Loss of the DNA Repair Gene RNase H2 Identifies a Unique Subset of DDR-Deficient Leiomyosarcomas
Bibliographic record
Abstract
Targeting the DNA damage response (DDR) pathway is an emerging therapeutic approach for leiomyosarcoma (LMS), and loss of RNase H2, a DDR pathway member, is a potentially actionable alteration for DDR-targeted treatments. Therefore, we designed a protein- and genomic-based RNase H2 screening assay to determine its prevalence and prognostic significance. Using a selective RNase H2 antibody on a pan-tumor microarray (TMA), RNase H2 loss was more common in LMS (11.5%, 9/78) than across all tumors (3.8%, 32/843). In a separate LMS cohort, RNase H2 deficiency was confirmed in uterine LMS (U-LMS, 21%, 23/108) and soft-tissue LMS (ST-LMS; 30%, 39/102). In the TCGA database, RNASEH2B homozygous deletions (HomDels) were found in 6% (5/80) of LMS cases, with a higher proportion in U-LMS (15%; 4/27) compared with ST-LMS (2%; 1/53). Using the SNiPDx targeted-NGS sequencing assay to detect biallelic loss of function in select DDR-related genes, we found RNASEH2B HomDels in 54% (19/35) of U-LMS cases with RNase H2 loss by IHC, and 7% (3/43) HomDels in RNase H2 intact cases. No RNASEH2B HomDels were detected in ST-LMS. In U-LMS patient cohort (n = 109), no significant overall survival difference was seen in patients with RNase H2 loss versus intact, or RNASEH2B HomDel (n = 12) versus Non-HomDel (n = 37). The overall diagnostic accuracy, sensitivity, and specificity of RNase H2 IHC for detecting RNA-SEH2B HomDels in U-LMS was 76%, 93%, and 71%, respectively, and it is being developed for future predictive biomarker driven clinical trials targeting DDR in U-LMS.
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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".