PATH-16. EVALUATION OF SEX-BASED DIFFERENCES IN CLINICAL OUTCOMES AND TUMOR GENOMICS IN PATIENTS WITH NEWLY DIAGNOSED IDH-WILDTYPE GLIOBLASTOMA RECEIVING CHEMORADIATION
Bibliographic record
Abstract
Abstract BACKGROUND We evaluated sex-based differences in clinical outcomes and tumor genomics in patients with newly-diagnosed GBM. METHODS We reviewed 665 IDH-wild type GBM patients with Karnofsky Performance Status (KPS) ≥60 treated at our institution from 2010-2019 including; 585 patients with targeted exome sequencing of 447 cancer associated genes (OncoPanel). Deleterious mutations were defined as homozygous deletions or loss of function mutations of known tumor suppressors (as reported in TCGA, ≥ 3 times in the COSMIC database, or predicted as “damaging” in SIFT and/or “probably damaging” in Polyphen 2) or known oncogenic mutations in proto-oncogenes (reported in TCGA or ≥ 3 times in COSMIC). RESULTS There were 384 (57.7%) males and 281 (42.3%) females. Median OS was 22.5 months for females and 19.3 months for males (hazard ratio [HR] 0.81, 95% CI 1.03-1.48, p = 0.02). On multivariable analysis adjusted for age, KPS ≥90, extent of resection, and MGMT methylation status, female sex (adjusted hazard ratio 0.78, 95% CI [0.64-0.95], p = 0.015) was associated with improved OS. Superior OS in females was observed in MGMT-unmethylated patients (HR 0.69, 95% CI [0.54-0.90], p = 0.005) but not MGMT-methylated patients. Thirteen genes were deleteriously altered in ≥5% of our cohort: CDK4 (12.1% male vs. 7.8% female), CDKN2A (46.5% vs. 45.7%), CDKN2B (41.8% vs. 43.3%), EGFR (34.7% vs. 40.0%, MTAP (18.2% vs. 18.8%), NF1 (11.5% vs. 9.4%), PTEN (28.2% vs. 29.8%), TP53 (28.2% vs. 30.2%), RB1 (5.6% vs. 6.5%), MDM4 (6.2% vs. 5.7%), ATM (5.9% vs. 3.7%), MDM2 (7.4% vs. 4.1%), PIK3R1 (6.2% vs. 4.1%). There were no differences in frequency of mutations in these individual genes between males and females (χ 2 [1, N=585] = 0.05-2.86, p = 0.09-0.86). CONCLUSIONS Female sex is associated with improved survival. We did not identify sex-based differences in deleterious genomic alterations amongst commonly altered genes in GBM.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".