Elucidating divergent biology in uterine carcinosarcoma
Bibliographic record
Abstract
OBJECTIVES: Uterine carcinosarcoma (UCS) is an aggressive malignancy characterized by epithelial (C) and mesenchymal (S) components, with complex biology and poor treatment response. This study aims to enhance understanding of UCS through genomic, epigenomic, and transcriptomic analysis. METHODS: Microdissected (C and S) tumor samples were processed for whole-genome sequencing (WGS), RNA-seqencing, and enzymatic methylation sequencing (EM-Seq). Multiplex immunohistochemistry (mIHC) and computational pathology techniques were employed to assess tumour microenvironment (TME). RESULTS: WGS and EM-seq of 18 samples from 9 patients revealed a low tumor mutation burden (TMB; median = 0.97 mutations/Mb) and no evidence of microsatellite instability (MSI). Driver mutations were identified in TP53 (94 %), PIK3CA (33 %), and PPP2R1A (22 %). Copy-number (CN) analysis revealed recurrent amplifications of MYC (67 %), PIK3CA (61 %), CCNE1 (56 %), AKT2 (44 %), and SMARCA4 (39 %). Comparative analysis of the C and S regions revealed no significant differences in mutation frequency, CN, transcriptomic and methylomic profiles. Both regions exhibited global hypomethylation, with functional enrichment for xenobiotic metabolism pathways in C and epithelial-to-mesenchymal transition pathways in S regions. Comparitive mIHC performed on 21 cases showed similar T cell and B cell densities, but a higher density of tumour-associated macrophages and PD-L1+ cells in the S component. Computational morphologic analysis showed substantial histomorphologic heterogeneity within and across UCS cases. CONCLUSION: By elucidating the complex interplay between the epithelial and mesenchymal components, this study enhances our understanding of UCS and informs the development of novel therapeutic strategies targeting both genomic alterations and the TME.
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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 | 0.000 |
| 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".