ETMR-09. Clinical and molecular characterization of DICER-mutant Central Nervous System Sarcoma
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background Central nervous system (CNS) sarcomas are rare mesenchymal non-meningothelial tumors accounting for 0.2% of brain tumors. Due to their rarity, there is a lack of substantive clinical, histological and molecular data. Methods Patients diagnosed with primary CNS sarcomas were identified through several international collaborations. DNA methylation profiles were generated using 850K microarray assays, followed by unsupervised hierarchical clustering and t-SNE analyses. Additionally, targeted DNA sequencing was employed to characterize the somatic landscape. Clinical data was collected and summarized using descriptive statistics, and survival estimates were calculated using the Kaplan-Meier method. Results We analyzed DNA methylation data of 65 tumor samples. Clustering analyses demonstrated DICER-1 CNS sarcomas segregate into two distinct epigenetic subgroups. Copy number analysis demonstrated frequent CNV alterations and chromothripsis in 35% of the samples. DICER-1 mutations were present in 85% of cases and associated with frequent additional alterations in TP53 (65%) and KRAS (51%). The median age at diagnosis was 10.4 years (r: 0.4 - 71 y) and 52% (n = 34) of subjects were male. Tumors were frequently (91%) located in the supratentorial compartment, frontal lobe involvement was noted in 62% of cases. Patients were treated with multimodal approaches including surgery, ICE chemotherapy, and focal radiation. At median follow-up time of 27.5 months, the 2-year progression-free and overall survival was 50.8% (r: 0.31-0.67) and 53.5% (r: 0.34-0.69) respectively. Conclusions DICER-1 CNS Sarcoma is an aggressive entity that segregates into two epigenetic subgroups, clinical and genotype correlations remain to be elucidated. Current multimodal approaches are only effective in a subset of patients. The identification of MAPK pathway alterations in most cases should be explored as a therapeutic avenue.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it