LGG-16. LOCATION AND HISTOLOGY DICTATE THE LIKELIHOOD OF MOLECULAR EVENTS IN PEDIATRIC LOW-GRADE GLIOMA
Bibliographic record
Abstract
Over the last decade, a plethora of genetic information in pediatric low-grade gliomas (pLGG) has been uncovered. Clinically, many of these molecular markers have been implicated in aiding in patient diagnosis, predicting prognosis, and stratifying the most effective therapeutic strategy. However, there has yet to be a consensus on what molecular markers to test for, how to test for them, or their incidence across tumour locations and histologies. We compiled a cohort of low-grade gliomas treated at the Hospital for Sick Children. MRI and pathology reviews confirmed tumour location and diagnosis. Molecular testing was conducted using a combination of the QX200 Bio-Rad Droplet-Digital PCR, NanoString nCounter, FISH, and RNA-seq depending on the alteration being tested for and the available material. Our cohort consisted of 480 patients with complete clinical and molecular data. The most frequent alteration in our cohort was KIAA1549-BRAF fusions (37%), followed by BRAF_V600E (20%). BRAF fusion events were most commonly seen in pilocytic and pilomyxoid astrocytoma (67% and 69%, respectively), whereas BRAF_V600E was primarily observed in ganglioglioma and PXA (52% and 85%, respectively). Interestingly, both low grade glioma, NOS and diffuse astrocytoma showed a wide array of molecular events, including BRAF_V600E, BRAF fusions, MYBL1 alterations and H3F3A_K27M. With respect to tumour location, the cerebellum showed significant enrichment for BRAF fusions (80%). In the hemispheres, diencephalon, brainstem and spinal cord, more mutational diversity was seen, with BRAF fusion and BRAF_V600E appearing most frequently, while additional events such as H3F3A_K27M, MYBL1 alterations, and FGFR1 fusions are also present. The work here shows the utility of targeted non-NGS techniques by which hotspot mutations and fusion events can be detected. Further, we show in which histological grades and locations specific events tend to cluster, providing a stepwise procedure by which the most likely molecular marks can be tested for.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".