LOGGIC Core BioClinical Data Bank: Added clinical value of RNA-Seq in an international molecular diagnostic registry for pediatric low-grade glioma patients
Bibliographic record
Abstract
BACKGROUND: The international, multicenter registry LOGGIC Core BioClinical Data Bank aims to enhance the understanding of tumor biology in pediatric low-grade glioma (pLGG) and provide clinical and molecular data to support treatment decisions and interventional trial participation. Hence, the question arises whether implementation of RNA sequencing (RNA-Seq) using fresh frozen (FrFr) tumor tissue in addition to gene panel and DNA methylation analysis improves diagnostic accuracy and provides additional clinical benefit. METHODS: Analysis of patients aged 0 to 21 years, enrolled in Germany between April 2019 and February 2021, and for whom FrFr tissue was available. Central reference histopathology, immunohistochemistry, 850k DNA methylation analysis, gene panel sequencing, and RNA-Seq were performed. RESULTS: FrFr tissue was available in 178/379 enrolled cases. RNA-Seq was performed on 125 of these samples. We confirmed KIAA1549::BRAF-fusion (n = 71), BRAF V600E-mutation (n = 12), and alterations in FGFR1 (n = 14) as the most frequent alterations, among other common molecular drivers (n = 12). N = 16 cases (13%) presented rare gene fusions (eg, TPM3::NTRK1, EWSR1::VGLL1, SH3PXD2A::HTRA1, PDGFB::LRP1, GOPC::ROS1). In n = 27 cases (22%), RNA-Seq detected a driver alteration not otherwise identified (22/27 actionable). The rate of driver alteration detection was hereby increased from 75% to 97%. Furthermore, FGFR1 internal tandem duplications (n = 6) were only detected by RNA-Seq using current bioinformatics pipelines, leading to a change in analysis protocols. CONCLUSIONS: The addition of RNA-Seq to current diagnostic methods improves diagnostic accuracy, making precision oncology treatments (MEKi/RAFi/ERKi/NTRKi/FGFRi/ROSi) more accessible. We propose to include RNA-Seq as part of routine diagnostics for all pLGG patients, especially when no common pLGG alteration was identified.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".