Addressing the Heterogeneity of Smooth Muscle Tumours by Bulk RNAseq Multilevel Clustering
Bibliographic record
Abstract
Leiomyosarcoma (LMS) is a malignant mesenchymal neoplasm with smooth muscle differentiation, presenting in abdominal, extremity, and uterine sites. As a primary site, uterine LMS (uLMS) management can be complicated, as benign uterine leiomyomas (uLM) can share indistinguishable similarities pre-operatively to uLMS. This work aims to investigate the heterogeneity of uterine smooth muscle tumours (USMTs) and all sites of LMS using an unsupervised bulk RNAseq transcriptional clustering pipeline, RACCOON. A spectrum across uLM, STUMP, and uLMS was identified, with the presence of a uLM/STUMP/uLMS cluster distinct from the independent uLMS cluster. Furthermore, 3 subtypes of LMS emerged: LMS A, LMS B, and uLMS, of which LMS B showed significantly prolonged metastatic-free survival and enrichment in mTOR and metabolic gene sets. Taken together, exploration of transcriptional relationships across smooth muscle tumours allows for cluster stratification, which may show benefit to tailored management plans, and offer insight into their biological mechanisms.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".