TRLS-14 CLINICOPATHOLOGIC CHARACTERISTICS AND OUTCOMES OF PATIENTS WITH KRAS G12C MUTANT NSCLC WITH BRAIN METASTASES
Bibliographic record
Abstract
Abstract INTRODUCTION KRAS G12C-mutated non-small cell lung cancer (NSCLC) is linked to higher risk of brain metastases (BM). However, clinicopathologic and genomic characteristics associated with BM in this population are poorly understood. METHODS This is a single-center, retrospective analysis of patients with KRAS G12C-mutated NSCLC. Clinicopathologic and genomic characteristics including PD-L1 status; tumor mutation burden (TMB); and specific co-mutations in TP53, STK11, and KEAP1 were assessed. Treatment patterns were characterized for the BM sub-cohort. Chi-squared, ANOVA, and Kaplan-Meier analyses were used to evaluate association of covariates with BM occurrence and overall survival. RESULTS Among 460 patients with KRAS G12C-mutated NSCLC treated from 2013-2023, 107 (23.3%) developed BM, with 54.2% of cases identified synchronously (within 60 days of initial diagnosis). Median time to metachronous BM diagnosis from primary diagnosis was 16.9 months. Solitary BM were present in 37.4% of all patients, while 28.0% had ≥4 lesions. For patients receiving comprehensive genomic testing (N=197), BM were associated with increased rates of co-mutations in TP53, STK11, and KEAP1 (p=0.049), but no differences in TMB or PD-L1. Patients with synchronous BM had median OS of 8.0 months from BM diagnosis while those with metachronous BM had median OS of 11.1 months, though this difference was not statistically significant. Upfront interventions following BM diagnosis were surgical resection (33.6%), stereotactic radiation (24.3%), systemic therapy (16.8%), and whole brain radiation (12.1%). Whole brain radiation differed for patients with one vs. multiple lesions (p=0.029). Patients who received no cancer-directed treatment due to poor performance had more frequent co-mutations. Discussion: BM in KRAS G12C-mutated NSCLC was associated with higher prevalence of co-mutations in TP53, STK11, and KEAP1. Given the heterogeneity of features influencing treatment response and observed variability in treatment strategies, larger studies examining patients with active CNS lesions are needed to inform management of BM in this population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".