protein kinases (or pseudokinases), which represent 10% of the human kinome with many of relevance in cancer. Among the so-called pseudotyrosine kinases, the PTK7 receptor tyrosine kinase (RTK) stands as a bona fide target overexpressed in several solid tumors and hematological malignancies and linked to metastasis, poor prognosis, and resistance to treatment. Despite the lack of catalytic activity, PTK7 has signaling capacities through heterodimerization with active RTKs and offers pharmacological targeting opportunities through its inactive kinase domain. Moreover, PTK7-targeting strategies based on antibody-drug conjugates, aptamers, and CAR-T cell-based therapies have demonstrated encouraging results in preclinical and clinical settings. We review the most recent data assigning to PTK7 a prominent role in cancer progression as well as current preclinical and clinical targeting strategies against RTK family pseudokinases including PTK7.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".