Molecular determinants of sotorasib clinical efficacy in KRASG12C-mutated non-small-cell lung cancer
Bibliographic record
Abstract
Molecular determinants of KRAS(G12C)inhibitor efficacy in KRASG12C-mutated non-small-cell lung cancer (NSCLC) remain poorly characterized. Here we report one of the largest integrated analyses to date of sotorasib clinical efficacy biomarkers from the phase 2 CodeBreaK 100 and phase 3 CodeBreaK 200 studies. We reveal differential sotorasib activity and relative benefit compared to docetaxel across KRASG12C-mutated NSCLC co-mutational subsets and transcriptional subtypes. We also identify low expression of TTF1 and KEAP1 co-mutations/NRF2 activation as major determinants of sotorasib anti-tumor efficacy and adverse prognostic features. Exploratory analyses highlight potential tumor cell-extrinsic contributors to sotorasib anti-tumor activity and suggest that early on-treatment clearance of KRASG12C- circulating tumor DNA may refine clinical response prediction algorithms. Our findings advance precision medicine for patients with KRASG12C-mutated NSCLC and establish a framework for patient stratification and selection for treatment intensification with rationally applied therapeutic combinations. This large integrated analysis of the KRASG12C inhibitor sotorasib clinical efficacy biomarkers from the phase 2 CodeBreaK 100 and phase 3 CodeBreaK 200 trials shows that low expression of TTF-1 and high expression of NRF2 determine anti-tumor efficacy of sotorasib in non–small-cell lung cancer.
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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 | 0.001 |
| 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) | 0.000 | 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".