Integrated Molecular Analysis of Undifferentiated Uterine Sarcomas Reveals Clinically Relevant Molecular Subtypes
Bibliographic record
Abstract
Abstract Purpose: Undifferentiated uterine sarcomas (UUS) are rare, extremely deadly, sarcomas with no effective treatment. The goal of this study was to identify novel intrinsic molecular UUS subtypes using integrated clinical, histopathologic, and molecular evaluation of a large, fully annotated, patient cohort. Experimental Design: Fifty cases of UUS with full clinicopathologic annotation were analyzed for gene expression (n = 50), copy-number variation (CNV, n = 40), cell morphometry (n = 39), and protein expression (n = 22). Gene ontology and network enrichment analysis were used to relate over- and underexpressed genes to pathways and further to clinicopathologic and phenotypic findings. Results: Gene expression identified four distinct groups of tumors, which varied in their clinicopathologic parameters. Gene ontology analysis revealed differential activation of pathways related to genital tract development, extracellular matrix (ECM), muscle function, and proliferation. A multivariable, adjusted Cox proportional hazard model demonstrated that RNA group, mitotic index, and hormone receptor expression influence patient overall survival (OS). CNV arrays revealed characteristic chromosomal changes for each group. Morphometry demonstrated that the ECM group, the most aggressive, exhibited a decreased cell density and increased nuclear area. A cell density cutoff of 4,300 tumor cells per mm2 could separate ECM tumors from the remaining cases with a sensitivity of 83% and a specificity of 94%. IHC staining of MMP-14, Collagens 1 and 6, and Fibronectin proteins revealed differential expression of these ECM-related proteins, identifying potential new biomarkers for this aggressive sarcoma subgroup. Conclusions: Molecular evaluation of UUS provides novel insights into the biology, prognosis, phenotype, and possible treatment of these tumors.
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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 | 0.001 |
| 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".