Abstract B029: Investigating therapeutic strategies to promote immune rejection of KRAS G12C inhibitor-resistant sub-populations in lung cancer
Bibliographic record
Abstract
Abstract Oncogenic KRAS signaling not only instigates aberrant growth in cancer cells but can further induce inhibitory effects on anti-tumor immune responses. Hence, recently developed KRAS G12C inhibitors (G12Ci) have the potential to alleviate immune suppression in the tumor microenvironment (TME) and reshape the therapy responses for patients with a KRAS G12C driver mutation. However, rewiring of oncogenic signaling circuits and acquisition of further mutations allows cancer cells to rapidly evolve drug resistance. In fact, clinical trial data from the recently approved G12Ci, adagrasib and sotorasib, showed a median progression-free survival in non-small cell lung cancer of just 7 months. In this project, we investigate how to combat G12Ci-resistant subpopulations in lung tumors by engaging an anti-tumor immune response. We mimic the development of drug resistance by combining traced isogenic G12C- and G12D-mutant lung cancer cells, where the KRAS G12D mutant cells are inherently resistant to G12Ci. By alleviating RAS-driven immune suppression and potentially inducing immunogenic cell death of G12Ci-responsive cells, we aim to further potentiate an adaptive immune response targeting the drug-resistant population. We found that G12Ci as monotherapy drives therapy resistance by giving the drug-resistant subpopulation a proliferative advantage. On the other hand, combining G12Ci with immunotherapy or a SHP2 inhibitor potentiated an adaptive immune response to induce complete eradication of both G12C and G12D KRAS mutant cancer cell populations and also immune memory towards drug-resistant KRAS G12D cells in mice. However, when repeating the combination treatment in Rag1 −/− mice, which lack B- and T-cells, there were no complete responders. Histopathological analysis of tumor sections in immune-competent mice revealed a strong T-cell infiltration in the TME of tumors treated with combination therapies. Although understanding the mechanistic changes in the TME induced by combination therapies is ongoing, these results indicate a role for the adaptive immune response in targeting G12Ci drug-resistant subpopulations. Understanding these underlying mechanisms will contribute to combating the development of drug resistance in patients by enhancing treatment regimens that promote immune-mediated destruction of drug-resistant tumor cell sub-populations. Citation Format: Mona Tomaschko, Miriam Molina-Arcas, Christopher Moore, Sareena Rana, Sophie de Carne, Panayiotis Anastasiou, Claire Pillsbury, Andrea de Castro, Julian Downward. Investigating therapeutic strategies to promote immune rejection of KRAS G12C inhibitor-resistant sub-populations in lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B029.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".