BIOM-47. PREDICTORS OF SEIZURE AT ONSET USING A FUNCTIONAL VARIANT ANALYSIS OF TARGETED NEXT GENERATION SEQUENCING IN GBM
Bibliographic record
Abstract
Abstract BACKGROUND Adverse events (AE) including seizures cause significant morbidity in patients with GBM. We propose a novel method for assessing genomic predictors of AEs using results from a clinical targeted sequencing platform with variant function analysis. METHODS We identified 1,011 consecutive adult patients with newly diagnosed, histologically confirmed IDH-wildtype GBM with targeted exome NGS (Oncopanel) at Dana-Farber Cancer Institute from 2013-2019. Seizure at presentation was retrospectively identified as an AE. Biologic function (high loss, low loss, neutral, low gain and high gain) was assigned to variants using a three-tiered approach leveraging a genetic variant database (OncoKB), followed by analysis using protein prediction tools (Sift, Polyphen2 and Provean). Univariate logistic regression was performed for each relevant altered gene against the outcome of interest with false-discovery rate correction. Genes associated with seizure at presentation were included iteratively in a multivariate logistic model including other predictors of the outcome. RESULTS Our analysis included 470 GBM patients with 107 genes and 12 whole chromosome or arm level candidate variants covered by all versions of Oncopanel and with >10% alteration. Seizure at presentation occurred in 143/463 patients (31%) and was associated with EGFR amplification (high gain) (OR: 2.76, 95% CI: 1.4-5.3, p = 0.04). In a multivariate analysis (including age, sex, and preoperative tumor volume), EGFR amplification remained statistically significant (OR: 1.5, 95% CI: 1.0-2.2, p = 0.03). CONCLUSION Genomic biomarkers based on functional variant analysis of a routine clinical panel may predict adverse events in GBM. Seizure at presentation was independently associated with EGFR amplification. Our ongoing analysis will look at predictors of myelosuppression, thromboembolism, pseudoprogression and early progression using a similar approach. Identifying molecular risk factors could improve the management of patients through supportive care and consideration of prophylactic therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".