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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".