ROR2 is a novel prognostic biomarker and a potential therapeutic target in leiomyosarcoma and gastrointestinal stromal tumour
Bibliographic record
Abstract
Soft-tissue sarcomas are a group of malignant tumours whose clinical management is complicated by morphological heterogeneity, inadequate molecular markers and limited therapeutic options. Receptor tyrosine kinases (RTKs) have been shown to play important roles in cancer, both as therapeutic targets and as prognostic biomarkers. An initial screen of gene expression data for 48 RTKs in 148 sarcomas showed that ROR2 was expressed in a subset of leiomyosarcoma (LMS), gastrointestinal stromal tumour (GIST) and desmoid-type fibromatosis (DTF). This was further confirmed by immunohistochemistry (IHC) on 573 tissue samples from 59 sarcoma tumour types. Here we provide evidence that ROR2 expression plays a role in the invasive abilities of LMS and GIST cells in vitro. We also show that knockdown of ROR2 significantly reduces tumour mass in vivo using a xenotransplantation model of LMS. Lastly, we show that ROR2 expression, as measured by IHC, predicts poor clinical outcome in patients with LMS and GIST, although it was not independent of other clinico-pathological features in a multivariate analysis, and that ROR2 expression is maintained between primary tumours and their metastases. Together, these results show that ROR2 is a useful prognostic indicator in the clinical management of these soft-tissue sarcomas and may represent a novel therapeutic target.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".