Clinical and molecular heterogeneity of pineal parenchymal tumors: a consensus study
Bibliographic record
Abstract
Abstract Background Recent genomic studies have shed light on the biology and inter-tumoral heterogeneity underlying pineal parenchymal tumors, in particular pineoblastomas (PBs) and pineal parenchymal tumors of intermediate differentiation (PPTIDs). Previous reports, however, had modest sample sizes and lacked power to integrate molecular and clinical findings. The different proposed subgroup structures also highlighted a need to reach consensus on a robust and relevant classification system. Methods We performed a meta-analysis on 221 patients with molecularly characterized PBs and PPTIDs. DNA methylation profiles were analyzed through complementary bioinformatic approaches and molecular subgrouping was harmonized. Demographic, clinical and genomic features of patients and samples from these pineal tumor subgroups were annotated. Findings Four clinically and biologically relevant consensus PB subgroups were defined: PB-miRNA1 (n=96), PB-miRNA2 (n=23), PB-MYC/FOXR2 (n=34) and PB-RB1 (n=25); with PPTID (n=43) remaining as a molecularly distinct entity. Genomic and transcriptomic profiling allowed the characterization of oncogenic drivers for individual subgroups, specifically, alterations in the microRNA processing pathway in PB-miRNA1/2, MYC amplification and FOXR2 overexpression in PB-MYC/FOXR2, RB1 alteration in PB-RB1, and KBTBD4 insertion in PPTID. Age at diagnosis, sex predilection and metastatic status varied significantly among tumor subgroups. While patients with PB-miRNA2 and PPTID had superior outcome, survival was intermediate for patients with PB-miRNA1, and dismal for those with PB-MYC/FOXR2 and PB-RB1. Interpretation We systematically interrogated the clinical and molecular heterogeneity within pineal parenchymal tumors and proposed a consensus nomenclature for disease subgroups, laying the groundwork for future studies as well as routine use in tumor classification.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".