Abstract 6909: KRASG12C inhibitor resistance in patient-derived non-small cell lung cancer models
Bibliographic record
Abstract
Abstract Introduction: One quarter of lung adenocarcinomas (LUAD) harbor KRAS-mutations, with KRASG12C accounting for the majority (40%). GDP-KRASG12C inhibitors (G12Ci) have been developed for this patient cohort, and two of these are approved for clinical use in the US. While response rates to G12Ci tested in clinical trials have been between 35-50%, studies have identified mechanisms of adaptive resistance to these novel agents, limiting their use as monotherapies. However, primary (intrinsic) resistance to these compounds has not been explored extensively. We leveraged our patient-derived xenograft (PDX) and xenograft-derived organoid (XDO) development programs to study primary resistance to the novel G12Ci, AZD4625. Methods: Twelve KRASG12C PDX models were established which recapitulated patient tumor histology, and genomic, transcriptomic, and methylome profiles. From these 12 PDX, six long-term (passage >10) XDO were generated. PDX were treated with AZD4625 chronically for four weeks as well as in an acute dosing pharmacodynamic study, where tumors were harvested for further analysis. Results: The AZD4625 drug screen in our PDX models reproduced the G12Ci response rate observed in clinical trials. Four of twelve tumor models reduced in size on treatment, while the remainder were considered resistant as these tumors increased in size on treatment. Sensitive tumors became necrotic while those resistant remained proliferative. XDO models recapitulated their originating PDX genomic alterations, histology, as well as responses to AZD4625. During the acute pharmacodynamic screen in our PDX models, we observed a decrease in pERK1/2 and pS6 protein expression in sensitive but not resistant models, despite acute DUSP6 gene expression decreases in every model. Global proteomic analysis highlighted differences between each model. Conclusion: PDX and XDO are useful models to study resistance to this novel class of inhibitor, with the KRASG12C NSCLC PDX models exhibiting a treatment response rate that is similar to those observed in clinical trials. Global proteomics suggests uniqueness of each model in our cohort, hinting that a universal combination of specific targeted agents to treat KRASG12C LUAD tumors may be insufficient. Citation Format: Joshua C. Rosen, Nhu-An Pham, Quan Li, Pinjiang Cao, Katrina Hueniken, Takamasa Koga, Nikolina Radulovich, Alex Koers, Michael Niedbala, Sarah Ross, Adrian Sacher, Ming-Sound Tsao. KRASG12C inhibitor resistance in patient-derived non-small cell lung cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6909.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".