Single-Agent Divarasib (GDC-6036) in Solid Tumors with a <i>KRAS</i> G12C Mutation
Bibliographic record
Abstract
Divarasib (GDC-6036) is a covalent KRAS G12C inhibitor that was designed to have high potency and selectivity. In a phase 1 study, we evaluated divarasib administered orally once daily (at doses ranging from 50 to 400 mg) in patients who had advanced or metastatic solid tumors that harbor a KRAS G12C mutation. The primary objective was an assessment of safety; pharmacokinetics, investigator-evaluated antitumor activity, and biomarkers of response and resistance were also assessed. A total of 137 patients (60 with non–small-cell lung cancer [NSCLC], 55 with colorectal cancer, and 22 with other solid tumors) received divarasib. No dose-limiting toxic effects or treatment-related deaths were reported. Treatment-related adverse events occurred in 127 patients (93%); grade 3 events occurred in 15 patients (11%) and a grade 4 event in 1 patient (1%). Treatment-related adverse events resulted in a dose reduction in 19 patients (14%) and discontinuation of treatment in 4 patients (3%). Among patients with NSCLC, a confirmed response was observed in 53.4% of patients (95% confidence interval [CI], 39.9 to 66.7), and the median progression-free survival was 13.1 months (95% CI, 8.8 to could not be estimated). Among patients with colorectal cancer, a confirmed response was observed in 29.1% of patients (95% CI, 17.6 to 42.9), and the median progression-free survival was 5.6 months (95% CI, 4.1 to 8.2). Responses were also observed in patients with other solid tumors. Serial assessment of circulating tumor DNA showed declines in KRAS G12C variant allele frequency associated with response and identified genomic alterations that may confer resistance to divarasib. Treatment with divarasib resulted in durable clinical responses across KRAS G12C–positive tumors, with mostly low-grade adverse events. (Funded by Genentech; ClinicalTrials.gov number, NCT04449874.)
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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 | 0.000 |
| 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 | 0.001 |
| 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".