INNV-53. Precision Medicine in Pediatric Neuro-Oncology-Experience from a tertiary care center in Canada
Bibliographic record
Abstract
Abstract INTRODUCTION The increased use of multi-omics testing in Neuro-Oncology has provided newer therapeutic options directed against molecular alterations for the treatment of Pediatric brain tumors, referred to as Precision medicine with targeted therapy (TT). We describe here the real-world use of innovative /targeted therapies for pediatric brain tumors from Cancer Care Manitoba in Canada. METHODS This is a retrospective study of all pediatric brain tumors diagnosed between 2010-2023 treated with at least one dose of TT. RESULTS Forty patients were treated with 50 lines of TT; Seven patients (N=7/40, 17.5%) received multiple lines of TT. Low grade gliomas (LGG) formed the largest cohort (N=26/40, 59.1%). Initial treatment included surgery (N=12/40, 30%), chemotherapy (N=15/40, 37.5%), radiotherapy (N=10/40, 25%) or any combination of the three modalities (N=9/40, 22.5%). Twenty-seven (N=27/50, 54%) patients were initiated with TT before first relapse. TT was initiated in 55% of the patients (N=22/40) based on molecular findings. Targetable genomic alterations were found mostly in RAS/MAPK and PI3K/AKT/mTOR pathway. Monotherapy with MEK inhibitors (Trametinib, Selumetinib, N=30/50, 60%) was the most common regime; 12 different combinations of TT were used; most common being Dabrafenib and Trametinib. Drugs were accessed through compassionate access programs in 94% of cases (N=47/50). Among all patients treated with TT, ten (N=10/50, 20%) had partial response and 28 (N=29/50, 63.6%) had stable disease; 11 patients (11/50, 22%) had progression while on TT; leading to a disease control rate (DCR) of 78% and a clinical benefit rate (CBR) of 63.3%. Adverse events involved most commonly the skin, hair and /nails (N=24, 66.7%). TT was stopped due to toxicities in 6 patients (N=6/50, 12%) with no deaths reported. CONCLUSION Our study confirms existing data in the literature regarding precision medicine in pediatric cancer with high CBR along with good quality of life and minimal toxicities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".