Efficacy and Safety of Olomorasib in Combination With Pembrolizumab in Treatment of Patients With KRAS G12C-Mutant Advanced NSCLC
Bibliographic record
Abstract
INTRODUCTION: Immunotherapy-based regimens are the standard first-line treatment for KRAS G12C-mutant NSCLC, but outcomes remain suboptimal. Prior attempts to combine KRAS G12C inhibitors with immunotherapy have been challenged by toxicity. We report results from LOXO-RAS-20001, a phase 1/2 trial evaluating the combination of olomorasib with pembrolizumab in KRAS G12C-mutant NSCLC, including in first-line setting. METHODS: Patients with advanced KRAS G12C-mutant NSCLC in any treatment line, including prior KRAS G12Ci and immunotherapy were eligible. Any PD-L1 level (0%-100%) was permitted. Three dose levels of olomorasib (50, 100, and 150 mg twice daily) plus pembrolizumab were evaluated, focusing on 50 and 100 mg twice daily for safety and efficacy. RESULTS: A total of 99 enrolled patients had a median age of 68 (range, 42-83) years; 52% of tumors were PD-L1 0% to 49%, 36% were PD-L1 more than or equal to 50%, and 51% were first line. Of 93 patients treated at 50 or 100 mg twice daily, 76 (81.7%) experienced treatment-related adverse event (TRAEs) of any grade, most often, diarrhea (34.4%) and increased alanine aminotransferase/aspartate aminotransferase level (24.7%/22.6%). Grade more than or equal to 3 TRAEs were found in 33.3%. TRAEs led to olomorasib dose reductions in 20.4% and discontinuation of both therapies in 6.5% of patients. Among 91 efficacy-evaluable patients, at a median follow-up of 12.5 months (interquartile range, 6.2-16.9), objective response rate was 57.1% (95% confidence interval [CI], 46.3-67.5) across all PD-L1 expression levels, 73.9% (95% CI, 58.9-85.7) in first line, and 90% (95% CI, 68.3-98.8) in first-line patients with PD-L1 more than or equal to 50%. CONCLUSION: Olomorasib plus pembrolizumab demonstrated manageable safety and promising antitumor activity in patients with KRAS G12C-mutant advanced NSCLC across PD-L1 expression levels, especially in the high PD-L1 first-line setting.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".