Modeling response to the KRAS-G12C inhibitor AZD4625 in KRASG12C NSCLC patient-derived xenografts reveals insights into primary resistance mechanisms
Bibliographic record
Abstract
KRAS G12C alterations are present in ~13% of lung adenocarcinomas. AZD4625 is a covalent small molecule inhibitor that selectively binds and inhibits GDP-KRAS G12C , leading to reduced cell viability and protein signaling responsible for tumor survival in models with this gain-of-function alteration. We studied short-term changes in signaling and mechanisms of primary resistance to AZD4625 in twelve KRAS G12C lung adenocarcinoma patient-derived xenografts (PDX) and six organoids derived from these twelve models. Sustained tumor regression in four (33%) PDXs was observed while the remaining eight models were intrinsically resistant to AZD4625. Organoid responses to AZD4625 were concordant with their derived PDXs. Acute AZD4625 exposure significantly decreased gene expression of the ERK1/2 negative regulator, DUSP6 , in all models while protein MAPK and AKT/mTOR signals were downregulated more frequently in the AZD4625-sensitive than AZD4625-resistant cohorts. Analyzing PDX transcriptomes and proteomes identified mTOR signaling as a putative mechanism of primary resistance to AZD4625. Our findings confirm AZD4625 as a highly active KRAS G12C inhibitor. This data also supports the use of PDX models in understanding resistance mechanisms that may be leveraged to develop more active combination therapies.
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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) | 0.000 | 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 | 0.000 |
| 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".