Therapeutic implications of improved molecular diagnostics for rare CNS embryonal tumor entities: results of an international, retrospective study
Bibliographic record
Abstract
BACKGROUND: Only few data are available on treatment-associated behavior of distinct rare CNS embryonal tumor entities previously treated as "CNS-primitive neuroectodermal tumors" (CNS-PNET). Respective data on specific entities, including CNS neuroblastoma, FOXR2 activated (CNS NB-FOXR2), and embryonal tumors with multilayered rosettes (ETMR) are needed for development of differentiated treatment strategies. METHODS: Within this retrospective, international study, tumor samples of clinically well-annotated patients with the original diagnosis of CNS-PNET were analyzed using DNA methylation arrays (n = 307). Additional cases (n = 66) with DNA methylation pattern of CNS NB-FOXR2 were included irrespective of initial histological diagnosis. Pooled clinical data (n = 292) were descriptively analyzed. RESULTS: DNA methylation profiling of "CNS-PNET" classified 58 (19%) cases as ETMR, 57 (19%) as high-grade glioma (HGG), 36 (12%) as CNS NB-FOXR2, and 89(29%) cases were classified into 18 other entities. Sixty-seven (22%) cases did not show DNA methylation patterns similar to established CNS tumor reference classes. Best treatment results were achieved for CNS NB-FOXR2 patients (5-year PFS: 63% ± 7%, OS: 85% ± 5%, n = 63), with 35/42 progression-free survivors after upfront craniospinal irradiation (CSI) and chemotherapy. The worst outcome was seen for ETMR and HGG patients with 5-year PFS of 18% ± 6% and 22% ± 7%, and 5-year OS of 24% ± 6% and 25% ± 7%, respectively. CONCLUSION: The historically reported poor outcome of CNS-PNET patients becomes highly variable when tumors are molecularly classified based on DNA methylation profiling. Patients with CNS NB-FOXR2 responded well to current treatments and a standard-risk CSI-based regimen may be prospectively evaluated. The poor outcome of ETMR across applied treatment strategies substantiates the necessity for evaluation of novel treatments.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".