Single-Agent Divarasib in Patients With <i>KRAS G12C</i> –Positive Non–Small Cell Lung Cancer: Long-Term Follow-Up of a Phase I Study
Bibliographic record
Abstract
Divarasib (GDC-6036), an oral, highly potent and selective next-generation KRAS G12C inhibitor, has demonstrated a manageable safety profile and promising antitumor activity in patients with advanced KRAS G12C –positive non–small cell lung cancer (NSCLC). Here, we report long-term (≥1 year) follow-up of single-agent divarasib from the ongoing, open-label, and multicenter phase I study (ClinicalTrials.gov identifier: NCT04449874 ). The primary objective was safety, and the other objectives included preliminary antitumor activity. Overall, 65 patients with advanced KRAS G12C –positive NSCLC received single-agent oral divarasib 50-400 mg once daily and 31 patients (48%) were treated beyond 1 year. Divarasib continued to be well tolerated, and the safety profile beyond 1 year was consistent with the overall safety profile. In patients with measurable disease at baseline across all dose levels (n = 63), the confirmed objective response rate was 55.6% (95% CI, 42.5 to 68.1), and the median duration of response was 18.0 months (95% CI, 11.1 to 24.9). The median progression-free survival was 13.8 months (95% CI, 9.8 to 25.4) in the overall population (N = 65) and 15.3 months (95% CI, 12.3 to 26.1) among patients assigned to the 400-mg dose level (n = 44). With extended follow-up, divarasib demonstrated long-term safety and antitumor activity in patients with advanced KRAS G12C –positive NSCLC.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".