An evaluation of the tumour endothelial marker <scp>CLEC14A</scp> as a therapeutic target in solid tumours
Bibliographic record
Abstract
Earlier studies identified the transmembrane cell surface C-type lectin CLEC14A as a putative tumour endothelial marker. For CLEC14A to progress as a vascular target in solid tumours an in-depth analysis of CLEC14A expression in human healthy and tumour tissue is needed. It is here shown that an analysis of 5332 RNA expression profiles in the public domain confirmed high expression of CLEC14A in tumour compared to healthy human tissue. It is further shown by immunohistochemistry that CLEC14A protein is absent, or expressed at a very low level, in healthy human and primate tissue. In contrast, CLEC14A is expressed on the vasculature of a range of human solid tumours, with particularly high expression in more than half of renal cell carcinomas. Elevated levels of CLEC14A transcripts were identified in some non-cancer pathologies; such comorbidities may need to be excluded from trials of therapies targeting this marker. It is further shown that, as CLEC14A expression can be induced by the absence of shear stress, it is imperative that freshly collected as opposed to aged or post-mortem tissue be analysed. We conclude that CLEC14A is a promising target to enable development of novel anti-cancer therapies for solid tumours.
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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 | 0.001 | 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".